Macrophage plasticity in the osteosarcoma tumor microenvironment: opportunities and challenges for immunotherapy.

Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice J Cancer Res Clin Oncol . 2026 Apr 13;152(4):88. doi: 10.1007/s00432-026-06471-3 Search in PMC Search in PubMed View in NLM Catalog Add to search Macrophage plasticity in the osteosarcoma tumor microenvironment: opportunities and challenges for immunotherapy Haifeng Li Haifeng Li 1 Orthopedic Diagnosis and Treatment Center, The Affiliated Hospital of Yanbian University (Yanbian Hospital), Yanji, 133000 Jilin China Find articles by Haifeng Li 1, # , Chengri Liu Chengri Liu 1 Orthopedic Diagnosis and Treatment Center, The Affiliated Hospital of Yanbian University (Yanbian Hospital), Yanji, 133000 Jilin China Find articles by Chengri Liu 1, # , Baojian Zhang Baojian Zhang 1 Orthopedic Diagnosis and Treatment Center, The Affiliated Hospital of Yanbian University (Yanbian Hospital), Yanji, 133000 Jilin China Find articles by Baojian Zhang 1 , Yanhu Zhang Yanhu Zhang 1 Orthopedic Diagnosis and Treatment Center, The Affiliated Hospital of Yanbian University (Yanbian Hospital), Yanji, 133000 Jilin China Find articles by Yanhu Zhang 1 , Yanqun Liu Yanqun Liu 1 Orthopedic Diagnosis and Treatment Center, The Affiliated Hospital of Yanbian University (Yanbian Hospital), Yanji, 133000 Jilin China Find articles by Yanqun Liu 1, ✉ Author information Article notes Copyright and License information 1 Orthopedic Diagnosis and Treatment Center, The Affiliated Hospital of Yanbian University (Yanbian Hospital), Yanji, 133000 Jilin China ✉ Corresponding author. # Contributed equally. Received 2026 Feb 5; Accepted 2026 Mar 26; Collection date 2026 Apr. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13076843  PMID: 41975053 Abstract Osteosarcoma is an aggressive primary malignancy bone tumor, characterized by a complex immune microenvironment, which poses significant challenges for immunotherapy. Tumor-associated macrophages (TAMs) are pivotal immune cells in the tumor microenvironment (TME), exhibiting remarkable plasticity, enabling them to switch between pro-tumorigenic and anti-tumorigenic phenotypes. Their functional polarization critically influences tumor progression and therapeutic response. This comprehensive review outlines the immune environment of osteosarcoma and the underlying mechanisms of macrophage plasticity. In this paper, we discuss the therapeutic possibilities through modulation of macrophages, their reprogramming and depletion, and the issues related to clinical translation. We describe how recent advances create an opportunity to target tumor-associated macrophages to improve patient responses. By synthesizing current knowledge on macrophage phenotypes, macrophage polarization, and preclinical-to-clinical therapeutic interventions, we present a strategic framework for development of macrophage-centric immunotherapies, highlighting a promising yet challenging avenue in improving patient outcome in osteosarcoma. Keywords: Osteosarcoma, Macrophage plasticity, Tumor microenvironment, Immunotherapy, Tumor-associated macrophages Introduction Osteosarcoma is the most common primary malignant bone tumor which mostly occurs in children and adolescents, and due to its tendency of being aggressive that leads to early metastasis, poor prognosis and finally results in mortality (Beird et al. 2022 ). The current standard-of-care for non-metastatic osteosarcoma involves neoadjuvant and adjuvant multi-agent chemotherapy, typically consisting of high-dose methotrexate, doxorubicin, and cisplatin (the MAP regimen), combined with wide surgical resection of the tumor (Robinson and Davis 2024 ). While this approach has significantly improved survival for patients with localized disease, further progress has been limited. Recent therapeutic developments have explored the addition of other agents such as ifosfamide and etoposide, particularly for high-risk or metastatic cases, although with varying degrees of success and increased toxicity (Lu et al. 2025a ). Furthermore, the integration of targeted therapies (e.g., tyrosine kinase inhibitors targeting VEGF or mTOR pathways) and immunotherapies (e.g., immune checkpoint inhibitors) into treatment regimens is an area of active investigation, aiming to overcome the limitations of conventional chemotherapy and improve outcomes for patients with advanced or refractory disease (Duffaud et al. 2019 ; Yang et al. 2022 ). Despite advances in multimodal treatment approaches, including surgical resection combined with chemotherapy, the survival outcomes for osteosarcoma patients have become stagnant over the past several decades, especially for those with metastatic or recurrent disease (Robinson and Davis 2024 ; Duffaud 2020 ). The 5-year overall survival rate remains below 70% for localized disease and drops to less than 30% in cases with lung metastases, which are the leading cause of mortality (Dharanikota et al. 2021 ; Yao et al. 2021 ). This clinical challenge underscores the urgent need for innovative therapeutic strategies and more precise prognostic biomarkers to improve patient management and outcomes. The tumor microenvironment (TME) of osteosarcoma is a complex network that is highly dynamic. It consists of various cellular and non-cellular components, including immune cells, stromal cells, extracellular matrix, cytokines, and chemokines, which collectively influence tumor progression, metastasis, and therapeutic response (Bader et al. 2020 ). Among the immune constituents, tumor-associated macrophages (TAMs) constitute a major population and play crucial roles to mediate the osteosarcoma TME (Luo et al. 2020 ). These macrophages exhibit remarkable plasticity, adapting their phenotype and functions in response to diverse signals in microenvironments. They can polarize into classically activated M1 macrophages, which are typically tumoricidal and pro-inflammatory, or alternatively activated M2 macrophages, which generally promote tumor growth, immune suppression, and metastasis. The predominance of M2 macrophages in osteosarcoma correlates with poor prognosis and enhanced metastatic potential, whereas M1 macrophage infiltration is linked with better survival (Wolf-Dennen et al. 2020 ). The plasticity of macrophages and their phenotypic switching is regulated by a complex network of signaling pathways and intercellular communications in the TME. Tumor-derived factors, including exosomes, cytokines, and growth factors, actively drive M2 polarization, thereby fostering an immunosuppressive niche that supports tumor progression. This phenomenon is compounded by the low immunogenicity of osteosarcoma and the presence of immune checkpoint molecules, which inhibit effective anti-tumor immune responses (Ying et al. 2023 ). Given this context, targeting macrophage polarization has emerged as a promising immunotherapeutic strategy to reprogram the TME and enhance the immunity against the tumor (Vitale et al. 2019 ). Given the dual role of macrophage plasticity, it is essential to comprehensively understand its regulatory mechanisms. Grasping the molecular mechanisms and signaling pathways that govern macrophage plasticity is crucial for developing effective immunotherapies. This review aims systematically examines the current knowledge surrounding macrophage plasticity in osteosarcoma, emphasizing both the opportunities and challenges in utilizing these cells for immunotherapeutic interventions. By integrating recent advances in characterizing macrophage phenotypes, intercellular communication, and therapeutic modulation, we aim to provide insights into future research directions that could ultimately enhance clinical outcomes for osteosarcoma patients. The immune landscape of osteosarcoma: cellular composition and functional implications Osteosarcoma is the most common primary malignant bone tumor, mainly affecting children and adolescents. It is characterized by a complex and dynamic TME, which profoundly influences disease progression, metastasis, and response to treatment (Liang et al. 2024 ). The immune cell composition of the TME in osteosarcoma mainly includes TAMs, myeloid-derived suppressor cells (MDSCs), tumor-associated neutrophils (TANs), dendritic cells (DCs), T and B lymphocytes, and natural killer (NK) cells (Inagaki et al. 2016 ; Liu et al. 2015 ). Increasing evidence indicates that various immune cells and their immunological pathways play important roles in promoting osteosarcoma progression (Fig. 1 ). It is worth noting that, unlike immunologically hot tumors which exhibit massive infiltration of cytotoxic T lymphocytes (CTLs), osteosarcoma is an immunologically cold tumor, characterized by sparse CTL infiltration, abundant immunosuppressive cell populations, and the generation of immunosuppressive networks that are prone to evade immune surveillance (Casey and Cheung 2020 ). Fig. 1. Open in a new tab The Immune landscape of osteosarcoma.The TME of osteosarcoma consists of tumor cells and numerous non-tumor cells within a remodeled extracellular matrix. These cells are broadly categorized as tumor-promoting or tumor-suppressing, exerting opposing effects through diverse mechanisms In the osteosarcoma TME, pro-tumor cells collaboratively establish an immunosuppressive environment through multiple mechanisms. Tumor cells actively remodel the TME and persist as circulating tumor cells (CTCs) facilitating distant recurrence (Zheng et al. 2018 ; Liu et al. 2019 ). M2-TAMs are the most abundant immune population. They promote angiogenesis, immune evasion, and the maintenance of cancer stem cells through cytokines such as IL-10 and TGF-β (Luo et al. 2020 ; He and Zhang 2021 ). Among these, a subset of FABP4 + TAMs, which have been likened to M3-like macrophages due to their association with alveolar macrophages in the lung, are enriched in lung metastatic lesions of osteosarcoma and promote tumor colonization by providing fatty acids to fuel cancer cell growth (Aran et al. 2019 ; Tang et al. 2023 ). MDSCs suppress T cell responses via arginase-1, reactive oxygen species (ROS), and inducible nitric oxide synthase (iNOS), while also promoting pre-metastatic niche formation and epithelial–mesenchymal transition (EMT) through VEGF, MMP9, and HGF (Haist et al. 2021 ; Ran and Wilber 2017 ; Tsubakihara and Moustakas 2018 ). Regulatory T cells (Tregs) further reinforce immunosuppression via inhibitory cytokines and direct cell contact mechanisms, with their infiltration correlating with poor prognosis (Li et al. 2023a ). TANs adopt an N2 pro-tumor phenotype under hypoxic conditions, and neutrophil extracellular traps (NETs) have been linked to metastasis and recurrence (Wu et al. 2019 ; Fu et al. 2021 ; Zhang et al. 2025 ; Tang et al. 2024 ). Cancer stem cells (CSCs), potentially originating from mesenchymal stem cells (MSCs),drive tumorigenesis and therapy resistance, while MSC-derived exosomes promoting Treg expansion and M2 polarization of TAMs (Chang et al. 2021 ; Zhang et al. 2018 ; Jia et al. 2016 ). Tumor-associated fibroblasts (TAFs) are reprogrammed by tumor-derived extracellular vesicles (EVs) and facilitate metastasis through extracellular matrix remodeling and the CXCL14-integrin α11β1 axis (Mazumdar et al. 2020 ; Xu et al. 2024 ). Collectively, these populations establish a highly immunosuppressive and pro-metastatic niche through metabolic competition, physical barrier formation, and multifaceted signaling. In the anti-tumor immune response, specific immune cells play crucial roles. Cytotoxic CD8 + T cells are central to tumor cell killing but often exhibit functional exhaustion characterized by elevated PD-1 and TIM-3 expression. In the context of osteosarcoma, these molecules mediate profound immunosuppression through distinct yet synergistic mechanisms. PD-1, primarily expressed on activated T cells, interacts with PD-L1 or PD-L2, which are frequently upregulated on osteosarcoma cells and TAMs. This engagement delivers potent inhibitory signals that dampen T-cell receptor (TCR) signaling pathways, leading to reduced production of effector cytokines (e.g., IFN-γ, TNF-α) and impaired cytotoxic granule release, effectively rendering T cells exhausted (Zhang et al. 2026 ). Concurrently, TIM-3 serves as a critical complementary checkpoint. Unlike PD-1, TIM-3 binds to a diverse array of ligands including galectin-9, HMGB1, and phosphatidylserine within the tumor microenvironment. The TIM-3/galectin-9 axis not only induces apoptosis in effector T cells but also actively drives the polarization of macrophages towards an immunosuppressive M2 phenotype. This dual functionality—directly inhibiting T-cell cytotoxicity while skewing the myeloid compartment towards a pro-tumorigenic state—establishes a robust barrier to immune surveillance in aggressive osteosarcoma (Li et al. 2017a ). Immune checkpoint blockade(ICB) can rejuvenate their activity, and higher infiltration levels correlate with improved prognosis (Liu et al. 2016 ). T cell function is suppressed by M2-TAMs, and depletion of CD163 + macrophages enhances T cell proliferation and cytokine production, underscoring the critical and targetable regulatory role of TAMs in immune modulation (Sun et al. 2021 ; Han et al. 2016 ). Macrophage functional heterogeneity is particularly noteworthy. Although M2-TAMs dominate the osteosarcoma microenvironment and are consistently associated with poor prognosis, angiogenesis, tumor cell invasion, and metastatic dissemination, M1-TAMs exert anti-tumor effects by secreting pro-inflammatory cytokines such as IL-12, IL-18, and TNF-α (Duluc et al. 2009 ). Single-cell analyses have further identified a C1Q + TAM subset linked to favorable prognosis, highlighting the functional diversity of macrophages in osteosarcoma (Tu et al. 2023 ). DCs are professional antigen-presenting cells whose maturation and function are often impaired by tumor-derived factors. Nonetheless, DC-based vaccines have demonstrated encouraging results in preclinical models (Le et al. 2021 ; He et al. 2016 ; Liu et al. 2021a ; Zhang et al. 2020a ; Miwa et al. 2017 ; Himoudi et al. 2012 ). NK cells mediate direct tumor lysis via perforin, granzymes, and death ligands, though their activity is suppressed by TGF-β and immune checkpoints such as TIGIT and PD-1; targeting these pathways may restore NK cytotoxicity (Prager and Watzl 2019 ; Zhang et al. 2019 ; Lazarova and Steinle 2019 ). B cells are associated with improved clinical outcomes and prolonged survival, suggesting a protective role, although regulatory B cells may also contribute to immunosuppression (Sarvaria et al. 2017 ; Li et al. 2021 ). Single-cell RNA sequencing has revolutionized our understanding of the osteosarcoma immune landscape. These studies reveal novel immune subsets and complex cellular crosstalk, emphasizing the plasticity and heterogeneity of macrophages (Liu et al. 2022 ; Wang et al. 2025 , 2023 ). These insights provide a foundation for developing precise immunotherapeutic strategies that target TAMs, whose functional reprogramming may reshape the entire immune ecosystem to favor tumor control. Biological basis of macrophages and their role in the tumor microenvironment Origin and classification of macrophages Macrophages are a highly heterogeneous and plastic population of immune cells that originate from distinct developmental pathways and exhibit diverse phenotypes and functions depending on their microenvironment. During embryonic development, tissue-resident macrophages arise from yolk sac-derived erythro-myeloid progenitors (EMPs) and fetal liver hematopoiesis, seeding various tissues prenatally where they self-renew through local proliferation without significant contribution from circulating monocytes in adulthood. This embryonic origin distinguishes these resident macrophages from bone marrow-derived monocytes, which differentiate into macrophages upon recruitment to tissues during inflammatory or pathological conditions (Kelleher and O'Sullivan 2017 ; Italiani and Boraschi 2014 ; Komohara et al. 2016 ; Cox et al. 2021 ). For example, cardiac macrophages comprise both embryonically derived self-renewing populations and monocyte-derived macrophages, each with distinct phenotypes and roles in homeostasis and injury response (Wang et al. 2020 ). In the thymus, two macrophage populations with distinct embryonic and adult hematopoietic origins coexist, demonstrating spatial and functional heterogeneity (Zhou et al. 2022 ). The bone marrow is the source of circulating monocytes that infiltrate tissues and contribute to macrophage pools under inflammatory stimuli and TME (Barsky et al. 2025 ). This dual origin underlies macrophage diversity in various organs and pathological states. From the perspectives of phenotype and functional characteristics, macrophages have been classically categorized into two polarized states: the classically activated M1 phenotype and the alternatively activated M2 phenotype. M1 macrophages, induced by interferon-γ and microbial products like lipopolysaccharide, exhibit pro-inflammatory, microbicidal, and tumoricidal activities, characterized by high production of pro-inflammatory cytokines, reactive oxygen species, and antigen presentation capacity. M2 macrophages, stimulated by IL-4, IL-13 or other anti-inflammatory signals, are involved in tissue repair, immune regulation, and tumor promotion through secretion of anti-inflammatory cytokines, growth factors, and extracellular matrix remodeling enzymes (Murray et al. 2014 ; Shapouri-Moghaddam et al. 2018 ; Pan et al. 2020 ; Boibessot et al. 2022 ). However, this binary M1/M2 paradigm is an oversimplification, as macrophages in vivo display a spectrum of activation states and phenotypes shaped by complex microenvironmental cues (Cassetta and Pollard 2018 ; Franklin et al. 2014 ; Mitsui and Satoh 2025 ). Using single-cell RNA sequencing and other omics technologies have revealed multiple macrophage subsets with distinct transcriptional signatures and metabolic profiles, challenging the classical dichotomy and highlighting the plasticity and heterogeneity of macrophage populations (Yu et al. 2023 ; Takabatake et al. 2025 ). Within this spectrum, the M2 phenotype is particularly diverse and can be further stratified into at least four functionally distinct subsets: M2a, M2b, M2c, and M2d. M2a macrophages, polarized by Th2 cytokines IL-4 and IL-13, are primarily involved in wound healing and fibrosis via factors like CCL17, CCL18, and TGF-β; in osteosarcoma, their pro-fibrotic activity likely contributes to the dense tumor stroma that impedes drug delivery. M2b macrophages, induced by immune complexes combined with Toll-like receptor (TLR) or IL-1R agonists, exhibit a mixed regulatory phenotype, secreting both anti-inflammatory IL-10 and pro-inflammatory IL-1β/TNF-α, thereby playing a complex role in modulating local immune responses. M2c macrophages, often termed "deactivated" macrophages, are driven by IL-10, TGF-β, and glucocorticoids to suppress inflammation and promote matrix deposition, creating an immunosuppressive niche. Crucially, M2d macrophages(frequently equated with tumor-associated macrophages in malignancy) are induced by TLR antagonists and adenosine A2A receptor (A2AR) agonists; this subset is strongly linked to tumor progression by promoting angiogenesis (via VEGF, IL-8), matrix remodeling, and potent immunosuppression while exhibiting reduced antigen presentation. In the specific context of osteosarcoma, the tumor microenvironment predominantly drives monocyte differentiation towards the M2d and M2c phenotypes, which synergistically foster an immunosuppressive, pro-angiogenic, and pro-metastatic niche (Shu-Jin et al. 2026 ). A schematic overview of macrophage origin and the main polarization pathways leading to these distinct macrophage phenotypes is provided in Fig. 2 . Fig. 2. Open in a new tab Macrophage origin and polarization pathways. Tissue macrophages arise via two distinct routes: A Embryonic, where yolk sac-derived EMPs seed tissues directly during development, and B Adult, where bone marrow HSCs generate circulating monocytes that infiltrate tissues upon inflammation to differentiate into macrophages via CSF-1/GM-CSF. Polarization: Environmental cues drive plasticity into distinct phenotypes In the TME, TAMs represent a major immune infiltrate and exhibit pronounced heterogeneity. TAMs can derive from both tissue-resident macrophages and recruited monocytes, with their relative contributions varying by tumor type and stage (Takabatake et al. 2025 ; Xu et al. 2022 ; Hourani et al. 2021 ). TAMs commonly adopt an M2-like immunosuppressive phenotype that promotes tumor growth, angiogenesis, invasion, and therapy resistance, although subsets with M1-like antitumor functions may also be present (Pan et al. 2020 ; Piontkowski et al. 2024 ). The phenotypic diversity of TAMs includes specialized subpopulations such as lipid-associated macrophages, inflammatory macrophages, and tissue-resident-like macrophages, each with unique gene expression profiles and functional roles (Keremitçi et al. 2025 ). This complexity is further compounded by spatial heterogeneity within tumors, where macrophage subsets localize to distinct niches and interact with other stromal and immune cells (Xiong et al. 2022 ). Emerging evidence suggests that the polarization state of TAMs significantly influences their functional specialization and response to therapeutic treatments, highlighting the importance of understanding their origin and classification for developing macrophage-targeted cancer therapies (Chi et al. 2024 ). Molecular mechanisms of macrophage plasticity in the TME Cytokine and growth factor signaling The recruitment and functional reprogramming of TAMs in osteosarcoma are orchestrated by a coordinated network of chemokines and cytokines that guide monocyte trafficking and polarize them toward a pro-tumor, immunosuppressive phenotype (Yan et al. 2020 ). Recruitment is primarily mediated by tumor and stroma-derived chemokines, including CCL2, CCL5, and CXCL12, which direct the homing of circulating monocytes to primary and metastatic sites (Franklin et al. 2014 ; Qian et al. 2011 ; Aldinucci and Colombatti 2014 ; Nagarsheth et al. 2017 ; Müller et al. 2001 ). Specifically, CCL2 acts as a potent chemoattractant for classical monocytes, binding to its receptor CCR2 to facilitate their transendothelial migration. In the osteosarcoma context, sustained CCL2 secretion not only ensures a continuous influx of these precursors but also primes them for rapid differentiation into immunosuppressive M2-like TAMs, thereby directly contributing to the exclusion of cytotoxic T cells and the establishment of a metastasis-permissive niche. The elevated levels of CCL2 and CXCL12 in serum and tumors are associated with lung metastasis and poor prognosis in patients with osteosarcoma, highlighting their clinical relevance (Chen et al. 2022 ). After monocytes infiltrate, they will reprogrammed by a microenvironment rich in cytokines, which will promotes M2-like polarization. Colony-stimulating factor 1 (CSF-1) is a central regulator, promoting TAM survival, proliferation, and expression of immunosuppressive markers through activation of the PI3K/Akt, JAK–STAT3 and NF-κB pathways (Pixley and Stanley 2004 ; Cornice et al. 2024 ). IL-4 and IL-13, which are produced by Th2 cells, eosinophils or tumor cells, work together to signal through the IL-4Rα–STAT6 pathway. This signaling leads to the induction of arginase-1 (ARG1), MRC1, and CCL17, which play significant roles in promoting tissue remodeling and facilitating immune evasion (Martinez et al. 2009 ). This polarization is further amplified by TGF-β and IL-10, often secreted by Tregs and cancer cells, which suppress MHC class II expression, inhibit pro-inflammatory cytokine production, and establish a self-reinforcing feedback loop that stabilizes the immunosuppressive TME (Li and Flavell 2008 ; Moore et al. 2001 ; Ouyang and O'Garra 2019 ). While IFN-γ can promote M1-like activation via STAT1, its effects are frequently blunted in osteosarcoma due to upregulation of SOCS proteins and PD-L1 (Inagaki-Ohara et al. 2013 ; Dong et al. 2002 ; Zaidi and Merlino 2011 ). The dominance of M2-polarizing signals underscores the therapeutic potential of targeting key pathways—such as CSF-1R, CCR2, or STAT6—in combination with immunotherapies to reprogram TAMs and restore anti-tumor immunity. However, soluble cytokines represent only the initial layer of regulation. To sustain this immunosuppressive phenotype against fluctuating environmental cues, osteosarcoma cells employ more sophisticated, contact-independent mechanisms to deliver functional cargo directly into macrophages, thereby reinforcing the transcriptional and metabolic programs initiated by cytokine signaling. Exosome-mediated intercellular communication Beyond soluble signals, tumor-derived exosomes serve as critical vehicles for intercellular communication, directly transferring diverse oncogenic cargo to macrophages that reprogram their phenotype and function. While much attention has focused on nucleic acids, emerging evidence highlights lipids—particularly long-chain fatty acids (LCFAs)—as key functional components of exosomal cargo. Macrophages expressing the fatty acid translocase CD36 efficiently internalize these lipid-laden exosomes. The internalized LCFAs are shuttled to mitochondria and utilized in fatty acid oxidation (FAO), a metabolic shift that is not merely supportive but instructive, actively driving macrophages toward an M2-like phenotype (Pascual et al. 2017 ). This exosome-induced metabolic reprogramming is characterized by increased secretion of pro-angiogenic factors, immunosuppressive cytokines, and intracellular lipid droplet accumulation, a hallmark of TAMs in multiple cancers, including osteosarcoma (Morrissey et al. 2021 ). Exosomal non-coding RNAs contribute to transcriptional reprogramming of TAMs (Raimondi et al. 2020 ). Osteosarcoma-derived exosomes deliver the long non-coding RNA ELFN1-AS1, which is internalized by macrophages and functions as a molecular sponge for miR-138-5p and miR-1291, thereby relieving their inhibition on the target gene CREB1; the subsequent upregulation of CREB1 drives the transcription of M2-specific markers CD206 and IL-10, mechanistically enforcing M2 polarization and facilitating tumor progression (Wang et al. 2022 ).Osteosarcoma-derived exosomes deliver the long non-coding RNA SCAMP1-AS1, which is internalized by osteosarcoma cells, where it regulates the LKB1-AMPK signaling pathway, leading to enhanced malignant characteristics such as proliferation, migration, and invasion, thus facilitating tumor progression (Li et al. 2025 ). Osteosarcoma-derived exosomes mediate M2 macrophage polarization through Tim-3, which in turn promotes the invasion and metastasis of osteosarcoma cells via the secretion of IL-10, TGF-β, and VEGF (Cheng et al. 2021 ). Osteosarcoma-derived exosomal miR-25-3p shuttles between cells, functioning intracellularly as an oncogene to promote tumor progression and drug resistance by targeting DKK3, and extracellularly to enhance angiogenesis (Yoshida et al. 2018 ). These nucleic acid-mediated signals often synergize with metabolic reprogramming, creating a robust immunosuppressive program. Crucially, the metabolic shifts induced by exosomal lipids (such as FAO) do not occur in isolation but are tightly coupled with the physical constraints of the tumor microenvironment. The hypoxic conditions prevalent in rapidly growing osteosarcoma masses further dictate these metabolic choices, creating a synergistic effect where exosome-mediated lipid supply and hypoxia-driven glycolysis jointly lock TAMs into a stable M2 state. Exosome-mediated signaling operates through both metabolic and transcriptional mechanisms to shape the TME. Targeting exosome biogenesis, CD36-mediated uptake, or downstream metabolic pathways may offer novel strategies to disrupt this axis and enhance immunotherapy efficacy in osteosarcoma. Hypoxia and metabolic reprogramming Hypoxia is a hallmark of solid tumors and profoundly influences the plasticity and function of TAMs (Becker et al. 2016 ). In the TME, hypoxia stabilizes hypoxia-inducible factors (HIFs), particularly HIF-1α and HIF-2α, which act as master regulators of cellular adaptation. Under normoxic conditions, HIF-α subunits are hydroxylated by prolyl hydroxylases (PHDs) and targeted for proteasomal degradation via the von Hippel-Lindau protein (pVHL). In hypoxia, PHD activity is suppressed, leading to HIF-α accumulation, nuclear translocation, and dimerization with HIF-1β. This complex binds to hypoxia-response elements (HREs) in target genes, driving the expression of factors that promote angiogenesis, extracellular matrix remodeling, and immunosuppression. In osteosarcoma, the over expression of HIF-1α is correlates with advanced stage, metastasis, and poor survival, underscoring its clinical significance (Majmundar et al. 2010 ; Semenza 2003 ; Yang et al. 2007 ). Hypoxia reprograms TAMs toward a glycolytic phenotype, enhancing glucose uptake and lactate production while suppressing oxidative phosphorylation (Palazon et al. 2014 ). HIF-1α upregulates key glycolytic enzymes and glucose transporters(such as GLUT1) and inhibits mitochondrial respiration by inducing pyruvate dehydrogenase kinase (PDK) (Kim et al. 2006 ). This metabolic shift supports TAM survival in nutrient-poor regions and reinforces an M2-like immunosuppressive state. For instance, HIF-driven expression of ARG1 diverts arginine metabolism away from nitric oxide synthesis, impairing T-cell function (Corzo et al. 2010 ). Importantly, this metabolic rewiring establishes a "metabolism-epigenetics-transcription" regulatory axis. The accumulation of glycolytic metabolites (e.g., lactate) and the reduction of TCA cycle intermediates (e.g., α-ketoglutarate) directly modulate the activity of epigenetic enzymes such as histone deacetylases (HDACs) and Jumonji C-domain demethylases. In osteosarcoma, hypoxia-induced suppression of α-ketoglutarate-dependent demethylases leads to the retention of repressive histone marks on M1 genes while preserving active marks on M2 loci, effectively locking the transcriptional program driven by HIF-1α and STAT6 into a stable, heritable state. This metabolic reprogramming synergizes with exosome-mediated signaling: hypoxia enhances CD36 expression in macrophages, promoting uptake of tumor-derived lipid-rich exosomes and subsequent fatty acid oxidation (FAO), further stabilizing the M2 phenotype (Crucet et al. 2013 ). Furthermore, hypoxia induces the recruitment and activation of immunosuppressive cells, including MDSCs and regulatory Tregs, via HIF-dependent chemokine secretion (Doedens et al. 2010 ; Noman et al. 2015 ). This creates a feedback loop that sustains TAM polarization, angiogenesis, and therapy resistance. The interplay between hypoxia and metabolic rewiring underscores TAM adaptability, making HIF pathways potential therapeutic targets to disrupt protumorigenic networks in osteosarcoma.Critically, this hypoxic drive acts synergistically with exosomal lipid supply, where hypoxia-induced CD36 upregulation maximizes the uptake of exogenous fatty acids that are essential to fuel FAO and sustain M2 polarization when endogenous oxidative metabolism is compromised. Crosstalk with other immune cells TAMs function as central orchestrators of immune suppression through dynamic, bidirectional crosstalk with diverse immune populations in the TME. A pivotal interaction occurs with T cells: TAMs express immune checkpoint ligands such as PD-L1, which engages PD-1 on cytotoxic T cells, leading to exhaustion, reduced cytokine production, and apoptosis (Li et al. 2023b ). TAMs also secrete CCL22 to recruit Tregs, which reciprocally produce IL-10 and TGF-β to reinforce M2 polarization and suppress antigen presentation—forming a self-sustaining immunosuppressive circuit (Yang and Zhang 2017 ). In osteosarcoma, spatial co-localization of TAMs and Tregs correlates with advanced disease and poor outcomes. TAMs also are also involved in the interactions between myeloid cells. Hypoxia and tumor-derived factors drive the expansion of MDSCs, which in turn secrete IL-10 and ARG1 to further polarize TAMs toward an M2 state. Conversely, TAM-derived IL-6 and IL-1β enhance MDSC survival and suppressive capacity, amplifying immune evasion (Kumar et al. 2016 ; Waight et al. 2011 ). With NK cells, TAMs inhibit cytotoxicity through multiple mechanisms: TGF-β downregulates NKG2D receptors on NK cells, while IL-10 and PGE2 impair IFN-γ production and lytic granule release (Castriconi et al. 2003 ; Wang et al. 2021 ; Santiso et al. 2024 ). Emerging evidence also implicates crosstalk with B cells, where TAM-secreted factors such as BAFF and IL-10 may promote regulatory B-cell differentiation, contributing to immune tolerance (Rosser and Mauri 2015 ; Shen and Fillatreau 2015 ). These multilayered interactions position TAMs as hub cells in the immunosuppressive network. Targeting TAMs offers a promising avenue to restore anti-tumor immunity in osteosarcoma. Targeting TAMs for osteosarcoma immunotherapy The pivotal role of TAMs in driving osteosarcoma progression, immunosuppression, and therapy resistance has established them as a compelling therapeutic target. Unlike broad surveys across malignancies, recent efforts in osteosarcoma have moved beyond theoretical mechanisms to evaluate specific interventions in orthotopic, metastatic, and patient-derived xenograft (PDX) models. This section focuses on strategies with demonstrated efficacy in osteosarcoma-specific contexts. Depletion and recruitment inhibition This strategy seeks to reduce the pro-tumoral influence of TAMs by directly eliminating them or preventing their influx into the TME (Rooijen and Hendrikx 2010 ). Depletion is commonly achieved by targeting the colony-stimulating factor 1 receptor (CSF-1R) signaling axis, which is critical for TAM survival and proliferation. In osteosarcoma, high CSF-1R expression correlates with metastasis and poor prognosis (Shang et al. 2025 ; Xiao et al. 2014 ). Small molecule inhibitors or monoclonal antibodies against CSF-1R can effectively induce TAM apoptosis, suppress tumor growth, and enhance T-cell infiltration in preclinical models (Ries et al. 2014 ). Inhibiting the recruitment of monocytic precursors by blocking key chemokine axes—such as CCL2/CCR2 or CXCL12/CXCR4 (using agents like plerixafor)—can significantly reduce TAM infiltration into tumors (Qian et al. 2011 ; Lee et al. 2013 ; Li et al. 2017b ). For instance, TIPE1 has been shown to suppress osteosarcoma growth by regulating macrophage infiltration via the MCP-1/CCR2 axis (Chen et al. 2019 ).Transient CCL2 blockade may trigger a rebound effect due to feedback upregulation, suggesting that intermittent dosing or combination with chemotherapy may be more effective (Bonapace et al. 2014 ; Caronni et al. 2023 ). However, these strategies face challenges, including compensatory cytokine release, the potential depletion of beneficial macrophage subsets involved in tissue homeostasis, and limited single-agent efficacy. Therefore, combination approaches are increasingly favored. The rationale for combining TAM-directed therapies with ICB is strongly supported by their intricate crosstalk. TAMs are a significant source of PD-L1 expression within the osteosarcoma TME (Koirala et al. 2016 ; Majzner et al. 2017 ). Furthermore, certain TAM subsets express PD-1, which can directly impair their phagocytic capacity and contribute to immune suppression (Gordon et al. 2017 ). Therefore, anti-PD-1/PD-L1 therapy may exert part of its effect by blocking these inhibitory signals on macrophages themselves, thereby rejuvenating their anti-tumor functions (Gong et al. 2018 ; He and Li 2019 ). This provides a compelling mechanistic basis for the superior efficacy of combination strategies. Reprogramming toward anti-tumor phenotypes Instead of eliminating TAMs, reprogramming strategies aim to functionally reprogram immunosuppressive M2-TAMs into immunostimulatory M1- macrophages. This approach leverages the inherent plasticity of TAMs, which in osteosarcoma is strongly associated with metastatic progression and poor survival. CD40 agonists engage CD40 on macrophages to mimic CD4 + T cell-derived co-stimulatory signals, triggering potent activation, pro-inflammatory cytokine production, and enhanced antigen presentation. In preclinical sarcoma models, CD40 stimulation reprograms TAMs, promotes dendritic cell maturation, and synergizes with PD-1 blockade to induce tumor regression (Anfray et al. 2019 ; Dalen et al. 2018 ). Beyond specific pathway targeting, broad-spectrum macrophage activation has achieved clinical translation in osteosarcoma. Liposomal muramyl tripeptide phosphatidyl ethanolamine (L-MTP-PE; mifamurtide), an immunostimulant derived from bacterial cell walls, is approved in the European Union (Mepact®) in combination with chemotherapy for non-metastatic osteosarcoma (Ando et al. 2011 ; Sone et al. 1984 ; Meyers et al. 2008 ; Kleinerman et al. 1991 ). It activates monocytes and macrophages to produce TNF-α and IL-6, driving a tumoricidal phenotype that suppresses metastasis and induces an intermediate activation state beyond the classical M1/M2 dichotomy (Punzo et al. 2020 ; Pahl et al. 2014 ; Kurzman et al. 1999 ). Toll-like receptor (TLR) agonists, such as CpG oligonucleotides (TLR9) or imiquimod (TLR7), directly activate NF-κB and IRF pathways, driving M1 polarization and type I interferon responses (Anfray et al. 2019 ). In preclinical osteosarcoma models, nanoparticle-delivered or chemotherapy-combined TLR agonists have been shown to overcome the TME and enhance anti-tumor immunity (Wang et al. 2019 ). Pharmacological inhibition of the PI3Kγ pathway targets a key node integrating inputs from immunosuppressive receptors like CSF-1R. This disruption reverses TAM immunosuppression, enhances T-cell infiltration, and in hypoxic regions of osteosarcoma, alters metabolic reprogramming (Henau et al. 2016 ). While earlier generations of CSF-1R inhibitors faced challenges such as compensatory GM-CSF upregulation and limited efficacy as monotherapies, novel agents now demonstrate improved selectivity and reprogramming potency. Recent studies highlight that next-generation small molecules and blocking antibodies can more precisely target the CSF-1R homodimer without affecting c-Kit, thereby reducing off-target toxicity. Crucially, preclinical data in osteosarcoma models indicate that these novel inhibitors, when combined with immune checkpoint blockers (e.g., anti-PD-1) or chemotherapy, effectively shift the TAM phenotype from an immunosuppressive M2-like state to a pro-inflammatory M1-like state. This reprogramming is characterized by increased expression of co-stimulatory molecules (CD80/CD86) and secretion of IL-12, which subsequently recruits cytotoxic CD8 + T cells and breaks the cycle of immune evasion. Furthermore, emerging dual-targeting strategies that simultaneously inhibit CSF-1R and other checkpoints (e.g., CD47 or PD-L1) are showing promising results in preventing metastatic relapse by sustaining the reprogrammed anti-tumor macrophage population within the lung niche (Dai et al. 2025 ). Several pharmacologically active agents have demonstrated TAM-reprogramming effects in osteosarcoma. All-trans retinoic acid (ATRA) inhibits M2 polarization and MMP12 secretion, thereby preventing metastasis and suppressing TAM-mediated enhancement of tumor initiation and stemness (Shao et al. 2019 ; Zhou et al. 2017 ). The EGFR inhibitor gefitinib reprograms TAMs by inhibiting RIPK2, blocking macrophage-promoted invasion and metastatic extravasation (Maloney et al. 2020 ; Kallis et al. 2020 ). Natural compounds like onionin A, epimedokoreanin B, corosolic acid, oleanolic acid, and wogonin exert anti-metastatic effects in preclinical models by suppressing STAT3-dependent M2 polarization (Nohara et al. 2017 ; Fujiwara et al. 2014 ; Kimura and Sumiyoshi 2013 , 2015 , 2016 ; Sumiyoshi et al. 2015 ). These findings highlight TAM reprogramming as a versatile immunomodulatory strategy. Its integration with chemotherapy, targeted therapy, or ICB offers a promising avenue for overcoming immunosuppression in osteosarcoma. Enhancing phagocytic function This strategy focuses on overcoming ‘don't eat me’ signals that tumor cells exploit to evade macrophage phagocytosis. The CD47-SIRPα axis is the most prominent target in this pathway. Osteosarcoma cells often overexpress CD47, which is associated with poor prognosis. This protein binds to SIRPα on the surface of macrophages, triggering inhibitory SHP-1/2 phosphatases, suppressing myosin-II accumulation at the phagocytic synapse and thereby blocking the phagocytosis (Liu et al. 2017 ; Willingham et al. 2012 ). Blocking this interaction with anti-CD47 or anti-SIRPα agents unleashes macrophage phagocytic activity, resulting in direct tumor cell clearance (Jalil et al. 2020 ; Zhang et al. 2020b ). This phagocytosis promotes antigen processing and cross-presentation to dendritic cells and T cells, thereby bridging innate and adaptive immunity. The effect is synergistically amplified when CD47 blockade with tumor-opsonizing antibodies, which engage Fc receptors on macrophages to drive antibody-dependent cellular phagocytosis (ADCP)—a synergistic mechanism actively explored in sarcoma models (Jiang et al. 2024 ; Jia et al. 2021 ). A key challenge for CD47-targeted therapy is on-target anemia due to its expression on red blood cells. Next-generation agents with tumor-selective binding or conditional activation are in development to improve the therapeutic window (Lu et al. 2025b ; Puro et al. 2020 ; Thaker et al. 2022 ). Emerging therapeutic approaches The frontier in TAM-targeted therapy is being shaped by the development of combinatorial, precision-engineered, and intelligently delivered strategies. In osteosarcoma, where single-agent immunotherapy has shown limited efficacy, rational combinations—such as CSF-1R inhibitors with PD-1/PD-L1 blockade or CD40 agonists with chemotherapy—are being evaluated in clinical trials and demonstrate enhanced anti-tumor activity by simultaneously targeting multiple immunosuppressive axes within the TME. Nanoparticle-based delivery systems are revolutionizing selective TAM modulation. By engineering lipid or polymeric nanoparticles with TAM-targeting ligands, therapeutics such as STAT6-siRNA (to disrupt M2 polarization) or TLR agonists (to drive M1 polarization) can be delivered directly to TAMs. In preclinical osteosarcoma models, such targeted delivery significantly enhances therapeutic efficacy while minimizing systemic toxicity (Patni et al. 2024 ; Pennisi et al. 2025 ; Zhao et al. 2021 ). Chimeric antigen receptor macrophages (CAR-M) represent a revolutionary approach (Sánchez-Paulete et al. 2022 ). Engineered to express tumor-specific receptors, CAR-M cells exhibit enhanced phagocytosis, antigen presentation, and pro-inflammatory cytokine secretion upon tumor engagement. In osteosarcoma models, CAR-M cells remodel the TME, recruit T cells, and exert potent anti-tumor effects (Liu et al. 2024 ; Maalej et al. 2023 ; Pierini et al. 2025 ). Although in early-stage clinical development, CAR-M therapy holds transformative potential for recalcitrant or metastatic osteosarcoma, marking a paradigm shift from modulating endogenous immunity to deploying engineered immune effector cells. Early-phase studies report that infused CAR-M cells are well-tolerated, with manageable cytokine release syndrome (CRS) profiles distinct from those observed in CAR-T therapy. Crucially, recent translational findings indicate that CAR-M cells not only persist within the hypoxic and immunosuppressive osteosarcoma microenvironment longer than anticipated but also function as effective antigen-presenting cells (APCs). By upregulating co-stimulatory molecules and secreting chemokines, these engineered macrophages successfully recruit and activate endogenous CD8 + T cells, thereby converting a "cold" tumor into an inflamed niche. Ongoing trials are now evaluating next-generation CAR-M constructs armed with checkpoint-resistant domains or secretable cytokines (e.g., IL-12) to further amplify this synergistic anti-tumor response in refractory osteosarcoma patients (Klichinsky et al. 2020 ; Cheng et al. 2024 ). In summary, while TAM depletion effectively reduces immunosuppressive burden, it risks compensatory rebound and loss of homeostatic functions. Reprogramming strategies restore anti-tumor activity but face challenges in maintaining stable polarization within the hypoxic microenvironment. Enhancing phagocytosis via CD47 blockade potently clears tumor cells yet is limited by on-target anemia. Finally, emerging approaches like CAR-M and nanotherapy offer high precision but require further validation for clinical scalability. Crucially, the design of such combinations must adhere to principles of mechanistic complementarity (targeting non-redundant pathways), temporal sequencing (depleting or reprogramming TAMs prior to T-cell activation), and toxicity mitigation to ensure synergistic efficacy without overlapping adverse events. Consequently, combining these complementary strategies is increasingly recognized as the optimal path to overcome individual limitations and maximize therapeutic efficacy in osteosarcoma. Challenges in clinical translation Despite the promising therapeutic potential of targeting TAMs, several formidable challenges impede their clinical translation in osteosarcoma: tumor and microenvironmental heterogeneity, the dynamic plasticity of TAMs, limited biomarkers for patient stratification, on-target off-tumor toxicity, and discrepancies between preclinical models and human disease. Tumor heterogeneity and TAM plasticity Osteosarcoma is characterized by profound genetic and phenotypic heterogeneity, both between patients and within individual lesion. This diversity extends to the TME, where the density, spatial distribution, and functional states of TAMs differ markedly across tumors and even in different regions of the same tumor (Welch et al. 2023 ; Rajan et al. 2023 ). Recent single-cell RNA sequencing analyses of human osteosarcoma samples have resolved TAMs into distinct transcriptional clusters that extend far beyond the classical M1/M2 dichotomy. Among these, SPP1 + (Osteopontin +) macrophages are notably enriched in metastatic lesions, where they drive extracellular matrix remodeling and facilitate lung colonization. Concurrently, MRC1 + (CD206 +) and FOLR2 + subsets exhibit potent immunosuppressive signatures characterized by high expression of TGFB1 and IL10, correlating strongly with poor overall survival and chemotherapy resistance. Furthermore, CXCL12 + macrophages, often localized near cancer-associated fibroblasts, form a supportive niche that promotes tumor cell proliferation and stemness. Although interferon-responsive and proliferative macrophage populations are present, they are frequently outnumbered by these pro-tumoral clusters in advanced disease, underscoring the dominance of an immunosuppressive microenvironment in osteosarcoma progression (Zhou et al. 2020 ; Liu et al. 2021b ). TAMs exhibit remarkable phenotypic plasticity, rapidly adapting to changing environmental cues such as hypoxia, chemotherapy, or immunotherapy. While this plasticity enables therapeutic reprogramming, it also poses a risk of therapy-induced resistance. For instance, an initial pro-inflammatory shift may be counteracted by compensatory immunosuppressive feedback loops involving IL-10, TGF-β, or regulatory Tregs (Kumar et al. 2020 ). Transient or incomplete reprogramming may fail to sustain anti-tumor immunity, underscoring the critical need for durable and context-aware modulation strategies. Lack of specific biomarkers A major barrier to the clinical development of TAM-directed therapies is the absence of reliable biomarkers. Current histopathological methods, such as immunohistochemical (IHC) staining for CD68 or CD163, offer only static snapshots of macrophage abundance and crude polarization statess. They fail to capture the dynamic functional status of TAMs or their critical spatial interactions with other immune and stromal cells in the TME (Xu et al. 2025 ). Furthermore, no consensus exists on which markers best define reprogrammed TAMs in vivo. While surface markers like CD80, CD86, or HLA-DR are generally associated with an M1-like activation state, their expression can be context-dependent and modulated by non-immunological factors. This gap highlights an urgent need for the development of advanced, dynamic biomarker strategies. These could include novel imaging-based biomarkers or liquid biopsy approaches to enable real-time monitoring of TAM responses during therapy. To achieve this, concrete technical routes should integrate spatial transcriptomics to map the precise neighborhood interactions between TAMs and osteosarcoma cells, alongside mass cytometry (CyTOF) for high-dimensional single-cell phenotyping that resolves complex TAM subpopulations beyond conventional markers. On-target off-tumor toxicity Macrophages are indispensable sentinels for maintaining tissue homeostasis, facilitating wound healing, and defending against pathogens throughout the body. The systemic activation or depletion strategies carry a relatively high risk of on-target off-tumor toxicity. This remains a pivotal challenge for TAM-targeted therapy, as many molecular targets expressed on TAMs are also ubiquitously expressed on normal tissue-resident macrophages and other cell types. During systemic drug administration, these therapeutics can disrupt homeostatic macrophage populations in vital organs——such as the liver, skin, and bone marrow—which are critical for maintaining tissue integrity and immune surveillance. This disruption can lead to adverse effects including hepatotoxicity, dermatological complications, and impaired overall immune function. For example, broad TAM activation via TLR agonists or CD40 stimulation can potentially trigger systemic inflammatory reactions, cytokine release syndrome (CRS), or hepatotoxicity (Hess et al. 2017 ). Although CSF-1R inhibitors can effectively reduce the infiltration of TAMs, their impact on the macrophage population outside the tumor can often lead to adverse reactions such as fatigue, edema, and elevated liver enzymes. (Dowlati et al. 2021 ). To mitigate these risks, next-generation therapeutic strategies are increasingly focusing on tumor-targeted delivery systems, such as nanoparticles functionalized with tumor-homing peptides or antibodies, and the design of prodrugs that are activated specifically within the acidic or hypoxic TME. These innovative approaches aim to confine macrophage modulation precisely to the tumor site, thereby sparing healthy tissues and minimizing systemic toxicity. Limitations of preclinical models The majority of our mechanistic understanding of TAM biology and the efficacy of targeting strategies is derived from murine models, which possess inherent limitations in faithfully recapitulating the complexity of human osteosarcoma. Specific limitations vary significantly by model type: Syngeneic mouse models, such as the highly metastatic K7M2 and LM8 lines (implanted in BALB/c or C57BL/6 mice) and the MOS-J model, retain a functional host immune system. However, while these models exhibit robust macrophage infiltration predominantly skewed toward an M2-like, immunosuppressive phenotype similar to human metastatic lesions, they lack the genomic diversity and heterogeneity characteristic of human disease, and their murine-specific cytokine networks may not fully mirror human TAM recruitment mechanisms. Conversely, standard xenograft models established using human cell lines (e.g., 143B, MG-63, Saos-2, or U2OS) in immunodeficient mice (e.g., nude or NOD/SCID)are incapable of assessing the activity of immune-modulating therapies within a functional, intact immune system, as they completely lack endogenous T cells and functional human-macrophage interactions.. Patient-derived xenografts (PDXs) implanted in humanized mice offer a improved level of fidelity by preserving some human-specific tumor and immune components. Recent studies utilizing osteosarcoma PDXs engrafted into humanized mice (e.g., NSG-SGM3 reconstituted with human CD34 + cells) have successfully demonstrated the recruitment of human monocytes that differentiate into tumor-associated macrophages. Notably, recent advances in "bone-humanized" mouse models, which involve implanting human bone fragments or engineering mice to express human bone-specific cytokines (e.g., M-CSF, IL-34), now provide a more physiologically accurate niche for studying human osteoclast-macrophage-tumor interactions in osteosarcoma. Key findings from these models include the identification of human-specific SPP1 + and MRC1 + TAM subsets that drive metastasis and confer resistance to PD-1 blockade—insights that were not replicable in standard syngeneic or xenograft settings. However, they remain expensive, low-throughput, and suffer from variable engraftment success rates and often exhibit incomplete myeloid lineage maturation. Moreover, fundamental differences between murine and human macrophages—in terms of receptor expression patterns, cytokine responsiveness, and metabolic regulation—can significantly affect the translatability of preclinical findings to the clinic (Okada et al. 2019 ). Emerging models, such as patient-derived organoids co-cultured with autologous immune cells or genetically engineered large animal models, hold considerable promise for bridging this translational gap but require further validation and standardization. Specifically, osteosarcoma organoid-immune cell co-culture systems have emerged as a powerful high-throughput platform, enabling the preservation of patient-specific tumor architecture while allowing real-time observation of autologous TAM infiltration, polarization dynamics, and response to immunotherapies in a controlled 3D environment. Thus, there is a critical and unmet need for the development of more physiologically relevant, robust, and scalable preclinical models. Such models must preserve the intricate crosstalk between human tumor, immune, and stromal cells and enable the longitudinal assessment of TAM dynamics in response to therapeutic intervention. Conclusion Osteosarcoma is a highly aggressive bone malignancy with stagnant therapeutic outcomes. TAMs are key immune cells in the microenvironment of osteosarcoma and exhibit remarkable plasticity. This plasticity enables it to undergo dynamic transitions between M1-like anti-tumor phenotype and M2-like pro-tumor phenotype—making it not only a key driver of disease progression but also a potential target for immunotherapy. In the osteosarcoma TME, TAMs are predominantly skewed toward an M2-like state through a complex network of signals, including cytokines, exosome-mediated intercellular communication, hypoxia-driven metabolic reprogramming, and crosstalk with other immune cells such as Tregs and MDSCs. This polarization in turn fuels immune evasion, angiogenesis, metastasis, and therapy resistance, correlating strongly with poor patient prognosis. Therapeutic strategies are therefore pivoting to harness this plasticity, aiming to deplete TAMs, inhibit their recruitment, reprogram them toward anti-tumor effector states, or unleash their phagocytic potential. Modulating key nodes such as CSF-1R, CD40, TLRs, and the CD47-SIRPα ‘don't eat me’ axis has shown promise in preclinical settings, particularly when combined with chemotherapy, immune checkpoint inhibitors, or other immunomodulatory agents. The clinical approval of mifamurtide in Europe represents a milestone in macrophage-targeted therapy for osteosarcoma; however, its mechanism remains partially debated, and it has not demonstrated consistent survival benefits across all patient populations, highlighting the complexity of translating innate immune modulation. Next-generation platforms like nanoparticle-based targeted delivery and CAR-M offer unprecedented opportunities to precisely engineer TAM function, yet these approaches remain largely experimental and face significant manufacturing, safety, and delivery hurdles before clinical viability can be assured. However, significant challenges remain in translating these advances into durable clinical benefits, and a critical appraisal of current limitations is essential to avoid repeating past failures. Substantial hurdles include profound tumor heterogeneity and the consequent adaptability of TAMs, which may lead to rapid compensatory resistance mechanisms similar to those observed with CSF-1R inhibitors in other solid tumors. Furthermore, the scarcity of validated predictive biomarkers poses a severe risk of enriching clinical trials with non-responders, potentially obscuring true therapeutic efficacy. The risk of on-target, off-tumor toxicities—given the essential homeostatic roles of macrophages—remains a significant safety concern. Crucially, as detailed earlier, the limited capacity of existing preclinical models to faithfully recapitulate the human immune context complicates the predictive accuracy of therapeutic efficacy, raising the possibility that many promising preclinical findings may represent false positives that will not translate to human patients. Overcoming these obstacles will require more than just a multidisciplinary approach integrating single-cell omics, spatial profiling, and humanized models; it demands a paradigm shift from broad immune modulation to precision targeting based on rigorous patient stratification. Therefore, while targeting macrophage plasticity holds theoretical promise for converting immunologically "cold" osteosarcomas into "hot" tumors, enthusiasm must be tempered with realistic expectations. History suggests that monotherapy targeting single myeloid checkpoints is unlikely to yield transformative results in such a heterogeneous disease. Continued innovation in immunotherapeutic design is necessary, but without addressing the fundamental issues of biomarker deficiency, model fidelity, and compensatory resistance, the full potential of macrophage-centered therapies may remain unrealized. Future research must prioritize the development of context-specific, combination-based, and spatially targeted interventions that are validated in human-relevant systems prior to large-scale clinical deployment. The overarching goal is to precisely engineer a sustained immune-activating niche within the TME, but this must be pursued with a clear understanding of the risks of toxicity and treatment failure. Through these cautious, evidence-based, and rigorously validated efforts, we may finally improve outcomes for patients with this devastating disease, avoiding the pitfalls of previous translational attempts and paving the way for a truly effective era in sarcoma immunotherapy. Author contributions Li.H., Liu.C., Zhang.B ., and Zhang.Y. conceptualized the review framework, conducted the comprehensive literature search, and drafted the original manuscript. Li.H. designed and prepared Figs. 1 and 2, including the graphical abstract of the osteosarcoma immune landscape and the macrophage origin and polarization pathways. Liu .Y. (corresponding author) supervised the entire project, critically revised the manuscript for important intellectual content, and secured the funding. All authors read, edited, and approved the final version of the manuscript and agreed to its submission. Funding The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the Yanbian Prefecture Science and Technology Development Program Project (2025NS15). Data availability This article is a review article and does not contain original research data. Therefore, no new datasets were generated or analyzed during this study. Declarations Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Haifeng Li and Chengri Liu are contributed to this work. References Aldinucci D, Colombatti A (2014) The inflammatory chemokine CCL5 and cancer progression. Mediators Inflamm 2014:292376 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ando K, Mori K, Corradini N, Redini F, Heymann D (2011) Mifamurtide for the treatment of nonmetastatic osteosarcoma. Expert Opin Pharmacother 12(2):285–292 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Anfray C, Ummarino A, Andón FT, Allavena P (2019) Current strategies to target tumor-associated-macrophages to improve anti-tumor immune responses. Cells. 10.3390/cells9010046 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Aran D, Looney AP, Liu L et al (2019) Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol 20(2):163–172 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Bader JE, Voss K, Rathmell JC (2020) Targeting metabolism to improve the tumor microenvironment for cancer immunotherapy. Mol Cell 78(6):1019–1033 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Barsky SH, Mcphail K, Wang J, Hoffman RM, Ye Y (2025) Bone marrow origin of mammary phagocytic intraductal macrophages (foam cells). Int J Mol Sci. 10.3390/ijms26041699 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Becker A, Thakur BK, Weiss JM, Kim HS, Peinado H, Lyden D (2016) Extracellular vesicles in cancer: cell-to-cell mediators of metastasis. Cancer Cell 30(6):836–848 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Beird HC, Bielack SS, Flanagan AM et al (2022) Osteosarcoma. Nat Rev Dis Primers 8(1):77 [ DOI ] [ PubMed ] [ Google Scholar ] Boibessot C, Molina O, Lachance G et al (2022) Subversion of infiltrating prostate macrophages to a mixed immunosuppressive tumor-associated macrophage phenotype. Clin Transl Med 12(1):e581 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Bonapace L, Coissieux M, Wyckoff J et al (2014) Cessation of CCL2 inhibition accelerates breast cancer metastasis by promoting angiogenesis. Nature 515(7525):130–133 [ DOI ] [ PubMed ] [ Google Scholar ] Caronni N, La Terza F, Vittoria FM et al (2023) IL-1β+ macrophages fuel pathogenic inflammation in pancreatic cancer. Nature 623(7986):415–422 [ DOI ] [ PubMed ] [ Google Scholar ] Casey DL, Cheung NV (2020) Immunotherapy of pediatric solid tumors: treatments at a crossroads, with an emphasis on antibodies. Cancer Immunol Res 8(2):161–166 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Cassetta L, Pollard JW (2018) Targeting macrophages: therapeutic approaches in cancer. Nat Rev Drug Discov 17(12):887–904 [ DOI ] [ PubMed ] [ Google Scholar ] Castriconi R, Cantoni C, Chiesa MD et al (2003) Transforming growth factor beta 1 inhibits expression of NKp30 and NKG2D receptors: consequences for the NK-mediated killing of dendritic cells. Proc Natl Acad Sci U S A 100(7):4120–4125 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Chang X, Ma Z, Zhu G, Lu Y, Yang J (2021) New perspective into mesenchymal stem cells: molecular mechanisms regulating osteosarcoma. J Bone Oncol 29:100372 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Chen P, Zhou J, Li J, Zhang Q, Zuo Q (2019) TIPE1 suppresses osteosarcoma tumor growth by regulating macrophage infiltration. Clin Transl Oncol 21(3):334–341 [ DOI ] [ PubMed ] [ Google Scholar ] Chen X, Zhang N, Zheng Y et al (2022) Identification of key genes and pathways in osteosarcoma by bioinformatics analysis. Comput Math Methods Med 2022:7549894 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Cheng Z, Wang L, Wu C, Huang L, Ruan Y, Xue W (2021) Tumor-derived exosomes induced M2 macrophage polarization and promoted the metastasis of osteosarcoma cells through Tim-3. Arch Med Res 52(2):200–210 [ DOI ] [ PubMed ] [ Google Scholar ] Cheng S, Wang H, Kang X, Zhang H (2024) Immunotherapy innovations in the fight against osteosarcoma: emerging strategies and promising progress. Pharmaceutics. 10.3390/pharmaceutics16020251 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Chi J, Gao Q, Liu D (2024) Tissue-resident macrophages in cancer: friend or foe. Cancer Med 13(21):e70387 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Cornice J, Verzella D, Arboretto P et al (2024) NF-κB: governing macrophages in cancer. Genes. 10.3390/genes15020197 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Corzo CA, Condamine T, Lu L et al (2010) HIF-1α regulates function and differentiation of myeloid-derived suppressor cells in the tumor microenvironment. J Exp Med 207(11):2439–2453 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Cox N, Pokrovskii M, Vicario R, Geissmann F (2021) Origins, biology, and diseases of tissue macrophages. Annu Rev Immunol 39:313–344 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Crucet M, Wüst SJA, Spielmann P, Lüscher TF, Wenger RH, Matter CM (2013) Hypoxia enhances lipid uptake in macrophages: role of the scavenger receptors Lox1, SRA, and CD36. Atherosclerosis 229(1):110–117 [ DOI ] [ PubMed ] [ Google Scholar ] Dai C, Shen B, Liu S et al (2025) Pharmacologic inhibition of CSF-1R suppresses intrinsic tumor cell growth in osteosarcoma with CSF-1R overexpression. J Transl Med 23(1):900 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] De Henau O, Rausch M, Winkler D et al (2016) Overcoming resistance to checkpoint blockade therapy by targeting PI3Kγ in myeloid cells. Nature 539(7629):443–447 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Dharanikota A, Arjunan R, Dasappa A (2021) Factors affecting prognosis and survival in extremity osteosarcoma. Indian J Surg Oncol 12(1):199–206 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Doedens AL, Stockmann C, Rubinstein MP et al (2010) Macrophage expression of hypoxia-inducible factor-1 alpha suppresses T-cell function and promotes tumor progression. Cancer Res 70(19):7465–7475 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Dong H, Strome SE, Salomao DR et al (2002) Tumor-associated B7-H1 promotes T-cell apoptosis: a potential mechanism of immune evasion. Nat Med 8(8):793–800 [ DOI ] [ PubMed ] [ Google Scholar ] Dowlati A, Harvey RD, Carvajal RD et al (2021) LY3022855, an anti-colony stimulating factor-1 receptor (CSF-1R) monoclonal antibody, in patients with advanced solid tumors refractory to standard therapy: phase 1 dose-escalation trial. Invest New Drugs 39(4):1057–1071 [ DOI ] [ PubMed ] [ Google Scholar ] Duffaud F (2020) Role of TKI for metastatic osteogenic sarcoma. Curr Treat Options Oncol 21(8):65 [ DOI ] [ PubMed ] [ Google Scholar ] Duffaud F, Mir O, Boudou-Rouquette P et al (2019) Efficacy and safety of regorafenib in adult patients with metastatic osteosarcoma: a non-comparative, randomised, double-blind, placebo-controlled, phase 2 study. Lancet Oncol 20(1):120–133 [ DOI ] [ PubMed ] [ Google Scholar ] Duluc D, Corvaisier M, Blanchard S et al (2009) Interferon-gamma reverses the immunosuppressive and protumoral properties and prevents the generation of human tumor-associated macrophages. Int J Cancer 125(2):367–373 [ DOI ] [ PubMed ] [ Google Scholar ] Franklin RA, Liao W, Sarkar A et al (2014) The cellular and molecular origin of tumor-associated macrophages. Science 344(6186):921–925 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Fu Y, Bao Q, Liu Z et al (2021) Development and validation of a hypoxia-associated prognostic signature related to osteosarcoma metastasis and immune infiltration. Front Cell Dev Biol 9:633607 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Fujiwara Y, Takeya M, Komohara Y (2014) A novel strategy for inducing the antitumor effects of triterpenoid compounds: blocking the protumoral functions of tumor-associated macrophages via STAT3 inhibition. Biomed Res Int 2014:348539 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Gong J, Chehrazi-Raffle A, Reddi S, Salgia R (2018) Development of PD-1 and PD-L1 inhibitors as a form of cancer immunotherapy: a comprehensive review of registration trials and future considerations. J Immunother Cancer 6(1):8 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Gordon SR, Maute RL, Dulken BW et al (2017) PD-1 expression by tumour-associated macrophages inhibits phagocytosis and tumour immunity. Nature 545(7655):495–499 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Haist M, Stege H, Grabbe S, Bros M (2021) The functional crosstalk between myeloid-derived suppressor cells and regulatory T cells within the immunosuppressive tumor microenvironment. Cancers (Basel). 10.3390/cancers13020210 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Han Q, Shi H, Liu F (2016) CD163(+) M2-type tumor-associated macrophage support the suppression of tumor-infiltrating T cells in osteosarcoma. Int Immunopharmacol 34:101–106 [ DOI ] [ PubMed ] [ Google Scholar ] He L, Li Z (2019) B7-H3 and its role in bone cancers. Pathol Res Pract 215(6):152420 [ DOI ] [ PubMed ] [ Google Scholar ] He Z, Zhang S (2021) Tumor-associated macrophages and their functional transformation in the hypoxic tumor microenvironment. Front Immunol 12:741305 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] He YT, Zhang QM, Kou QC, Tang B (2016) In vitro generation of cytotoxic T lymphocyte response using dendritic cell immunotherapy in osteosarcoma. Oncol Lett 12(2):1101–1106 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Hess NJ, Felicelli C, Grage J, Tapping RI (2017) TLR10 suppresses the activation and differentiation of monocytes with effects on DC-mediated adaptive immune responses. J Leukoc Biol 101(5):1245–1252 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Himoudi N, Wallace R, Parsley KL et al (2012) Lack of T-cell responses following autologous tumour lysate pulsed dendritic cell vaccination, in patients with relapsed osteosarcoma. Clin Transl Oncol 14(4):271–279 [ DOI ] [ PubMed ] [ Google Scholar ] Hourani T, Holden JA, Li W, Lenzo JC, Hadjigol S, O’Brien-Simpson NM (2021) Tumor associated macrophages: origin, recruitment, phenotypic diversity, and targeting. Front Oncol 11:788365 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Inagaki Y, Hookway E, Williams KA et al (2016) Dendritic and mast cell involvement in the inflammatory response to primary malignant bone tumours. Clin Sarcoma Res 6:13 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Inagaki-Ohara K, Kondo T, Ito M, Yoshimura A (2013) SOCS, inflammation, and cancer. JAK-STAT 2(3):e24053 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Italiani P, Boraschi D (2014) From monocytes to M1/M2 macrophages: phenotypical vs. functional differentiation. Front Immunol 5:514 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Jalil AR, Andrechak JC, Discher DE (2020) Macrophage checkpoint blockade: results from initial clinical trials, binding analyses, and CD47-SIRPα structure-function. Antibody Ther 3(2):80–94 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Jia XH, Feng GW, Wang ZL et al (2016) Activation of mesenchymal stem cells by macrophages promotes tumor progression through immune suppressive effects. Oncotarget 7(15):20934–20944 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Jia X, Yan B, Tian X et al (2021) CD47/SIRPα pathway mediates cancer immune escape and immunotherapy. Int J Biol Sci 17(13):3281–3287 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Jiang C, Sun H, Jiang Z, Tian W, Cang S, Yu J (2024) Targeting the CD47/SIRPα pathway in malignancies: recent progress, difficulties and future perspectives. Front Oncol 14:1378647 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Kallis MP, Maloney C, Blank B, Soffer SZ, Symons M, Steinberg BM (2020) Pharmacological prevention of surgery-accelerated metastasis in an animal model of osteosarcoma. J Transl Med 18(1):183 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Kelleher FC, O’Sullivan H (2017) Monocytes, macrophages, and osteoclasts in osteosarcoma. J Adolesc Young Adult Oncol 6(3):396–405 [ DOI ] [ PubMed ] [ Google Scholar ] Keremitçi D, Tuna Ö, Houdjedj A, Kazan H, Kaymaz Y (2025) Transcriptional states of lung cancer microenvironment reveal macrophage subtype dynamics linked to disease progression. J Immunol [ DOI ] [ PubMed ] Kim J, Tchernyshyov I, Semenza GL, Dang CV (2006) HIF-1-mediated expression of pyruvate dehydrogenase kinase: a metabolic switch required for cellular adaptation to hypoxia. Cell Metab 3(3):177–185 [ DOI ] [ PubMed ] [ Google Scholar ] Kimura Y, Sumiyoshi M (2013) Anti-tumor and anti-metastatic actions of wogonin isolated from Scutellaria baicalensis roots through anti-lymphangiogenesis. Phytomedicine 20(3–4):328–336 [ DOI ] [ PubMed ] [ Google Scholar ] Kimura Y, Sumiyoshi M (2015) Antitumor and antimetastatic actions of dihydroxycoumarins (esculetin or fraxetin) through the inhibition of M2 macrophage differentiation in tumor-associated macrophages and/or G1 arrest in tumor cells. Eur J Pharmacol 746:115–125 [ DOI ] [ PubMed ] [ Google Scholar ] Kimura Y, Sumiyoshi M (2016) Resveratrol prevents tumor growth and metastasis by inhibiting lymphangiogenesis and M2 macrophage activation and differentiation in tumor-associated macrophages. Nutr Cancer 68(4):667–678 [ DOI ] [ PubMed ] [ Google Scholar ] Kleinerman ES, Snyder JS, Jaffe N (1991) Influence of chemotherapy administration on monocyte activation by liposomal muramyl tripeptide phosphatidylethanolamine in children with osteosarcoma. J Clin Oncol 9(2):259–267 [ DOI ] [ PubMed ] [ Google Scholar ] Klichinsky M, Ruella M, Shestova O et al (2020) Human chimeric antigen receptor macrophages for cancer immunotherapy. Nat Biotechnol 38(8):947–953 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Koirala P, Roth ME, Gill J et al (2016) Immune infiltration and PD-L1 expression in the tumor microenvironment are prognostic in osteosarcoma. Sci Rep 6:30093 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Komohara Y, Fujiwara Y, Ohnishi K, Takeya M (2016) Tumor-associated macrophages: potential therapeutic targets for anti-cancer therapy. Adv Drug Deliv Rev 99(Pt B):180–185 [ DOI ] [ PubMed ] [ Google Scholar ] Kumar V, Patel S, Tcyganov E, Gabrilovich DI (2016) The nature of myeloid-derived suppressor cells in the tumor microenvironment. Trends Immunol 37(3):208–220 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Kumar S, Ramesh A, Kulkarni A (2020) Targeting macrophages: a novel avenue for cancer drug discovery. Expert Opin Drug Discov 15(5):561–574 [ DOI ] [ PubMed ] [ Google Scholar ] Kurzman ID, Shi F, Vail DM, MacEwen EG (1999) In vitro and in vivo enhancement of canine pulmonary alveolar macrophage cytotoxic activity against canine osteosarcoma cells. Cancer Biother Radiopharm 14(2):121–128 [ DOI ] [ PubMed ] [ Google Scholar ] Lazarova M, Steinle A (2019) Impairment of NKG2D-mediated tumor immunity by TGF-β. Front Immunol 10:2689 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Le T, Su S, Kirshtein A, Shahriyari L (2021) Data-driven mathematical model of osteosarcoma. Cancers (Basel). 10.3390/cancers13102367 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Lee H, Choi H, Ha S, Lee K, Kwon Y (2013) Recruitment of monocytes/macrophages in different tumor microenvironments. Biochim Biophys Acta 1835(2):170–179 [ DOI ] [ PubMed ] [ Google Scholar ] Li MO, Flavell RA (2008) TGF-beta: a master of all T cell trades. Cell 134(3):392–404 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Li X, Chen Y, Liu X et al (2017a) Tim3/Gal9 interactions between T cells and monocytes result in an immunosuppressive feedback loop that inhibits Th1 responses in osteosarcoma patients. Int Immunopharmacol 44:153–159 [ DOI ] [ PubMed ] [ Google Scholar ] Li X, Yao W, Yuan Y et al (2017b) Targeting of tumour-infiltrating macrophages via CCL2/CCR2 signalling as a therapeutic strategy against hepatocellular carcinoma. Gut 66(1):157–167 [ DOI ] [ PubMed ] [ Google Scholar ] Li GQ, Wang YK, Zhou H et al (2021) Application of immune infiltration signature and machine learning model in the differential diagnosis and prognosis of bone-related malignancies. Front Cell Dev Biol 9:630355 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Li F, Tang H, Luo X et al (2023a) Interaction gene set between osteoclasts and regulatory CD4+ T cells can accurately predict the prognosis of patients with osteosarcoma. Cancer Sci 114(7):3014–3026 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Li J, Sun J, Zeng Z et al (2023b) Tumour-associated macrophages in gastric cancer: from function and mechanism to application. Clin Transl Med 13(8):e1386 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Li Y, Zou X, Feng X, Xia J, Wu Z, Ma H (2025) Exosomal LncRNA SCAMP1-AS1 enhances osteosarcoma malignancy by regulating the LKB1-AMPK signaling pathway. Sci Rep 15(1):29560 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Liang H, Cui M, Tu J, Chen X (2024) Advancements in osteosarcoma management: integrating immune microenvironment insights with immunotherapeutic strategies. Front Cell Dev Biol 12:1394339 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Liu T, Fang XC, Ding Z, Sun ZG, Sun LM, Wang YL (2015) Pre-operative lymphocyte-to-monocyte ratio as a predictor of overall survival in patients suffering from osteosarcoma. FEBS Open Bio 5:682–687 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Liu J, Zhang S, Hu Y et al (2016) Targeting PD-1 and Tim-3 pathways to reverse CD8 T-cell exhaustion and enhance ex vivo T-cell responses to autologous dendritic/tumor vaccines. J Immunother 39(4):171–180 [ DOI ] [ PubMed ] [ Google Scholar ] Liu X, Kwon H, Li Z, Fu Y (2017) Is CD47 an innate immune checkpoint for tumor evasion. J Hematol Oncol 10(1):12 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Liu T, Ma Q, Zhang Y et al (2019) Self-seeding circulating tumor cells promote the proliferation and metastasis of human osteosarcoma by upregulating interleukin-8. Cell Death Dis 10(8):575 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Liu R, Hu Y, Liu T, Wang Y (2021a) Profiles of immune cell infiltration and immune-related genes in the tumor microenvironment of osteosarcoma cancer. BMC Cancer 21(1):1345 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Liu Y, Feng W, Dai Y et al (2021b) Single-cell transcriptomics reveals the complexity of the tumor microenvironment of treatment-naive osteosarcoma. Front Oncol 11:709210 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Liu W, Hao Y, Tian X, Jiang J, Qiu Q (2022) The role of NR4A1 in the pathophysiology of osteosarcoma: a comprehensive bioinformatics analysis of the single-cell RNA sequencing dataset. Front Oncol 12:879288 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Liu Y, Xiao L, Yang M et al (2024) CAR-armored-cell therapy in solid tumor treatment. J Transl Med 22(1):1076 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Lu X, Jie X, Xin S et al (2025a) Apatinib plus ifosfamide and etoposide versus ifosfamide and etoposide in patients with advanced osteosarcomas (OAIE/PKUPH-sarcoma 11): a randomized phase II study. Nat Commun 16(1):10473 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Lu X, Chen Z, Yi C et al (2025b) An anti-CD47 antibody binds to a distinct epitope in a novel metal ion-dependent manner to minimize cross-linking of red blood cells. J Biol Chem 301(8):110420 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Luo ZW, Liu PP, Wang ZX, Chen CY, Xie H (2020) Macrophages in osteosarcoma immune microenvironment: implications for immunotherapy. Front Oncol 10:586580 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Maalej KM, Merhi M, Inchakalody VP et al (2023) CAR-cell therapy in the era of solid tumor treatment: current challenges and emerging therapeutic advances. Mol Cancer 22(1):20 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Majmundar AJ, Wong WJ, Simon MC (2010) Hypoxia-inducible factors and the response to hypoxic stress. Mol Cell 40(2):294–309 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Majzner RG, Simon JS, Grosso JF et al (2017) Assessment of programmed death-ligand 1 expression and tumor-associated immune cells in pediatric cancer tissues. Cancer 123(19):3807–3815 [ DOI ] [ PubMed ] [ Google Scholar ] Maloney C, Kallis MP, Edelman M et al (2020) Gefitinib inhibits invasion and metastasis of osteosarcoma via inhibition of macrophage receptor interacting serine-threonine kinase 2. Mol Cancer Ther 19(6):1340–1350 [ DOI ] [ PubMed ] [ Google Scholar ] Martinez FO, Helming L, Gordon S (2009) Alternative activation of macrophages: an immunologic functional perspective. Annu Rev Immunol 27:451–483 [ DOI ] [ PubMed ] [ Google Scholar ] Mazumdar A, Urdinez J, Boro A et al (2020) Osteosarcoma-derived extracellular vesicles induce lung fibroblast reprogramming. Int J Mol Sci. 10.3390/ijms21155451 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Meyers PA, Schwartz CL, Krailo MD et al (2008) Osteosarcoma: the addition of muramyl tripeptide to chemotherapy improves overall survival–a report from the Children’s Oncology Group. J Clin Oncol 26(4):633–638 [ DOI ] [ PubMed ] [ Google Scholar ] Mitsui Y, Satoh T (2025) Functional diversity of disorder-specific macrophages involved in various diseases. Inflamm Regen 45(1):29 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Miwa S, Nishida H, Tanzawa Y et al (2017) Phase 1/2 study of immunotherapy with dendritic cells pulsed with autologous tumor lysate in patients with refractory bone and soft tissue sarcoma. Cancer 123(9):1576–1584 [ DOI ] [ PubMed ] [ Google Scholar ] Moore KW, de Waal Malefyt R, Coffman RL, O’Garra A (2001) Interleukin-10 and the interleukin-10 receptor. Annu Rev Immunol 19:683–765 [ DOI ] [ PubMed ] [ Google Scholar ] Morrissey SM, Zhang F, Ding C et al (2021) Tumor-derived exosomes drive immunosuppressive macrophages in a pre-metastatic niche through glycolytic dominant metabolic reprogramming. Cell Metab 33(10):2040-2058.e10 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Müller A, Homey B, Soto H et al (2001) Involvement of chemokine receptors in breast cancer metastasis. Nature 410(6824):50–56 [ DOI ] [ PubMed ] [ Google Scholar ] Murray PJ, Allen JE, Biswas SK et al (2014) Macrophage activation and polarization: nomenclature and experimental guidelines. Immunity 41(1):14–20 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Nagarsheth N, Wicha MS, Zou W (2017) Chemokines in the cancer microenvironment and their relevance in cancer immunotherapy. Nat Rev Immunol 17(9):559–572 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Nohara T, Fujiwara Y, El-Aasr M et al (2017) Antitumor allium sulfides. Chem Pharm Bull (Tokyo) 65(3):209–217 [ DOI ] [ PubMed ] [ Google Scholar ] Noman MZ, Hasmim M, Messai Y et al (2015) Hypoxia: a key player in antitumor immune response. A Review in the Theme: Cellular Responses to Hypoxia. Am J Physiol Cell Physiol 309(9):C569–C579 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Okada S, Vaeteewoottacharn K, Kariya R (2019) Application of highly immunocompromised mice for the establishment of patient-derived xenograft (PDX) models. Cells. 10.3390/cells8080889 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ouyang W, O’Garra A (2019) IL-10 family cytokines IL-10 and IL-22: from basic science to clinical translation. Immunity 50(4):871–891 [ DOI ] [ PubMed ] [ Google Scholar ] Pahl JHW, Kwappenberg KMC, Varypataki EM et al (2014) Macrophages inhibit human osteosarcoma cell growth after activation with the bacterial cell wall derivative liposomal muramyl tripeptide in combination with interferon-γ. J Exp Clin Cancer Res 33:27 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Palazon A, Goldrath AW, Nizet V, Johnson RS (2014) HIF transcription factors, inflammation, and immunity. Immunity 41(4):518–528 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Pan Y, Yu Y, Wang X, Zhang T (2020) Tumor-associated macrophages in tumor immunity. Front Immunol 11:583084 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Pascual G, Avgustinova A, Mejetta S et al (2017) Targeting metastasis-initiating cells through the fatty acid receptor CD36. Nature 541(7635):41–45 [ DOI ] [ PubMed ] [ Google Scholar ] Patni H, Chaudhary R, Kumar A (2024) Unleashing nanotechnology to redefine tumor-associated macrophage dynamics and non-coding RNA crosstalk in breast cancer. Nanoscale 16(39):18274–18294 [ DOI ] [ PubMed ] [ Google Scholar ] Pennisi G, Valeri F, Burattini B et al (2025) Targeting macrophages in glioblastoma: current therapies and future directions. Cancers (Basel). 10.3390/cancers17162687 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Pierini S, Gabbasov R, Oliveira-Nunes MC et al (2025) Chimeric antigen receptor macrophages (CAR-M) sensitize HER2+ solid tumors to PD1 blockade in pre-clinical models. Nat Commun 16(1):706 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Piontkowski ZT, Hayes DC, McDonald A, Pattison K, Butler KS, Timlin JA (2024) Label-free, noninvasive bone cell classification by hyperspectral confocal raman microscopy. Chem Biomed Imaging 2(2):147–155 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Pixley FJ, Stanley ER (2004) CSF-1 regulation of the wandering macrophage: complexity in action. Trends Cell Biol 14(11):628–638 [ DOI ] [ PubMed ] [ Google Scholar ] Prager I, Watzl C (2019) Mechanisms of natural killer cell-mediated cellular cytotoxicity. J Leukoc Biol 105(6):1319–1329 [ DOI ] [ PubMed ] [ Google Scholar ] Punzo F, Bellini G, Tortora C et al (2020) Mifamurtide and TAM-like macrophages: effect on proliferation, migration and differentiation of osteosarcoma cells. Oncotarget 11(7):687–698 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Puro RJ, Bouchlaka MN, Hiebsch RR et al (2020) Development of AO-176, a next-generation humanized anti-CD47 antibody with novel anticancer properties and negligible red blood cell binding. Mol Cancer Ther 19(3):835–846 [ DOI ] [ PubMed ] [ Google Scholar ] Qian B, Li J, Zhang H et al (2011) CCL2 recruits inflammatory monocytes to facilitate breast-tumour metastasis. Nature 475(7355):222–225 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Raimondi L, De Luca A, Gallo A et al (2020) Osteosarcoma cell-derived exosomes affect tumor microenvironment by specific packaging of microRNAs. Carcinogenesis 41(5):666–677 [ DOI ] [ PubMed ] [ Google Scholar ] Rajan S, Franz EM, McAloney CA et al (2023) Osteosarcoma tumors maintain intra-tumoral transcriptional heterogeneity during bone and lung colonization. BMC Biol 21(1):98 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ran S, Wilber A (2017) Novel role of immature myeloid cells in formation of new lymphatic vessels associated with inflammation and tumors. J Leukoc Biol 102(2):253–263 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ries CH, Cannarile MA, Hoves S et al (2014) Targeting tumor-associated macrophages with anti-CSF-1R antibody reveals a strategy for cancer therapy. Cancer Cell 25(6):846–859 [ DOI ] [ PubMed ] [ Google Scholar ] Robinson MJ, Davis EJ (2024) Neoadjuvant chemotherapy for adults with osteogenic sarcoma. Curr Treat Options Oncol 25(11):1366–1373 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Rosser EC, Mauri C (2015) Regulatory B cells: origin, phenotype, and function. Immunity 42(4):607–612 [ DOI ] [ PubMed ] [ Google Scholar ] Sánchez-Paulete AR, Mateus-Tique J, Mollaoglu G et al (2022) Targeting macrophages with CAR T cells delays solid tumor progression and enhances antitumor immunity. Cancer Immunol Res 10(11):1354–1369 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Santiso A, Heinemann A, Kargl J (2024) Prostaglandin E2 in the tumor microenvironment, a convoluted affair mediated by EP receptors 2 and 4. Pharmacol Rev 76(3):388–413 [ DOI ] [ PubMed ] [ Google Scholar ] Sarvaria A, Madrigal JA, Saudemont A (2017) B cell regulation in cancer and anti-tumor immunity. Cell Mol Immunol 14(8):662–674 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Semenza GL (2003) Targeting HIF-1 for cancer therapy. Nat Rev Cancer 3(10):721–732 [ DOI ] [ PubMed ] [ Google Scholar ] Shang Q, Zhang P, Lei X, Du L, Qu B (2025) Insights into CSF-1/CSF-1R signaling: the role of macrophage in radiotherapy. Front Immunol 16:1530890 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Shao X, Xiang S, Chen Y et al (2019) Inhibition of M2-like macrophages by all-trans retinoic acid prevents cancer initiation and stemness in osteosarcoma cells. Acta Pharmacol Sin 40(10):1343–1350 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Shapouri-Moghaddam A, Mohammadian S, Vazini H et al (2018) Macrophage plasticity, polarization, and function in health and disease. J Cell Physiol 233(9):6425–6440 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Shen P, Fillatreau S (2015) Antibody-independent functions of B cells: a focus on cytokines. Nat Rev Immunol 15(7):441–451 [ DOI ] [ PubMed ] [ Google Scholar ] Shu-Jin L, Xiao-He W, Ling-Rui L, Lei C, Zhi-Jun S (2026) Leveraging macrophage plasticity for precision-targeted tumor immunotherapy. Biochim Biophys Acta Mol Basis Dis 1872(4):168176 [ DOI ] [ PubMed ] [ Google Scholar ] Sone S, Mutsuura S, Ogawara M, Tsubura E (1984) Potentiating effect of muramyl dipeptide and its lipophilic analog encapsulated in liposomes on tumor cell killing by human monocytes. J Immunol 132(4):2105–2110 [ PubMed ] [ Google Scholar ] Sumiyoshi M, Taniguchi M, Baba K, Kimura Y (2015) Antitumor and antimetastatic actions of xanthoangelol and 4-hydroxyderricin isolated from Angelica keiskei roots through the inhibited activation and differentiation of M2 macrophages. Phytomedicine 22(7–8):759–767 [ DOI ] [ PubMed ] [ Google Scholar ] Sun CY, Zhang Z, Tao L et al (2021) T cell exhaustion drives osteosarcoma pathogenesis. Ann Transl Med 9(18):1447 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Takabatake K, Tianyan P, Arashima T et al (2025) Refining the role of tumor-associated macrophages in oral squamous cell carcinoma. Cancers (Basel). 10.3390/cancers17172770 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Tang F, Tie Y, Lan TX et al (2023) Surgical treatment of osteosarcoma induced distant pre-metastatic niche in lung to facilitate the colonization of circulating tumor cells. Adv Sci 10(28):e2207518 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Tang H, Xie J, Du YX, Tan ZJ, Liang ZT (2024) Osteosarcoma neutrophil extracellular trap network-associated gene recurrence and metastasis model. J Cancer Res Clin Oncol 150(2):48 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Thaker YR, Rivera I, Pedros C et al (2022) A novel affinity engineered anti-CD47 antibody with improved therapeutic index that preserves erythrocytes and normal immune cells. Front Oncol 12:884196 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Tsubakihara Y, Moustakas A (2018) Epithelial-mesenchymal transition and metastasis under the control of transforming growth factor β. Int J Mol Sci. 10.3390/ijms19113672 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Tu J, Wang D, Zheng X, Liu B (2023) Single-cell RNA datasets and bulk RNA datasets analysis demonstrated C1Q+ tumor-associated macrophage as a major and antitumor immune cell population in osteosarcoma. Front Immunol 14:911368 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] van Rooijen N, Hendrikx E (2010) Liposomes for specific depletion of macrophages from organs and tissues. Methods Mol Biol 605:189–203 [ DOI ] [ PubMed ] [ Google Scholar ] van Dalen FJ, van Stevendaal MHME, Fennemann FL, Verdoes M, Ilina O (2018) Molecular repolarisation of tumour-associated macrophages. Molecules. 10.3390/molecules24010009 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Vitale I, Manic G, Coussens LM, Kroemer G, Galluzzi L (2019) Macrophages and metabolism in the tumor microenvironment. Cell Metab 30(1):36–50 [ DOI ] [ PubMed ] [ Google Scholar ] Waight JD, Hu Q, Miller A, Liu S, Abrams SI (2011) Tumor-derived G-CSF facilitates neoplastic growth through a granulocytic myeloid-derived suppressor cell-dependent mechanism. PLoS ONE 6(11):e27690 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Wang S, Wang Z, Yu G et al (2019) Tumor-specific drug release and reactive oxygen species generation for cancer chemo/chemodynamic combination therapy. Adv Sci 6(5):1801986 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Wang Z, Lu YL, Zhao WT et al (2020) Distinct origins and functions of cardiac orthotopic macrophages. Basic Res Cardiol 115(2):8 [ DOI ] [ PubMed ] [ Google Scholar ] Wang Z, Guan D, Huo J et al (2021) IL-10 enhances human natural killer cell effector functions via metabolic reprogramming regulated by mTORC1 signaling. Front Immunol 12:619195 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Wang B, Wang X, Li P et al (2022) Osteosarcoma cell-derived exosomal ELFN1-AS1 mediates macrophage M2 polarization via sponging miR-138-5p and miR-1291 to promote the tumorgenesis of osteosarcoma. Front Oncol 12:881022 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Wang T, Wang L, Zhang L, Long Y, Zhang Y, Hou Z (2023) Single-cell RNA sequencing in orthopedic research. Bone Res 11(1):10 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Wang Q, Huang M, Guo JJ (2025) From cells to clinic: single-cell transcriptomics shaping the future of orthopedics. J Orthop Transl 53:1–11 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Welch DL, Fridley BL, Cen L et al (2023) Modeling phenotypic heterogeneity towards evolutionarily inspired osteosarcoma therapy. Sci Rep 13(1):20125 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Willingham SB, Volkmer JP, Gentles AJ et al (2012) The CD47-signal regulatory protein alpha (SIRPa) interaction is a therapeutic target for human solid tumors. Proc Natl Acad Sci U S A 109(17):6662–6667 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Wolf-Dennen K, Gordon N, Kleinerman ES (2020) Exosomal communication by metastatic osteosarcoma cells modulates alveolar macrophages to an M2 tumor-promoting phenotype and inhibits tumoricidal functions. Oncoimmunology 9(1):1747677 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Wu L, Saxena S, Awaji M, Singh RK (2019) Tumor-associated neutrophils in cancer: going pro. Cancers (Basel). 10.3390/cancers11040564 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Xiao Q, Zhang X, Wu Y, Yang Y (2014) Inhibition of macrophage polarization prohibits growth of human osteosarcoma. Tumour Biol 35(8):7611–7616 [ DOI ] [ PubMed ] [ Google Scholar ] Xiong X, Xie X, Wang Z, Zhang Y, Wang L (2022) Tumor-associated macrophages in lymphoma: From mechanisms to therapy. Int Immunopharmacol 112:109235 [ DOI ] [ PubMed ] [ Google Scholar ] Xu C, Xiao M, Li X et al (2022) Origin, activation, and targeted therapy of glioma-associated macrophages. Front Immunol 13:974996 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Xu Y, Deng C, Chen H et al (2024) Osteosarcoma cells secrete CXCL14 that activates integrin α11β1 on fibroblasts to form a lung metastatic niche. Cancer Res 84(7):994–1012 [ DOI ] [ PubMed ] [ Google Scholar ] Xu J, Ding L, Mei J et al (2025) Dual roles and therapeutic targeting of tumor-associated macrophages in tumor microenvironments. Signal Transduct Target Ther 10(1):268 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Yan L, Wang J, Cai X et al (2020) Macrophage plasticity: signaling pathways, tissue repair, and regeneration. MedComm 5(8):e658 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Yang L, Zhang Y (2017) Tumor-associated macrophages: from basic research to clinical application. J Hematol Oncol 10(1):58 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Yang Q, Zeng B, Dong Y, Shi Z, Jiang Z, Huang J (2007) Overexpression of hypoxia-inducible factor-1alpha in human osteosarcoma: correlation with clinicopathological parameters and survival outcome. Jpn J Clin Oncol 37(2):127–134 [ DOI ] [ PubMed ] [ Google Scholar ] Yang W, Fan T, Chongqi T, Francis H, Zhenfeng D, Li M (2022) Immune checkpoints in osteosarcoma: recent advances and therapeutic potential. Cancer Lett 547:215887 [ DOI ] [ PubMed ] [ Google Scholar ] Yao Z, Tan Z, Yang J et al (2021) Prognostic nomogram for predicting 5-year overall survival in Chinese patients with high-grade osteosarcoma. Sci Rep 11(1):17728 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ying H, Li ZQ, Li MP, Liu WC (2023) Metabolism and senescence in the immune microenvironment of osteosarcoma: focus on new therapeutic strategies. Front Endocrinol (Lausanne) 14:1217669 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Yoshida A, Fujiwara T, Uotani K et al (2018) Clinical and functional significance of intracellular and extracellular microRNA-25-3p in osteosarcoma. Acta Med Okayama 72(2):165–174 [ DOI ] [ PubMed ] [ Google Scholar ] Yu L, Zhang Y, Liu C et al (2023) Heterogeneity of macrophages in atherosclerosis revealed by single-cell RNA sequencing. FASEB J 37(3):e22810 [ DOI ] [ PubMed ] [ Google Scholar ] Zaidi MR, Merlino G (2011) The two faces of interferon-γ in cancer. Clin Cancer Res 17(19):6118–6124 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Zhang Q, Fu L, Liang Y et al (2018) Exosomes originating from MSCs stimulated with TGF-β and IFN-γ promote Treg differentiation. J Cell Physiol 233(9):6832–6840 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Zhang ML, Chen L, Li YJ, Kong DL (2019) PD‑L1/PD‑1 axis serves an important role in natural killer cell‑induced cytotoxicity in osteosarcoma. Oncol Rep 42(5):2049–2056 [ DOI ] [ PubMed ] [ Google Scholar ] Zhang C, Zheng JH, Lin ZH et al (2020a) Profiles of immune cell infiltration and immune-related genes in the tumor microenvironment of osteosarcoma. Aging (Albany NY) 12(4):3486–3501 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Zhang W, Huang Q, Xiao W et al (2020b) Advances in anti-tumor treatments targeting the CD47/SIRPα axis. Front Immunol 11:18 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Zhang W, Xie Y, Chen F, Xie B, Yin Z (2025) Development and validation of a neutrophil extracellular traps-related gene signature for lower-grade gliomas. Comput Biol Med 188:109844 [ DOI ] [ PubMed ] [ Google Scholar ] Zhang K, Wang Z, He J et al (2026) Advances in T cell-based immunotherapy for osteosarcoma. Front Immunol 17:1769847 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Zhao M, Li J, Liu J et al (2021) Charge-switchable nanoparticles enhance cancer immunotherapy based on mitochondrial dynamic regulation and immunogenic cell death induction. J Control Release 335:320–332 [ DOI ] [ PubMed ] [ Google Scholar ] Zheng Y, Wang G, Chen R, Hua Y, Cai Z (2018) Mesenchymal stem cells in the osteosarcoma microenvironment: their biological properties, influence on tumor growth, and therapeutic implications. Stem Cell Res Ther 9(1):22 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Zhou Q, Xian M, Xiang S et al (2017) All-trans retinoic acid prevents osteosarcoma metastasis by inhibiting M2 polarization of tumor-associated macrophages. Cancer Immunol Res 5(7):547–559 [ DOI ] [ PubMed ] [ Google Scholar ] Zhou Y, Yang D, Yang Q et al (2020) Single-cell RNA landscape of intratumoral heterogeneity and immunosuppressive microenvironment in advanced osteosarcoma. Nat Commun 11(1):6322 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Zhou TA, Hsu HP, Tu YH, et al. (2022) Thymic macrophages consist of two populations with distinct localization and origin. Elife 11 [ DOI ] [ PMC free article ] [ PubMed ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement This article is a review article and does not contain original research data. Therefore, no new datasets were generated or analyzed during this study. Articles from Journal of Cancer Research and Clinical Oncology are provided here courtesy of Springer ACTIONS View on publisher site PDF (1.7 MB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top