ABCA8 drives bone loss via disrupting Th17/Treg imbalance: a novel immunometabolic target for osteoporosis.

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Learn more: PMC Disclaimer | PMC Copyright Notice Inflamm Res . 2026 Jun 2;75(1):123. doi: 10.1007/s00011-026-02259-w ABCA8 drives bone loss via disrupting Th17/Treg imbalance: a novel immunometabolic target for osteoporosis Wenjing Yi 1, 8 , Wenan Xu 1, 2 , Tao Yang 1, 3 , Xiaolin Lyu 1, 2 , Ziting Wang 1, 2 , Leyi Chen 1, 2 , Jingya Zhang 1, 4 , Yiming Zhong 1, 7 , Songlin Wang 5, 6, ✉ , Buling Wu 1, 7, ✉ Author information Article notes Copyright and License information 1 Shenzhen Clinical College of Stomatology, School of Stomatology, Southern Medical University, Shenzhen, 518000 Guangdong China 2 Department of Pediatric Stomatology, Shenzhen Stomatology Hospital (Pingshan) of Southern Medical University, Shenzhen, 518000 Guangdong China 3 Department of Oral Implantology, Shenzhen Stomatology Hospital (Pingshan) of Southern Medical University, Shenzhen, 518000 Guangdong China 4 Department of Oral Restoration, Shenzhen Stomatology Hospital (Pingshan) of Southern Medical University, Shenzhen, 518000 Guangdong China 5 Salivary Gland Disease Center, Beijing Key Laboratory of Tooth Regeneration and Function Reconstruction, Beijing Laboratory of Oral Health, Beijing Stomatological Hospital, Capital Medical University, Beijing, 100070 China 6 Department of Stomatology, Laboratory of Oral Homeostatic Medicine, School of Medicine and SUSTech Homeostatic Medicine Institute (SHMI), Southern University of Science and Technology, Shenzhen, 518055 China 7 Department of Endodontics, Shenzhen Stomatology Hospital (Pingshan) of Southern Medical University, Shenzhen, 518000 Guangdong China 8 Department of Orthodontics, Shenzhen Stomatology Hospital (Pingshan) of Southern Medical University, Guangdong 518000 Shenzhen, China Communicated by John Di Battista ✉ Corresponding author. Received 2025 Sep 9; Revised 2026 Apr 14; Accepted 2026 Apr 16; Issue date 2026. © 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: PMC13230258  PMID: 42228211 Abstract Background Osteoporosis is a metabolic bone disease characterized by reduced bone mass and microarchitectural deterioration, with complex involvement of molecular networks and immune-associated transcriptional dysregulation. Objective This study aimed to elucidate the immune-associated regulatory features of ATP-binding cassette subfamily A member 8 (ABCA8) in osteoporosis. Methods Differential gene expression analysis was performed using the GSE35958 dataset, and 384 up-regulated genes and 1143 down-regulated genes were identified. Weighted gene co-expression network analysis (WGCNA) and machine learning–based feature selection (LASSO, random forest, and Boruta) were applied to identify key osteoporosis-associated genes. Gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) were conducted to explore immune-associated transcriptional signatures, including Th17- and regulatory T cell (Treg)–related signatures. In vivo validation was conducted in ovariectomized (OVX) rats, with gene modulation experiments evaluating bone phenotypes, osteogenic and osteoclastic markers, and inflammatory cytokine profiles. Results ABCA8 was significantly upregulated in osteoporotic samples and associated with osteoporosis-related transcriptional features. High ABCA8 expression was associated with altered immune-associated signatures, including Th17- and regulatory T cell (Treg)–related transcriptional features, as revealed by ssGSEA. Pathway analyses further linked ABCA8 expression to immune regulation and inflammatory signaling pathways. In OVX rats, ABCA8 knockdown improved trabecular bone microarchitecture, upregulated osteogenic markers (Runt-related transcription factor 2 (RUNX2), osteocalcin (OCN), and osteoprotegerin (OPG)), and downregulated bone resorption markers (receptor activator of nuclear factor κB ligand (RANKL) and tartrate-resistant acid phosphatase (TRAP)). Conversely, ABCA8 overexpression aggravated bone loss and was accompanied by disruption of the Th17/Treg balance and increased levels of interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and interleukin-17 (IL-17). Conclusion ABCA8 is closely associated with osteoporosis-related immune-associated transcriptional alterations and contributes to disease progression through dysregulation of osteoimmune balance. These findings suggest that ABCA8 may represent a potential immunoregulatory target for therapeutic intervention in osteoporosis. Supplementary Information The online version contains supplementary material available at 10.1007/s00011-026-02259-w. Keywords: Osteoporosis, ABCA8, Immune-associated signatures, Machine learning, ssGSEA, Th17/Treg balance Introduction Osteoporosis is a common metabolic bone disease associated with aging and menopause, affecting over 200 million people globally, particularly among the elderly, women, and Caucasians [ 1 ]. With the aging population, the risk and diagnosis rates of osteoporosis are rising, which will further exacerbate its medical and socioeconomic impact [ 2 , 3 ]. A meta-analysis indicates regional differences in osteoporosis prevalence worldwide, with developing countries showing higher rates than developed ones [ 3 ]. The disease is caused by an imbalance between bone resorption and bone formation, with bone mineral density assessments and dual-energy X-ray absorptiometry (DXA) being the primary tools for fracture risk evaluation [ 4 ]. In recent years, omics and machine learning have advanced personalized risk assessments [ 5 ]. Current treatments for osteoporosis primarily focus on inhibiting bone resorption and promoting bone formation. Common medications include bisphosphonates [ 6 ], denosumab [ 7 ], and teriparatide [ 8 ]. Additionally, regenerative medicine treatments, such as stem cell therapy [ 9 ] and bioactive hydrogels [ 10 ], are gradually being implemented to restore bone density and improve bone structure [ 11 ]. Beyond pharmacological treatments, lifestyle interventions are also crucial. Appropriate physical activity, a balanced diet, and avoiding harmful habits like smoking and excessive alcohol consumption can help slow down bone loss [ 12 ]. However, despite existing treatments showing some efficacy in improving the quality of life for osteoporosis patients, treatment adherence remains a challenge. Statistics indicate that approximately 30%-50% of patients do not maintain long-term adherence to their medication, affecting its effectiveness [ 13 ]. Therefore, enhancing patient awareness of osteoporosis and strengthening proactive disease management are essential for controlling disease progression. The primary cause of osteoporosis is the reduction of estrogen levels, leading to an imbalance between bone formation and bone resorption, with resorption exceeding formation [ 1 ]. In recent years, with advances in scientific research, it has become increasingly recognized that the pathogenesis of osteoporosis is multifactorial. In addition to estrogen deficiency, other contributing factors include oxidative stress, aging, epigenetic modifications, and inflammatory responses [ 14 ]. Among these, the balance between T helper 17 (Th17) cells and regulatory T (Treg) cells plays a dual role in bone metabolism. On one hand, Th17 cells promote bone loss by differentiating and secreting interleukin-17 (IL-17); on the other hand, Treg cells protect bone integrity by producing interleukin-10 (IL-10) [ 15 ]. Disruption of the Th17/Treg balance further accelerates the progression of osteoporosis. Therefore, targeting the Th17/Treg imbalance is considered a promising therapeutic strategy for osteoporosis [ 16 ]. ABCA8 (ATP-binding cassette sub-family A member 8) is a newly discovered member of the ABC superfamily, primarily involved in the transport of exogenous substances [ 17 ]. The human ABCA8 protein consists of 1581 amino acids and includes 14 predicted transmembrane domains, exhibiting ATP-dependent drug transport capabilities [ 18 ]. ABCA8 is expressed in various organs, including the heart, liver, and muscles, suggesting its potential roles in various physiological and pathological processes [ 17 ]. In multi-system atrophy (MSA), overexpression of ABCA8 leads to increased levels of α-synuclein and p25α, which may be related to lipid dysfunction in myelin membranes [ 19 ]. Furthermore, ABCA8 may be involved in chemotherapy resistance; research has shown that ABCA8 expression is significantly elevated in human pancreatic cancer cells after treatment with gemcitabine (GEM), especially in resistant cells, while knockdown of ABCA8 significantly reduces cancer cell sensitivity [ 20 ]. Conversely, in colorectal cancer (CRC) patients, ABCA8 affects the malignant progression of CRC by inhibiting the Wnt/β-catenin signaling pathway, suggesting it may be a new therapeutic target for CRC [ 21 ]. Additionally, ABCA8 is associated with prognosis in gastric adenocarcinoma (STAD) and positively correlates with M2 macrophages [ 22 ]. Despite the preliminary confirmation of ABCA8’s role in the aforementioned cancers, its function in osteoporosis remains completely unexplored and represents a significant knowledge gap. Intriguingly, given ABCA8’s established roles in lipid transport and cellular signal regulation, we hypothesized a novel and pioneering link: ABCA8 might critically influence osteoporosis pathogenesis by disrupting the pivotal Th17/Treg immune balance. This potential immunomodulatory role of ABCA8 in bone metabolism has never been investigated. Therefore, this study aims to provide the first evidence investigating the regulatory role of ABCA8 in osteoporosis through integrated bioinformatics analyses and in vivo animal experiments, with a central focus on elucidating its specific impact on the Th17/Treg equilibrium as a core underlying mechanism. Materials and methods Data acquisition Gene Expression Omnibus (GEO) [ 22 ] database was searched to retrieve publicly available gene expression datasets related to osteoporosis. After screening with the keyword “osteoporosis”, the GSE35958 dataset was selected for subsequent analyses. The GSE35958 dataset contains gene expression profiles from osteoporosis patients and healthy control samples and was used for differential expression analysis, weighted gene co-expression network analysis, functional enrichment analysis, machine learning-based feature selection, gene set enrichment analysis, and immune-associated signature analysis. Differential expression analysis Raw gene expression data from the GSE35958 dataset were preprocessed and normalized to ensure data quality and comparability across samples. Low-expression genes were filtered out prior to downstream analysis. Samples were divided into osteoporosis and healthy control groups according to the dataset annotation. Differential expression analysis was performed using the Limma package [ 23 ]. Genes with an absolute log2 fold change (|log2FC|) > 1 and an adjusted P value < 0.05 were considered significantly differentially expressed and were defined as differentially expressed genes (DEGs). These DEGs were used for subsequent analyses. Functional enrichment analysis To explore the biological functions and pathways associated with osteoporosis-related genes, functional enrichment analyses were performed using the clusterProfiler package [ 24 , 25 ]. Gene Ontology (GO) enrichment analysis was conducted using the enrichGO function, including Biological Process (BP), Molecular Function (MF), and Cellular Component (CC) categories. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using the enrichKEGG function. Enrichment results were visualized using bar plots and dot plots, and pathways with an adjusted P value < 0.05 were considered significantly enriched. WGCNA analysis Weighted gene co-expression network analysis was performed using the WGCNA packageto identify gene modules associated with osteoporosis. Genes with high expression variability were selected to construct the co-expression network. A soft-thresholding power was chosen based on the scale-free topology criterion, and an adjacency matrix was constructed to describe the correlation strength between genes. The adjacency matrix was then transformed into a topological overlap matrix, followed by hierarchical clustering to identify gene modules with similar expression patterns. Module eigengenes were calculated to represent the overall expression profiles of each module. The correlation between module eigengenes and osteoporosis status was evaluated to identify disease-associated modules. Genes within the osteoporosis-associated module were intersected with DEGs to obtain a refined set of candidate genes for further analysis, including ABCA8. Protein–protein interaction (PPI) network analysis To investigate potential functional interactions among candidate genes, a PPI network was constructed using the STRING database, with a minimum required interaction score set to 0.4. The network was visualized using Cytoscape software. The PPI network was used to illustrate the interaction landscape among osteoporosis-related genes and to provide functional context for subsequent analyses. Machine learning–based feature selection To identify robust feature genes associated with osteoporosis, multiple machine learning algorithms were applied to the candidate gene set, including least absolute shrinkage and selection operator (LASSO) regression, random forest, and Boruta feature selection. LASSO regression was used to identify genes with non-zero coefficients through cross-validation. Random forest analysis was performed to rank genes based on their importance scores, while Boruta analysis was applied to confirm informative features. Genes consistently identified across these methods were considered key feature genes. Construction of the ABCA8-based nomogram Based on the feature genes identified by machine learning analyses, a nomogram model was constructed to evaluate the potential predictive value of ABCA8 in combination with other key genes. The nomogram integrates gene expression levels to estimate the probability of osteoporosis, providing a visual and quantitative tool for risk assessment. Gene set enrichment analysis (GSEA) To further explore biological pathways associated with ABCA8 expression, GSEA analysis was performed. Samples were stratified into high- and low-ABCA8 expression groups based on the median expression level of ABCA8. GSEA was conducted to identify pathways differentially enriched between the two groups, with a focus on immune-related and metabolic pathways. Pathways with a FDR < 0.25 and nominal P value < 0.05 were considered significantly enriched. Immune-associated signature analysis based on ssGSEA To assess immune-associated transcriptional features related to ABCA8 expression, single-sample ssGSEA analysis was performed. Immune-related gene sets representing 29 immune cell-associated signatures were used to calculate ssGSEA scores for each sample. In addition, a Th17-related gene set (GOBP_T_HELPER_17_CELL_DIFFERENTIATION.v2025.1.Hs) was included to specifically evaluate Th17-associated signatures. Differences in immune-associated signature scores between samples were compared to characterize the overall immune-associated transcriptional landscape. Furthermore, correlations between ABCA8 expression and immune-related signatures were analyzed using Spearman’s correlation analysis. These analyses were used to characterize immune-associated transcriptional patterns rather than direct immune cell infiltration. Animal model establishment and grouping Six-week-old female Sprague-Dawley (SD) rats were randomly assigned to a Control group or an OVX group. The Control group underwent a sham operation in which only the periadnexal adipose tissue was removed, while the OVX group underwent bilateral ovariectomy under anesthesia to establish a postmenopausal osteoporosis (PMOP) model. Model success was assessed at 6 and 12 weeks post-surgery by hematoxylin-eosin (H&E) staining of femoral tissues. Structural disruption of trabecular bone, decreased trabecular number, and widened trabecular spaces under microscopy were considered indicative of successful modeling. All animal procedures were approved by the Experimental Animal Ethics Committee of the Guangdong Medical Laboratory Animal Center (Approval No. D202506-6) and were conducted in accordance with institutional guidelines for the care and use of laboratory animals. According to the approved protocol, 10 rats were initially allocated to each experimental group. After verification, OVX rats were randomly divided into four groups: OVX+siNC (intramedullary injection of lentivirus carrying negative control siRNA), OVX + si-ABCA8 (lentivirus carrying ABCA8 siRNA), OVX+vector (lentivirus carrying negative control overexpression plasmid), and OVX+ABCA8 (lentivirus carrying ABCA8 overexpression plasmid). Rats were randomly assigned to each group with equal numbers to ensure balanced baseline conditions. After model verification and quality control during the experimental procedures, 6 rats per group were ultimately included in the final analyses. Each rat received 25 µL of the corresponding lentivirus injected into the femoral marrow cavity every two weeks. At 12 weeks post-intervention, femoral tissues, spleens, and peripheral blood samples were harvested for subsequent analysis. Histopathological observation of femoral tissues by H&E staining Fixed femurs were decalcified in 10% ethylenediaminetetraacetic acid (EDTA) for 30 days, dehydrated, cleared in xylene, embedded in paraffin, and sectioned at 4 μm thickness. Sections underwent standard H&E staining and were examined under a light microscope (Olympus CX33). Bone morphometric parameters, including bone volume fraction (BV/TV, %), osteoid surface (OS/BS, %), and eroded surface (ES/BS, %), were analyzed. ELISA analysis of serum bone turnover markers and cytokines Serum samples stored at − 80 °C were analyzed using ELISA kits to measure levels of alkaline phosphatase (ALP), procollagen type I N-terminal propeptide (PINP), C-terminal telopeptide of type I collagen (CTX-1), N-terminal telopeptide (NTX), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), interleukin-17 (IL-17), and interleukin-10 (IL-10), following the manufacturers’ instructions. Flow cytometric analysis of Treg and Th17 cells in spleen and peripheral blood Spleen tissues were homogenized in sterile grinding tubes to prepare single-cell suspensions, which were centrifuged at 4 °C, 3000 r/min for 5 min. After discarding the supernatant, the cell pellets were resuspended in complete culture medium. Surface staining was performed using CD4+-FITC and CD25+-PerCP antibodies, followed by fixation, permeabilization, and intracellular staining with IL-17-APC and Foxp3-PE antibodies for 30 min. The proportions of Th17 cells (CD4 + IL-17+) and Treg cells (CD4 + CD25+Foxp3+) were analyzed using a flow cytometer (BD FACSCanto II). The same procedure was applied for determining Th17 (CD4 + IL-17 A+) and Treg (CD4 + CD25+FOXP3+) cell percentages in peripheral blood. qRT-PCR analysis of ABCA8 mRNA expression in rat femoral tissues Total RNA was extracted from femoral tissues using Trizol and reverse-transcribed into cDNA. cDNA concentration and purity were assessed before amplification according to the RT-PCR kit instructions. Cycling conditions were 95°C for 1 min; 35 cycles of 95 °C for 40 s, 58 °C for 40 s, and 72 °C for 45 s; followed by 72 °C for 10 min. Relative mRNA expression was calculated using the 2^−ΔΔCt method with GAPDH as the internal control. Primer sequences: ABCA8 forward 5’-ATAAGTGTGCGCCAACAAACT-3’; reverse 5’-TGACAGGCGTGTACCCTATC-3’; GAPDH forward 5’-CCACTCCTCCACCTTTG-3’, reverse 5’-CACCACCCTGTTGCTGT-3’. Western blotting Total protein was extracted from rat femoral tissues, and concentrations were measured. Equal amounts (20 µg) of protein were mixed with 4 µL of 2× SDS loading buffer, denatured at 100 °C for 10 min, and separated by 10% SDS-PAGE, then transferred onto PVDF membranes. Membranes were blocked with 5% skim milk for 1 h, washed with PBS, and incubated overnight at 4 °C with primary antibodies against ABCA8 (ab230896), Runt-related transcription factor 2 (RUNX2, 57 kDa, ab236639), osteocalcin (OCN, 11 kDa, ab93876), Osterix (45 kDa, ab209484), receptor activator of nuclear factor κB ligand (RANKL, 35 kDa, ab239607), osteoprotegerin (OPG, 46 kDa, ab73400), receptor activator of nuclear factor κB (RANK, 80 kDa, ab305233), and tartrate-resistant acid phosphatase (TRAP, 32 kDa, ab133238). After PBS washing, membranes were incubated with secondary antibodies at room temperature for 30 min, followed by additional PBS washes. Protein bands were visualized using enhanced chemiluminescence (ECL) and analyzed with a gel imaging system. GAPDH was used as the internal control. Experiments were performed in triplicate. Statistical analysis All statistical analyses were performed using R software (version 4.2.0) and GraphPad Prism (version 9.0). For bioinformatics analyses, differential gene expression was assessed using the Limma package, and adjusted P values were calculated using the Benjamini–Hochberg method to control the false discovery rate. Correlation analyses were conducted using Spearman’s correlation coefficient unless otherwise specified. For comparisons between two groups, Student’s t-test was applied when data followed a normal distribution, while the Mann–Whitney U test was used for non-normally distributed data. Comparisons among multiple groups were performed using one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test, or Kruskal–Wallis test with Dunn’s multiple comparisons test when appropriate. Experimental data are presented as mean ± standard deviation (SD). All experiments were performed with at least three independent biological replicates. A two-tailed P value < 0.05 was considered statistically significant. Results Identification of differentially expressed genes related to osteoporosis To identify genes dysregulated in osteoporosis, differential expression analysis was performed using the GSE35958 dataset. Under the threshold of |log2FC| > 1 and adjusted P value < 0.05, a set of significantly differentially expressed genes (DEGs) was identified between osteoporosis patients and healthy controls. The volcano plot illustrated the global distribution of DEGs, in which ABCA8 was identified as a significantly dysregulated gene (Fig. 1 A). Consistently, hierarchical clustering analysis based on the top DEGs demonstrated a clear separation between osteoporosis and control samples, with ABCA8 exhibiting a distinct expression pattern across samples, consistent with its differential expression identified in the DEG analysis (Fig. 1 B). These results indicate that ABCA8 is significantly associated with osteoporosis at the transcriptional level. Fig. 1. Open in a new tab Differential expression analysis based on the GSE35958 dataset. A Volcano plot showing the distribution of differentially expressed genes (DEGs) between osteoporosis patients and healthy controls under the threshold of |log2FC| > 1 and adjusted P < 0.05. Red dots indicate upregulated genes, blue dots indicate downregulated genes, and gray dots represent genes without significant differential expression. B Heatmap of the top differentially expressed genes illustrating distinct expression patterns between osteoporosis and control samples, including ABCA8 among the displayed genes. Color intensity represents the relative expression level of genes across samples. Construction of weighted gene co-expression network and identification of osteoporosis-related modules To further explore gene co-expression patterns associated with osteoporosis, a WGCNA was conducted using the GSE35958 dataset. An appropriate soft-thresholding power was selected to achieve a scale-free topology (Fig. 2 A), and genes were subsequently clustered into distinct co-expression modules based on topological overlap (Fig. 2 B–C). Module–trait relationship analysis revealed that the turquoise module exhibited the strongest positive correlation with osteoporosis status (Fig. 2 D). Notably, ABCA8 was located within this osteoporosis-associated module, suggesting that it participates in a coordinated transcriptional program related to disease progression. Intersection analysis between DEGs and genes within the turquoise module further refined a set of osteoporosis-related candidate genes, including ABCA8 (Fig. 2 E). Fig. 2. Open in a new tab Construction of the weighted gene co-expression network. A Scale-free topology model fit (left) and mean connectivity (right) analysis used to determine the optimal soft-thresholding power for network construction. B , C Hierarchical clustering dendrogram of genes generated by weighted gene co-expression network analysis (WGCNA), with modules identified using dynamic tree cutting and represented by different colors. D Heatmap showing correlations between module eigengenes and clinical traits, with color intensity indicating the strength and direction of correlation, and the turquoise module exhibiting the strongest association with osteoporosis. E Venn diagram illustrating the intersection between DEGs and genes within the osteoporosis-associated module identified by WGCNA. Protein–protein interaction network analysis of osteoporosis-related genes To investigate potential biological interactions among osteoporosis-related genes, a PPI network was constructed using the intersected gene set. The resulting network revealed extensive interactions among candidate genes, highlighting a complex interaction landscape related to immune regulation and inflammatory signaling (Fig. 3 ). Notably, ABCA8 was included in the PPI network, suggesting that it may participate in protein interaction networks associated with osteoporosis-related biological processes. Further inspection of the network indicated that ABCA8 exhibited a direct first-order interaction with SLCO1A2, a solute carrier transporter involved in the transmembrane transport of organic anions and metabolic substrates. These results provide supportive evidence for the involvement of ABCA8 within a broader functional interaction context. Fig. 3. Open in a new tab Protein–protein interaction (PPI) network constructed from the intersected gene set using the STRING database. Each node represents a protein encoded by the intersected genes, and edges indicate predicted or experimentally supported protein–protein interactions derived from the STRING database (interaction score ≥ 0.4). Red nodes highlight hub genes with higher connectivity within the network Functional enrichment analysis of ABCA8-associated genes To elucidate the biological functions and signaling pathways associated with ABCA8-related genes, GO and KEGG enrichment analyses were performed on genes derived from the osteoporosis-associated module and intersected gene set. GO enrichment analysis revealed significant enrichment in biological processes related to cell chemotaxis, immune response, metabolic regulation, and cellular response to external stimuli (Fig. 4 A–B). KEGG pathway analysis further demonstrated enrichment in pathways involved in PI3K–Akt signaling, MAPK signaling, NF-κB signaling, and Th17 cell differentiation, among others (Fig. 4 C–D). These results suggest that ABCA8-associated genes are functionally linked to immune regulation and inflammatory signaling pathways relevant to osteoporosis. Fig. 4. Open in a new tab Functional enrichment analysis of genes associated with the osteoporosis-related module. A , B Gene Ontology (GO) enrichment analysis illustrating significantly enriched biological processes. Bar plots (A) and dot plots (B) display the top enriched GO biological process terms, where the x-axis represents the gene count and color intensity indicates the adjusted P value C , D Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showing pathways related to immune regulation, inflammatory signaling, and cell differentiation, including Th17 cell differentiation–related pathways. Bar plots (C) and dot plots (D) present the significantly enriched KEGG pathways, with dot size representing gene count and color indicating the adjusted P value. Machine learning identifies ABCA8 as a robust osteoporosis-associated feature gene To evaluate the robustness and predictive relevance of ABCA8, multiple machine learning algorithms were applied to the candidate gene set. LASSO regression identified a subset of genes with non-zero coefficients associated with osteoporosis status (Fig. 5 A, B). Random forest analysis ranked genes according to their importance scores (Fig. 5 C, D), while Boruta feature selection further confirmed a set of stable and informative features (Fig. 5 E). Intersection analysis across these three algorithms demonstrated that ABCA8 was consistently retained, indicating its robustness as a key feature gene associated with osteoporosis (Fig. 5 F). Fig. 5. Open in a new tab Machine learning approaches used to identify robust osteoporosis-associated feature genes. A , B Least absolute shrinkage and selection operator (LASSO) regression analysis showing the coefficient profiles of candidate genes ( A ) and the cross-validation curve for selecting the optimal penalty parameter λ ( B ). C , D Random forest analysis illustrating feature importance rankings based on mean decrease accuracy ( C ) and the stability of variable importance across repeated classifier runs ( D ). E Boruta feature selection confirming informative genes based on importance scores compared with shadow features. F Venn diagram showing the intersection of feature genes identified by LASSO, random forest, and Boruta algorithms. The numbers represent the counts of genes in each intersection. Construction of a nomogram model based on key feature genes Based on the feature genes identified by machine learning, a nomogram model was constructed to assess the potential predictive value of ABCA8 in combination with other selected genes. The nomogram incorporated ABCA8, DAGLA (Diacylglycerol lipase alpha), and PFKL (Phosphofructokinase, liver type), assigning each gene a weighted score contributing to the overall risk estimation (Fig. 6 ). The model visually illustrates the contribution of ABCA8 to disease prediction, supporting its potential value as a biomarker associated with osteoporosis. Fig. 6. Open in a new tab Nomogram model constructed based on selected feature genes. The model integrates ABCA8, DAGLA, and PFKL to estimate the probability of osteoporosis, illustrating the relative contribution of each gene to disease risk prediction. For each gene, the corresponding expression value is projected to the “Points” scale to obtain an individual score. The total points are calculated by summing the scores of all variables, which are then mapped to the predicted probability of osteoporosis on the bottom scale Gene set enrichment analysis reveals immune-related pathways associated with ABCA8 expression To further investigate the biological pathways associated with ABCA8 expression, GSEA was performed by stratifying samples into high- and low-ABCA8 expression groups. GSEA revealed that samples with high ABCA8 expression were significantly enriched in pathways related to T cell differentiation, Th17 cell differentiation, amino acid metabolism, and immune regulatory processes (Fig. 7 ). These findings provide pathway-level evidence linking ABCA8 expression to immune-related biological functions. Fig. 7. Open in a new tab Gene set enrichment analysis (GSEA) comparing samples with high and low ABCA8 expression. Samples were stratified into high- and low-ABCA8 expression groups based on the median expression level of ABCA8. Representative enriched pathways are shown, including immune-related and metabolic pathways. The running enrichment score (ES) curves indicate the degree to which each gene set is overrepresented at the top or bottom of the ranked gene list. Vertical bars represent the positions of genes within each gene set in the ranked dataset, and the bottom plot shows the ranked metric distribution. Pathways with nominal P < 0.05 and false discovery rate (FDR) < 0.25 were considered significantly enriched Immune landscape associated with ABCA8 expression in osteoporosis To characterize the immune landscape associated with ABCA8, ssGSEA analysis was conducted to estimate immune cell–related signature scores. The relative distribution of immune-associated signatures differed between osteoporosis and control groups (Fig. 8 A). Correlation analysis among immune-related signatures revealed coordinated relationships between multiple immune cell–associated activities, reflecting the overall immune-associated transcriptional landscape in osteoporosis (Fig. 8 B). These results suggest that ABCA8 is closely linked to systemic immune-associated transcriptional features in osteoporosis. Fig. 8. Open in a new tab Immune-associated transcriptional features estimated by single-sample gene set enrichment analysis (ssGSEA) analysis. A Relative distribution of immune-related signature scores across samples estimated by ssGSEA, comparing osteoporosis and control groups. The y-axis represents the relative proportion of each immune-related signature in individual samples. B Correlation heatmap illustrating the relationships among different immune-related signature scores, reflecting the overall immune-associated transcriptional landscape. Color intensity represents the strength and direction of Spearman correlation analysis between immune-related signatures. Association between ABCA8 expression and Th17/Treg-associated immune signatures Given the enrichment of immune-related pathways, particularly those related to Th17 cell differentiation, the association between ABCA8 expression and Th17/Treg-related immune signatures was further examined. As shown in Fig. 9 A, the overall distribution of immune-associated signature scores differed between osteoporosis and control samples, indicating global alterations in immune-related transcriptional patterns. Furthermore, correlation analysis (Fig. 9 B) demonstrated that ABCA8 expression was significantly associated with multiple immune-related signatures, including Th17- and Treg–associated transcriptional features. These results indicate that ABCA8 expression is significantly associated with alterations in Th17- and Treg-associated transcriptional features, reflecting immune-related transcriptional patterns, suggesting a potential link between ABCA8 and immune imbalance in osteoporosis. Fig. 9. Open in a new tab Relationship between ABCA8 expression and Th17/Treg-associated immune signatures estimated by ssGSEA analysis. A Comparison of ssGSEA-derived immune-related signature scores between osteoporosis and control samples, showing the overall immune-associated transcriptional landscape. Immune-related signature scores were calculated using the ssGSEA algorithm. Samples were grouped according to disease status (control vs. osteoporosis). B Correlation heatmap illustrating the associations between ABCA8 expression and multiple immune-related signature scores, including Th17- and Treg–associated transcriptional features. Correlations were calculated using Spearman’s correlation analysis. Color intensity represents the strength and direction of correlation, with statistical significance indicated (* P < 0.05, ** P < 0.01). Expression changes and experimental modulation of ABCA8 in femoral tissues of OVX Rats To explore the potential role of ABCA8 in the development of osteoporosis, we first examined its expression levels in femoral tissues of OVX model rats. qRT-PCR and Western blot analyses revealed that both mRNA and protein levels of ABCA8 were significantly elevated in OVX rats at 6 and 12 weeks post-surgery compared to controls, with even higher expression observed at 12 weeks (Fig. 10 A, B). These results suggest that ABCA8 expression increases progressively with the advancement of osteoporosis and may be involved in disease pathogenesis. Fig. 10. Open in a new tab Elevated and experimentally modulated ABCA8 expression in femoral tissues of OVX rats. A qRT-PCR analysis of ABCA8 mRNA expression in femoral tissues from Control rats, OVX rats at 6 weeks post-surgery, and OVX rats at 12 weeks post-surgery. B Western blot analysis of ABCA8 protein levels in femoral tissues from the same groups. C qRT-PCR analysis of ABCA8 mRNA levels in femoral tissues from Control, OVX, OVX+siNC, OVX + si-ABCA8, OVX+vector, and OVX+ABCA8 groups. D Western blot analysis of ABCA8 protein levels in femoral tissues from the same groups. Data are presented as mean ± SD ( n = 6). Statistical comparisons were performed using one-way ANOVA followed by Tukey’s post hoc test.** P < 0.05 vs. Control; ## P < 0.05 vs. OVX+siNC; && P < 0.05 vs. OVX+vector. Based on these findings, we further investigated the functional role of ABCA8 by modulating its expression in OVX rats via intramedullary injection of lentivirus carrying either ABCA8 siRNA or overexpression constructs. qRT-PCR and Western blot results confirmed that ABCA8 expression was significantly reduced in the OVX + si-ABCA8 group and markedly increased in the OVX+ABCA8 group compared to their respective negative controls (Fig. 10 C, D). No significant differences were observed among the OVX, OVX+siNC, and OVX+vector groups, indicating the specificity and effectiveness of the viral interventions. Effects of ABCA8 modulation on femoral bone morphology in OVX rats Histological assessment of bone morphology is shown in Fig. 11 . In the Control group, trabeculae were evenly distributed, with normal thickness and orderly arrangement. In contrast, trabeculae in the OVX, OVX+siNC, and OVX+vector groups were thinner, partially lost, irregularly arranged, and accompanied by enlarged marrow cavities and uneven matrix staining. In the OVX + si-ABCA8 group, trabecular thickness increased, the arrangement became more regular, marrow spaces were reduced, and matrix staining was more uniform compared to the OVX+siNC group. Conversely, the OVX+ABCA8 group exhibited further trabecular loss, thinning, network disruption, expanded marrow cavities, and uneven staining compared to the OVX+vector group (Fig. 11 A). Fig. 11. Open in a new tab Effects of ABCA8 modulation on femoral bone morphology in OVX rats. A Representative hematoxylin and eosin (H&E) staining images showing femoral trabecular morphology in each group. Scale bar = 50 μm. B – D Quantitative analysis of bone volume fraction (BV/TV, %), osteoid surface per bone surface (OS/BS, %), and eroded surface per bone surface (ES/BS, %). Data are presented as mean ± SD ( n = 6). Statistical comparisons were performed using one-way ANOVA followed by Tukey’s post hoc test. ** P < 0.05 vs. Control; ## P < 0.05 vs. OVX+siNC; && P < 0.05 vs. OVX+vector Bone morphometric analysis showed that OVX significantly decreased BV/TV and OS/BS and increased ES/BS compared to controls ( P < 0.05). ABCA8 silencing significantly improved these parameters, whereas ABCA8 overexpression worsened them ( P < 0.05). No significant differences were observed among the OVX, OVX+siNC, and OVX+vector groups ( P > 0.05) (Fig. 11 B–D). These results suggest that ABCA8 promotes bone resorption and aggravates osteoporosis progression. Effects of ABCA8 modulation on bone formation and metabolic markers in OVX rats We next examined the effects of ABCA8 modulation on bone formation and metabolic markers in OVX rats. Compared to the Control group, OVX rats showed significantly decreased serum levels of ALP and PINP, and increased levels of CTX-1 and NTX ( P < 0.05). Silencing ABCA8 (OVX + si-ABCA8 group) significantly elevated ALP and PINP levels and reduced CTX-1 and NTX levels compared to the OVX+siNC group ( P < 0.05), while ABCA8 overexpression (OVX+ABCA8 group) had the opposite effects compared to the OVX+vector group ( P < 0.05). No significant differences were observed among the OVX, OVX+siNC, and OVX+vector groups ( P > 0.05) (Fig. 12 A–D). Fig. 12. Open in a new tab Effects of ABCA8 modulation on bone formation and metabolic markers in OVX rats. A – D ELISA analysis of serum levels of ALP, PINP, CTX-1, and NTX in each group. E Western blot analysis of RUNX2, OCN, Osterix, RANKL, OPG, RANK, and TRAP protein levels in femoral tissues. Data are presented as mean ± SD ( n = 6). Statistical comparisons were performed using one-way ANOVA followed by Tukey’s post hoc test. ** P < 0.01 vs. Control; ## P < 0.01 vs. OVX+siNC; && P < 0.01 vs. OVX+vector Western blot analysis revealed that, relative to the Control group, OVX rats had significantly reduced expression of osteogenic proteins (RUNX2, OCN, Osterix, and OPG) and increased expression of bone resorption markers (RANKL, RANK, and TRAP) ( P < 0.05). Silencing ABCA8 upregulated osteogenic proteins and downregulated resorption markers compared to the OVX+siNC group, while ABCA8 overexpression produced the opposite pattern compared to the OVX+vector group ( P < 0.05) (Fig. 12 E). These results indicate that ABCA8 silencing ameliorates OVX-induced bone metabolic imbalance by promoting bone formation and inhibiting resorption, whereas ABCA8 overexpression exacerbates bone metabolic disruption, supporting a regulatory role of ABCA8 in osteoblast–osteoclast balance during osteoporosis progression. Effects of ABCA8 modulation on Th17/Treg balance and inflammatory cytokines in OVX rats Finally, we investigated the effects of ABCA8 modulation on the Th17/Treg balance and inflammatory cytokine levels in OVX rats. Flow cytometry analysis showed that OVX rats had increased Th17 and decreased Treg cell percentages in spleen and peripheral blood compared to controls ( P < 0.05). Silencing ABCA8 (OVX + si-ABCA8 group) reversed these changes, while ABCA8 overexpression exacerbated them ( P < 0.05), with no significant differences among OVX, OVX+siNC, and OVX+vector groups ( P > 0.05) (Fig. 13 A, B). These results indicate that ABCA8 overexpression shifts the Th17/Treg balance toward a pro-inflammatory state. Fig. 13. Open in a new tab Effects of ABCA8 modulation on Th17/Treg balance and inflammatory cytokine levels in OVX rats. A Representative flow cytometry plots illustrating the gating strategy for Th17 (CD4⁺IL-17 A⁺) and regulatory T (Treg; CD4⁺CD25⁺Foxp3⁺) cells. B Quantification of Th17 and Treg cell proportions in spleen and peripheral blood across experimental groups. C – F ELISA analysis of serum IL-6, TNF-α, IL-17, and IL-10 levels in each group. IL-6, Interleukin-6; TNF-α, tumor necrosis factor-α; IL-17, Interleukin-17; IL-10, Interleukin-10. Data are presented as mean ± SD ( n = 6). Statistical comparisons were performed using one-way ANOVA followed by Tukey’s post hoc test. ** P < 0.05 vs. Control; ## P < 0.05 vs. OVX+siNC; && P < 0.05 vs. OVX+vector Further ELISA analysis demonstrated that serum levels of IL-6, TNF-α, and IL-17 were significantly elevated, and IL-10 levels were significantly decreased in OVX rats compared to controls ( P < 0.05). Silencing ABCA8 significantly decreased IL-6, TNF-α, and IL-17 levels and increased IL-10 levels compared to the OVX+siNC group, while ABCA8 overexpression produced the opposite effects compared to the OVX+vector group ( P < 0.05) (Fig. 13 C–F). These findings suggest that ABCA8 overexpression exacerbates systemic inflammation by disrupting the Th17/Treg immune balance in OVX rats. Discussion An increasing body of research indicates that the immune system plays a significant role in bone metabolism and the pathogenesis of osteoporosis [ 26 ]. Osteoporosis is characterized by decreased bone density and deterioration of bone structure, leading to an increased risk of fractures. Recent studies have found that inflammatory factors, immune cells, and signaling pathways may contribute to the pathophysiology of osteoporosis. Notably, immune-associated transcriptional alterations have been observed in osteoporosis patients, involving dysregulation of specific immune cell–related signatures, including those associated with T cells and B cells [ 27 , 28 ]. For instance, alterations in regulatory T cell (Treg)–related activity have been reported to be associated with osteoporosis, potentially through effects on bone formation and resorption [ 15 ]. In the present study, our bioinformatics analysis revealed that ABCA8 expression is associated with distinct immune-associated transcriptional patterns, including signatures related to Th17 and Treg cells. However, these findings are derived from ssGSEA-based transcriptomic analyses and therefore reflect relative enrichment of immune-related gene sets rather than direct changes in immune cell proportions. These results suggest a potential association between ABCA8 expression and systemic immune-associated transcriptional regulation in osteoporosis. Importantly, these findings are derived from transcriptomic analyses of peripheral blood–related datasets and therefore reflect systemic immune-associated transcriptional features, rather than direct alterations within the bone immune microenvironment. In contrast, our in vivo experiments provide functional evidence at the cellular level. Nevertheless, the biological relevance of this systemic immune association was further supported by our in vivo experiments, in which modulation of ABCA8 expression in ovariectomized rats led to consistent alterations in Th17/Treg balance, inflammatory cytokine profiles, and bone structural parameters. Specifically, ABCA8 overexpression promoted a pro-inflammatory shift characterized by increased Th17 cells and decreased Treg cells, whereas ABCA8 silencing exerted the opposite effects. Together, these findings suggest that ABCA8 may influence osteoimmune regulation through systemic immune-associated dysregulation that is functionally linked to bone pathology, with transcriptomic analyses providing complementary evidence and in vivo experiments offering direct functional validation, rather than solely through mechanisms inferred from bioinformatics analyses. Understanding how ABCA8 affects immune cell regulation across systemic and bone-related contexts may provide new targets and strategies for treating osteoporosis. Moreover, macrophages and bone-resorption-related inflammatory cytokines, such as TNF-α and IL-6, have been widely reported to play a promoting role in the development of osteoporosis [ 29 , 30 ]. Inflammatory signaling pathways and immune-associated cytokine dysregulation are increasingly recognized as critical contributors to osteoimmune imbalance, rather than reflecting classical immune microenvironment remodeling. In the present study, bioinformatics analyses based on transcriptomic data revealed that ABCA8 expression was associated with immune-related transcriptional features, particularly those linked to T cell–associated regulatory pathways. Notably, our in vivo experiments further demonstrated that modulation of ABCA8 expression in ovariectomized rats led to significant alterations in inflammatory cytokine profiles, including IL-6, TNF-α, and IL-17, which are known to promote osteoclast differentiation and bone resorption. Elevated levels of these cytokines have been reported to exacerbate bone loss by enhancing osteoclast activity and suppressing osteoblast function [ 31 ]. Taken together, these findings suggest that ABCA8 may influence bone metabolism through regulation of inflammatory and immune-associated signaling pathways, thereby contributing to the disruption of osteoimmune homeostasis in osteoporosis. This immuno-inflammatory regulatory axis may represent an important mechanism by which ABCA8 participates in osteoporosis progression. To further investigate the role of ABCA8 in osteoporosis and its immune-associated inflammatory regulation, we established a postmenopausal osteoporosis model in rats using OVX. We found that ABCA8 mRNA and protein levels in femoral tissues were significantly elevated as osteoporosis progressed, suggesting that ABCA8 may contribute to the disease pathogenesis. While the observed upregulation of ABCA8 in OVX rats aligns with reports implicating other ABCA family members (e.g., ABCA1 [ 32 ]) in bone metabolism, our study suggests a distinct regulatory role for ABCA8. Unlike ABCA1, which primarily influences osteoblast function through cholesterol efflux, our data provide evidence that ABCA8 is associated with osteoporosis progression at least in part through immunomodulatory effects, particularly involving disruption of the Th17/Treg balance. This highlights a potentially non-redundant role for ABCA8 within the ABC transporter family in the context of bone disease. Moreover, ABCA8 expression exhibited a progressive increase from 6 to 12 weeks post-ovariectomy, indicating a potential dose-dependent association between ABCA8 levels and osteoporosis severity. Lentivirus-mediated ABCA8 silencing and overexpression in OVX rats successfully modulated ABCA8 expression in femoral tissues, with silencing effectively reducing and overexpression increasing its levels, further supporting the potential role of ABCA8 in osteoporosis progression. H&E staining of femoral tissues provided histopathological evidence supporting osteoporotic changes. Following ovariectomy, rats exhibited classical osteoporotic features, including trabecular thinning, disorganization, and marrow cavity expansion, consistent with the pathological characteristics of postmenopausal osteoporosis [ 33 ]. Notably, silencing ABCA8 significantly improved trabecular structure, as indicated by increased thickness, more regular arrangement, and reduced marrow spaces, whereas ABCA8 overexpression aggravated trabecular deterioration. These histological findings were highly consistent with bone morphometric data (BV/TV, OS/BS, and ES/BS), suggesting that ABCA8 expression plays an important regulatory role in maintaining the balance between bone resorption and formation. According to the International Osteoporosis Foundation and the International Federation of Clinical Chemistry, serum levels of ALP, PINP, CTX-1, and NTX are key markers recommended for evaluating bone turnover [ 34 ]. Among them, PINP and ALP reflect osteoblastic activity, while CTX-1 and NTX indicate osteoclastic bone resorption. In this study, OVX rats showed significantly decreased ALP and PINP levels and increased CTX-1 and NTX levels, consistent with estrogen deficiency–associated acceleration of bone turnover [ 35 ]. Furthermore, ABCA8 silencing restored bone metabolic balance, as evidenced by elevated bone formation markers (ALP, PINP) and reduced bone resorption markers (CTX-1, NTX). These results were further supported by Western blot analyses showing corresponding changes in osteogenesis-related proteins (RUNX2, OCN, Osterix, OPG) and bone resorption markers (RANKL, RANK, TRAP), further supporting a critical regulatory role of ABCA8 in osteoporosis progression. The role of immune regulation in bone metabolic diseases has received increasing attention, with Th17/Treg imbalance and the resulting inflammatory responses recognized as important contributors to osteoporosis progression. Studies have shown that ovariectomy leads to a significant expansion of Th17 cells and a reduction of Treg cells in mice, disrupting immune homeostasis [ 36 ]. As a key pro-inflammatory T-cell subset, Th17 cells have been reported to promote bone resorption through two mechanisms: directly expressing RANKL to induce osteoclast precursor differentiation, and secreting inflammatory cytokines such as IL-17 and TNF-α to activate the NF-κB signaling pathway, further upregulating RANKL expression [ 15 ]. In contrast, Treg cells suppress RANKL expression and IL-17 production and inhibit osteoclastogenesis through direct cell-cell interactions [ 37 ]. Notably, under estrogen-deficient conditions, Treg cells may exhibit phenotypic instability or partial transdifferentiation toward Th17 cells, thereby intensifying osteoclastogenic signaling [ 38 ]. Thus, targeting the Th17/Treg balance may offer a novel therapeutic approach for postmenopausal osteoporosis. While this study provides important insights into the role of ABCA8 in osteoporosis, several limitations should be noted. First, although bioinformatics analyses and the OVX rat model were used to investigate the association between ABCA8 and immune imbalance in osteoporosis, the OVX model cannot fully recapitulate the pathological complexity of postmenopausal osteoporosis in humans, particularly with respect to dynamic hormonal fluctuations and the influence of comorbid metabolic factors. In addition, although the animal experiments completed group intervention and functional verification, the sample size of rats included in the final analyses for each group was relatively limited, and its potential impact on statistical power was not further evaluated. Moreover, ABCA8 was investigated mainly through public dataset mining combined with animal experiments, and clinical osteoporosis specimens were not included for in vivo or in vitro validation, which may limit the translational applicability of these findings. Second, the immune-associated effects of ABCA8 were primarily explored through bulk transcriptomic data and flow cytometry; advanced techniques such as single-cell RNA sequencing or spatial transcriptomics may provide deeper insights into its cell-type-specific functions. Third, the present study mainly identifies an association between ABCA8 expression and Th17/Treg immune imbalance, and the potential confounding influence of other immunometabolic regulators cannot be completely excluded. Therefore, the findings should be interpreted primarily at the level of correlation rather than direct causal inference. In addition, the current work mainly focused on the immune–bone axis, whereas ABCA8 may also participate in other metabolic or signaling pathways that remain to be elucidated. Future studies integrating multi-omics approaches and longitudinal designs are warranted to further clarify the broader biological functions and regulatory mechanisms of ABCA8 in osteoporosis. In our study, OVX rats exhibited a significant increase in Th17 cells and a decrease in Treg cells in spleen and peripheral blood, accompanied by elevated serum levels of IL-6, TNF-α, and IL-17, and reduced IL-10 levels. Silencing ABCA8 markedly reduced pro-inflammatory Th17 cells, increased anti-inflammatory Treg cells, decreased IL-6, TNF-α, and IL-17 levels, and elevated IL-10 levels, shifting the Th17/Treg balance toward an anti-inflammatory state and mitigating osteoporosis progression. Conversely, ABCA8 overexpression shifted the balance toward a pro-inflammatory state, exacerbating osteoporosis. Collectively, these findings suggest that ABCA8 is closely associated with osteoporosis progression and may contribute to bone loss, at least in part, through disruption of the Th17/Treg balance and enhancement of systemic inflammatory responses. Conclusion This study identifies ABCA8 as a novel gene closely associated with osteoporosis progression and reveals its previously unrecognized involvement in immune-associated regulatory processes. By integrating bioinformatics analyses with in vivo functional validation, we demonstrate that ABCA8 expression is associated with bone loss, impaired bone formation, enhanced bone resorption, and alterations in the Th17/Treg balance accompanied by inflammatory changes. These findings suggest that ABCA8 may contribute to osteoporosis progression, at least in part, through dysregulation of osteoimmune balance, particularly in the context of postmenopausal osteoporosis. Collectively, our results highlight the potential of ABCA8 as an immunoregulatory target for osteoporosis and provide a foundation for future studies aimed at clinical validation and therapeutic exploration. Supplementary Information Below is the link to the electronic supplementary material. Supplementary Material 1 (4.7MB, zip) Acknowledgements Not applicable. Author contributions Wenjing Yi: Study design, Methodology, Formal analysis, Data interpretation, Writing – original draft. Wenan Xu: Study design, Experimental work, Data acquisition, Statistical analysis, Writing – original draft. Tao Yang: Investigation, Experimental work, Data curation. Xiaolin Lyu: Sample preparation, Laboratory analysis, Data validation. Ziting Wang: Investigation, Resources, Technical support. Leyi Chen: Data acquisition, Literature search. Jingya Zhang: Data validation, Figure preparation. Yiming Zhong: Software, Data visualization, Figure preparation. Songlin Wang: Supervision, Project administration, Funding acquisition, Writing – review & editing. Buling Wu: Supervision, Conceptualization, Funding acquisition, Writing – review & editing. All authors read and approved the final manuscript. Funding This study was supported by Guangdong Provincial Medical Science and Technology Research Fund Project(B2025406). Data availability The data used to support the findings of this study are available from the corresponding author upon request. Declarations Competing interests The authors declare no competing interests. Ethical approval All procedures were approved by the Ethical Committee of Laboratory Animals, Guangdong Provincial Medical Experimental Animal Center(Approval No.D202506-6) and conducted in compliance with the institutional guidelines (Directive 2010/63/EU in Europe) for the care and use of animals. Footnotes President’s Fund of Southern Medical University Shenzhen Stomatological Hospital (Pingshan) (Grant No. B2025B001) Guangdong Medical Science and Technology Research Fund (Grant No. B2025406) Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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