Expanding role of cell-free DNA for the early diagnosis and monitoring of pulmonary diseases.

Expanding role of cell-free DNA for the early diagnosis and monitoring of pulmonary diseases - PMC 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 Chin Med J (Engl) . 2026 Jan 28;139(8):1168–1180. doi: 10.1097/CM9.0000000000003955 Search in PMC Search in PubMed View in NLM Catalog Add to search Expanding role of cell-free DNA for the early diagnosis and monitoring of pulmonary diseases Liuqing Yang Liuqing Yang 1 Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Targeted Tracer Research and Development Laboratory, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, Sichuan 610041, China Find articles by Liuqing Yang 1 , Yu Gu Yu Gu 2 West China School of Medicine, Sichuan University, Chengdu, Sichuan 610041, China Find articles by Yu Gu 2 , Jun Shao Jun Shao 1 Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Targeted Tracer Research and Development Laboratory, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, Sichuan 610041, China Find articles by Jun Shao 1 , Li Zhang Li Zhang 1 Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Targeted Tracer Research and Development Laboratory, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, Sichuan 610041, China Find articles by Li Zhang 1 , Chengdi Wang Chengdi Wang 1 Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Targeted Tracer Research and Development Laboratory, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, Sichuan 610041, China Find articles by Chengdi Wang 1, ✉ Editor: Xiangxiang Pan Author information Article notes Copyright and License information 1 Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Targeted Tracer Research and Development Laboratory, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, Sichuan 610041, China 2 West China School of Medicine, Sichuan University, Chengdu, Sichuan 610041, China ✉ Correspondence to: Chengdi Wang, Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Targeted Tracer Research and Development Laboratory, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, Sichuan 610041, China E-Mail: [email protected] Received 2024 Jul 5; Issue date 2026 Apr 20. Copyright © 2026 The Chinese Medical Association, produced by Wolters Kluwer, Inc. under the CC-BY-NC-ND license. This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. PMC Copyright notice PMCID: PMC13090082  PMID: 41603032 Abstract Circulating cell-free DNA (cfDNA) has firmly established itself as a cornerstone of liquid biopsy, advancing the noninvasive diagnosis and monitoring of pulmonary diseases. Its molecular characteristics, particularly methylation profiles, fragmentation patterns, and mutations, now enable a range of clinical applications—from early detection of lung cancer to rapid pathogen identification and severity assessment in pneumonia, and to precise risk stratification in chronic conditions such as chronic obstructive pulmonary disease and asthma. Beyond diagnostic applications, dynamic changes in cfDNA levels and profiles provide critical insights into disease monitoring across a spectrum of pulmonary disorders. While challenges in detection sensitivity, analytical standardization, and clinical validation remain, the ongoing integration of multi-omics data and artificial intelligence is refining the predictive power of cfDNA-based models. Future developments are expected to consolidate the role of cfDNA analysis as an indispensable tool in precision pulmonology, ultimately transforming diagnostic pathways and enabling more personalized, proactive management of respiratory health. Keywords: Cell-free DNA, Liquid biopsy, Pulmonary diseases, DNA methylation, Biomarkers Introduction Pulmonary diseases represent a major global health burden, significantly contributing to morbidity and mortality worldwide. [ 1 ] These include lung cancer, chronic obstructive pulmonary disease (COPD), pneumonia, asthma, pulmonary tuberculosis, and pulmonary fibrosis, among others. Traditional methods, such as imaging, tissue biopsy, and pulmonary function tests, face limitations including invasiveness, low sensitivity for early detection, and challenges in dynamic monitoring. Furthermore, the high heterogeneity of pulmonary diseases highlights the urgent need for noninvasive, highly sensitive biomarker technologies to enable early screening, precise diagnosis, treatment evaluation, and prognosis prediction. The emergence of liquid biopsy technologies, such as cell-free DNA (cfDNA) analysis, offers a transformative solution to address these challenges. [ 2 ] Liquid biopsy is a revolutionary technology that enables the analysis of disease-related molecular markers in biofluids such as blood, urine, saliva, cerebrospinal fluid, and pleural effusion. [ 3 ] The noninvasive approach aids in various clinical settings, including the detection and monitoring of diseases, offering patients a more convenient and potentially less risky alternative to traditional tissue biopsies. [ 4 ] cfDNA, circulating tumor cells (CTCs), and exosomes are the key components of liquid biopsy. [ 5 ] Among them, cfDNA stands out as the primary subject of research attention. cfDNA is found in the bloodstream and is primarily released from apoptotic and necrotic cells, as well as from active cells. [ 6 , 7 ] In healthy individuals, cfDNA is mainly derived from blood cells, including white blood cells (55%), erythrocyte progenitors (30%), and other tissues, such as vascular endothelial cells (10%) and liver cells (1%). [ 7 , 8 ] The length of cfDNA is usually concentrated at approximately 160 base pairs (bp), whereas in certain pathological conditions such as cancer, the length distribution of cfDNA may vary. [ 9 ] The concentration of cfDNA in the plasma of healthy individuals is typically low, ranging from 1 to 50 ng/mL. [ 7 ] In various pathological conditions, such as in patients with cancers, inflammatory diseases, and tissue trauma, the concentration of cfDNA is increased, even exceeding 1000 ng/mL in cancer patients. [ 7 , 10 ] The marked elevation in circulating cfDNA levels and their dynamic fluctuations across disease states establish a quantitative biological foundation for liquid biopsy technologies, enabling noninvasive disease screening and real-time monitoring through blood-based assays [Figure 1 ]. Figure 1. Open in a new tab Integrating multi-omics data for precision diagnosis and monitoring of pulmonary diseases. Circulating cfDNA, through its epigenetic signatures integrated with multi-omics data and other information, serves as a powerful biomarker for pulmonary disease early detection, auxiliary diagnosis, treatment monitoring, and prognosis prediction. cfDNA: Cell-free DNA. cfDNA exhibits a diverse range of molecular characteristics, including mutations, methylation alterations, fragmentation patterns, and topological structures [Figure 2 ]. [ 11 ] The various characteristics of cfDNA reflect different dimensions of information, indicating their potential as disease biomarkers. [ 6 ] In pulmonary diseases, such as lung cancer, pneumonia, COPD, asthma, tuberculosis, pulmonary fibrosis, and other significant pulmonary conditions, a number of research findings concerning cfDNA have emerged in the realm of diagnosis and treatment. These advances have positioned cfDNA as a transformative tool in pulmonary diseases, bridging molecular insights with clinical decision making. We comprehensively present a thorough review of the latest developments in cfDNA testing and its application in the diagnosis and monitoring of pulmonary diseases. Figure 2. Open in a new tab Methylation, fragmentation, mutation, and topology of cell-free DNA in liquid biopsies. Methylation refers to the chemical modification process of adding methyl groups to the DNA molecules. Fragmentomics refers to the fragmentation patterns and sizes. Mutation refers to permanent alterations that occur in the DNA sequence, such as base substitution, insertion, and deletion. Topology is the physical structure of cfDNA, which includes double-stranded linear molecules, single-stranded forms, and extrachromosomal circular DNA. These characteristics provide complementary information for disease detection and monitoring. cfDNA: Cell-free DNA; DNMT: DNA methyltransferase. Alterations in DNA Methylation in Pulmonary Diseases DNA methylation is facilitated by DNA methyltransferases (DNMTs), which catalyze the transfer of a methyl group on S-adenosylmethionine (SAM) to the 5′ position of a cytosine nucleotide, resulting in the formation of 5-methylcytosine (5-mC) within the genomic cytosine-guanosine dinucleotide (CpG). [ 12 ] Methylation of genes at different locations has different effects on gene expression: methylation of CpG islands near the transcriptional start point (TSS) induces gene silencing, whereas methylation inside the gene body activates gene expression. [ 13 ] Emerging evidence has indicated that DNA methylation potentially participates in disease pathogenesis. [ 14 , 15 , 16 ] The methylation of certain genes has been identified as a factor that can accelerate the progression of diseases toward tumorigenesis. Methylation of coiled coil domain containing 37 ( CCDC37 ) and microtubule-associated protein 1B ( MAP1B ) plays a role in lung cancer development among patients with COPD. [ 16 ] The CCDC37 protein regulates ciliary motility, and epigenetic silencing of this gene provokes mucus accumulation and amplifies pulmonary inflammation. MAP1B is one of the major cytoskeletal proteins involved in a variety of cellular activities, including molecular trafficking, actin-based cell motility, and autophagy. The light chain of MAP1B interacts with Pes1 and p53 to regulate cell proliferation and apoptosis, and the loss of this gene’s function may contribute to the ability of cancer cells to evade death signals and proliferate. The development of lung cancer involves the disruption of tumor suppressor networks by abnormal DNA methylation. Specific methylation patterns activate distinct cancer pathways. The Ras association domain family 1 isoform A ( RASSF1A ) functions as a tumor suppressor. Its inactivation due to methylation prevents its ability to induce cellular apoptosis, consequently contributing to the initiation and progression of cancer [Supplementary Figure 1, http://links.lww.com/CM9/C728 ]. In non-small cell lung cancer (NSCLC), the methylation of sperm-associated antigen 6 ( SPAG6 ) and LINE-1 type transposase domain-containing 1 ( L1TD1 ) was associated with the absence of protein expression. [ 15 ] Loss of protein expression due to methylation prevented L1TD1 from reducing tumor growth in vivo , but no effect of SPAG6 alteration on tumor cell lines was observed. Methylation profiles are also altered in interstitial lung diseases such as pulmonary fibrosis. These changes contribute to the pathogenesis of idiopathic pulmonary fibrosis (IPF). [ 17 ] In pulmonary fibrosis, hypermethylation of chromosome 8 open reading frame 4 ( c8orf4 ) leads to its transcriptional silencing and reduced expression. This downregulation results in decreased levels of downstream prostaglandin E2 (PGE2), impairing its antifibrotic functions and ultimately promoting fibrosis progression. [ 18 , 19 ] Collectively, these findings establish DNA methylation as a pivotal epigenetic driver in pulmonary disease pathogenesis, underscoring its potential as a source of clinically actionable biomarkers for early detection and precision-guided therapeutic interventions via liquid biopsy. Method Used for the cfDNA Test The methylation, mutation, fragmentation, and topological features of cfDNA can be detected by different sequencing methods [Table 1 ]. [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ] The methods used to detect cfDNA methylation fall into two categories: genome-wide and targeted methods. [ 11 ] Genome-wide methods, such as methylated CpG tandem amplification and sequencing (MCTA-seq) and cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq), enable the capture of genome-wide methylation patterns and hold broad potential for the discovery of disease-specific methylation sites. [ 25 ] Targeted methods are also robust and common methylation detection methods that use DNA probes or primers to capture methylation patterns in specific genomic regions through next-generation sequencing (NGS), allowing for deep sequencing in a more cost-effective way. [ 11 ] Table 1. Technology and applications for cell-free DNA analysis. Techniques Test type/genomic space Molecular feature Characteristics Application examples References WGS Genome-wide Mutation Comprehensive Dynamic tumor burden tracking and postoperative residual disease detection [ 20 ] WGBS Genome-wide Methylation Gold standard in DNA methylation analysis Diagnosis of multiple cancer diseases [ 21 , 22 ] WES Genome-wide Mutation Economical Monitoring of recurrence of non-small cell lung cancer after treatment [ 23 ] MCTA-seq Genome-wide Methylation Efficient Detection of tumor [ 24 ] cfMeDIP-seq Genome-wide Methylation Cost-effectiveness Detection and classification of tumor [ 25 ] MSRE-qPCR Targeted Methylation Quantification Diagnosis of cancer [ 26 ] CAPP-Seq Multilocus Mutation Economical, quantification Somatic mutation detection and tumor burden quantitation [ 27 ] TAm-Seq Multilocus Mutation Quantification Identify mutations such as p53 in plasma samples from patients with ovarian cancer [ 28 ] BEAMing Single-locus Mutation Quantification Detecting T790M to predict the outcome for osimertinib [ 29 , 30 ] ddPCR Single-locus Mutation Quantification Testing for EGFR variants in cfDNA in patients with NSCLC [ 31 ] DELFI Genome-wide Fragmentation Cost-effectiveness Detection of lung cancer [ 32 ] EPIC-seq Targeted Fragmentation High-throughput Detection and classification of lung cancer [ 33 ] Circle-seq Genome-wide Topology Qualitative Detection of cancer [ 34 ] Open in a new tab BEAMing: Beads, emulsion, amplification, and magnetics; CAPP-Seq: Cancer personalized profiling by deep sequencing; cfDNA: Cell-free DNA; cfMeDIP-seq: Cell-free methylated DNA immunoprecipitation and high-throughput sequencing; Circle-seq: Circularization for in vitro reporting of cleavage effects by sequencing; ddPCR: Droplet digital polymerase chain reaction; DELFI: DNA evaluation of fragments for early interception; EPIC-seq: Epigenetic expression inference from cell-free DNA-sequencing; MCTA-seq: Methylated CpG tandem amplification and sequencing; MSRE-qPCR: Methylation-sensitive restriction enzyme-quantitative polymerase chain reaction; NSCLC: Non-small cell lung cancer; TAm-Seq: Tagged-amplificon deep sequencing; WES: Whole exome sequencing; WGBS: Whole genome bisulfite sequencing; WGS: Whole genome sequencing. Furthermore, cfDNA typically contains specific gene mutation sites for detection. Techniques such as droplet digital polymerase chain reaction (ddPCR) and NGS can identify significant genetic characteristics and quantify variant allele frequency (VAF), a measure of the relative abundance of mutant alleles. [ 2 , 35 ] ddPCR accurately detects and quantifies known DNA mutations without the need for a separate calibration reaction, enabling improved VAF detection limits and more precise VAF quantification. [ 9 ] However, the high cost and limited number of installed devices pose challenges for extensive cfDNA analysis. In comparison, NGS offers significantly greater multiplexing capabilities, making it the primary method for cfDNA mutation analysis. [ 9 ] Currently, cfDNA library preparation is primarily achieved through two targeted methods: hybrid capture and multiplex PCR. The former often results in incomplete enrichment due to the nonspecific binding of cfDNA amplicons to probes or magnetic beads. In comparison, the latter significantly enhances target enrichment rates, as the concentration of amplicons at the sites of interest doubles with each PCR cycle, thereby reducing costs. [ 36 ] The core characteristics of cfDNA fragmentation, such as size profile, end motifs, and nucleosome occupancy, can indirectly reflect the status of gene expression regulation in vivo and are primarily detected. [ 37 ] DNA evaluation of fragments for early interception (DELFI) technology, which is based on whole-genome sequencing (WGS) analysis of cfDNA fragmentation patterns, facilitates cancer detection by examining the length of cfDNA across various regions of the genome. [ 32 , 38 ] Subsequently, the epigenetic expression inference from cell-free DNA-sequencing (EPIC-seq) technology, which utilizes targeted deep sequencing of the flanking regions of the cfDNA transcription start site (TSS) alongside machine learning techniques, was developed to accurately correlate the characteristics of cfDNA fragment length diversity with gene expression levels. [ 33 ] The detection methods above focus primarily on linear segments; however, other forms of cfDNA, such as circular molecules, also provide valuable insights into the disease. [ 6 ] This highlights the topological characteristics of cfDNA. The difference in the detection methods used between circular cfDNA and linear cfDNA is a digestion step that involves the addition of the restriction enzyme Bfa I before the sequencing library is prepared. This step ensures that both ends of the circular molecule have an enzyme cleavage signature, whereas only one end of the linear molecule has this characteristic. [ 39 ] Another topological feature of cfDNA in plasma is its double-stranded and single-stranded properties. Single-stranded DNA library preparation methods improve the detection of short plasma DNA. [ 40 , 41 ] Advances in the Application of cfDNA in Pulmonary Diseases Currently, the advancement of cfDNA testing technology has facilitated its clinical use. Completed and ongoing clinical studies investigating cfDNA in various pulmonary diseases have demonstrated significant potential for improving detection sensitivity, diagnostic accuracy, therapeutic decision making, and prognostic stratification [Supplementary Table 1, http://links.lww.com/CM9/C728 and Figure 3 ]. Figure 3. Open in a new tab Methylated genes in pulmonary diseases and their accuracy for precision diagnosis and prognostic evaluation. Representative gene combinations and their diagnostic and prognosis performance (sensitivity/specificity) are shown for lung cancer, pneumonia, asthma, tuberculosis, COPD, and IPF. COPD: Chronic obstructive pulmonary disease; IPF: Idiopathic pulmonary fibrosis. Lung cancer Early-stage lung cancer is usually asymptomatic, resulting in delayed diagnosis until the disease progresses to an advanced stage when clinical manifestations become evident. Identifying the condition at an early stage is crucial for increasing the likelihood of survival. [ 42 ] DNA methylation alterations have been indicated to occur prior to the emergence of atypical adenomatous hyperplasia (AAH) in the progression of lung adenocarcinoma. [ 43 ] And emerging methodologies now enable the reconstruction of tumor evolutionary history from methylation patterns, underscoring their potential in detecting early malignant transformation. [ 44 ] Thus, DNA methylation represents a valuable detection marker for identifying early-stage lung cancer. [ 43 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ] A machine-learning model known as lung cancer likelihood in plasma (Lung-CLiP) uses cfDNA and matched leukocyte DNA for targeted sequencing. This model effectively distinguished NSCLC patients from risk-matched controls, with a specificity of 80% and a sensitivity of 63% in patients with stage I disease. [ 48 ] Similarly, fragmentomic analyses like EPIC-seq, a technique in which cfDNA fragmentation patterns are used to infer gene expression noninvasively, can distinguish histological subtypes of NSCLC. The classifier generated by EPIC-seq has shown robust performance in cross-validation with an area under the curve (AUC) of 0.9, assisting in determining optimal treatment approaches for patients. [ 33 ] Additionally, a study indicates that the fragmentation patterns may be closely related to genetic and epigenetic landscapes, such as the methylation status of CpG sites controlling the fragmentation patterns around them, suggesting the potential for mutual derivation between these features. [ 52 ] Approaches that rely on a single data modality are inherently limited, while clinicians routinely integrate and analyze multi-omics data for more comprehensive diagnoses. [ 53 ] The development of multimodal systems that integrate data from diverse modalities is anticipated to bridge this gap. A model combining methylation of SHOX2 / PTGER4 and LDCT images performs better than LDCT in the diagnosis of pulmonary nodules, showing the potential of multiomics fusion. [ 54 ] The PulmoSeek Plus model, which integrates clinical symptoms, image features, and the methylation profiles of 100 cfDNA sites, stratifies the risk of pulmonary nodules. [ 49 , 55 ] The model categorizes participants with pulmonary nodules into low-risk, medium-risk, and high-risk groups according to PulmoSeek Plus scores and further provides personalized management recommendations based on the level of risk. The ASCEND-LUNG study developed an artificial intelligence (AI)-aided diagnostic model integrating chest CT images and cfDNA methylation profiles, which implemented a dual-score risk stratification system and demonstrated an accuracy of 80.3% in distinguishing benign from malignant pulmonary nodules in an external validation set. [ 56 ] The role of cfDNA methylation in the classification of small cell lung cancer (SCLC) subtypes has also been investigated, and the accuracy of the typing model can reach more than 90% in the validation set. [ 47 , 57 ] Extensive research in multicancer early screening has validated the utility of cfDNA analysis, demonstrating its significant contribution to the early detection of multiple cancer types, including lung cancer. [ 58 , 59 , 60 , 61 , 62 ] The blood-based multicancer early detection (MCED) test from the Circulating Cell-free Genome Atlas (CCGA) study leverages cfDNA sequencing and machine learning to detect diverse cancer signals and predict the cancer signal origin (CSO) with high precision. [ 60 , 61 ] MCED not only offered diagnostic solutions to the majority of participants within three months but also contributed to a reduction in unnecessary tests and surgeries. [ 63 ] The THUNDER study developed an MCED model demonstrating high sensitivity and specificity for six types of cancer: lung, colorectal, esophageal, liver, ovarian, and pancreatic cancers, providing a noninvasive approach for pan-cancer screening. [ 58 ] In the study, the MCDBT-1 model for the general population and the MCDBT-2 model for high-risk populations were constructed based on different specificities, leading to a 38.7% to 46.4% reduction in the incidence of advanced-stage disease and a 33.1% to 40.4% improvement in the 5-year survival rate. The application of cfDNA in early screening of cancer is highly important for improving patient compliance and reducing cancer-related deaths, but it is also necessary to avoid potential harms caused by overdiagnosis. Circulating tumor DNA (ctDNA) is a type of cfDNA derived from tumor cells and is valuable for detecting genetic mutations, predicting the recurrence of lung cancer and estimating the overall survival (OS). [ 64 , 65 ] The positive predictive value of minimal residual disease (MRD) testing utilizing ctDNA for relapse prediction approaches 89.1%, and the accuracy can be further enhanced through integration with additional omics data. [ 66 ] For example, the PET/CT-based habitat imaging framework can extract imaging features from tumor subregions to divide lung cancer patients into three subtypes with different prognoses, and the model can be optimized after further fusion of ctDNA status, clinical features, and tumor volume. [ 67 ] Another work demonstrated that integrating tumor features, radiomics, and ctDNA analysis enables refined prognostication in NSCLC patients undergoing chemoradiotherapy (CRT). [ 68 ] The integrated risk model effectively stratifies patients into low-risk and high-risk subgroups. In training and validation cohorts, the model achieved concordance statistics (C statistics) of 0.81 and 0.79, respectively, significantly outperforming single-modality approaches and supporting response-adapted therapeutic strategies. In predicting lung cancer brain metastases, a model based on ctDNA achieved an AUC of 0.80, demonstrating its strong predictive power. [ 69 ] Separately, the TRACERx study confirmed that ctDNA effectively tracks lung cancer recurrence and metastasis, providing a median lead time of 119 days for recurrence prediction. [ 70 ] These ctDNA-derived molecular profiles provide critical insights for refining pathological subtyping and therapeutic decision making, advancing the implementation of personalized treatment strategies. Pneumonia Pneumonia remains a leading global cause of mortality and the most fatal infectious disease worldwide. [ 1 ] Research on cfDNA in pneumonia has focused primarily on pathogen identification and severity prediction. For pathogen detection, plasma metagenomic next-generation sequencing (mNGS) has demonstrated high efficacy, identifying one or more clinically confirmed pneumonia pathogens in 67% of cases. [ 71 ] Notably, in addition to bacterial pathogens, the study also detected the invasive fungal species Histoplasma capsulatum in patients presenting with disseminated infections. In research focusing on other fungal pneumonias, such as Pneumocystis pneumonia (PCP), plasma cfDNA PCR has demonstrated its potential as a reliable noninvasive diagnostic alternative, enabling early and accurate PCP diagnosis, especially for patients contraindicated for bronchoscopy. [ 72 ] Regarding prognosis prediction, a thorough epigenome-wide association study (EWAS) was conducted on viral pneumonia patients to pinpoint potential DNA methylation sites linked to disease severity, specifically those related to respiratory failure. [ 73 ] This analysis revealed that methylation of 44 CpG sites, including loci within the absent in melanoma 2 ( AIM2 ) and major histocompatibility complex, class I C ( HLA-C ) genes, was correlated with the clinical severity of viral pneumonia. The epigenomic signature model (EPICOVID) derived from these sites using a meta-model created by six distinct machine learning algorithms achieved an accuracy of 90.18% in predicting severity. However, the analysis was limited to individuals aged 61 years or younger, making it unrepresentative of the overall population, especially elderly individuals, who have a higher burden of disease. Further investigations found that the differential methylation of CpG sites common to severe and mild cases was related mainly to the activation of interferon signaling pathways and the overactivation of B and T lymphocytes. [ 74 , 75 ] These pathways are associated with the severity of viral pneumonia according to transcriptome studies. The cfDNA profile can identify patients at high risk of severe illness and death, offering a tool for early intervention. [ 76 ] A study by Cheng et al . [ 77 ] further revealed that critically ill coronavirus disease 2019 (COVID-19) patients exhibited significantly elevated proportions of plasma cfDNA derived from the lungs, liver, and erythroid progenitor cells. Furthermore, the total cfDNA concentration strongly correlated with the WHO ordinal scale for disease progression. These findings not only reveal the multiorgan injury characteristics of COVID-19 but also highlight the potential of dynamic cfDNA monitoring as a noninvasive approach to assess organ involvement and predict the risk of clinical deterioration in real time. Additionally, in terms of long-term prognosis, the cfDNA methylation profile of patients with post-acute sequelae of pneumonia was different from that of healthy participants, indicating the feasibility of identifying post-acute sequelae of COVID-19 (PASC) with cfDNA methylation levels and stratifying its severity. [ 78 ] These findings indicate that analyzing specific features of cfDNA released into the bloodstream by host cells or microorganisms during infection may aid in detecting and differentiating among various pathogens. Nevertheless, extensive clinical research is scarce, and the application of cfDNA for pneumonia diagnosis is currently in its early stages. In the future, whether cfDNA can be successfully applied as a noninvasive technology for pneumonia in clinical practice will depend crucially on accurately identifying its irreplaceable application scenarios. For instance, for critically ill patients who urgently need an etiological diagnosis, the rapid detection of cfDNA can fully demonstrate its core advantages. Moreover, the interpretation of cfDNA test results should also be comprehensively considered in combination with clinical conditions. Chronic obstructive pulmonary disease For a long time, therapeutic strategies for COPD have mainly focused on symptom management and quality-of-life enhancement, whereas no curative therapies exist to halt disease progression or reverse pathological changes. Therefore, elucidating biomarkers and identifying novel therapeutic targets for COPD are critical for addressing this unmet clinical need. Many studies have investigated the association between DNA methylation patterns and COPD, predominantly through the analysis of circulating blood cells. Genome-wide genetic associations have identified several variants linked to COPD, including family with sequence similarity 13 member A ( FAM13A ) and Serpin family A member 1 ( SERPINA1 ). [ 79 , 80 ] An investigation into blood-based epigenome-wide analyses of 19 common disease states also found associations between CpG methylation and COPD, especially between the baseline level of CpG methylation and the incidence of COPD. [ 81 ] DNA methylation profiling of fetal and neonatal samples has revealed COPD-associated markers, establishing a causal relationship between early-life methylation alterations and the risk of COPD progression. [ 82 , 83 ] The methylation changes associated with COPD occur early in the pathogenesis of the disease. [ 84 ] Consequently, analyzing the methylation patterns within DNA isolated from peripheral blood has emerged as a critical approach for detecting early signs and monitoring the progression of COPD. [ 85 ] The methylation profile in COPD patients has been preliminarily confirmed to be related to the severity of airflow limitation. [ 86 ] Another study revealed 28 DNA methylation sites associated with respiratory function and COPD, 14 of which are not linked to smoking status. [ 87 ] Research has also investigated the predictive value of differentially methylated sites (DMSs) in COPD. Integrating the DMSs into a baseline model that included factors such as age, sex, height, as well as smoking status and intensity, significantly improved the AUC by 0.039 ( P = 0.025). Furthermore, in specific populations such as the people living with HIV (PLWH), findings revealed that those with airflow obstruction display distinct blood DNA methylation patterns compared to those normal lung function. [ 88 ] Studies focusing on cfDNA have demonstrated that cfDNA levels are associated with COPD exacerbation and mortality risk. [ 89 , 90 ] More specifically, elevated levels of cell-free mitochondrial DNA (cf-mtDNA) were associated with an increased rate of COPD exacerbation in a prospective cohort comprising 2128 participants, whereas elevated cell-free nuclear DNA (cf-nDNA) levels were significantly associated with reduced survival. Further combined analysis revealed that participants with low cf-mtDNA and high cf-nDNA levels exhibited significantly worse outcomes among patients with COPD. Unlike genetic mutations, epigenetic marks are reversible, making them appealing for targeted therapy. A comprehensive understanding of the pathogenesis and pathology of COPD is essential for developing innovative early diagnostic methods and disease-modifying treatments. [ 84 ] However, although endeavors have been made to identify and compare differentially methylated CpGs associated with COPD in lung tissue and blood, high-reliability markers are lacking due to considerable heterogeneity and different analytical statistics. Asthma Asthma affects approximately 300 million people globally. [ 91 , 92 ] Recent estimates suggest that 10% of children and 6–7% of adults experience asthma symptoms worldwide. [ 93 , 94 , 95 ] Currently, the diagnosis of asthma remains challenging due to its variability of asthma, which can result in the absence of clear objective signs at the time of assessment. [ 96 ] DNA methylation represents a promising epigenetic biomarker for asthma subtyping and risk stratification. [ 97 , 98 , 99 , 100 , 101 ] Evidence from genome-wide analyses has demonstrated distinct methylation patterns associated with asthma severity. [ 102 ] A case-control study of asthma in the Agricultural Health study divided adults into atopy without asthma, non-atopic asthma, atopic asthma, and non-case groups and analyzed differentially methylated CpG sites in the other three groups with non-case as control. [ 97 ] The analysis revealed three distinct patterns: no significant methylation differences in participants with atopy without asthma; 524 differential methylation sites in non-atopic asthma; and 1086 in atopic asthma. These two sets of asthma-associated methylation sites partially overlapped. Epigenome-wide methylation profiling of bronchial biopsy samples from patients with active asthma versus those in remission identified 4 differentially methylated CpG sites and 42 DMRs. [ 103 ] Notably, two CpG loci (cg08364654 and cg00741675) were inversely correlated with the transcriptional activity of ACKR2 and DGKQ , respectively, suggesting their regulatory roles in airway inflammation resolution. The nasal epithelium, which serves as a surrogate for bronchial tissue, exhibits unique DNA methylation patterns, allowing for the development of noninvasive biomarkers for asthma. [ 104 ] An EWAS of nasal epithelial cells revealed that cg08844313 (annotated to the PDE6A gene) was significantly associated with asthma in a meta-analysis of cross-ethnic cohorts (Dutch, Puerto Rican, and African American). This analysis also identified 16 DMRs linked to asthma. Notably, nasal methylation profiles showed a minimal but significant predictive accuracy for asthma in validation set, underscoring their potential as pediatric-friendly biomarkers to circumvent invasive bronchial sampling. Genome-wide DNA methylation sequencing of blood samples revealed overall hypomethylation of gene promoter regions in children with asthma. [ 105 ] Research in the field of epigenetics has conducted a comprehensive assessment of the relationship between DNA methylation and a range of clinical asthma markers, revealing robust, persistent epigenetic signals in whole blood. [ 106 ] These discoveries have significant implications for identifying connections between different types of asthma and may offer insights into the origins of the disease, paving the way for enhanced treatment approaches. The therapeutic efficacy of bronchodilators, a cornerstone in asthma management, is typically evaluated through bronchodilator response (BDR) assessment. [ 107 ] Research on the connections between blood DNA methylation patterns and BDR in pediatric asthma identified BDR-associated DMRs, and the most important regions were annotated to CCAAT/enhancer-binding protein δ (CEBPD), which regulates the expression of pro-inflammatory cytokines interleukin 5 (IL-5) and interleukin 6 (IL-6), providing potential therapeutic targets for asthma. [ 108 ] Furthermore, an epigenetic classifier for BDR was developed based on 70 CpGs, which demonstrated excellent performance in the training set (AUC: 0.99) and moderate performance in validation set (AUC: 0.70–0.71). This study suggested a potential role for epigenetics in the clinical prediction of BDR. The expanding understanding of asthma-associated methylation signatures is driving transformative applications in disease management. Future investigations should focus on elucidating the causal relationships between methylation dynamics and asthma endotypes and identifying methylation-regulated pathways as novel therapeutic targets. These efforts will offer potential to revolutionize asthma management. Pulmonary tuberculosis Pulmonary tuberculosis remains a major global health burden, with an annual incidence of over 10 million cases. [ 109 , 110 ] Current diagnostic tests for tuberculosis rely heavily on the collection of pathogen-containing sputum from patients, yet samples obtained in clinical settings are frequently of poor quality. [ 111 ] Additionally, obtaining adequate sputum samples is particularly challenging in individuals living with HIV, severely ill patients, and children. [ 112 ] These challenges contribute to delayed diagnosis and treatment initiation, perpetuating tuberculosis transmission and mortality. cfDNA is a promising biomarker for diagnosing pulmonary Mycobacterium tuberculosis ( M. tuberculosis ) infection. [ 111 ] A CRISPR-Cas12a-powered fluorescence assay demonstrated high diagnostic accuracy for detecting M. tuberculosis cfDNA ( Mtb -cfDNA) in blood, achieving a sensitivity of 96% in the adult cohort and 83% in the pediatric cohort. [ 113 ] Additionally, high initial levels of Mtb -cfDNA in the blood of hospitalized children living with HIV (CLHIV) appeared to be linked to higher mortality rates, indicating a potential association between Mtb -cfDNA positivity and short-term mortality. However, the correlation requires validation in prospective cohorts. The use of targeted next-generation sequencing (tNGS) in tuberculosis testing has also been evaluated. One study applied tNGS to detect cfDNA from bronchoalveolar lavage fluid (BALF) and found that its sensitivity for diagnosing pulmonary tuberculosis was comparable to that of the Xpert MTB/RIF assay (75.5% vs . 74.5%, respectively). [ 114 ] Furthermore, this study demonstrated that tNGS exhibited sensitivity and specificity ranging from 80% to 100% for detecting rifampicin (RIF) and isoniazid (INH) resistance, which was highly consistent with the phenotypic drug susceptibility test (pDST) results. These findings indicate that cfDNA tNGS has the potential to serve as a valuable tool for identifying the drug sensitivity of M. tuberculosis and may guide clinical treatment. Transrenal urine cfDNA also shows promise as a noninvasive diagnostic biomarker for pulmonary tuberculosis. [ 115 , 116 , 117 , 118 ] In active tuberculosis patients, tuberculosis-specific cfDNA fragments are released into the bloodstream, some of which are filtered through the kidneys and expelled in the urine as transrenal cfDNA. [ 116 ] The application of a sequence-specific cfDNA assay to urine samples demonstrated a sensitivity of 84% and a specificity of 100% for diagnosing active tuberculosis, highlighting its potential as a high-accuracy diagnostic tool. [ 116 ] Moreover, emerging evidence suggests that detecting DNA methylation signatures from buccal swabs represents a promising approach for tuberculosis detection. [ 119 ] Despite these advancements, significant challenges remain in translating these innovations into widespread clinical practice. A significant journey still lies ahead in the advancement of tuberculosis diagnosis and treatment. Large-scale validation studies are needed to confirm the utility of these biomarkers across diverse populations and healthcare settings. Idiopathic pulmonary fibrosis IPF is a chronic and progressive lung disease, characterized by a grim prognosis once it advances to the point of manifesting clinical symptoms and imaging abnormalities. [ 120 ] Therefore, early-stage detection is important for the management of IPF. [ 121 ] Methylation plays a key role in the regulation of gene expression, promoting the formation of fibroblast foci and pulmonary fibrosis. [ 122 ] Multiple studies have identified differential methylation patterns in IPF samples compared to normal lung tissue or samples from other pulmonary disease. [ 17 , 123 ] Through comparative analysis of IPF and healthy control lung tissues, Sanders et al [ 17 ] identified 870 differentially methylated genes (DMGs) out of 14,000 interrogated genes. Among these genes, 53% were hypermethylated, and 47% were hypomethylated, indicating bidirectional epigenetic perturbations in IPF pathogenesis. McErlean et al [ 124 ] employed Illumina EPIC methylation arrays to analyze alveolar macrophages (AMs) in patients with IPF and demonstrated significant heterogeneity in DNA methylation. These epigenetic alterations were closely linked to macrophage differentiation and metabolic reprogramming. For example, the methylation levels of LPCAT1 and PFKFB3 were markedly altered in IPF patients and were negatively correlated with pulmonary function metrics such as forced vital capacity (FVC). These findings suggest that epigenetic dysregulation may drive fibrotic progression by influencing the metabolic phenotypes of macrophages. Additionally, another study confirmed that aberrant DNA methylation of MUC5B and DSP is closely linked to the pathogenesis of IPF. [ 125 ] Currently, numerous studies have investigated whether the disease status of pulmonary fibrosis can be identified through cfDNA methylation analysis of plasma samples. For example, based on DNA methylation analysis of lung tissue from patients with lung cancer, pulmonary fibrosis, and COPD, potential markers were screened, and their diagnostic performance was assessed using serum cfDNA. The study revealed that methylation markers associated with genes such as HOXD10 , PAV9 , PTPRN2 , and STAG3 could successfully identify these diseases, but further optimization is needed to enhance the sensitivity and specificity. [ 123 ] Considering that it is involved in the progression of IPF, DNA methylation has attracted attention as a promising target for therapeutic intervention. [ 126 ] Explosive advances in molecular genetics, epigenetics, and multiomics have led to tremendous progress in uncovering the mechanisms that cause disease. [ 127 ] The discovery of extensive epigenetic changes and related alterations in gene expression in the lungs of patients with IPF suggests that it is possible to explore epigenetic therapies for this devastating disease. For example, the DNA demethylating agent 5-aza-2′-deoxycytidine (5aza), an FDA-approved epigenetic therapy for specific cancers, has been shown in murine models to alleviate pulmonary fibrosis by targeting the DNMT1/DNMT3a and the peroxisome proliferator-activated receptor γ (PPAR-γ) axis. [ 128 ] This mechanism involves demethylation of the PPAR-γ promoter, restoration of PPAR-γ expression, and subsequent attenuation of fibrotic pathways. However, despite this promising mechanism, translating DNA methylation-based therapies into clinical practice for IPF remains challenging. The dynamic nature of epigenetic characteristics, their variances in cell- or tissue-specific manners, and their susceptibility to aging and various environmental influences all contribute to this hurdle. A limited number of studies have focused directly on cfDNA in the context of pulmonary fibrosis. However, with the continuous progress of technology and in-depth research, cfDNA detection is expected to play an increasingly important role in the diagnosis and monitoring of pulmonary fibrosis. Other pulmonary diseases The potential applications of cfDNA in the diagnosis and monitoring of other respiratory diseases have also been actively investigated. In pulmonary embolism (PE), for example, the plasma concentration of cfDNA is substantially greater in patients with massive PE than in those with submassive PE. [ 129 ] The study revealed that the concentrations of plasma mitochondrial DNA (mt-DNA) and nuclear DNA (n-DNA) were 2.3 and 1.9 times higher, respectively, in non-survivors than in survivors. Accordingly, plasma mt-DNA achieved an AUC of 0.89 for predicting 15-day mortality. In the context of sarcoidosis, epigenetic mechanisms may play a significant role in the pathogenesis and progression of the disease. [ 130 ] However, study on DNA methylation and gene expression in lung cells had not revealed statistically significant changes associated with the disease. [ 131 ] Therefore, the generalizability of these findings remains uncertain and warrants further investigation in large cohorts. In addition to research on the correlation between cfDNA and the abovementioned major lung diseases, many scientists have conducted studies on the status of pulmonary function and cfDNA. [ 132 , 133 ] For example, research conducted among the broader population revealed that hypomethylation of the aryl hydrocarbon receptor repressor ( AHRR ) was correlated with decreased pulmonary function, accelerated deterioration in pulmonary function, and an increased likelihood of experiencing respiratory issues. [ 133 ] These findings play a crucial role in assessing patients’ smoking status and forecasting lung damage, which is highly important in both scientific investigations and medical practice. Limitations and Challenges Currently, applications such as disease diagnosis and prognosis prediction based on cfDNA remain challenging. A major obstacle that hinders its application is the limited sensitivity of the detection methods. [ 4 ] To address the challenge, efforts have been made to develop faster and more precise methods for cfDNA testing. Most advancements have focused on in vitro strategies, such as developing new sequencing techniques and preparing libraries. [ 134 ] Innovation has also been conducted in other direction, with the aim of concentrating cfDNA in the blood before a sample is collected. [ 135 ] The method involves the injection of priming agents into the blood, which temporarily hinders the degradation of cfDNA by enzymes and the engulfment of cfDNA by liver-resident macrophages, thus enabling greater retention of cfDNA and increasing the detection sensitivity. Experiments in mice demonstrated that intravenous administration of the promoter increased the concentration of ctDNA in blood samples by 60-fold. Additionally, it led to a more comprehensive molecular profile from ctDNA and enhanced the ability to detect small tumors from less than 10% to more than 75%. Improving the sensitivity and specificity of the cfDNA model is crucial for broader clinical application. Considerable inconsistency exists in the performance of cfDNA-based diagnostic models for lung cancer across various studies. The underlying reasons include inconsistent inclusion criteria for participants and limited sample sizes. To construct a clinical model with high sensitivity and generalizability, large-scale, multicenter, and multimodal studies are essential. [ 136 , 137 , 138 ] Accelerated development of AI technology has provided crucial technical support for achieving this goal. [ 139 ] For example, radiological, pathological, and genomic information can be integrated by machine-learning approach to predict the response of patients with NSCLC to immunotherapy. [ 137 ] Currently, the development and clinical implementation of integrated predictive models for pulmonary diseases remain underexplored areas that warrant further investigation. Additionally, reducing costs and improving the accessibility of cfDNA testing are crucial for its broader clinical implementation. Several studies have explored potential approaches to reduce the cost of cfDNA testing. For example, the cfMethyl-Seq approach focuses on CpG islands, which constitute approximately 3% of the human whole genome, consequently reducing sequencing costs by approximately 12 times. [ 140 ] Despite advancements, the current cost of cfDNA-based diagnostic tests still limits their clinical translation, necessitating research and technological innovation to improve cost-effectiveness. As research advances, the use of cfDNA is expected to become more prevalent. When delving into the utilization of cfDNA, it is essential to carefully consider the following points. First, the sensitivity and specificity of cfDNA need to be explored as a potential marker for the early detection of disease, while avoiding unnecessary panic. Second, although cfDNA can detect specific diseases, the underlying biological mechanisms require further elucidation to support its application in disease management. Ultimately, despite the effectiveness of cfDNA shown in numerous studies, its widespread clinical use still requires substantial progress. Over the past decades, liquid biopsy has been transforming the landscape of pulmonary disease detection, diagnosis, treatment and prognosis, paving the way for more personalized approaches. The integration of cfDNA analysis—spanning malignancies, infections, and chronic conditions—has revealed its multifaceted utility, from early detection of lung cancer to real-time monitoring of pathogen dynamics and treatment response. Despite persisting challenges in sensitivity, bioinformatic complexity, and cost-effectiveness, advancements in sequencing, AI-driven multi-omics integration, and biomarker co-detection are poised to overcome these limitations. As these innovations transition from bench to bedside, cfDNA-based liquid biopsy is set to fulfill key unmet needs in precision medicine, offering minimally invasive, dynamic, and personalized solutions. Acknowledgements We acknowledged the BioRender.com for the support of figures design. Funding The study was supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project of China (No. 2024ZD0528604/2024ZD0528600), National Natural Science Foundation of China (No. 82470109), Natural Science Foundation of Sichuan Province (No. 2026NSFSCZY0142), 1.3.5 Project for Disciplines Excellence, West China Hospital, Sichuan University (No. ZYYC23027), and 1.3.5 Project of State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University (No. RHM24208). Conflicts of interest None. Supplementary Material cm9-139-1168-s001.docx (283.3KB, docx) Open in a new tab Footnotes Liuqing Yang and Yu Gu contributed equally to this work. How to cite this article: Yang LQ, Gu Y, Shao J, Zhang L, Wang CD. Expanding role of cell-free DNA for the early diagnosis and monitoring of pulmonary diseases. Chin Med J 2026;139:1168–1180. doi: 10.1097/CM9.0000000000003955 References 1. 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