Front Oncol. 2026 Aug 27;16:1919279. doi: 10.3389/fonc.2026.1919279. eCollection 2026.

ABSTRACT

Lung Cancer (LC) continues to be the biggest cause of cancer-related deaths around the world, mostly because of delayed diagnosis. Even if tissue biopsies and circulating tumor DNA (ctDNA) tests have revolutionized clinical management of LC patients, their effectiveness is restricted in settings with lower tumor burden, molecular heterogeneity, and bias in sampling approaches. In this scenario, the epigenetic profiling of cell-free DNA (cfDNA) stands out as a promising, less invasive approach, accurately detect cancer traces. Evidence from stage I-II disease and CT-detected pulmonary nodules supports the diagnostic potential of cfDNA methylation, although further validation in prospective screening cohorts remains necessary. Beyond genomic alterations, cfDNA epigenetic changes, including DNA methylation, chromatin organization, nucleosome positioning, and fragmentation patterns, reflect multi-dimensional complexity of tumor biology. These properties convey both the functional status and the origin of the circulating DNA fragments, accelerating for tumor integrating genomic analysis. Within this group, DNA methylation is the biologically robust and clinically well-established epigenetic marker, as alterations in methylation linked to cancer often occur in the early stages of tumorigenesis and are commonly found across different cancer cell types. Here, we explored the biological and clinical relevance of the epigenetic landscape of cfDNA in LC patients, particularly focusing on DNA methylation-based biomarkers and their evolving applications towards early diagnosis and post-surgical monitoring of minimal residual disease (MRD). We aimed to comprehensively overview analytical approaches for cfDNA methylation analysis, including targeted and genome-wide profiling strategies, and discuss their integration with machine learning (ML) and multi-omics frameworks in order to improve diagnostic performance and clinical applicability in LC management.

PMID:42724581 | PMC:PMC13559918 | DOI:10.3389/fonc.2026.1919279

Anal Bioanal Chem. 2026 Jul 27. doi: 10.1007/s00216-026-06702-z. Online ahead of print.

ABSTRACT

Protein glycosylation is a major post-translational modification that regulates tumor initiation and progression; however, its dynamic modeling during multistep evolution of lung adenocarcinoma (LUAD) remains poorly understood, particularly in clinically archived tissues. Here, we established an integrated multi-omics workflow combining global proteomes, N-glycans, and site-specific intact N-glycopeptides to comprehensively characterize glycosylation in formalin-fixed paraffin-embedded (FFPE) specimens spanning four pathological stages of LUAD progression: inflammatory nodules (IN), atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), and invasive adenocarcinoma (IAC). Using optimized protein extraction, hydrophilic interaction liquid chromatography (HILIC)-based glycopeptide enrichment, and high-resolution LC-MS/MS, we achieved large-scale identification of proteins, N-glycans, and intact glycopeptides from archival clinical samples. Integrated analyses revealed progressive remodeling of site-specific N-glycosylation during malignant transformation, characterized by increased glycan branching, fucosylation, and sialylation during the transition from premalignant lesions to invasive cancer. Sialylated glycans reached their highest abundance in the premalignant AAH stage, whereas highly branched and fucosylated complex N-glycans predominated in invasive adenocarcinoma, indicating stage-dependent glycan remodeling throughout disease progression. Functional enrichment analyses linked these glycosylation alterations to extracellular matrix organization, neutrophil degranulation, and immune-associated pathways, while representative glycoproteins, including CEACAM6 and FGB, exhibited coordinated changes in protein abundance and site-specific glycoform micro-heterogeneity across pathological stages. Collectively, this study demonstrates the feasibility of deep glycoproteomic profiling using archived FFPE tissues and provides a comprehensive molecular atlas of glycosylation remodeling during LUAD progression. These findings establish a valuable resource for elucidating disease mechanisms and identifying stage-specific glycosylation biomarkers and potential glycan-targeted therapeutic candidates for early lung adenocarcinoma.

PMID:42509285 | DOI:10.1007/s00216-026-06702-z

Crit Rev Oncol Hematol. 2026 Sep;225:105483. doi: 10.1016/j.critrevonc.2026.105483. Epub 2026 Jul 10.

ABSTRACT

Low-dose CT (LDCT) lung cancer screening significantly reduces mortality but has dramatically increased the detection of pulmonary nodules. Most of these nodules are benign, leading to a high false-positive rate that triggers unnecessary invasive procedures and patient anxiety, underscoring the need for more precise noninvasive diagnostic tools. Critically, single-modality liquid biopsy biomarkers, including circulating tumor cells, cell-free DNA mutations, or individual microRNAs, have demonstrated insufficient sensitivity or specificity for independent clinical deployment when used in isolation. This necessitates a paradigm shift toward multimodal molecular integration, wherein complementary biomarker classes are combined to overcome the inherent limitations of any single analyte. Traditional clinical prediction models (Mayo, VA, Brock, Herder) assist in estimating malignancy risk, yet their accuracy remains modest. Emerging approaches harness radiomics and artificial intelligence (AI) to extract high-dimensional imaging features from chest CT scans, improving risk stratification beyond human assessment alone. In parallel, minimally invasive liquid biopsy biomarkers offer complementary avenues to detect occult malignancy signals. Additionally, bronchial airway gene expression classifiers leverage the "field-of-injury" effect in normal respiratory epithelium to help identify lung cancer even when the nodule itself cannot be directly sampled via biopsy. Integrating these radiologic and molecular data streams into a multi-omics framework has the potential to enhance diagnostic precision for indeterminate pulmonary nodules, enabling more confident discrimination between benign and malignant lesions. However, most of these emerging tools have not yet been validated in large prospective trials and face technological barriers as well as challenges in real-world implementation. This review focuses primarily on LDCT screening detected pulmonary nodules, while incorporating evidence from incidentally detected and other indeterminate nodule cohorts when relevant to broader CT based management. By synthesizing advances in radiomics, AI, liquid biopsy, airway classifiers, and multi-omics integration, we highlight the need for prospective validation and multidisciplinary collaboration to translate these approaches into clinically useful pathways that improve early lung cancer detection, reduce unnecessary interventions, and enhance patient outcomes.

PMID:42431477 | DOI:10.1016/j.critrevonc.2026.105483

Front Immunol. 2026 Jun 18;17:1855906. doi: 10.3389/fimmu.2026.1855906. eCollection 2026.

ABSTRACT

Pulmonary gas exchange and host defense depend on the dynamic coordination of resident and recruited macrophage populations. Historically, macrophage functions have often been interpreted through the classic M1/M2 dichotomy; however, this binary framework does not capture the heterogeneity and context-dependent plasticity of macrophage states within the lung microenvironment. Advances in single-cell RNA sequencing and spatial multi-omics have substantially refined our understanding of this complex macrophage network. Here, we synthesize evidence from human studies and experimental models to summarize macrophage functional states in homeostasis and across chronic obstructive pulmonary disease, asthma, idiopathic pulmonary fibrosis, pulmonary hypertension, acute lung injury/acute respiratory distress syndrome, and lung cancer. We highlight how macrophage transcriptional programs are shaped by ontogeny, tissue niche, and epigenetic-metabolic regulation, and how these programs are linked to disease-specific remodeling of the pulmonary microenvironment. Across diverse respiratory diseases, persistent tissue injury and microenvironmental stress remodel resident macrophage programs and are frequently accompanied by the expansion and context-dependent differentiation of recruited monocyte-derived macrophages. These macrophage states are associated with inflammatory amplification, epithelial and endothelial barrier dysfunction, extracellular matrix remodeling, and tumor immune evasion. Ligand-receptor and spatial analyses further identify candidate communication axes linking macrophages with stromal, epithelial, endothelial, and immune cells, some of which appear partially conserved across disease contexts. Emerging macrophage-targeted strategies are increasingly being explored beyond broad depletion, with growing interest in context-specific reprogramming and niche modulation, including antibody-based, nanocarrier-mediated, and engineered-cell approaches. Decoding the spatiotemporal trajectories and cell-cell communication networks of specific macrophage subsets, while considering tissue context, species differences, and levels of experimental support, may help clarify mechanisms of tissue remodeling, therapeutic resistance, and macrophage-targeted intervention in complex pulmonary diseases.

PMID:42396453 | PMC:PMC13322945 | DOI:10.3389/fimmu.2026.1855906

Chin Med J Pulm Crit Care Med. 2026 Jun 6;4(2):99-116. doi: 10.1016/j.pccm.2026.05.005. eCollection 2026 Jun.

ABSTRACT

Lung diseases-including lung cancer, chronic obstructive pulmonary disease (COPD), asthma, interstitial lung diseases (ILDs), and rare conditions like cystic fibrosis-remain major drivers of global morbidity and mortality. Timely diagnosis and individualized treatment are frequently challenged by heterogeneous clinical phenotypes and the complexity of multimodal data. This review provides a critical synthesis of the transformative role of artificial intelligence (AI) in respiratory care, tracing the paradigm shift from classical machine learning to emerging large language models (LLMs) and multimodal foundation models. We evaluate the performance of AI across the patient care continuum: beginning with radiologist-level nodule detection and automated diagnostics, advancing into AI-powered clinical decision support systems (CDSS) and surgical/radiotherapeutic interventions, and culminating in prognostic modeling and "digital twin" simulations for longitudinal patient management. Furthermore, we explore the translational frontier of precision medicine, examining how AI leverages multi-omics and liquid biopsies to drive novel biomarker discovery and accelerate drug repurposing. Finally, we address persistent sociotechnical barriers-including data sovereignty, legal liability, and the critical need for prospective clinical validation-proposing a translational roadmap for the safe integration of generalist medical AI into clinical workflows.

PMID:42396189 | PMC:PMC13323542 | DOI:10.1016/j.pccm.2026.05.005

Curr Med Imaging. 2026 Jun 22. doi: 10.2174/0115734056460566260609044755. Online ahead of print.

ABSTRACT

Lung cancer is the most common and fatal malignant tumour. Early detection and treatment are likely to reduce mortality, but most pulmonary nodules identified during routine health checks are harmless. Consequently, a clear distinction between benign and malignant nodules is vital to improve early detection and reduce unnecessary interventions. Radiomics, a new omics technology, can be used to extract high-dimensional quantitative features from medical images, providing a profound understanding of tumour pathophysiology. Radiomics has attracted the attention of medical researchers since its formal definition by the Dutch researcher Lambin et al. in 2012. The number of research papers on radiomics has grown tremendously over the past few years. At present, it is used to predict pulmonary nodule malignancy, for noninvasive risk stratification, for integration with genomics to identify genetic mutations associated with lung cancer, and for evaluation of therapeutic responses. With this review, we summarise the literature on radiomics of pulmonary nodules, discuss how it could be used in nodule management, and address the current challenges and future directions for improving precision oncology.

PMID:42333843 | DOI:10.2174/0115734056460566260609044755

J Thorac Dis. 2026 May 31;18(5):537. doi: 10.21037/jtd-2026-1-0315. Epub 2026 Apr 30.

ABSTRACT

BACKGROUND AND OBJECTIVE: Lung cancer remains one of the leading causes of cancer-related death worldwide. Although low-dose computed tomography (LDCT) has improved early detection, false-positive results, overdiagnosis, and interobserver variability continue to limit screening efficiency and downstream management of pulmonary nodules. This narrative review summarizes recent progress in artificial intelligence (AI)-assisted screening, radiomics-based nodule characterization, and multi-omics integration for the precision diagnosis of lung cancer.

METHODS: A narrative review with thematic analysis was conducted using representative literature on AI-assisted lung cancer screening, quantitative imaging analysis of pulmonary nodules, radiogenomic and multi-omics integration, and clinical translation challenges. Studies were synthesized to highlight technical advances, diagnostic performance, strengths, limitations, and barriers to implementation.

KEY CONTENT AND FINDINGS: AI improves nodule detection, second-reader support, workflow efficiency, and malignancy-risk estimation in LDCT screening. Radiomics converts CT images into quantitative features that can improve discrimination between benign and malignant nodules, especially when combined with clinical variables or deep-learning models. Beyond imaging alone, radiogenomic and other multi-omics approaches link imaging phenotypes with molecular alterations, treatment response, and prognosis, thereby supporting more individualized management. However, current evidence remains limited by dataset heterogeneity, retrospective design, limited interpretability, and insufficient multicenter prospective validation.

CONCLUSIONS: AI-based imaging and multi-omics integration offer a promising pathway toward earlier detection and more precise diagnosis of lung cancer. Broader clinical adoption will depend on standardized data acquisition, robust external validation, interpretable models, and careful governance of privacy, ethics, and workflow integration.

PMID:42306713 | PMC:PMC13266817 | DOI:10.21037/jtd-2026-1-0315

J Transl Med. 2026 Jun 8;24(1):776. doi: 10.1186/s12967-026-08382-w.

ABSTRACT

BACKGROUND: The molecular distinction between benign and malignant pulmonary nodules remains a significant diagnostic challenge. While genomic drivers are well studied, multiomic integration of the epigenetic-transcriptional landscape and its translation into noninvasive tools are lacking.

METHODS: We performed a multiomic characterization (genomic, epigenomic, and transcriptomic) of 158 pulmonary nodules. Unsupervised factor analysis integrated these layers to identify core regulatory axes. A 9-gene cell-free DNA (cfDNA) methylation classifier was developed and validated in blood and tissue cohorts.

RESULTS: Genomic profiling revealed EGFR mutations (exclusive to malignant nodules) and MYC amplification as fundamental initiators of malignancy. Multiomic factor analysis (Factor 1) revealed profound genetic‒epigenetic synergy, in which these alterations dictate a permissive methylome, leading to aberrant epigenetic programming of chromatin accessibility, as well as epigenetic-transcriptional effects: hypomethylation at the promoters of cell cycle genes that augments their expression, and hypermethylation at immune related pathways gene loci that silences their transcription. This effect orchestrates formation of proproliferative (E2F target/G2M checkpoint) and "immune-cold" malignant phenotype, characterized by elevated Treg/CD8+ ratios and fibroblast recruitment. Notably, we observed a gradual accumulation of methylation aberrations along the premalignant-to-invasive continuum (adenocarcinoma in situ [AIS]→minimally invasive adenocarcinoma [MIA]→adenocarcinoma [ADC]), identifying progressive epigenetic dysregulation as a hallmark of tumor aggressiveness. Global methylome remodeling drives ADC progression through hypermethylation-mediated silencing of tumor suppressors (RASA3 and PPARG) and hypomethylation-activated oncogenic axes, specifically the GDF15 axis, which independently predict poor survival in patients with lung ADC in the TCGA cohort. We translated these tissue-derived insights into a 9-gene cfDNA methylation classifier, which achieved exceptional diagnostic accuracy across independent cohorts (training AUC = 1.00; test AUC = 0.93; tissue AUC = 0.96). Rooted in the biological "ground truth" of tissue dysregulation, this classifier functions specifically as a functional readout of the core cell cycle and proliferative pathways, offering a robust, noninvasive tool for the biology-informed risk assessment of pulmonary nodules.

CONCLUSIONS: This study delineates an epigenetic-transcriptional regulatory network that drives nodule malignancy. Our findings provide a robust theoretical foundation and a high-performance liquid biopsy tool for the precise, noninvasive diagnosis of pulmonary nodules.

PMID:42260586 | PMC:PMC13274191 | DOI:10.1186/s12967-026-08382-w

J Thorac Dis. 2026 Apr 30;18(4):417. doi: 10.21037/jtd-2025-1-2697. Epub 2026 Mar 26.

ABSTRACT

BACKGROUND AND OBJECTIVE: Lung cancer remains the leading cause of cancer related mortality worldwide, and early detection and precise stratified management are crucial for improving patient outcomes. Tumor growth kinetics, as a characterization of its proliferation and malignant differentiation, is a key decision-making factor and research hotspot in clinical practice today. This study aimed to elucidate the growth kinetics of solid nodular non-small cell lung cancer (NSCLC) as a critical determinant of early diagnosis, prognostic evaluation, and treatment strategy selection, and to address the challenge that significant heterogeneity in tumor growth poses to risk stratification and clinical decision-making.

METHODS: We conducted a retrospective search of PubMed, Embase, Web of Science, and Scopus databases, focusing on the current research status of solid nodular NSCLC, particularly in terms of molecular mechanisms, prognosis, modeling prediction, and management strategies related to its growth heterogeneity, with the aim of exploring future research directions.

KEY CONTENT AND FINDINGS: Volume doubling time (VDT) serves as a key metric for evaluating nodule dynamics. While earlier studies suggested a generally rapid growth pattern (VDT <400 days) in solid nodular NSCLC, recent evidence reveals considerable heterogeneity, with some tumors demonstrating indolent growth pattern (VDT >40-600 days). The prognosis of rapidly growing nodules is usually poor, so nodule management recommendations should be personalized based on growth dynamics and patient characteristics. Traditional radiological features, and deep learning models show promise for growth risk stratification but require large-scale external validation and refinement. Molecular and pathological studies suggest that the tumor microenvironment and immune cell infiltration may contribute to growth heterogeneity, though direct mechanistic evidence remains limited. Artificial intelligence (AI) based approaches exhibit significant potential in predicting individual tumor growth behavior.

CONCLUSIONS: Growth heterogeneity in solid nodular NSCLC carries substantial clinical significance but remains insufficiently studied. Future research should prioritize imaging based modeling to predict individualized growth dynamics. Integrating multi-omics analyses may help elucidate the molecular factors underlying growth heterogeneity. AI driven risk stratification based on large-scale multi center sequence data can achieve truly personalized and growth oriented management strategies.

PMID:42182806 | PMC:PMC13190150 | DOI:10.21037/jtd-2025-1-2697

Transl Cancer Res. 2026 Apr 30;15(4):314. doi: 10.21037/tcr-2025-aw-2503. Epub 2026 Mar 20.

ABSTRACT

BACKGROUND: Lung adenocarcinoma (LUAD) is one of the most common lung cancer subtypes worldwide, and its aggressive subtype invasive adenocarcinoma (IAC) has low survival rates. The precise identification of IAC is vital for the clinical diagnosis and treatment. The purpose of this study is to identify novel biomarkers for LUAD using single-cell and bulk RNA sequencing, so as to provide theoretical basis and practical support for the diagnosis, treatment and prognosis evaluation of lung invasive adenocarcinoma.

METHODS: We employed a combination of transcriptomic analysis and single-cell analysis to investigate the molecular characteristics and immune microenvironment of four subtypes of LUAD, including atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and IAC, with the aim of screening for biomarkers to differentiate pre-invasive lesions from invasive lesions.

RESULTS: Transcriptomic and single-cell analyses revealed that IAC subtypes demonstrated the most substantial molecular differences, particularly in immune cell infiltration and immune-related gene expression. Three genes-CD27, TIGIT, and TNFRSF18-that were significantly upregulated in IAC, predominantly expressed in immune cells and closely linked to immune regulatory pathways. We further analyzed T cell subpopulations in the IAC subtype and explored the expression of transcription factors (TFs) corresponding to these three genes, revealing their critical roles in immune cell function. Additionally, communication between T cells and other cells showed significantly enhanced signaling pathways, particularly those related to immune co-stimulatory molecules and inflammation pathways. Immunohistochemical validation of clinical samples showed that these three genes have high diagnostic value in IAC subtypes. These findings establish a crucial biological foundation for diagnosis, classification, and immunotherapy of LUAD, which contributes to the development of individualized treatment strategies.

CONCLUSIONS: This study identifies a three-gene signature (CD27, TIGIT, and TNFRSF18) that not only distinguishes invasive from pre-invasive LUAD with high precision by capturing the immune checkpoint disequilibrium characteristic of IAC, but also provides a clinically actionable biomarker panel for preoperative diagnosis and personalized immunotherapy strategies.

PMID:42180871 | PMC:PMC13190665 | DOI:10.21037/tcr-2025-aw-2503

NPJ Precis Oncol. 2026 May 12;10(1):284. doi: 10.1038/s41698-026-01471-5.

ABSTRACT

Lung adenocarcinoma (LUAD) accounts for approximately 40% of non-small cell lung cancer. Although the microbiome may play a role in LUAD, a comprehensive understanding of its ecological landscape and interactions with the tumor host, particularly during early development of LUAD, remains lacking. Here we employed a multi-omic approach to assess the dynamics of the tumor microbiota-host interaction across stages of early LUAD, including benign nodules, adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC). We found a strong and intricate interaction between the microbiome and host immune and metabolic pathways in AIS, while microbiome-host interactions substantially diminish in MIA and IAC. Serum metabolites and CT-based radiological features, such as atropaldehyde, sterculic acid, nodule morphology and maximum nodule diameter, were closely associated with the microbiome-host interaction network, suggesting they could be non-invasive markers indicating tumor ecological and pathological changes. Multi-omic integration revealed an optimal performance in classifying individual LUAD stages, particularly between AIS and MIA that was otherwise challenging to differentiate using a single data type. Our results highlight the dynamic interaction between microbiome and host during early LUAD, which can be partially reflected in systemic metabolic and radiological manifestations, providing a novel framework for understanding early-stage LUAD.

PMID:42120518 | PMC:PMC13388699 | DOI:10.1038/s41698-026-01471-5

Front Immunol. 2026 Apr 14;17:1744789. doi: 10.3389/fimmu.2026.1744789. eCollection 2026.

ABSTRACT

Psoriasis vulgaris (PV), a chronic immune-mediated inflammatory dermatosis, is associated with a significant burden of systemic comorbidities. Traditional comorbidity research methods struggle to reveal its complex interconnectedness. Based on large-scale retrospective cohort data, we constructed a PV comorbidity network using the Ising model from statistical physics. Weighted network centrality analysis was used to identify core and hub nodes and elucidate shared molecular mechanisms at the multiomics level (nontargeted proteomics and lipid peroxidation metabolomics). Finally, the impact of IL-17A inhibition (IL-17Ai) on PV and atherosclerosis (assessed by carotid Doppler color ultrasound) was evaluated using a prospective intervention study. The Ising model identified atherosclerosis- coronary heart disease (CHD) as the core comorbidity (degree centrality >10), with pulmonary nodules, hypertension, and fatty liver serving as key hub nodes (betweenness centrality >60). Multiomics analysis revealed a core molecular mechanism in PV, involving immune inflammation, oxidative stress, lipid metabolism disorder, and coagulation abnormalities, where the oxidative stress molecule GPX3 acts as a critical hub. Following IL-17Ai intervention, both skin lesions and early atherosclerosis markers significantly improved, accompanied by downregulation of the proinflammatory peripheral blood factor S100A9 and upregulation of anti-inflammatory lipid peroxidation metabolites (e.g., 17(R)-RVD1). This study systematically revealed the modular hierarchical structure of PV comorbidities at the network topology and molecular mechanism levels, confirming the central role of the IL-17 signaling pathway in driving the comorbidity network. This conclusion was further clinically validated by IL-17Ai intervention outcomes. This research provides theoretical and clinical evidence for early identification, prioritized management, and "one drug, multiple targets" therapeutic strategies for treating PV comorbidities.

PMID:42058202 | PMC:PMC13121148 | DOI:10.3389/fimmu.2026.1744789

Semin Oncol. 2026 Aug;53(4):152497. doi: 10.1016/j.seminoncol.2026.152497. Epub 2026 Apr 3.

ABSTRACT

Although exosome research has advanced considerably, clinical implementation remains limited. In contrast to previous reviews that catalog numerous biomarkers, this review focuses on translationally ready candidates for breast, lung, and colorectal cancers. Exosomal signatures address key diagnostic challenges, such as enabling tissue-free molecular subtyping in breast cancer, distinguishing malignant from benign nodules in lung cancer, and detecting CEA-negative or premalignant adenomas in colorectal cancer. This review critically assesses clinically advanced biomarkers-including miRNAs, lncRNAs, circRNAs, and proteins-and highlights those with validated, high diagnostic performance relative to current standards (e.g., CEA, imaging). Single biomarkers often fail to capture tumor heterogeneity, whereas multi-omic panels integrated with clinical data improve diagnostic accuracy. Exosomal biomarkers should, therefore, be considered adjuncts to standard diagnostics, functioning as triage tools to enhance patient management. The integration of exosomes into clinical workflows holds significant promise for non-invasive, early cancer detection, and personalized medicine.

PMID:42054834 | DOI:10.1016/j.seminoncol.2026.152497

Cell Rep. 2026 May 26;45(5):117215. doi: 10.1016/j.celrep.2026.117215. Epub 2026 Apr 28.

ABSTRACT

Early-stage lung adenocarcinoma (LUAD) in never smokers exhibits distinct biological features, yet the metabolic programs driving early invasion remain unclear. We integrate proteomic and lipidomic profiling of primary LUAD tumors from never smokers, matched normal adjacent tissues (NATs), and benign pulmonary nodules (BPNs). Integrated multi-omics analysis reveals coordinated dysregulation of lipid metabolism and immune signaling in early LUAD. Proteome-based network fusion stratifies invasive LUAD into immune-metabolic synergistic (IMS) and metabolic-stress-driven (MSD) subtypes. IMS tumors retain apolipoprotein-associated lipid modules and favorable immune features, whereas MSD tumors exhibit stress-response programs. Mechanistically, APOA1 and APOC1 emerge as key nodes linking lipid homeostasis to invasion, and their depletion promotes LUAD cell migration and invasion. We establish a two-protein, four-lipid diagnostic panel demonstrating robust performance across tissue and plasma cohorts. These findings provide a molecular basis for early detection and risk stratification in never smokers.

PMID:42054209 | DOI:10.1016/j.celrep.2026.117215

J Cancer Res Clin Oncol. 2026 Apr 22;152(4):92. doi: 10.1007/s00432-026-06465-1.

ABSTRACT

Lung ground-glass nodules (GGNs) represent a critical early imaging manifestation of lung adenocarcinoma, and exploring the relationship between their CT imaging features and oncogenic driver genes holds significant promise for precision diagnosis and personalized treatment. In recent years, artificial intelligence (AI) technologies, particularly deep learning and machine learning methods, have demonstrated remarkable potential in the integrative analysis of radiomic and genomic data. This review summarizes the current advances in AI applications for extracting CT imaging features of lung GGNs, identifying oncogenic driver genes, and analyzing their correlations. Key AI-driven techniques enabling the construction of a bridge between imaging phenotypes and genetic alterations are discussed, alongside challenges such as data heterogeneity, limited annotated datasets, and interpretability. Future research directions emphasize the development of robust, explainable AI models and multi-omics integration to enhance early lung cancer diagnosis and therapeutic strategies. By providing a comprehensive overview of the intersection between AI, radiomics, and genomics in lung GGN adenocarcinoma, this article aims to offer theoretical insights and technical references to advance early detection and precision oncology.

PMID:42017975 | PMC:PMC13103144 | DOI:10.1007/s00432-026-06465-1

Transl Lung Cancer Res. 2026 Mar 23;15(3):62. doi: 10.21037/tlcr-2025-1-1477. Epub 2026 Mar 18.

ABSTRACT

BACKGROUND AND OBJECTIVE: In 2025, lung cancer research advanced rapidly across the disease continuum, from population-level risk assessment and screening to mechanistic studies of early carcinogenesis and therapeutic innovation in perioperative and metastatic settings. A key shift moved beyond a smoking-centred paradigm toward a multidimensional risk framework reflecting the growing burden among never-smokers and the roles of air pollution, occupational exposures, and systemic metabolic-inflammatory states. This narrative review aims to synthesize influential 2025 evidence across prevention, diagnosis, treatment, and survivorship, and to identify convergent themes and translational gaps relevant to clinical practice and policy.

METHODS: We performed a narrative synthesis of influential lung cancer studies published in major international journals in 2025. Evidence was organized along a clinically oriented pathway spanning carcinogenesis and screening, precision diagnosis, treatment optimization in resectable and advanced disease, and survivorship, emphasizing practice-informing trials, high-impact translational research, and implementation-relevant technologies.

KEY CONTENT AND FINDINGS: Lineage tracing, single-cell and spatial omics, and evolutionary inference refined concepts of field cancerization, clonal selection, and copy-number-driven fitness. In small-cell lung cancer, evidence further supported neuronal coupling and synapse-like programs as potentially tractable vulnerabilities. Clinically, low-dose computed tomography (CT) strategies and data-informed nodule thresholds aimed to balance under-detection against over-surveillance harms. In diagnostics, artificial intelligence (AI) models increasingly inferred molecular features from routine histopathology ("virtual molecular testing") and should be regarded as decision support requiring prospective validation, population calibration, and explicit failure-mode reporting. Multimodal approaches integrating imaging with circulating tumor DNA (ctDNA) improved feasibility in tissue-limited settings, but clinical utility remains contingent on assay standardization and pathway-level implementation. In resectable disease, longer follow-up consolidated neoadjuvant chemo-immunotherapy for selected patients, while ctDNA kinetics emerged as a candidate biomarker for response-adaptive escalation and de-escalation. In advanced non-small cell lung cancer (NSCLC), phase III evidence for antibody-drug conjugates and bispecific antibodies began reshaping sequencing, while highlighting challenges in toxicity, access, affordability, and immature overall survival in several programs.

CONCLUSIONS: The 2025 landscape reflects coordinated progress in risk conceptualization, biology, diagnostics, and therapeutics, yet gaps in validation, standardization, and real-world deliverability persist. Priorities include prospective evaluation of AI- and ctDNA-enabled pathways, toxicity-informed sequencing, and equitable implementation aligned with health-system capacity.

PMID:41982682 | PMC:PMC13071762 | DOI:10.21037/tlcr-2025-1-1477

J Multidiscip Healthc. 2026 Mar 11;19:584071. doi: 10.2147/JMDH.S584071. eCollection 2026.

ABSTRACT

Non-small cell lung cancer (NSCLC) is one of the malignant tumors with the highest incidence and mortality rates. Zeqi Decoction has the functions of "promoting diuresis and reducing swelling, resolving phlegm and dispersing nodules", embodying the unique approach of traditional Chinese medicine in treating lung cancer by "strengthening the body's resistance and eliminating pathogenic factors". Modern research shows that Zeqi Decoction exerts anti-NSCLC effects through multiple pathways and targets. In terms of the material basis of its efficacy, its active ingredients (such as diterpene esters and flavonoids contained in Zeqi) have the ability to directly inhibit the proliferation, invasion and migration of tumor cells and induce apoptosis. In terms of the mechanism of action, basic experiments have revealed that Zeqi Decoction can down-regulate the S100A9/STAT3 signaling pathway, inhibit the immunosuppressive activity of myelium-derived suppressor cells (MDSCs), reshape the tumor microenvironment, thereby enhancing the cytotoxic function of CD8⁺T cells, and can also regulate the EGFR/PI3K/Akt pathway to affect PD-L1 expression. Intervene in tumor immune escape; In terms of clinical transformation, the combination of Zexi Decoction with chemotherapy and targeted therapy can improve patients' symptoms such as cough and pleural effusion, prolong progression-free survival, and alleviate the toxic and side effects of Western medical treatment. In addition, Zexi Decoction also shows potential value in reversing drug resistance such as gemcitabine. At present, there are still problems such as the lack of standardized protocols and unclear molecular mechanisms in the research. In the future, it is necessary to combine new technologies such as network pharmacology and multi-omics analysis to deepen the research on the pharmacological material basis, dose-effect relationship and evidence-based medicine of Zeqi Decoction, so as to promote the clinical application and transformation of the combination of traditional Chinese and Western medicine in the treatment of NSCLC.

PMID:41847115 | PMC:PMC12991379 | DOI:10.2147/JMDH.S584071

Am J Surg Pathol. 2026 Jun 1;50(6):695-704. doi: 10.1097/PAS.0000000000002533. Epub 2026 Mar 13.

ABSTRACT

With the application of molecular techniques in pathologic diagnosis, several novel primary pulmonary epithelial tumors have been continuously discovered and classified under the WHO classification of thoracic tumors. Recently, a pulmonary tumor with NFATC2 :: NUTM2B fusion was first documented, but the spectrum of NFATC2::NUTM2 fusion variants and their associated pathologic features remains incompletely characterized. Coincidentally, we also found and described 6 primary pulmonary tumors harboring recurrent NFATC2::NUTM2A/E fusions through integrated genomic analysis. These patients, including 4 females and 2 males, with a median age of 53 years, presented with incidentally detected peripheral lung nodules composed of monotonous epithelioid cells arranged in cords, nests, and trabeculae within a prominent desmoplastic stroma. All tumors exhibited a consistent immunophenotype: CK5/6+/GATA3+/calponin+/EMA+/DOG1 (perinuclear dot-like staining)/p63-. High-throughput chromosome conformation capture (Hi-C) analysis showed the structural variation of NFATC2::NUTM2E in all 6 cases, whereas RNA sequencing detected the fusion transcripts in 5 cases ( NFATC2::NUTM2A , n=2; NFATC2::NUTM2E , n=3). Ultrastructural examination of 1 case suggested epithelial differentiation. All patients remained disease-free after complete resection (median follow-up: 24 mo; range: 9 to 41 mo). These findings define a novel primary pulmonary tumor entity driven by NFATC2::NUTM2 fusions, and characterized by a distinctive immunophenotype, expanding the spectrum of NUTM2 -associated neoplasms. Our study underscores the utility of multiomics approaches for characterizing rare neoplasms and provides a diagnostic framework for this entity.

PMID:41821426 | DOI:10.1097/PAS.0000000000002533

Transl Cancer Res. 2026 Feb 28;15(2):127. doi: 10.21037/tcr-2025-664. Epub 2026 Feb 25.

ABSTRACT

BACKGROUND: In recent years, multi-omics models based on a variety of biomarkers have been continuously developed and increasingly applied in the field of oncology, especially in the early diagnosis of lung cancer. This study aimed to integrate computed tomography (CT) radiomics with seven lung cancer-associated autoantibodies (AABs) to develop multi-omics predictive models for pulmonary nodule (PN) characterization.

METHODS: This retrospective study enrolled 179 patients with PNs measuring from 5 to 30 mm in diameter who underwent thoracic surgery at Zhongda Hospital, Southeast University between January 2020 and December 2024. The patients were pathologically categorized into lung cancer (n=87) and non-lung cancer (n=92) groups, and then randomly allocated into training and test sets at a ratio of 7 to 3. Least absolute shrinkage and selection operator (LASSO) regression was used for feature screening to construct a clinical model based on five clinical characteristics. A radiomics prediction model was constructed based on the radiomics features identified after delineating the regions of interest and extracting the radiomics features; the rad-score for each patient was calculated to develop a multi-analytic comprehensive model by combining different markers. The diagnostic performances of the models were compared using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value.

RESULTS: The multi-omics model demonstrated superior diagnostic accuracy with an AUC of 0.902 [95% confidence interval (CI): 0.817-0.986], accuracy of 82.4%, sensitivity of 88.5%, and specificity of 80.0%, outperforming the clinical (AUC =0.848; 95% CI: 0.777-0.919) and radiomics (AUC =0.854; 95% CI: 0.786-0.922) models. Notably, the radiomics model exhibited high sensitivity (96.6%) but poor specificity (63.6%), while the multi-omics model resolved this trade-off via the synergistic integration of clinical-radiomic-biomarker features, achieving significant improvements in the PPV (81.5% vs. 72.7%) compared to the clinical model.

CONCLUSIONS: Integrating CT radiomics with seven lung cancer-AABs established a robust multi-omics framework for PN diagnosis. Compared to the standalone clinical or radiomics models, this comprehensive model demonstrated superior diagnostic performance.

PMID:41815158 | PMC:PMC12971553 | DOI:10.21037/tcr-2025-664

Front Cell Infect Microbiol. 2026 Feb 23;16:1732958. doi: 10.3389/fcimb.2026.1732958. eCollection 2026.

ABSTRACT

INTRODUCTION: Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer, is a form of malignant pulmonary nodule that requires clinical differentiation from benign pulmonary nodules (BPN). The mechanisms underlying the development of LUAD are complex, and effective non-invasive methods for differentiating BPN from LUAD are lacking. This study aimed not only to distinguish BPN from LUAD using gut fungi and serum metabolites, but also to establish an integrated network of gut fungi-metabolite-cytokine interactions.

METHODS: Fecal and serum samples from individuals with BPN and patients with LUAD were subjected to internal transcribed spacer sequencing, ultra-performance liquid chromatography-tandem mass spectrometry, and multiplex Luminex assays to quantify gut fungi, metabolites, and cytokines, respectively.

RESULTS: A significant difference in gut fungal communities was observed between the BPN and LUAD groups. Multiple genera and species were more abundant in LUAD than in BPN. Docosapentaenoic acid n-6 (DPAn-6), indole-3-propionic acid (IPA), and interferon-γ-induced protein 10 (IP-10) were significantly elevated in the LUAD group. The integrated model established using a combination of gut fungi and metabolites demonstrated excellent performance in distinguishing BPN from LUAD. A network of interactions was established among differentially abundant gut fungi, serum metabolites, and cytokines.

CONCLUSION: Our study identifies a novel panel of fungal and metabolite biomarkers for differentiating between BPN and LUAD, and constructs a multi-omics network that provides new insights into investigating the mechanistic role of gut mycobiota dysbiosis in LUAD.

PMID:41809995 | PMC:PMC12968269 | DOI:10.3389/fcimb.2026.1732958