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