World J Oncol. 2026 Sep 4;17(5):683-704. doi: 10.14740/wjon2815. eCollection 2026 Oct.

ABSTRACT

BACKGROUND: Lung cancer (LC) and venous thromboembolism (VTE) are closely associated, with VTE contributing to morbidity and mortality among patients with LC. We aimed to identify and characterize a mitochondrial-related transcriptomic signature shared between LC and VTE and to explore its association with immune microenvironment features.

METHODS: We applied a multiomics approach focused on mitochondrial-related signaling pathways. Publicly available transcriptomic datasets were analyzed using differential expression profiling and weighted gene co-expression network analysis to identify key regulatory genes. These genes were intersected with a mitochondrial gene set and subjected to functional enrichment analysis. Least absolute shrinkage and selection operator (LASSO) regression was used to identify candidate diagnostic genes validation. Immune cell infiltration was quantified, and associated regulatory mechanisms were explored.

RESULTS: Thirty-nine shared crosstalk genes were identified and were primarily enriched in mitochondrial metabolic processes. LASSO regression identified a five-gene candidate signature (ACAA1, HSD17B10, MTIF2, THOP1, and PDE2A). The model exhibited promising discriminatory performance (area under the curve > 0.9 in LC dataset and 0.7-0.9 in VTE dataset). These genes were significantly dysregulated and were associated with altered immune cell infiltration, particularly in dendritic cell and T cell subsets.

CONCLUSION: We identified a mitochondrial-related gene signature reflecting shared transcriptomic correlates between LC and VTE. The signature showed variable performance across disease contexts and correlative associations with immune features, supporting its role as a candidate biomarker for further investigation. Prospective validation in independent clinical cohorts is required before any translational application.

PMID:42730163 | PMC:PMC13568737 | DOI:10.14740/wjon2815

Front Pharmacol. 2026 Aug 28;17:1899849. doi: 10.3389/fphar.2026.1899849. eCollection 2026.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with limited therapeutic options and marked molecular heterogeneity. Despite available antifibrotic therapies, disease progression remains poorly predictable, highlighting the need for improved mechanistic understanding and therapeutic targeting. This review summarizes recent advances in multi-omics research to elucidate the molecular mechanisms underlying IPF and to identify potential biomarkers and pharmacological targets. Multi-omics studies, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiome profiling, and single-cell sequencing, have revealed key pathogenic mechanisms in IPF. Genetic susceptibility factors such as MUC5B promoter variants and telomere-related genes contribute to disease risk. Epigenetic regulation, including DNA methylation, histone modifications, and non-coding RNAs, plays a central role in fibrotic remodeling. Transcriptomic and proteomic analyses have identified dysregulated signaling pathways, including TGF-β, mTOR, cellular senescence, and extracellular matrix remodeling. Metabolomic alterations indicate disrupted lipid and amino acid metabolism. Importantly, integration of multi-omics datasets enables the identification of molecular endotypes, candidate biomarkers, and potential therapeutic targets. However, challenges including data integration, tissue heterogeneity, limited cohort size, and the need for functional validation remain important barriers to clinical translation. Continued development of multi-omics approaches may facilitate more accurate disease classification and support the development of personalized therapeutic strategies for IPF.

PMID:42729333 | PMC:PMC13561894 | DOI:10.3389/fphar.2026.1899849

Front Genet. 2026 Aug 28;17:1900277. doi: 10.3389/fgene.2026.1900277. eCollection 2026.

ABSTRACT

BACKGROUND: Chronic obstructive pulmonary disease (COPD) and lung cancer are major risk factors for invasive pulmonary fungal infection (IPFI), carrying an attributable mortality of 30%-80%. Their coexistence further amplifies immunosuppression, while current diagnostic criteria remain inadequate for early risk identification.

METHODS: Transcriptomic data from the GEO dataset GSE296912 (scRNA-seq; 12,078 cells from normal and COPD lung tissue) and The Cancer Genome Atlas (TCGA)-lung adenocarcinoma (LUAD) bulk RNA-seq cohort (539 tumor and 59 normal samples) underwent differential expression and cross-omics integration analysis. Five machine learning models were constructed: logistic regression, SVM, random forest, XGBoost, and LASSO. Candidate genes were validated by qRT-PCR in A549 cells and THP-1-derived macrophages stimulated with heat-inactivated Aspergillus fumigatus conidia, a protocol selected to ensure BSL-2 biosafety compliance and isolate PAMP-mediated innate immune signaling. Model performance was evaluated using 5-fold stratified cross-validation with AUC, calibration curves, and decision curve analysis.

RESULTS: Single-cell transcriptomic analysis of 12,078 cells identified 14 distinct cell populations, with marked myeloid expansion and immune dysregulation in COPD lung tissue. Cross-omics integration with TCGA-LUAD data identified 1,145 shared genes (79 immune-related), converging on NF-κB, TLR4, and cytokine receptor signaling. The random forest model achieved excellent discriminative performance (5-fold CV AUC = 0.988), with Treg infiltration, TLR4, and MMP9 as the top predictors. qRT-PCR confirmed significant upregulation of all five candidate genes (DEFB4A, S100A8, IL-8, MMP9, and TLR4) in both A549 and THP-1 cells following fungal stimulation.

CONCLUSION: This multi-omics machine learning model integrating scRNA-seq and TCGA transcriptomic data demonstrates excellent discriminative performance (AUC = 0.988), with mechanistic convergence of NF-κB, TLR4, and oncogenic signaling pathways identified across shared immune gene signatures. In vitro qRT-PCR validation confirms the biological relevance of five key antifungal immune genes, providing a transcriptomic foundation for future prospective IPFI risk stratification in patients with COPD and lung cancer.

PMID:42725278 | PMC:PMC13561498 | DOI:10.3389/fgene.2026.1900277

Front Med (Lausanne). 2026 Aug 27;13:1865050. doi: 10.3389/fmed.2026.1865050. eCollection 2026.

ABSTRACT

Transformed small-cell lung cancer (T-SCLC) is a clinically important form of histologic transformation and a mechanism of acquired resistance in non-small-cell lung cancer (NSCLC). It is associated with poor prognosis, with a median overall survival of only about 9-13 months. This review summarizes recent advances in the mechanisms, diagnosis, monitoring, and treatment of T-SCLC. Repeat biopsy remains the gold standard for confirming histologic transformation, whereas molecular profiling and liquid biopsy may facilitate early detection and longitudinal disease monitoring. Platinum-etoposide remains the most commonly used clinical standard after transformation, but its benefit is typically transient and durable disease control remains uncommon. Continuation of EGFR tyrosine kinase inhibitors combined with chemotherapy may prolong progression-free survival in selected patients but has not consistently improved overall survival. Anti-angiogenic therapy, particularly anlotinib, and chemo-immunotherapy have shown encouraging activity in selected patients, while emerging strategies targeting DLL3, MYC, SOX2, and epigenetic regulators may broaden the therapeutic landscape. Prospective studies integrating repeat tissue sampling, comprehensive genomic profiling, biomarker-guided patient stratification, pharmacogenomics, functional drug-sensitivity testing where feasible, and integrated multi-omics approaches are needed to advance molecularly guided and individualized treatment for T-SCLC.

PMID:42724635 | PMC:PMC13560167 | DOI:10.3389/fmed.2026.1865050

J Thorac Dis. 2026 Aug 31;18(8):883. doi: 10.21037/jtd-2026-0807. Epub 2026 Jul 21.

ABSTRACT

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous lung condition not fully captured by spirometry. Quantitative computed tomography (QCT) enables objective characterization of emphysema, airway remodeling, and other structural abnormalities, playing key roles in early recognition, phenotyping, and prognosis. Despite growing literature in this field, no comprehensive bibliometric synthesis has mapped the intellectual structure, collaborative networks, or thematic evolution of QCT research in COPD. This study aims to fill this gap by providing a structured overview of the field over the past two decades.

METHODS: A systematic search was performed in the Web of Science Core Collection (WoSCC) using the topic formula: TS=(("quantitative computed tomography" OR "quantitative CT" OR "QCT" OR "CT quantification" OR "quantitative CT assessment") AND ("chronic obstructive pulmonary disease" OR "COPD" OR "chronic obstructive pulmonary disease*")). Publications from 2005 to 2025 were included, limited to English original articles and reviews. Titles and abstracts were independently screened by two reviewers; studies not primarily focusing on QCT-based quantitative analysis in COPD were excluded. Disagreements were resolved through discussion. Bibliometric and visual analyses were conducted using CiteSpace 6.4.R1, VOSviewer 1.6.19, and the R package bibliometrix.

RESULTS: A total of 300 publications (279 original articles, 21 reviews) were included. The United States was the leading contributor in overall output and international collaboration. The University of Iowa was the most productive institution, Hoffman EA was the most prolific author, and the International Journal of Chronic Obstructive Pulmonary Disease was the most productive journal. Keyword and thematic analyses revealed a clear evolutionary trajectory: early research (2005-2012) focused on technical quantification of emphysema and airway abnormalities; a transitional phase (2013-2018) emphasized "phenotypes" and disease heterogeneity; and the recent period (2019-2025) has seen rising attention to prognostic evaluation, mortality prediction, and artificial intelligence-assisted analysis.

CONCLUSIONS: This study confirms a shift from morphologic quantification toward clinically actionable imaging biomarkers. However, the existing literature suffers from several critical gaps: lack of standardized acquisition and analysis protocols, predominance of cross-sectional designs, and insufficient external validation of artificial intelligence models. Future research should prioritize multicenter prospective validation, integration with multi-omics data for endotyping, and development of open-source automated pipelines to facilitate clinical translation.

PMID:42724634 | PMC:PMC13559334 | DOI:10.21037/jtd-2026-0807

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

J Thorac Dis. 2026 Aug 31;18(8):950. doi: 10.21037/jtd-2026-1704. Epub 2026 Aug 28.

ABSTRACT

BACKGROUND AND OBJECTIVE: Traditionally, two primary systems have been employed for staging small cell lung cancer (SCLC): the Veterans Administration Lung Study Group (VALG) system and the International Association for the Study of Lung Cancer (IASLC) tumor, node, metastasis (TNM) system. The term "limited disease" is defined differently: VALG characterizes it as disease encompassed within a single tolerable radiation field, while IASLC defines it as the lack of distant metastases (M0). Patients with N3 disease frequently satisfy VALG extensive-stage (ES) criteria while meeting IASLC limited-stage (LS) criteria, resulting in a notable staging discrepancy. Therefore, this review aims to clarify the clinical challenges posed by this staging overlap and provide insights for standardizing staging terminology and optimizing therapeutic decision-making in N3 SCLC.

METHODS: A narrative review utilizing a systematized search strategy was conducted. While strict adherence to PRISMA guidelines was not pursued because the extensive heterogeneity of the literature precluded a formal meta-analysis, rigorous search criteria were applied to minimize selection bias. Databases including PubMed, Web of Science, Embase, the Cochrane Library, and China National Knowledge Infrastructure (CNKI) were searched for literature from January 2000 to March 2026. Studies examining stage N3 SCLC, spatial metastatic burden, and definitional inconsistencies between the VALG and IASLC staging systems were analyzed to assess their effects on treatment dosimetry, systemic therapy, and survival outcomes.

KEY CONTENT AND FINDINGS: The staging overlap in N3 SCLC leads to heterogeneous clinical management depending on its spatial metastatic burden, and this highly variable cohort can be stratified into distinct prognostic subgroups based on the anatomical distribution (single-region vs. multi-region) of the involved lymph nodes.

CONCLUSIONS: These findings should guide clinical trial design and terminology. Clinical decision-making must transcend historical paradigms and technical constraints. Future strategies must incorporate spatial evaluations of metastatic burden alongside innovative multimodal tools, such as artificial intelligence (AI) and multi-omics, to facilitate tailored therapy for SCLC.

PMID:42724560 | PMC:PMC13559235 | DOI:10.21037/jtd-2026-1704

Ultrasound Med Biol. 2026 Sep 10:S0301-5629(26)00316-9. doi: 10.1016/j.ultrasmedbio.2026.08.004. Online ahead of print.

ABSTRACT

Cancer immunotherapy has revolutionized the treatment landscape, yet its clinical efficacy is limited by modest objective response rates and the emergence of atypical response patterns such as pseudoprogression and hyperprogression. Conventional RECIST criteria relying on anatomical size changes and invasive tissue biopsies suffer from prominent sampling bias and cannot dynamically reflect the heterogeneous tumor immune microenvironment (TIME), creating an urgent demand for non-invasive, real-time functional imaging tools to longitudinally profile intra-tumoral immune landscapes. Ultrasound molecular imaging (USMI) stands out as a distinctive imaging modality complementary to PET-CT and MRI, featuring radiation-free operation, low cost, superior spatiotemporal resolution and repeatable whole-tumor visualization-advantages that overcome the limitations of ionizing radiation, high expense and static single-spot sampling inherent to mainstream molecular imaging modalities. This review systematically elaborates state-of-the-art advances in USMI for visualizing tumor immune biomarkers, with in-depth dissection of core acoustic imaging mechanisms, rational design and multi-functional optimization strategies of immune-targeted microbubble/nanobubble probes and comprehensive collation of landmark pre-clinical investigations across melanoma, hepatocellular carcinoma, non-small cell lung cancer, colorectal and breast cancers. We thoroughly correlate USMI signal readouts with pathological immunohistochemistry, transcriptomic profiles and longitudinal immunotherapy outcomes, and elaborate on its core translational applications: dynamic tracking of immune cell infiltration and spatial distribution, quantitative mapping of global immune checkpoint expression, early prediction of therapeutic efficacy and differential diagnosis of pseudoprogression, hyperprogression and true tumor progression. We further highlight the inherent uniqueness of USMI for TIME surveillance and its complementary value relative to PET/MRI and objectively dissect critical translational bottlenecks, including probe off-target binding, insufficient standardized quantitative pipelines and deep-tissue ultrasound attenuation. Rather than overstating preliminary exploratory work, we rationally discuss the synergistic integration of USMI with multi-omics and artificial intelligence radiomics as a forward-looking developmental direction and propose theranostic probe engineering and standardized multi-center validation frameworks to accelerate clinical translation. This review constructs a complete theoretical and technical framework positioning USMI as a novel functional assessment paradigm for tumor immunotherapy, clarifies its irreplaceable strengths in immune molecular imaging and provides targeted insights to advance precision tumor immunotherapy evaluation.

PMID:42722534 | DOI:10.1016/j.ultrasmedbio.2026.08.004

Thorax. 2026 Sep 10:thorax-2026-225147. doi: 10.1136/thorax-2026-225147. Online ahead of print.

ABSTRACT

INTRODUCTION: Most research on idiopathic pulmonary fibrosis (IPF) has focused on the interplay among fibroblasts, the immune system and epithelial cells. There is growing evidence that microvascular dysfunction also plays a role in disease progression, but large human translational studies are lacking. In this research, we aim to identify a proteomic signature of microvascular instability and assess the impact of current therapeutics on the microvasculature.

METHODS: Olink proteomic data from patients with IPF were obtained from the Pulmonary Fibrosis Foundation Patient Registry (PFF-PR) (n=914) and an independent validation cohort (n=366). Among the PFF-PR, 640 patients also have whole-blood RNA sequencing data available. A subset of 79 microvascular-associated proteins was curated, and their associations with disease severity and transplant-free survival were examined. An adaptive least absolute shrinkage and selection operator was used to generate a novel microvascular risk score.

RESULTS: Higher plasma levels of five microvascular-associated proteins (SDC1, MMP10, THBS2, HGF and SERPINA5) were associated with lung function and survival in both cohorts. Whole-blood RNA sequencing of patients with microvascular risk revealed enrichment of immune-mediated processes. Patients with higher microvascular risk who were subsequently put on nintedanib in the following year had significantly better 3-year transplant-free survival compared with patients who did not receive antifibrotic intervention (HR 0.56, 95% CI 0.35 to 0.89, p=0.0142).

DISCUSSION: Integrative multi-omics analyses suggest that perturbations to microvascular remodelling contribute to disease severity and progression in IPF. This analysis offers a framework for a precision medicine approach for IPF.

PMID:42722423 | DOI:10.1136/thorax-2026-225147

Drug Des Devel Ther. 2026 Sep 5;20:603123. doi: 10.2147/DDDT.S603123. eCollection 2026.

ABSTRACT

BACKGROUND: High-altitude hypoxia can trigger maladaptive cardiopulmonary responses, with hypoxia-induced pulmonary hypertension (HPH) representing a major clinical challenge with limited therapeutic options. Shengxian Decoction (SXT), a classical traditional Chinese medicine formula for treating "qi deficiency and sinking", has shown clinical benefits, but the molecular pathways associated with its effects remain incompletely understood.

METHODS: Male Sprague-Dawley rats were exposed to simulated high altitude (5000 m; 404 mmHg, 10.8% O₂) for 28 days and treated with SXT at three doses (1.8, 3.6, or 7.2 g/kg/day; n = 6/group). Integrated serum metabolomics (UHPLC-Q-TOF-MS) and lung transcriptomics (RNA-seq) were applied. Multivariate analysis, pathway enrichment, weighted gene co-expression network analysis, and cross-omics correlation were used for data integration. After randomization, allocation concealment and blinding were strictly implemented throughout all experimental procedures, with all interventions and outcome assessments performed by personnel blinded to group assignment until completion of data analysis.

RESULTS: Chronic hypoxia induced HPH with elevated mPAP, RVHI, RVWI and pulmonary vascular remodeling (increased WT% and WA%), while SXT dose-dependently ameliorated these abnormalities and restored hypoxia-disrupted metabolomic and transcriptomic profiles, with the high-dose group showing the most pronounced effect. Chronic hypoxia induced pronounced metabolic and transcriptional remodeling, with model animals clearly separated from controls in principal component analysis. Most differentially expressed genes exhibited downregulated expression, indicating global transcriptional suppression. SXT treatment dose-dependently restored both metabolomic and transcriptomic profiles, with the high-dose group most closely resembling controls. These pyruvate-proximal nodes may represent potential points of convergence through which SXT-associated metabolic and transcriptional alterations are coordinated. The relationships reported here are based on cross-omics associations, and causal inference will require further functional validation.

CONCLUSION: These findings suggest that SXT may ameliorate HPH partly through coordinated regulation of metabolic pathways and gene networks, particularly those related to energy metabolism, rather than fully explaining disease pathogenesis. The study provides multi-omics evidence supporting the traditional concept of "replenishing qi and elevating sunken qi" and identifies candidate metabolic biomarkers for further investigation. However, the results should be interpreted cautiously because of the relatively small sample size, the lack of functional validation experiments, and the exploratory nature of the biomarker findings. Further mechanistic and clinical studies are required to confirm these observations.

PMID:42719424 | PMC:PMC13557172 | DOI:10.2147/DDDT.S603123

Transl Oncol. 2026 Sep 9;73:103020. doi: 10.1016/j.tranon.2026.103020. Online ahead of print.

ABSTRACT

BACKGROUND: The prognostic relevance of inflammatory bowel disease (IBD)-colorectal cancer (CRC) shared host-microbe signatures in non-intestinal epithelial malignancies remains unclear. This study aimed to evaluate the prognostic and biological significance of an IBD-CRC shared host-microbe interactome signature in bladder urothelial carcinoma (BLCA).

METHODS: Gene set variation analysis (GSVA) was used to assess the activity of the IBD-CRC shared signature across The Cancer Genome Atlas (TCGA) pan-cancer solid tumor cohorts, including lung, liver, colorectal, and urinary system tumors. In BLCA, weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO)-Cox regression were applied to construct a prognostic risk model, which was validated in independent transcriptomic cohorts. An attention-based multiple instance learning (MIL) model was developed to predict the LASSO-derived high- or low-risk group from H&E whole-slide images (WSIs), using TCGA cases for training and internal validation and an independent institutional cohort of 39 BLCA patients for external validation. Molecular subtype, immune infiltration, immunohistochemistry (IHC), machine learning, single nucleotide variation/copy number variation (SNV/CNV), single-cell/spatial transcriptomics, and WSI-based deep learning analyses were integrated to characterize the biological relevance of the signature.

RESULTS: High GSVA scores were significantly associated with poor prognosis in BLCA. The LASSO-derived high-risk group was enriched in basal/squamous molecular features and exhibited an immune-infiltrated but immunosuppressive tumor microenvironment, characterized by increased immunosuppressive cell infiltration and elevated immune checkpoint expression. Conventional IHC markers supported distinct subtype-related protein phenotypes between risk groups. Single-cell and spatial transcriptomic analyses revealed that malignant cells with high signature activity were enriched in Wnt, Hippo, and cell adhesion pathways. The WSI-based MIL model achieved an area under the curve (AUC) of 0.852 in the internal validation cohort. Machine learning and SNV/CNV analyses further characterized key molecular features associated with the LASSO risk score, including AKR1B1, LY6E, MEST, and others. Pan-cancer characterization of AKR1B1 across multiple malignancies, including lung adenocarcinoma (LUAD), liver hepatocellular carcinoma (LIHC), and kidney renal clear cell carcinoma (KIRC), revealed cancer-type-specific associations with immunosuppressive microenvironmental features and tumor stemness.

CONCLUSION: The IBD-CRC shared host-microbe signature has significant prognostic value in BLCA and is associated with basal/squamous differentiation, immunosuppressive microenvironmental features, genomic alteration patterns, and malignant cell functional heterogeneity. The integrated multi-omics framework and externally validated pathology AI model provide potential tools for BLCA risk stratification and biological interpretation.

PMID:42715652 | DOI:10.1016/j.tranon.2026.103020

J Vis Exp. 2026 Sep 8;(235). doi: 10.3791/71404.

ABSTRACT

Severe asthma is a difficult-to-control airway disease with pronounced heterogeneity in both clinical manifestations and underlying inflammatory mechanisms. This review examines the mechanisms, biomarkers, and biologic therapies of severe asthma, with a focus on biomarker-guided treatment selection and emerging precision strategies for type 2-high (T2-high) and type 2-low (T2-low) disease. The development of biologic therapies has changed the treatment paradigm, particularly for patients with T2-high inflammation. By targeting immunoglobulin E (IgE), interleukin-5 (IL-5), interleukin-4 receptor alpha (IL-4Rα), and thymic stromal lymphopoietin (TSLP)-related pathways, these agents can decrease exacerbations, improve lung function and symptom control, and enhance quality of life. Biomarkers, including blood eosinophils, fractional exhaled nitric oxide (FeNO), total IgE, and sputum eosinophils, have been incorporated into clinical decision-making to support patient stratification. Emerging markers such as periostin, epithelial alarmins, gene-expression patterns, microRNAs, and multi-omics signatures are under investigation for more accurate phenotyping and response prediction. Despite these advances, current biomarkers do not always provide sufficient predictive accuracy, targeted options for T2-low asthma remain limited, biologics are costly, and long-term outcome data remain incomplete. Overall, integrating biomarker findings with clinical phenotype, comorbidities, and treatment history remains central to individualized biologic selection.

PMID:42714006 | DOI:10.3791/71404

CA Cancer J Clin. 2026 Sep-Oct;76(5):e70100. doi: 10.3322/caac.70100.

ABSTRACT

The clinical landscape of early stage nonsmall cell lung cancer is at transformative crossroads. Driven by the widespread adoption of low-dose computed tomography screening, the frequent detection of ground-glass opacities, and a rising incidence among never-smokers, the diagnostic center of gravity has shifted toward earlier, potentially curable disease. This shift has been accompanied by equally important therapeutic advances, including parenchyma-sparing surgical techniques, minimally invasive platforms enhanced by digital navigation, and the transformative integration of perioperative immunotherapy and targeted agents. Concurrently, noninvasive monitoring approaches, such as liquid biopsy, have emerged as powerful tools to guide precision management. Despite this progress, substantial barriers to achieving a universal cure persist. Clinicians continue to face uncertainty in the management of ground-glass opacities, the anatomy-based TNM staging system fails to capture the biologic heterogeneity of early tumors, and global disparities in access to innovation remain unresolved. To address these challenges, the authors propose a shift toward a risk-adaptive management paradigm that harnesses artificial intelligence-driven analytics and multi-omics profiling to tailor treatment intensity according to each patient's biologic risk. Such an approach would enable appropriate escalation for high-risk individuals while permitting safe de-escalation for those at low risk. This holistic, lifespan-oriented strategy must be embraced to deliver equitable and durable cures for patients with early stage nonsmall cell lung cancer.

PMID:42713910 | PMC:PMC13555834 | DOI:10.3322/caac.70100

Front Immunol. 2026 Aug 25;17:1902981. doi: 10.3389/fimmu.2026.1902981. eCollection 2026.

ABSTRACT

BACKGROUND: Coronaviruses (CoVs), including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Middle East respiratory syndrome (MERS-CoV), cause respiratory infections with distinct clinical outcomes and case fatality rates. However, the molecular basis of these differences remains unclear. In this study, we sought to define virus-specific host metabolic programs by directly comparing multiomics profiles of the lungs of lethally infected mouse models.

METHODS: We performed integrated multiomics analyses, including untargeted metabolomics, transcriptomics, and targeted lipidomics, of lung tissues from human angiotensin-converting enzyme 2 (hiACE2)-human dipeptidyl peptidase 4 (hDPP4) double-knock-in (DKI) mice infected in SARS-CoV-2 or MERS-CoV. Data Integration Analysis and Biomarker discovery using Latent cOmponents (DIABLO) was applied across all three omics layers to identify key distinguishing molecular patterns. Additionally, in vitro lipid droplet kinetics were examined in infected Vero E6 cells to validate temporal differences in lipid remodeling.

RESULTS: We identified two distinct strategies for lipid utilization. SARS-CoV-2 infection showed strong activation of energy and amino acid metabolism at an early stage of infection (3 days post infection, DPI), whereas MERS-CoV infection was characterized by sustained alterations in lipid and nucleotide metabolism. Integrative DIABLO analysis of all three omics layers revealed that the key distinguishing features clustered into virus-specific molecular signatures: a triacylglycerol-lipid droplet-interferon axis for SARS-CoV-2 and a phospholipid-sphingolipid-membrane hub for MERS-CoV. In vitro lipid droplet kinetics in infected Vero E6 cells confirmed this temporal difference, with SARS-CoV-2 peaking earlier than MERS-CoV.

CONCLUSION: These findings show that β-CoVs exploit host lipid metabolism through virus-specific and time-dependent remodeling programs, providing a framework for understanding differential pathogenesis and developing host-directed antiviral strategies.

PMID:42712680 | PMC:PMC13550176 | DOI:10.3389/fimmu.2026.1902981

Transl Oncol. 2026 Sep 8;73:102990. doi: 10.1016/j.tranon.2026.102990. Online ahead of print.

ABSTRACT

BACKGROUND: Lung adenocarcinoma (LUAD) is a predominant and lethal subtype of non-small cell lung cancer, with a lack of reliable prognostic biomarkers to guide clinical management. Basigin (BSG) has been implicated in tumor progression across multiple cancers, yet its expression pattern, prognostic significance, and underlying mechanisms in LUAD remain incompletely elucidated.

METHODS: We integrated multi-omics data from TCGA, GTEx, CCLE, and GEO databases to analyze BSG expression profiles. Clinical correlations were assessed via Kruskal-Wallis tests. Prognostic value was determined using Kaplan-Meier survival analysis, univariate/multivariate Cox regression, and nomogram construction with calibration curves. Functional enrichment (GO/KEGG) and immune infiltration analyses were performed to explore BSG-related mechanisms, followed by immunohistochemical (IHC) validation in A549 cells and clinical LUAD tissue microarrays.

RESULTS: BSG was significantly upregulated in LUAD tissues versus normal/paired adjacent tissues, correlating with advanced T/N/pathologic stages. High BSG expression predicted worse survival outcomes in TCGA-LUAD, which was validated in GEO datasets. Multivariate Cox regression identified BSG as an independent prognostic factor, with a well-calibrated nomogram for survival prediction. Functional exploration indicated that BSG mainly participated in tumor-associated and immunological pathways. Immune infiltration analysis indicated that BSG was significantly correlated with the infiltration of various immune cells. Moreover, BSG exhibited a strong association with immune checkpoint proteins, chemokines, chemokine receptors, and MHC genes. IHC further confirmed its cytoplasmic/membranous localization and prognostic relevance.

CONCLUSION: BSG serves as an independent prognostic biomarker and potential therapeutic target in LUAD, shedding light on its regulatory roles in tumor progression and immune microenvironment remodeling.

PMID:42710246 | DOI:10.1016/j.tranon.2026.102990

Respirology. 2026 Sep 7. doi: 10.1002/resp.70309. Online ahead of print.

ABSTRACT

Multidisciplinary discussion remains the cornerstone of ILD diagnosis and management, with recent advances further strengthening the integration of clinical, radiological, pathological, and molecular information. Updated ILD nomenclature and classification better align disease terminology with underlying morphology and pathobiology. Growing emphasis is being placed on the early detection of ILD and on identifying individuals at high risk of progression among those with interstitial lung abnormalities (ILAs). Emerging multi-omic biomarkers and quantitative imaging techniques are enhancing prognostic stratification and may support future precision medicine approaches. Novel antifibrotic therapies and targeted treatments are expanding therapeutic options beyond IPF, although important unmet needs remain regarding patient selection, treatment response, and disease modification. The integration of clinical, radiological, functional, and molecular information will be fundamental to optimize individualized management and improve long-term outcomes in patients with ILDs.

PMID:42706010 | DOI:10.1002/resp.70309

Zhongguo Fei Ai Za Zhi. 2026 Jul 20;29(7):540-547. doi: 10.3779/j.issn.1009-3419.2026.101.17.

ABSTRACT

Immune checkpoint inhibitors (ICIs) have significantly improved the prognosis of patients with lung cancer. However, checkpoint inhibitor-related pneumonitis (CIP), as one of the most severe immune-related adverse events, lacks well-defined diagnostic criteria and reliable risk stratification tools. Radiomics enables high-throughput feature extraction from computed tomography images and provides a non-invasive technical approach for the early identification and risk stratification of CIP. This article systematically reviews the recent advances in the application of radiomics to risk prediction, diagnosis and differential diagnosis, and prognostic evaluation of CIP in lung cancer immunotherapy. Furthermore, it explores the value of integrating radiomics with multi-omics data in elucidating the pathogenesis of CIP, as well as the role of explainable artificial intelligence (XAI) in enhancing the clinical trustworthiness of models. .

PMID:42705857 | DOI:10.3779/j.issn.1009-3419.2026.101.17

Curr Probl Cancer. 2026 Sep 7;65:101333. doi: 10.1016/j.currproblcancer.2026.101333. Online ahead of print.

ABSTRACT

Heterogeneous tumor microenvironment (TME) in lung cancer plays a crucial role in disease progression and resistance to therapy. Despite advances in single-cell and bulk omics profiling, these methods often overlook spatial context, which is vital for understanding cell-cell interactions and regional heterogeneity. In recent years, spatial omics technologies-including spatial genomics, transcriptomics, proteomics, and metabolomics-have revolutionized the ability to map molecular landscapes while maintaining tissue architecture. These advancements have become essential components of next-generation lung cancer management. By providing unprecedented resolution in characterizing the lung cancer TME, spatial omics could reveal prognostic and predictive biomarkers and identify new therapeutic vulnerabilities. This review will provide the first critical evaluation of spatial multi-omics approaches for lung cancer prognosis. It will also assess various integration strategies for multi-omics data to explore the clinical translational potential of these tools for therapy selection and patient stratification. Therefore, a deeper understanding of spatial omics technologies and their application in lung cancer can significantly improve precision diagnostics and therapeutic decision-making.

PMID:42705130 | DOI:10.1016/j.currproblcancer.2026.101333

J Inflamm Res. 2026 Sep 2;19:633944. doi: 10.2147/JIR.S633944. eCollection 2026.

ABSTRACT

Lung inflammation and immune dysregulation are central to the pathogenesis of a broad spectrum of respiratory diseases, yet the cellular and molecular mechanisms underlying these processes remain incompletely understood. This review examines mechanisms of pulmonary inflammation across major respiratory diseases, including asthma, chronic obstructive pulmonary disease, acute lung injury/acute respiratory distress syndrome, and pulmonary fibrosis. We discuss the roles of dysregulated innate and adaptive immunity, persistent inflammation, tissue remodeling, and impaired resolution in the pathogenesis of respiratory diseases, thereby highlighting mechanisms that are broadly conserved across these conditions as well as those that diverge in a disease-specific manner. This synthesis offers a framework for understanding pulmonary immune dysregulation and identifying new biomarkers and therapeutic strategies to restore pulmonary immune homeostasis. We also discuss how endotyping, single-cell transcriptomics, spatial biology, and multi-omics approaches are refining mechanistic understanding and enabling precision immunomodulatory interventions.

PMID:42703518 | PMC:PMC13546649 | DOI:10.2147/JIR.S633944

Pediatr Res. 2026 Sep 5. doi: 10.1038/s41390-026-05417-2. Online ahead of print.

ABSTRACT

Bronchopulmonary dysplasia (BPD) is the most common chronic pulmonary complication of extreme prematurity in infants, now understood as a disorder of disrupted lung development rather than acute injury alone. Conventional clinical and functional criteria fail to capture the full heterogeneity of outcomes or the persistence of pulmonary morbidity into adulthood. Epigenetic mechanisms-including DNA methylation, histone modifications, and non-coding RNAs-provide a unifying biological framework linking perinatal exposures to long-term lung dysfunction. In the preterm lung, hyperoxia, inflammation, infection, pharmacologic interventions, and microbiome alterations durably influence gene expression without changing the DNA sequence, contributing to impaired alveolarization, pulmonary vascular growth, antioxidant defenses, immune regulation, and cellular senescence. Hyperoxia, specifically, has been associated with lasting epigenetic changes in redox-sensitive pathways, angiogenic signaling, and cell cycle control, while inflammatory stimuli may establish epigenetic "memory" that is consistent with the persistence of chronic inflammation and defective repair. Collectively, these processes support a model in which epigenetically programmed lung phenotypes may emerge, characterized by reduced pulmonary reserve, accelerated lung aging, and heightened vulnerability to respiratory disease across the lifespan. Although evidence for transgenerational inheritance in humans is limited, inherited susceptibility remains plausible, but unproven. Framing BPD as a disorder of biological memory emphasizes the need for epigenetic biomarkers, longitudinal cohort studies, and targeted preventive or therapeutic strategies to improve lifelong outcomes in survivors. IMPACT: This review reframes Bronchopulmonary Dysplasia as a disorder of developmental programming and biological memory, integrating hyperoxia, inflammation, and pharmacologic exposures within a DOHaD-based epigenetic framework to explain long-term pulmonary and systemic heterogeneity. It synthesizes experimental, translational, and clinical evidence-including redox epigenetics, trained immunity, sex-specific responses, and lung-brain-immune interactions-supporting a model in which early-life exposures shape lifelong respiratory and extra-pulmonary outcomes, while acknowledging that some mechanisms remain unproven. It identifies key translational priorities, including validation of epigenetic biomarkers, longitudinal multi-omics studies, and cautious development of epigenetic therapies, while emphasizing current methodological limitations and remaining evidence gaps.

PMID:42701161 | DOI:10.1038/s41390-026-05417-2