Probiotics Antimicrob Proteins. 2026 Sep 14. doi: 10.1007/s12602-026-11134-3. Online ahead of print.

ABSTRACT

Gastric cancer (GC) remains a major global health burden and is frequently diagnosed at advanced stages owing to the limited sensitivity, invasiveness, and restricted availability of current screening strategies. Increasing evidence indicates that the gastric microbiome including Helicobacter pylori and diverse non-H. pylori bacteria, fungi, and viruses actively contribute to gastric carcinogenesis by modulating mucosal immunity, chronic inflammation, epithelial barrier integrity, and metabolism. High-throughput sequencing has revealed reproducible dysbiosis signatures in GC, characterized by enrichment of taxa such as Lactobacillus, Streptococcus, and Fusobacterium, and depletion of beneficial commensals, including Bifidobacterium and short-chain fatty acid producing anaerobes, some of which show promise as diagnostic or prognostic biomarkers. This review summarizes current knowledge on bacterial, fungal, and viral inhabitants of gastric tumors, highlighting their mechanistic roles in tumor initiation and progression and their potential as microbial indicators of disease. It further evaluates noninvasive and minimally invasive early detection strategies based on fecal and salivary microbiota profiling, urinary extracellular vesicles, and metabolomic fingerprints, alongside multi-omics integration and machine-learning models that combine microbial and host features to improve risk stratification. Finally, the review discusses therapeutic avenues including microbiota modulation, immunotherapy microbiome interactions, and personalized medicine frameworks that incorporate microbial signatures into clinical decision-making, underscoring the need for standardized, multicenter studies to translate gastric microbiome insights into robust tools for early detection and targeted intervention in GC.

PMID:42734869 | DOI:10.1007/s12602-026-11134-3

Probiotics Antimicrob Proteins. 2026 Sep 14. doi: 10.1007/s12602-026-11134-3. Online ahead of print.

ABSTRACT

Gastric cancer (GC) remains a major global health burden and is frequently diagnosed at advanced stages owing to the limited sensitivity, invasiveness, and restricted availability of current screening strategies. Increasing evidence indicates that the gastric microbiome including Helicobacter pylori and diverse non-H. pylori bacteria, fungi, and viruses actively contribute to gastric carcinogenesis by modulating mucosal immunity, chronic inflammation, epithelial barrier integrity, and metabolism. High-throughput sequencing has revealed reproducible dysbiosis signatures in GC, characterized by enrichment of taxa such as Lactobacillus, Streptococcus, and Fusobacterium, and depletion of beneficial commensals, including Bifidobacterium and short-chain fatty acid producing anaerobes, some of which show promise as diagnostic or prognostic biomarkers. This review summarizes current knowledge on bacterial, fungal, and viral inhabitants of gastric tumors, highlighting their mechanistic roles in tumor initiation and progression and their potential as microbial indicators of disease. It further evaluates noninvasive and minimally invasive early detection strategies based on fecal and salivary microbiota profiling, urinary extracellular vesicles, and metabolomic fingerprints, alongside multi-omics integration and machine-learning models that combine microbial and host features to improve risk stratification. Finally, the review discusses therapeutic avenues including microbiota modulation, immunotherapy microbiome interactions, and personalized medicine frameworks that incorporate microbial signatures into clinical decision-making, underscoring the need for standardized, multicenter studies to translate gastric microbiome insights into robust tools for early detection and targeted intervention in GC.

PMID:42734869 | DOI:10.1007/s12602-026-11134-3

Cancer Rep (Hoboken). 2026 Sep;9(9):e70685. doi: 10.1002/cnr2.70685.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and remains a major cause of cancer-related mortality worldwide. Its poor clinical outcomes are largely attributed to late-stage diagnosis and the limited accuracy of currently available diagnostic and prognostic biomarkers. Therefore, identifying novel molecular markers with improved sensitivity, specificity, and therapeutic relevance is essential for enhancing early detection and guiding personalized treatment strategies.

AIMS: To identify and prioritize novel candidate HCC biomarkers with diagnostic and prognostic value and potential therapeutic vulnerability using integrated multi-omics, survival, functional dependency, and tumor microenvironment analyses.

METHODS AND RESULTS: We examined the mRNA and protein expression levels of 8 DEGs in HCC tissues in the TCGA and CPTAC datasets using UALCAN, which showed that MSTO1 and LIG1 were overexpressed consistently in HCC relative to normal liver tissues. Moreover, elevated expression levels of these genes were significantly associated with higher tumor grade and advanced stage. Kaplan-Meier plotter survival data confirmed that increased expression of MSTO1 and LIG1 was associated with poorer overall survival. The DepMap CRISPR knockout data confirmed a functional dependency of both genes in HCC cell lines. CBioPortal analyses provided characterization of genomic alterations and enabled enrichment analysis of co-expressed genes, and the TCGA-UALCAN pan-cancer analyses supported the assessment of tissue specificity across tumor types. TIMER3 analyses linked candidate gene expression with immune cell infiltration patterns. Diagnostic performance by ROC analysis showed excellent discrimination for MSTO1 (AUC = 0.987) and good discrimination for LIG1 (AUC = 0.897). Multivariate Cox regression with Benjamini-Hochberg FDR correction across the eight genes supported MSTO1 as a candidate independent prognostic factor after adjustment for tumor stage, grade, etiology, age, and sex (HR = 1.29, p = 0.035), whilst LIG1 showed no independent prognostic value. Promoter methylation of MSTO1 and ADH4, assessed via UALCAN, showed that both genes were significantly differentially methylated in the promoter region of primary HCC tissues compared with normal liver tissues. Our study also confirmed the biological and clinical relevance of established HCC biomarkers: TERT, IRAK1, and ADH4.

CONCLUSION: MSTO1 and LIG1 emerged as candidate diagnostic biomarkers in HCC. Additionally, MSTO1 showed a candidate prognostic association with overall survival that remained significant after adjusting for tumor stage, grade, and etiology, as well as patients' age, but not after further adjustment for AFP status. Functional data also highlighted MSTO1 as a candidate therapeutic dependency. On the other hand, LIG1 showed no independent prognostic association in either multivariate model. Their differential expression and functional essentiality in HCC cell lines highlighted their value for further experimental and independent-cohort validation before potential integration into biomarker development pipelines aimed at improving early detection and targeted therapy in HCC.

PMID:42732946 | DOI:10.1002/cnr2.70685

Int Immunopharmacol. 2026 Sep 13;189:117355. doi: 10.1016/j.intimp.2026.117355. Online ahead of print.

ABSTRACT

Immune evasion in hepatocellular carcinoma (HCC) represents a major biological barrier limiting the efficacy of immunotherapy, yet its molecular basis remains incompletely understood. Increasing evidence indicates that tumor metabolic reprogramming and ferroptosis-related signaling play critical roles in shaping an immunosuppressive tumor microenvironment (TME); however, the specific regulatory factors involved remain unclear. This study aims to systematically elucidate the functional role of S100 calcium-binding protein A10 (S100A10) in immune evasion in HCC, with a particular focus on the molecular mechanisms by which S100A10 regulates CD8⁺ T-cell exhaustion through arachidonic acid (AA) metabolism and ferroptosis, as well as its potential therapeutic implications. To this end, data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort are integrated to analyze the expression patterns of S100A10, its prognostic value, and its association with the immune microenvironment. S100A10 overexpression and knockout models are established in HCCLM3 and MHCC97L cell lines, and S100A10-mediated metabolic pathway reprogramming is characterized using transcriptomic profiling, untargeted metabolomics, and ferroptosis-related functional assays. In parallel, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics are employed to delineate the cell-type specificity and spatial distribution of S100A10. Furthermore, human CD8⁺ T-cell co-culture systems and orthotopic mouse HCC models are used to evaluate the impact of S100A10 on immune function and responsiveness to anti-programmed cell death protein 1 (anti-PD-1) therapy. The results demonstrate that S100A10 is significantly upregulated in HCC and is closely associated with poor prognosis and an immunosuppressive state. Mechanistically, S100A10 activates cytosolic phospholipase A₂-arachidonate 5-lipoxygenase (cPLA₂-5-LOX)-mediated AA oxidative metabolism, leading to the accumulation of lipid peroxidation products and ferroptosis-associated signals, thereby driving CD8⁺ T-cell exhaustion and promoting immune evasion. Significantly, inhibition of S100A10 reshapes the tumor immune microenvironment (TIME) and enhances the therapeutic efficacy of anti-PD-1 treatment. Collectively, these findings identify S100A10 as a critical regulator of metabolic-immune coupling in HCC and provide a theoretical basis for combinatorial strategies targeting metabolism and immunotherapy.

PMID:42732672 | DOI:10.1016/j.intimp.2026.117355

Cancer Rep (Hoboken). 2026 Sep;9(9):e70685. doi: 10.1002/cnr2.70685.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and remains a major cause of cancer-related mortality worldwide. Its poor clinical outcomes are largely attributed to late-stage diagnosis and the limited accuracy of currently available diagnostic and prognostic biomarkers. Therefore, identifying novel molecular markers with improved sensitivity, specificity, and therapeutic relevance is essential for enhancing early detection and guiding personalized treatment strategies.

AIMS: To identify and prioritize novel candidate HCC biomarkers with diagnostic and prognostic value and potential therapeutic vulnerability using integrated multi-omics, survival, functional dependency, and tumor microenvironment analyses.

METHODS AND RESULTS: We examined the mRNA and protein expression levels of 8 DEGs in HCC tissues in the TCGA and CPTAC datasets using UALCAN, which showed that MSTO1 and LIG1 were overexpressed consistently in HCC relative to normal liver tissues. Moreover, elevated expression levels of these genes were significantly associated with higher tumor grade and advanced stage. Kaplan-Meier plotter survival data confirmed that increased expression of MSTO1 and LIG1 was associated with poorer overall survival. The DepMap CRISPR knockout data confirmed a functional dependency of both genes in HCC cell lines. CBioPortal analyses provided characterization of genomic alterations and enabled enrichment analysis of co-expressed genes, and the TCGA-UALCAN pan-cancer analyses supported the assessment of tissue specificity across tumor types. TIMER3 analyses linked candidate gene expression with immune cell infiltration patterns. Diagnostic performance by ROC analysis showed excellent discrimination for MSTO1 (AUC = 0.987) and good discrimination for LIG1 (AUC = 0.897). Multivariate Cox regression with Benjamini-Hochberg FDR correction across the eight genes supported MSTO1 as a candidate independent prognostic factor after adjustment for tumor stage, grade, etiology, age, and sex (HR = 1.29, p = 0.035), whilst LIG1 showed no independent prognostic value. Promoter methylation of MSTO1 and ADH4, assessed via UALCAN, showed that both genes were significantly differentially methylated in the promoter region of primary HCC tissues compared with normal liver tissues. Our study also confirmed the biological and clinical relevance of established HCC biomarkers: TERT, IRAK1, and ADH4.

CONCLUSION: MSTO1 and LIG1 emerged as candidate diagnostic biomarkers in HCC. Additionally, MSTO1 showed a candidate prognostic association with overall survival that remained significant after adjusting for tumor stage, grade, and etiology, as well as patients' age, but not after further adjustment for AFP status. Functional data also highlighted MSTO1 as a candidate therapeutic dependency. On the other hand, LIG1 showed no independent prognostic association in either multivariate model. Their differential expression and functional essentiality in HCC cell lines highlighted their value for further experimental and independent-cohort validation before potential integration into biomarker development pipelines aimed at improving early detection and targeted therapy in HCC.

PMID:42732946 | DOI:10.1002/cnr2.70685

Int Immunopharmacol. 2026 Sep 13;189:117355. doi: 10.1016/j.intimp.2026.117355. Online ahead of print.

ABSTRACT

Immune evasion in hepatocellular carcinoma (HCC) represents a major biological barrier limiting the efficacy of immunotherapy, yet its molecular basis remains incompletely understood. Increasing evidence indicates that tumor metabolic reprogramming and ferroptosis-related signaling play critical roles in shaping an immunosuppressive tumor microenvironment (TME); however, the specific regulatory factors involved remain unclear. This study aims to systematically elucidate the functional role of S100 calcium-binding protein A10 (S100A10) in immune evasion in HCC, with a particular focus on the molecular mechanisms by which S100A10 regulates CD8⁺ T-cell exhaustion through arachidonic acid (AA) metabolism and ferroptosis, as well as its potential therapeutic implications. To this end, data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort are integrated to analyze the expression patterns of S100A10, its prognostic value, and its association with the immune microenvironment. S100A10 overexpression and knockout models are established in HCCLM3 and MHCC97L cell lines, and S100A10-mediated metabolic pathway reprogramming is characterized using transcriptomic profiling, untargeted metabolomics, and ferroptosis-related functional assays. In parallel, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics are employed to delineate the cell-type specificity and spatial distribution of S100A10. Furthermore, human CD8⁺ T-cell co-culture systems and orthotopic mouse HCC models are used to evaluate the impact of S100A10 on immune function and responsiveness to anti-programmed cell death protein 1 (anti-PD-1) therapy. The results demonstrate that S100A10 is significantly upregulated in HCC and is closely associated with poor prognosis and an immunosuppressive state. Mechanistically, S100A10 activates cytosolic phospholipase A₂-arachidonate 5-lipoxygenase (cPLA₂-5-LOX)-mediated AA oxidative metabolism, leading to the accumulation of lipid peroxidation products and ferroptosis-associated signals, thereby driving CD8⁺ T-cell exhaustion and promoting immune evasion. Significantly, inhibition of S100A10 reshapes the tumor immune microenvironment (TIME) and enhances the therapeutic efficacy of anti-PD-1 treatment. Collectively, these findings identify S100A10 as a critical regulator of metabolic-immune coupling in HCC and provide a theoretical basis for combinatorial strategies targeting metabolism and immunotherapy.

PMID:42732672 | DOI:10.1016/j.intimp.2026.117355

Transl Res. 2026 Sep 13:S1931-5244(26)00195-7. doi: 10.1016/j.trsl.2026.09.009. Online ahead of print.

ABSTRACT

Gemcitabine resistance remains a major barrier to effective therapy in pancreatic ductal adenocarcinoma (PDAC), and current combination regimens show potential to overcome this resistance. Here, we identify the mitochondrial ribosomal proteins MRPS22 and MRPL3 as key metabolic gatekeepers that maintain mitochondrial OXPHOS and pyrimidine metabolism, thereby promoting pancreatic cancer cell proliferation and chemoresistance. Across independent cohorts, high MRPS22/MRPL3 expression associates with poorer survival. Depletion of either gene in PDAC curtailed cell proliferation and xenograft growth, which might be due to an impaired mitochondria function, including destabilized respiratory super-complex assembly, diminished ATP production, and increased oxidative stress. Multi-omics profiling revealed a broad reduction of central-carbon intermediates and a pronounced blockade of de novo pyrimidine synthesis at the dihydroorotate dehydrogenase (DHODH) node. MRPS22 depletion hampered nucleotide-pool generation, and exogenous deoxynucleotides partially rescued PDAC cell growth when MRPs were knocked down. Pharmacologic OXPHOS inhibition increased gemcitabine sensitivity, whereas gemcitabine-resistant derivatives exhibited heightened OXPHOS activity and upregulated mitochondrial ribosomal programs. Co-targeting OXPHOS (antimycin A) or DHODH (brequinar) with gemcitabine produced Loewe synergy in vitro and suppressed growth of gemcitabine-resistant xenografts without affecting body weight. Collectively, these findings established MRPS22/MRPL3 as translation-level drivers of PDAC metabolic fitness and nominate OXPHOS/DHODH blockade as a rational combination strategy to overcome gemcitabine resistance.

PMID:42732873 | DOI:10.1016/j.trsl.2026.09.009

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 Oncol. 2026 Aug 28;16:1907210. doi: 10.3389/fonc.2026.1907210. eCollection 2026.

ABSTRACT

Clonal evolution in hepatocellular carcinoma (HCC), esophageal squamous cell carcinoma (ESCC), and gastric cancer (GC) reflects the interaction of genetic diversification, cell-state plasticity, and tissue-specific selection. Multi-region and single-cell DNA sequencing resolve truncal and subclonal lineages, whereas single-cell and spatial transcriptomics, proteomics, and serial liquid biopsy characterize cellular states, ecological niches, and temporal dynamics. The three cancers differ in dissemination timing, dominant selective pressures, and biomarker maturity: early seeding is best supported in selected HCC cohorts, ESCC is strongly influenced by field cancerization and epithelial-stromal crosstalk, and GC follows subtype- and ecotype-dependent trajectories. We discuss the assumptions and sampling biases that constrain phylogenetic inference, the causal limits of cross-sectional tumor atlases, clonal hematopoiesis, and the incremental value of broad multi-omics over focused assays. Liquid-biopsy detection of minimal residual disease is prognostic, but treatment benefit from marker-guided intervention remains context dependent. Near-term translation requires standardized, decision-linked assays; adaptive therapy and evolutionary steering remain investigational.

PMID:42729528 | PMC:PMC13563159 | DOI:10.3389/fonc.2026.1907210

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

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

Front Pharmacol. 2026 Aug 26;17:1881703. doi: 10.3389/fphar.2026.1881703. eCollection 2026.

ABSTRACT

BACKGROUND: Gastric intestinal metaplasia (GIM) is a typical precancerous lesion of gastric cancer (PLGC). Previous studies have demonstrated that Xinkai Kujiang formula can effectively alleviate GIM, but its underlying mechanism remains largely unclear.

METHODS: The GIM rat model was established using 2% sodium salicylate and 20 mmol/L sodium deoxycholate, and then the rats were treated with Banxia Xiexin Decoction (BXD) and Xinkai Kujiang Decoction (XKD) for 4 weeks. Multi-omics analyses including 16 S ribosomal RNA gene sequencing, transcriptomics, single-cell RNA sequencing, network pharmacology, and component identification were performed to explore the therapeutic mechanisms of Xinkai Kujiang formula on GIM.

RESULTS: In the model rats, severe gastric mucosal atrophy was observed, characterized by disordered glands and goblet cells. Following intervention with BXD and XKD, gastric mucosal thickness was restored, glandular structures became regularly arranged, and the number of metaplastic goblet cells markedly decreased. Microbiota profiling of gastric mucosa revealed significant enrichment of Lactobacillus and Enterococcus in the model group. These abundances were reduced in the BXD group, and short-chain fatty acid-producing bacteria such as Alistipes and Lachnospira were enriched. In the intestine, opportunistic pathogens like Streptococcus and Enterococcus were enriched in the model group, whereas Corynebacterium and Bifidobacterium were enriched in the XKD group. Transcriptomic analysis presented that BXD upregulated innate immune-related genes in the gastric mucosa, and single-cell RNA sequencing (scRNA-Seq) showed that XKD alleviated GIM by inhibiting the VEGF and HIF-1α pathways, reducing angiogenesis, suppressing inflammatory infiltration, and regulating energy metabolism.

CONCLUSION: BXD and XKD improve gastrointestinal microbiota disorders and metabolic disorders, enhance gastric mucosal immunity, and inhibit the VEGF and HIF-1α pathway. Collectively, these multi-omics data provide novel insights into the therapeutic mechanisms of Xinkai Kujiang formula for GIM.

PMID:42718732 | PMC:PMC13553361 | DOI:10.3389/fphar.2026.1881703