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

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

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

Cytotechnology. 2026 Jun;78(3):119. doi: 10.1007/s10616-026-00993-x. Epub 2026 May 14.

ABSTRACT

Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer worldwide, with increasing incidence and mortality rates. Although several targeted therapies are currently available, the therapeutic outcomes remain unsatisfactory due to the high heterogeneity and drug resistance of HCC. Therefore, novel molecular mechanisms and therapeutic strategies urgently need to be explored. In this study, we obtained the GSE39791 dataset from the GEO database and identified 1,186 differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was conducted to obtain 776 key module genes, which were intersected with 11,671 HCC-related genes from the GeneCards database, resulting in 226 candidate genes. A protein-protein interaction (PPI) network was constructed using the STRING database, and the top 20 hub genes were identified using the MNC algorithm in Cytoscape. Among these, the five most significant hub genes-RFC4, TOP2A, AURKA, HSP90AA1, and MCM4-were selected for further analysis. KEGG enrichment analysis was performed to explore their functional pathways. Potential therapeutic agents were predicted using the CMap database, and molecular docking was conducted via AutoDock Vina. To validate the computational predictions, a quercetin intervention model was established. The optimal dose was determined through CCK-8 assays in HepG2 cells, and the expression of the five hub genes was examined in normal liver cells (LO2), HepG2 cells, and HepG2 cells treated with quercetin using RT-qPCR. The five hub genes-RFC4, TOP2A, AURKA, HSP90AA1, and MCM4-were significantly overexpressed in both HCC tissues and cell lines. Enrichment analysis revealed that these genes were mainly involved in cancer-related pathways, including the cell cycle, p53 signaling pathway, and FoxO signaling pathway. Drug prediction analysis showed that quercetin exhibited a negative regulatory pattern with respect to HCC and displayed binding energies below - 5 kcal/mol with all five hub proteins. CCK-8 assays confirmed the dose-dependent inhibitory effect of quercetin on HepG2 cell viability. RT-qPCR results demonstrated that quercetin significantly downregulated the expression of the five hub genes, consistent with the bioinformatics predictions. This study integrated multi-omics analysis and experimental validation to identify five core genes closely associated with HCC and suggested that quercetin may exert anti-HCC effects partly associated with the regulation of these genes. Our findings offer new insights into the molecular mechanisms of HCC and provide a promising strategy for the development of targeted therapeutics.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10616-026-00993-x.

PMID:42145839 | PMC:PMC13176377 | DOI:10.1007/s10616-026-00993-x

J Ethnopharmacol. 2026 Mar 30;365:121612. doi: 10.1016/j.jep.2026.121612. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: Airway inflammation is one of the primary pathological characteristics of asthma. Soufeng Yuchuan (SFYC) decoction, a compound formula derived from multiple traditional Chinese medicine prescriptions, is widely applied clinically and exhibits significant therapeutic efficacy against asthma. However, its anti-asthmatic mechanisms remain incompletely understood.

MATERIALS AND METHODS: Asthmatic rat models induced by ovalbumin (OVA) and ferroptosis models induced by erastin in BEAS-2B cells were established. Proteomics and metabolomics analyses were conducted on lung tissues and serum. Key ferroptosis-related targets (GPX4, SLC7A11/SLC3A2, GCLC, GSS, and VDAC2) were validated using Western blotting, RT-qPCR, and biochemical assays. The direct anti-ferroptosis effects of SFYC-containing serum were compared with ferrostatin-1 and blank serum in vitro.

RESULTS: Integrated omics analysis revealed that ferroptosis, glutathione metabolism, and ROS signaling pathways were the core targets modulated by SFYC. In vivo, SFYC significantly reduced airway inflammation and ROS accumulation, restored pulmonary GSH levels, upregulated the expression of GPX4, GCLC, GSS, SLC7A11, and SLC3A2, and downregulated VDAC2 expression (P < 0.05). In vitro, SFYC-containing serum effectively reversed erastin-induced lipid peroxidation, iron overload, GSH depletion, ROS elevation, and apoptosis in BEAS-2B cells, demonstrating comparable or superior efficacy to ferrostatin-1.

CONCLUSION: SFYC alleviates airway inflammation in asthma primarily by inhibiting ferroptosis. This study provides evidence that SFYC exerts anti-asthmatic effects, at least in part, via the regulation of ferroptosis pathways.

PMID:41921764 | DOI:10.1016/j.jep.2026.121612

Nat Commun. 2026 Mar 20;17(1):2655. doi: 10.1038/s41467-026-70193-w.

ABSTRACT

Pulmonary fibrosis represents a progressive interstitial lung disease marked by excessive extracellular matrix deposition and architectural distortion. Vascular endothelial cells critically contribute to fibrogenesis through paracrine secretion of pro-fibrotic mediators, yet their mechanobiological regulation remains elusive. Using integrated single-cell multi-omics profiling of human pulmonary fibrosis specimens and experimental fibrosis models induced by bleomycin or silica, we identify mechanosensitive Piezo1 upregulation in Endothelial cells as a hallmark of fibrotic progression. Endothelial-specific Piezo1 knockout significantly attenuates Bleomycin-induced fibrotic remodeling in male mice, establishing its pathogenic necessity. Mechanistically, PIEZO1 activation promotes pulmonary fibrosis development via CAPN2-mediated STAT3 phosphorylation, which may regulate the secretion of the pro-fibrotic molecule interleukin-33. These findings suggest that the endothelial PIEZO1-CAPN2-STAT3-IL33 axis is a potential therapeutic target for PF intervention.

PMID:41862476 | PMC:PMC13004862 | DOI:10.1038/s41467-026-70193-w