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

Front Oncol. 2026 Feb 25;16:1743620. doi: 10.3389/fonc.2026.1743620. eCollection 2026.

ABSTRACT

Lung cancer remains the leading cause of cancer-related mortality, yet current precision oncology approaches remain overwhelmingly tumor-centric, guided by genomic alterations and immune biomarkers, while largely neglecting the profound impact of aging biology on treatment response. While emerging evidence suggests that aging biology can modify therapeutic benefit and toxicity, its clinical integration remains uneven and largely investigational. In this review, we explicitly distinguish the chronological aging from biological aging to clarify how host biology modifies therapeutic benefit and toxicity. We synthesize mechanistic, translational, and early clinical evidence, while explicitly noting areas where prospective validation is lacking, to reframe personalization of lung cancer therapy through an age-conscious lens. We summarize data indicating that immunosenescence is associated with T-cell exhaustion, myeloid dominance, and extracellular matrix stiffening, features that may contribute to immune-evasive tumor phenotypes and attenuated responses to immune checkpoint blockade in subsets of patients, while pediatric cases, though rare, illustrate how global precision initiatives like iTHER and ZERO enable cautious adaptation of adult therapies. Moving beyond chronological age, we discuss biological age biomarkers, including PhenoAgeAccel, epigenetic clocks, telomere length, and frailty indices, which outperform traditional metrics in predicting risk, resistance, and toxicity, and propose integrating these tools into trial design, screening, and care planning which show promise for risk stratification and toxicity prediction but are not yet validated for routine treatment selection. Looking forward, we outline investigational strategies at the intersection of geroscience and oncology, including immune engineering, senolytics, microenvironmental modulation, and AI-driven multi-omic modeling. Overall, this review argues that biological age represents a critical but still underdeveloped dimension of precision oncology, and highlights key evidence gaps that must be addressed before age-aware personalization can be implemented in routine lung cancer care.

PMID:41821888 | PMC:PMC12975599 | DOI:10.3389/fonc.2026.1743620