Evidence map›Paper›PMID 42715652›Full record

ArticleTranslational oncology2026

Pan-cancer screening and integrative multi-omics and deep learning reveal the prognostic significance of an IBD-CRC shared host-microbe signature in bladder urothelial carcinoma.

Changxin Zhan, Ting Xu, Hongshen Lai, Yifeng He, Fengjie Chen, Ruijian Liu, Bangqi Wang, Zhu Wang, Hui Liang, Deqin Zeng

Abstract read
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Article in Translational oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Changxin ZhanDepartment of Urology, The People's Hospital of Longhua Shenzhen, 38 Jinglong Jianshe Road, Longhua District, Shenzhen, Guangdong, 518109, China.
Ting XuDepartment of Urology, The People's Hospital of Longhua Shenzhen, 38 Jinglong Jianshe Road, Longhua District, Shenzhen, Guangdong, 518109, China.
Hongshen LaiDepartment of Urology, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, 466 Xingang Middle Road, Haizhu District, Guangzhou, Guangdong, 510317, China.
Yifeng HeGeneral Management Department, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, 290 Jichang Road, Baiyun District, Guangzhou, Guangdong, 510410, China.
Fengjie ChenGeneral Management Department, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, 290 Jichang Road, Baiyun District, Guangzhou, Guangdong, 510410, China.
Ruijian LiuMinimally Invasive Hyperhidrosis Center, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, 466 Xingang Middle Road, Haizhu District, Guangzhou, Guangdong, 510317, China.
Bangqi WangDepartment of Urology, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, 466 Xingang Middle Road, Haizhu District, Guangzhou, Guangdong, 510317, China. Electronic address: wangbangqi0618@163.com.
Zhu WangDepartment of Urology, The People's Hospital of Longhua Shenzhen, 38 Jinglong Jianshe Road, Longhua District, Shenzhen, Guangdong, 518109, China. Electronic address: wangzhu@smu.edu.cn.
Hui LiangDepartment of Urology, The People's Hospital of Longhua Shenzhen, 38 Jinglong Jianshe Road, Longhua District, Shenzhen, Guangdong, 518109, China. Electronic address: lianghui8689@smu.edu.cn.
Deqin ZengGeneral Management Department, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, 290 Jichang Road, Baiyun District, Guangzhou, Guangdong, 510410, China. Electronic address: mhyuanqu321@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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).

methodsGene 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.

resultsHigh 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.

conclusionThe 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.

Indexed as

Bladder urothelial carcinomaHost–microbe interactionMulti-omicsPan-cancerPathology artificial intelligenceTumor microenvironment

Identifiers

PMID42715652
PMCPMC13579696

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.