Evidence map›Paper›PMID 41533176›Full record

ArticleDiscover oncology2026

Single-cell and immune-context integration identifies basement-membrane/metastasis signatures that sharpen bladder-cancer diagnosis and prognosis.

Ji Chen, Xiaobing Liu, Rongyin Ren, Renzheng Yi, Xiongfeng Zhang, Chaoqun Xie, Xinyu Liu, Weibing Long

Abstract read
In one paragraph

Article in Discover 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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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Ji ChenDepartment of Urology, Loudi City Central Hospital, Loudi, 417000, Hunan, China.
Xiaobing LiuDepartment of Urology, Loudi City Central Hospital, Loudi, 417000, Hunan, China.
Rongyin RenDepartment of Otolaryngology Head and Neck Surgery, Loudi Central Hospital, Loudi, 417011, Hunan, China.
Renzheng YiDepartment of Urology, Loudi City Central Hospital, Loudi, 417000, Hunan, China.
Xiongfeng ZhangDepartment of Urology, Loudi City Central Hospital, Loudi, 417000, Hunan, China.
Chaoqun XieDepartment of Urology, Loudi City Central Hospital, Loudi, 417000, Hunan, China.
Xinyu LiuDepartment of Otolaryngology Head and Neck Surgery, Loudi Central Hospital, Loudi, 417011, Hunan, China.
Weibing LongDepartment of Urology, Loudi City Central Hospital, Loudi, 417000, Hunan, China. longweibingldszxyy@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBladder cancer(BLCA) has a high recurrence rate and metastasis, and its process is closely related to basement membrane remodeling. we developed an interpretable prognostic model based on metastasis-basement membrane-related genes (MBRGs) to enhance clinical and personalized treatment strategies.

methodDifferentially expressed MBRGs from TCGA and GEO cohorts were analyzed. Prognostic genes were identified by univariate Cox and LASSO regression. A six-MBRG risk model was built and externally validated. SHAP analysis quantified feature contributions. Functional enrichment analyzed via GSEA and KEGG. Immune cell profiles estimated with CIBERSORT and ssGSEA. Immunotherapy response predicted using TMB, TIDE, and mutation frequency. Single-cell and spatial transcriptomics localized key genes to cancer-associated fibroblasts(CAFs).

resultsThrough analysis of metastasis and basement membrane-associated DEGs, 18 candidate MBRGs were identified and refined via univariate Cox and SHAP to a 6-gene signature (SERPINF1, DDR2, SLIT2, HSPG2, ECM1, RECK). This signature demonstrated prognostic power with AUCs of 0.638-0.674 in TCGA and 0.602-0.742 in GEO cohorts. A clinical nomogram achieved an AUC of 0.827. The high-risk group exhibited elevated M2 macrophages and TIDE scores(a computational metric for predicting tumor immune evasion and immunotherapy response), whereas the low-risk group showed enriched CD8⁺ T cells. Drug assays indicated dasatinib sensitivity in low-risk patients, and LGK974, LY2109761, and Wnt-C59 in high-risk patients. Single-cell RNA-seq and IHC confirmed CAF-specific overexpression of DDR2 and SERPINF1.

conclusionThe MBRG-based model effectively predicts BLCA prognosis, integrates mechanisms of basement membrane remodeling, EMT, and immune suppression, and identifies DDR2 and SERPINF1 in CAFs as potential targets for personalized therapy.

Identifiers

PMID41533176
PMCPMC12891303

What Socratic holds

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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.