Evidence map›Paper›PMID 41870815›Full record

ArticleDiscover oncology2026

Construction and evaluation of a bladder cancer prognosis model based on super-enhancer-associated genes.

Lieyu Xu, Zhenhao Zeng, Xinchang Zou, Zunwei Zhu, Tao Zeng

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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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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Lieyu Xu *Jiangxi Medical College, Nanchang University, Nanchang, China.
Zhenhao Zeng *Department of Urology, Jiangxi Provicial People's Hospital (The first Affiliated Hospital of Nanchang Medical College), Nanchang, China.
Xinchang Zou *Jiangxi Medical College, Nanchang University, Nanchang, China.
Zunwei ZhuDepartment of Urology, Jiangxi Provicial People's Hospital (The first Affiliated Hospital of Nanchang Medical College), Nanchang, China.
Tao ZengJiangxi Medical College, Nanchang University, Nanchang, China. taozeng40709@sina.com.

Funding

Jiangxi Provincial Health Commission project 202610361Science and Technology Program Project of Jiangxi Provincial Administration of Traditional Chinese Medicine 2020A0384Science and Technology Program Project of Jiangxi Provincial Health Commission 202130051
6 · The paper itself

Abstract

introductionBladder cancer (BLCA) is a malignant tumour that occurs on the mucosa of the bladder. It accounts for the first place in the incidence of genitourinary tumours in China. BLCA is characterized by high recurrence rate and poor survival rate. There is still a research gap regarding super-enhancer-related genes (SERGs) in BLCA.

methodsThe The Cancer Genome Atlas Bladder Urothelial Carcinoma (TCGA-BLCA) and GSE31684 were subjected into this study. In addition, the Super-Enhancer Archive database was used to identify SERGs. Differential expression analysis was used to analyse the differentially expressed genes (DEGs) between the BLCA and control groups. The DEGs were overlapped with SERGs to get candidate genes in TCGA-BLCA, which were analyzed for Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Univariate Cox, Least Absolute Shrinkage and Selection Operator (Lasso) regression and multivariate Cox regression analyses were used to build the risk model for BLCA. Survival analyses and validation of the model were performed by Kaplan-Meier (K-M) curves and Receiver Operating Characteristic (ROC) curve, respectively. In addition, using the estimating relative subsets of RNA transcripts (CIBERSORT) algorithm, 22 immune cell proportions were calculated. The drug sensitivity was also analyzed in this study.

resultsFirst of all, based on the TCGA-BLCA, 70 DE-SERGs were yielded. A prognosis model based on MXRA7, PLEKHG4B and ATP2B4 was finally constructed. ROC curves revealed that the prognosis model was a good predictor of BLCA outcomes. Immune infiltration analysis revealed that risk score was positively associated with T cells CD4 memory resting, Mast cells resting and Macrophages M2 and negatively associated with Dendritic cells activated and T cells CD8. Besides, AZD8186, BMS-754,807, JQ1, KRAS (G12C) Inhibitor and NU7441 were the top five sensitivity drugs for BLCA.

conclusionThree genes (MXRA7, PLEKHG4B and ATP2B4) were identified to construct a SERG-related model in BLCA, which provides a basis for understanding BLCA pathogenesis and new insights into BLCA treatment.

Indexed as

Bladder cancerGEOPrognosis modelSuper-enhancer-related genesTCGA

Identifiers

PMID41870815
PMCPMC13133299

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.