Evidence map›Paper›PMID 40635219›Full record

ArticleCurrent cancer drug targets2026

Analysis of Single-Cell RNA-Seq Data to Investigate Tumor Cell Heterogeneity in Uroepithelial Bladder Cancer and Predict Immunotherapy Response.

Lu Zhang, Yu Wang, Jianjun Tan

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Article in Current cancer drug targets, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Lu ZhangDepartment of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China.
Yu WangDepartment of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China.
Jianjun TanDepartment of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China.ORCID 0000-0003-3573-0807

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNumerous studies have suggested a close association between cancer stem cells (CSCs) and the tumor microenvironment (TME), suggesting that cancer stemness might also contribute to ICI resistance. However, the interplay between these physiological processes in urothelial bladder cancer (UBC) remains unclear.

methodsWe performed differential expression analysis using UBC single-cell RNA-seq dataset and obtained tumor stemness gene sets. The relationship between Ste.genes and ICI response, as well as response to drug therapy, was investigated using Tumour Immune Dysfunction and Exclusion (TIDE) and drug sensitivity analyses. Machine learning based on Ste.genes was also used to predict ICI response.

resultsA hypoxia-related tumor subgroup associated with angiogenesis and tumor metastasis was identified, and prognostic models were constructed based on hypoxic tumor subgroups. It was also found that the Ste.genes score was associated with cellular immunity, tumor immunotherapy response, and drug sensitivity. Multiple machine learning models were used to predict ICI response based on Ste.genes, and the AUC was greater than 0.7, indicating that Ste.genes can predict ICI response effectively.

conclusionIn this study, the analysis of UBC scRNA-seq data provided further insight into the role of hypoxic tumor subpopulations in tumor development in UBC, and a prognostic model was constructed. Additionally, an association was found between cell stemness and resistance to immunotherapy as well as drug sensitivity in UBC. Ste.genes were extracted and utilized to predict the ICI response.

Indexed as

Biomarkers, TumorImmune Checkpoint InhibitorsImmunotherapyRNA-SeqUrinary Bladder NeoplasmsGene Expression Regulation, NeoplasticHumansMachine LearningNeoplastic Stem CellsPrognosisSingle-Cell AnalysisSingle-Cell Gene Expression AnalysisTumor MicroenvironmentBiomarkers, TumorImmune Checkpoint Inhibitorshypoxiaimmune checkpoint therapySingle-cell RNA-sequencingstemnessurothelial bladder cancer

Identifiers

PMID40635219

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