A novel efferocytosis-related gene signature for predicting prognosis and therapeutic response in bladder cancer

Efferocytosis, the process by which phagocytes like macrophages and dendritic cells clear apoptotic cells, is crucial for maintaining tissue homeostasis. However, its function in bladder cancer (BLCA) remains unclear and warrants further exploration. This study seeks to establish a prognostic and treatment response signature based on efferocytosis-related genes (EFRGs) for bladder cancer patients. BLCA-related datasets were sourced from the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/ ) and the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/ ). A comprehensive analysis was performed on 28 prognostic EFRGs. Clustering analysis was carried out using ConsensusClusterPlus. Prognostic differentially expressed genes (DEGs) were identified based on expression variations across the subtypes. A prognostic model was subsequently developed using least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. Lastly, a thorough analysis was conducted to explore the relationship between risk scores and the tumor immune microenvironment, somatic mutations, as well as responses to immunotherapy and chemotherapy. Consensus clustering revealed two efferocytosis subtypes, Cluster A and Cluster B, and identified 61 prognostic DEGs between them. A risk scoring model, incorporating four key DEGs-SERPINE2, DPYSL3, CTSE, and KRT16-was constructed and validated. This model successfully stratified patients into high-risk and low-risk groups, with high-risk patients showing worse prognosis, increased immune infiltration, and higher immune checkpoint gene expression. The risk scores also provide insights into patient responsiveness to treatment. In conclusion, we identified four key genes-SERPINE2, DPYSL3, CTSE, and KRT16-that can be used to develop a prognostic model for bladder cancer. These findings may provide valuable molecular targets for the clinical diagnosis and therapeutic strategies of bladder cancer.

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Publication Details

Journal
Scientific Reports
Published
2025-06-06
DOI
https://doi.org/10.1038/s41598-025-04037-w
Citations
4
Primary Topic
Phagocytosis and Immune Regulation
Type
article
Field-Weighted Citation Impact
3.19
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article

A novel efferocytosis-related gene signature for predicting prognosis and therapeutic response in bladder cancer

Weitao Yu, Xueming Ma, Junqiang Tian, Dongnuan Yao et al.
4 citations
Scientific Reports
Phagocytosis and Immune Regulation
3.19
article

A novel efferocytosis-related gene signature for predicting prognosis and therapeutic response in bladder cancer

Weitao Yu, Xueming Ma, Junqiang Tian, Dongnuan Yao, Juanjuan Hou
article en
4 citations

Abstract

Efferocytosis, the process by which phagocytes like macrophages and dendritic cells clear apoptotic cells, is crucial for maintaining tissue homeostasis. However, its function in bladder cancer (BLCA) remains unclear and warrants further exploration. This study seeks to establish a prognostic and treatment response signature based on efferocytosis-related genes (EFRGs) for bladder cancer patients. BLCA-related datasets were sourced from the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/ ) and the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/ ). A comprehensive analysis was performed on 28 prognostic EFRGs. Clustering analysis was carried out using ConsensusClusterPlus. Prognostic differentially expressed genes (DEGs) were identified based on expression variations across the subtypes. A prognostic model was subsequently developed using least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. Lastly, a thorough analysis was conducted to explore the relationship between risk scores and the tumor immune microenvironment, somatic mutations, as well as responses to immunotherapy and chemotherapy. Consensus clustering revealed two efferocytosis subtypes, Cluster A and Cluster B, and identified 61 prognostic DEGs between them. A risk scoring model, incorporating four key DEGs-SERPINE2, DPYSL3, CTSE, and KRT16-was constructed and validated. This model successfully stratified patients into high-risk and low-risk groups, with high-risk patients showing worse prognosis, increased immune infiltration, and higher immune checkpoint gene expression. The risk scores also provide insights into patient responsiveness to treatment. In conclusion, we identified four key genes-SERPINE2, DPYSL3, CTSE, and KRT16-that can be used to develop a prognostic model for bladder cancer. These findings may provide valuable molecular targets for the clinical diagnosis and therapeutic strategies of bladder cancer.

Scientific ReportsVol. 15(1)
Lanzhou University Second Hospital (CN), Gansu Provincial Center for Disease Control and Prevention (CN), Lanzhou University (CN)
Good health and well-being
Openalex Percentile: Top 8%
Phagocytosis and Immune Regulation
3.19
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