Predicting survival in bladder cancer with a novel apoptotic gene-related prognostic model

Ding-Ming Song; Kun Feng; Wen-Fei Luo; Dong-Shan Lv; Li-Po Zhou; Yi-Bo He; Yanyang Jin

doi:10.1007/s12672-024-01575-z

Predicting survival in bladder cancer with a novel apoptotic gene-related prognostic model

Discov Oncol. 2024 Nov 24;15(1):702. doi: 10.1007/s12672-024-01575-z.

Authors

Ding-Ming Song^#¹, Kun Feng^#¹, Wen-Fei Luo², Dong-Shan Lv³, Li-Po Zhou³, Yi-Bo He⁴, Yanyang Jin⁵

Affiliations

¹ Department of Urology, Jinzhou Medical University, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China.
² Jinzhou Medical University, Jinzhou, Liaoning, China.
³ Department of Urology, Jinzhou Medical University, The Third Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China.
⁴ Department of Clinical Lab, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, China. heyb1992@126.com.
⁵ Department of Urology, Jinzhou Medical University, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China. jinyy@jzmu.edu.cn.

^# Contributed equally.

PMID: 39580765
DOI: 10.1007/s12672-024-01575-z

Abstract

Background: Apoptosis and apoptotic genes play a critical role in the carcinogenesis and progression of bladder cancer. However, there is no prognostic model established by apoptotic genes.

Methods: Messenger RNA (mRNA), Expression data, and related clinical data were obtained from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. After extracting the apoptosis-related genes, the survival-related apoptosis genes were screened by univariate Cox regression analysis in the TCGA cohort. Following the Least Absolute Shrinkage and Selection Operator (LASSO) regression method, these genes were modeled by multivariate Cox analysis. The predictive abilities of the Apoptosis-Related Gene Model (ARGM) for overall survival (OS) rate, disease-specific survival (DSS) measures, and progression-free survival (PFS) were verified by the Kaplan-Meier(K-M)survival analysis and time-dependent Receiver Operating Characteristic (ROC) curve. Functional enrichment analyses were performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG). CIBERSORT and Single-Sample Gene Set Enrichment Analysis (ssGSEA) were used to calculate the type of immune cell infiltration and immune functions. The model's predictive ability for immunotherapy were evaluated using Tumor Immune Dysfunction and Exclusion (TIDE) and the Imvigor210 study.The single-cell sequencing was used to display the expression level of the ARGM.Finally,qRT-PCR was executed to validate the expression level of ARGM.

Results: Several apoptosis genes were identified through the model, including ANXA1, CASP6, CD2, F2, PDGFRB, SATB1, and TSPO. The prognostic value of the model for OS, DSS, and PFS were verified using the TCGA and GEO cohort. The model can predict patient response to immunotherapy treatment as established through the model's score which was linked to different types of immune cell infiltration and identified significant differences in the signal pathways between high-risk and low-risk groups. Nomogram variables, prompted from ARGM and clinical parameters, also generate a high predictive value for patient survival.

Conclusion: Ourestablished apoptosis-related gene model (ARGM) has a substantial predictive value for prognosis and immunotherapy of bladder cancer. It may help with clinical consultation, clinical stratification, and treatment selection. The immune infiltration status and signal pathway of different risk groups also provide direction for further research.

Keywords: Apoptosis; Bladder cancer; GEO; Immune infiltration; Prognostic model; TCGA.