Frontiers in immunology · 2025
Development of a prognostic model based on immunogenic cell death/ferroptosis-related genes and the study of TREX1 effects on prostate cancer cells.
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Wen Yaoan, Zhan Shuyuan, Chen Jianhui, Yang Jiangbin, Chen Dandong, Zheng Song, Zhu Shaoxing
Аннотация
BACKGROUND: Understanding the interplay between immunogenic cell death (ICD), ferroptosis, and prostate cancer (PCa) is critical for elucidating the underlying mechanisms of PCa pathogenesis. This study aimed to establish a prognostic model for PCa based on ICD- and ferroptosis-related genes (IFRGs) and to evaluate its potential clinical applicability.
METHODS: RNA sequencing data and clinical information of PCa patients were obtained from The Cancer Genome Atlas (TCGA-PRAD) database. Candidate IFRGs were identified through Pearson correlation and differential expression analyses. A prognostic model was constructed using univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and Kaplan-Meier survival analyses, and subsequently validated in an external cohort (GSE70769). In addition, siRNA-mediated knockdown of the key gene TREX1 was performed in PC-3 cells, and EdU and Transwell assays were conducted to assess its effects on tumor cell proliferation, migration, and invasion.
RESULTS: A three-gene IFRG-based prognostic model was developed, which effectively stratified PCa patients into high- and low-risk groups with significantly different survival outcomes. Multivariate Cox regression analysis confirmed the model as an independent prognostic factor. Functional experiments further demonstrated that TREX1 serves as a critical risk gene, and its knockdown markedly suppressed the proliferative, migratory, and invasive capacities of PCa cells.
CONCLUSION: The three-gene IFRG-based prognostic model may serve as a promising prognostic biomarker for PCa, providing predictive value and novel insights into the complex interactions between IFRGs and PCa progression. Moreover, TREX1 was identified as a potential therapeutic target, offering new perspectives for prognostic assessment and the development of immunotherapy strategies in PCa.
METHODS: RNA sequencing data and clinical information of PCa patients were obtained from The Cancer Genome Atlas (TCGA-PRAD) database. Candidate IFRGs were identified through Pearson correlation and differential expression analyses. A prognostic model was constructed using univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and Kaplan-Meier survival analyses, and subsequently validated in an external cohort (GSE70769). In addition, siRNA-mediated knockdown of the key gene TREX1 was performed in PC-3 cells, and EdU and Transwell assays were conducted to assess its effects on tumor cell proliferation, migration, and invasion.
RESULTS: A three-gene IFRG-based prognostic model was developed, which effectively stratified PCa patients into high- and low-risk groups with significantly different survival outcomes. Multivariate Cox regression analysis confirmed the model as an independent prognostic factor. Functional experiments further demonstrated that TREX1 serves as a critical risk gene, and its knockdown markedly suppressed the proliferative, migratory, and invasive capacities of PCa cells.
CONCLUSION: The three-gene IFRG-based prognostic model may serve as a promising prognostic biomarker for PCa, providing predictive value and novel insights into the complex interactions between IFRGs and PCa progression. Moreover, TREX1 was identified as a potential therapeutic target, offering new perspectives for prognostic assessment and the development of immunotherapy strategies in PCa.