Disulfidptosis-related genes serve as potential prognostic biomarkers and indicate tumor microenvironment characteristics and immunotherapy response in prostate cancer

Rongbin Zhou, Dingjin Lu, Junhao Mi,Chengbang Wang,Wenhao Lu,Zuheng Wang, Xiao Li,Chunmeng Wei,Huiyong Zhang,Jin Ji, Yifeng Zhang, Duobing Zhang,Fubo Wang

crossref(2024)

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摘要
Abstract Disulfidptosis, a novel programmed cell death pathway identified in prostate cancer (PCa), is intricately linked to intracellular disulfide stress and glycolysis. This research sought to elucidate the expression patterns and prognostic significance of disulfidptosis-related genes (DRGs) in PCa. We analyzed PCa datasets from TCGA, GEO, and MSKCC, accounting for batch effects and standardizing data. Using consistent cluster analysis, we categorized samples into subtypes based on 24 DRGs. Further, we employed univariate and LASSO regression analyses to construct a DRG-based risk score model. Its predictive accuracy was confirmed through Kaplan-Meier and ROC analyses. We also developed and validated prognostic nomograms for forecasting 1-, 3-, and 5-year progression-free survival, integrating risk scores with clinical factors, yielding area under the curve (AUC) values of 0.895, 0.825, and 0.768, respectively. This risk score model effectively stratified PCa patients into high- and low-risk groups. Utilizing the CIBERSORT algorithm, we explored the association between risk score and immune cell infiltration, and examined the tumor microenvironment and somatic mutations in different risk groups. Additionally, we investigated immunotherapy responses and drug sensitivities, uncovering significant links between DRG risk scores, clinical features, tumor microenvironment, and treatment outcomes. Notably, we identified PROK1 as a crucial prognostic marker in PCa, with its reduced expression correlating with disease progression. In summary, our study comprehensively assessed the clinical implications of DRGs in PCa progression and prognosis, offering vital insights for tailored precision medicine approaches.
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