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

被引:2
|
作者
Rongbin Zhou [1 ]
Dingjin Lu [2 ]
Junhao Mi [3 ]
Chengbang Wang [1 ]
Wenhao Lu [2 ]
Zuheng Wang [4 ]
Xiao Li [1 ]
Chunmeng Wei [2 ]
Huiyong Zhang [4 ]
Jin Ji [5 ]
Yifeng Zhang [2 ]
Duobing Zhang [4 ]
Fubo Wang [2 ]
机构
[1] Guangxi Medical University,Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co
[2] Guangxi Medical University,Constructed By the Province and Ministry
[3] People’s Hospital of Beihai,Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine
[4] The First Affiliated Hospital of Guangxi Medical University,Department of Urology
[5] Guangxi Medical University,Department of Urology
[6] Guangxi Medical University,School of Life Sciences
[7] Affiliated Tumor Hospital of Guangxi Medical University,Department of Urology
[8] Guangxi Medical University,School of Public Health
[9] Guangxi Medical University,Department of Urology
[10] Shanghai Changhai Hospital,Department of Urology
[11] Naval Medical University,Department of Urology
[12] Naval Medical Center,undefined
[13] Naval Medical Univiersiy,undefined
[14] Suzhou Hospital of Anhui Medical University,undefined
关键词
Disulfidptosis; Prostate cancer; Prognosis; Tumor microenvironment; Immunotherapy;
D O I
10.1038/s41598-024-61679-y
中图分类号
学科分类号
摘要
Disulfidptosis, a newly identified programmed cell death pathway in prostate cancer (PCa), is closely associated with intracellular disulfide stress and glycolysis. This study aims to elucidate the roles of disulfidptosis-related genes (DRGs) in the pathogenesis and progression of PCa, with the goal of improving diagnostic and therapeutic approaches. We analyzed PCa datasets and normal tissue transcriptome data from TCGA, GEO, and MSKCC. Using consensus clustering analysis and LASSO regression, we developed a risk scoring model, which was validated in an independent cohort. The model's predictive accuracy was confirmed through Kaplan–Meier curves, receiver operating characteristic (ROC) curves, and nomograms. Additionally, we explored the relationship between the risk score and immune cell infiltration, and examined the tumor microenvironment and somatic mutations across different risk groups. We also investigated responses to immunotherapy and drug sensitivity. Our analysis identified two disulfidosis subtypes with significant differences in survival, immune environments, and treatment responses. According to our risk score, the high-risk group exhibited poorer progression-free survival (PFS) and higher tumor mutational burden (TMB), associated with increased immune suppression. Functional enrichment analysis linked high-risk features to key cancer pathways, including the IL-17 signaling pathway. Moreover, drug sensitivity analysis revealed varied responses to chemotherapy, suggesting the potential for disulfidosis-based personalized treatment strategies. 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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