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Exploration of Redox-Related Molecular Patterns and the Redox Score for Prostate Cancer.

Yue WuXi ZhangHuan FengBintao HuZhiyao DengChengwei WangBo LiuYang LuanYajun RuanXiaming LiuZhuo LiuJihong LiuTao Wang
Published in: Oxidative medicine and cellular longevity (2021)
Redox homeostasis is the key to cell survival, and its imbalance can promote the occurrence and progression of tumors. However, it remains unclear whether these redox-related genes (RRGs) have potential roles in the tumor microenvironment, immunotherapy, and drug sensitivity. Here, we performed a systematic and comprehensive analysis of 489 prostate cancer (PC) samples from The Cancer Genome Atlas database and 214 PC samples from 8 datasets in the Gene Expression Omnibus database to determine redox modification patterns and the redox scoring system for PC. We identified two modification patterns (Redox_A and Redox_B) in PC using unsupervised consensus clustering based on 1410 differential expression RRGs. We then compared the prognostic value, tumor microenvironment characteristics, immune cell infiltration, and molecular characteristics of the two patterns. The Redox_A pattern was significantly enriched in the carcinogenic activation signaling pathways and had a poor prognosis, while the Redox_B pattern was mainly enriched in a variety of metabolic and redox pathways and had a good prognosis. Next, redox-related characteristic genes were extracted from these two patterns, and a scoring system (Redox_score) was constructed to evaluate PC patients. Further analysis indicated that lower Redox_score patients had a better prognosis, while higher Redox_score patients had a higher tumor mutation burden, driver gene mutation rate, and immune checkpoint inhibitor gene expression. We also found that higher Redox_score patients were more responsive to anti-PD-1 immunotherapy. Moreover, Redox_score was determined to be significantly correlated with anticancer drug sensitivity and resistance. Our study provides a comprehensive analysis of redox modifications in PC and reveals new patterns of PC based on RRGs, which will provide insights into the complex mechanisms of PC and develop more effective individualized therapeutic strategies.
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