A novel prognosis and drug-susceptibility predictor based inflammatory-related genes signature in prostate cancer

Prostate cancer (PCa) is a widespread global health concern affecting males. Numerous investigations have shown the substantial implications of inflammation to PCa progression. To evaluate the predictive capacity of inflammatory-related genes (IRGs) in PCa, we conducted univariate Cox regression analysis and the Least Absolute Shrinkage and Selector Operation (LASSO) regression to formulate a prognostic model based on IRG expression. The training dataset comprised The Cancer Genome Atlas (TCGA) cohort, with validation performed using the cBioPortal cohort. Subsequently, an overall survival (OS) prediction was performed using a nomogram within the TCGA-PCa cohort, and we explored the association between the risk model and various factors such as immune cell infiltration, immune-related pathway functionality, tumor microenvironment characteristics, cancer stem cell scores and drug sensitivity. A comprehensive selection of 17 IRGs was identified to establish this prognostic risk model, where individuals with elevated risk scores exhibited unfavorable prognosis. The genomic nomogram, constructed based on these central IRGs, demonstrated remarkable accuracy in predicting PCa prognosis. Evaluation of tumor-infiltrating immune cells and immune-related pathways suggested that high-risk groups may manifest an immune-active phenotype. Moreover, there was a significant correlation between the expression levels of IRGs and tumor cell sensitivity to chemotherapeutic drugs. In conclusion, these findings indicate that an IRG-based risk model associated with inflammation can effectively predict PCa prognosis, presenting a promising strategy for further investigation.

Prostate cancer; TCGA; Inflammatory-related genes; Immune; LASSO regression

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