Exploring the Effects of Opioid-Related Drugs on the Clinical Outcome of Prostate Cancer Patients Via Integrated Bioinformatics Analysis.

Opioids are the primary regimens for perioperative analgesia with controversial effects on oncological survival. The underlying mechanism remains unexplored. This study developed survival-related gene co-expression networks based on RNA-seq and clinical characteristics from TCGA cohort. Two survival-related networks were identified, and drug-induced transcriptional profiles were predicted. Immune cell infiltration algorithm, least absolute shrinkage and selection operator (LASSO) regression, and cox proportional models were executed to explore the correlation between opioid-related drugs and prostate cancer patient prognosis. The opioid receptor agonists, represented by tramadol, were evidenced for anti-survival effects on prostate cancer by facilitating the DNA replication and cell cycle, and immune cell infiltration. Conversely, opioid receptor antagonists showed pro-survival effects. A novel prognostic model containing CNIH2, MCCC1, and Gleason scores was established and validated in two independent cohorts. This study revealed opioids' effect on prostate cancer progression, and provided a novel model to predict these regulations in clinical outcomes.