Inflammation-related signature for prognostic prediction, tumor immune, genomic heterogeneity, and drug choices in prostate cancer: Integrated analysis of bulk and single-cell RNA-sequencing

Background: Prostate cancer (PCa) ranks as the second most prevalent malignancy among males on a global scale. Accumulating evidence suggests that inflammation has an intricate relationship with tumorigenesis, tumor progression and tumor immune microenvironment. However, the overall impact of inflammation-related genes on the clinical prognosis and tumor immunity in PCa remains unclear.Methods: Machine learning methods were utilized to construct and validate a signature using The Cancer Genome Atlas (TCGA) for training, while the Memorial Sloan Kettering Cancer Center (MSKCC) and GSE70769 cohorts for independent validation. The efficacy of the signature in predicting outcomes and its clinical utility were assessed through a series of investigations encompassing in vitro experiments, survival analysis, and nomogram development. The association between the signature and precision medicine was explored via tumor immunity, genomic heterogeneity, therapeutic response, and molecular docking analyses, using bulk and single-cell RNA-sequencing data.Results: We identified 7 inflammation-related genes with prognostic significance and developed an inflammation-related prognostic signature (IRPS) with 6 genes. Furthermore, we demonstrated that both the IRPS and a nomogram integrating risk score and pathologic T stage exhibited excellent predictive ability for the survival outcomes in PCa patients. Moreover, the IRPS was found to be significantly associated with the tumor immune, genomic heterogeneity, therapeutic response, and drug selection. Conclusion: IRPS can serve as a reliable predictor for PCa patients. The signature may provide clinicians with valuable information on the efficacy of therapy and help personalize treatment for PCa patients.

Automated summary

This study constructed an inflammation-related prognostic signature (IRPS) using machine learning methods and validated its predictive ability for survival outcomes in prostate cancer patients. The IRPS was found to be significantly associated with tumor immunity, genomic heterogeneity, therapeutic response, and drug selection.