Signature for Prostate Cancer Based on Autophagy-Related Genes and a Nomogram for Quantitative Risk Stratification

Abstract

Background: Prostate cancer (PCa) ranks as the most common malignancy and the second leading cause of cancer-related death among males worldwide. The essential role of autophagy in the progression of PCa and treatment resistance has been preliminarily revealed. However, comprehensive molecular elucidations of the correlation between PCa and autophagy are rare.

Method: We obtained transcription information and corresponding clinicopathological profiles of PCa patients from TCGA, MSKCC, and GEO datasets. LAASO analysis was employed to select gene signatures and estimate the autophagy score for each patient. Correlations between the signature and prognosis of PCa were investigated by K-M and multivariate Cox regression analyses. A nomogram was established on the basis of the above results. Further validations relied on ROC, calibration analysis, decision curve analysis, and external cohorts. Variable activated signaling pathways were revealed using GSVA algorithms, and the genetic alteration landscape was elucidated via the oncodrive module from the "maftools" R package. In addition, we also examined the therapeutic role of the signature based on phenotype data from GDSC 2016.

Result: Six autophagy-related genes were eventually selected to establish the signature, including ULK1, CAPN10, FKBP5, UBE2T, NLRC4, and BNIP3L. We used these genes and corresponding coefficients to calculate an autophagy score (AutS) for each patient in this study. A high AutS group and a low AutS group were divided on the mean AutS of the patients. Longer overall survival, higher Gleason score and PSA, and better response to ADT were observed in patients with high AutS. Meanwhile, we found that high AutS PCa was related to more proliferation-associated signaling activation and higher genetic mutation frequencies, manifesting a poor prognosis. A nomogram was constructed based on GS, T stage, PSA, and AutS as covariates. Its discriminative efficacy and clinical value were validated using robust statistical methods. Finally, we tested its prognostic value through two external cohorts and six published signatures.

Conclusion: The autophagy-related gene signature is a highly discriminative model for risk stratification and drug therapy in PCa, and a nomogram incorporating AutS might be a promising tool for precision medicine.