Biochemical recurrence related metabolic novel signature associates with immunity and ADT treatment responses in prostate cancer.

Abstract

Prostate cancer (PCa) is a unique cancer from a metabolic perspective. Androgen receptor assumes a vital part in normal and malignant prostate cells regarding almost all aspects of cell metabolism, such as glucose, fat, amino acids, nucleotides, and so on. We used The Cancer Genome Atlas database as training set, Memorial Sloan-Kettering Cancer Center cohort as validation set, and Gene Expression Omnibus database (GSE70769) as test set to identify the optimal prognostic signature. We evaluated the signature in terms of biochemical progression-free survival (bPFS), ROC curve, clinicopathological features, independent prognostic indicators, tumor microenvironment, and infiltrating immune cells. Nomogram was built dependent on the results of cox regression analyses. GSEA algorithm was used to evaluate differences in metabolism. The signature's prediction of androgen deprivation therapy (ADT) response was validated based on two groups of basic cytological experiments treat with ADT (GSE143408 and GSE120343) and the transcriptional information of pre-ADT/post-ADT of six local PCa patients. We finally input four screened genes into the stepwise regression model to construct metabolism-related signature. The signature shows good prediction performance in training set, verification set, and test set. A nomogram based on the PSA, Gleason score, T staging, and the signature risk score could predict 1-, 3-, and 5-year bPFS with the high area under curve values. Based on gene-set enrichment analysis, the characteristics of four genes signature could influence some important metabolic biological processes of PCa and were serendipitously found to be significantly related to androgen response. Subsequently, two cytological experimental data sets and our local patient sequencing data set verified that the signature may be helpful to evaluate the therapeutic response of PCa to ADT. Our systematic study definite a metabolism-related gene signature to foresee prognosis of PCa patients which might add to individual prevention and treatment.