Identification and validation of a novel anoikis-related prognostic model for prostate cancer

Abstract

Abstract In this study, we collected gene expression profile, single nucleotide polymorphism mutation data, and copy number variation (CNV) info of 495 prostate cancer (PCa) patients from the TCGA database and downloaded 140 PCa samples from the MSKCC dataset as an external cohort. We extracted 434 anoikis-related genes from GeneCards and previous publications. We used unsupervised consensus cluster analysis to identify two molecular subtypes (C1 and C2). C1 exhibited a higher level of CNV amplification, immune score, stromal score, aneuploidy score, homologous recombination deficiency, intratumor heterogeneity, single-nucleotide variant neoantigens, and tumor mutational burden compared to C2. C2 showed a better survival outcome and was found to have a high level of gamma delta T cell and activated B cell infiltration. We then constructed a novel risk signature consisting of four genes (HELLS, ZWINT, ABCC5, and TPSB2) for predicting the overall survival of PCa patients using the univariate Cox regression analysis and the least absolute shrinkage and selection operator algorithm, which we validated using the MSKCC dataset. The receiver operating characteristic curve of the signature indicated that the area under the curve was 0.780, suggesting good predictive accuracy. We found that the risk signature was an independent prognostic factor for overall survival in PCa patients. Additionally, we identify four CTRP-derived compounds (cucurbitacin I, SB−743921, paclitaxel, and GSK461364) and four PRISM-derived compounds (volasertib, LY2606368, mitoxantrone, and dolastatin−10) for the treatment of high-risk group patients. Our findings may provide a new perspective for the treatment of anoikis-related PCa.