Gene expression analysis reveals a 5-gene signature for progression-free survival in prostate cancer

• Published in: Frontiers in oncology 2022-08, Vol.12, p.914078-914078
• Main Authors: Mou, Zhuofan, Spencer, Jack, Knight, Bridget, John, Joseph, McCullagh, Paul, McGrath, John S., Harries, Lorna W.
• Format: Article
• Language: English
• Subjects: gene signature; Oncology; prediction model; prognosis; prostate cancer; transcriptomics
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2022.914078

Prostate cancer (PCa) is the second most common male cancer worldwide, but effective biomarkers for the presence or progression risk of disease are currently elusive. In a series of nine matched histologically confirmed PCa and benign samples, we carried out an integrated transcriptome-wide gene expression analysis, including differential gene expression analysis and weighted gene co-expression network analysis (WGCNA), which identified a set of potential gene markers highly associated with tumour status (malignant vs . benign). We then used these genes to establish a minimal progression-free survival (PFS)-associated gene signature (GS) ( PCBP1 , PABPN1 , PTPRF , DANCR , and MYC ) using least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox regression analyses from The Cancer Genome Atlas prostate adenocarcinoma (TCGA-PRAD) dataset. Our signature was able to predict PFS over 1, 3, and 5 years in TCGA-PRAD dataset, with area under the curve (AUC) of 0.64–0.78, and our signature remained as a prognostic factor independent of age, Gleason score, and pathological T and N stages. A nomogram combining the signature and Gleason score demonstrated improved predictive capability for PFS (AUC: 0.71–0.85) and was superior to the Cambridge Prognostic Group (CPG) model alone and some conventionally used clinicopathological factors in predicting PFS. In conclusion, we have identified and validated a novel five-gene signature and established a nomogram that effectively predicted PFS in patients with PCa. Findings may improve current prognosis tools for PFS and contribute to clinical decision-making in PCa treatment.