A four‐gene signature associated with clinical features can better predict prognosis in prostate cancer. (13th September 2020) - Yuan, Penghui; Ling, Le; Fan, Qing; Gao, Xintao; Sun, Taotao; Miao, Jianping; Yuan, Xianglin; Liu, Jihong; Liu, Bo
Abstract: Prostate cancer (PCa) is one of the most deadly urinary tumors in men globally, and the 5‐year over survival is poor due to metastasis of tumor. It is significant to explore potential biomarkers for early diagnosis and personalized therapy of PCa. In the present study, we performed an integrated analysis based on multiple microarrays in the Gene Expression Omnibus (GEO) dataset and obtained differentially expressed genes (DEGs) between 510 PCa and 259 benign issues. The weighted correlation network analysis indicated that prognostic profile was the most relevant to DEGs. Then, univariate and multivariate COX regression analyses were conducted and four prognostic genes were obtained to establish a four‐gene prognostic model. And the predictive effect and expression profiles of the four genes were well validated in another GEO dataset, The Cancer Genome Atlas and the Human Protein Atlas datasets. Furthermore, combination of four‐gene model and clinical features was analyzed systematically to guide the prognosis of patients with PCa to a largest extent. In summary, our findings indicate that four genes had important prognostic significance in PCa and combination of four‐gene model and clinical features could achieve a better prediction to guide the prognosis of patients with PCa. Abstract : Prostate cancer (PCa) is one of the most deadly urinary tumors in men globally, and the progression and development of PCa is associated with copious genetic aberrations. This study is aimed to add novel biomarkers of PCa development and prognosis by analyzing the genetic changes and clinical traits comprehensively. Based on integrated analysis, four genes were significantly related to the prognosis of PCa and well validated in other datasets. Furthermore, combination of four genes and clinical features achieved a better prediction to guide the prognosis of patients with PCa.