Department of Urology Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Cancer Med. 2020 Nov;9(21):8202-8215. doi: 10.1002/cam4.3453. Epub 2020 Sep 13.
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.
前列腺癌（PCa）是全球男性中最致命的泌尿系统肿瘤之一，由于肿瘤转移，5 年生存率较差。探索前列腺癌早期诊断和个体化治疗的潜在生物标志物具有重要意义。本研究基于 GEO 数据集的多个微阵列进行了综合分析，获得了 510 例前列腺癌和 259 例良性病例之间的差异表达基因（DEGs）。加权相关网络分析表明，预后特征与 DEGs 最相关。然后进行单变量和多变量 COX 回归分析，得到 4 个预后基因，建立 4 基因预后模型。该模型在另一个 GEO 数据集、癌症基因组图谱和人类蛋白质图谱数据集中得到了很好的验证。此外，还系统地分析了四基因模型与临床特征的结合，以最大限度地指导前列腺癌患者的预后。总之，我们的研究结果表明，这四个基因在前列腺癌中有重要的预后意义，四基因模型与临床特征的结合可以更好地预测前列腺癌患者的预后。
