Data_Sheet_6_Identification of Potential Key Genes for Pathogenesis and Prognosis in Prostate Cancer by Integrated Analysis of Gene Expression Profiles and the Cancer Genome Atlas.XLSX

Background: Prostate cancer (PCa)is a malignancy of the urinary system with a high incidence, which is the second most common male cancer in the world. There are still huge challenges in the treatment of prostate cancer. It is urgent to screen out potential key biomarkers for the pathogenesis and prognosis of PCa.
Methods: Multiple gene differential expression profile datasets of PCa tissues and normal prostate tissues were integrated analysis by R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the overlapping Differentially Expressed Genes (DEG) were performed. The STRING online database was used in conjunction with Cytospace software for protein-protein interaction (PPI) network analysis to define hub genes. The relative mRNA expression of hub genes was detected in Gene Expression Profiling Interactive Analysis (GEPIA) database. A prognostic gene signature was identified by Univariate and multivariate Cox regression analysis.
Results: Three hundred twelve up-regulated genes and 85 down-regulated genes were identified from three gene expression profiles (GSE69223, GSE3325, GSE55945) and The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset. Seven hub genes (FGF2, FLNA, FLNC, VCL, CAV1, ACTC1, and MYLK) further were detected, which related to the pathogenesis of PCa. Seven prognostic genes (BCO1, BAIAP2L2, C7, AP000844.2, ASB9, MKI67P1, and TMEM272) were screened to construct a prognostic gene signature, which shows good predictive power for survival by the ROC curve analysis.
Conclusions: We identified a robust set of new potential key genes in PCa, which would provide reliable biomarkers for early diagnosis and prognosis and would promote molecular targeting therapy for PCa.

背景：前列腺癌（Prostate cancer, PCa）是一种高发的泌尿系统恶性肿瘤，亦是全球范围内第二大常见的男性恶性肿瘤。当前前列腺癌的临床诊疗仍存在诸多严峻挑战，亟需筛选出与前列腺癌发病机制及预后相关的潜在关键生物标志物。
方法：本研究借助R软件对前列腺癌组织与正常前列腺组织的多组基因差异表达谱数据集开展整合分析；对筛选得到的重叠差异表达基因（Differentially Expressed Genes, DEG）进行基因本体论（Gene Ontology, GO）富集分析与京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）通路富集分析。联合使用STRING在线数据库与Cytospace软件开展蛋白质相互作用（protein-protein interaction, PPI）网络分析以鉴定核心基因；通过基因表达谱交互式分析数据库（Gene Expression Profiling Interactive Analysis, GEPIA）验证核心基因的相对mRNA表达水平。采用单因素及多因素Cox回归分析构建预后基因标记物。
结果：从3组基因表达谱数据集（GSE69223、GSE3325、GSE55945）及癌症基因组图谱前列腺腺癌数据集（The Cancer Genome Atlas Prostate Adenocarcinoma, TCGA-PRAD）中，共筛选得到312个上调差异表达基因与85个下调差异表达基因。进一步筛选获得7个与前列腺癌发病机制相关的核心基因（FGF2、FLNA、FLNC、VCL、CAV1、ACTC1及MYLK）。通过筛选得到7个预后相关基因（BCO1、BAIAP2L2、C7、AP000844.2、ASB9、MKI67P1及TMEM272）以构建预后基因标记物，经受试者工作特征（Receiver Operating Characteristic, ROC）曲线分析证实该标记物具备良好的生存预测效能。
结论：本研究鉴定得到一组稳健的前列腺癌新型潜在关键基因，可为前列腺癌的早期诊断与预后评估提供可靠的生物标志物，并有望推动前列腺癌的分子靶向治疗研究。