Post-Doctoral Research Center, Longgang Central Hospital, Shenzhen Clinical Medical Institute, Guangzhou University of Chinese Medicine, Shenzhen 518116, China; Department of Urology, Juntendo University Graduate School of Medicine, Tokyo 1138421, Japan.
Evidence Based Medicine Center, School of Basic Medical Science, Lanzhou University, Lanzhou 730000, China; Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province, Lanzhou 730000, China.
EBioMedicine. 2018 Jun;32:234-244. doi: 10.1016/j.ebiom.2018.05.010. Epub 2018 Jun 1.
Prostate cancer (PCa) is the most commonly diagnosed cancer in males in the Western world. Although prostate-specific antigen (PSA) has been widely used as a biomarker for PCa diagnosis, its results can be controversial. Therefore, new biomarkers are needed to enhance the clinical management of PCa. From publicly available microarray data, differentially expressed genes (DEGs) were identified by meta-analysis with RankProd. Genetic algorithm optimized artificial neural network (GA-ANN) was introduced to establish a diagnostic prediction model and to filter candidate genes. The diagnostic and prognostic capability of the prediction model and candidate genes were investigated in both GEO and TCGA datasets. Candidate genes were further validated by qPCR, Western Blot and Tissue microarray. By RankProd meta-analyses, 2306 significantly up- and 1311 down-regulated probes were found in 133 cases and 30 controls microarray data. The overall accuracy rate of the PCa diagnostic prediction model, consisting of a 15-gene signature, reached up to 100% in both the training and test dataset. The prediction model also showed good results for the diagnosis (AUC = 0.953) and prognosis (AUC of 5 years overall survival time = 0.808) of PCa in the TCGA database. The expression levels of three genes, FABP5, C1QTNF3 and LPHN3, were validated by qPCR. C1QTNF3 high expression was further validated in PCa tissue by Western Blot and Tissue microarray. In the GEO datasets, C1QTNF3 was a good predictor for the diagnosis of PCa (GSE6956: AUC = 0.791; GSE8218: AUC = 0.868; GSE26910: AUC = 0.972). In the TCGA database, C1QTNF3 was significantly associated with PCa patient recurrence free survival (P < .001, AUC = 0.57). In this study, we have developed a diagnostic and prognostic prediction model for PCa. C1QTNF3 was revealed as a promising biomarker for PCa. This approach can be applied to other high-throughput data from different platforms for the discovery of oncogenes or biomarkers in different kinds of diseases.
前列腺癌（PCa）是西方男性中最常见的癌症。尽管前列腺特异性抗原（PSA）已被广泛用作 PCa 诊断的生物标志物，但它的结果可能存在争议。因此，需要新的生物标志物来增强 PCa 的临床管理。通过公开的微阵列数据，使用 RankProd 进行荟萃分析来识别差异表达基因（DEGs）。引入遗传算法优化的人工神经网络（GA-ANN）来建立诊断预测模型并筛选候选基因。在 GEO 和 TCGA 数据集上研究了预测模型和候选基因的诊断和预后能力。通过 qPCR、Western Blot 和组织微阵列进一步验证候选基因。通过 RankProd 荟萃分析，在 133 例病例和 30 例对照微阵列数据中发现 2306 个显著上调和 1311 个下调探针。由 15 个基因组成的 PCa 诊断预测模型的整体准确率在训练和测试数据集均达到 100%。该预测模型在 TCGA 数据库中还显示出对 PCa 诊断（AUC=0.953）和预后（5 年总生存时间的 AUC=0.808）的良好结果。通过 qPCR 验证了 FABP5、C1QTNF3 和 LPHN3 三个基因的表达水平。Western Blot 和组织微阵列进一步验证了 C1QTNF3 在 PCa 组织中的高表达。在 GEO 数据集，C1QTNF3 是 PCa 诊断的良好预测因子（GSE6956：AUC=0.791；GSE8218：AUC=0.868；GSE26910：AUC=0.972）。在 TCGA 数据库中，C1QTNF3 与 PCa 患者无复发生存率显著相关（P<.001，AUC=0.57）。在这项研究中，我们开发了一种用于 PCa 的诊断和预后预测模型。C1QTNF3 被揭示为 PCa 的一种很有前途的生物标志物。这种方法可以应用于不同平台的其他高通量数据，以发现不同类型疾病中的癌基因或生物标志物。
