RankProd Combined with Genetic Algorithm Optimized Artificial Neural Network Establishes a Diagnostic and Prognostic Prediction Model that Revealed C1QTNF3 as a Biomarker for Prostate Cancer

• Published in: EBioMedicine Vol. 32; pp. 234 - 244
• Main Authors: Hou, Qi, Bing, Zhi-Tong, Hu, Cheng, Li, Mao-Yin, Yang, Ke-Hu, Mo, Zu, Xie, Xiang-Wei, Liao, Ji-Lin, Lu, Yan, Horie, Shigeo, Lou, Ming-Wu
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.06.2018; Elsevier
• Subjects: Advanced Basic Science; Artificial neural network; Biomarker; Biomarkers, Tumor - genetics; Fatty Acid-Binding Proteins - genetics; Gene Expression Regulation, Neoplastic; Genetic algorithm; Humans; Internal Medicine; Male; Neural Networks, Computer; Prognosis; Prostate cancer; Prostatic Neoplasms - diagnosis; Prostatic Neoplasms - genetics; Prostatic Neoplasms - pathology; RankProd; Receptors, G-Protein-Coupled - genetics; Receptors, Peptide - genetics; Research Paper; Tissue Array Analysis; Tumor Necrosis Factors - genetics; RankProd; Biomarker; Artificial neural network; Genetic algorithm; Prostate cancer
• ISSN: 2352-3964; 2352-3964
• DOI: 10.1016/j.ebiom.2018.05.010

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. •Fifteen gene signatures have high accuracy for diagnosing prostate cancer.•Fifteen gene signatures have promising values for the OS and RFS prediction of PCa patients.•C1QTNF3 is revealed as a promising biomarker of prostate cancer. 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 prostate cancer (PCa). Based on public microarray data of PCa patients' samples in the GEO and TCGA database, we used RankProd to conduct a meta-analysis of mRNA expression microarray data. Then, genetic algorithm optimized artificial neural network (GA-ANN) was introduced to establish a diagnostic prediction model and to filter candidate genes. The expression levels of candidate genes were validated by qPCR, Western Blot and Tissue microarray. In this study, we have developed a diagnostic and prognostic prediction model for PCa. C1QTNF3 was revealed as a promising biomarker for PCa.