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

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....

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Main Authors: Qi Hou, Zhi-Tong Bing, Cheng Hu, Mao-Yin Li, Ke-Hu Yang, Zu Mo, Xiang-Wei Xie, Ji-Lin Liao, Yan Lu, Shigeo Horie, Ming-Wu Lou
Format: Article
Language:English
Published: Elsevier 2018-06-01
Series:EBioMedicine
Online Access:http://www.sciencedirect.com/science/article/pii/S2352396418301658
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record_format Article
collection DOAJ
language English
format Article
sources DOAJ
author Qi Hou
Zhi-Tong Bing
Cheng Hu
Mao-Yin Li
Ke-Hu Yang
Zu Mo
Xiang-Wei Xie
Ji-Lin Liao
Yan Lu
Shigeo Horie
Ming-Wu Lou
spellingShingle Qi Hou
Zhi-Tong Bing
Cheng Hu
Mao-Yin Li
Ke-Hu Yang
Zu Mo
Xiang-Wei Xie
Ji-Lin Liao
Yan Lu
Shigeo Horie
Ming-Wu Lou
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
EBioMedicine
author_facet Qi Hou
Zhi-Tong Bing
Cheng Hu
Mao-Yin Li
Ke-Hu Yang
Zu Mo
Xiang-Wei Xie
Ji-Lin Liao
Yan Lu
Shigeo Horie
Ming-Wu Lou
author_sort Qi Hou
title 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
title_short 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_sort 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
publisher Elsevier
series EBioMedicine
issn 2352-3964
publishDate 2018-06-01
description 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. Keywords: RankProd, Artificial neural network, Genetic algorithm, Prostate cancer, Biomarker
url http://www.sciencedirect.com/science/article/pii/S2352396418301658
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spelling doaj-362b4eadd53246ebaf8345cdd61947d62020-11-25T01:49:52ZengElsevierEBioMedicine2352-39642018-06-0132234244RankProd Combined with Genetic Algorithm Optimized Artificial Neural Network Establishes a Diagnostic and Prognostic Prediction Model that Revealed C1QTNF3 as a Biomarker for Prostate CancerQi Hou0Zhi-Tong Bing1Cheng Hu2Mao-Yin Li3Ke-Hu Yang4Zu Mo5Xiang-Wei Xie6Ji-Lin Liao7Yan Lu8Shigeo Horie9Ming-Wu Lou10Post-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, JapanEvidence 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, ChinaDepartment of Urology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, ChinaDepartment of Urology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, ChinaEvidence 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, ChinaDepartment of Urology, Longgang Central Hospital, Shenzhen Clinical Medical Institute, Guangzhou University of Chinese Medicine, Shenzhen 518116, ChinaDepartment of Urology, Longgang Central Hospital, Shenzhen Clinical Medical Institute, Guangzhou University of Chinese Medicine, Shenzhen 518116, ChinaDepartment of Urology, Longgang Central Hospital, Shenzhen Clinical Medical Institute, Guangzhou University of Chinese Medicine, Shenzhen 518116, ChinaDepartment of Urology, Juntendo University Graduate School of Medicine, Tokyo 1138421, JapanDepartment of Urology, Juntendo University Graduate School of Medicine, Tokyo 1138421, JapanPost-Doctoral Research Center, Longgang Central Hospital, Shenzhen Clinical Medical Institute, Guangzhou University of Chinese Medicine, Shenzhen 518116, China; Corresponding author.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. Keywords: RankProd, Artificial neural network, Genetic algorithm, Prostate cancer, Biomarkerhttp://www.sciencedirect.com/science/article/pii/S2352396418301658