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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| Published in | EBioMedicine Vol. 32; pp. 234 - 244 |
|---|---|
| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier B.V
01.06.2018
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2352-3964 2352-3964 |
| DOI | 10.1016/j.ebiom.2018.05.010 |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. 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. 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 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. AbstractProstate 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. |
| Author | Mo, Zu Hou, Qi Lu, Yan Horie, Shigeo Bing, Zhi-Tong Yang, Ke-Hu Lou, Ming-Wu Liao, Ji-Lin Xie, Xiang-Wei Hu, Cheng Li, Mao-Yin |
| AuthorAffiliation | b Department of Urology, Juntendo University Graduate School of Medicine, Tokyo 1138421, Japan f Department of Urology, Longgang Central Hospital, Shenzhen Clinical Medical Institute, Guangzhou University of Chinese Medicine, Shenzhen 518116, China c Evidence Based Medicine Center, School of Basic Medical Science, Lanzhou University, Lanzhou 730000, China e Department of Urology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China d Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province, Lanzhou 730000, China a Post-Doctoral Research Center, Longgang Central Hospital, Shenzhen Clinical Medical Institute, Guangzhou University of Chinese Medicine, Shenzhen 518116, China |
| AuthorAffiliation_xml | – name: a Post-Doctoral Research Center, Longgang Central Hospital, Shenzhen Clinical Medical Institute, Guangzhou University of Chinese Medicine, Shenzhen 518116, China – name: f Department of Urology, Longgang Central Hospital, Shenzhen Clinical Medical Institute, Guangzhou University of Chinese Medicine, Shenzhen 518116, China – name: b Department of Urology, Juntendo University Graduate School of Medicine, Tokyo 1138421, Japan – name: c Evidence Based Medicine Center, School of Basic Medical Science, Lanzhou University, Lanzhou 730000, China – name: e Department of Urology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China – name: d Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province, Lanzhou 730000, China |
| Author_xml | – sequence: 1 givenname: Qi orcidid: 0000-0002-4597-7013 surname: Hou fullname: Hou, Qi organization: Post-Doctoral Research Center, Longgang Central Hospital, Shenzhen Clinical Medical Institute, Guangzhou University of Chinese Medicine, Shenzhen 518116, China – sequence: 2 givenname: Zhi-Tong surname: Bing fullname: Bing, Zhi-Tong organization: Evidence Based Medicine Center, School of Basic Medical Science, Lanzhou University, Lanzhou 730000, China – sequence: 3 givenname: Cheng surname: Hu fullname: Hu, Cheng organization: Department of Urology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China – sequence: 4 givenname: Mao-Yin surname: Li fullname: Li, Mao-Yin organization: Department of Urology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China – sequence: 5 givenname: Ke-Hu surname: Yang fullname: Yang, Ke-Hu organization: Evidence Based Medicine Center, School of Basic Medical Science, Lanzhou University, Lanzhou 730000, China – sequence: 6 givenname: Zu surname: Mo fullname: Mo, Zu organization: Department of Urology, Longgang Central Hospital, Shenzhen Clinical Medical Institute, Guangzhou University of Chinese Medicine, Shenzhen 518116, China – sequence: 7 givenname: Xiang-Wei surname: Xie fullname: Xie, Xiang-Wei organization: Department of Urology, Longgang Central Hospital, Shenzhen Clinical Medical Institute, Guangzhou University of Chinese Medicine, Shenzhen 518116, China – sequence: 8 givenname: Ji-Lin surname: Liao fullname: Liao, Ji-Lin organization: Department of Urology, Longgang Central Hospital, Shenzhen Clinical Medical Institute, Guangzhou University of Chinese Medicine, Shenzhen 518116, China – sequence: 9 givenname: Yan surname: Lu fullname: Lu, Yan organization: Department of Urology, Juntendo University Graduate School of Medicine, Tokyo 1138421, Japan – sequence: 10 givenname: Shigeo surname: Horie fullname: Horie, Shigeo organization: Department of Urology, Juntendo University Graduate School of Medicine, Tokyo 1138421, Japan – sequence: 11 givenname: Ming-Wu surname: Lou fullname: Lou, Ming-Wu email: mingwulou@sina.com organization: Post-Doctoral Research Center, Longgang Central Hospital, Shenzhen Clinical Medical Institute, Guangzhou University of Chinese Medicine, Shenzhen 518116, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29861410$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1056/NEJMoa0810696 10.1109/72.846725 10.1371/journal.pone.0076746 10.3322/caac.21338 10.1172/JCI38012 10.1016/j.ebiom.2015.07.017 10.1093/bioinformatics/btl476 10.3322/caac.21442 10.1016/j.juro.2008.06.038 10.1186/s12885-017-3103-1 10.1016/S0928-0987(97)10028-8 10.1001/jama.295.7.801 10.3390/ijms150916544 10.18632/oncotarget.6141 10.3892/etm.2015.2527 10.1371/journal.pone.0134006 10.1093/bioinformatics/btx292 10.1002/phar.1333 10.1158/0008-5472.CAN-07-2608 10.1093/bioinformatics/bts108 10.1111/gbb.12224 10.1007/s13273-016-0018-x 10.1073/pnas.1013699108 10.1016/j.cmpb.2015.11.009 10.18632/oncotarget.8217 10.1073/pnas.1215870110 10.1158/0008-5472.CAN-10-0021 10.1002/jcc.21471 10.1002/ijc.23016 10.1126/scisignal.2004088 10.1111/bju.12262 10.1089/omi.2011.0118 10.1016/j.eururo.2015.01.009 10.1371/journal.pone.0018640 10.3322/caac.21349 10.1016/j.ccr.2010.05.026 |
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| Keywords | RankProd Biomarker Artificial neural network Genetic algorithm Prostate cancer |
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| References | Cima, Schiess, Wild, Kaelin, Schuffler, Lange (bb0040) 2011; 108 Wu, Mei, Wen, Liao, Chen, Shen (bb0130) 2010; 31 Lee, Lindquist, Segal, Covinsky (bb0165) 2006; 295 Ankerst, Groskopf, Day, Blase, Rittenhouse, Pollock (bb0045) 2008; 180 Hou, Lin, Huang, Li, Feng, Mao (bb0205) 2015; 10 Taylor, Schultz, Hieronymus, Gopalan, Xiao, Carver (bb0150) 2010; 18 Zhang, Xiang, Liu, Fang, Ruan, Sun (bb0010) 2004; 23 Wu, Schroeder, Ma, Cutie, Wu, Salunga (bb0180) 2013; 110 Andriole, Grubb, Buys, Chia, Church, Fouad (bb0025) 2009; 360 Forootan, Bao, Forootan, Kamalian, Zhang, Bee (bb0190) 2010; 36 Bourquin, Schmidli, van Hoogevest, Leuenberger (bb0100) 1998; 7 Bismar, Alshalalfa, Petersen, Teng, Gerke, Bakkar (bb0185) 2014; 113 Lin, Xu, Tian, Gao, Chen, Gu (bb0195) 2007; 121 Huang, Zheng, Qin, Cheng, Chen, Wan (bb0175) 2010; 120 Stattin, Vickers, Sjoberg, Johansson, Granfors, Johansson (bb0035) 2015; 68 Wallace, Prueitt, Yi, Howe, Gillespie, Yfantis (bb0135) 2008; 68 Rossadams, Lamb, Dunning, Halim, Lindberg, Massie (bb0155) 2015; 2 Nanni, Brahnam, Lumini (bb0060) 2012; 28 Rau, Hsu, Lin, Atique, Fuad, Wei (bb0110) 2016; 125 Yao, Ren, Zhang, Du, Zhu, Yu (bb0055) 2015; 6 Peng, Fang, Li, Ou, Jiang, Lu (bb0105) 2016; 7 Planche, Bacac, Provero, Fusco, Delorenzi, Stehle (bb0145) 2011; 6 Bruxel, Salatino-Oliveira, Akutagava-Martins, Tovo-Rodrigues, Genro, Zeni (bb0200) 2015; 14 Miller, Siegel, Lin, Mariotto, Kramer, Rowland (bb0020) 2016; 66 Siegel, Miller, Di (bb0005) 2018; 68 Bengio, Bengio (bb0065) 2000; 11 Strand, Orntoft, Sorensen (bb0050) 2014; 15 Del Carratore, Jankevics, Eisinga, Heskes, Hong, Breitling (bb0070) 2017; 33 Lee, Kim, Park, Kim, Myeong, Kwon (bb0090) 2016; 12 Hong, Breitling, McEntee, Wittner, Nemhauser, Chory (bb0075) 2006; 22 Saadah, Chedid, Sohail, Nazzal, Al Kaabi, Rahmani (bb0115) 2014; 34 Wang, Xia, Jia, Sawyers, Yao, Wang-Rodriquez (bb0140) 2010; 70 Gao, Aksoy, Dogrusoz, Dresdner, Gross, Sumer (bb0170) 2013; 6 Yu, Wang, Han, He (bb0160) 2012; 16 Sun, Yuan, Zhang, Ma, Zhang, Tian (bb0095) 2015; 10 Chen, Yang, Chiu (bb0125) 2009 Loeb, Gashti, Catalona (bb0030) 2009; 27 Chow, Alias, Jamal (bb0085) 2017; 17 Haupt, Haupt, Haupt (bb0120) 1998; vol. 2 Chen, Zheng, Baade, Zhang, Zeng, Bray (bb0015) 2016; 66 Peri, Devarajan, Yang, Knudson, Balachandran (bb0080) 2013; 8 Andriole (10.1016/j.ebiom.2018.05.010_bb0025) 2009; 360 Haupt (10.1016/j.ebiom.2018.05.010_bb0120) 1998; vol. 2 Peri (10.1016/j.ebiom.2018.05.010_bb0080) 2013; 8 Wallace (10.1016/j.ebiom.2018.05.010_bb0135) 2008; 68 Siegel (10.1016/j.ebiom.2018.05.010_bb0005) 2018; 68 Wang (10.1016/j.ebiom.2018.05.010_bb0140) 2010; 70 Wu (10.1016/j.ebiom.2018.05.010_bb0130) 2010; 31 Rossadams (10.1016/j.ebiom.2018.05.010_bb0155) 2015; 2 Huang (10.1016/j.ebiom.2018.05.010_bb0175) 2010; 120 Wu (10.1016/j.ebiom.2018.05.010_bb0180) 2013; 110 Miller (10.1016/j.ebiom.2018.05.010_bb0020) 2016; 66 Lin (10.1016/j.ebiom.2018.05.010_bb0195) 2007; 121 Yao (10.1016/j.ebiom.2018.05.010_bb0055) 2015; 6 Chen (10.1016/j.ebiom.2018.05.010_bb0015) 2016; 66 Strand (10.1016/j.ebiom.2018.05.010_bb0050) 2014; 15 Taylor (10.1016/j.ebiom.2018.05.010_bb0150) 2010; 18 Stattin (10.1016/j.ebiom.2018.05.010_bb0035) 2015; 68 Bengio (10.1016/j.ebiom.2018.05.010_bb0065) 2000; 11 Rau (10.1016/j.ebiom.2018.05.010_bb0110) 2016; 125 Sun (10.1016/j.ebiom.2018.05.010_bb0095) 2015; 10 Peng (10.1016/j.ebiom.2018.05.010_bb0105) 2016; 7 Ankerst (10.1016/j.ebiom.2018.05.010_bb0045) 2008; 180 Bourquin (10.1016/j.ebiom.2018.05.010_bb0100) 1998; 7 Chen (10.1016/j.ebiom.2018.05.010_bb0125) 2009 Bruxel (10.1016/j.ebiom.2018.05.010_bb0200) 2015; 14 Saadah (10.1016/j.ebiom.2018.05.010_bb0115) 2014; 34 Forootan (10.1016/j.ebiom.2018.05.010_bb0190) 2010; 36 Cima (10.1016/j.ebiom.2018.05.010_bb0040) 2011; 108 Hou (10.1016/j.ebiom.2018.05.010_bb0205) 2015; 10 Loeb (10.1016/j.ebiom.2018.05.010_bb0030) 2009; 27 Yu (10.1016/j.ebiom.2018.05.010_bb0160) 2012; 16 Gao (10.1016/j.ebiom.2018.05.010_bb0170) 2013; 6 Hong (10.1016/j.ebiom.2018.05.010_bb0075) 2006; 22 Lee (10.1016/j.ebiom.2018.05.010_bb0090) 2016; 12 Nanni (10.1016/j.ebiom.2018.05.010_bb0060) 2012; 28 Del Carratore (10.1016/j.ebiom.2018.05.010_bb0070) 2017; 33 Chow (10.1016/j.ebiom.2018.05.010_bb0085) 2017; 17 Zhang (10.1016/j.ebiom.2018.05.010_bb0010) 2004; 23 Lee (10.1016/j.ebiom.2018.05.010_bb0165) 2006; 295 Bismar (10.1016/j.ebiom.2018.05.010_bb0185) 2014; 113 Planche (10.1016/j.ebiom.2018.05.010_bb0145) 2011; 6 |
| References_xml | – volume: 113 start-page: 309 year: 2014 end-page: 319 ident: bb0185 article-title: Interrogation of ERG gene rearrangements in prostate cancer identifies a prognostic 10-gene signature with relevant implication to patients' clinical outcome publication-title: BJU Int. – volume: 22 start-page: 2825 year: 2006 end-page: 2827 ident: bb0075 article-title: RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis publication-title: Bioinformatics – volume: 18 start-page: 11 year: 2010 end-page: 22 ident: bb0150 article-title: Integrative genomic profiling of human prostate cancer publication-title: Cancer Cell – volume: 295 start-page: 801 year: 2006 end-page: 808 ident: bb0165 article-title: Development and validation of a prognostic index for 4-year mortality in older adults publication-title: J. Am. Med. Assoc. – volume: 14 start-page: 419 year: 2015 end-page: 427 ident: bb0200 article-title: LPHN3 and attention-deficit/hyperactivity disorder: a susceptibility and pharmacogenetic study publication-title: Genes Brain Behav. – volume: 180 start-page: 1303 year: 2008 end-page: 1308 ident: bb0045 article-title: Predicting prostate cancer risk through incorporation of prostate cancer gene 3 publication-title: J. Urol. – volume: 10 year: 2015 ident: bb0205 article-title: CTRP3 stimulates proliferation and anti-apoptosis of prostate cells through PKC signaling pathways publication-title: PLoS One – volume: 68 start-page: 207 year: 2015 end-page: 213 ident: bb0035 article-title: Improving the specificity of screening for lethal prostate Cancer using prostate-specific antigen and a panel of Kallikrein markers: a nested case-control study publication-title: Eur. Urol. – volume: 2 start-page: 1133 year: 2015 end-page: 1144 ident: bb0155 article-title: Integration of copy number and transcriptomics provides risk stratification in prostate cancer: a discovery and validation cohort study publication-title: Ebiomedicine – year: 2009 ident: bb0125 article-title: Artificial neural network prediction for cancer survival time by gene expression data publication-title: Paper Presented at: Bioinformatics and Biomedical Engineering, 2009 ICBBE 2009 3rd International Conference on (IEEE). – volume: 6 year: 2011 ident: bb0145 article-title: Identification of prognostic molecular features in the reactive stroma of human breast and prostate Cancer publication-title: PLoS One – volume: 31 start-page: 1956 year: 2010 end-page: 1968 ident: bb0130 article-title: A self-adaptive genetic algorithm-artificial neural network algorithm with leave-one-out cross validation for descriptor selection in QSAR study publication-title: J. Comput. Chem. – volume: vol. 2 year: 1998 ident: bb0120 article-title: Practical Genetic Algorithms – volume: 360 start-page: 1310 year: 2009 ident: bb0025 article-title: Mortality results from a randomized prostate-cancer screening trial publication-title: N. Engl. J. Med. – volume: 12 start-page: 139 year: 2016 end-page: 148 ident: bb0090 article-title: Systematic identification of novel biomarker signatures associated with acquired erlotinib resistance in cancer cells publication-title: Mol. Cell. Toxicol. – volume: 7 start-page: 5 year: 1998 end-page: 16 ident: bb0100 article-title: Advantages of Artificial Neural Networks (ANNs) as alternative modelling technique for data sets showing non-linear relationships using data from a galenical study on a solid dosage form publication-title: Eur. J. Pharm. Sci. – volume: 7 start-page: 22939 year: 2016 end-page: 22947 ident: bb0105 article-title: A scoring system based on artificial neural network for predicting 10-year survival in stage II a colon cancer patients after radical surgery publication-title: Oncotarget – volume: 110 start-page: 6121 year: 2013 end-page: 6126 ident: bb0180 article-title: Development and validation of a 32-gene prognostic index for prostate cancer progression publication-title: Proc. Natl. Acad. Sci. U. S. A. – volume: 108 start-page: 3342 year: 2011 end-page: 3347 ident: bb0040 article-title: Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer publication-title: Proc. Natl. Acad. Sci. U. S. A. – volume: 28 start-page: 1151 year: 2012 end-page: 1157 ident: bb0060 article-title: Combining multiple approaches for gene microarray classification publication-title: Bioinformatics – volume: 68 start-page: 927 year: 2008 end-page: 936 ident: bb0135 article-title: Tumor immunobiological differences in prostate cancer between African-American and European-American men publication-title: Cancer Res. – volume: 36 start-page: 69 year: 2010 end-page: 76 ident: bb0190 article-title: Atelocollagen-delivered siRNA targeting the FABP5 gene as an experimental therapy for prostate cancer in mouse xenografts publication-title: Int. J. Oncol. – volume: 120 start-page: 223 year: 2010 end-page: 241 ident: bb0175 article-title: Genetic and epigenetic silencing of SCARA5 may contribute to human hepatocellular carcinoma by activating FAK signaling publication-title: J. Clin. Investig. – volume: 10 start-page: 743 year: 2015 end-page: 748 ident: bb0095 article-title: Crosstalk analysis of pathways in breast cancer using a network model based on overlapping differentially expressed genes publication-title: Exp. Ther. Med. – volume: 11 start-page: 550 year: 2000 end-page: 557 ident: bb0065 article-title: Taking on the curse of dimensionality in joint distributions using neural networks publication-title: IEEE Trans. Neural Netw. – volume: 68 year: 2018 ident: bb0005 article-title: Cancer statistics, 2018 publication-title: CA Cancer J Clin. – volume: 125 start-page: 58 year: 2016 end-page: 65 ident: bb0110 article-title: Development of a web-based liver cancer prediction model for type II diabetes patients by using an artificial neural network publication-title: Comput. Methods Prog. Biomed. – volume: 33 start-page: 2774 year: 2017 end-page: 2775 ident: bb0070 article-title: RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets publication-title: Bioinformatics – volume: 17 start-page: 120 year: 2017 ident: bb0085 article-title: Meta-analysis of gene expression in relapsed childhood B-acute lymphoblastic leukemia publication-title: BMC Cancer – volume: 16 start-page: 284 year: 2012 end-page: 287 ident: bb0160 article-title: clusterProfiler: an R package for comparing biological themes among gene clusters publication-title: OMICS J. Integr. Biol. – volume: 34 start-page: 251 year: 2014 end-page: 259 ident: bb0115 article-title: Palivizumab prophylaxis during nosocomial outbreaks of respiratory syncytial virus in a neonatal intensive care unit: predicting effectiveness with an artificial neural network model publication-title: Pharmacotherapy – volume: 27 start-page: 64 year: 2009 end-page: 66 ident: bb0030 article-title: Exclusion of inflammation in the differential diagnosis of an elevated prostate-specific antigen (PSA) publication-title: Urol. Oncol.-Semin. Orig. Investig. – volume: 66 start-page: 115 year: 2016 ident: bb0015 article-title: Cancer statistics in China, 2015 publication-title: CA Cancer J. Clin. – volume: 15 start-page: 16544 year: 2014 end-page: 16576 ident: bb0050 article-title: Prognostic DNA methylation markers for prostate Cancer publication-title: Int. J. Mol. Sci. – volume: 6 start-page: 40611 year: 2015 end-page: 40621 ident: bb0055 article-title: Identification of specific DNA methylation sites on the Y-chromosome as biomarker in prostate cancer publication-title: Oncotarget – volume: 70 start-page: 6448 year: 2010 end-page: 6455 ident: bb0140 article-title: In silico estimates of tissue components in surgical samples based on expression profiling data publication-title: Cancer Res. – volume: 66 start-page: 271 year: 2016 ident: bb0020 article-title: Cancer treatment and survivorship statistics, 2016 publication-title: CA Cancer J. Clin. – volume: 6 start-page: pl1 year: 2013 ident: bb0170 article-title: Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal publication-title: Sci. Signal. – volume: 121 start-page: 2596 year: 2007 end-page: 2605 ident: bb0195 article-title: Identification of candidate prostate cancer biomarkers in prostate needle biopsy specimens using proteomic analysis publication-title: Int. J. Cancer – volume: 8 year: 2013 ident: bb0080 article-title: Meta-analysis identifies NF-kappaB as a therapeutic target in renal cancer publication-title: PLoS One – volume: 23 start-page: 555 year: 2004 ident: bb0010 article-title: Trends analysis of common urologic neoplasm incidence of elderly people in Shanghai, 1973–1999 publication-title: Chinese J. Cancer – volume: 360 start-page: 1310 year: 2009 ident: 10.1016/j.ebiom.2018.05.010_bb0025 article-title: Mortality results from a randomized prostate-cancer screening trial publication-title: N. Engl. J. Med. doi: 10.1056/NEJMoa0810696 – volume: 11 start-page: 550 year: 2000 ident: 10.1016/j.ebiom.2018.05.010_bb0065 article-title: Taking on the curse of dimensionality in joint distributions using neural networks publication-title: IEEE Trans. Neural Netw. doi: 10.1109/72.846725 – volume: 8 year: 2013 ident: 10.1016/j.ebiom.2018.05.010_bb0080 article-title: Meta-analysis identifies NF-kappaB as a therapeutic target in renal cancer publication-title: PLoS One doi: 10.1371/journal.pone.0076746 – volume: 66 start-page: 115 year: 2016 ident: 10.1016/j.ebiom.2018.05.010_bb0015 article-title: Cancer statistics in China, 2015 publication-title: CA Cancer J. Clin. doi: 10.3322/caac.21338 – volume: 120 start-page: 223 year: 2010 ident: 10.1016/j.ebiom.2018.05.010_bb0175 article-title: Genetic and epigenetic silencing of SCARA5 may contribute to human hepatocellular carcinoma by activating FAK signaling publication-title: J. Clin. Investig. doi: 10.1172/JCI38012 – volume: 2 start-page: 1133 year: 2015 ident: 10.1016/j.ebiom.2018.05.010_bb0155 article-title: Integration of copy number and transcriptomics provides risk stratification in prostate cancer: a discovery and validation cohort study publication-title: Ebiomedicine doi: 10.1016/j.ebiom.2015.07.017 – volume: 22 start-page: 2825 year: 2006 ident: 10.1016/j.ebiom.2018.05.010_bb0075 article-title: RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis publication-title: Bioinformatics doi: 10.1093/bioinformatics/btl476 – volume: 68 year: 2018 ident: 10.1016/j.ebiom.2018.05.010_bb0005 article-title: Cancer statistics, 2018 publication-title: CA Cancer J Clin. doi: 10.3322/caac.21442 – volume: 36 start-page: 69 year: 2010 ident: 10.1016/j.ebiom.2018.05.010_bb0190 article-title: Atelocollagen-delivered siRNA targeting the FABP5 gene as an experimental therapy for prostate cancer in mouse xenografts publication-title: Int. J. Oncol. – volume: 23 start-page: 555 year: 2004 ident: 10.1016/j.ebiom.2018.05.010_bb0010 article-title: Trends analysis of common urologic neoplasm incidence of elderly people in Shanghai, 1973–1999 publication-title: Chinese J. Cancer – volume: 180 start-page: 1303 year: 2008 ident: 10.1016/j.ebiom.2018.05.010_bb0045 article-title: Predicting prostate cancer risk through incorporation of prostate cancer gene 3 publication-title: J. Urol. doi: 10.1016/j.juro.2008.06.038 – volume: 17 start-page: 120 year: 2017 ident: 10.1016/j.ebiom.2018.05.010_bb0085 article-title: Meta-analysis of gene expression in relapsed childhood B-acute lymphoblastic leukemia publication-title: BMC Cancer doi: 10.1186/s12885-017-3103-1 – volume: 7 start-page: 5 year: 1998 ident: 10.1016/j.ebiom.2018.05.010_bb0100 article-title: Advantages of Artificial Neural Networks (ANNs) as alternative modelling technique for data sets showing non-linear relationships using data from a galenical study on a solid dosage form publication-title: Eur. J. Pharm. Sci. doi: 10.1016/S0928-0987(97)10028-8 – volume: vol. 2 year: 1998 ident: 10.1016/j.ebiom.2018.05.010_bb0120 – volume: 295 start-page: 801 year: 2006 ident: 10.1016/j.ebiom.2018.05.010_bb0165 article-title: Development and validation of a prognostic index for 4-year mortality in older adults publication-title: J. Am. Med. Assoc. doi: 10.1001/jama.295.7.801 – volume: 15 start-page: 16544 year: 2014 ident: 10.1016/j.ebiom.2018.05.010_bb0050 article-title: Prognostic DNA methylation markers for prostate Cancer publication-title: Int. J. Mol. Sci. doi: 10.3390/ijms150916544 – volume: 6 start-page: 40611 year: 2015 ident: 10.1016/j.ebiom.2018.05.010_bb0055 article-title: Identification of specific DNA methylation sites on the Y-chromosome as biomarker in prostate cancer publication-title: Oncotarget doi: 10.18632/oncotarget.6141 – volume: 10 start-page: 743 year: 2015 ident: 10.1016/j.ebiom.2018.05.010_bb0095 article-title: Crosstalk analysis of pathways in breast cancer using a network model based on overlapping differentially expressed genes publication-title: Exp. Ther. Med. doi: 10.3892/etm.2015.2527 – volume: 10 year: 2015 ident: 10.1016/j.ebiom.2018.05.010_bb0205 article-title: CTRP3 stimulates proliferation and anti-apoptosis of prostate cells through PKC signaling pathways publication-title: PLoS One doi: 10.1371/journal.pone.0134006 – volume: 33 start-page: 2774 year: 2017 ident: 10.1016/j.ebiom.2018.05.010_bb0070 article-title: RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets publication-title: Bioinformatics doi: 10.1093/bioinformatics/btx292 – volume: 34 start-page: 251 year: 2014 ident: 10.1016/j.ebiom.2018.05.010_bb0115 article-title: Palivizumab prophylaxis during nosocomial outbreaks of respiratory syncytial virus in a neonatal intensive care unit: predicting effectiveness with an artificial neural network model publication-title: Pharmacotherapy doi: 10.1002/phar.1333 – volume: 68 start-page: 927 year: 2008 ident: 10.1016/j.ebiom.2018.05.010_bb0135 article-title: Tumor immunobiological differences in prostate cancer between African-American and European-American men publication-title: Cancer Res. doi: 10.1158/0008-5472.CAN-07-2608 – volume: 28 start-page: 1151 year: 2012 ident: 10.1016/j.ebiom.2018.05.010_bb0060 article-title: Combining multiple approaches for gene microarray classification publication-title: Bioinformatics doi: 10.1093/bioinformatics/bts108 – volume: 14 start-page: 419 year: 2015 ident: 10.1016/j.ebiom.2018.05.010_bb0200 article-title: LPHN3 and attention-deficit/hyperactivity disorder: a susceptibility and pharmacogenetic study publication-title: Genes Brain Behav. doi: 10.1111/gbb.12224 – year: 2009 ident: 10.1016/j.ebiom.2018.05.010_bb0125 article-title: Artificial neural network prediction for cancer survival time by gene expression data – volume: 12 start-page: 139 year: 2016 ident: 10.1016/j.ebiom.2018.05.010_bb0090 article-title: Systematic identification of novel biomarker signatures associated with acquired erlotinib resistance in cancer cells publication-title: Mol. Cell. Toxicol. doi: 10.1007/s13273-016-0018-x – volume: 108 start-page: 3342 year: 2011 ident: 10.1016/j.ebiom.2018.05.010_bb0040 article-title: Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer publication-title: Proc. Natl. Acad. Sci. U. S. A. doi: 10.1073/pnas.1013699108 – volume: 125 start-page: 58 year: 2016 ident: 10.1016/j.ebiom.2018.05.010_bb0110 article-title: Development of a web-based liver cancer prediction model for type II diabetes patients by using an artificial neural network publication-title: Comput. Methods Prog. Biomed. doi: 10.1016/j.cmpb.2015.11.009 – volume: 7 start-page: 22939 year: 2016 ident: 10.1016/j.ebiom.2018.05.010_bb0105 article-title: A scoring system based on artificial neural network for predicting 10-year survival in stage II a colon cancer patients after radical surgery publication-title: Oncotarget doi: 10.18632/oncotarget.8217 – volume: 110 start-page: 6121 year: 2013 ident: 10.1016/j.ebiom.2018.05.010_bb0180 article-title: Development and validation of a 32-gene prognostic index for prostate cancer progression publication-title: Proc. Natl. Acad. Sci. U. S. A. doi: 10.1073/pnas.1215870110 – volume: 70 start-page: 6448 year: 2010 ident: 10.1016/j.ebiom.2018.05.010_bb0140 article-title: In silico estimates of tissue components in surgical samples based on expression profiling data publication-title: Cancer Res. doi: 10.1158/0008-5472.CAN-10-0021 – volume: 31 start-page: 1956 year: 2010 ident: 10.1016/j.ebiom.2018.05.010_bb0130 article-title: A self-adaptive genetic algorithm-artificial neural network algorithm with leave-one-out cross validation for descriptor selection in QSAR study publication-title: J. Comput. Chem. doi: 10.1002/jcc.21471 – volume: 121 start-page: 2596 year: 2007 ident: 10.1016/j.ebiom.2018.05.010_bb0195 article-title: Identification of candidate prostate cancer biomarkers in prostate needle biopsy specimens using proteomic analysis publication-title: Int. J. Cancer doi: 10.1002/ijc.23016 – volume: 27 start-page: 64 year: 2009 ident: 10.1016/j.ebiom.2018.05.010_bb0030 article-title: Exclusion of inflammation in the differential diagnosis of an elevated prostate-specific antigen (PSA) publication-title: Urol. Oncol.-Semin. Orig. Investig. – volume: 6 start-page: pl1 year: 2013 ident: 10.1016/j.ebiom.2018.05.010_bb0170 article-title: Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal publication-title: Sci. Signal. doi: 10.1126/scisignal.2004088 – volume: 113 start-page: 309 year: 2014 ident: 10.1016/j.ebiom.2018.05.010_bb0185 article-title: Interrogation of ERG gene rearrangements in prostate cancer identifies a prognostic 10-gene signature with relevant implication to patients' clinical outcome publication-title: BJU Int. doi: 10.1111/bju.12262 – volume: 16 start-page: 284 year: 2012 ident: 10.1016/j.ebiom.2018.05.010_bb0160 article-title: clusterProfiler: an R package for comparing biological themes among gene clusters publication-title: OMICS J. Integr. Biol. doi: 10.1089/omi.2011.0118 – volume: 68 start-page: 207 year: 2015 ident: 10.1016/j.ebiom.2018.05.010_bb0035 article-title: Improving the specificity of screening for lethal prostate Cancer using prostate-specific antigen and a panel of Kallikrein markers: a nested case-control study publication-title: Eur. Urol. doi: 10.1016/j.eururo.2015.01.009 – volume: 6 year: 2011 ident: 10.1016/j.ebiom.2018.05.010_bb0145 article-title: Identification of prognostic molecular features in the reactive stroma of human breast and prostate Cancer publication-title: PLoS One doi: 10.1371/journal.pone.0018640 – volume: 66 start-page: 271 year: 2016 ident: 10.1016/j.ebiom.2018.05.010_bb0020 article-title: Cancer treatment and survivorship statistics, 2016 publication-title: CA Cancer J. Clin. doi: 10.3322/caac.21349 – volume: 18 start-page: 11 year: 2010 ident: 10.1016/j.ebiom.2018.05.010_bb0150 article-title: Integrative genomic profiling of human prostate cancer publication-title: Cancer Cell doi: 10.1016/j.ccr.2010.05.026 |
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| Snippet | 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... AbstractProstate cancer (PCa) is the most commonly diagnosed cancer in males in the Western world. Although prostate-specific antigen (PSA) has been widely... |
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| SubjectTerms | 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 |
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| 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 |
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