Risk analysis of colorectal cancer incidence by gene expression analysis
Background Colorectal cancer (CRC) is one of the leading cancers worldwide. Several studies have performed microarray data analyses for cancer classification and prognostic analyses. Microarray assays also enable the identification of gene signatures for molecular characterization and treatment pred...
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| Format: | Article |
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PeerJ Inc.
2017-02-01
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| Online Access: | https://peerj.com/articles/3003.pdf |
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| author | Wei-Chuan Shangkuan Hung-Che Lin Yu-Tien Chang Chen-En Jian Hueng-Chuen Fan Kang-Hua Chen Ya-Fang Liu Huan-Ming Hsu Hsiu-Ling Chou Chung-Tay Yao Chi-Ming Chu Sui-Lung Su Chi-Wen Chang |
| author_facet | Wei-Chuan Shangkuan Hung-Che Lin Yu-Tien Chang Chen-En Jian Hueng-Chuen Fan Kang-Hua Chen Ya-Fang Liu Huan-Ming Hsu Hsiu-Ling Chou Chung-Tay Yao Chi-Ming Chu Sui-Lung Su Chi-Wen Chang |
| author_sort | Wei-Chuan Shangkuan |
| collection | DOAJ |
| description | Background Colorectal cancer (CRC) is one of the leading cancers worldwide. Several studies have performed microarray data analyses for cancer classification and prognostic analyses. Microarray assays also enable the identification of gene signatures for molecular characterization and treatment prediction. Objective Microarray gene expression data from the online Gene Expression Omnibus (GEO) database were used to to distinguish colorectal cancer from normal colon tissue samples. Methods We collected microarray data from the GEO database to establish colorectal cancer microarray gene expression datasets for a combined analysis. Using the Prediction Analysis for Microarrays (PAM) method and the GSEA MSigDB resource, we analyzed the 14,698 genes that were identified through an examination of their expression values between normal and tumor tissues. Results Ten genes (ABCG2, AQP8, SPIB, CA7, CLDN8, SCNN1B, SLC30A10, CD177, PADI2, and TGFBI) were found to be good indicators of the candidate genes that correlate with CRC. From these selected genes, an average of six significant genes were obtained using the PAM method, with an accuracy rate of 95%. The results demonstrate the potential of utilizing a model with the PAM method for data mining. After a detailed review of the published reports, the results confirmed that the screened candidate genes are good indicators for cancer risk analysis using the PAM method. Conclusions Six genes were selected with 95% accuracy to effectively classify normal and colorectal cancer tissues. We hope that these results will provide the basis for new research projects in clinical practice that aim to rapidly assess colorectal cancer risk using microarray gene expression analysis. |
| first_indexed | 2024-03-09T07:59:51Z |
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| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T07:59:51Z |
| publishDate | 2017-02-01 |
| publisher | PeerJ Inc. |
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| series | PeerJ |
| spelling | doaj.art-a85d6338c42649e086809ddc343d0d032023-12-03T00:49:02ZengPeerJ Inc.PeerJ2167-83592017-02-015e300310.7717/peerj.3003Risk analysis of colorectal cancer incidence by gene expression analysisWei-Chuan Shangkuan0Hung-Che Lin1Yu-Tien Chang2Chen-En Jian3Hueng-Chuen Fan4Kang-Hua Chen5Ya-Fang Liu6Huan-Ming Hsu7Hsiu-Ling Chou8Chung-Tay Yao9Chi-Ming Chu10Sui-Lung Su11Chi-Wen Chang12National Defense Medical Center, Taipei, TaiwanNational Defense Medical Center, Taipei, TaiwanNational Defense Medical Center, Taipei, TaiwanNational Defense Medical Center, Taipei, TaiwanDepartment of Pediatrics, Tungs’ Taichung MetroHarbor Hospital, Wuchi, Taichung, TaiwanDepartment of Nursing, College of Medicine, Chang Gung University, Taoyuan, TaiwanSection of Biostatistics and Informatics, Department of Epidemiology, School of Public Health, National Defense Medical Center, Taipei, TaiwanNational Defense Medical Center, Taipei, TaiwanDepartment of Nursing, Far Eastern Memorial Hospital and Oriental Institute of Technology, New Taipei City, TaiwanDepartment of Emergency, Cathay General Hospital and School of Medicine, Fu-Jen Catholic University, Taipei, TaiwanNational Defense Medical Center, Taipei, TaiwanNational Defense Medical Center, Taipei, TaiwanRN, PhD, Assistant Professor, School of Nursing, College of Medicine, Chang Gung University & Assistant Research Fellow, Division of Endocrinology, Department of Pediatrics, Linkou Chang Gung Memorial Hospital, TaiwanBackground Colorectal cancer (CRC) is one of the leading cancers worldwide. Several studies have performed microarray data analyses for cancer classification and prognostic analyses. Microarray assays also enable the identification of gene signatures for molecular characterization and treatment prediction. Objective Microarray gene expression data from the online Gene Expression Omnibus (GEO) database were used to to distinguish colorectal cancer from normal colon tissue samples. Methods We collected microarray data from the GEO database to establish colorectal cancer microarray gene expression datasets for a combined analysis. Using the Prediction Analysis for Microarrays (PAM) method and the GSEA MSigDB resource, we analyzed the 14,698 genes that were identified through an examination of their expression values between normal and tumor tissues. Results Ten genes (ABCG2, AQP8, SPIB, CA7, CLDN8, SCNN1B, SLC30A10, CD177, PADI2, and TGFBI) were found to be good indicators of the candidate genes that correlate with CRC. From these selected genes, an average of six significant genes were obtained using the PAM method, with an accuracy rate of 95%. The results demonstrate the potential of utilizing a model with the PAM method for data mining. After a detailed review of the published reports, the results confirmed that the screened candidate genes are good indicators for cancer risk analysis using the PAM method. Conclusions Six genes were selected with 95% accuracy to effectively classify normal and colorectal cancer tissues. We hope that these results will provide the basis for new research projects in clinical practice that aim to rapidly assess colorectal cancer risk using microarray gene expression analysis.https://peerj.com/articles/3003.pdfCancerMicroarray analysisGene expressionGene ontologyPrediction analysis for microarrays |
| spellingShingle | Wei-Chuan Shangkuan Hung-Che Lin Yu-Tien Chang Chen-En Jian Hueng-Chuen Fan Kang-Hua Chen Ya-Fang Liu Huan-Ming Hsu Hsiu-Ling Chou Chung-Tay Yao Chi-Ming Chu Sui-Lung Su Chi-Wen Chang Risk analysis of colorectal cancer incidence by gene expression analysis PeerJ Cancer Microarray analysis Gene expression Gene ontology Prediction analysis for microarrays |
| title | Risk analysis of colorectal cancer incidence by gene expression analysis |
| title_full | Risk analysis of colorectal cancer incidence by gene expression analysis |
| title_fullStr | Risk analysis of colorectal cancer incidence by gene expression analysis |
| title_full_unstemmed | Risk analysis of colorectal cancer incidence by gene expression analysis |
| title_short | Risk analysis of colorectal cancer incidence by gene expression analysis |
| title_sort | risk analysis of colorectal cancer incidence by gene expression analysis |
| topic | Cancer Microarray analysis Gene expression Gene ontology Prediction analysis for microarrays |
| url | https://peerj.com/articles/3003.pdf |
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