Risk analysis of colorectal cancer incidence by gene expression analysis

PeerJ

Chung-Tay Yao¹  Kang-Hua Chen²  Hsiu-Ling Chou³  Hueng-Chuen Fan⁴  Sui-Lung Su⁵  Chen-En Jian⁵  Hung-Che Lin⁵  Chi-Ming Chu⁵  Wei-Chuan Shangkuan⁵  Huan-Ming Hsu⁵  Yu-Tien Chang⁵  Chi-Wen Chang⁶  Ya-Fang Liu⁷ 

[1] Department of Emergency, Cathay General Hospital and School of Medicine, Fu-Jen Catholic University,Taipei, Taiwan;Department of Nursing, College of Medicine, Chang Gung University, Taoyuan, Taiwan;Department of Nursing, Far Eastern Memorial Hospital and Oriental Institute of Technology, New Taipei City, Taiwan;Department of Pediatrics, Tungs’ Taichung MetroHarbor Hospital, Wuchi, Taichung, Taiwan;National Defense Medical Center, Taipei, Taiwan;RN, 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, Taiwan;Section of Biostatistics and Informatics, Department of Epidemiology, School of Public Health, National Defense Medical Center, Taipei, Taiwan;

关键词: Cancer;    Microarray analysis;    Gene expression;    Gene ontology;    Prediction analysis for microarrays;   

DOI  :  10.7717/peerj.3003

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown