Risk analysis of colorectal cancer incidence by gene expression analysis

PeerJ

article

Wei-Chuan Shangkuan¹  Hung-Che Lin¹  Yu-Tien Chang¹  Chen-En Jian¹  Hueng-Chuen Fan⁴  Kang-Hua Chen⁷  Ya-Fang Liu³  Huan-Ming Hsu¹  Hsiu-Ling Chou^1,10  Chung-Tay Yao^1,11  Chi-Ming Chu¹  Sui-Lung Su¹  Chi-Wen Chang^1,12 

[1] National Defense Medical Center;Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center;Section of Biostatistics and Informatics, Department of Epidemiology, School of Public Health, National Defense Medical Center;Department of Pediatrics, Tungs’ Taichung MetroHarbor Hospital;Department of Medical Research, Tungs’ Taichung MetroHarbor Hospital;Department of Nursing, Jen-Teh Junior College of Medicine, Nursing and Management;Department of Nursing, College of Medicine, Chang Gung University;Department of Education and Research, Shin Kong Wu Ho-Su Memorial Hospital;Division of General Surgery, Department of Surgery, Tri-Service General Hospital Songshan Branch, National Defense Medical Center;Department of Nursing, Far Eastern Memorial Hospital and Oriental Institute of Technology;Department of Emergency, Cathay General Hospital and School of Medicine, Fu-Jen Catholic University;School of Nursing, College of Medicine, Chang Gung University, Division of Endocrinology, Department of Pediatrics, Linkou Chang Gung Memorial Hospital

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

DOI  :  10.7717/peerj.3003

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

BackgroundColorectal 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.ObjectiveMicroarray gene expression data from the online Gene Expression Omnibus (GEO) database were used to to distinguish colorectal cancer from normal colon tissue samples.MethodsWe 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.ResultsTen 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.ConclusionsSix 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】

CC BY