Risk analysis of colorectal cancer incidence by gene expression analysis

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

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

30 Scopus citations

Abstract

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 thePAMmethod, with an accuracy rate of 95%. The results demonstrate the potential of utilizing a model with thePAMmethod 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.

Original languageEnglish
Article numbere3003
JournalPeerJ
Volume2017
Issue number2
DOIs
StatePublished - 2017

Bibliographical note

Publisher Copyright:
© 2017 Shangkuan et al.

Keywords

  • Cancer
  • Gene expression
  • Gene ontology
  • Microarray analysis
  • Prediction analysis for microarrays

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