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Genes 2018, 9(8), 397; https://doi.org/10.3390/genes9080397

An Integrated Approach for Identifying Molecular Subtypes in Human Colon Cancer Using Gene Expression Data

1
State Key Laboratory of Organ Failure Research, Division of Nephrology, Southern Medical University, Guangzhou 510515, China
2
Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
3
Network Information Center, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510655, China
4
Center for Systems Medical Genetics, Department of Obstetrics & Gynecology Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
5
Laboratory of Systems Neuroscience, Institute of Mental Health Southern Medical University, Southern Medical University, Guangzhou 510515, China
*
Author to whom correspondence should be addressed.
Received: 22 May 2018 / Revised: 18 July 2018 / Accepted: 27 July 2018 / Published: 2 August 2018
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Abstract

Identifying molecular subtypes of colorectal cancer (CRC) may allow for more rational, patient-specific treatment. Various studies have identified molecular subtypes for CRC using gene expression data, but they are inconsistent and further research is necessary. From a methodological point of view, a progressive approach is needed to identify molecular subtypes in human colon cancer using gene expression data. We propose an approach to identify the molecular subtypes of colon cancer that integrates denoising by the Bayesian robust principal component analysis (BRPCA) algorithm, hierarchical clustering by the directed bubble hierarchical tree (DBHT) algorithm, and feature gene selection by an improved differential evolution based feature selection method (DEFSW) algorithm. In this approach, the normal samples being completely and exclusively clustered into one class is considered to be the standard of reasonable clustering subtypes, and the feature selection pays attention to imbalances of samples among subtypes. With this approach, we identified the molecular subtypes of colon cancer on the mRNA gene expression dataset of 153 colon cancer samples and 19 normal control samples of the Cancer Genome Atlas (TCGA) project. The colon cancer was clustered into 7 subtypes with 44 feature genes. Our approach could identify finer subtypes of colon cancer with fewer feature genes than the other two recent studies and exhibits a generic methodology that might be applied to identify the subtypes of other cancers. View Full-Text
Keywords: subtypes of cancer; colon cancer; Bayesian robust principal component; hierarchical clustering; feature selection subtypes of cancer; colon cancer; Bayesian robust principal component; hierarchical clustering; feature selection
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Wang, W.-H.; Xie, T.-Y.; Xie, G.-L.; Ren, Z.-L.; Li, J.-M. An Integrated Approach for Identifying Molecular Subtypes in Human Colon Cancer Using Gene Expression Data. Genes 2018, 9, 397.

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