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Int. J. Mol. Sci. 2016, 17(5), 696; doi:10.3390/ijms17050696

Identification of More Feasible MicroRNA–mRNA Interactions within Multiple Cancers Using Principal Component Analysis Based Unsupervised Feature Extraction

Department of Physics, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan
Academic Editor: Martin Pichler
Received: 19 March 2016 / Revised: 13 April 2016 / Accepted: 27 April 2016 / Published: 10 May 2016
(This article belongs to the Special Issue MicroRNA Regulation)
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Abstract

MicroRNA(miRNA)–mRNA interactions are important for understanding many biological processes, including development, differentiation and disease progression, but their identification is highly context-dependent. When computationally derived from sequence information alone, the identification should be verified by integrated analyses of mRNA and miRNA expression. The drawback of this strategy is the vast number of identified interactions, which prevents an experimental or detailed investigation of each pair. In this paper, we overcome this difficulty by the recently proposed principal component analysis (PCA)-based unsupervised feature extraction (FE), which reduces the number of identified miRNA–mRNA interactions that properly discriminate between patients and healthy controls without losing biological feasibility. The approach is applied to six cancers: hepatocellular carcinoma, non-small cell lung cancer, esophageal squamous cell carcinoma, prostate cancer, colorectal/colon cancer and breast cancer. In PCA-based unsupervised FE, the significance does not depend on the number of samples (as in the standard case) but on the number of features, which approximates the number of miRNAs/mRNAs. To our knowledge, we have newly identified miRNA–mRNA interactions in multiple cancers based on a single common (universal) criterion. Moreover, the number of identified interactions was sufficiently small to be sequentially curated by literature searches. View Full-Text
Keywords: principal component analysis; feature extraction; miRNA–mRNA interaction; hepatocellular carcinoma; non-small cell lung cancer; esophageal squamous cell carcinoma; prostate cancer; colorectal/colon cancer; breast cancer principal component analysis; feature extraction; miRNA–mRNA interaction; hepatocellular carcinoma; non-small cell lung cancer; esophageal squamous cell carcinoma; prostate cancer; colorectal/colon cancer; breast cancer
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Taguchi, Y.-H. Identification of More Feasible MicroRNA–mRNA Interactions within Multiple Cancers Using Principal Component Analysis Based Unsupervised Feature Extraction. Int. J. Mol. Sci. 2016, 17, 696.

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