Screening candidate microRNA-mRNA regulatory pairs for predicting the response to chemoradiotherapy in rectal cancer by a bioinformatics approach

Extensive efforts have been undertaken in search of biomarkers for predicting the chemoradiotherapy response in rectal cancer. However, most attention on treatment efficiency prediction in carcinoma is addicted to single or limited molecules. Network biomarkers are considered to outperform single mo...

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Bibliographic Details
Published in:Scientific reports 2017-09, Vol.7 (1), p.11312-11, Article 11312
Main Authors: Peng, Qiliang, Zhu, Junjia, Shen, Peipei, Yao, Wenyan, Lei, Yu, Zou, Li, Xu, Yingying, Shen, Yuntian, Zhu, Yaqun
Format: Article
Language:English
Subjects:
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Bioinformatics
Biomarkers
Chemoradiotherapy
Chemotherapy
Colorectal cancer
Gene expression
Humanities and Social Sciences
Ionizing radiation
miRNA
multidisciplinary
Radiation therapy
Rectum
Science
Science (multidisciplinary)
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Summary:Extensive efforts have been undertaken in search of biomarkers for predicting the chemoradiotherapy response in rectal cancer. However, most attention on treatment efficiency prediction in carcinoma is addicted to single or limited molecules. Network biomarkers are considered to outperform single molecules in predictive power. In this study, candidate microRNAs (miRNAs) were identified from the PubMed citations and miRNA expression profiles. Targets of miRNAs were obtained from four experimentally confirmed interactions and three computationally predicted databases. Functional enrichment analysis of all the targets revealed their associations with chemoradiotherapy response, indicating they could be promising biomarkers. Two lists of key target mRNAs of the candidate miRNAs were retrieved from protein–protein interaction (PPI) network and mRNA expression profiles, respectively. Pathway analysis and literature validation revealed that the mRNA lists were highly related to the ionizing radiation. The above miRNAs along with the key miRNA targets provide potential miRNA-mRNA regulatory pairs as network biomarkers in which all the network components may be used for predicting the chemoradiotherapy response. These results demonstrated that the network biomarkers could provide a useful model for predicting the chemoradiotherapy response and help in further understanding the molecular basis of response differences, which should be prioritized for further study.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-017-11840-7