Research Papers:

Identification of biomarker microRNAs for predicting the response of colorectal cancer to neoadjuvant chemoradiotherapy based on microRNA regulatory network

Yaqun Zhu, Qiliang Peng, Yuxin Lin, Li Zou, Peipei Shen, Feifei Chen, Ming Min, Li Shen, Jiajia Chen and Bairong Shen _

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Oncotarget. 2017; 8:2233-2248. https://doi.org/10.18632/oncotarget.13659

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Yaqun Zhu1,2,3,*, Qiliang Peng1,2,3,*, Yuxin Lin4,*, Li Zou1,2,3, Peipei Shen1,2,3, Feifei Chen4, Ming Min4, Li Shen4,5, Jiajia Chen6, Bairong Shen4,7

1Department of Radiotherapy and Oncology, Second Affiliated Hospital of Soochow University, Suzhou, China

2Institute of Radiotherapy and Oncology, Soochow University, Suzhou, China

3Suzhou Key Laboratory for Radiation Oncology, Suzhou, China

4Center for Systems Biology, Soochow University, Suzhou, China

5Institute of Biological Sciences and Biotechnology, Donghua University, Shanghai, China

6School of Chemistry, Biology and Material Engineering, Suzhou University of Science and Technology, Suzhou, China

7Key laboratory of Systems Biology, Shanghai Institute of Biological Sciences, Chinese Academy of Sciences, Shanghai, China

*These authors contributed equally to this work

Correspondence to:

Bairong Shen, email: [email protected]

Yaqun Zhu, email: [email protected]

Keywords: biomarker microRNA, colorectal cancer, neoadjuvant chemoradiotherapy, microRNA regulatory network, bioinformatics model

Received: September 09, 2016     Accepted: November 18, 2016     Published: November 26, 2016


Preoperative radiotherapy or chemoradiotherapy has become a standard procedure for treatment of patients with locally advanced colorectal cancer (CRC). However, patients’ responses to treatment are different and personalized. MicroRNAs (miRNAs) are promising biomarkers for predicting personalized responses. In this study, we collected 30 publicly reported miRNAs associated with chemoradiotherapy of CRC. We extracted 46 differentially expressed miRNAs from samples of responders and non-responders to preoperative radiotherapy from the Gene Expression Omnibus dataset (Student’s t test, p-value < 0.05 and |fold-change| > 2). We performed a systematic and integrative bioinformatics analysis to identify biomarker miRNAs for prediction of CRC responses to chemoradiotherapy. Using the bioinformatics model, miR-198, miR-765, miR-671-5p, miR-630, miR-371-5p, miR-575, miR-202, miR-483-5p and miR-513a-5p were screened as putative biomarkers for treatment response. Literature validation and functional enrichment analysis were exploited to confirm the reliability of the predicted miRNAs. Quantitative polymerase chain reaction showed that seven of the candidates were significantly differentially expressed between radiosensitive and insensitive CRC cell lines. The unique target genes of miR-198 and miR-765 were altered significantly upon transfection of specific miRNA mimics in the radiosensitive cell line. These results demonstrated the predictive power of our model and suggested that miR-198, miR-765, miR-630, miR-371-5p, miR-575, miR-202 and miR-513a-5p could be used for predicting the response of CRC to preoperative chemoradiotherapy.

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