Identification of chemoresistance-associated miRNAs in breast cancer
Authors Lou W, Liu J, Ding B, Xu L, Fan W
Received 6 May 2018
Accepted for publication 8 August 2018
Published 23 October 2018 Volume 2018:10 Pages 4747—4757
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 4
Editor who approved publication: Dr Antonella D'Anneo
Weiyang Lou,1–3,* Jingxing Liu,4,* Bisha Ding,1–3 Liang Xu,1–3 Weimin Fan1–3,5
1Program of Innovative Cancer Therapeutics, Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China; 2Key Laboratory of Organ Transplantation, Zhejiang Province, Hangzhou 310003, China; 3Key Laboratory of Combined Multi-organ Transplantation, Ministry of Public Health, Hangzhou 310000, China; 4Department of Intensive Care Unit, Changxing People’s Hospital of Zhejiang, Zhejiang Province, Huzhou 313100, China; 5Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, SC 29425, USA
*These authors contributed equally to this work
Background: Neoadjuvant chemotherapy (NAC) is an effective therapeutic regimen for patients with breast cancer. However, some individuals cannot benefit from NAC because of drug resistance. To date, valid strategies about enhancing sensitivity of breast cancer to NAC are still scarce. miRNAs have been reported to proverbially be involved in the onset and development of malignancies including drug resistance.
Methods: GSE73736 was downloaded from the GEO database. Student’s t-test was conducted to acquire differentially expressed-miRNAs (DE-miRNAs). Potential target genes of DE-miRNAs were predicted by miRTarBase. Gene Ontology annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses for these target genes were performed by database for annotation, visualization, and integrated discovery. Protein–protein interaction network was constructed by STRING database and visualized through Cytoscape software. The hub target gene-miRNA network was also established by Cytoscape software. Next, the expression of potential functional miRNAs in breast cancer cell lines and tissues was determined. Finally, the roles of miR-3617-3p, miR-3136-3p, and miR-520b in modulating breast cancer chemoresistance were further examined.
Results: A total of 123 DE-miRNAs were identified, including 60 upregulated miRNAs and 63 downregulated miRNAs in the chemoresistant breast cancer group when compared with the chemosensitive group. Six hundred and seventeen and 1,146 potential target genes for the top 10 most upregulated and downregulated miRNAs were predicted, respectively. Enrichment analyses revealed that these target genes were enriched in some cancer-associated or chemoresistance-associated pathways, such as MAPK signaling pathway, wnt signaling pathway, and p53 signaling pathway. MAPK1 and PRDM10 were identified as hub genes in the protein–protein interaction network. The top 25 hub genes were potentially regulated by 16 DE-miRNAs, among which miR-3617-3p and miR-3136-3p were commonly upregulated, whereas miR-520b was downregulated in two chemoresistant breast cancer cells compared with chemosensitive cell. By analyzing TCGA data, we found that expression of miR-3136-3p and miR-520b was increased and decreased in breast cancer tissues, respectively. Moreover, functional experiments demonstrated that miR-3136-3p and miR-3617-3p could reduce chemosensitivity of breast cancer, whereas miR-520b could reverse chemoresistance.
Conclusion: The present study, based on bioinformatics analysis and experimental validation, brings to light novel mechanisms of breast cancer NAC resistance.
Keywords: breast cancer, chemoresistance, neoadjuvant chemotherapy miRNA, bioinformatics analysis, enrichment analysis
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