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Identification of circulating miRNAs as novel prognostic biomarkers for bladder cancer

1 Department of Urology, The Affiliated Wenling Hospital of Wenzhou Medical University, Wenling 317500, China
2 Department of Urology, Zhejiang Rongjun Hospital, Jiaxing 314000, China

Special Issues: Advanced Big Data Analysis for Precision Medicine

MicroRNAs (miRNAs) are a type of ncRNAs with 18–25 nucleotides in length and reported to play crucial roles in human cancers. Bladder cancer is one of the most common causes of cancer-related death. The discovery of new early biomarkers for BC may improve the patients' response to the treatment thus obtaining higher outcomes. The present study identified 7 miRNAs were up-regulated in bladder serum cancer samples compared to normal samples, including hsa-miR-185-5p, hsa-miR-663a, hsa-miR-30c-5p, hsa-miR-3648, hsa-miR-1270, hsa-miR-200c-3p, and hsa-miR-29c-5p. The dysregulation of these miRNAs were correlated to advanced stage and overall survival time in bladder cancer patients. Moreover, we identified a predictive model to predict the prognosis of bladder cancer. Kaplan-Meier survival curve analyses showed that bladder cancer patients with high-risk scores had significantly worse overall survival time than bladder patients with lower risk scores. Furthermore, we constructed a miRNA-mRNA regulating network. Bioinformatics analysis showed these miRNAs were involved in regulating sarcomere organization, positive regulation of multicellular organism growth, phosphorylation, phosphatidylinositol-mediated signaling, and peroxisome proliferator activated receptor signaling pathway. We thought this study could provide novel noninvasive early biomarkers for bladder cancer.
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References

1. A. Q. Gomes, S. Nolasco and H. Soares, Non-coding RNAs: Multi-tasking molecules in the cell, Int. J. Mol. Sci., 14 (2013), 16010-16039.

2. N. Asano, J. Matsuzaki, M. Ichikawa, et al., A serum microRNA classifier for the diagnosis of sarcomas of various histological subtypes, Nat. Commun., 10 (2019), 1299.

3. L. Falzone, S. Candido, R. Salemi, et al., Computational identification of microRNAs associated to both epithelial to mesenchymal transition and NGAL/MMP-9 pathways in bladder cancer, Oncotarget, 7 (2016), 72758-72766.

4. L. Falzone, G. Lupo, G. R. M. L. Rosa, et al., Identification of Novel MicroRNAs and Their Diagnostic and Prognostic Significance in Oral Cancer, Cancers, 11 (2019), 610.

5. P. Anand, A. B. Kunnumakara, C. Sundaram, et al., Cancer is a Preventable Disease that Requires Major Lifestyle Changes, Pharm. Res., 25 (2008), 2097-2116.

6. T. Inamoto, H. Uehara, Y. Akao, et al., A Panel of MicroRNA Signature as a Tool for Predicting Survival of Patients with Urothelial Carcinoma of the Bladder, Dis. Markers, 2018 (2018), 5468672.

7. Y. He, J. Lin, D. Kong, et al., Current State of Circulating MicroRNAs as Cancer Biomarkers, Clin. Chem., 61 (2015), 1138-1155.

8. X. Jiang, L. Du, W. Duan, et al., Serum microRNA expression signatures as novel noninvasive biomarkers for prediction and prognosis of muscle-invasive bladder cancer, Oncotarget, 7 (2016), 36733-36742.

9. W. Usuba, F. Urabe, Y. Yamamoto, et al., Circulating miRNA panels for specific and early detection in bladder cancer, Cancer Sci., 110 (2019), 408-419.

10. D. W. Huang, B. T. Sherman and R. A. Lempicki, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources, Nat. Protoc., 4 (2009), 44-57.

11. Z. Wang, M. A. Jensen and J. C. Zenklusen, A Practical Guide to The Cancer Genome Atlas (TCGA), in Statistical Genomics, Humana Press, New York, (2016), 111-141.

12. N. Wong, Y. Chen, S. Chen, et al., OncomiR: An online resource for exploring pan-cancer microRNA dysregulation, Bioinformatics, 34 (2018), 713-715.

13. C. Sticht, C. D. La Torre, A. Parveen, et al., miRWalk: An online resource for prediction of microRNA binding sites, Plos One, 13 (2018), e0206239.

14. N. Wong and X. Wang, miRDB: An online resource for microRNA target prediction and functional annotations, Nucleic Acids Res., 43 (2015), D146-D152.

15. J. Yang, J. Li, P. Shao, et al., starBase: A database for exploring microRNA-mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data, Nucleic Acids Res., 39 (2011), D202-D209.

16. P. Shannon, A. Markiel, O. Ozier, et al., Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks, Genome Res., 13 (2003), 2498-2504.

17. S. Ostadrahimi, S. Fayaz, M. Parvizhamidi, et al., Downregulation of miR-1266-5P, miR-185-5P and miR-30c-2 in prostatic cancer tissue and cell lines, Oncol. Lett., 15 (2018), 8157-8164.

18. F. Qu, X. Cui, Y. Hong, et al., MicroRNA-185 suppresses proliferation, invasion, migration, and tumorigenicity of human prostate cancer cells through targeting androgen receptor, Mol. Cell. Biochem., 377 (2013), 121-130.

19. C. Zhang, B. Chen, A. Jiao, et al., miR-663a inhibits tumor growth and invasion by regulating TGF-beta1 in hepatocellular carcinoma, BMC Cancer, 18 (2018), 1179.

20. Y. Zhang, X. Xu, M. Zhang, et al., MicroRNA-663a is downregulated in non-small cell lung cancer and inhibits proliferation and invasion by targeting JunD, BMC Cancer, 16 (2016), 315.

21. H. Tang, M. Deng, Y. Tang, et al., miR-200b and miR-200c as Prognostic Factors and Mediators of Gastric Cancer Cell Progression, Clin. Cancer Res., 19 (2013), 5602-5612.

© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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