IDENTIFICATION AND FUNCTIONAL ANALYSIS OF microRNA AS PROSPECTIVE PROGNOSTIC MARKERS OF COLORECTAL CANCER


Maliborska 1, 2, T. Zadvornyi3

1 Communal non-profit enterprise «Prykarpatsky Clinical Oncology Center of the Ivano-Frankivsk Regional Council», Ivano-Frankivsk,
2 Department of Oncology, Ivano-Frankivsk National Medical University, Ivano-Frankivsk,
3 R.E. Kavetsky Institute of Experimental Pathology, Oncology and Radiobiology, NAS of Ukraine, Kyiv, Ukraine

DOI: https://doi.org/10.15407/oncology.2023.03.207

 

Summary. Colorectal cancer (CRC) is one of the main causes of death from malignant neoplasms. Despite the progress achieved in the diagnosis and treatment of CRC, long-term results still remain unsatisfactory. That is why the search for specific biomarkers for predicting the course of the tumor process and personalizing the therapy of this form of cancer is relevant. Aim: to identify microRNAs and determine their functional role in the occurrence and progression of CRC, with the aim of further using them as prognostic markers of this oncopathology. Object and methods: KEGG analysis of signaling pathways was performed using DIANA miRPath v.3.0. Analysis of the functional role of the target genes of the microRNAs was carried out using the DAVID online tool. The study of overall survival rates of patients with CRC depending on the expression level of the miRNAs was conducted using the ENCORI Pan-Cancer Analysis Platform databases. Results: based on the data presented in the databases miR2Disease, HMDD v.3.2, PhenomiR v.2.0, we identified that 3 microRNAs are involved in the occurrence and progression of CRC and can be considered as prognostic markers — hsa-mir-100-5p, hsa-mir-125b- 5p, hsa-mir- 200b-3p. It was established that the studied microRNAs are involved in the regulation of 24 signaling pathways, among which the strongest signaling associations were characteristic of Proteoglycans in cancer (hsa05205), Fatty acid biosynthesis (hsa00061), Fatty acid metabolism (hsa01212) and ErbB signaling pathway (hsa04012). It was found that high expression rates of hsa-mir-200b- 3p in CRC tissue are associated with better rates of overall patient survival. Conclusions: the results obtained by us indicate the need for further research of the role of hsamir- 100-5p, hsa-mir-125b-5p and hsa-mir-200b-3p in the mechanisms of the occurrence and progression of CRC and indicate the perspective of using hsa-mir-200b-3p as a prognostic marker associated with the aggressiveness of the tumor process.

 

References

  1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71: 209–49. doi: 10.3322/caac.21660.
  2. Hughes LAE, Simons CCJM, van den Brandt PA, et al. Lifestyle, diet, and colorectal cancer risk according to (epi)genetic instability: current evidence and future directions of molecular pathological epidemiology. Curr Colorectal Cancer Rep 2017; 13: 455–69. doi: 10.1007/s11888-017-0395-0.
  3. Yamamoto T, Kawada K, Obama K. Inflammation-related biomarkers for the prediction of prognosis in colorectal cancer patients. Int J Mol Sci 2021; 22: 8002. doi: 10.3390/ijms22158002.
  4. Oh HH, Joo YE Novel biomarkers for the diagnosis and prognosis of colorectal cancer. Intestinal Res 2020; 18 (2): 168–83. doi: 10.5217/ir.2019.00080.
  5. Bonfrate L, Altomare DF, Di Lena M, et al. MicroRNA in colorectal cancer: new perspectives for diagnosis, prognosis and treatment. J Gastrointestin Liver Dis 2013; 22(3): 311–20. PMID: 24078989
  1. Burt RW, Barthel JS, Dunn KB, et al. NCCN clinical practice guidelines in oncology. Colorectal cancer screening. JNCCN 2010; 8 (1): 8–61. doi: 10.6004/jnccn.2010.0003.
  2. Koncina E, Haan S, Rauh S, Letellier E. Prognostic and predictive molecular biomarkers for colorectal cancer: updates and challenges. Cancers 2020; 12 (2): 319. doi: 10.3390/cancers12020319.
  3. Gonzalez-Pons M, Cruz-Correa M. Colorectal cancer biomarkers: where are we now? BioMed Res Int 2015; 2015: 149014. doi: 10.1155/2015/149014.
  4. Zhu J, Xu Y, Liu S, et al. MicroRNAs associated with colon cancer: new potential prognostic markers and targets for therapy. Front Bioeng Biotechnol 2020; 8: 176. doi: 10.3389/fbioe.2020.00176.
  5. Lv Y, Duanmu J, Fu X, et al. Identifying a new microRNA signature as a prognostic biomarker in colon cancer. PloS one 2020; 15 (2): e0228575. doi: 10.1371/journal.pone.0228575.
  6. Hussen BM, Hidayat HJ, Salihi A, et al. MicroRNA: a signature for cancer progression. Biomed Pharmacother 2021; 138: 111528. doi: 10.1016/j.biopha.2021.111528.
  7. Xiao G, Tang H, Wei W, et al. Aberrant expression of microRNA-15a and microRNA-16 synergistically associates with tumor progression and prognosis in patients with colorectal cancer. Gastroenterol Res Pract 2014; 2014: 364549. doi: 10.1155/2014/364549.
  8. Schetter AJ, Leung SY, Sohn JJ, et al. MicroRNA expression profiles associated with prognosis and therapeutic outcome in colon adenocarcinoma. JAMA 2008; 299: 425–36. doi: 10.1001/jama.299.4.425.
  9. Lukianova NYu, Borikun TV, Bazas VM, et al. Circulating microRNAs: prospects of use for early diagnostics and monitoring of tumor process. Oncology 2019; 21 (3): 181–91.
  10. Chang L, Zhou G, Soufan O, Xia J. miRNet 2.0: network-based visual analytics for miRNA functional analysis and systems biology. Nucleic Acids Res 2020; 48 (W1): W244–251. doi: 10.1093/nar/gkaa467.
  11. Huang HY, Lin YC, Li J, et al. miRTarBase 2020: updates to the experimentally validated microRNA-target interaction database. Nucleic Acids Res 2020; 48 (D1), D148–154. doi: 10.1093/nar/gkz896.
  12. Karagkouni D, Paraskevopoulou MD, Chatzopoulos S, et al. DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA-gene interactions. Nucleic Acids Res 2018; 46 (D1): D239–45. doi: 10.1093/nar/gkx1141.
  13. Xiao F, Zuo Z, Cai G, et al. miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res 2009; 37: D105–10. doi: 10.1093/nar/gkn851.
  14. Wilkinson MD, Dumontier M, Aalbersberg IJ, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016; 3: 160018. doi: 10.1038/sdata.2016.18.
  15. Vlachos IS, Zagganas K, Paraskevopoulou MD, et al. DIANA-miRPath v3.0: deciphering microRNA function with experimental support. Nucleic Acids Res 2015; 43 (W1): W460–66. doi: 10.1093/nar/gkv403.
  16. Li JH, Liu S, Zhou H, et al. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res 2014; 42: D92–7. doi: 10.1093/nar/gkt1248.
  17. Cekaite L, Eide PW, Lind GE, et al. MicroRNAs as growth regulators, their function and biomarker status in colorectal cancer. Oncotarget 2016; 7: 6476–505. doi: 10.18632/oncotarget.6390.
  18. Ahrens TD, Bang-Christensen SR, Jørgensen AM, et al. The role of proteoglycans in cancer metastasis and circulating tumor cell analysis. Front Cell Dev Biol 2020; 8: 749. doi: 10.3389/fcell.2020.00749.
  19. Wang H, Xi Q, Wu G. Fatty acid synthase regulates invasion and metastasis of colorectal cancer via Wnt signaling pathway. Cancer Med 2016; (7): 1599–606. doi: 10.1002/cam4.711.
  20. Park H, Kim I, Kim J, Nam T. Induction of apoptosis and the regulation of ErbB signaling by laminarin in HT-29 human colon cancer cells. Int J Mol Med 2013; 32 (2): 291–5. doi: 10.3892/ijmm.2013.1409.
  21. Chen L, Wang X, Zhu Y, et al. miR‑200b‑3p inhibits proliferation and induces apoptosis in colorectal cancer by targeting Wnt1. Mol Med Rep 2018; 18: 2571–80. doi: 10.3892/mmr.2018.9287.

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