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Identifying miRNA biomarkers for breast cancer and ovarian cancer: a text mining perspective

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Abstract

Background

microRNA (miRNAs) are small, non-coding RNAs that mediate post-transcriptional gene silencing. Numerous studies have demonstrated the critical role of miRNAs in the development of breast cancer and ovarian cancer. To reduce potential bias from individual studies, a more comprehensive approach of exploring miRNAs in cancer research is essential. This study aims to explore the role of miRNAs in the development of breast cancer and ovarian cancer.

Methods

Abstracts of the publications were tokenized and the biomedical terms (miRNA, gene, disease, species) were identified and extracted for vectorization. Predictive analyses were conducted with four machine learning models: K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), and Naïve Bayes. Both holdout validation and cross-validation were utilized. Feature importance will be identified for miRNA-cancer networks construction.

Results

We found that miR-182 is highly specific to female cancers. miR-182 targets different genes in regulating breast cancer and ovarian cancer. Naïve Bayes provided a promising prediction model for breast cancer and ovarian cancer with miRNAs and genes combination, with an accuracy score greater than 60%. Feature importance identified miR-155 and miR-199 are critical for breast cancer and ovarian cancer prediction, with miR-155 being highly related to breast cancer, whereas miR-199 being more associated with ovarian cancer.

Conclusion

Our approach effectively identified potential miRNA biomarkers associated with breast cancer and ovarian cancer, providing a solid foundation for generating novel research hypotheses and guiding future experimental studies.

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Data availability

The code used in this study is available upon request.

References

  1. Lagos-Quintana M, Rauhut R, Lendeckel W, Tuschl T (2001) Identification of novel genes coding for small expressed RNAs. Science 294(5543):853–858

    Article  CAS  PubMed  Google Scholar 

  2. Lau NC, Lim LP, Weinstein EG, Bartel DP (2001) An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science 294(5543):858–862

    Article  CAS  PubMed  Google Scholar 

  3. Lee RC, Feinbaum RL, Ambros V (1993) The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 75(5):843–854

    Article  CAS  PubMed  Google Scholar 

  4. Yoshida K, Miki Y (2004) Role of BRCA1 and BRCA2 as regulators of DNA repair, transcription, and cell cycle in response to DNA damage. Cancer Sci 95(11):866–871

    Article  CAS  PubMed  Google Scholar 

  5. Lee SWL, Paoletti C, Campisi M, Osaki T, Adriani G, Kamm RD, Mattu C, Chiono V (2019) MicroRNA delivery through nanoparticles. J Control Release 313:80–95

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Kaboli PJ, Rahmat A, Ismail P, Ling KH (2015) MicroRNA-based therapy and breast cancer: a comprehensive review of novel therapeutic strategies from diagnosis to treatment. Pharmacol Res 97:104–121

    Article  CAS  PubMed  Google Scholar 

  7. Maryam M, Naemi M, Hasani SS (2021) A comprehensive review on oncogenic miRNAs in breast cancer. J Genet. https://doi.org/10.1007/s12041-021-01265-7

    Article  PubMed  Google Scholar 

  8. Alshamrani AA (2020) Roles of microRNAs in ovarian cancer tumorigenesis: two decades later, what have we learned? Front Oncol 10:1084

    Article  PubMed  PubMed Central  Google Scholar 

  9. Jin Y, Wei J, Xu S, Guan F, Yin L, Zhu H (2019) miR-210-3p regulates cell growth and affects cisplatin sensitivity in human ovarian cancer cells via targeting E2F3. Mol Med Rep 19(6):4946–4954

    CAS  PubMed  Google Scholar 

  10. Nguyen VHL, Yue C, Du KY, Salem M, O’Brien J, Peng C (2020) The role of microRNAs in epithelial ovarian cancer metastasis. Int J Mol Sci. https://doi.org/10.3390/ijms21197093

    Article  PubMed  PubMed Central  Google Scholar 

  11. Shi H, Shen H, Xu J, Zhao S, Yao S, Jiang N (2018) MiR-143-3p suppresses the progression of ovarian cancer. Am J Transl Res 10(3):866–874

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI (2015) Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 13:8–17

    Article  CAS  PubMed  Google Scholar 

  13. Kocher M, Ruge MI, Galldiks N, Lohmann P (2020) Applications of radiomics and machine learning for radiotherapy of malignant brain tumors. Strahlenther Onkol 196(10):856–867

    Article  PubMed  PubMed Central  Google Scholar 

  14. Yang Y, Xu L, Sun L, Zhang P, Farid SS (2022) Machine learning application in personalised lung cancer recurrence and survivability prediction. Comput Struct Biotechnol J 20:1811–1820

    Article  PubMed  PubMed Central  Google Scholar 

  15. Fang C, Pan Y, Zhao L, Niu Z, Guo Q, Zhao B (2022) A machine learning-based approach to predict prognosis and length of hospital stay in adults and children with traumatic brain injury: retrospective cohort study. J Med Internet Res 24(12):e41819

    Article  PubMed  PubMed Central  Google Scholar 

  16. Olsen CR, Mentz RJ, Anstrom KJ, Page D, Patel PA (2020) Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure. Am Heart J 229:1–17

    Article  PubMed  Google Scholar 

  17. Rigatti SJ (2017) Random forest. J Insur Med 47(1):31–39

    Article  PubMed  Google Scholar 

  18. Wei CH, Kao HY, Lu Z (2013) PubTator: a web-based text mining tool for assisting biocuration. Nucleic Acids Res 41:W518–W522

    Article  PubMed  PubMed Central  Google Scholar 

  19. Griffiths-Jones S (2004) The microRNA registry. Nucleic Acids Res 32:D109-111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ambros V, Bartel B, Bartel DP, Burge CB, Carrington JC, Chen X, Dreyfuss G, Eddy SR, Griffiths-Jones S, Marshall M, Matzke M, Ruvkun G, Tuschl T (2003) A uniform system for microRNA annotation. RNA 9(3):277–279

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lo Surdo P, Iannuccelli M, Contino S, Castagnoli L, Licata L, Cesareni G, Perfetto L (2022) SIGNOR 3.0, the SIGnaling network open resource 3.0: 2022 update. Nucleic Acids Res. https://doi.org/10.1093/nar/gkac883

    Article  PubMed Central  Google Scholar 

  22. Silveri L, Tilly G, Vilotte JL, Le Provost F (2006) MicroRNA involvement in mammary gland development and breast cancer. Reprod Nutr Dev 46(5):549–556

    Article  CAS  PubMed  Google Scholar 

  23. Cissell KA, Rahimi Y, Shrestha S, Hunt EA, Deo SK (2008) Bioluminescence-based detection of microRNA, miR21 in breast cancer cells. Anal Chem 80(7):2319–2325

    Article  CAS  PubMed  Google Scholar 

  24. Bearfoot JL, Choong DY, Gorringe KL, Campbell IG (2008) Genetic analysis of cancer-implicated MicroRNA in ovarian cancer. Clin Cancer Res 14(22):7246–7250

    Article  CAS  PubMed  Google Scholar 

  25. Eitan R, Kushnir M, Lithwick-Yanai G, David MB, Hoshen M, Glezerman M, Hod M, Sabah G, Rosenwald S, Levavi H (2009) Tumor microRNA expression patterns associated with resistance to platinum based chemotherapy and survival in ovarian cancer patients. Gynecol Oncol 114(2):253–259

    Article  CAS  PubMed  Google Scholar 

  26. Lu C, Zhao Y, Wang J, Shi W, Dong F, Xin Y, Zhao X, Liu C (2021) Breast cancer cell-derived extracellular vesicles transfer miR-182-5p and promote breast carcinogenesis via the CMTM7/EGFR/AKT axis. Mol Med 27(1):78

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wu X, Chen H, Wu M, Peng S, Zhang L (2020) Downregulation of miR-182-5p inhibits the proliferation and invasion of triple-negative breast cancer cells through regulating TLR4/NF-kappaB pathway activity by targeting FBXW7. Ann Transl Med 8(16):995

    Article  PubMed  PubMed Central  Google Scholar 

  28. Soheilifar MH, Vaseghi H, Seif F, Ariana M, Ghorbanifar S, Habibi N, Papari Barjasteh F, Pornour M (2021) Concomitant overexpression of mir-182-5p and mir-182-3p raises the possibility of IL-17-producing Treg formation in breast cancer by targeting CD3d, ITK, FOXO1, and NFATs: a meta-analysis and experimental study. Cancer Sci 112(2):589–603

    Article  CAS  PubMed  Google Scholar 

  29. Lin G, Li J, Cai J, Zhang H, Xin Q, Wang N, Xie W, Zhang Y, Xu N (2021) RNA-binding protein MBNL2 regulates cancer cell metastasis through MiR-182-MBNL2-AKT pathway. J Cancer 12(22):6715–6726

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Chiang CH, Chu PY, Hou MF, Hung WC (2016) MiR-182 promotes proliferation and invasion and elevates the HIF-1alpha-VEGF-A axis in breast cancer cells by targeting FBXW7. Am J Cancer Res 6(8):1785–1798

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Sang Y, Chen B, Song X, Li Y, Liang Y, Han D, Zhang N, Zhang H, Liu Y, Chen T, Li C, Wang L, Zhao W et al (2021) circRNA_0025202 regulates tamoxifen sensitivity and tumor progression via regulating the miR-182-5p/FOXO3a axis in breast cancer. Mol Ther 29(12):3525–3527

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Jia XN, Yin SD, Wei Y, Chen L (2020) MiR-182-5p inhibited proliferation and migration of ovarian cancer cells by targeting BNIP3. Eur Rev Med Pharmacol Sci 24(15):7912

    PubMed  Google Scholar 

  33. Wang A, Jin C, Li H, Qin Q, Li L (2018) LncRNA ADAMTS9-AS2 regulates ovarian cancer progression by targeting miR-182-5p/FOXF2 signaling pathway. Int J Biol Macromol 120(Pt B):1705–1713

    Article  CAS  PubMed  Google Scholar 

  34. Lu W, Lu T, Wei X (2016) Downregulation of DNMT3a expression increases miR-182-induced apoptosis of ovarian cancer through caspase-3 and caspase-9-mediated apoptosis and DNA damage response. Oncol Rep 36(6):3597–3604

    Article  CAS  PubMed  Google Scholar 

  35. Lu JT, Tan CC, Wu XR, He R, Zhang X, Wang QS, Li XQ, Zhang R, Feng YM (2020) FOXF2 deficiency accelerates the visceral metastasis of basal-like breast cancer by unrestrictedly increasing TGF-beta and miR-182-5p. Cell Death Differ 27(10):2973–2987

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Zhang X, Ma G, Liu J, Zhang Y (2017) MicroRNA-182 promotes proliferation and metastasis by targeting FOXF2 in triple-negative breast cancer. Oncol Lett 14(4):4805–4811

    Article  PubMed  PubMed Central  Google Scholar 

  37. Yu J, Shen W, Gao B, Zhao H, Xu J, Gong B (2017) MicroRNA-182 targets FOXF2 to promote the development of triple-negative breast cancer. Neoplasma 64(2):209–215

    Article  CAS  PubMed  Google Scholar 

  38. O’Bryan S, Dong S, Mathis JM, Alahari SK (2017) The roles of oncogenic miRNAs and their therapeutic importance in breast cancer. Eur J Cancer 72:1–11

    Article  CAS  PubMed  Google Scholar 

  39. Feng X, Wang Z, Fillmore R, Xi Y (2014) MiR-200, a new star miRNA in human cancer. Cancer Lett 344(2):166–173

    Article  CAS  PubMed  Google Scholar 

  40. Arghiani N, Matin MM (2021) miR-21: a key small molecule with great effects in combination cancer therapy. Nucleic Acid Ther 31(4):271–283

    Article  CAS  PubMed  Google Scholar 

  41. Wang J, Wu J (2012) Role of miR-155 in breast cancer. Front Biosci 17(6):2350–2355

    Article  Google Scholar 

  42. Bacci M, Giannoni E, Fearns A, Ribas R, Gao Q, Taddei ML, Pintus G, Dowsett M, Isacke CM, Martin LA, Chiarugi P, Morandi A (2016) miR-155 drives metabolic reprogramming of er+ breast cancer cells following long-term estrogen deprivation and predicts clinical response to aromatase inhibitors. Cancer Res 76(6):1615–1626

    Article  CAS  PubMed  Google Scholar 

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Funding

This study has no external funding source. The corresponding author has full access to all data in the research and took final responsibility for the decision to submit for publication.

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Author notes

  1. Xin Li and Andrea Dai contributed equally to this work.

Authors and Affiliations

  1. Ophthalmology Department, Central Hospital Affiliated to Shandong First Medical University, Jinan, 250013, Shandong, China

    Xin Li

  2. Oakland University William Beaumont School of Medicine, Rochester, MI, 48309, USA

    Andrea Dai

  3. Masters Program in Computer Science, University of Chicago, Chicago, IL, 20833, USA

    Richard Tran

  4. Applied Data Science Program, Syracuse University, Syracuse, NY, 13244, USA

    Jie Wang

  5. MDSight, LLC, Brookeville, MD, 20833, USA

    Jie Wang

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  1. Xin Li
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Correspondence to Jie Wang.

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Li, X., Dai, A., Tran, R. et al. Identifying miRNA biomarkers for breast cancer and ovarian cancer: a text mining perspective. Breast Cancer Res Treat 201, 5–14 (2023). https://doi.org/10.1007/s10549-023-06996-y

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