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

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.