Identifying miRNA biomarkers for breast cancer and ovarian cancer: a text mining perspective
- 17.06.2023
- Review
Erschienen in:
- Verfasst von
- Xin Li
- Andrea Dai
- Richard Tran
- Jie Wang
- Erschienen in
- Breast Cancer Research and Treatment | Ausgabe 1/2023
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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- Titel
- Identifying miRNA biomarkers for breast cancer and ovarian cancer: a text mining perspective
- Verfasst von
-
Xin Li
Andrea Dai
Richard Tran
Jie Wang
- Publikationsdatum
- 17.06.2023
- Verlag
- Springer US
- Erschienen in
-
Breast Cancer Research and Treatment / Ausgabe 1/2023
Print ISSN: 0167-6806
Elektronische ISSN: 1573-7217 - DOI
- https://doi.org/10.1007/s10549-023-06996-y
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