Text Mining Strategy Identifies Gene Networks Under Control of miR‐21 in Breast Cancer Development
ABSTRACT Background MicroRNAs (miRNAs) are small regulatory molecules that play a critical role in various biological processes by regulating gene expression. They have emerged as crucial players in cancer development, including breast cancer. However, individual research studies may be subject to s...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-07-01
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| Series: | Cancer Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/cam4.70986 |
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| Summary: | ABSTRACT Background MicroRNAs (miRNAs) are small regulatory molecules that play a critical role in various biological processes by regulating gene expression. They have emerged as crucial players in cancer development, including breast cancer. However, individual research studies may be subject to specific biases. Methods To gain a more comprehensive understanding of miRNA involvement in breast cancer, we employed a large‐scale analysis of miRNA studies retrieved from PubMed. Our approach involved tokenizing abstracts to identify key biomedical entities (e.g., miRNA, gene, disease) and constructing miRNA‐cancer co‐occurrence networks using bioinformatic analysis. Results This analysis revealed miR‐21 as the most frequently studied miRNA in breast cancer research, with a significant difference compared to other miRNAs. Network analysis identified SMAD3, PIK3R1, STAT3, and TP53 as key regulators potentially affecting pathways like TGF‐β signaling and p53 signaling. Additionally, our analysis suggests that genes associated with miR‐21 are often downregulated in tumors and exhibit a positive correlation with T cell infiltration, particularly CD8+ T cells, potentially indicating a favorable prognosis. Conclusion Our findings highlight miR‐21 as a central regulatory hub and potential biomarker in breast cancer. While informative, the results are derived from literature‐based data and may be influenced by text‐mining limitations, underscoring the need for experimental validation. |
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| ISSN: | 2045-7634 |