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Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence
Published on: November 2, 2014
A diagnostic miRNA panel to detect recurrence of ovarian cancer through artificial intelligence approaches
Reyhaneh Aghayousefi1, Seyed Mahdi Hosseiniyan Khatibi2,3,4, Sepideh Zununi Vahed4
1Department of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran.
Background:
Ovarian Cancer (OC) is the deadliest gynecology malignancy, whose high recurrence rate in OC patients is a challenging object. Therefore, having deep insights into the genetic and molecular mechanisms of OC recurrence can improve the target therapeutic procedures. This study aimed to discover crucial miRNAs for the detection of tumor recurrence in OC by artificial intelligence approaches.
Method:
Through the ANOVA feature selection method, we selected 100 candidate miRNAs among 588 miRNAs. For their classification, a deep-learning model was employed to validate the significance of the candidate miRNAs. The accuracy, F1-score (high-risk), and AUC-ROC of classification test data based on the 100 miRNAs were 73%, 0.81, and 0.65, respectively. Association rule mining was used to discover hidden relations among the selected miRNAs.
Result:
Five miRNAs, including miR-1914, miR-203, miR-135a-2, miR-149, and miR-9-1, were identified as the most frequent items among high-risk association rules. The identified miRNAs may target genes/proteins involved in epithelial-mesenchymal transition (EMT), resistance to therapy, and cancer stem cells; being responsible for the heterogeneity and plasticity of the tumor. Our conclusion presents mir-1914 as the significant candidate miRNA and the most frequent item. Current knowledge indicates that the dysregulated miR-1914 may function as a tumor suppressor or oncogene in the development of cancer.
Conclusion:
These candidate miRNAs can be considered a powerful tool in the diagnosis of OC recurrence. We hypothesize that mir-1914 might open a new line of research in the realm of managing the recurrence of OC and could be a significant factor in triggering OC recurrence.
Insights
Artificial intelligence identified five microRNAs (miRNAs) crucial for detecting ovarian cancer recurrence. MiR-1914 shows particular promise as a biomarker for managing ovarian cancer recurrence.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Ovarian cancer (OC) is a leading cause of gynecologic cancer mortality.
- High recurrence rates in OC patients present a significant clinical challenge.
- Understanding OC recurrence mechanisms is vital for improving targeted therapies.
Purpose of the Study:
- To identify key microRNAs (miRNAs) indicative of tumor recurrence in ovarian cancer.
- To leverage artificial intelligence (AI) approaches for discovering novel OC recurrence biomarkers.
- To explore the potential of miRNAs in improving the diagnosis and management of OC recurrence.
Main Methods:
- Utilized ANOVA feature selection to identify 100 candidate miRNAs from 588.
- Employed a deep-learning model for classifying and validating miRNA significance.
- Achieved classification accuracy of 73%, F1-score of 0.81, and AUC-ROC of 0.65 for high-risk cases.
- Applied association rule mining to uncover relationships among selected miRNAs.
Main Results:
- Identified five significant miRNAs: miR-1914, miR-203, miR-135a-2, miR-149, and miR-9-1.
- These miRNAs are implicated in epithelial-mesenchymal transition (EMT), therapy resistance, and cancer stem cells.
- miR-1914 was highlighted as the most frequent and significant candidate miRNA.
- Dysregulated miR-1914 may act as a tumor suppressor or oncogene in cancer development.
Conclusions:
- The identified candidate miRNAs represent a powerful tool for diagnosing OC recurrence.
- miR-1914 presents a potential new research avenue for managing OC recurrence.
- miR-1914 could be a significant factor in the initiation of OC recurrence.
