From Deep LSTM-Based Gene Expression Modeling to microRNA Disease Association; hsa-miR-133b identified as a Potentially Functionally Conserved Tumor Suppressor Across Cancers
Eletr, L., Elzeiny, H., Marey, G., Amen, A., Elshazly, A., Elgebaly, A., Herajy, M., Hassan, M. (2025). From Deep LSTM-Based Gene Expression Modeling to microRNA Disease Association; hsa-miR-133b identified as a Potentially Functionally Conserved Tumor Suppressor Across Cancers. EKB Journal Management System, 6(3), 321-341. doi: 10.21608/ajbas.2025.386713.1256
Loai Eletr; Hazem Elzeiny; Ganna Marey; Aalaa Amen; Abdelruhman Elshazly; Abdelrahman Elgebaly; Mostafa Herajy; Mohamed Hassan. "From Deep LSTM-Based Gene Expression Modeling to microRNA Disease Association; hsa-miR-133b identified as a Potentially Functionally Conserved Tumor Suppressor Across Cancers". EKB Journal Management System, 6, 3, 2025, 321-341. doi: 10.21608/ajbas.2025.386713.1256
Eletr, L., Elzeiny, H., Marey, G., Amen, A., Elshazly, A., Elgebaly, A., Herajy, M., Hassan, M. (2025). 'From Deep LSTM-Based Gene Expression Modeling to microRNA Disease Association; hsa-miR-133b identified as a Potentially Functionally Conserved Tumor Suppressor Across Cancers', EKB Journal Management System, 6(3), pp. 321-341. doi: 10.21608/ajbas.2025.386713.1256
Eletr, L., Elzeiny, H., Marey, G., Amen, A., Elshazly, A., Elgebaly, A., Herajy, M., Hassan, M. From Deep LSTM-Based Gene Expression Modeling to microRNA Disease Association; hsa-miR-133b identified as a Potentially Functionally Conserved Tumor Suppressor Across Cancers. EKB Journal Management System, 2025; 6(3): 321-341. doi: 10.21608/ajbas.2025.386713.1256

From Deep LSTM-Based Gene Expression Modeling to microRNA Disease Association; hsa-miR-133b identified as a Potentially Functionally Conserved Tumor Suppressor Across Cancers

Alfarama Journal of Basic & Applied Sciences
Volume 6, Issue 3, July 2025, Page 321-341  XML PDF (1.75 MB)
Document Type: Original Article
DOI: 10.21608/ajbas.2025.386713.1256
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Authors
Loai Eletr email 1; Hazem Elzeiny1; Ganna Marey1; Aalaa Amen1; Abdelruhman Elshazly1; Abdelrahman Elgebaly1; Mostafa Herajy2; Mohamed Hassan3
1Computing and Bioinformatics Program, Faculty of Science, Port Said University, Port Said, Egypt
2Department of Mathematics and Computer Science, Faculty of Science, Port Said University, 42521-Port Said, Egypt
3Biotechnology program, Zoology Dept., Faculty of Science, Port Said University
Abstract
Background: Cancer remains a global health burden, and early, accurate diagnosis is vital. Most deep learning models focus on binary classification, with limited work on multiclass tasks. The roles of miRNAs in cancer have also been studied. Objective: This study proposed a hybrid deep learning model based on Deep Long Short-Term Memory (D-LSTM) for binary and multi-class cancer classification. The important genes identified by the model were then used to predict target miRNAs. Methods: The D-LSTM model was trained on GEO dataset GSE203024, covering 14 cancers, colon polyps, and normal samples. Gene selection was per- formed using ANOVA-F and CFS, and SMOTE was used to handle class imbalance. Performance was evaluated using the accuracy, precision, recall, and F1-score. miRNAs were identified using miRNet, and regulatory networks were visualized using Cytoscape. Results: The model achieved 98.38% accuracy (binary) and 99.88–100% (multi-class). hsa-miR-133b has been linked to 14 cancers and colon polyps, targeting CTNND1, CCNB1, and SUZ12. Conclusion: While demonstrating strong diagnostic potential, in silico findings require further biological validation. The comprehensive evaluation of hyperparameters, activation functions, and performance metrics of the model provides a flexible framework for cancer detection. Future studies should focus on in vivo/in vitro validation of hsa-miR-133b’s clinical relevance.
Keywords
Cancer; Deep learning; Gene expression; D-LSTM; MicroRNA
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