Computational Approach to Identify Regulatory Biomarkers in the Pathogenesis of Breast Carcinoma

Abstract: Breast Cancer is reckoned amongst the most common cause of morbidity and mortality among women, adversely affecting female population irrespective of age. The poor survival rate reported in invasive carcinoma cases demands the identification of early developmental stage key markers. MicroRNAs are contributing a critical role in gene regulation potential markers. Over 2000 miRNAs have been identified and considered to offer a unique opportunity for early detection of diseases. In this study, a gene-miRNA-TF interaction network was constructed from the differentially expressed genes obtained from the invasive lobular and invasive ductal carcinoma samples. The network consists of experimentally validated miRNAs and transcription factors were identified for the target genes, followed by thermodynamics studies to identify the binding free energy between mRNA-miRNA. Our analysis identified miRNA; hsa-miR-28-5p binds with MAD2L1 with unexpectedly high binding free energy equivalent to -92.54kcal/mol and also makes canonical triplex with hsa-miR-203a, which acts as a catalyst to initialize the MAD2L1 regulation. For the identified regulatory elements, we proposed a mathematical model and feed-forward loops that may serve in understanding the regulatory mechanisms in breast cancer pathogenesis and progression.

Ghazala Sultan, Swaleha Zubair, Inamul Hasan Madar and Harishchander Anandaram. “Computational Approach to Identify Regulatory Biomarkers in the Pathogenesis of Breast Carcinoma”. International Journal of Advanced Computer Science and Applications (IJACSA) 13.6 (2022). http://dx.doi.org/10.14569/IJACSA.2022.0130641

@article{Sultan2022,
title = {Computational Approach to Identify Regulatory Biomarkers in the Pathogenesis of Breast Carcinoma},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130641},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130641},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {6},
author = {Ghazala Sultan and Swaleha Zubair and Inamul Hasan Madar and Harishchander Anandaram}
}