Decoding the Role of Epigenetics in Breast Cancer Using Formal Modeling and Machine-Learning Methods

Ayesha Asim; Yusra Sajid Kiani; Muhammad Tariq Saeed; Ishrat Jabeen

doi:10.3389/fmolb.2022.882738

Decoding the Role of Epigenetics in Breast Cancer Using Formal Modeling and Machine-Learning Methods

Front Mol Biosci. 2022 Jul 11:9:882738. doi: 10.3389/fmolb.2022.882738. eCollection 2022.

Authors

Ayesha Asim¹, Yusra Sajid Kiani¹, Muhammad Tariq Saeed¹, Ishrat Jabeen¹

Affiliation

¹ School of Interdisciplinary Engineering and Sciences (SINES), National University of Sciences and Technology, (NUST), Islamabad, Pakistan.

Abstract

Breast carcinogenesis is known to be instigated by genetic and epigenetic modifications impacting multiple cellular signaling cascades, thus making its prevention and treatments a challenging endeavor. However, epigenetic modification, particularly DNA methylation-mediated silencing of key TSGs, is a hallmark of cancer progression. One such tumor suppressor gene (TSG) RUNX3 (Runt-related transcription factor 3) has been a new insight in breast cancer known to be suppressed due to local promoter hypermethylation mediated by DNA methyltransferase 1 (DNMT1). However, the precise mechanism of epigenetic-influenced silencing of the RUNX3 signaling resulting in cancer invasion and metastasis remains inadequately characterized. In this study, a biological regulatory network (BRN) has been designed to model the dynamics of the DNMT1-RUNX3 network augmented by other regulators such as p21, c-myc, and p53. For this purpose, the René Thomas qualitative modeling was applied to compute the unknown parameters and the subsequent trajectories signified important behaviors of the DNMT1-RUNX3 network (i.e., recovery cycle, homeostasis, and bifurcation state). As a result, the biological system was observed to invade cancer metastasis due to persistent activation of oncogene c-myc accompanied by consistent downregulation of TSG RUNX3. Conversely, homeostasis was achieved in the absence of c-myc and activated TSG RUNX3. Furthermore, DNMT1 was endorsed as a potential epigenetic drug target to be subjected to the implementation of machine-learning techniques for the classification of the active and inactive DNMT1 modulators. The best-performing ML model successfully classified the active and least-active DNMT1 inhibitors exhibiting 97% classification accuracy. Collectively, this study reveals the underlined epigenetic events responsible for RUNX3-implicated breast cancer metastasis along with the classification of DNMT1 modulators that can potentially drive the perception of epigenetic-based tumor therapy.

Keywords: Dnmt1; RUNX3 signaling pathway; SMBioNet; c-myc; machine learning; qualitative modeling.