AUTHOR=Asim Ayesha , Kiani Yusra Sajid , Saeed Muhammad Tariq , Jabeen Ishrat 

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

JOURNAL=Frontiers in Molecular Biosciences

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2022.882738

DOI=10.3389/fmolb.2022.882738

ISSN=2296-889X

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 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 DNMT1-RUNX3 network augmented by other regulators such as p21, c-myc and p53 etc. For this purpose, René Thomas qualitative modeling was applied to compute the unknown parameters and the subsequent trajectories signified important behaviors of DNMT1-RUNX3 network (i.e. recovery cycle, homeostasis, bifurcation state). As a result, the biological system was observed to invade cancer metastasis due to persistent activation of oncogene c-myc accompanied with consistent downregulation of TSG RUNX3. Conversely, the 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 for 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 of RUNX3 implicated breast cancer metastasis along with classification of DNMT1 modulators that can potentially drive the perception of epigenetic based tumor therapy.