Epigenetic influence of Curcumin on Histone signatures in Cancer using 3D Model (ECHC)

Abstract

Aberrant epigenetic alterations are known to lead to cancer arising from normal non-cancerous cells. Unlike genetic mutations, these dysregulations can be reversible and are potential targets for anticancer drugs. Curcumin, a natural plant-derived compound, has been shown to have an anticancer effect via its influence on epigenetic regulation. However, the exact identity of those epigenetic changes, as well as their mechanisms of action, remain largely unknown. The goal of this study is to use a 3D network model to identify what histone codes are being modified by curcumin and the enzymatic pathways through which those changes occur in breast cancer. We analyzed multi-omic data from the NIH LINCS program and identified two novel histone marks associated with curcumin that are influenced by twenty-three phosphoproteins involved in cell signaling which are profiled in this paper. These histone and phosphoprotein signatures can be used as potential biomarkers for future chromatin-based drug therapies in breast cancer.

Contents

All data files used are found in the data folder. Data originally generated by the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) Program. Preprocessing R script for data files is available in the code folder. To run analysis, you will need to install the gen3DNet R package (developed by Mollah lab). You can install it from GitHub using:

install.packages("remotes")
remotes::install_github("MollahLab/gen3DNet")

Gen3DNet results for the MCF7 cell line is provided in the results folder.

Citation

1. Tina Tang, Mikhail Berezin, Shamim Mollah. Deciphering epigenetic influence of curcumin on histone signatures in breast cancer using 3D network. bioRxiv 2024.11.13.623008; doi: https://doi.org/10.1101/2024.11.13.623008
2. Mollah, S. A., & Subramaniam, S. (2020). Histone Signatures Predict Therapeutic Efficacy in Breast Cancer. IEEE open journal of engineering in medicine and biology, 1, 74–82. https://doi.org/10.1109/OJEMB.2020.2967105
3. Paul Morrison, Tina Tang, Charles Lu, Shamim Mollah. gen3DNet: An R Package for Generating 3D Network Models. https://doi.org/10.1101/2024.11.11.623060