Active natural compounds perturb the melanoma risk-gene network

by Selleckchem | Jan 07 2024

Cutaneous melanoma is an aggressive type of skin cancer with a complex genetic landscape caused by the malignant transformation of melanocytes. This study aimed at providing an in-silico network model based on the systematic profiling of the melanoma-associated genes considering germline mutations, somatic mutations, and genome-wide association studies (GWAS) signals accounting for a total of 232 unique melanoma risk genes. A protein-protein interaction (PPI) network was constructed using the melanoma risk genes as seeds and evaluated to describe the functional landscape in which the melanoma genes operate within the cellular milieu. Not only were the majority of the melanoma risk genes able to interact with each other at the protein level within the core of the network, but this showed significant enrichment for genes whose expression is altered in human melanoma specimens. Functional annotation showed the melanoma risk network to be significantly associated with processes related to DNA metabolism and telomeres, DNA damage and repair, cellular ageing, and response to radiation. We further explored whether the melanoma risk network could be used as an in-silico tool to predict the efficacy of anti-melanoma phytochemicals, that are considered active molecules with potentially less systemic toxicity than classical cytotoxic drugs. A significant portion of the melanoma risk network showed differential expression when SK-MEL-28 human melanoma cells were exposed to the phytochemicals harmine and berberine chloride. This reinforced our hypothesis that the network modelling approach not only provides an alternative way to identify molecular pathways relevant to disease, but it may also represent an alternative screening approach to prioritize potentially active compounds.

Comments:

That's a comprehensive study! It's fascinating how they utilized a network modeling approach to understand the functional landscape of melanoma-associated genes. By constructing a protein-protein interaction network and evaluating gene interactions, they gained insights into how these genes operate within cellular contexts.

The identification of significant enrichment in processes related to DNA metabolism, telomeres, DNA damage and repair, cellular aging, and response to radiation within the melanoma risk network provides valuable information about the underlying mechanisms of melanoma development and progression.

Moreover, the exploration of using this network as a predictive tool for the efficacy of anti-melanoma phytochemicals is intriguing. Finding differential expression in melanoma cells when exposed to these phytochemicals suggests that the network model might serve as a means to predict or prioritize potentially active compounds, offering a new avenue for screening compounds with therapeutic potential.

This study demonstrates the multifaceted utility of network modeling in understanding disease pathways, identifying potential therapeutic targets, and predicting the efficacy of compounds—a promising approach that could contribute significantly to melanoma research and drug development.