Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.04.04.535515v1?rss=1
Authors: Levy, C., Elkoshi, N., Parikh, S., Mahameed, S., Meidan, A., Rubin, E.
Abstract:
There are significant differences in the prevalence of cancer type, primary tumor body site, and mutation load between men and women, but the mechanisms underlying these sex-dependent differences is mostly unknown. Here we used binary classification machine-learning methodology to study sex-correlated somatic mutations signatures in cutaneous melanoma. We identified a number of genes that are more frequently mutated in females compared to males. Mutations in two genes, LAMA2 and TPTE, together with a set of specific genes that are not mutated, can predict sex of melanoma patients. Over representation analysis of genes clustered with LAMA2 revealed significant enrichment in androgen and estrogen biosynthesis and metabolism pathways, suggesting that mutation of LAMA2 might be involved in biased sex hormone synthesis in melanoma. Taken together, our analysis shows that gender can be predicted based on mutation status of genes in melanoma and that certain mutations are predictive of survival beyond sex differences. Our results will lead to better diagnosis and more effective personalized treatment of melanoma.
Copy rights belong to original authors. Visit the link for more info
Podcast created by Paper Player, LLC
