Molecular landscape of endometrioid Cancer: Integrating multiomics and deep learning for personalized survival prediction

Abstract

Background The endometrioid subtype of endometrial cancer is a significant health concern for women, making it crucial to study the factors influencing patient outcomes. Method This study presents a novel survival analysis pipeline applied to multiomics data, including transcriptome, methylation, and proteome data, extracted from endometrioid samples in the TCGA-UCEC project to identify potential survival biomarkers. A major innovation in our work was the development of a deep learning autoencoder designed to capture the complex non-linear relationships between biological variables and survival outcomes. To achieve this, we defined a new loss function specifically for the autoencoder. Result The newly defined loss function can lead to extracting more survival information. The output of our pipeline includes 346 features ranked by their survival importance based on SHAP analysis, with a focus on the top 30 …

Fatemeh Zare

Associate Professor

My research interests include bioinformatics, computational biology and artificial intelligence.