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

The endometrioid subtype of endometrial cancer is a significant health concern for women, making it crucial to study the factors influencing patient outcomes.

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.

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 features. We analyzed the biological pathways enriched by these omics data and their contributions. As a result, we identified a relationship between Vitamin D, its receptor, and the Galanin receptor pathways with survival in endometrioid cancer.

This study introduces an innovative approach to survival analysis using multi-omics data and deep learning, with a greater focus on censored data to extract more survival information. It offers potential biomarkers for improved prognostic evaluation in endometrial cancer and presents pathway associations related to survival. These findings contribute to a better understanding of the progression of endometrial cancer.