Construction of an immune-related gene prognostic model for obese endometrial cancer patients based on bioinformatics analysis.

Background: The tumor microenvironment (TME) affected the prognosis of tumors. However, its effect on the outcomes of obese endometrial cancer (EC) patients had not been reported.

Methods: This research performed a retrospective analysis of the transcriptome profiles and medical data of 503 EC patients.

Predicting drug synergy using a network propagation inspired machine learning framework.

Combination therapy is a promising strategy for cancers, increasing therapeutic options and reducing drug resistance. Yet, systematic identification of efficacious drug combinations is limited by the combinatorial explosion caused by a large number of possible drug pairs and diseases. At present, machine learning techniques have been widely applied to predict drug combinations, but most studies rely on the response of drug combinations to specific cell lines and are not entirely satisfactory in terms of mechanism interpretability and model scalability.