Abstract

Endometrial cancer ranks among the most prevalent tumours in women, with approximately 80–85% classified as endometrioid type. Evidence has suggested a significant link between obesity and the development of endometrial endometrioid cancer (EEC). We analysed RNA sequencing data encompassing 407 EEC samples and 35 normal endometrial tissue samples sourced from TCGA, and created a risk score model built on differentially expressed genes related to fatty acid metabolism within the tumour microenvironment (TME). Patients categorised as high-risk scores exhibited a higher prevalence of dMMR and TP53 mutations, fewer mutations in KRAS and PTEN, and worse prognostic outcomes. Further investigation indicated that the two patient subgroups showed varying sensitivities to different chemotherapeutic agents. Additionally, the composition of immune cell infiltration in the TME diverged between these subgroups. Tumour Immune Dysfunction and Exclusion analysis suggested that individuals with high-risk scores are not suited for immunotherapeutic intervention. Based on bioinformatic analyses, this research offers a novel framework for the classification and therapeutic direction of EEC.