Background/Objectives: Lipid metabolism plays a crucial role in uterine corpus endometrial carcinoma (UCEC); however, its specific mechanisms remain to be fully elucidated. This study aimed to construct a lipid-metabolism-related prognostic model and explore its association with the tumor immune microenvironment. Methods: A total of 552 UCEC and 35 normal tissue samples from The Cancer Genome Atlas (TCGA) database were analyzed to identify differentially expressed lipid-metabolism-related genes (DE-LMRGs). A prognostic risk model was established using univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression, and its clinical utility was assessed through nomogram construction. Functional enrichment analysis was performed to explore the biological pathways involved. Tumor immune infiltration patterns were evaluated using single-sample Gene Set Enrichment Analysis (ssGSEA), Estimation of Stromal and Immune Cells in Malignant Tumors using Expression Data (ESTIMATE), and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms. Results: Multivariate analysis indicated that the prognostic model had robust predictive value, with AUCs of 0.701, 0.746, and 0.790 for 1-, 3-, and 5-year overall survival predictions. High-risk patients exhibited a suppressed immune microenvironment characterized by reduced immune cell infiltration, lower tumor mutation burden (TMB), and elevated TIDE scores, suggesting potential resistance to immunotherapy. Furthermore, LIPG was identified as a key hub gene through the intersection of nine machine learning algorithms, demonstrating strong associations with both cancer progression and immune infiltration. Functional validation using Cell Counting Kit-8 (CCK-8), wound healing, and transwell migration assays following small interfering RNA (siRNA) transfection demonstrated that LIPG promotes UCEC cell proliferation and migration in vitro. Conclusions: These findings highlight the critical role of lipid metabolism in UCEC progression and immune modulation, with LIPG emerging as a potential prognostic biomarker. The identified lipid-metabolism-related gene signature may provide new insights into tumor microenvironment interactions.

Endometrial cancer (EC) is an immunogenic tumor, with CD8⁺ T cells playing a pivotal role in antitumor immunity. Overexpression of PD1 suppresses T cell function by inhibiting CD28, a critical co-stimulatory molecule. Classifying CD8⁺ T cells based on PD1 and CD28 expression provides valuable insights into the immune microenvironment of EC. Peripheral blood samples from 120 EC patients and tumor tissue samples from 81 EC patients were analyzed via flow cytometry. CD8⁺ T cells were categorized according to PD1 and CD28 expression, and their associations with clinical characteristics were systematically evaluated. Peripheral CD28⁻/CD8⁺ and PD1⁺/CD8⁺ T cell proportions were significantly associated with several high-risk factors, including deep myometrial invasion, and LVSI, as well as metabolic disorders such as dyslipidemia. Peripheral CD28⁺PD1⁻/CD8⁺ T cells were associated with stage, grade, and LVSI, inversely correlated with age, and elevated in patients with hypertension or dyslipidemia. Tumor-infiltrating CD28⁺PD1⁻/CD8⁺ T cells were associated with tumor grade and LVSI, with multivariate analysis identifying low proportions as an independent predictor of relapse. In summary, CD8⁺CD28⁻ and CD8⁺PD1⁺ T cells are linked to high-risk clinical features in EC, while tumor-infiltrating CD8⁺CD28⁺PD1⁻ T cells serve as a key independent prognostic marker for relapse. Additionally, CD8⁺CD28⁻, CD8⁺PD1⁺, and CD8⁺CD28⁺PD1⁻ T cell proportions in PBMC are closely associated with metabolic disorders, emphasizing their potential as biomarkers for immune and metabolic interactions in EC.