A lipid metabolism and lysosome-based risk signature for prognosis and immune response prediction in uterine corpus endometrial carcinoma.

Abstract

Background: The dysregulation of genes related to lipid metabolism and lysosomal function has been reported to significantly contribute to tumor progression. In this study, we systematically explored the roles played by lipid metabolism and lysosomes in uterine corpus endometrial carcinoma (UCEC), aiming to identify potential biomarkers for predicting prognosis and immune checkpoint therapy efficacy.

Methods: Genes associated with lipid metabolism and lysosomal function were retrieved from the MSigDB and GO databases. Transcriptomic data and clinical information of patients were acquired from The Cancer Genome Atlas database. A prognostic model was constructed using consensus clustering, univariate Cox regression, and LASSO regression. ROC curves, Kaplan-Meier plots, and calibration curves were employed to assess the predictive capacity of the model, while ssGSEA, TIDE, and IPS were used to evaluate the response of high- and low-risk groups to immunotherapy. Drug sensitivity was assessed with the "oncoPredict" R package. Given that we identified a strong association between PLAAT1 and CD8⁺ T-cell infiltration, this gene was selected for loss-of-function assays in UCEC cells, including the evaluation of their proliferative, invasive, and migratory potential.

Results: An eight-gene (LAMP3, RNF183, EEF1A2, PLAAT1, ELAPOR1, B4GALT1, ATP10B, and PLA2G10) risk signature based on lipid metabolism and lysosomal function was constructed to distinguish high-risk and low-risk UCEC patients. Subsequent analyses showed that patients classified as high risk had higher TIDE scores, whereas those categorized as low risk exhibited higher MSI scores and greater levels of CD8⁺ T-cell infiltration. All evidence suggested that patients in the low-risk group displayed greater immunogenicity and sensitivity to both immunotherapy and chemotherapy. Analysis using the TIMER database indicated that among the eight risk genes, PLAAT1 showed the strongest association with CD8⁺ T-cell immune infiltration in UCEC. Cytological experiments confirmed that the knockdown of PLAAT1 effectively suppressed the proliferation and motility of endometrial cancer cells.

Conclusion: We constructed a risk prognostic model for UCEC based on a combination of lysosomal- and lipid metabolism-related genes. Our findings highlight the oncogenic potential of PLAAT1 in endometrial cancer and provide novel insights into the diagnosis and therapy of this cancer type.