Lipid-Metabolism-Related Gene Signature Predicts Prognosis and Immune Microenvironment Alterations in Endometrial Cancer

<b>Background/Objectives</b>: 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. <b>Methods</b>: 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. <b>Conclusions</b>: 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.

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Publication Details

Journal
Biomedicines
Published
2025-04-26
DOI
https://doi.org/10.3390/biomedicines13051050
Citations
1
Primary Topic
Cancer, Lipids, and Metabolism
Type
article
Field-Weighted Citation Impact
0.94

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article

Lipid-Metabolism-Related Gene Signature Predicts Prognosis and Immune Microenvironment Alterations in Endometrial Cancer

Tianhui He, Lixiang Xue, Yufei Nie, Deshui Kong et al.
1 citations
Biomedicines
Cancer, Lipids, and Metabolism
0.94
article

Lipid-Metabolism-Related Gene Signature Predicts Prognosis and Immune Microenvironment Alterations in Endometrial Cancer

Tianhui He, Lixiang Xue, Yufei Nie, Deshui Kong, Hongyan Guo, Jie Zhang, Chunliang Shang, Zhangxin Wu, Kaiyi Zhang
article en
1 citations

Abstract

<b>Background/Objectives</b>: 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. <b>Methods</b>: 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. <b>Conclusions</b>: 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.

BiomedicinesVol. 13(5)
Peking University (CN), Peking University Third Hospital (CN)
National Key Research and Development Program of China
Reduced inequalities
Openalex Percentile: Top 26%
Cancer, Lipids, and Metabolism
0.94
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