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Related Experiment Video

Updated: Jul 8, 2026

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer

Published on: March 17, 2016

Characterization and validation of fatty acid metabolism-related genes predicting prognosis, immune infiltration, and

Haojia Li¹, Ting Zhou¹, Qi Zhang¹

¹Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.

Biotechnology and Applied Biochemistry

|April 15, 2024

Summary

This study identifies key fatty acid metabolism genes that predict endometrial cancer prognosis. These genes influence tumor immunity and may predict response to immunotherapy, offering new risk assessment tools.

Keywords:

drug sensitivity analysis endometrial cancer fatty acid metabolism immune infiltration prognosis

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Author Spotlight: Advancing Reproductive Immunology with a Protocol for the Quantitative Evaluation of Endometrial Immune Cells

Author Spotlight: Advancing Reproductive Immunology with a Protocol for the Quantitative Evaluation of Endometrial Immune Cells

Published on: October 13, 2023

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Last Updated: Jul 8, 2026

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer

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Published on: March 17, 2016

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Author Spotlight: Advancing Reproductive Immunology with a Protocol for the Quantitative Evaluation of Endometrial Immune Cells

Author Spotlight: Advancing Reproductive Immunology with a Protocol for the Quantitative Evaluation of Endometrial Immune Cells

Published on: October 13, 2023

Area of Science:

Oncology
Molecular Biology
Metabolomics

Background:

Endometrial cancer is a prevalent gynecologic malignancy with poor prognosis in advanced stages.
The role of fatty acid metabolism in endometrial cancer progression is not well understood.
Accurate prognostic markers are crucial for guiding treatment decisions.

Purpose of the Study:

To construct and validate a prognostic risk model for endometrial cancer based on fatty acid metabolism.
To investigate the relationship between fatty acid metabolism, tumor immune microenvironment, and immunotherapy response.
To identify key genes involved in endometrial cancer development and progression.

Main Methods:

Utilized The Cancer Genome Atlas (TCGA) database for transcriptome and clinical data.
Employed least absolute shrinkage and selection operator (LASSO) regression to build a prognostic model.
Analyzed tumor immune microenvironment using CIBERSORT and gene set variation analysis (GSVA).
Validated model genes in vitro (cell assays) and in vivo (xenograft models).

Main Results:

A 14-gene prognostic model for endometrial cancer was developed and validated across multiple cohorts.
Significant differences in immune cell infiltration were observed between high-risk and low-risk groups.
The high-risk group showed potential for better immunotherapy response.
Knockdown of EPHX2 and ACOXL, and overexpression of HPGDS, inhibited endometrial cancer cell growth in vitro and in vivo.

Conclusions:

Fatty acid metabolism-related prognostic models can aid in endometrial cancer risk assessment.
Key genes (HPGDS, EPHX2, ACOXL) identified in the model play a significant role in endometrial cancer development.
This research provides insights into potential therapeutic targets and immunotherapy strategies for endometrial cancer.