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ORIGINAL RESEARCH

Identification of a Prognostic Gene Signature Based on Lipid Metabolism-Related Genes in Esophageal Squamous Cell Carcinoma

, , , , , & show all
Pages 959-972 | Received 09 Aug 2023, Accepted 16 Oct 2023, Published online: 03 Nov 2023
 

Abstract

Background

Dysregulation of lipid metabolism is common in cancer. However, the molecular mechanism underlying lipid metabolism in esophageal squamous cell carcinoma (ESCC) and its effect on patient prognosis are not well understood. The objective of our study was to construct a lipid metabolism-related prognostic model to improve prognosis prediction in ESCC.

Methods

We downloaded the mRNA expression profiles and corresponding survival data of patients with ESCC from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. We performed differential expression analysis to identify differentially expressed lipid metabolism-related genes (DELMGs). We used Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses to establish a risk model in the GEO cohort and used data of patients with ESCC from the TCGA cohort for validation. We also explored the relationship between the risk model and the immune microenvironment via infiltrated immune cells and immune checkpoints.

Results

The result showed that 132 unique DELMGs distinguished patients with ESCC from the controls. We identified four genes (ACAA1, ACOT11, B4GALNT1, and DDHD1) as prognostic gene expression signatures to construct a risk model. Patients were classified into high- and low-risk groups as per the signature-based risk score. We used the receiver operating characteristic (ROC) curve and the Kaplan-Meier (KM) survival analysis to validate the predictive performance of the 4-gene signature in both the training and validation sets. Infiltrated immune cells and immune checkpoints indicated a difference in the immune status between the two risk groups.

Conclusion

The results of our study indicated that a prognostic model based on the 4-gene signature related to lipid metabolism was useful for the prediction of prognosis in patients with ESCC.

Data Sharing Statement

All data generated or analysed during this study are included in this article. Further enquiries can be directed to the corresponding author.

Ethics Approval and Consent to Participate

This study was conducted with approval from the Ethics Committee of Inner Mongolia Cancer Hospital (KY202210). This study was conducted in accordance with the declaration of Helsinki. Informed consent was obtained from the study participants for the use of the tissue samples in your in viva validation prior to study commencement.

Acknowledgments

We would like to acknowledge the hard and dedicated work of all the staff that implemented the intervention and evaluation components of the study.

Disclosure

The authors declare that they have no competing interests in this work.

Additional information

Funding

1. Natural Science Foundation of Fujian Province (No. 2023J011809). 2. Zhangzhou Hospital Doctor Studio Climbing Project (PDA202205). 3. Health Science and Technology Program of Inner Mongolia (No.202201363): Establishment and operation of a biological sample library for thoracic surgery in tumor specialized hospitals. 4. Beijing Medical Award Fund (YXJL202007850351): The effect of neoadjuvant chemotherapy combined with immunotherapy on postoperative pathology and related survival indicators of esophageal squamous cell carcinoma.