ArticlePharmacogenomics and personalized medicine2023
Identification of a Prognostic Gene Signature Based on Lipid Metabolism-Related Genes in Esophageal Squamous Cell Carcinoma.
Article in Pharmacogenomics and personalized medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 5 citations in OpenAlex.
- Establishment of prognostic prediction model based on lipid metabolism related genes in esophageal squamous cell carcinoma by machine learning algorithms.BMC gastroenterology · 2026Article
- Ethnic genomic differences in esophageal squamous cell carcinoma: Whole-exome sequencing of Han and Kazakh populations in China.World journal of gastroenterology · 2025Article
- KLF13 promotes esophageal cancer progression and regulates triacylglyceride and free fatty acid metabolism through GPIHBP1.Cell death & disease · 2025Article
- Identification of biomarkers and potential drug targets for esophageal cancer: a Mendelian randomization study.Scientific reports · 2025Article
- Establishment and validation of a prognostic model for idiopathic pulmonary fibrosis based on mitochondrial-related genes.Journal of thoracic disease · 2024Article
- From Classical to Alternative Pathways of 2-Arachidonoylglycerol Synthesis: AlterAGs at the Crossroad of Endocannabinoid and Lysophospholipid Signaling.Molecules (Basel, Switzerland) · 2024Review
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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