ArticleGenes2022
Bioinformatics Analysis and Structure of Gastric Cancer Prognosis Model Based on Lipid Metabolism and Immune Microenvironment.
Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Metabolic reprogramming as a key regulator in Helicobacter pylori-infected gastric cancer.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026Review
- The role and research progress of FGF21 in breast cancer: a review.Frontiers in oncology · 2026Review
- Exploring the role of lipid metabolism genes in gastric cancer prognosis and tumor immune microenvironment.The Journal of international medical research · 2025Article
- The Impact of Transcriptional Profiling Cadherin Family and Therapeutic Approaches of Gastric Cancer: A Translational Outlook on Multi-omics Data Analysis.Applied biochemistry and biotechnology · 2024Article
- Delineation of fatty acid metabolism in gastric cancer: Therapeutic implications.World journal of clinical cases · 2023Article
- Analyzing the impact of metabolism on immune cells in tumor microenvironment to promote the development of immunotherapy.Frontiers in immunology · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe reprogramming of lipid metabolism is a new trait of cancers. However, the role of lipid metabolism in the tumor immune microenvironment (TIME) and the prognosis of gastric cancer remains unclear.
methodsConsensus clustering was applied to identify novel subgroups. ESTIMATE, TIMER, and MCPcounter algorithms were used to determine the TIME of the subgroups. The underlying mechanisms were elucidated using functional analysis. The prognostic model was established using the LASSO algorithm and multivariate Cox regression analysis.
resultsThree molecular subgroups with significantly different survival were identified. The subgroup with relatively low lipid metabolic expression had a lower immune score and immune cells. The differentially expressed genes (DEGs) were concentrated in immune biological processes and cell migration via GO and KEGG analyses. GSEA analysis showed that the subgroups were mainly enriched in arachidonic acid metabolism. Gastric cancer survival can be predicted using risk models based on lipid metabolism genes.
conclusionsThe TIME of gastric cancer patients is related to the expression of lipid metabolism genes and could be used to predict cancer prognosis accurately.
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