Research Paper Volume 16, Issue 10 pp 8552—8571
Identification of a fatty acid metabolism-related gene signature to predict prognosis in stomach adenocarcinoma
- 1 Department of Gastroenterology, Strategic Support Force Medical Center, Beijing 100101, China
- 2 Department of Spinal Surgery, Strategic Support Force Medical Center, Beijing 100101, China
- 3 Department of Hematopathology, Strategic Support Force Medical Center, Beijing 100101, China
Received: November 2, 2023 Accepted: March 13, 2024 Published: May 13, 2024
https://doi.org/10.18632/aging.205823How to Cite
Copyright: © 2024 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background: Fatty acid metabolism (FAM) contributes to tumorigenesis and tumor development, but the role of FAM in the progression of stomach adenocarcinoma (STAD) has not been comprehensively clarified.
Methods: The expression data and clinical follow-up information were obtained from The Cancer Genome Atlas (TCGA). FAM pathway was analyzed by gene set enrichment analysis (GSEA) and single-sample GSEA (ssGSEA) methods. Univariate Cox regression analysis was conducted to select prognosis genes. Molecular subtypes were classified by consensus clustering analysis. Furthermore, least absolute shrinkage and selection operator (Lasso) analysis was employed to develop a risk model. ESTIMATE and tumour immune dysfunction and exclusion (TIDE) algorithm were used to assess immunity. pRRophetic package was conducted to predict drug sensitivity.
Results: Based on 14 FAM related prognosis genes (FAMRG), 2 clusters were determined. Patients in C2 showed a worse overall survival (OS). Furthermore, a 7-FAMRG risk model was established as an independent predictor for STAD, with a higher riskscore indicating an unfavorable OS. High riskscore patients had higher TIDE score and these patients were more sensitive to anticancer drugs such as Bortezomib, Dasatinib and Pazopanib. A nomogram based on riskscore was an effective prediction tool applicable to clinical settings. The results from pan-cancer analysis supported a prominent application value of riskscore model in other cancer types.
Conclusion: The FAMRGs model established in this study could help predict STAD prognosis and offer new directions for future studies on dysfunctional FAM-induced damage and anti-tumor drugs in STAD disease.
Abbreviations
AUC: Area under ROC curve; CDF: Cumulative distribution function; CNV: Copy number variation; DCA: Decision curve analysis; DFI: Disease free interval; DSS: Disease specific survival; ESTIMATE: Estimation of STromal and Immune cells in MAlignant Tumors using Expression data; EMT: Epithelial-mesenchymal transition; FAM: Fatty acid metabolism; FAMRGs: FAM-related genes; GEO: Gene Expression Omnibus; GSEA: Gene set enrichment analysis; HR: Hazard ratio; ICIs: immune checkpoint inhibitors; IC50: The half maximal inhibitory concentration; K-M analysis: Kaplan-Meier analysis; LASSO: Least absolute shrinkage and selection operator; MCP-counter: Microenvironment Cell Populations-counter; OS: Overall Survival; PCA: Principal component analysis; PD: Progressive disease; PFI: Progression free survival; ROC: Receiver operating characteristic analysis; SD: Stable disease; SNV: Single nucleotide mutation; ssGSEA: Single-sample gene set enrichment analysis; STAD: Stomach adenocarcinoma; TCGA: The Cancer Genome Atlas; TIDE: Tumor Immune Dysfunction and Exclusion; TMB: Tumor Mutation Burden; TME: Tumor microenvironment.