Identification of a fatty acid metabolism-related gene signature to predict prognosis in stomach adenocarcinoma

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.

Keywords: STAD; fatty acid metabolism; immunotherapy; nomogram; riskscore model.

Figure 1

Fatty acid metabolism pathway and related prognostic gene analysis. (A, B) Fatty acid metabolism pathway analysis via ssGSEA and GSEA. (C) Genes closely related to FAM related gene pathway score in TCGA-STAD dataset. (D) Gene expression levels between STAD cancer tissue and para-carcinoma tissue. (E) Single nucleotide variation analysis of genes in TCGA-STAD dataset. (F) Copy number variation analysis. ^*p < 0.05, ^***p < 0.001, ^****p < 0.0001.

Figure 2

Identification of molecular subtypes. (A) Cumulative distribution function. (B) Delta area. (C, D) Heatmap and PCA plots of sample clustering when k = 2 in TCGA-STAD. (E, F) K-M survival analysis of C1 and C2 in TCGA-STAD and GSE84437 datasets. (G) Expression levels of 14 genes in C1 and C2 subtypes based on TCGA-STAD dataset.

Figure 3

Analysis of immune infiltration and gene set enrichment analysis. (A) Analysis of 28 immune cells using CIBERSORT. (B) The distribution of innate and acquired immunity in TCGA-STAD dataset. (C) Analysis of immune infiltration using ESTIMATE. (D) Analysis of immune infiltration using MCP-counter. (E) GSEA pathways score analysis between C1 and C2 in TCGA-STAD. ^*p < 0.05, ^**p < 0.01, ^***p < 0.001, ^***p < 0.0001. Abbreviation: ns: no significance.

Figure 4

Construction and validation of a prognostic risk signature in TCGA-STAD based on hub FAM-related genes. (A) Lambda trajectory of differentially expressed genes. (B) Confidence interval under lambda. (C) Forest map of FAM-related hub genes. (D) ROC and K-M survival analysis of riskscore in GSE84437-train dataset. (E) ROC and K-M survival analysis of riskscore in GSE84437-test dataset. (F) ROC and K-M survival analysis of riskscore in TCGA dataset. (G) ROC and K-M survival analysis of riskscore in GSE84437 dataset.

Figure 5

Somatic mutation analysis and clinicopathological characteristics based on riskscore in TCGA cohort. (A) Analysis of somatic mutation in risk groups. (B) Distribution of TMB in risk groups. (C) K-M curve combining risk grouping with TMB. (D) The distribution of riskscore among different clinicopathological characteristics in the TCGA queue.

Figure 6

Establishment and assessment of the nomogram in TCGA-STAD queue. Forest plot of the (A) univariate and (B) multivariate Cox regression analyses. (C) The nomogram plot was constructed based on Age, M stage and FAMRGs riskscore. (D) Calibration plot of the nomogram. (E) DCA of the nomogram for 1-, 2- and 4-year OS.

Figure 7

Immunotherapy and drug sensitivity assessment. (A) TIDE score between high- and low-risk groups. (B–E) Riskscore survival curve and immunotherapy distribution in IMvigor210, GSE135222, GSE78220 and GSE91061 datasets. (F) Differential heatmap analysis of 62 chemotherapeutics in high and low riskscore groups. ^*p < 0.05, ^**p < 0.01, ^***p < 0.001, ^***p < 0.0001.

Figure 8

Prognostic analysis of our riskscore signature at different times in pan cancer. (The numbers inside represent the HR value, and the ^*in parentheses after it represents the log Rank P-value of HR. ^*P < 0.05, ^**P < 0.01, ^***P < 0.001, and ^****P < 0.0001. The gray NA represents that there is no corresponding survival time and status in the tumor or that HR values cannot be calculated).