Characterization of lipid droplet metabolism patterns identified prognosis and tumor microenvironment infiltration in gastric cancer

Abstract

Background: Gastric cancer is one of the common malignant tumors of the digestive system worldwide, posing a serious threat to human health. A growing number of studies have demonstrated the important role that lipid droplets play in promoting cancer progression. However, few studies have systematically evaluated the role of lipid droplet metabolism-related genes (LDMRGs) in patients with gastric cancer.

Methods: We identified two distinct molecular subtypes in the TCGA-STAD cohort based on LDMRGs expression. We then constructed risk prediction scoring models in the TCGA-STAD cohort by lasso regression analysis and validated the model with the GSE15459 and GSE66229 cohorts. Moreover, we constructed a nomogram prediction model by cox regression analysis and evaluated the predictive efficacy of the model by various methods in STAD. Finally, we identified the key gene in LDMRGs, ABCA1, and performed a systematic multi-omics analysis in gastric cancer.

Results: Two molecular subtypes were identified based on LDMRGs expression with different survival prognosis and immune infiltration levels. lasso regression models were effective in predicting overall survival (OS) of gastric cancer patients at 1, 3 and 5 years and were validated in the GEO database with consistent results. The nomogram prediction model incorporated additional clinical factors and prognostic molecules to improve the prognostic predictive value of the current TNM staging system. ABCA1 was identified as a key gene in LDMRGs and multi-omics analysis showed a strong correlation between ABCA1 and the prognosis and immune status of patients with gastric cancer.

Conclusion: This study reveals the characteristics and possible underlying mechanisms of LDMRGs in gastric cancer, contributing to the identification of new prognostic biomarkers and providing a basis for future research.

Keywords: gastric cancer; lipid droplet metabolism; prognostic model; subtypes; tumor immunity.

Figure 1

The differential expression and functional enrichment analysis of LDMRGs in STAD. (A) Differential expression analysis of LDMRGs in STAD. (B) Correlation analysis of the expression of LDMRGs in STAD. (C) GO and KEGG pathway enrichment analysis of LDMRGs. (D) Construction of PPI interaction network for LDMRGs using the STRING database. (E) Identification of the top 5 hub genes in the PPI Interaction network of LDMRGs by cytoscape software. (F, G) Identification of key network modules in PPI interaction network for LDMRGs via cytoscape software. (ns: p > 0.05,*: p ≤0.05, **: p ≤ 0.01, ***: p ≤ 0.001).

Figure 2

Multi-omics analysis of LDMRGs in STAD. (A) Landscape analysis of genetic alterations of LDMRGs in STAD using the cBioPortal database. (B) Differential analysis of TMB, MSI in different genetic alteration groups in STAD using the cBioPortal database. (C–E) Correlation analysis of mRNA expression levels of LDMRGs and CNV, methylation, and immune infiltration levels in STAD. (****: p ≤ 0.0001).

Figure 3

Identification of subtypes associated with LDMRGs in STAD. (A–C) The optimal number of clusters (K=2) was determined for classification based on the cumulative distribution function (CDF) curve. (D) Heat map of the expression of LDMRGs in different subgroups, red represents high expression and blue represents low expression. (E) Principal component analysis (PCA) of 375 patients with STAD, with each point representing one sample. (F) Survival analysis between different subgroups in the TCGA-STAD cohort, including OS, PFS, and DSS. (G) Differential expression analysis of LDMRGs between different subgroups in the TCGA-STAD cohort. (ns: p > 0.05, *: p ≤0.05, **: p ≤ 0.01, ***: p ≤ 0.001).

Figure 4

Functional enrichment analysis of DEGs between the two subgroups. (A, B) Volcano and heat maps of differentially expressed genes between the two subgroups in the TCGA-STAD cohort. (C) GO and KEGG pathway enrichment analysis of differentially expressed genes that were dwon-regulated. (D) GO and KEGG pathway enrichment analysis of differentially expressed genes that were up-regulated.

Figure 5

Characteristic analysis of different subgroups in the TCGA-STAD cohort. (A, B) Analysis of differences in the level of immune infiltration between the two subgroups. (C) Differential expression analysis of immune checkpoint-related genes between the two subgroups. (D) Analysis of the differences in TIDE scores between the two subgroups. (E) Heat map of differential expression of m6A methylation-related genes between the two subgroups. (F) Analysis of differences in tumor stemness scores between the two subgroups. (*: p ≤0.05, **: p ≤ 0.01, ***: p ≤ 0.001, ****: p ≤ 0.0001).

Figure 6

Construction and validation of the Lasso regression model. (A) Trajectory plots of variables for Lasso regression analysis. (B) Screening of coefficients for Lasso regression analysis variables. (C–E) Construction and evaluation of a risk prediction scoring system in the TCGA-STAD cohort. (F–H) Validation and evaluation of risk prediction scoring systems in the GSE15459 cohort. (I–K) Validation and evaluation of risk prediction scoring systems in the GSE66229 cohort.

Figure 7

Construction and evaluation of nomogram prediction models in the TCGA-STAD cohort. (A) Construction of a nomogram prediction model. (B) Evaluation of calibration curve on the predictive value of the nomogram model. (C) Evaluation of ROC curve on the predictive value of the nomogram model. (D–F) Evaluation of DCA curve on the clinical utility value of the nomogram model.

Figure 8

Expression and prognostic analysis of ABCA1 in gastric cancer. (A) Identification of the key gene, ABCA1, in LDMRGs in STAD. (B–E) Differential expression analysis of ABCA1 in gastric cancer using TCGA and GEO databases. (F) The diagnostic value of ABCA1 expression in gastric cancer was analyzed by ROC curve in STAD. (G) The correlation between ABCA1 expression and clinical characteristics was analyzed by logistic regression in STAD. (H–J) Correlation analysis of ABCA1 expression and survival prognosis in the TCGA-STAD cohort, including OS, DSS, and PFI. (K, L) Correlation analysis of ABCA1 expression and OS prognosis in the GSE15459 and GSE26253 cohorts. (*: p ≤0.05, ***: p ≤ 0.001).

Figure 9

Gene function enrichment analysis of ABCA1 in STAD. (A, B) Volcano and heat maps of DEGs in the ABCA1 high and low expression groups in the TCGA-STAD cohort. (C, D) GO and KEGG pathway enrichment analysis of DEGs in STAD. (E, F) GSEA analysis based on ABCA1 expression in STAD.

Figure 10

Correlation analysis of ABCA1 expression and immune characteristics in STAD. (A, B) Differential analysis of the level of immune infiltration between high and low expression groups of ABCA1 was performed in STAD by the ssGSEA and estimate algorithms. (C, D) Differential analysis of ABCA1 expression levels between different immunological and molecular subtypes in STAD using the TISIDB database. (E-G) Differential analysis of TMB, MSI, and neoantigen loads between high and low ABCA1 expression groups was performed in STAD using the CAMOIP database. (H) Differential analysis of TIDE scores between high and low ABCA1 expression groups in STAD. (ns: p > 0.05, *: p ≤0.05, **: p ≤ 0.01, ***: p ≤ 0.001, ****: p ≤ 0.0001).

Figure 11

Mutation analysis and drug sensitivity analysis of ABCA1 in STAD. (A) Lollipop plot of ABCA1 mutation distribution in the genome. (B) A waterfall map of the somatic mutation landscape in the TCGA-STAD cohort, including the top 10 mutation-frequency genes and ABCA1. (C) Correlation analysis of ABCA1 mRNA expression and drug sensitivity in pan-cancer using the GSCA database.