Lactate metabolism-related genes serve as potential biomarkers for predicting gastric cancer progression and immunotherapy

Abstract

Background: Targeting lactate metabolism represents a promising therapeutic strategy to enhance anti-tumor immune responses. In this study, we developed a novel model based on lactate metabolism-related genes (LRGs) to predict survival, characterize the immune microenvironment, and assess immunotherapy response in gastric cancer (GC), with the potential to identify new biomarkers.

Methods: Data sets of GC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. LRGs were sourced from the MSigDB database. Five key prognostic LRGs (MMP11, MMP12, HBB, VSIG2, and SERPINE1) were identified using univariate COX regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Patients were classified into high-risk and low-risk groups based on a median risk score. We conducted prognostic analysis, gene set enrichment analysis (GSEA), immune microenvironment analysis, immunotherapy responsiveness evaluation, and drug screening in these groups.

Results: The high-risk group exhibited poorer prognosis compared to the low-risk group, as predicted by our nomogram for overall survival. Notably, the high-risk group, marked by higher stromal cell infiltration and RNA stemness scores (RNAss), showed increased susceptibility to immune evasion. In contrast, the low-risk group demonstrated better responses to immunotherapy and greater sensitivity to chemotherapy. Single-cell analysis revealed that SERPINE1 is predominantly positively correlated with immune checkpoint expression, while VSIG2 exhibits a negative correlation.

Conclusions: We have developed and validated a novel lactate metabolism-associated model, providing new insights into the prognosis and immunotherapy of GC patients. The five identified LRGs offer potential as prognostic biomarkers and therapeutic targets in GC.

Keywords: Gastric cancer; Immunotherapy; Lactate metabolism; Prognosis; Tumor microenvironment.

Fig. 1

Flow chart of the study

Fig. 2

Identification of LRGs in patients with GC. a. Waterfall plots summarizing the mutations in GC patients. b. Localization of 36 LRGs in chromosomal regions. c. Copy number variations (CNVs) of 36 LRGs in TCGA-STAD. d. The 39 LRGs related differentially expressed genes were identified. e. The network diagram showed the correlations between the top 32 LRGs. The red connecting lines represent positive correlations, while the blue represents negative correlations

Fig. 3

Constructing risk model and validating its validity. a. Coefficient profile plots of 5 prognostic LAGs. Vertical dashed lines are plotted at the best lambda. b. LASSO Cox regression analysis of 26 prognosis-related differentially expressed LRGs. c. Differences in risk scores between the two subtypes. d. Expression of differentially expressed genes between high- and low-risk groups. e–g. Kaplan–Meier survival curves of the OS of patients in the Entire set, Test set and Train set. h–j. ROC curve of the Entire set, Test set and Train set

Fig. 4

Correlation analysis between risk score and clinicopathological features. a–c. Distribution of LAGs model presented based on Entire set, Test set and Train set. d–f. Survival time and survival status of low- and high-risk groups for the Entire set, Test set and Train set. g–i. Heat-maps of 5 LAGs expressions in the Entire set, Test set and Train set. j. Forest plot summary of multivariable Cox regression analyses of the clinical features as well as risk score in GC patients. k. Nomogram plot based on LAGs scores and clinicopathological factors

Fig. 5

Functional enrichment analysis. a–d. GO and KEGG enrichment analyses of DEGs among two LAGs subtypes. *p < 0.05; **p < 0.01; ***p < 0.001

Fig. 6

Immune cell infiltration assessment. a–h. Correlations between LRGs scores and immune cell types. i. Correlations between 5 model genes and immune cell types. j. TME Estimate-Scores, Immunity-Scores, and Stromal-Scores measured between high- and low-risk groups. k. Immune infiltration patterns of two subtype groups were obtained using ssGSEA

Fig. 7

Analysis of immunotherapeutic responses between different risk groups. a. The expression of 15 immune checkpoints in the high- and low-risk groups. b. Correlations between the 5 model-related genes and risk score and immune checkpoints. c, d. Differences in MSI between patients in the high- and low-risk groups. e. Differences in TIDE scores between high- and low-risk groups. f. Relationships between LRG_score and CSC index. g. Comparison of IPS between the GC patients with high- and low-risk groups in the CTLA4 negative/positive or PD-1 negative/positive groups. CTLA4_positive or PD1_positive represents anti-CTLA4 or anti-PD-1/PD-L1 therapy, respectively

Fig. 8

Chemotherapeutic drug sensitivity analysis. Identification of sensitive chemotherapy drugs based on the risk scores. Box plots depicts the differences in the estimated IC50 levels of Bleomycin (a) Oxaliplatin (b) Epirubicin (c) Docetaxe (d) Cisplatin (e) Irinotecan (f) Paclitaxel (g) Uprosertib (h) 5-Fluorouracil (i) Gefitinib between the high- and low-risk groups

Fig. 9

Four LAGs expression in single-cell RNA sequencing. a, b. Annotation of all cell types in GSE134520 and the percentage of each cell type. c, d. Expression of SERPINE1, MMP11, HBB and VSIG2 in each cell type