Fatty acid metabolism is related to the immune microenvironment changes of gastric cancer and RGS2 is a new tumor biomarker

Abstract

BackgroundAlterations in lipid metabolism promote tumor progression. However, the role of lipid metabolism in the occurrence and development of gastric cancer have not been fully clarifiedMethodHere, genes that are related to fatty acid metabolism and differentially-expressed between normal and gastric cancer tissues were identified in the TCGA-STAD cohort. The intersection of identified differentially-expressed genes with Geneset was determined to obtain 78 fatty acid metabolism-related genes. The ConsensusClusterPlus R package was used to perform differentially-expressed genes, which yielded divided two gastric cancer subtypes termed cluster 1 and cluster 2.ResultsPatients in cluster 2 was found to display poorer prognosis than patients in cluster 1. Using machine learning method to select 8 differentially expressed genes among subtypes to construct fatty acid prognostic risk score model (FARS), which was found to display good prognostic efficacy. We also identified that certain anticancer drugs, such as bortezomib, elesclomol, GW843682X, and nilotinib, showed significant sensitivity in the high FARS score group. RGS2 was selected as the core gene upon an analysis of the gastric cancer single-cell, and Western blotting and immunofluorescence staining results revealed high level of expression of this gene in gastric cancer cells. The results of immunohistochemical staining showed that a large amount of RGS2 was deposited in the stroma in gastric cancer. A pan-cancer analysis also revealed a significant association of RGS2 with TMB, TIDE, and CD8+ T-cell infiltration in other cancer types as well. RGS2 may thus be studied further as a new target for immunotherapy in future studies on gastric cancer.ConclusionIn summary, the FARS model developed here enhances our understanding of lipid metabolism in the TME in gastric cancer, and provides a theoretical basis for predicting tumor prognosis and clinical treatment.

Exploring the current landscape of single‐cell RNA sequencing applications in gastric cancer research

“…ML approaches also contributed to the development of a fatty acid prognostic risk model (FARS), which showed promising prognostic efficacy. Through single‐cell analysis, differentially expressed genes and fatty acid metabolism in gastric tumour tissues were analysed, leading to the discovery of a novel biomarker, the RGS2 gene 32 . These ML methods have proven valuable in predicting treatment response, identifying prognostic markers and uncovering biological insights, offering the potential for improving tumour prognosis and clinical decision‐making.…”

“…(2022) found regulator of G protein signalling 2 (RGS2) to be a promising immunotherapy target, and Jiang et al. (2022) identified PD‐1 expression in CD8+ T cells as a predictive marker for treatment response 27,32 . Kang et al.…”

“…Pseudo‐time analysis and trajectory inference algorithms have played important roles in understanding the progression and cellular dynamics of gastric tumours 32 . Spatial transcriptomics and single‐cell assays for transposase‐accessible chromatin sequencing (scATAC‐seq) provide valuable spatial context and information on transcription factors (TFs) 8 .…”

“…23 However, it is critical to further investigate the generalisability of ML models, as their performance varied when validated with clinical data from the STAD-TCGA dataset. 32 ML approaches also contributed to the development of a fatty acid prognostic risk model (FARS), which showed promising prognostic efficacy. Through single-cell analysis, differentially expressed genes and fatty acid metabolism in gastric tumour tissues were analysed, leading to the discovery of a novel biomarker, the RGS2 gene.…”

“…3 Pseudo-time analysis and trajectory inference algorithms have played important roles in understanding the progression and cellular dynamics of gastric tumours. 32 Spatial transcriptomics and single-cell assays for transposase-accessible chromatin sequencing (scATAC-seq) provide valuable spatial context and information on transcription factors (TFs). 8 Computational tools like Harmony have been developed to address batch effects and enable the integration and comparison of scRNA-seq datasets from different experiments or platforms.…”