O-GlcNAcylation-related genes mediate tumor microenvironment characteristics and prediction of immunotherapy response in gastric cancer

Abstract

We aim to identify molecular clusters related to O-GlcNAcylation and establish a novel scoring system for predicting prognosis and immunotherapy efficacy in patients with gastric cancer (GC). The transcriptomic and clinical data are obtained from XENA-UCSC and GEO databases. The O-GlcNAcylation-related genes are obtained from the GSEA database. Consensus clustering analysis is employed to identify O-GlcNAcylation-related molecular clusters, and principal component analysis (PCA) is utilized to develop a novel prognostic scoring system for predicting GC outcomes and immunotherapy efficacy. The prognostic accuracy of the scoring system is assessed across five real-world cohorts. The biological function of actin alpha 2, smooth muscle (ACTA2) in GC is determined through experimental verification. Using 34 O-GlcNAcylation-related genes associated with prognosis in GC patients, these individuals are divided into two distinct subgroups characterized by different outcomes, tumor microenvironment profiles, and clinical case characteristics. The DEGs between the two subgroups are subsequently used to further divide the GC patients into two subgroups by consensus cluster analysis. PCA is used to construct a prognostic scoring system, which reveal that patients in the low-score subgroup have a better prognosis and greater benefit from immunotherapy. The accuracy of the scoring system is confirmed through validation in a cohort of patients receiving immunotherapy in the real world. ACTA2 promotes proliferation and inhibits apoptosis in GC cells. These findings suggest that we successfully establish molecular clusters associated with O-GlcNAcylation and develop a scoring system that demonstrates strong performance in predicting the prognosis of patients with GC and the effect of immunotherapy interventions.

1. Introduction

Gastric cancer (GC) is the fifth most common cause of cancer death worldwide [1]. Because GC is often diagnosed at an advanced stage, it has a high mortality rate and is the third leading cause of cancer-related death worldwide, with 784,000 deaths in 2018 [2]. Despite advances in checkpoint inhibitor-based cancer immunotherapy [3], the prognosis for patients with GC remains unfavorable, which poses challenges for clinicians. Therefore, it is necessary to further study the mechanisms of different responses to immunotherapy and to design prognostic and efficacy prediction tools.

O-linked N-acetylglucosaminylation is a form of glycosylation that occurs when monosaccharide O-GlcNAc is added to serine or threonine residues of nuclear or cytoplasmic proteins by O-GlcNAc transferase (OGT) and can be reversibly removed by O-GlcNAcase (OGA) [4]. O-GlcNAcylation is the subject of extensive research in the field of oncology. It is significantly upregulated in most cancer types and plays a key role in integrating nutrient fluxes with metabolic pathways essential for tumor cell proliferation and growth. Moreover, O-GlcNAcylation has a regulatory function on many proteins involved in the initiation and development of cancer [5,6]. For example, O-GlcNAcylation plays a significant role in enhancing the stability of the Reticulon 2 protein, thereby contributing to GC progression [7]. Wu L et al. [8] focused on the role of O-GlcNAcylation in tumor immune evasion. Specifically, O-GlcNAcylation plays a catalytic role in this process by inhibiting the lysosomal degradation of programmed death-ligand 1 (PD-L1). This mechanism suggests that O-GlcNAcylation is a potential intervention target for enhancing the tumor immune response. OGT is also involved in transcriptional regulation by regulating transcription factors [9–11]. Studies have shown that O-GlcNAcylation of CD36 enhances cellular fatty acid uptake activity, which is critical for GC invasion [12]. Although many studies have investigated the relationship between O-GlcNAcylation and GC, relatively few studies have investigated O-GlcNAcylation as a predictive biomarker. Thus, novel predictive biomarkers are needed to improve the accuracy of immune checkpoint inhibitor (ICI) therapy in GC patients.