Endoplasmic reticulum stress-related gene expression patterns in glioblastoma models identified by single-cell RNA sequencing: Implications for prognosis and treatment

Abstract

Objective: Endoplasmic reticulum (ER) stress has therapeutic potential for a variety of malignancies, including glioma. In this study, bioinformatics was used to analyze ER stress-related genes (ERGs) in glioblastoma (GBM), explore their functions and pathways, construct prognostic models, and explore new treatment strategies. Methods: Various bioinformatics algorithms were utilized to screen for ERGs and construct a risk model. According to the expression of ERGs, different subtypes were classified using the consensus clustering method. Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA) were performed on the subtypes. Based on screened risk genes, GBM patients were divided into Train and Test groups in a 1:1 ratio. The prognostic model was validated through Receiver Operating Characteristic (ROC) curve analysis and independent prognostic analysis. The model was further validated by comparing the risk scores between high-risk and low-risk groups, and comparisons were made in terms of survival time, immune microenvironment, and pathway regulation. Drug sensitivity was used to screen drugs for low- and high- risk group, and single-cell RNA sequencing (scRNA-seq) analysis were utilized to explore the expression distribution of risk genes in GBM. Results: According to the ERGs, GBM samples can be divided into two groups with significant differences. Cluster A showed better survival rates compared to Cluster B. GSVA and GSEA analysis revealed that Cluster A was mainly enriched in glutamate receptor signaling pathway, synaptic transmission between neurons, postsynaptic density membrane, postsynaptic membrane, and synaptic vesicle membrane functions. It is worth noting that 8 ERGs were screened as model genes, which can effectively and independently predicate the survival risk of GBM patients with high accuracy and discrimination ability. Subsequently, changes in immune cell populations were observed in high-risk and low-risk groups, with differences in memory B cells and resting CD4 memory T cells between the high-risk and low-risk groups. The high-risk group had higher levels of memory B cells, while the low-risk group had higher levels of resting CD4 memory T cells. Furthermore, potential therapeutic strategies were identified, with BI-2536, Daporinad, SB505124, UMI-77, and Vorinostat identified for the low-risk group, while AZD8055, Camptothecin, Gemcitabine, PD0325901, and Topotecan identified for the high-risk group. scRNA-seq identified Ribosomal Protein L10 (RPL10) as one of the eight ER stress-related genes, primarily expressed in malignant cells of various tumors. Conclusion: This study identified eight ERGs and constructed a risk model based on bioinformatics analysis, which can be used for prognosis prediction and drug screening.