Screening of key genes co-regulating immune and mitochondrial energy metabolism and analysis of immune infiltration in glioma based on the Cancer Genome Atlas database
10.3760/cma.j.cn115355-20240322-00128
- VernacularTitle:基于癌症基因组图谱数据库的胶质瘤中协同调控免疫和线粒体能量代谢关键基因筛选及其免疫浸润分析
- Author:
Dan HUA
1
;
Qiang GE
;
Liushuan CHANG
;
Yifan HE
;
Yongheng SHI
Author Information
1. 天津医科大学总医院 天津市神经病学研究所神经病理学研究室，天津 300052
- Keywords:
Glioma;
Immune infiltration;
Mitochondria;
Energy metabolism
- From:
Cancer Research and Clinic
2024;36(7):496-502
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To screen key genes that co-regulate immune and mitochondrial energy metabolism through bioinformatics methods and to investigate the relationship between the key genes and immune infiltration.Methods:A total of 671 glioma samples (the tumor group) and 5 non-tumor brain tissue samples (the control group) were collected from the Cancer Genome Atlas (TCGA) database on November 13, 2023. Through a comprehensive search of the GeneCards database and immune-related genes (IRG) and mitochondrial energy metabolism-related genes (MEMRG) in previous published literatures, 76 IRG and MEMRG (IR & MEMRG) were obtained by taking the intersection of IRG and MEMRG after merging and deduplicating. The limma package in R software was used to screen the differentially expressed genes (DEG) between the tumor group and the control group. Then, immune-related & mitochondrial energy metabolism-related differentially expressed genes (IR&MEMRDEG) were obtained by intersecting with IR & MEMRG. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on the IR&MEMRDEG through the clusterProfiler package in R software. The STRINGv12.0 online database (https://cn.string-db.org/) was employed to construct a protein interaction network based on IR&MEMRDEG and to identify the top 5 key core genes. Single-sample gene-set enrichment analysis (ssGSEA) was used to determine the relative abundance of immune cell infiltration in all samples, and the immune cell infiltration matrices for both the tumor and the control groups were acquired. The expression differences in infiltration abundance of the immune cells in the tumor group and the control group were analyzing by using the ggplot2 package in R software. The heat map was drawn by utilizing the R software pheatmap package to show self-correlation of immune cells. The correlation between the top 5 key genes in the protein interaction network and immune cells was calculated by using the Spearman algorithm and the R software ggplot2 package.Results:A total of 3 623 DEGs were identified from the TCGA database in both groups, including 1 711 up-regulated genes and 1 912 down-regulated genes. After taking the intersection of DEG and IR&MEMRG, 11 IR&MEMRDEG were obtained including EIF4EBP1, TP53, IDH1, PRCKZ, CD200, GPI, PGM2, PKLR, AK2, ATP4A, and ALDH3B1. GO enrichment analysis results showed that 11 IR&MEMRDEG were mainly enriched in ADP metabolic process, ATP metabolic process, purine nucleoside diphosphate metabolic process, purine ribonucleoside diphosphate metabolic process, and ribonucleoside diphosphate metabolic process at the biological level; in the fibronectin-1 rich granule, secretory granule lumen, cytoplasmic vesiclelumen, vesiclelumen, and nuclear matrix at the cellular component level; in magnesium ion binding, potassium ion binding, and alkali metal ion binding at the molecular functional level. The KEGG enrichment analysis results showed that 11 IR&MEMRDEGs were mainly enriched in glycolysis/gluconeogenesis, carbon metabolism, insulin signaling pathway, pentose phosphate pathway, starch and sucrose metabolism signaling pathways. The protein interaction network analysis from the STRING database revealed that 5 highest scoring core proteins were identified, namely EIF4EBP1, TP53, IDH1, PRKCZ, and AK2.The immune infiltration abundances of 28 immune cells were calculated by using the ssGSEA algorithm. The infiltration abundance of 15 immune cells in the tumor group was higher than that in the control group, and the differences were statistically significant (all P < 0.05). The findings from the immune infiltration analysis indicated a positive correlation among 15 types of immune cells, in which there was a strongest correlation between effector memory CD8 + T cell and myeloid derived suppressor cells. EIF4EBP1, TP53, IDH1, and AK2 exhibited a positive correlation with a large number of immune cells (all P < 0.05), whereas PRKCZ demonstrated a negative correlation with more immune cells (all P < 0.05). Conclusions:PRKCZ, AK2, and EIF4EBP1 have the potential to be the new targets of immunotherapy for gliomas.
