检索历史 清除

目的:基于生物信息学方法筛选胶质瘤中协同调控免疫和线粒体能量代谢的关键基因，探讨这些基因与免疫浸润的关系。方法:从癌症基因组图谱（TCGA）数据库中收集671例胶质瘤样本（肿瘤组）和5例非肿瘤脑组织样本（对照组），数据下载时间为2023年11月13日。检索GeneCards数据库和已发表文献中免疫相关基因（IRG）和线粒体能量代谢相关基因（MEMRG），经合并去重复后，对IRG和MEMRG取交集共得到76个免疫和线粒体能量代谢共相关基因（IR&MEMRG）。使用R软件limma包，筛选肿瘤组和对照组中的差异表达基因（DEG），与IR&MEMRG取交集得到胶质瘤中免疫和线粒体能量代谢共相关差异表达基因（IR&MEMRDEG）。采用R软件中clusterProfiler包对IR&MEMRDEG进行基因本体（GO）及京都基因和基因组百科全书（KEGG）富集分析。使用STRINGv12.0在线数据库（https：//cn.string-db.org/），利用获得的IR&MEMRDEG构建蛋白质互作网络，获得排名前5位的关键核心基因。采用单样本基因集富集分析（ssGSEA）分析所有样本中各免疫细胞浸润的相对丰度，得出样本中免疫细胞浸润矩阵。利用R软件中ggplot2包分析免疫细胞浸润丰度在肿瘤组和对照组间的差异；R软件pheatmap包绘制相关性热图展示免疫细胞自身的相关性，基于Spearman算法并利用R软件ggplot2包计算蛋白质互作网络内排名前5位的关键核心基因与免疫细胞的相关性。结果:TCGA数据库中，肿瘤组和对照组有3 623个DEG；其中上调表达基因1 711个，下调表达基因1 912个。对DEG与获得的IR&MEMRG取交集，得到11个IR&MEMRDEG，分别为EIF4EBP1、TP53、IDH1、PRCKZ、CD200、GPI、PGM2、PKLR、AK2、ATP4A和ALDH3B1。GO富集分析结果显示，11个IR&MEMRDEG在生物过程层面主要富集在ATP代谢和ADP代谢、嘌呤核苷二磷酸代谢、嘌呤核糖核苷二磷酸代谢和核糖核苷二磷酸代谢；细胞成分层面主要富集在纤维胶凝蛋白-1富集颗粒、分泌颗粒腔、细胞质囊泡腔、囊泡腔和核基质中；分子功能层面主要富集于镁离子结合、钾离子结合和碱金属离子结合。KEGG富集分析结果显示，11个IR&MEMRDEG主要富集在糖酵解/糖异生、碳代谢、胰岛素信号通路、磷酸戊糖通路、淀粉和蔗糖代谢信号通路中。采用STRING在线数据库对11个IR&MEMRDEG进行分析，得到前5个得分最高的节点蛋白，分别为EIF4EBP1、TP53、IDH1、PRKCZ和AK2。通过ssGSEA算法计算28种免疫细胞的免疫浸润丰度，肿瘤组15种免疫细胞浸润丰度均高于对照组，差异均有统计学意义（均 P<0.05）。15种免疫细胞间均呈正相关，其中效应记忆性CD8 + T细胞与髓源性抑制细胞的相关性最强；EIF4EBP1、TP53、IDH1和AK2与较多免疫细胞之间均呈正相关（均 P<0.05）；PRKCZ与较多免疫细胞均呈负相关（均 P<0.05）。 结论:胶质瘤中PRKCZ、AK2和EIF4EBP1可能是胶质瘤免疫治疗新靶点。

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.