基于公共数据库的胶质瘤预后相关基因筛选

doi:10.3969/j.issn.1004-616x.2024.03.005

Abstract

Abstract: OBJECTIVE：Due to the high invasiveness and mortality of glioma，it is necessary to identify prognostic markers，such as glioma-associated hub genes，for improved treatment of this cancer. METHODS：Based on the Gene Expression Omnibus (GEO) database and limma R package，differentially expressed genes of glioma were downloaded，and oxidative stress-related genes based on the Genecard database. GSE31095 dataset (population from Netherlands and Sweden) was downloaded from the GEO database. Based on the GSE31095 dataset and limma R package，differentially expressed genes of glioma were identified. Hub genes were investigated using the protein-protein interaction (PPI)，the Gene Ontology (GO)，and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The Cancer Genome Atlas (TCGA) databases (population from the USA) and Chinese Glioma Genome Atlas (CGGA) databases (population from China) were used to verify the hub genes. Subsequently，random forest analysis，Kaplan-Meier analysis，and Cox proportional hazard analysis were conducted on the hub genes using the clinical data from the CGGA databases (mRNAseq_325). These analyses aimed to elucidate the diagnostic and prognostic significance of the identified hub genes. RESULTS：214 differentially expressed genes were identified，of which 205 were up-regulated and 9 were down-regulated. GO function enrichment analysis yielded 3 entries，including biosynthetic processes，translation processes，and ribosomes. The KEGG pathway enrichment analysis yielded 2 signaling pathways which were mainly involved in the immune system and antigen presentation. Ten hub genes were selected，and they were consistent with the results verified by the TCGA and CGGA cohorts. Four key genes，RPL7，RPL8，RPS3A，and RPS7，were identified with the overlap results from random forest algorithm，KM，and ggrisk analyses. The area under the ROC curve for the risk model for prognosis of gliomas was 0.691 at 1 year，0.687 at 3 years，and 0.685 at 5 years. CONCLUSION：Utilizing bioinformatics methods，the identification of hub genes in gliomas showed a novel avenue that could serve as a reference point for both clinical prognostic assessment and the development of new therapeutic strategies.

Key words: glioma, bioinformatics, hub gene, prognosis

CLC Number:

R739.41

ZHANG Yi, GAO Han, ZHENG Zhanyue, TAN Qitao, YANG Minli, SUN Yan. Screening of key genes for prognosis of glioma based on public databases[J]. Carcinogenesis, Teratogenesis & Mutagenesis, 2024, 36(3): 195-201.

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Screening of key genes for prognosis of glioma based on public databases

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