Gliomas is a solid tumor of the primary central nervous system that originates from glial cells and it is highly aggressive and fatal. [1] Glioma is the most common type of primary intracranial malignant tumor and, accounts for ∼ 51.4% of all primary brain tumors [2]. Moreover, glioma accounts for ∼80% of primary malignant tumors of the central nervous system. In 2016, the World Health Organization (WHO) classified glioma into grades I-IV, which is categorized via malignancy, active mitosis and necrosis [3]. WHO grades I / II represent low-grade gliomas, whereas WHO grades III/ IV indicate high-grade gliomas, whose malignancy is increasing [4]. Each grade has relative specificity to guide subsequent clinical treatment. Furthermore, other than a small number of low-grade gliomas, such as hair-cell astrocytoma, that can be cured via surgery, the vast majority of gliomas are WHO grade III anaplastic glioblastoma (GBM) and WHO Grade IV GBM, which are considered to be nearly incurable, which the average survival time ∼1 year. Previous studies have reported a 5-year survival rate of only 9.8% [5]. Currently, surgical resection adjuvant radiotherapy and temozolomide adjuvant chemotherapy combined with radiotherapy are the standard treatment for this disease. However, the overall survival of patients with GBM ∼15 months [4], which may be attributed to the limitations of treatment, the diffuse nature of GBM, and the incomplete understanding of tumor pathophysiology [3]. Moreover, gliomas are resistant to chemoradiotherapy, which leads to a high recurrence rate and poor therapeutic effect. As a result, patients with GBM do not significantly benefit from standard treatment. Numerous tumors respond poorly to conventional chemotherapy and radiation, and chemotherapies that are able to control the tumor often lack a lasting therapeutic response [6]. Although numerous underlying mechanisms in glioma tumorigenesis have been identified, including p53 and receptor tyrosine kinase signaling pathways, little is known about the genetic factors and specific underlying mechanisms of human gliomas. [7] There is therefore an urgent need to develop new diagnostic methods and understand oncogenic signaling pathways to develop more effective therapeutic strategies.

With the popularization and development of computer technology, there are increasing numbers of high-throughput platforms for gene expression analysis. Microarray analysis and next-generation sequencing technologies are becoming more and more increasingly important in the field of oncology medicine and have a wide been demonstrated to have a wide range of clinical applications [8]. Microarray analysis based on high-throughput platforms is highly effective tool for screening virtual genetic or epigenetic changes in cancer development. Microarray analysis is also frequently used for cancer diagnosis and in prognostic applications. [9].

Large-scale gene expression profiles provide a powerful means of identifying transcriptional networks associated with up-regulated signaling pathways. This approach also allows for the discovery of previously unrecognized tumor subtypes with different molecular and/or clinical phenotype or therapeutics responses. Gene expression profiles can also be used to detect specific mutations linked to unique molecular characteristics [10].

Numerous tumor-related databases have previously been established. Over the past decade, the Gene Expression Omnibus (GEO) database has become the primary public archive of high-throughput microarray and sequence-based functional genome datasets. Furthermore, as well as archiving, cross-linking, and making large amounts of data available for free download, GEO offers several user-friendly web-based tools and policies to assist user in querying and analyzing the data [11]. Moreover, the applications of bioinformatics analysis are constantly changing. This therefore indicates that genetic molecular diagnosis and genetic analysis are serving an important role in disease [9].

Gene expression microarrays help to identify differentially expressed genes (DEGs) between tumor and normal tissues. These genes may be meaningful biomarkers for cancer diagnosis and treatment. However, the key molecular targets of glioma have not been identified.

Using different bioinformatics analysis tools, such as gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, to study expression profile data of certain diseases, bioinformatics analysis is a means to find potential biomarkers that are associated with diseases.

In order to improve the diagnosis and treatment of gliomas, the present study searched for protein-coding genes that were differentially expressed between glioma and normal tissues via bioinformatics analysis of the GEO database. Bioinformatics analysis tools, such as GO functional enrichment analysis were used to identify the biological functions of the identified genes. The protein-protein interaction (PPI) network of DEGs was constructed to identify the hub genes. In the present study, data was extracted from the GSE108474, GSE109857 and GSE116520 datasets to screen 400 common DEGs in normal and glioma patient tissues. Subsequently, the functions and enrichment pathways of the DEGs were analyzed. Furthermore, the PPI networks of DEGs was constructed to identify hub genes. The key genes identified were analyzed via reverse transcription-quantitative PCR (RT-qPCR) to verify results, and the analysis results were further verified by western blot.