Glioblastoma multiforme (GBM) is categorized by rapid malignant cellular growth in the central nervous system (CNS) tumors. It is one of the most prevailing primary brain tumors, particularly in human male adults. Even though the combination therapy comprises surgery, chemotherapy, and adjuvant therapies, the survival rate is on average 14.6 months. Glioma stem cells (GSCs) have key roles in tumorigenesis, progression, and counteracting chemotherapy and radiotherapy. In our study, firstly, the gene expression dataset GSE45117 was retrieved and differentially expressed genes (DEGs) were spotted. The co-expression network analysis was employed on DEGs to find the significant modules. The most significant module resulting from co-expression analysis was the turquoise module. The turquoise module related to the tumor cells, hypoxia, normoxic treatments of glioblastoma tumor (GBT), and GSCs were screened. Sixty-one common genes in the turquoise module were selected generated through the co-expression analysis and protein-protein interaction (PPI) network. Moreover, the GO and KEGG pathway enrichment results were studied. Twenty common hub genes were screened by the NetworkAnalyst web instrument constructed on the PPI network through the STRING database. After survival analysis via the Kaplan-Meier (KM) plotter from The Cancer Genome Atlas (TCGA) database, we identified the five most significant hub genes strongly related to the progression of GBM. We further observed these five most significant hub genes also up-regulated in another GBM gene expression dataset. The protein-protein interaction (PPI) network of the turquoise module genes was constructed and a KEGG pathway enrichments study of the turquoise module genes was performed. The VEGF signaling pathway was emphasized because of the strong link with GBM. A gene-disease association network was further constructed to demonstrate the information of the progression of GBM and other related brain neoplasms. All hub genes assessed through this study would be potential markers for the prognosis and diagnosis of GBM.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8951270PMC
http://dx.doi.org/10.3390/genes13030518DOI Listing

Publication Analysis

Top Keywords

hub genes
20
turquoise module
20
co-expression analysis
12
ppi network
12
genes
9
tumor cells
8
gene expression
8
expression dataset
8
protein-protein interaction
8
interaction ppi
8

Similar Publications

Identification, Clinical Values, and Prospective Pathway Signaling of Lipid Metabolism Genes in Epilepsy and AED Treatment.

Mol Neurobiol

January 2025

Department of Neurology, School of Medicine, Affiliated ZhongDa Hospital, Southeast University, Dingjiaqiao 87, Nanjing, 210009, Jiangsu, China.

The dysregulation of lipid metabolism has been associated with the etiology and progression of the neurological pathology. However, the roles of lipid metabolism and the molecular mechanism in epilepsy and the use of antiepileptic drugs (AEDs) are relatively understudied. Gene expression profiles of GSE143272 from blood samples were included for differential analysis, and the lipid metabolism-related differentially expressed genes (DEGs) were identified.

View Article and Find Full Text PDF

Objective: Rosmarinic acid (RosA) is a natural polyphenol compound that has been shown to be effective in the treatment of inflammatory disease and a variety of malignant tumors. However, its specific mechanism for the treatment of lung adenocarcinoma (LUAD) has not been fully elucidated. Therefore, this study aims to clarify the mechanism of RosA in the treatment of LUAD by integrating bioinformatics, network pharmacology and in vivo experiments, and to explore the potential of the active ingredients of traditional Chinese medicine in treating LUAD.

View Article and Find Full Text PDF

Objectives: This study aimed to comprehensively investigate the molecular landscape of gastric cancer (GC) by integrating various bioinformatics tools and experimental validations.

Methodology: GSE79973 dataset, limma package, STRING, UALCAN, GEPIA, OncoDB, cBioPortal, DAVID, TISIDB, Gene Set Cancer Analysis (GSCA), tissue samples, RT-qPCR, and cell proliferation assay were employed in this study.

Results: Analysis of the GSE79973 dataset identified 300 differentially expressed genes (DEGs), from which COL1A1, COL1A2, CHN1, and FN1 emerged as pivotal hub genes using protein-protein interaction network analysis.

View Article and Find Full Text PDF

Background: Alopecia areata (AA) is a common non-scarring hair loss disorder associated with autoimmune conditions. However, the pathobiology of AA is not well understood, and there is no targeted therapy available for AA.  METHODS: In this study, differential gene expression analysis, immune status assessment, weighted correlation network analysis (WGCNA), and functional enrichment analysis were performed to identify shared genes associated with both immunological response and AA.

View Article and Find Full Text PDF

Testicular germ cell tumour (TGCT) is a malignancy with known inherited risk factors, affecting young men. We have previously identified several hundred differentially abundant circulating RNAs in pre-diagnostic serum from TGCT cases compared to healthy controls. In this study, we performed Weighted Gene Co-expression Network Analysis (WGCNA) on mRNA and miRNA data from these samples.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!