Gene co-expression network and function modules in three types of glioma

COL3A1 and SNAP91: novel glioblastoma markers with diagnostic and prognostic value

Although patients with glioblastoma (GBM) have grave prognosis, significant variability in patient outcome is observed. This study aims to identify novel targets for GBM diagnosis and therapy. Microarray data (GSE4290, GSE7696, and GSE4412) obtained from the Gene Expression Omnibus was used to identify the differentially expressed genes (DEGs) by significant analysis of microarray (SAM). Intersection of the identified DEGs for each profile revealed 46 DEGs in GBM. A subset of common DEGs were validated by real-time reverse transcription quantitative PCR (qPCR). The prognostic value of some of the markers was also studied. We determined that RRM2 and COL3A1 were increased and directly correlated with glioma grade, while SH3GL2 and SNAP91 were decreased in GBM and inversely correlated with glioma grade. Kaplan-Meir analysis of GSE7696 revealed that COL3A1 and SNAP91 correlated with survival, suggesting that COL3A1 and SNAP91 may be suitable biomarkers for diagnostic or therapeutic strategies for GBM.

The Identification of Key Genes and Pathways in Glioma by Bioinformatics Analysis

Glioma is the most common malignant tumor in the central nervous system. This study aims to explore the potential mechanism and identify gene signatures of glioma. The glioma gene expression profile GSE4290 was analyzed for differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were applied for the enriched pathways. A protein-protein interaction (PPI) network was constructed to find the hub genes. Survival analysis was conducted to screen and validate critical genes. In this study, 775 downregulated DEGs were identified. GO analysis demonstrated that the DEGs were enriched in cellular protein modification, regulation of cell communication, and regulation of signaling. KEGG analysis indicated that the DEGs were enriched in the MAPK signaling pathway, endocytosis, oxytocin signaling, and calcium signaling. PPI network and module analysis found 12 hub genes, which were enriched in synaptic vesicle cycling rheumatoid arthritis and collecting duct acid secretion. The four key genes CDK17, GNA13, PHF21A, and MTHFD2 were identified in both generation (GSE4412) and validation (GSE4271) dataset, respectively. Regression analysis showed that CDK13, PHF21A, and MTHFD2 were independent predictors. The results suggested that CDK17, GNA13, PHF21A, and MTHFD2 might play important roles and potentially be valuable in the prognosis and treatment of glioma.

Integrating Epigenomics into the Understanding of Biomedical Insight

Epigenetics is one of the most rapidly expanding fields in biomedical research, and the popularity of the high-throughput next-generation sequencing (NGS) highlights the accelerating speed of epigenomics discovery over the past decade. Epigenetics studies the heritable phenotypes resulting from chromatin changes but without alteration on DNA sequence. Epigenetic factors and their interactive network regulate almost all of the fundamental biological procedures, and incorrect epigenetic information may lead to complex diseases. A comprehensive understanding of epigenetic mechanisms, their interactions, and alterations in health and diseases genome widely has become a priority in biological research. Bioinformatics is expected to make a remarkable contribution for this purpose, especially in processing and interpreting the large-scale NGS datasets. In this review, we introduce the epigenetics pioneering achievements in health status and complex diseases; next, we give a systematic review of the epigenomics data generation, summarize public resources and integrative analysis approaches, and finally outline the challenges and future directions in computational epigenomics.