Screening key genes and pathways in glioma based on gene set enrichment analysis and meta-analysis

Stoichioproteomics reveal oxygen usage bias, key proteins and pathways in glioma

Background

The five-year survival rate and therapeutic effect of malignant glioma is low. Identification of key/associated proteins and pathways in glioma is necessary for developing effective diagnosis and targeted therapy of glioma. In addition, Glioma involves hypoxia-specific microenvironment, whether hypoxia restriction influences the stoichioproteomic characteristics of expressed proteins is unknown.

Methods

In this study, we analyzed the most comprehensive immunohistochemical data from 12 human glioma samples and 4 normal cell types of cerebral cortex, identified differentially expressed proteins (DEPs), and researched the oxygen contents of DEPs, highly and lowly expressed proteins. Further we located key genes on human genome to determine their locations and enriched them for key functional pathways.

Results

Our results showed that although no difference was detected on whole proteome, the average oxygen content of highly expressed proteins is 6.65% higher than that of lowly expressed proteins in glioma. A total of 1480 differentially expressed proteins were identified in glioma, including 226 up regulated proteins and 1254 down regulated proteins. The average oxygen content of up regulated proteins is 2.56% higher than that of down regulated proteins in glioma. The localization of differentially expressed genes on human genome showed that most genes were on chromosome 1 and least on Y. The up regulated proteins were significantly enriched in pathways including cell cycle, pathways in cancer, oocyte meiosis, DNA replication etc. Functional dissection of the up regulated proteins with high oxygen contents showed that 51.28% of the proteins were involved in cell cycle and cyclins.

Conclusions

Element signature of oxygen limitation could not be detected in glioma, just as what happened in plants and microbes. Unsaved use of oxygen by the highly expressed proteins and DEPs were adapted to the fast division of glioma cells. This study can help to reveal the molecular mechanism of glioma, and provide a new approach for studies of cancer-related biomacromolecules. In addition, this study lays a foundation for application of stoichioproteomics in precision medicine.

Electronic supplementary material

The online version of this article (10.1186/s12920-019-0571-y) contains supplementary material, which is available to authorized users.

Gene set enrichment analysis and meta-analysis identified 12 key genes regulating and controlling the prognosis of lung adenocarcinoma

The aim of the present study was to analyze lung adenocarcinoma-associated microarray data and identify potentially crucial genes. The gene expression profiles were downloaded from the Gene Expression Omnibus database and 6 datasets, of which 2 were discarded and 4 were retained, were preprocessed using packages in the R computing language. Subsequently, Gene Set Enrichment Analysis (GSEA) and meta-analysis was used to screen the common pathways and differentially expressed genes at the transcriptional level. The genes detected from GSEA through The Cancer Genome Atlas databases were subsequently examined, and the crucial genes by survival data were identified. Pathways of the crucial genes were obtained using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of the online website Database for Annotation, Visualization and Integrated Discovery (DAVID) tool, and the pathways of crucial genes that were upregulated or downregulated were matched using the Venn method to identify the common crucial pathways. Furthermore, on the basis of the common crucial pathways, key genes that are closely associated with the development and progression of lung adenocarcinoma were identified with the KEGG pathway of DAVID. Additional information was obtained through Gene Ontology annotation. A total of two key pathways, including cell cycle and DNA replication, as well as 12 key genes [DNA polymerase δ subunit 2, DNA replication licensing factor MCM4, MCM6, mitotic checkpoint serine/threonine-protein kinase BUB1, BUB1β, mitotic spindle assembly checkpoint protein MAD2A, dual specificity protein kinase TTK, M-phase inducer phosphatase 1, cell division control protein 45 homolog, cyclin-dependent kinase inhibitor 1C, pituitary tumor-transforming gene 1 protein and polo-like kinase 1] were identified. These key pathways and genes may be studied in future studies involving gene transfection/knockdown, which may provide insights into the prognosis of lung adenocarcinoma. Additional studies are required to confirm their biological function.

A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma

Differential co-expression network analyses have recently become an important step in the investigation of cellular differentiation and dysfunctional gene-regulation in cell and tissue disease-states. The resulting networks have been analyzed to identify and understand pathways associated with disorders, or to infer molecular interactions. However, existing methods for differential co-expression network analysis are unable to distinguish between various forms of differential co-expression. To close this gap, here we define the three different kinds (conserved, specific, and differentiated) of differential co-expression and present a systematic framework, CSD, for differential co-expression network analysis that incorporates these interactions on an equal footing. In addition, our method includes a subsampling strategy to estimate the variance of co-expressions. Our framework is applicable to a wide variety of cases, such as the study of differential co-expression networks between healthy and disease states, before and after treatments, or between species. Applying the CSD approach to a published gene-expression data set of cerebral cortex and basal ganglia samples from healthy individuals, we find that the resulting CSD network is enriched in genes associated with cognitive function, signaling pathways involving compounds with well-known roles in the central nervous system, as well as certain neurological diseases. From the CSD analysis, we identify a set of prominent hubs of differential co-expression, whose neighborhood contains a substantial number of genes associated with glioblastoma. The resulting gene-sets identified by our CSD analysis also contain many genes that so far have not been recognized as having a role in glioblastoma, but are good candidates for further studies. CSD may thus aid in hypothesis-generation for functional disease-associations.

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

The aim of the present study was to identify the disease‑associated genes and their functions involved in the development of three types of glioma (astrocytoma, glioblastoma and oligodendroglioma) with DNA microarray technology, and to analyze their differences and correlations. First, the gene expression profile GSE4290 was downloaded from the Gene Expression Omnibus database, then the probe‑level data were pre‑processed and the differentially expressed genes (DEGs) were identified with limma package in R language. Gene functions of the selected DEGs were further analyzed with the Database for Annotation, Visualization and Integrated Discovery. After the co‑expression network of DEGs was constructed by Cytoscape, the functional modules were mined and enrichment analysis was performed, and then the similarities and differences between any two types of glioma were compared. A total of 1151 genes between normal and astrocytoma tissues, 684 genes between normal and malignant glioma tissues, and 551 genes between normal and oligodendroglioma tissues were filtered as DEGs, respectively. By constructing co‑expression networks of DEGs, a total of 77232, 455 and 987 interactions were involved in the differentially co‑expressed networks of astrocytoma, oligodendroglioma and glioblastoma, respectively. The functions of DEGs were consistent with the modules in astrocytoma, glioblastoma and oligodendroglioma, which were mainly enriched in neuron signal transmission, immune responses and synthesis of organic acids, respectively. Model functions of astrocytoma and glioblastoma were similar (mainly related with immune response), while the model functions of oligodendroglioma differed markedly from that of the other two types. The identification of the associations among these three types of glioma has potential clinical utility for improving the diagnosis of different types of glioma in the future. In addition, these results have marked significance in studying the underlying mechanisms, distinguishing between normal and cancer tissues, and examining novel therapeutic strategies for patients with glioma.