Identification of potential key genes associated with glioblastoma based on the gene expression profile

Bo, Lulong; Wei, Bo; Li, Chaohui; Wang, Zhanfeng; Gao, Zheng; Zhang, Miao

doi:10.3892/ol.2017.6460

Cited by 15 publications

(13 citation statements)

References 67 publications

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“…By querying the public TCGA stomach adenocarcinoma sequencing data, 78 genes were implicated possibly being co-expressed with cyclin E. Pearson correlation coefficient evaluation of the correlation between the co-expression of these genes and cyclin E used a coefficient threshold set at 0.4. 19 , 20 The 78 genes included 5 non-coding genes ( LOC642852 , PHF2P1 , BAIAP2-AS1 , CSNK1A1P1 , and MIMT1 ). These non-coding genes and their transcripts (i.e.…”

Section: Discussionmentioning

confidence: 99%

“…This study sought to identify genes potentially co-expressed with the cyclin E oncogene in gastric cancer and to clarify the probable regulatory mechanisms. By querying the public TCGA stomach adenocarcinoma sequencing data, 78 genes were implicated possibly being co-expressed with cyclin E. Pearson correlation coefficient evaluation of the correlation between the co-expression of these genes and cyclin E used a coefficient threshold set at 0.4 19, 20. The 78 genes included 5 non-coding genes ( LOC642852 , PHF2P1 , BAIAP2-AS1 , CSNK1A1P1 , and MIMT1 ).…”

Section: Discussionmentioning

confidence: 99%

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Analysis of cyclin E co‐expression genes reveals nuclear transcription factor Y subunit alpha is an oncogene in gastric cancer

Bie

et al. 2019

Chronic Diseases and Translational Medicine

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Objective To explore genes potentially co-expressed with cyclin E in gastric cancer and discover possible targets for gastric cancer treatment. Methods The Cancer Genome Atlas (TCGA) stomach adenocarcinoma sequencing data were used to predict genes co-expressed with cyclin E. Co-expression genes predicted by cBioPortal online analysis with Pearson correlation coefficient ≥0.4 were analyzed by gene ontology (GO) enrichment annotation using the PANTHER online platform (Ver. 7). Interactions between proteins encoded by these genes were analyzed using the STRING online platform (Ver. 10.5) and Cytoscape software (Ver. 3.5.1). Genes displaying a high degree of connection were analyzed by transcription factor enrichment prediction using FunRich software (Ver. 3). The significant transcription factor and cyclin E expression levels and their impact on gastric cancer progression were analyzed by Western blotting and Kaplan–Meier survival curve analysis. Results After filtering the co-expression gene prediction results, 78 predicted genes that included 73 protein coding genes and 5 non-coding genes with Pearson correlation coefficient ≥0.4 were selected. The expressions of the genes were considered to be correlated with cyclin E expression. Among the 78 genes co-expressed with cyclin E, 19 genes at the central of the regulatory network associated with cyclin E were discovered. Nuclear transcription factor Y subunit alpha (NF-YA) was identified as a significant transcription factor associated with cyclin E co-expressing genes. Analysis of specimen donors’ clinical records revealed that high expression of NF-YA tended to be associated with increased cyclin E expression. The expression of both was associated with progression of gastric cancer. Western blotting results showed that compared with normal tissues, NF-YA and cyclin E were highly expressed in tumor tissues ( P < 0.001). Survival curve analysis clearly demonstrated relatively poor overall survival of gastric cancer patients with high cyclin E or high NF-YA expression level, compared to patients with low cyclin E or NF-YA expression ( P < 0.05). Conclusions NF-YA may promote gastric cancer progression by increasing the transcription of cyclin E and other cell cycle regulatory genes. NF-YA might be a potential therapeutically useful prognostic factor for gastric cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Analysis of cyclin E co‐expression genes reveals nuclear transcription factor Y subunit alpha is an oncogene in gastric cancer

Bie

et al. 2019

Chronic Diseases and Translational Medicine

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“…Nonetheless, no subtypespecific genetic signature was found [64]. There are also a number of other research papers devoted to the study of the molecular characteristics of one or two types of glial tumours [65][66][67]. Our study is characterized by a large number of samples and tumour subtypes (939 samples of 5 different subtypes of gliomas were analysed) and an integrative approach, which consists in the analysis of signalling pathways, survival and the search for subtype-specific markers among DEGs and DE miRNAs.…”

Section: Discussionmentioning

confidence: 99%

Integrative analysis of mRNA and miRNA sequencing data for gliomas of various grades

Gvaldin

Pushkin

Тимошкина

et al. 2020

Egypt J Med Hum Genet

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Background The purpose of this study was to characterize subtype-specific patterns of mRNA and miRNA expression of gliomas using The Cancer Genome Atlas (TCGA) data to search for genetic determinants that predict prognosis in terms of overall survival and to create interaction networks for grade 2 and 3 (G2 and G3) astrocytomas, oligodendrogliomas and grade 4 (G4) glioblastoma multiforme. Based on open-access TCGA data, 5 groups were formed: astrocytoma G2 (n = 58), astrocytoma G3 (n = 128), oligodendroglioma G2 (n = 102), oligodendroglioma G3 (n = 72) and glioblastoma G4 (n = 564); normal samples of brain tissue were also analysed (n = 15). Data of patient age, sex, survival and expression patterns of mRNA and miRNA were extracted for each sample. After stratification of the data into groups, a differential analysis of expression was carried out, genes and miRNAs that affect overall survival were identified and gene set enrichment analysis (GSEA) and interaction analysis were performed. Results A total of 939 samples of glial tumours were analysed, for which subtype-specific expression profiles of genes and miRNAs were identified and networks of mRNA-miRNA interactions were constructed. Genes whose aberrant expression level was associated with survival were determined, and pairwise correlations between differential gene expression (DEG) and differential miRNA expression (DE miRNA) were calculated. Conclusions The developed panel of genes and miRNAs allowed us to differentiate glioma subtypes and evaluate prognosis in terms of the overall survival of patients. The regulatory miRNA-mRNA pairs unique to the five glioma subtypes identified in this study can stimulate the development of new therapeutic approaches based on subtype-specific mechanisms of oncogenesis.

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“…With the help of gene microarray and RNA-seq, the aberrant expression profiles of GBM in genome and transcriptome level were increasing reported. Using the gene expression data from Gene Expression Omnibus (GEO) database, Bo et al [6] identified a total of 431 differentially expressed genes (DEGs) between GBM and normal samples. After various bioinformatics analysis, 69 DEGs were identified significantly associated with GBM prognosis.…”

Section: Introductionmentioning

confidence: 99%

Integrative analysis of DNA methylation and gene expression to identify key epigenetic genes in glioblastoma

Jia

Lin

Tang

et al. 2019

Aging

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Glioblastoma (GBM) ranks the most common and aggressive primary brain malignant tumor worldwide. However, the survival rates of patients remain very poor. Therefore, molecular oncology of GBM are urgently needed. In this study, we performed an integrative analysis of DNA methylation and gene expression to identify key epigenetic genes in GBM. The methylation and gene expression of GBM patients in The Cancer Genome Atlas (TCGA) database were downloaded. After data preprocessing, we identified 4,881 differentially expressed genes (DEGs) between tumor and normal samples, including 1,111 upregulated and 3,770 downregulated genes. Then, we randomly separated all samples into training set (n = 69) and testing set (n = 69). We next obtained 11,269 survival-methylation sites by univariate and multivariate Cox regression analyses. In the correlation analysis, we defined 198 low promoter methylation with high gene expression as epigenetically induced (EI) genes and 111 high promoter methylation with low gene expression as epigenetically suppressed (ES) genes. Key markers including C1orf61 and FAM50B were selected with a Pearson correlation coefficient greater than 0.75. Further, we chose the 20 CpG methylation sites of above two genes in unsupervised clustering analysis using the Euclidean distance. We found that the prognosis of the hypomethylated group was significantly better than that in the hypermethylated group (log-rank test p -value = 0.011). Based on the validation in the TCGA testing set and GEO dataset, we validated the prognostic value of our signature ( p -value = 0.02 in TCGA and 0.012 in GEO). In conclusion, our findings provided predictive and prognostic value as methylation-based biomarkers for the diagnosis and treatment of GBM.

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Identification of potential key genes associated with glioblastoma based on the gene expression profile

Cited by 15 publications

References 67 publications

Analysis of cyclin E co‐expression genes reveals nuclear transcription factor Y subunit alpha is an oncogene in gastric cancer

Analysis of cyclin E co‐expression genes reveals nuclear transcription factor Y subunit alpha is an oncogene in gastric cancer

Integrative analysis of mRNA and miRNA sequencing data for gliomas of various grades

Integrative analysis of DNA methylation and gene expression to identify key epigenetic genes in glioblastoma

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