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

Liu, Mingfa; Xu, Zhennan; Du, Zhifeng; Wu, Bing‐Li; Jin, Tao; Xu, Ke; Xu, Li; Li, En‐Min; Xu, Haixiong

doi:10.1155/2017/1278081

Cited by 45 publications

(38 citation statements)

References 41 publications

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“…The recently adopted high-throughput gene microarray analysis of tumors and samples from patients and healthy people allows us to share and explore global molecular tumors at different levels of the landscape from somatic mutations and copy number changes to genome-level gene expression at the transcriptome level, as well as epigenetic changes (Liu et al, 2017;Sun et al, 2017;Chen et al, 2017). In this study, we downloaded the GSE117606 dataset from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo) database using R software for the comprehensive identification of differentially expressed genes (DEGs).…”

Section: Introductionmentioning

confidence: 99%

Identification of Core Gene Expression Signature and Key Pathways in Colorectal Cancer

2020

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Objective: Colorectal cancer (CRC) is considered the most prevalent malignant tumor that contributes to high cancer-related mortality. However, the signaling pathways involved in CRC and CRC-driven genes are largely unknown. We sought to discover a novel biomarker in CRC. Materials and Methods:All clinical CRC samples (n = 20) were from Renmin Hospital of Wuhan University. We first selected MAD2L1 by integrated bioinformatics analysis of a GSE dataset. Next, the expression of MAD2L1 in tissues and cell lines was verified by quantitative real-time PCR. The effects of MAD2L1 on cell growth, proliferation, the cell cycle, and apoptosis were examined by in vitro assays. Results:We identified 683 shared DEGs (420 upregulated and 263 downregulated), and the top twenty genes (CDK1, CCNA2, TOP2A, PLK1, MAD2L1, AURKA, BUB1B, UBE2C, TPX2, RRM2, KIF11, NCAPG, MELK, NUSAP1, MCM4, RFC4, PTTG1, CHEK1, CEP55, DTL) were selected by integrated analysis. These hub genes were significantly overexpressed in CRC samples and were positively correlated. Our data revealed that the expression of MAD2L1 in CRC tissues is higher than that in normal tissues. MAD2L1 knockdown significantly suppressed CRC cell growth by impairing cell cycle progression and inducing cell apoptosis.Conclusion: MAD2L1,asanoveloncogenicgene,playsaroleinregulatingcancercellgrowth and apoptosis and could be used as a new biomarker for diagnosis and therapy in CRC.

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Section: Introductionmentioning

confidence: 99%

Identification of Core Gene Expression Signature and Key Pathways in Colorectal Cancer

2020

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“…Dysfunction of CDKs contributes to cell proliferation, tumor genesis, and tumor progression including breast cancer, lung carcinoma, and melanoma . Recently, CDK13 participates a lot in many cancers . In HCC, frequent amplification of CDK13 is observed .…”

Section: Discussionmentioning

confidence: 99%

“…[35][36][37] Recently, CDK13 participates a lot in many cancers. 38,39 In HCC, frequent amplification of CDK13 is observed. 40 Many other mRNA transcripts can also be F I G U R E 8 A schematic diagram of LINC00152/miR-215/ CDK13 axis.…”

Section: Discussionmentioning

confidence: 99%

Insight into the molecular mechanism of LINC00152/miR‐215/CDK13 axis in hepatocellular carcinoma progression

Wang

Zhang

et al. 2019

J of Cellular Biochemistry

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Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. Nevertheless, its underlying molecular mechanisms are largely unknown. LINC00152 are recently investigated in several cancer types. In our current investigation, we observed LINC00152 was obviously upregulated in HCC cells. LINC00152 was significantly downregulated by infecting LV‐shLINC00152 in HepG2 and SNU449 cells. Loss of LINC00152 remarkably repressed HCC cell proliferation, cell colony formation, induced cell apoptosis, and restrained cell migration/invasion. Growing evidence has reported long noncoding RNAs can sponge microRNAs to modulate cancer process. Here, we indicated miR‐215 was greatly decreased in HCC and LINC00152 regulated HCC development via sponging miR‐215. For another, the binding association between LINC00152 and miR‐215 was proved by a series of functional assays. CDK13 was predicted as the target of miR‐215. Upregulation of miR‐215 greatly depressed CDK13 in HCC cells. Subsequently, the in vivo results demonstrated that silence of LINC00152 restrained HCC development via modulating miR‐215 to up‐regulate CDK13. Therefore, it was revealed that LINC00152 contributed to the progression of HCC by the modulation of miR‐215 and CDK13.

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“…The current exploitation of high-throughput gene microarray to analyze normal and tumor tissue samples from patients confers us an opportunity to detect and explore the comprehensive molecular landscapes of tumors at multiple levels ranging from somatic mutations and copy number alteration at the genome level to gene expression changes at transcriptome level [4][5][6]. While, the use of microarrays in clinic is greatly restricted because of countless genes detected by gene profiling, lack of independent stability, likewise the complex statistical analyses.…”

Section: Ivyspringmentioning

confidence: 99%

The Identification of Key Gene Expression Signature and Biological Pathways in Metastatic Renal Cell Carcinoma

et al. 2020

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Purpose: To investigate the potential mechanisms contributing to metastasis of clear cell renal cell carcinoma (ccRCC), screen the hub genes, associated pathways of metastatic ccRCC and identify potential biomarkers. Methods: The ccRCC metastasis gene expression profile GSE47352 was employed to analyze the differentially expressed genes (DEGs). DAVID was performed to assess Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The protein-protein interaction (PPI) network and modules were constructed. The function pathway, prognostic and diagnostic analysis of these hub genes was picked out to estimate their potential effects on metastasis of ccRCC. Results: A total of 873 DEGs were identified (503 upregulated genes and 370 downregulated genes). Meanwhile, top 20 hub genes were displayed. GO analysis showed that the top 20 hub genes were enriched in regulation of phosphatidylinositol 3-kinase signaling, positive regulation of DNA replication, protein autophosphorylation, protein tyrosine kinase activity, etc. KEGG analysis indicated these hub genes were enriched in the Ras signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, Pathways in cancer, etc. The GO and KEGG enrichment analyses for the hub genes disclosed important biological features of metastatic ccRCC. PPI network showed the interaction of top 20 hub genes. Gene Set Enrichment Analysis (GSEA) revealed that some of the hub genes was associated with metastasis, epithelial mesenchymal transition (EMT), hypoxia cancer and adipogenesis of ccRCC. Some top hub genes were distinctive and new discoveries compared with that of the existing associated researches. Conclusions: Our analysis uncovered that changes in signal pathways such as Ras signaling pathway, PI3K-Akt signaling pathway, etc. may be the main signatures of metastatic ccRCC. We identified several candidate biomarkers related with overall survival (OS) and disease-free survival (DFS) of ccRCC patients. Accordingly, they might be novel therapeutic targets and used as potential biomarkers for diagnosis, prognosis of ccRCC.

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

Cited by 45 publications

References 41 publications

Identification of Core Gene Expression Signature and Key Pathways in Colorectal Cancer

Identification of Core Gene Expression Signature and Key Pathways in Colorectal Cancer

Insight into the molecular mechanism of LINC00152/miR‐215/CDK13 axis in hepatocellular carcinoma progression

The Identification of Key Gene Expression Signature and Biological Pathways in Metastatic Renal Cell Carcinoma

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