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      Identification of genes related to low‐grade glioma progression and prognosis based on integrated transcriptome analysis

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      https://www.riss.kr/link?id=O113187224

      • 저자
      • 발행기관
      • 학술지명
      • 권호사항
      • 발행연도

        2020년

      • 작성언어

        -

      • Print ISSN

        0730-2312

      • Online ISSN

        1097-4644

      • 등재정보

        SCI;SCIE;SCOPUS

      • 자료형태

        학술저널

      • 수록면

        3099-3111   [※수록면이 p5 이하이면, Review, Columns, Editor's Note, Abstract 등일 경우가 있습니다.]

      • 소장기관
      • 구독기관
        • 전북대학교 중앙도서관  
        • 성균관대학교 중앙학술정보관  
        • 부산대학교 중앙도서관  
        • 전남대학교 중앙도서관  
        • 제주대학교 중앙도서관  
        • 중앙대학교 서울캠퍼스 중앙도서관  
        • 인천대학교 학산도서관  
        • 숙명여자대학교 중앙도서관  
        • 서강대학교 로욜라중앙도서관  
        • 계명대학교 동산도서관  
        • 충남대학교 중앙도서관  
        • 한양대학교 백남학술정보관  
        • 이화여자대학교 중앙도서관  
        • 고려대학교 도서관  
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      부가정보

      다국어 초록 (Multilingual Abstract)

      Glioma is one of the most common types of human brain tumor, with high mortality in high‐grade gliomas (HGG). Low‐grade gliomas (LGG) can progress into HGG, leading to poor prognosis. However, it is unclear what factors affect the progression of L...

      Glioma is one of the most common types of human brain tumor, with high mortality in high‐grade gliomas (HGG). Low‐grade gliomas (LGG) can progress into HGG, leading to poor prognosis. However, it is unclear what factors affect the progression of LGG to HGG. This study aims to explore the function of the crosstalk genes on the progression and prognosis of LGG using bioinformatics analysis. Integrated transcriptome analysis was used to screen differentially expressed genes (DEGs). Then, gene ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to investigate the association between DEGs and gene functions and pathways by ClusterProfiler package and ClueGO plug‐in. Protein‐protein interaction (PPI) network analysis was applied to explore the connection between genes and biological processes. Subsequently, the gene clusters were analyzed using the Centiscape and molecular complex detection (MCODE) plug‐in in Cytoscape software, where the crosstalk genes were identified for further study. Ultimately, the UALCAN website and Gene Expression Profiling Interactive Analysis (GEPIA) website were performed to visualize the expression levels and survival curves of genes, respectively. There were 74 DEGs identified in glioma, including 55 upregulated genes and 19 downregulated genes, which mainly were enriched in extracellular matrix (ECM)‐receptor interaction, focal adhesion, PI3K‐Akt signaling pathway, and so on. Then, six crosstalk genes were selected, including COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, and COL5A2 genes. Overall survival (OS) analysis of crosstalk genes was conducted on the GEPIA website. High expression levels of crosstalk genes were closely related to the low survival rate of patients with LGG. The overexpressed crosstalk genes, such as COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, and COL5A2 may participate in the progression and poor prognosis of LGG through the ECM‐receptor interaction pathway.
      Integrated transcriptome analysis was used to screen differentially expressed genes. And the CentiScape and molecular complex detection plug‐in identified gene clusters in Cytoscape software for finding the crosstalk genes. Ultimately, the overall survival analysis of crosstalk genes was conducted. The crosstalk genes with high expressions, such as COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, and COL5A2 may participate in the progression and poor prognosis of low‐grade gliomas through the extracellular matrix–receptor interaction pathway.

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