Literature DB >> 28145562

Methylated of genes behaving as potential biomarkers in evaluating malignant degree of glioblastoma.

Wan-Shun Wen1, Sheng-Li Hu2, Zhibing Ai3, Lin Mou4, Jing-Min Lu5, Sen Li6.   

Abstract

Abnormal methylation genes usually act as oncogenes or anti-oncogenes in the occurrence and development of tumor, indicating their potential role as biomarkers in the evaluation of malignant tumor. However, the research on methylation's association with glioblastoma was rare. We attempted to figure out whether the methylation of genes could serve as the biomarker in evaluating the malignant degree of GBM. Methylation microarray data of 275 GBM patients have been downloaded from The Cancer Genome Atlas (TCGA) dataset. Logistic regression was used to find the methylated genes associated with the malignant degree of patients with the tumor. Functional enrichment analysis and network analysis were further performed on these selected genes. A total of 668, 412, 470, and 620 genes relevant with the methylation or demethylation were found to be associated with the malignant degree, Grades 1-4 of tumor. The higher the degree of malignant tumor, the higher of its methylation degree of its corresponding genes. GO and KEGG analysis results showed that these methylated genes were enriched in many functions as cell adhesion, abnormal transcription, and cell cycle disorder, etc. Of note, CCL11 and LCN11 were found to be significantly related to the progression of GBM. Critical genes associated with cell cycle as CCL11 and LCN1 may play essential roles in the occurrence, development, and transition of glioblastoma. More research was needed to explore its potential molecular mechanism.
© 2017 Wiley Periodicals, Inc.

Entities:  

Keywords:  biomarkers; glioblastoma; mRNA; malignant degree; methylation

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Year:  2017        PMID: 28145562     DOI: 10.1002/jcp.25831

Source DB:  PubMed          Journal:  J Cell Physiol        ISSN: 0021-9541            Impact factor:   6.384


  4 in total

1.  Ferroptosis related genes are regulated by methylation and predict the prognosis of glioblastoma patients.

Authors:  Hongliang Zhong; Yu Wang; Jianwen Jia; Hongchao Yang; Haoyu Zhang; Tong Li; He Liu; Yang Wang
Journal:  Transl Cancer Res       Date:  2022-04       Impact factor: 0.496

2.  Identification of key candidate genes and pathways in glioblastoma by integrated bioinformatical analysis.

Authors:  Lei Li; Xiaohui Liu; Xiaoye Ma; Xianyu Deng; Tao Ji; Pingping Hu; Ronghao Wan; Huijia Qiu; Daming Cui; Liang Gao
Journal:  Exp Ther Med       Date:  2019-09-05       Impact factor: 2.447

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

Authors:  Danyun Jia; Wei Lin; Hongli Tang; Yifan Cheng; Kaiwei Xu; Yanshu He; Wujun Geng; Qinxue Dai
Journal:  Aging (Albany NY)       Date:  2019-08-08       Impact factor: 5.682

4.  A comprehensive prognostic signature for glioblastoma patients based on transcriptomics and single cell sequencing.

Authors:  Fan Fan; Hao Zhang; Ziyu Dai; Yakun Zhang; Zhiwei Xia; Hui Cao; Kui Yang; Shui Hu; Yong Guo; Fengqin Ding; Quan Cheng; Nan Zhang
Journal:  Cell Oncol (Dordr)       Date:  2021-06-17       Impact factor: 6.730

  4 in total

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