Literature DB >> 33614485

Identification of a Glycolysis-Related LncRNA Signature to Predict Survival in Diffuse Glioma Patients.

Yangyang Wang1, Wenjianlong Zhou1, Shunchang Ma2, Xiudong Guan1, Dainan Zhang1, Jiayi Peng1, Xi Wang1, Linhao Yuan1, Peiliang Li1, Beibei Mao1, Peng Kang1, Deling Li1, Chuanbao Zhang1, Wang Jia1,2,3.   

Abstract

Glycolysis refers to one of the critical phenotypes of tumor cells, regulating tumor cell phenotypes and generating sufficient energy for glioma cells. A range of noticeable genes [such as isocitrate dehydrogenase (IDH), phosphatase, and tensin homolog (PTEN), or Ras] overall impact cell proliferation, invasion, cell cycle, and metastasis through glycolysis. Moreover, long non-coding RNAs (LncRNAs) are increasingly critical to disease progression. Accordingly, this study aimed to identify whether glycolysis-related LncRNAs have potential prognostic value for glioma patients. First, co-expression network between glycolysis-related protein-coding RNAs and LncRNAs was established according to Pearson correlation (Filter: |r| > 0.5 & P < 0.001). Furthermore, based on univariate Cox regression, the Least Absolute Shrinkage and Selection Operator (LASSO) analysis and multivariate Cox regression, a predictive model were built; vital glycolysis-related LncRNAs were identified; the risk score of every single patient was calculated. Moreover, receiver operating characteristic (ROC) curve analysis, gene set enrichment analysis (GSEA), GO and KEGG enrichment analysis were performed to assess the effect of risk score among glioma patients. 685 cases (including RNA sequences and clinical information) from two different cohorts of the Chinese Glioma Genome Atlas (CGGA) database were acquired. Based on the mentioned methods, the risk score calculation formula was yielded as follows: Risk score = (0.19 × EXPFOXD2-AS1) + (-0.27 × EXPAC062021.1) + (-0.16 × EXPAF131216.5) + (-0.05 × EXPLINC00844) + (0.11 × EXPCRNDE) + (0.35 × EXPLINC00665). The risk score was independently related to prognosis, and every single mentioned LncRNAs was significantly related to the overall survival of patients. Moreover, functional enrichment analysis indicated that the biologic process of the high-risk score was mainly involved in the cell cycle and DNA replication signaling pathway. This study confirmed that glycolysis-related LncRNAs significantly impact poor prognosis and short overall survival and may act as therapeutic targets in the future.
Copyright © 2021 Wang, Zhou, Ma, Guan, Zhang, Peng, Wang, Yuan, Li, Mao, Kang, Li, Zhang and Jia.

Entities:  

Keywords:  glioma; glycolysis; long non-coding RNAs (LncRNA); prognosis; risk model

Year:  2021        PMID: 33614485      PMCID: PMC7892596          DOI: 10.3389/fonc.2020.597877

Source DB:  PubMed          Journal:  Front Oncol        ISSN: 2234-943X            Impact factor:   6.244


  7 in total

1.  A Prognostic Signature of Glycolysis-Related Long Noncoding RNAs for Molecular Subtypes in the Tumor Immune Microenvironment of Lung Adenocarcinoma.

Authors:  Na Li; Mu Su; Louyin Zhu; Li Wang; Yonggang Peng; Bo Dong; Liya Ma; Yongyu Liu
Journal:  Int J Gen Med       Date:  2021-11-27

2.  A Novel Inflammatory lncRNAs Prognostic Signature for Predicting the Prognosis of Low-Grade Glioma Patients.

Authors:  Zijin Xiang; Xueru Chen; Qiaoli Lv; Xiangdong Peng
Journal:  Front Genet       Date:  2021-08-02       Impact factor: 4.599

3.  Identification of an epithelial-mesenchymal transition-related lncRNA prognostic signature for patients with glioblastoma.

Authors:  XinJie Yang; Sha Niu; JiaQiang Liu; Jincheng Fang; ZeYu Wu; Shizhang Ling; GuangFu Di; XiaoChun Jiang
Journal:  Sci Rep       Date:  2021-12-08       Impact factor: 4.379

4.  An Effective Hypoxia-Related Long Non-Coding RNA Assessment Model for Prognosis of Lung Adenocarcinoma.

Authors:  Yuanshuai Li; Xiaofang Sun
Journal:  Front Genet       Date:  2022-03-16       Impact factor: 4.599

Review 5.  The Emerging Roles of LINC00665 in Human Cancers.

Authors:  Jing Zhu; Yirao Zhang; Xuyu Chen; Yibo Bian; Juan Li; Keming Wang
Journal:  Front Cell Dev Biol       Date:  2022-03-09

6.  Tumor Heterogeneity and Molecular Characteristics of Glioblastoma Revealed by Single-Cell RNA-Seq Data Analysis.

Authors:  Dhanusha Yesudhas; S Akila Parvathy Dharshini; Y-H Taguchi; M Michael Gromiha
Journal:  Genes (Basel)       Date:  2022-02-25       Impact factor: 4.096

7.  Metabolism-related long non-coding RNAs (lncRNAs) as potential biomarkers for predicting risk of recurrence in breast cancer patients.

Authors:  Jian-Ying Ma; Shao-Hua Liu; Jie Chen; Qin Liu
Journal:  Bioengineered       Date:  2021-12       Impact factor: 3.269

  7 in total

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