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

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.

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

“…Currently, increasing studies dedicated to develop prognostic signatures composed of various tumor hallmarks-related lncRNAs, providing promising prognostic indicators and personalized treatments for tumor malignancies. Wang et al established a valuable glycolysis-related lncRNA signature to predict the prognosis of diffuse glioma patients through univariate Cox regression analysis, the Least Absolute Shrinkage and Selection Operator (LASSO) analysis and multivariate Cox regression analysis 23 . Luan et al constructed a prognostic signature composed of 10 autophagy-related lncRNAs for glioma patients 24 .…”

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

“…Metabolic pathways such as glycolysis have distinct, essential roles in tumorigenicity and cancer progression [ 14 ]. Wang et al [ 37 ] indicated by Pearson’s correlation analysis that FOXD2-AS1 is associated with glycolysis-related lncRNAs. However, the authors did not explore how FOXD2-AS1 affects the development and progression of BC by regulating metabolism.…”

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

“…HOXA-AS3 is a risk factor in a variety of tumors, including glioma ( Chen et al, 2020 ). AC062021.1 is involved in the formation of glycolysis-related lncRNA signature that predict the survival in patients with diffuse glioma ( Wang et al, 2020 ).…”