ArticleCell division2023
Development and validation of a two glycolysis-related LncRNAs prognostic signature for glioma and in vitro analyses.
Article in Cell division, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- Cost-effectiveness of dapagliflozin for the treatment of heart failure: a systematic review.Frontiers in pharmacology · 2025Pooled it
- Associations between migrasome-related genes and long non-coding rnas in glioma and their prognostic relevance to the tumor microenvironment.IBRO neuroscience reports · 2026Article
- Multi-Omics Integration for Advancing Glioma Precision Medicine.Annals of clinical and translational neurology · 2026Review
- Neuron-Glioma Synapses in Tumor Progression.Biomedicines · 2025Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundMounting evidence suggests that there is a complex regulatory relationship between long non-coding RNAs (lncRNAs) and the glycolytic process during glioma development. This study aimed to investigate the prognostic role of glycolysis-related lncRNAs in glioma and their impact on the tumor microenvironment.
methodsThis study utilized glioma transcriptome data from public databases to construct, evaluate, and validate a prognostic signature based on differentially expressed (DE)-glycolysis-associated lncRNAs through consensus clustering, DE-lncRNA analysis, Cox regression analysis, and receiver operating characteristic (ROC) curves. The clusterProfiler package was applied to reveal the potential functions of the risk score-related differentially expressed genes (DEGs). ESTIMATE and Gene Set Enrichment Analysis (GSEA) were utilized to evaluate the relationship between prognostic signature and the immune landscape of gliomas. Furthermore, the sensitivity of patients to immune checkpoint inhibitor (ICI) treatment based on the prognostic feature was predicted with the assistance of the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. Finally, qRT-PCR was used to verify the difference in the expression of the lncRNAs in glioma cells and normal cell.
resultsBy consensus clustering based on glycolytic gene expression profiles, glioma patients were divided into two clusters with significantly different overall survival (OS), from which 2 DE-lncRNAs, AL390755.1 and FLJ16779, were obtained. Subsequently, Cox regression analysis demonstrated that all of these lncRNAs were associated with OS in glioma patients and constructed a prognostic signature with a robust prognostic predictive efficacy. Functional enrichment analysis revealed that DEGs associated with risk scores were involved in immune responses, neurons, neurotransmitters, synapses and other terms. Immune landscape analysis suggested an extreme enrichment of immune cells in the high-risk group. Moreover, patients in the low-risk group were likely to benefit more from ICI treatment. qRT-PCR results showed that the expression of AL390755.1 and FLJ16779 was significantly different in glioma and normal cells.
conclusionWe constructed a novel prognostic signature for glioma patients based on glycolysis-related lncRNAs. Besides, this project had provided a theoretical basis for the exploration of new ICI therapeutic targets for glioma patients.
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