ArticleScientific reports2023
Comprehensive analysis of oxidative stress-related lncRNA signatures in glioma reveals the discrepancy of prognostic and immune infiltration.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Integrative RNA-Seq Analysis Reveals Stress Type-Dependent lncRNA-Centered Co-Expression Networks Across Human Cellular Stress Contexts.International journal of molecular sciences · 2026Article
- Integrating single-cell RNA-Seq and machine learning to dissect a novel Palmitoylation-related prognostic signature of glioblastoma.BMC neurology · 2025Article
- Oxidative Stress and Antioxidants in Glioblastoma: Mechanisms of Action, Therapeutic Effects and Future Directions.Antioxidants (Basel, Switzerland) · 2025Review
- Long Non-Coding RNAs in Malignant Human Brain Tumors: Driving Forces Behind Progression and Therapy.International journal of molecular sciences · 2025Review
- Integrative Analysis of Whole-Genome and Transcriptomic Data Reveals Novel Variants in Differentially Expressed Long Noncoding RNAs Associated with Asthenozoospermia.Non-coding RNA · 2025Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
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Abstract
Oxidative stress refers to the process of reactive oxide species (ROS) increase in human body due to various factors, which leads to oxidative damage in human tissues. Current studies have confirmed that sustained oxidative stress is one of the distinctive features throughout the development of tumors. Numerous reports have shown that lncRNAs can regulate the process of oxidative stress through multiple pathways. However, the relationship between glioma-associated oxidative stress and lncRNAs is not clearly investigated. RNA sequencing data of GBM (glioblastoma) and LGG (low grade glioma) and corresponding clinical data were retrieved from the TCGA database. Oxidative stress related lncRNAs (ORLs) were identified by Pearson correlation analysis. Prognostic models for 6-ORLs were structured in the training cohort by univariate Cox regression analysis, multivariate Cox regression analysis and LASSO regression analysis. We constructed the nomogram and verified its predictive efficacy by Calibration curves and DCA decision curves. The biological functions and pathways of 6-ORLs-related mRNAs were inferred by Gene Set Enrichment Analysis. Immune cell abundance and immune function associated with risk score (RS) were estimated by ssGSEA, CIBERSORT and MCPcounter synthetically. External validation of the signature was completed using the CGGA-325 and CGGA-693 datasets. 6-ORLs signature-AC083864.2, AC107294.1, AL035446.1, CRNDE, LINC02600, and SNAI3-AS1-were identified through our analysis as being predictive of glioma prognosis. Kaplan-Meier and ROC curves indicated that the signature has a dependable predictive efficacy in the TCGA training cohort, validation cohort and CGGA-325/CGGA-693 test cohort. The 6-ORLs signature were verified to be independent prognostic predictors by multivariate cox regression and stratified survival analysis. Nomogram built with risk scores had strong predictive efficacy for patients' overall survival (OS). The outcomes of the functional enrichment analysis revealing potential molecular regulatory mechanisms for the 6-ORLs. Patients in the high-risk subgroup presented a significant immune microenvironment of macrophage M0 and cancer-associated fibroblast infiltration which was associated with a poorer prognosis. Finally, the expression levels of 6-ORLs in U87/U251/T98/U138 and HA1800 cell lines were verified by RT-qPCR. The nomogram in this study has been made available as a web version for clinicians. This 6-ORLs risk signature has the capabilities to predict the prognosis of glioma patients, assist in evaluating immune infiltration, and assess the efficacy of various anti-tumor systemic therapy regimens.
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