ArticleDiscover oncology2025
Prognostic modeling of disulfidptosis gene-associated lncRNAs aids in identifying the tumor microenvironment and guiding the selection of therapy.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionGliomas, a type of malignant tumor, are marked by a short survival period and an unfavorable prognosis. Disulfide stress, which arises from an overabundance of intracellular cystine, can initiate disulfidoptosis, an emerging form of cell death. The link between gliomas and disulfidoptosis has not been extensively explored. This study breaks new ground by investigating the correlation between glioma prognosis and lncRNAs associated with disulfidoptosis, with the aim of improving glioma treatment strategies.
methodsWe analyzed 10 long non-coding RNAs (lncRNAs) co-expressed with disulfidoptosis genes, retrieved clinical information and gene expression profiles from glioma and normal groups in the TCGA database, and developed a prognostic model for lncRNAs based on this data. The receiver operating characteristic curve (ROC) was used to evaluate and validate the model's reliability. Furthermore, the Kaplan-Meier survival curve was employed to assess the disparity in overall survival (OS) among patients with varying risk scores. We also examined the tumor microenvironment (TME), immune cell infiltration, immune-related functions, tumor mutational burden (TMB), and OncoPredict in samples with differing risk scores. To confirm the expression variations of genes associated with prognostic models in cell lines, quantitative polymerase chain reaction (qPCR) was employed.
resultsEleven long non-coding RNAs (lncRNAs) were identified for constructing prognostic models by analyzing lncRNAs associated with disulfidoptosis genes using Cox regression and LASSO regression analyses. The study's findings indicate that these 11 key lncRNAs serve as independent predictors of overall survival (OS) in glioma patients. Moreover, the frequency with which patients of varying risk scores opt for immune checkpoint blockade (ICB) therapy and chemotherapy not only differs but also their responses to these treatments are significantly distinct, suggesting that the risk score could be a predictive factor for treatment response.
conclusionsThis research sheds light on the characteristics of disulfidoptosis in glioma, revealing that patterns of disulfidoptosis in patients can be effectively assessed using a risk score. Consequently, the judicious application of this prognostic model can significantly inform clinical treatment strategies and precision medicine for glioma, potentially improving patient outcomes.
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