ArticleIranian journal of public health2024
Identification and Verification of a Novel Disulfidptosis-Related lncRNAs Prognostic Signature to Predict the Prognosis and Immune Activity of Head and Neck Squamous Carcinoma.
Article in Iranian journal of public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- VPS9D1-AS1: a critical oncogenic long non-coding RNA in human malignancies.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Long Non-Coding RNA VPS9D1-AS1 in Human Cancer: Functions, Mechanisms, and Clinical Utility.Anti-cancer agents in medicinal chemistry · 2026Review
- Development and validation of a novel endoplasmic reticulum stress-related lncRNA signature in laryngeal squamous cell carcinoma.Scientific reports · 2025Article
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Authors and funding
6 authors.
Funding
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Abstract
Background: We aimed to explore the prediction value of disulfidptosis-related long noncoding RNAs (lncRNAs) on the prognosis and immunotherapy efficiency of patients with head and neck squamous carcinoma (HNSCC). Methods: Clinical and RNA-seq information were collected from The Cancer Genome Atlas (TCGA) and Genome Data Sharing (GDC) portal. The Pearson correlation analysis, univariate COX regression analysis, the least absolute shrinkage and selection operator (LASSO) COX regression were employed to construct the disulfidptosis-related lncRNAs (DRLs) prognostic model. The Kaplan-Meier survival curve, principal component analysis (PCA), receiver operating characteristic (ROC) curves and areas under the curves (AUCs) were used to examine the accuracy of the prognostic model. ssGSEA, mutation and functional and gene set enrichment analysis was performed to quantify the immune cell infiltration, immune function and functional enrichments. Finally, the mRNA expression of the DRLs was verified by real-time PCR (RT-PCR) in HNSCC cells. Results: A new DRLs prognostic model ( Conclusion: We developed a new DRLs prognostic model of HNSCC, which could effectively predicate the prognosis and therapy response of HNSCC patients and provide insights into personalized therapeutics.
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