ArticleScientific reports2023
Construction and validation of a prognostic signature based on seven endoplasmic reticulum stress-related lncRNAs for patients with head and neck squamous cell carcinoma.
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 3 papers.
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3 citing papers in PubMed, 4 citations in OpenAlex.
- Machine learning-based model for CD4Scientific reports · 2024Article
- Development and validation of a novel disulfidptosis-related lncRNAs signature in patients with HPV-negative oral squamous cell carcinoma.Scientific reports · 2024Article
- Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response.Frontiers in immunology · 2024Article
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6 authors at 2 institutions in 1 country.
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Abstract
Endoplasmic reticulum stress (ERS) occurs when misfolded or unfolded proteins accumulate in the endoplasmic reticulum (ER), and it is often observed in tumors, including head and neck squamous cell carcinoma (HNSCC). Relevant studies have demonstrated the prognostic significance of ERS-related long non-coding RNAs (lncRNAs) in various cancers. However, the relationship between ERS and lncRNAs in HNSCC has received limited attention in previous studies. In this study, we aimed to develop an ERS-related lncRNAs prognostic model using correlation analysis, Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression analysis based on data from The Cancer Genome Atlas (TCGA) database. The survival and predictive ability of this model were evaluated using Kaplan-Meier analysis and time-dependent receiver operating characteristics (ROC), while nomograms and calibration curves were constructed. Then, functional enrichment analyses, tumor mutation burden (TMB), tumor infiltration of immune cells, single sample Gene Set Enrichment Analysis (ssGSEA), and drug sensitivity analysis were performed. Additionally, we conducted a consensus cluster analysis to compare differences between subtypes of tumors. Finally, we validated the expression of the ERS-related lncRNAs that constructed prognostic risk score model in HNSCC tissues through quantitative real-time PCR (qRT-PCR). We developed a prognostic signature based on seven ERS-related lncRNAs, which showed better predictive performance than other clinicopathological features. The high-risk poor prognosis group had a poorer prognosis in comparison to the low-risk good prognosis. The area under the ROC curve (AUC) predicted by this model for 3-year survival rates of HNSCC patients was 0.805. Enrichment analysis revealed that the differentially expressed genes were primarily enriched in pathways related to immune responses and signal transduction. Low-risk patients had lower TMB, more immune cell infiltrations, and enhanced anti-tumor immunity. Cluster analysis indicated that cluster 3 may have a better prognosis and immunotherapy effect. In addition, the result of qRT-PCR was consistent with our analysis. This prognostic model based on seven ERS-related lncRNAs is a promising tool for risk stratification, survival prediction, and immune cell infiltration status assessment.
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