ArticleBioinformatics and biology insights2022
Comprehensive Analysis of the Significance of Ferroptosis-Related Genes in the Prognosis and Immunotherapy of Oral Squamous Cell Carcinoma.
Article in Bioinformatics and biology insights, 2022. 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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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
5 citing papers in PubMed, 8 citations in OpenAlex.
- Review
- Bioinformatics identification and validation of m6A/m1A/m5C/m7G/ac4 C-modified genes in oral squamous cell carcinoma.BMC cancer · 2025Article
- Review
- Ferroptosis and oral squamous cell carcinoma: connecting the dots to move forward.Frontiers in oral health · 2024Review
- HECT, UBA and WWE domain containing 1 represses cholesterol efflux during CD4Frontiers in pharmacology · 2023Article
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oral squamous cell carcinoma (OSCC) is a life-threatening disease, associated with poor prognosis and the absence of specific biomarkers. Studies have shown that the ferroptosis-related genes (FRGs) can be used as tumor prognostic markers. However, FRGs' prognostic value in OSCC needs further exploration. In our study, gene expression profile and clinical data of OSCC patients were collected from a public domain. We performed univariate and multivariate Cox regression analyses to construct a multigene signature. The Kaplan-Meier and receiver operating characteristic (ROC) methods were used to test the effectiveness of the signature, followed by the expression analysis of human leukocyte antigen (HLA) and immune checkpoints. The Cox regression analysis identified 4 hubs from 103 FRGs expressed in OSCC that were associated with overall survival (OS). A risk model based on the 4 FRGs was established to classify patients into high-risk and low-risk groups. Compared with the low-risk group, the survival time of the high-risk group was significantly reduced. According to the multivariate Cox regression analysis, the risk score acted as an independent predictor for OS. The accuracy of the 4 FRGs risk predictive model was confirmed by ROC curve analysis. Moreover, the low-risk group had the characteristics of higher expression of HLA and immune checkpoints, a lower tumor purity, and a higher immune infiltration, indicating a more sensitive response to immunotherapy. The novel FRGs-OSCC risk score system can be used to predict the prognosis of OSCC patients and their response to immunotherapy.
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