AI Article Synopsis

  • Oral squamous cell carcinoma (OSCC) is the most common oral cancer, characterized by a poor prognosis; this study focuses on the role of ferroptosis-related long non-coding RNAs (FRLs) in predicting patient outcomes.
  • Researchers used data from The Cancer Genome Atlas and FerrDb to construct a prognostic model based on 10 FRLs, which successfully categorized patients into low and high-risk groups.
  • The study found that high-risk patients had worse outcomes, and identified potential treatments, including cisplatin and docetaxel, while also highlighting a specific lncRNA, STARD4-AS1, that warrants further investigation.

Article Abstract

Background: Oral squamous cell carcinoma (OSCC) stands as the predominant form of oral cancer, marked by a poor prognosis. Ferroptosis, a type of programmed cell death, plays a critical role in the initiation and progression of various cancers. Long non-coding RNAs (lncRNAs) are prominent in modulating cancer development. Nevertheless, the prognostic significance of ferroptosis-related lncRNAs (FRLs) in OSCC remains inadequately explored. This study aims to develop a predictive signature based on FRLs to forecast the prognosis of OSCC patients.

Methods: We gathered expression profiles of FRLs along with clinical data from The Cancer Genome Atlas (TCGA) and FerrDb databases. A prognostic model based on 10 FRLs were constructed using Cox regression analyses with LASSO algorithms, and their predictive power was evaluated. Then, the model was used to investigate functional enrichment, immune landscape, m6A genes, somatic variations, and drug response in different risk cohorts of patients. Finally, the expression and function of STARD4-AS1 (steroidogenic acute regulator protein-related lipid transfer domain containing 4-antisense RNA 1), a potential prognostic marker for OSCC screening based on our bioinformatics analysis, were investigated in vitro.

Results: We developed a signature comprising 10 FRLs to stratify patients into two risk cohorts according to their calculated risk scores. Patients classified as high-risk exhibited significantly poorer prognoses compared to those in the low-risk cohort. Furthermore, survival analysis, patient risk heat plot, and risk curve verified the accuracy of the signature. The role of this signature in OSCC was well investigated using immune microenvironment, mutational, and gene set enrichment analysis (GSEA). Moreover, seven drugs, including cisplatin and docetaxel, were identified as potential treatments for patients with high-risk cancers. In addition, the knockdown of STARD4-AS1 in OSCC cell lines markedly inhibited cell proliferation and migration and induced ferroptosis.

Conclusion: Using this signature may improve overall survival predictions in OSCC, throwing new light on immunotherapies and targeted therapies. Moreover, STARD4-AS1 might regulate the process of ferroptosis and could be used as a novel biomarker of OSCC.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11250877PMC
http://dx.doi.org/10.1016/j.heliyon.2024.e33193DOI Listing

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