Ferroptosis-related gene signature predicts the prognosis in Oral squamous cell carcinoma patients

• Published in: BMC cancer Vol. 21; no. 1; pp. 1 - 835
• Main Authors: Li, Hongyu, Zhang, Xiliu, Yi, Chen, He, Yi, Chen, Xun, Zhao, Wei, Yu, Dongsheng
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 20.07.2021; BioMed Central; BMC
• Subjects: Apoptotic proteins; Bioinformatics; Cancer therapies; Cell death; Correlation analysis; Ferroptosis; Gene expression; Gene prognosis signature; Gene set enrichment analysis; Genetic aspects; Genomes; Health aspects; Immune status; Immunity; Medical prognosis; Metabolism; Mouth cancer; Nomogram; Nomograms; Oral cancer; Oral squamous cell carcinoma; Patients; Prognosis; Proteins; Risk groups; Software; Squamous cell carcinoma; Tumors; China
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-021-08478-0

Background The prognosis of oral squamous cell carcinoma (OSCC) patients is difficult to predict or describe due to its high-level heterogeneity and complex aetiologic factors. Ferroptosis is a novel form of iron-dependent cell death that is closely related to tumour growth and progression. This study aims to clarify the predictive value of ferroptosis-related genes (FRGs) on the overall survival(OS) of OSCC patients. Methods The mRNA expression profile of FRGs and clinical information of patients with OSCC were collected from the TCGA database. Candidate differentially expressed ferroptosis-related genes (DE-FRGs) were identified by analysing differences between OSCC and adjacent normal tissues. A gene signature of prognosis-related DE-FRGs was established by univariate Cox analysis and LASSO analysis in the training set. Patients were then divided into high- and low-risk groups according to the cut-off value of risk scores, A nomogram was constructed to quantify the contributions of gene signature and clinical parameters to OS. Then several bioinformatics analyses were used to verify the reliability and accuracy of the model in the validation set. Finally, single-sample gene set enrichment analysis (ssGSEA) was also performed to reveal the underlying differences in immune status between different risk groups. Results A prognostic model was constructed based on 10 ferroptosis-related genes. Patients in high-risk group had a significantly worse OS (p < 0.001). The gene signature was verified as an independent predictor for the OS of OSCC patients (HR > 1, p < 0.001). The receiver operating characteristic curve displayed the favour predictive performance of the risk model. The prediction nomogram successfully quantified each indicator's contribution to survival and the concordance index and calibration plots showed its superior predictive capacity. Finally, ssGSEA preliminarily indicated that the poor prognosis in the high-risk group might result from the dysregulation of immune status. Conclusion This study established a 10-ferroptosis-releated gene signature and nomogram that can be used to predict the prognosis of OSCC patients, which provides new insight for future anticancer therapies based on potential FRG targets. Keywords: Oral squamous cell carcinoma, Ferroptosis, Gene prognosis signature, Nomogram, Immunity