A new ferroptosis-related genetic mutation risk model predicts the prognosis of skin cutaneous melanoma

Jia He; Wenting Huang; Xinxin Li; Jingru Wang; Yaxing Nie; Guiqiang Li; Xiaoxiang Wang; Huili Cao; Xiaodong Chen; Xusheng Wang

doi:10.3389/fgene.2022.988909

A new ferroptosis-related genetic mutation risk model predicts the prognosis of skin cutaneous melanoma

Front Genet. 2023 Jan 5:13:988909. doi: 10.3389/fgene.2022.988909. eCollection 2022.

Authors

Jia He^{1

2}, Wenting Huang¹, Xinxin Li¹, Jingru Wang², Yaxing Nie³, Guiqiang Li², Xiaoxiang Wang², Huili Cao¹, Xiaodong Chen², Xusheng Wang¹

Affiliations

¹ School of Pharmaceutical Sciences (Shenzhen), Sun Yat-Sen University, Guangzhou, China.
² Department of Burn Surgery, The First People's Hospital of Foshan, Foshan, China.
³ CAS Key Laboratory of Molecular Virology and Immunology, Institut Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai, China.

Abstract

Background: Ferroptosis is an iron-dependent cell death mode and closely linked to various cancers, including skin cutaneous melanoma (SKCM). Although attempts have been made to construct ferroptosis-related gene (FRG) signatures for predicting the prognosis of SKCM, the prognostic impact of ferroptosis-related genetic mutations in SKCM remains lacking. This study aims to develop a prediction model to explain the relationship between ferroptosis-related genetic mutations and clinical outcomes of SKCM patients and to explore the potential value of ferroptosis in SKCM treatment. Methods: FRGs which significantly correlated with the prognosis of SKCM were firstly screened based on their single-nucleotide variant (SNV) status by univariate Cox regression analysis. Subsequently, the least absolute shrinkage and selection operator (LASSO) and Cox regressions were performed to construct a new ferroptosis-related genetic mutation risk (FerrGR) model for predicting the prognosis of SKCM. We then illustrate the survival and receiver operating characteristic (ROC) curves to evaluate the predictive power of the FerrGR model. Moreover, independent prognostic factors, genomic and clinical characteristics, immunotherapy, immune infiltration, and sensitive drugs were compared between high-and low-FerrGR groups. Results: The FerrGR model was developed with a good performance on survival and ROC analysis. It was a robust independent prognostic indicator and followed a nomogram constructed to predict prognostic outcomes for SKCM patients. Besides, FerrGR combined with tumor mutational burden (TMB) or MSI (microsatellite instability) was considered as a combined biomarker for immunotherapy response. The high FerrGR group patients were associated with an inhibitory immune microenvironment. Furthermore, potential drugs target to high FerrGR samples were predicted. Conclusion: The FerrGR model is valuable to predict prognosis and immunotherapy in SKCM patients. It offers a novel therapeutic option for SKCM.

Keywords: ferroptosis; genetic mutation; prognosis; single nucleotide variant; skin cutaneous melanoma; tumor immunity.