A Novel Necroptosis-Related Gene Signature in Skin Cutaneous Melanoma Prognosis and Tumor Microenvironment

Binyu Song; Pingfan Wu; Zhen Liang; Jianzhang Wang; Yu Zheng; Yuanyong Wang; Hao Chi; Zichao Li; Yajuan Song; Xisheng Yin; Zhou Yu; Baoqiang Song

doi:10.3389/fgene.2022.917007

A Novel Necroptosis-Related Gene Signature in Skin Cutaneous Melanoma Prognosis and Tumor Microenvironment

Front Genet. 2022 Jul 11:13:917007. doi: 10.3389/fgene.2022.917007. eCollection 2022.

Authors

Binyu Song¹, Pingfan Wu², Zhen Liang¹, Jianzhang Wang¹, Yu Zheng³, Yuanyong Wang⁴, Hao Chi⁵, Zichao Li¹, Yajuan Song¹, Xisheng Yin⁵, Zhou Yu¹, Baoqiang Song¹

Affiliations

¹ Department of Plastic Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
² Department of Burn and Plastic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China.
³ Hospital for Skin Disease (Institute of Dermatology), Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China.
⁴ Department of Thoracic Surgery, Tangdu Hospital of Air Force Military Medical University, Xi'an, China.
⁵ Clinical Medical College, Southwest Medical University, Luzhou, China.

Abstract

Background: Necroptosis has been identified recently as a newly recognized programmed cell death that has an impact on tumor progression and prognosis, although the necroptosis-related gene (NRGs) potential prognostic value in skin cutaneous melanoma (SKCM) has not been identified. The aim of this study was to construct a prognostic model of SKCM through NRGs in order to help SKCM patients obtain precise clinical treatment strategies. Methods: RNA sequencing data collected from The Cancer Genome Atlas (TCGA) were used to identify differentially expressed and prognostic NRGs in SKCM. Depending on 10 NRGs via the univariate Cox regression analysis usage and LASSO algorithm, the prognostic risk model had been built. It was further validated by the Gene Expression Omnibus (GEO) database. The prognostic model performance had been assessed using receiver operating characteristic (ROC) curves. We evaluated the predictive power of the prognostic model for tumor microenvironment (TME) and immunotherapy response. Results: We constructed a prognostic model based on 10 NRGs (FASLG, TLR3, ZBP1, TNFRSF1B, USP22, PLK1, GATA3, EGFR, TARDBP, and TNFRSF21) and classified patients into two high- and low-risk groups based on risk scores. The risk score was considered a predictive factor in the two risk groups regarding the Cox regression analysis. A predictive nomogram had been built for providing a more beneficial prognostic indicator for the clinic. Functional enrichment analysis showed significant enrichment of immune-related signaling pathways, a higher degree of immune cell infiltration in the low-risk group than in the high-risk group, a negative correlation between risk scores and most immune checkpoint inhibitors (ICIs), anticancer immunity steps, and a more sensitive response to immunotherapy in the low-risk group. Conclusions: This risk score signature could be applied to assess the prognosis and classify low- and high-risk SKCM patients and help make the immunotherapeutic strategy decision.

Keywords: SKCM; TCGA; TME; necroptosis; prognostic signature.