A novel Cuprotosis-related signature predicts the prognosis and selects personal treatments for melanoma based on bioinformatics analysis

Bingqian Hu; Alphonse Houssou Hounye; Zheng Wang; Min Qi; Jianglin Zhang

doi:10.3389/fonc.2023.1108128

A novel Cuprotosis-related signature predicts the prognosis and selects personal treatments for melanoma based on bioinformatics analysis

Front Oncol. 2023 Feb 6:13:1108128. doi: 10.3389/fonc.2023.1108128. eCollection 2023.

Authors

Bingqian Hu¹, Alphonse Houssou Hounye², Zheng Wang³, Min Qi⁴, Jianglin Zhang^{5

6}

Affiliations

¹ Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
² School of Mathematics and Statistics, Central South University, Changsha, China.
³ School of Computer Science, Hunan First Normal University, Changsha, China.
⁴ Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China.
⁵ Department of Dermatology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China.
⁶ Candidate Branch of National Clinical Research Center for Skin Diseases, Shenzhen People's Hospital, Shenzhen, Guangdong, China.

Abstract

Background: Melanoma is a common and aggressive cutaneous malignancy characterized by poor prognosis and a high fatality rate. Recently, due to the application of Immune-checkpoint inhibitors (ICI) in melanoma treatment, melanoma patients' prognosis has been tremendously improved. However, the treatment effect varies quite differently from patient to patient. In this study, we aim to construct and validate a Cuproptosis-related risk model to improve outcome prediction of ICIs in melanoma and divide patients into subtypes with different Cuproptosis-related genes.

Methods: Here, according to differentially expressed genes from four melanoma datasets in GEO (Gene Expression Omnibus), and one in TCGA (The Cancer Genome Atlas) database, a novel signature was developed through LASSO and Cox regression analysis. We used 781 melanoma samples to examine the molecular subtypes associated with Cuproptosis-related genes and studied the related gene mutation and TME cell infiltration. Patients with melanoma can be divided into at least three subtypes based on gene expression profile. Survival pan-cancer analysis was also conducted for melanoma patients.

Results: The Cuproptosis risk score can predict tumor immunity, subtype, survival, and drug sensitivity for melanoma. And Cuproptosis-associated subtypes can help predict therapeutic outcomes.

Conclusion: Cuproptosis risk score is a promising potential biomarker in cancer diagnosis, molecular subtypes determination, TME cell infiltration characteristics, and therapy response prediction in melanoma patients.

Keywords: bioinformatic; cuproptosis; immune–checkpoint inhibitors; melanoma; tumor immune microenvironment.

Grants and funding

This work was supported by the Projects of the National Natural Science Foundation of China (grant number 82073019 and 82073018), the Shenzhen Science and Technology Innovation Commission, China (Natural Science Foundation of Shenzhen, grant number JCYJ20210324113001005 and JCYJ20210324114212035), Hunan provincial nature science foundation of China (2022JJ30189), Teaching Reform Research Project of Universities in Hunan Province (HNJG-2021-1120).