Construction of prognostic model and tumor microenvironment landscape based on cuproptosis-related subtypes in melanoma

Zishen Xia; Nan Gao; Jianwen Wang; Lizhao Yan; Cong Ma; Kangwei Wang; Yuxiong Weng

doi:10.71321/vxy0xd87

Authors

Zishen Xia Union Hospital, Tongji Medical College, Huazhong University of Science and Technology‌
Nan Gao Union Hospital, Tongji Medical College, Huazhong University of Science and Technology‌
Jianwen Wang Union Hospital, Tongji Medical College, Huazhong University of Science and Technology‌
Lizhao Yan Union Hospital, Tongji Medical College, Huazhong University of Science and Technology‌
Cong Ma Union Hospital, Tongji Medical College, Huazhong University of Science and Technology‌
Kangwei Wang Union Hospital, Tongji Medical College, Huazhong University of Science and Technology‌
Yuxiong Weng Department of Hand Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

DOI:

https://doi.org/10.71321/vxy0xd87

Keywords:

melanoma, cuproptosis, tumor microenvironment, differentially expressed genes, risk score, bioinformatics analysis

Abstract

Background: Melanoma, known for its aggressive nature and poor prognosis, may be impacted by cuproptosis, a recently discovered form of programmed cell death. Despite its unclear mechanisms, preliminary studies suggested a link between cuproptosis and cancer progression and metastasis. We aimed to investigate the association between cuproptosis-related genes (CRGs) and melanoma to enhance prognostic and therapeutic strategies.
Method: In this study, we downloaded transcriptome RNA-seqs and clinical information of all melanoma patients from The Cancer Genome Atlas (TCGA) database, selected a dataset from Gene Expression Omnibus (GEO) databases, and merged the two datasets. After univariate regression analysis, all the samples were categorized into three groups based on expression levels of CRGs. Differential expression analysis was carried out for three CRG clusters to obtain the significant differentially expressed genes (DEGs). After univariate Cox regression analysis, multivariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) algorithm were performed on DEGs, the prognosis related genes were screened to establish a prognosis prediction model. The model's accuracy was validated through Kaplan-Meier analysis, receiver operating characteristic (ROC) curve, nomogram, and independent prognostic analysis. Additionally, we compared the immune scores of the tumor microenvironment, tumor mutation burden, tumor immune dysfunction and exclusion, and drug sensitivity between high-risk and low-risk groups.
Results: Through algorithm analysis, eight genes significantly related to prognosis were identified, among which SLFN13, CAMK4, TLR8, EIF4E3, and CLEC2B were low-risk genes, OCA2, NAIP, and SAMD9 were high-risk genes. Using these genes, we established a prognostic model that effectively distinguishes between different survival outcomes, with the low-risk group showing a markedly higher long-term survival rate.
Conclusion: In conclusion, based on the research of cuproptosis subtypes, we identify the DEG with predictive potential and establish a prognosis prediction model. This study may provide a reference for the prognosis and clinical treatment of melanoma patients from the perspective of cuproptosis.

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