The role and prognostic value of PANoptosis-related genes in skin cutaneous melanoma

Huijing Feng¹Linzi Jia²YANAN MA¹Pengmin Liu¹Xiaoling Yang¹Lina Hu¹Kai Xu¹Fan Yang²Dongfeng Zhang³Jian Li⁴Qi Mei¹Fei Han^2*

• ¹Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, Shanxi Province, China
• ²Shanxi Provincial Cancer Hospital, Taiyuan, Shanxi Province, China
• ³Linfen Central Hospital, Linfen, Shanxi Province, China
• ⁴University Hospital Bonn, Bonn, Nordrhein-Westfalen, Germany

Skin cutaneous melanoma (SKCM) is a malignant tumor, with PANoptosis playing a key role in its progression and metastasis. However, the precise mechanisms underlying its function in SKCM remain unclear. This study aims to develop a novel prognostic model for SKCM based on PANoptosis. SKCM-related datasets were retrieved from public databases. The differentially expressed PANoptosis-related genes (DEPRGs) were determined by intersecting differentially expressed genes from differential expression analysis, and the key module genes from weighted gene co-expression network analysis (WGCNA) . Prognostic genes for SKCM were derived using Cox analysis and machine learning algorithms, leading to the construction and validation of a prognostic model. Independent prognostic factors for patients with SKCM were identified, and a nomogram was developed. Additionally, enrichment analysis and immune infiltration analysis were performed for the two risk groups. A competitive endogenous RNA (ceRNA) network was constructed, and potential SKCM therapeutic drugs based on prognostic genes were predicted. Finally, bioinformatics findings were experimentally validated using reverse transcription quantitative PCR (RT-qPCR).CD8A, ADAMDEC1, CD69, CRIP1, LSP1, BCL11B, and CCR7 were identified as prognostic genes.The risk model and nomogram indicated excellent predictive abilities for SKCM patients. Genes in both high-and low-risk groups were linked to cytokine-regulated immune responses, with nine differential immune cells identified between the groups. The ceRNA network revealed that the prognostic genes were found to be regulated by several miRNAs (such as hsa-miR-330-5p) and lncRNAs (such as AL355075.4).. Additionally, MPPG and DT-1687, associated with LSP1, may offer promising treatment options for SKCM. RT-qPCR validation revealed significant differences in the expression levels of CD8A, ADAMDEC1, CD69, CRIP1, and BCL11B between SKCM and control samples. This study presents a robust prognostic model for SKCM based on PANoptosisrelated genes, providing a theoretical foundation for SKCM treatment.