INTRODUCTION

Skin cutaneous melanoma (SKCM), a malignant tumor, has PANoptosis implicated in its progression and metastasis. However, the exact mechanisms remain unclear. This study aims to develop a prognostic model for SKCM based on PANoptosis.

METHODS

SKCM - related datasets were retrieved from public databases. Differentially expressed PANoptosis - related genes (DEPRGs) were determined by intersecting differentially expressed genes from differential expression analysis and 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 were identified, and a nomogram was developed. Enrichment analysis and immune infiltration analysis were performed for the two risk groups. A competitive endogenous RNA (ceRNA) network was constructed, and potential therapeutic drugs were predicted. Bioinformatics findings were validated experimentally using reverse transcription quantitative PCR (RT - qPCR).

RESULTS

CD8A, ADAMDEC1, CD69, CRIP1, LSP1, BCL11B, and CCR7 were identified as prognostic genes. The risk model and nomogram showed 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 prognostic genes were regulated by several miRNAs (such as hsa-miR-330-5p) and lncRNAs (such as AL355075.4). MPPG and DT - 1687, associated with LSP1, may offer promising treatment options. RT - qPCR validation confirmed significant expression differences of CD8A, ADAMDEC1, CD69, CRIP1, and BCL11B between SKCM and control samples.

DISCUSSION

This study presents a robust prognostic model for SKCM based on PANoptosis - related genes, providing a theoretical foundation for SKCM treatment.