{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T13:52:56Z","timestamp":1773841976418,"version":"3.50.1"},"reference-count":64,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T00:00:00Z","timestamp":1749168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>This study presents a robust prognostic model for SKCM based on PANoptosis - related genes, providing a theoretical foundation for SKCM treatment.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fimmu.2025.1605977","type":"journal-article","created":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T05:22:27Z","timestamp":1749187347000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["The role and prognostic value of PANoptosis-related genes in skin cutaneous 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