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Updated: Jan 13, 2026
A 3D Organotypic Melanoma Spheroid Skin Model
Published on: May 18, 2018
Screening of PANoptosis regulator-associated long noncoding RNAs and construction of a survival prognostic model in
Jingshu Xiong1, Mingyang Wu1, Jinquan Liu2
1Department of Pathology, Hospital for Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, China.
Background:
Cutaneous melanoma (SKCM) remains a lethal malignancy with complex molecular mechanisms. PANoptosis, a coordinated cell death pathway, and long noncoding RNAs (lncRNAs) have emerged as critical modulators influencing oncogenic pathways and tumor development through multifaceted regulatory mechanisms. This study aimed to identify PANoptosis regulator (PANR)-associated lncRNAs and construct a prognostic model to predict SKCM outcomes and to clarify their associations with immune infiltration, drug sensitivity, and molecular pathways.
Methods:
Gene expression data from 471 The Cancer Genome Atlas (TCGA)-human skin SKCM tumors, 214 Gene Expression Omnibus-SKCM samples, and 812 Genotype-Tissue Expression normal tissues were merged after batch correction. A PANR set (n=300) was integrated to identify differentially expressed PANRs (DE-PANRs) and identify differentially expressed lncRNAs (DE-lncRNAs) using the "limma" package [false discovery rate (FDR) <0.05 and |log2 fold change| >1]. Associations between DE-lncRNAs and DE-PANRs were established through Pearson correlation analysis and followed by functional enrichment via Database for Annotation, Visualization and Integrated Discovery (DAVID). Prognostic DE-lncRNAs were selected via univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. A risk score (RS) model was developed and validated in the TCGA and GSE65904 cohorts. Nomogram construction, immune profiling (CIBERSORT), drug sensitivity (pRRophetic), and pathway analyses [gene set enrichment analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG)] were performed.
Results:
Differential analysis identified 995 DE-lncRNAs and 142 DE-PANRs, with 83 PANR-associated lncRNAs forming a regulatory network. Six prognostic lncRNAs (MIR155HG, LINC01501, NRIR, HLA-DQB1-AS1, USP30-AS1, and LINC00674) were optimized via LASSO. Survival disparities were observed between the high- and low-risk cohorts stratified by the RS model [TCGA cohort: hazard ratio (HR) =2.72, P<0.001; GSE65904 cohort: HR =1.85, P=0.002]. The nomogram integrating RS, age, and tumor stage could predict the 1-, 3-, and 5-year survival (concordance index =0.81). High-risk patients exhibited immunosuppressive profiles and showed differential drug response patterns, with predicted increased sensitivity to 14 therapeutic agents. Enriched pathways included apoptosis, inflammatory response, and KRAS signaling. Mutational analysis revealed the top 20 mutated genes that differed the most between risk groups.
Conclusions:
This study established a PANR-associated lncRNA prognostic model with robust predictive accuracy for SKCM survival. The risk stratification system correlates with immune dysregulation, therapy response, and pathway activation, offering a potential tool for personalized prognosis and treatment strategies.
