Abstract
BackgroundAutophagy, a highly conserved lysosomal degradation pathway, is associated with the prognosis of melanoma. However, prognostic prediction models based on autophagy related genes (ARGs) have never been recognized in melanoma. In the present study, we aimed to establish a novel nomogram to predict the prognosis of melanoma based on ARGs signature and clinical parameters.MethodsData from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases were extracted to identify the differentially expressed ARGs. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate analysis were used to select the prognostic ARGs. ARGs signature, age and stage were then enrolled to establish a nomogram to predict the survival probabilities of melanoma. The nomogram was evaluated by concordance index (C-index), receiver operating characteristic (ROC) curve and calibration curve. Decision curve analysis (DCA) was performed to assess the clinical benefits of the nomogram and TNM stage model. The nomogram was validated in GEO cohorts.ResultsFive prognostic ARGs were selected to construct ARGs signature model and validated in the GEO cohort. Kaplan-Meier survival analysis suggested that patients in high-risk group had significantly worse overall survival than those in low-risk group in TCGA cohort (P = 5.859 × 10–9) and GEO cohort (P = 3.075 × 10–9). We then established and validated a novel promising prognostic nomogram through combining ARGs signature and clinical parameters. The C-index of the nomogram was 0.717 in TCGA training cohort and 0.738 in GEO validation cohort. TCGA/GEO-based ROC curve and decision curve analysis (DCA) demonstrated that the nomogram was better than traditional TNM staging system for melanoma prognosis.ConclusionWe firstly developed and validated an ARGs signature based-nomogram for individualized prognosis prediction in melanoma patients, which could assist with decision making for clinicians.
Highlights
Cutaneous melanoma is one of the most aggressive skin malignancies, characterized by its high potential for invasiveness and metastasis, and limited response to treatment [1]
Data collection and processing All the RNA sequencing data were extracted from The Cancer Genome Atlas (TCGA) dataset, Gene Expression Omnibus (GEO) dataset and the Genotype-Tissue Expression (GTEx) project using the University of California Santa Cruz (UCSC) Xena website
Multivariable Cox analysis further showed that APOL1 (HR = 0.86, 95% CI: 0.78–0.95), ATG16L2 (HR = 0.72, 95% CI: 0.54–0.97), DAPK2 (HR = 0.58, 95% CI: 0.29–1.16) were considered as protective genes, while ATG9B (HR = 1.41, 95% CI: 1.04–1.92) and EGFR (HR = 1.24, 95% CI: 1.07–1.45) were risk genes for melanoma overall survival (Fig. 3D)
Summary
Cutaneous melanoma (thereafter as “melanoma”) is one of the most aggressive skin malignancies, characterized by its high potential for invasiveness and metastasis, and limited response to treatment [1]. Until now, the prognostic prediction still relies too much on the American Joint Committee on Cancer’s (AJCC) staging system for tumor-node-metastasis (TNM), which remains limitations because melanoma patients at the same stage vary widely in the survival outcomes [4, 5]. A highly conserved lysosomal degradation pathway, is associated with the prognosis of melanoma. Prognostic prediction models based on autophagy related genes (ARGs) have never been recognized in melanoma. We aimed to establish a novel nomogram to predict the prognosis of melanoma based on ARGs signature and clinical parameters
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