ArticleBMC cancer2021
Nomogram based on autophagy related genes for predicting the survival in melanoma.
Article in BMC cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Prognostic nomogram for external ear melanoma patients in the elderly: a SEER-based study.Journal of cancer research and clinical oncology · 2023Trial
- Development and validation of a nomogram for predicting overall survival in patients with lower extremity melanoma: based on the Surveillance, Epidemiology, and End Results (SEER) database.Archives of dermatological research · 2025Article
- Establishment and validation of a nomogram model for predicting the specific mortality risk of melanoma in upper limbs based on the SEER database.Scientific reports · 2024Article
- Prognostic nomograms for predicting long-term overall survival in spindle cell melanoma: a population-based study.Frontiers in endocrinology · 2024Article
- A comprehensive signature based on endoplasmic reticulum stress-related genes in predicting prognosis and immunotherapy response in melanoma.Scientific reports · 2023Article
- A Novel Necroptosis-Related Gene Signature in Skin Cutaneous Melanoma Prognosis and Tumor Microenvironment.Frontiers in genetics · 2022Article
- Development and Validation of a Combined Ferroptosis and Immune Prognostic Model for Melanoma.Journal of oncology · 2022Article
- A combined aging and immune prognostic signature predict prognosis and responsiveness to immunotherapy in melanoma.Frontiers in pharmacology · 2022Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
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.
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