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Background: Autophagy, 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.Methods: Data 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.Results: Five 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.Conclusion: We firstly developed and validated an ARGs signature based-nomogram for individualized prognosis prediction in melanoma patients, which could assist with decision making for clinicians. [ABSTRACT FROM AUTHOR]

Metabolic reprogramming is closely related to melanoma. However, the prognostic role of metabolism-related genes (MRGs) remains to be elucidated. We aimed to establish a nomogram by combining MRGs signature and clinicopathological factors to predict melanoma prognosis. Eighteen prognostic MRGs between melanoma and normal samples were identified using The Cancer Genome Atlas (TCGA) and GSE15605. WARS (HR = 0.881, 95% CI = 0.788–0.984, P = 0.025) and MGST1 (HR = 1.124, 95% CI = 1.007–1.255, P = 0.037) were ultimately identified as independent prognostic MRGs with LASSO regression and multivariate Cox regression. The MRGs signature was established according to these two genes and externally validated in the Gene Expression Omnibus (GEO) dataset. Kaplan-Meier survival analysis indicated that patients in the high-risk group had significantly poorer overall survival (OS) than those in the low-risk group. Furthermore, the MRGs signature was identified as an independent prognostic factor for melanoma survival. An MRGs nomogram based on the MRGs signature and clinicopathological factors was developed in TCGA cohort and validated in the GEO dataset. Calibration plots showed good consistency between the prediction of nomogram and actual observation. The receiver operating characteristic curve and decision curve analysis indicated that MRGs nomogram had better OS prediction and clinical net benefit than the stage system. To our knowledge, we are the first to develop a prognostic nomogram based on MRGs signature with better predictive power than the current staging system, which could assist individualized prognosis prediction and improve treatment. [ABSTRACT FROM AUTHOR]

Objective: To establish nomogram based on inflammatory indices for differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC). Methods: A cohort of 422 patients with HCC or ICC hospitalized at Xiangya Hospital between January 2014 and December 2018 was included in the study. Univariate and multivariate analysis was performed to identify the independent differential factors. Through combining these independent differential factors, a nomogram was established for differential diagnosis between ICC and HCC. The accuracy of nomogram was evaluated by using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). The results were validated using a prospective study on 98 consecutive patients operated on from January 2019 to November 2019 at the same institution. Results: Sex (OR = 9.001, 95% CI: 3.268‐24.792, P <.001), hepatitis (OR = 0.323, 95% CI: 0.121‐0.860, P =.024), alpha‐fetoprotein (AFP) (OR = 0.997, 95% CI: 0.995‐1.000, P =.046), carbohydrate antigen 19‐9 (CA199) (OR = 1.016, 95% CI: 1.007‐1.025, P <.001), and aspartate transaminase‐to‐neutrophil ratio index (ANRI) (OR = 0.904, 95% CI: 0.843‐0.969, P =.004) were the independent differential factors for ICC. Nomogram was established with well‐fitted calibration curves through incorporating these 5 factors. Comparing model 1 including gender, hepatitis, AFP, and CA199 (C index = 0.903, 95% CI: 0.849‐0.957) and model 2 enrolling AFP and CA199 (C index = 0.850, 95% CI: 0.791‐0.908), the nomogram showed a better discrimination between ICC and HCC, with a C index of 0.920 (95% CI, 0.872‐0.968). The results were consistent in the validation cohort. DCA also confirmed the conclusion. Conclusion: A nomogram was established for the differential diagnosis between ICC and HCC preoperatively, and better therapeutic choice would be made if it was applied in clinical practice. [ABSTRACT FROM AUTHOR]