A Novel Glycolysis-Related Gene Signature Predicts Prognosis For Cutaneous Melanoma

Abstract

Background: There exists a lack of effective tools predicting prognosis for cutaneous melanoma patients. Glycolysis plays an essential role in the carcinogenesis process. Objective: : We intended to construct a new prognosis model for cutaneous melanoma. Method: Based on the data from TCGA database, we conducted univariate Cox regression analysis and identified prognostic glycolysis-related genes (GRGs). Meanwhile, GSE15605 dataset was used to identify differentially expressed genes (DEGs). The intersection of prognostic GRGs and DEGs was extracted for the subsequent multivariate Cox regression analysis. Results: A prognostic signature containing ten GRGs was built, and the TCGA cohort was classified into high and low risk subgroups based on risk score of each patient. K-M analysis manifested that the overall survival of high-risk group was statistically worse than that of low-risk group. Further study indicated that the risk-score could be used as an independent prognostic factor which effectively predicted the clinical prognosis in patients with different age, gender and stage. GO and KEGG enrichment analysis showed DEGs between high and low risk groups were enriched in immune-related functions and pathways. In addition, a significant difference existed between high and low risk groups in infiltration pattern of immune cell and expression levels of inhibitory immune checkpoint genes. Conclusion: A new glycolysis-related gene signature was established for identifying cutaneous melanoma patients with poor prognosis and formulating individualized treatment for them.

A BRAF mutation-associated gene risk model for predicting the prognosis of melanoma

“…Predicting the accurate prognosis of melanoma patients is essential for clinical practice. Earlier prognostic models for melanoma patients were developed based on hypoxia score [ 13 ], ferroptosis-related gene [ 14 ], immune-related gene [ 15 ], glycolysis-related gene [ 16 ], and pyroptosis-related lncRNA [ 17 ]. It is not appropriate to make definitive comparisons among these studies since different data sources were used to evaluate the prognostic models.…”

“…Several prognostic prediction models for melanoma have been constructed based on hypoxia score [ 13 ], ferroptosis-related gene [ 14 ], immune-related gene [ 15 ], glycolysis-related gene [ 16 ], and pyroptosis-related lncRNA [ 17 ]. However, the prediction ability of these prognostic models needs further evaluation due to poor predictive performance in the validation cohort (AUC = 0.628) [ 13 ], the absence of time-dependent receiver operative characteristic (ROC) analysis [ 14 , 15 ], or the lack of a validation cohort [ 16 , 17 ]. To the best of our knowledge, there was no prediction model based on BRAF mutation status in melanoma.…”