ArticleFrontiers in oncology2021
A Four-Gene-Based Prognostic Model Predicts Overall Survival in Patients With Cutaneous Melanoma.
Article in Frontiers in oncology, 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, 14 citations in OpenAlex.
- A development and validation of predictive model based on novel immune-related gene-based subtypes for the risk assessment of cutaneous melanoma.Translational cancer research · 2025Article
- Heat shock protein HSPA13 promotes hepatocellular carcinoma progression by stabilizing TANK.Cell death discovery · 2023Article
- Conjugation of the 9-kDa Isoform of Granulysin with Liposomes Potentiates Its Cytotoxicity.International journal of molecular sciences · 2022Article
- Preclinical Studies of Granulysin-Based Anti-MUC1-Tn Immunotoxins as a New Antitumoral Treatment.Biomedicines · 2022Article
- Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View.International journal of molecular sciences · 2022Review
- Identification of mRNA Signature for Predicting Prognosis Risk of Rectal Adenocarcinoma.Frontiers in genetics · 2022Article
- The Prognostic and Predictive Role of Xeroderma Pigmentosum Gene Expression in Melanoma.Frontiers in oncology · 2022Article
- Development and Validation of a Combined Glycolysis and Immune Prognostic Model for Melanoma.Frontiers in immunology · 2021Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCutaneous melanoma (CM) is one of the most aggressive cancers with highly metastatic ability. To make things worse, there are limited effective therapies to treat advanced CM. Our study aimed to investigate new biomarkers for CM prognosis and establish a novel risk score system in CM.
methodsGene expression data of CM from Gene Expression Omnibus (GEO) datasets were downloaded and analyzed to identify differentially expressed genes (DEGs). The overlapped DEGs were then verified for prognosis analysis by univariate and multivariate COX regression in The Cancer Genome Atlas (TCGA) datasets. Based on the gene signature of multiple survival associated DEGs, a risk score model was established, and its prognostic and predictive role was estimated through Kaplan-Meier (K-M) analysis and log-rank test. Furthermore, the correlations between prognosis related genes expression and immune infiltrates were analyzed
resultsA total of 103 DEGs were obtained based on GEO cohorts, and four genes were verified in TCGA datasets. Subsequently, four genes (
conclusionsThe four-gene risk score model functions well in predicting the prognosis and treatment response in CM and will be useful for guiding therapeutic strategies for CM patients. Additional clinical trials are needed to verify our findings.
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