ArticlePloS one2020
Predicting the clinical outcome of melanoma using an immune-related gene pairs signature.
Article in PloS one, 2020. 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.
- Determinants of resistance and response to melanoma therapy.Nature cancer · 2024Review
- A seven-immune-genes risk model predicts the survival and suitable treatments for patients with skin cutaneous melanoma.Heliyon · 2023Article
- Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View.International journal of molecular sciences · 2022Review
- Predicting the Survival and Immune Landscape of Colorectal Cancer Patients Using an Immune-Related lncRNA Pair Model.Frontiers in genetics · 2021Article
- Article
- Identification of a Set of Genes Improving Survival Prediction in Kidney Renal Clear Cell Carcinoma through Integrative Reanalysis of Transcriptomic Data.Disease markers · 2020Article
- Development of an Immune-Related Gene Signature for Prognosis in Melanoma.Frontiers in oncology · 2020Article
- Construction and Validation of a Novel Immune-Related Gene Pairs-Based Prognostic Model in Lung Adenocarcinoma.Cancer control : journal of the Moffitt Cancer CenterArticle
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9 authors.
Funding
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Abstract
objectiveMelanoma is rare but dangerous skin cancer, and it can spread rather quickly in the advanced stages of the tumor. Abundant evidence suggests the relationship between tumor development and progression and the immune system. A robust gene risk model could provide an accurate prediction of clinical outcomes. The present study aimed to explore a robust signature of immune-related gene pairs (IRGPs) for estimating overall survival (OS) in malignant melanoma.
methodsClinical and genetic data of skin cutaneous melanoma (SKCM) patients from The Cancer Genome Atlas (TCGA) was performed as a training dataset to identify candidate IRGPs for the prognosis of melanoma. Two independent datasets from the Gene Expression Omnibus (GEO) database (GSE65904) and TCGA dataset (TCGA-UVM) were selected for external validation. Univariate and multivariate Cox regression analyses were then performed to explore the prognostic power of the IRGPs signature and other clinical factors. CIBERSORTx was applied to estimate the fractions of infiltrated immune cells in bulk tumor tissues.
resultsA signature consisted of 33 IRGPs was established which was significantly associated with patients' survival in the TCGA-SKCM dataset (P = 2.0×10-16, Hazard Ratio (HR) = 4.220 (2.909 to 6.122)). We found the IRGPs signature exhibited an independent prognostic factor in all the three independent cohorts in both the univariate and multivariate Cox analysis (P<0.01). The prognostic efficacy of the signature remained unaffected regardless of whether BRAF or NRAS was mutated. As expected, the results were verified in the GSE65904 dataset and the TCGA-UVM dataset. We found an apparent shorter OS in patients of the high-risk group in the GSE65904 dataset (P = 2.1×10-3; HR = 1.988 (1.309 to 3.020)). The trend in the results of the survival analysis in TCGA-UVM was as we expected, but the result was not statistically significant (P = 0.117, HR = 4.263 (1.407 to 12.91)). CD8 T cells, activated dendritic cells (DCs), regulatory T cells (Tregs), and activated CD4 memory T cells presented a significantly lower fraction in the high-risk group in the TCGA-SKCM dataset(P <0.01).
conclusionThe results of the present study support the IRGPs signature as a promising marker for prognosis prediction in melanoma.
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