Melanoma 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.,Clinical 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.,A 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).,The results of the present study support the IRGPs signature as a promising marker for prognosis prediction in melanoma.
The metastatic Skin Cutaneous Melanoma (SKCM) has been associated with diminished survival rates and high mortality rates worldwide.,Thus, segregating metastatic melanoma from the primary tumors is crucial to employ an optimal therapeutic strategy for the prolonged survival of patients.,The SKCM mRNA, miRNA and methylation data of TCGA is comprehensively analysed to recognize key genomic features that can segregate metastatic and primary tumors.,Further, machine learning models have been developed using selected features to distinguish the same.,The Support Vector Classification with Weight (SVC-W) model developed using the expression of 17 mRNAs achieved Area under the Receiver Operating Characteristic (AUROC) curve of 0.95 and an accuracy of 89.47% on an independent validation dataset.,This study reveals the genes C7, MMP3, KRT14, LOC642587, CASP7, S100A7 and miRNAs hsa-mir-205 and hsa-mir-203b as the key genomic features that may substantially contribute to the oncogenesis of melanoma.,Our study also proposes genes ESM1, NFATC3, C7orf4, CDK14, ZNF827, and ZSWIM7 as novel putative markers for cutaneous melanoma metastasis.,The major prediction models and analysis modules to predict metastatic and primary tumor samples of SKCM are available from a webserver, CancerSPP (http://webs.iiitd.edu.in/raghava/cancerspp/).