Immune-related risk score: An immune-cell-pair-based prognostic model for cutaneous melanoma

Abstract

BackgroundMelanoma is among the most malignant immunologic tumor types and is associated with high mortality. However, a considerable number of melanoma patients cannot benefit from immunotherapy owing to individual differences. This study attempts to build a novel prediction model of melanoma that fully considers individual differences in the tumor microenvironment.MethodsAn immune-related risk score (IRRS) was constructed based on cutaneous melanoma data from The Cancer Genome Atlas (TCGA). Single-sample gene set enrichment analysis (ssGSEA) was used to calculate immune enrichment scores of 28 immune cell signatures. We performed pairwise comparisons to obtain scores for cell pairs based on the difference in the abundance of immune cells within each sample. The resulting cell pair scores, in the form of a matrix of relative values of immune cells, formed the core of the IRRS.ResultsThe area under the curve (AUC) for the IRRS was over 0.700, and when the IRRS was combined with clinical information, the AUC reached 0.785, 0.817, and 0.801 for the 1-, 3-, and 5-year survival, respectively. Differentially expressed genes between the two groups were enriched in staphylococcal infection and estrogen metabolism pathway. The low IRRS group showed a better immunotherapeutic response and exhibited more neoantigens, richer T-cell receptor and B-cell receptor diversity, and higher tumor mutation burden.ConclusionThe IRRS enables a good prediction of prognosis and immunotherapy effect, based on the difference in the relative abundance of different types of infiltrating immune cells, and could provide support for further research in melanoma.

T cell-related ubiquitination genes as prognostic indicators in hepatocellular carcinoma

“…This study employed the ssGSEA algorithm to accurately calculate the infiltration abundance of 28 immune cell types in each liver cancer patient and analyzed the correlation between riskscore and immune cells ( 21 , 22 ). MSI, as an important prognostic factor and treatment target in tumors, its score differences between high and low-risk groups can effectively predict the efficacy of immunotherapy.…”

Construction and validation of eight cuproptosis-related lncRNAs signature for predicting prognosis and immune response in melanoma

“…The single-sample genomic enrichment analysis (ssGSEA) algorithm is a method commonly used for immune cell in ltration analysis. The ssGSEA algorithm was performed by the "GSVA" package in R software to investigate the differences between two groups of samples in terms of the proportion of immune cell in ltration and immune-related functions [19]. The expression levels of 13 immune-related pathways as well as 16 immune-related cells, which are listed in Supplementary Table S4, were obtained.…”

Table_1.xlsx

“…This study employed the ssGSEA algorithm to accurately calculate the infiltration abundance of 28 immune cell types in each liver cancer patient and analyzed the correlation between riskscore and immune cells (21,22). MSI, as an important prognostic factor and treatment target in tumors, its score differences between high and low-risk groups can effectively predict the efficacy of immunotherapy.…”