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. 2023 Feb 15:14:1112181.
doi: 10.3389/fimmu.2023.1112181. eCollection 2023.

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

Mingjia Li  1   2   3   4   5   6 , Xinrui Long  1   2   3   4   5 , Wenbo Bu  7 , Guanxiong Zhang  1   2   3   4   5 , Guangtong Deng  1   2   3   4   5 , Yuancheng Liu  1   2   3   4   5 , Juan Su  1   2   3   4   5 , Kai Huang  1   2   3   4   5
Affiliations

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

Mingjia Li et al. Front Immunol. .

Abstract

Background: Melanoma 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.

Methods: An 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.

Results: The 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.

Conclusion: The 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.

Keywords: cell pair; cutaneous melanoma; immunotherapy response; prognosis model; tumor infiltrating immune cell.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer MP declared a shared affiliation with the authors ML, XL, GZ, GD, YL, JS and KH to the handling editor at time of review.

Figures

Figure 1
Figure 1
Overview of the workflow. (A) The data of this study were from The Cancer Genome Atlas (TCGA) and GEO cohorts. (B) The immune-related risk score (IRRS) was constructed by the relative value of cell abundance, that is, cell pairs. (C) Discovered the difference in the genomic features between the high and low IRRS through DEG, mutation, and CNV. (D) Immunologic changes based on the IRRS was analyzed by the TCR and BCR, MHC, neoantigens, and checkpoints. (E) The prognosis and immunotherapy predictive effects were confirmed. (F) A nomogram based on the IRRS was constructed and validated.
Figure 2
Figure 2
Construction and validation of the IRRS. (A) The 19 candidate cells screened based on uni-Cox analysis. (B–D) The Kaplan–Meier curves of survival probability for patients in the TCGA-SKCM, GSE54467, and GSE65904 cohorts. (E) The ROC curve for patients in the TCGA-SKCM cohort. (F) Comparison of immunotherapeutic responses (P< 0.01) for patients in the GSE91061, GSE115821, and Liu et al. cohorts. (G) Comparison of C-index between the IRRS and tumor stage, TMB, and driver mutations (BRAF, NF1, and RAS) in the TCGA.
Figure 3
Figure 3
The Kaplan–Meier survival curves according to different TNM stages of patients from the TCGA-SKCM classified into high- and low-risk groups based on the IRRS score.
Figure 4
Figure 4
Screening of differentially expressed genes and the master regulator. (A) Volcano plot of differentially expressed genes between the low- and high-risk groups in the TCGA cohort. (B) KEGG enrichment of differentially expressed genes. (C) Gene set enrichment analysis of the high IRRS and low IRRS groups. (D) Network of the MRs and DEGs upregulated in the high IRRS group. Orange: eight MRs with the most nodes. Genes related to the KEGG enrichment pathway corresponding to each color: yellow, Staphylococcus aureus infection; green, estrogen signaling pathway; blue, both above; dark blue, arachidonic acid metabolism; purple, other DEGs.
Figure 5
Figure 5
Genomic features and immunologic changes of the high- and low-score groups. (A) Mutation of top 20 most frequently altered genes in melanoma patients with high and low IRRS. (B) Cosmic mutation signature 7, tumor mutation burden, microsatellite instability, neoantigens, and TIDE score in the high- and low-score groups. (C) Heatmap depicting the co-occurrence or exclusivity of the top 25 most mutated genes in the high IRRS group (left upper corner) and the low IRRS group (lower right corner). (D) Association between HLA and immune checkpoint molecules and the IRRS. *P<0.05 **P<0.01 ***P<0.001.
Figure 6
Figure 6
Copy number alterations in the high- and low-score groups. (A) Copy number profiles of the high IRRS score (above) and low IRRS score (below) groups. (B) Detailed cytobands with focal amplification (red) and deletion (blue) peaks identified in the high IRRS group. (C) Detailed cytobands with focal amplification (red) and deletion (blue) peaks identified in the low IRRS group. (D) Circular plot of the top 10 biological processes and corresponding enriched genes in the high IRRS. (E) Circular plot of the top 10 biological processes and corresponding enriched genes in the low IRRS.
Figure 7
Figure 7
Construction of the nomogram. (A) The prognostic clinical factors screened based on uni-Cox regression. (B) The nomogram for predicting the survival rate of melanoma patients, including four independent clinical prognostic factors and the IRRS. (C) The online version of the nomogram. (D) The ROC analysis of the nomogram. (E) DCA of the nomogram. (F) The calibration curve of the nomogram. ***P<0.001.
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