The Identification and Validation of a Robust Immune-Associated Gene Signature in Cutaneous Melanoma

Abstract

Background: Cutaneous melanoma is defined as one of the most aggressive skin tumors in the world. An increasing body of evidence suggested an indispensable association between immune-associated gene (IAG) signature and melanoma. This article is aimed at formulating an IAG signature to estimate prognosis of melanoma.

Methods: 434 melanoma patients were extracted from The Cancer Genome Atlas (TCGA) database, and 1811 IAGs were downloaded from the ImmPort database in our retrospective study. The Cox regression analysis and LASSO regression analysis were utilized to establish a prognostic IAG signature. The Kaplan-Meier (KM) survival analysis was performed, and the time-dependent receiver operating characteristic curve (ROC) analysis was further applied to assess the predictive value. Besides, the propensity score algorithm was utilized to balance the confounding clinical factors between the high- and low-risk groups.

Results: A total of six prognostic IAGs comprising of INHA, NDRG1, IFITM1, LHB, GBP2, and CCL8 were eventually filtered out. According to the KM survival analysis, the results displayed a shorter overall survival (OS) in the high-risk group compared to the low-risk group. In the multivariate Cox model, the gene signature was testified as a remarkable prognostic factor (HR = 45.423, P < 0.001). Additionally, the ROC curve analyses were performed which demonstrated our IAG signature was superior to four known biomarkers mentioned in the study. Moreover, the IAG signature was significantly related to immunotherapy-related biomarkers.

Conclusion: Our study demonstrated that the six IAG signature played a critical role in the prognosis and immunotherapy of melanoma, which might help clinicians predict patients' survival and provide individualized treatment.

Figure 1

Correlation between the six IAGs and overall survival of cutaneous melanoma. (a) The least absolute shrinkage and selection operator (LASSO) Cox regression method. (b) The regression coefficient of the optimal prognostic IAGs. (c) Kaplan-Meier curves of overall survival of the high-risk and low-risk groups in (c) the training set and (d) the testing set. The ROC curves of 1-year, 3-year and 5-year OS in (e) the training set and (f) the testing set. IAGs: immune-associated genes; OS: overall survival; ROC: receiver operating characteristic.

Figure 2

The heat map that showed the expression of six IAGs and the distribution of clinicopathological variables. IAGs: immune-associated genes.

Figure 3

The univariate and multivariate Cox regression analyses of clinicopathological factors (including the risk score) and OS in the TCGA database. OS: overall survival. ^∗P < 0.05, ^∗∗P < 0.01, and ^∗∗∗P < 0.001.

Figure 4

The AUC value for 1-year, 3-year, and 5-year OC of our six-IAG signature compared with other four gene-associated signatures.

Figure 5

The relationship between the IAG signature and immunotherapy-related biomarkers. (a) The propensity score between the high-risk and low-risk groups. (b) CYT, (c) GEP, (d) BCR richness, and (e) TCR richness. (f) Six immune cell populations, including B cell, macrophage, myeloid dendritic cell, neutrophil, T cell CD4+, and T cell CD8+ between the high- and low-risk groups. (g) Differences in the mRNA expression level of 34 immune checkpoints. (h) Comparison of immunotherapy response rate between the high- and low-risk groups.