Predicting the Risk of Melanoma Metastasis Using an Immune Risk Score in the Melanoma Cohort

Abstract

Melanoma is a highly aggressive cancer, attracting increasing attention worldwide. The 5-year survival rate of patients with metastatic melanoma is low. Therefore, it is critical to identify potential effective biomarkers for diagnosis of melanoma metastasis. In the present study, the melanoma cohort and immune genes were obtained from the Cancer Genome Atlas (TCGA) database and the ImmPort database, respectively. Then, we constructed the immune risk score (IRS) using univariate and multivariate logistic analysis. The area under the curve (AUC) of IRS in sequencing samples and the initial diagnosis patients was 0.90 and 0.80, respectively. Besides, IRS could add benefits for metastasis diagnosis. For sequencing samples, IRS (OR = 16.35, 95% CI = 8.74-30.59) increased the odds for melanoma metastasis. Similar results were obtained in the initial diagnosis patients (OR = 8.93, 95% CI = 3.53-22.61). A composite nomogram was built based on IRS and clinical information with well-fitted calibration curves. We further used other independent melanoma cohorts from Gene Expression Omnibus (GEO) databases to confirm the reliability and validity of the IRS (AUC > 0.75, OR > 1.04, and P value < 0.01 in all cohorts). In conclusion, IRS is significantly associated with melanoma metastasis and can be a novel effective signature for predicting the metastasis risk.

Keywords: immune risk score; metastasis risk; metastatic melanoma; nomogram; primary melanoma.

FIGURE 1

Construction of the immune risk score. (A) The volcano plot of significantly differentially expressed IRGs. (B) The significantly differentially expressed IRGs involved KEGG pathways. (C) and (D) Distribution of the IRS and gene expression among sequencing samples and the initial patients.

FIGURE 2

Decision curve analyses of the IRS. (A) Decision curve analyses for the sequencing samples. (B) Decision curve analyses for the initial diagnosis patients. (C–E) Decision curve analyses for melanoma cohorts from GEO datasets (C: GSE8401, D: GSE15605, E: GSE46517).

FIGURE 3

Construction of nomogram. (A) Nomogram for predicting melanoma metastasis for the sequencing samples. (B) Calibration curves of nomograms in terms of agreement between predicted and observed in the sequencing samples. (C) Nomogram for predicting melanoma metastasis for the initial diagnosis patients. (D) Calibration curves of nomograms in terms of agreement between predicted and observed in the initial diagnosis patients.