A novel predictive model incorporating immune-related gene signatures for overall survival in melanoma patients

Abstract

Melanoma is the most invasive type of skin cancer, in which the immune system plays a vital role. In this study, we aimed to establish a prognostic prediction nomogram for melanoma patients that incorporates immune-related genes (IRGs). Ninety-seven differentially expressed IRGs between melanoma and normal skin were screened using gene expression omnibus database (GEO). Among these IRGs, a two-gene signature consisting of CCL8 and DEFB1 was found to be closely associated with patient prognosis using the cancer genome atlas (TCGA) database. Survival analysis verified that the IRGs score based on the signature gene expressions efficiently distinguished between high-and low-risk patients, and was identified to be an independent prognostic factor. A nomogram integrating the IRGs score, age and TNM stage was established to predict individual prognosis for melanoma. The prognostic performance was validated by the TCGA/GEO-based concordance indices and calibration plots. The area under the curve demonstrated that the nomogram was superior than the conventional staging system, which was confirmed by the decision curve analysis. Overall, we developed and validated a nomogram for prognosis prediction in melanoma based on IRGs signatures and clinical parameters, which could be valuable for decision making in the clinic. Abbreviations PD-1 Programmed death-1 CLTA-4 Cytotoxic T-lymphocyte antigen 4 GEO Gene expression omnibus TCGA The cancer genome atlas DEGs Differentially expressed genes IRGs Immune-related genes DE-IRGs Differentially expressed immune-related genes KEGG Kyoto encyclopedia of genes and genomes GO Gene ontology PPI Protein-protein interactions LASSO Least absolute shrinkage and selection operator OS Overall survival GEPIA Gene expression profiling interactive analysis ROC Receiver operating characteristic AUC Area under the curve DCA Decision curve analysis HR Hazard ratio

Development of an Immune-Related Gene Signature for Prognosis in Melanoma

“…Liao et al. developed a predictive model based on two gene signatures including CCL8 and DEFB1 but lacked an exploration of its relationship with immune cells ( 20 ). Meng et al.…”

Coexpressed Genes That Promote the Infiltration of M2 Macrophages in Melanoma Can Evaluate the Prognosis and Immunotherapy Outcome

“…We compared the prognosis model of M2 macrophages of melanoma in this paper with the prognosis model of immune-related proposed by other scholars [ 48 , 49 ]. The area under curve (AUC) value of Yansig was 0.655, that of Liaosig was 0.566, and that of Songsig was 0.579 ( Figure 10 ).…”

A Novel 23-Gene Expression Signature is Predictor of Inferior Prognosis in Melanoma

“…Sha et al developed a 8-gene risk score based on the linear combination of GPR87, KIT, SH3GL3, PVRL1, ATP1B1, CDAN1, FAU, and TNFSF14 expression levels and found the risk score was effectively predictive of increased mortality rate in melanoma patients [10]. Stanley developed a two-gene signature consisting of CCL8 and DEFB1 (IRGs score) and a nomogram integrating the IRGs score, age and TNM stage that could effectively perform prognosis prediction in melanoma [11]. Despite signi cant advances in the risk classi cation of melanoma, the accuracies of these methods are still needed to be improved.…”

“…Recent whole-genome mRNA expression pro ling studies have successfully strati ed melanoma tumors into distinct molecular subtypes which are associated with clinical properties and prognosis based on gene expression patterns [8,9]. Several gene patterns in melanoma have been reported to independently predict melanoma patients with a high risk of poor survival [10,11]. However, these studies have focused on immune markers and their relationship with prognosis.…”