Construction and Identification of an NLR-Associated Prognostic Signature Revealing the Heterogeneous Immune Response in Skin Cutaneous Melanoma.

Abstract

Skin cutaneous melanoma (SKCM) is the deadliest dermatology tumor. Ongoing researches have confirmed that the NOD-like receptors (NLRs) family are crucial in driving carcinogenesis. However, the function of NLRs signaling pathway-related genes in SKCM remains unclear. To establish and identify an NLRs-related prognostic signature and to explore its predictive power for heterogeneous immune response in SKCM patients. Establishment of the predictive signature using the NLRs-related genes by least absolute shrinkage and selection operator-Cox regression analysis (LASSO-COX algorithm). Through univariate and multivariate COX analyses, NLRs signature's independent predictive effectiveness was proven. CIBERSORT examined the comparative infiltration ratios of 22 distinct types of immune cells. RT-qPCR and immunohistochemistry implemented expression validation for critical NLRs-related prognostic genes in clinical samples. The prognostic signature, including 7 genes, was obtained by the LASSO-Cox algorithm. In TCGA and validation cohorts, SKCM patients with higher risk scores had remarkably poorer overall survival. The independent predictive role of this signature was confirmed by multivariate Cox analysis. Additionally, a graphic nomogram demonstrated that the risk score of the NLRs signature has high predictive accuracy. SKCM patients in the low-risk group revealed a distinct immune microenvironment characterized by the significantly activated inflammatory response, interferon-α/γ response, and complement pathways. Indeed, several anti-tumor immune cell types were significantly accumulated in the low-risk group, including M1 macrophage, CD8 T cell, and activated NK cell. It is worth noting that our NLRs prognostic signature could serve as one of the promising biomarkers for predicting response rates to immune checkpoint blockade (ICB) therapy. Furthermore, the results of expression validation (RT-qPCR and IHC) were consistent with the previous analysis. A promising NLRs signature with excellent predictive efficacy for SKCM was developed.