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

Abstract

Background: 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.

Objective: To establish and identify an NLRs-related prognostic signature and to explore its predictive power for heterogeneous immune response in SKCM patients.

Methods: 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.

Results: 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.

Conclusion: A promising NLRs signature with excellent predictive efficacy for SKCM was developed.

Keywords: NLR proteins; cutaneous melanoma; immune; prognosis; survival analysis.

Figure 1

Critical NLRs signaling pathway-related genes in TCGA SKCM cohort. (A) Genomic profile of top 20 NLRs signaling pathway-related genes with higher somatic mutation frequency. (B) Forest plot showing the hazard ratio and p-value of the NLRs signaling pathway-related genes from univariate Cox regression analysis in the TCGA cohort. (C) Kaplan-Meier curves for OS by expression of CCL8, CCL5, PSTPIP1, NFKBIA, BIRC3, NLRC4, IL18, and CASP8, where the median expression was cut-off.

Figure 2

Construction of the prognostic NLRs-related gene signature in the TCGA cohort. (A) LASSO coefficient profiles of the prognostic genes. (B) Cross-validation for turning parameter selection in the LASSO regression model. Two vertical dashed lines indicated the optimal values using the minimum criteria. Optimal RNAs with the best discriminative capability were selected for developing the risk score. (C) LASSO coefficients of the 7 optimized genes for constructing the prognostic model. (D) The distribution of the risk scores. (E) The distributions of OS status, OS time, and risk score in TCGA SKCM cohort. (F) Heatmap indicated mRNA expression distribution of the 7 genes between the high-risk and low-risk groups. (G) Kaplan-Meier curves for the OS by risk groups. (H) The ROC curves of the risk score in predicting 1-, 3-, and 5-year OS.

Figure 3

Validation of the prognostic NLRs-related gene signature in the two independent validation cohorts. (A and B) Kaplan-Meier curves for the OS by risk groups for GSE133713 and GSE54467 datasets, respectively. (C and D) The ROC curves of the risk score in predicting 1-, 3-, and 5-year OS in GSE133713 and GSE54467 datasets, respectively.

Figure 4

Independent predictive efficacy of the NLRs signature and its association with clinicopathological features. Distribution of the risk score by different age (A), gender (B), and tumor stage (C) groups in TCGA SKCM cohort. Results of the univariate (D) and multivariate (E) Cox regression analyses regarding OS of risk score and clinicopathological features in the TCGA cohort.

Figure 5

The construction and verification of the nomogram in the TCGA cohort. (A) A nomogram based on the NLRs prognostic signature risk score, tumor stage, and age for 1-, 3- and 5-year OS prediction. (B) The calibration plot showed the agreement between 1-, 3- and 5-year OS prediction and actual observation. (C) Results of the decision curve analysis (DCA).

Figure 6

The NLRs signature characterized differential tumor molecular features in the TCGA cohort. (A and B) Oncoprint depicts the recurrent somatic mutations with maximum mutation frequency in the different risk groups of the TCGA SKCM cohort. (C) The ssGSEA activity score of the 50 cancer hallmark pathways between the high-and low-risk groups in the TCGA cohort.

Figure 7

The NLRs signature indicated the heterogeneous tumor immune microenvironment. Scatter plots depict the correlations between the risk score and leukocyte fraction (A), stromal score (B), immune score (C), and tumor purity (D). (E) Comparison of 22 immune cell subtypes infiltration between high- and low-risk groups in TCGA SKCM cohort. (F) Heatmap depicting the correlation between the 7 signature genes expressions, risk score, and the infiltration scores of 22 immune cells; *P < 0.05; **P < 0.01; ***P < 0.001; ****P <0.0001.

Figure 8

Potential clinical application of NLR signature in immunotherapy. (A) Scatter plots depicting correlations between the risk score and the expression of four immune checkpoint genes (CD276, CD274, PDCD1, CTLA4). (B) Comparison of the mutation burden, neoantigen load, and TCR diversity by different risk groups. (C) Kaplan-Meier curves for the OS of patients treated with ICB by different risk groups. (D) Distribution of the risk score between responders and non-responders. (E) The proportion of responders and non-responders between the high- and low-risk groups.

Figure 9

Expression Validation of the NLR-related prognostic gene signature in Clinical Samples. (A) Expressions of 7 critical NLR signature genes in tumor and para-tumor samples by RT-qPCR. (B) The immunostaining scores of SKCM tissues (n = 10) and nevus tissues (n = 8) were displayed using t-test. (C) Immunohistochemistry staining for MAPK10 in clinical SKCM (Left) and nevus tissue (Right) (Scale bar, 250 μm); *P < 0.05; **P < 0.01; ***P < 0.001.