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

Yi Geng; Yu-Jie Sun; Hao Song; Qiu-Ju Miao; Yi-Fei Wang; Jin-Liang Qi; Xiu-Lian Xu; Jian-Fang Sun

doi:10.2147/CCID.S410723

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

Clin Cosmet Investig Dermatol. 2023 Jun 26:16:1623-1639. doi: 10.2147/CCID.S410723. eCollection 2023.

Authors

Yi Geng¹, Yu-Jie Sun¹, Hao Song¹, Qiu-Ju Miao¹, Yi-Fei Wang¹, Jin-Liang Qi², Xiu-Lian Xu¹, Jian-Fang Sun¹

Affiliations

¹ Institute of Dermatology, Peking Union Medical College and Chinese Academy of Medical Sciences, Nanjing, 210042, People's Republic of China.
² State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, 210023, People's Republic of China.

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.

Grants and funding

This work was supported by the National Natural Science Foundation of China (81772916, 82103470) and the Jiangsu Natural Science Foundation (BK20171132).