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. 2022 Jul 12:15:6145-6163.
doi: 10.2147/IJGM.S367693. eCollection 2022.

Identification of a Novel Pyroptosis-Related Gene Signature Indicative of Disease Prognosis and Treatment Response in Skin Cutaneous Melanoma

An-An Li  1   2 , Yu Zhang  1   2 , Wei-Lai Tong  1   2 , Jiang-Wei Chen  1 , Shan-Hu Huang  1 , Jia-Ming Liu  1 , Zhi-Li Liu  1   2
Affiliations

Identification of a Novel Pyroptosis-Related Gene Signature Indicative of Disease Prognosis and Treatment Response in Skin Cutaneous Melanoma

An-An Li et al. Int J Gen Med. .

Abstract

Purpose: Pyroptosis plays an important role in the occurrence and progression of many tumors; however, the specific mechanisms involved remain unknown. Here, we construct a pyroptosis-related gene signature that can be used to predict survival prognosis of skin cutaneous melanoma (SKCM) and provide guidance for clinical treatment.

Methods: By integrating data from the two databases from the GTEx and TCGA, differentially expressed genes (DEGs) from normal tissues and skin cutaneous tumor tissues were identified. The main signaling pathways and function enrichment of these differential genes were determined. Univariate and multivariate COX regression analysis, and risk score analysis were used to construct a signature to assess its predictive value for overall survival. The mRNA expression of these five genes in melanoma cells was determined by quantitative polymerase chain reaction (qPCR). The pRRophetic algorithm was used to estimate the half-maximal inhibitory concentration (IC50) of chemotherapy drugs in SKCM patients. The expression of multiple immune checkpoint genes also was evaluated.

Results: Sixteen DEGs associated with pyroptosis in SKCM and normal skin tissues were identified. Of these, 12 pyroptosis-related DEGs were associated with the prognosis of SKCM. A five-gene signature (GSDMA, GSDMC, IL-18, NLRP6, and AIM2) model was constructed. Patients were divided into high-risk and low-risk groups using the risk scores. Of these, the high-risk group had a worse survival prognosis. There are significant differences in the predicted sensitivity of the high-risk and low-risk groups to chemotherapeutic drugs. In addition, compared with the high-risk group, the low-risk group showed higher expression of PD-1, PDL-1, CTLA-4, LAG-3, and VSIR.

Conclusion: In this study, we constructed a novel prognostic pyroptosis-related gene-signature for SKCM. These genes showed good predictive value for patient prognosis and could provide guidance for better treatment of SKCM patients.

Keywords: prognosis; pyroptosis; risk model; signature; skin cutaneous melanoma.

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Conflict of interest statement

The authors report no conflicts of interest in this work.

Figures

Figure 1
Figure 1
Differential gene expression analysis based on skin cutaneous melanoma (SKCM) tissue and normal tissue in colon cancer. (A–C) Heatmaps, volcano maps, boxplots (green: low expression; red: high expression) revealing the expression of differentially expressed genes (DEGs) in SKCM and normal tissues. (D) PPI network showing the interactions for 13 of 16 DEGs (interaction score=0.7). (E and F) Circular plots for GO and KEGG enrichment analysis of the 16 DEGs.
Figure 2
Figure 2
Tumor classification based on DEGs. (A) 471 SKCM patients were divided into two clusters according to the consensus clustering matrix (k=2). (B) Heatmap and the clinical characters of the two clusters classified by 16 DEGs. (C) Kaplan–Meier OS curves for the two clusters.
Figure 3
Figure 3
Construction of five pyroptosis-related prognostic genes signature. Construction of the pyroptosis related gene signature and prognostic analysis. (A–C) Risk score distribution, survival status and heatmap of gene expression among patients with SKCM. (D) Kaplan–Meier OS curve for high-risk subgroup and low-risk subgroup.
Figure 4
Figure 4
Prognostic analysis of the pyroptosis related gene signature. (A and B) Univariate (A) and multivariate (B) Cox analysis of clinical characteristics in the TCGA cohort. (C) AUC value predicts clinical characteristics and risk score. (D) The relationship between the high-risk and low-risk groups and clinically relevant characteristics, *P<0.05; ***P<0.001. (E) 1-, 2-, and 3-year survival rates by scoring the clinical characteristics in the nomogram.
Figure 5
Figure 5
Protein expression of five key genes in SKCM patients. (A–E) Immunohistochemical staining images from The HPA of five key genes in the SKCM tissue.
Figure 6
Figure 6
Correlation between the five DEGs and immune cell infiltration from TIMER in SKCM. (A) Correlation between AIM2 and immune cell infiltration; (B) correlation between GSDMA and immune cell infiltration; (C) correlation between GSDMC and immune cell infiltration; (D) correlation between IL18 and immune cell infiltration; (E) correlation between NLRP6 and immune cell infiltration.
Figure 7
Figure 7
The differential expression of 22 kind of immune cells in both the high- and low-risk groups.
Figure 8
Figure 8
Correlation of risk scores with expression of different chemotherapy drugs and immune checkpoint molecules. (A1–E1) Sensitivity of high- and low-risk patients to cisplatin (A1), docetaxel (B1), paclitaxel (C1), sorafenib (D1), or PD0325901 (E1). (A2–E2) differential expression of PD-1 (A2), PDL-1 (B2), CTLA-4 (C2), LAG-3 (D2), and VSIR (E2) between the high-risk and low-risk groups. ***P<0.001.
Figure 9
Figure 9
Expression Levels of GSDMA, GSDMC, IL18, AIM2 and NLRP6. (A–E) The level of mRNA expression of five DEGs in normal skin epithelial (HaCaT) and three melanoma tumor cell lines (A375, HS294T, M14). “nc”, P>0.05; *P<0.05; **P<0.01; ***P<0.001.
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