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

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.