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To construct a prognosis model of melanoma based on pyroptosis related genes. Methods: Melanoma RNA-sequencing data was downloaded from TCGA. First, the lnRNAs related to pyroptosis were obtained through Pearson correlation analysis. Then, the prognosis model of pyroptosis related genes was constructed by Cox regression and Lasso regression. Melanoma patients were divided into high-risk and low-risk groups by risk score, and the differences in prognosis and immune microenvironment between the two groups were explored. Results: We found that the high-risk group had a significantly poorer prognosis, and different groups differed in immune infiltration, m6A methylation, and immune checkpoint. Conclusion: Our prognostic model can provide a reference for the study of pyroptosis in melanoma cells and provide a new idea for melanoma treatment.

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Hepatocellular carcinoma (HCC) is one of the most common cancers in the world and is often associated with a poor prognosis. The main reason for this poor prognosis is that inconspicuous early symptoms lead to delayed diagnosis. Treatment options for advanced HCC remain limited and ineffective. In this context, the exploration of the immune microenvironment in HCC becomes attractive. In this study, we divided HCC into immune cell and non-immune cell subtypes, by single-cell sequencing analysis of GEO dataset GSE146115. We found differentially expressed genes in the two subtypes, which we used to construct a prognostic model for HCC through Cox and Lasso regressions. Our prognostic model can accurately evaluate the prognosis of HCC patients, and provide a reference for the design of immunotherapy for HCC.