Melanoma has a high degree of malignancy and mortality. While there are some hopeful clinical trials for melanoma treatment in progress, they have not yet to yield significant long-term cure rates. Cancer vaccines including mRNA are currently one of the most promising strategy for tumor immunotherapy. The aim of this study was to analyze the potential tumor antigens in melanoma that could be used to develop mRNA vaccines and identify suitable vaccine populations. The gene expression data and complete clinical information of 471 melanoma samples and 1 normal tissue were retrieved from TCGA. Then, 812 samples of normal skin and their corresponding gene expression data were obtained from GTEx. Overexpressed genes, mutated genes and IRDEGs are used to identify potential tumor antigens. The relationship between the expression level of potential antigen and prognosis was analyzed in GEPIA, and then the immune cell infiltration was estimated based on TIMER algorithm. The expression profiles of IRDEGs were used to identify consensus clusters and immune subtypes of melanoma. Finally, mutational status and immune microenvironment characterization in immune subtypes were analyzed. Five tumor antigens (PTPRC, SIGLEC10, CARD11, LILRB1, ADAMDEC1) were identified as potential tumor antigens according to overexpressed genes, mutated genes and immune-related genes. They were all associated with OS, DFS and APCs. We identified two immune subtypes of melanoma, named IS1 and IS2, which exhibit different clinical features and immune landscapes. Based on the different immune landscape, we may conclude that IS1 is immunophenotypically “cold”, while IS2 is "hot". The present research implicates that PTPRC, SIGLEC10, CARD11, LILRB1 and ADAMDEC1 may be the antigenic targets for melanoma mRNA vaccines and IS2 patients may be more effective to these vaccines.

Background. Vacuolar protein sorting 16 (VPS16) overexpression was recently considered related to cancer growth and drug resistance; however, little is known about whether VPS16 plays a vital role in liver hepatocellular carcinoma (LIHC). Methods. The TIMER2 online database was used to analyze the expression of VPS16 in pancancer, and the Xena Browser was used to explore the correlation between VPS16 expression level and survival time. R language was used to test the survival data of 374 LIHC cases in the TCGA database. DESeq2 was used for differentially expressed gene (DEG) analysis. The HPA database was used to verify the expression level of VPS16 in LIHC. The clusterProfiler package was used to analyze functions and related signaling pathways via GO/KEGG enrichment analysis. Drug sensitivity analysis and molecular docking technology were used to screen the most sensitive drugs targeting VPS16 molecules. Results. Pancancer analysis showed that VPS16 was highly expressed in various tumors, especially in LIHC. With the increase in the T stage and grade of LIHC, the expression level of VPS16 was also increased. The expression of VPS16 was negatively correlated with the overall survival of LIHC patients. The stage can be used as an independent prognostic factor. A total of 63 sensitive drugs were found, and 19 drugs were displaying strong molecular binding energy with VPS16. Conclusion. VPS16 may be a potential biomarker for the diagnosis and prognosis of LIHC. Drugs targeting VPS16 may be used in the treatment of LIHC in the future.

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