Multi-omics analysis of tumor mutation burden combined with immune infiltrates in melanoma.

BACKGROUND

In multiple malignancies, whether tumor mutation burden (TMB) correlated with increased survival or promotion of immunotherapy remained a debate. Our aim was to analyze the prognosis of TMB and the possible connection with immune infiltration of the skin cutaneous melanoma (SKCM).

METHODS

We gathered somatic mutation data from the 472 SKCM patients using the Cancer Genome Atlas (TCGA) database and analyzed the mutation profiles using ""maftools" package. TMB was determined and samples were divided into high and low TMB groups. We undertook differential analysis to determine the profiles of expression between two groups using the "limma" package and established the 10 Hub TMB signature from a batch survival study. Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Set Enrichment Analysis (GSEA) were performed in order to test considerably enriched pathways between the two groups. The connections of 10 TMB-related signature mutants with immune infiltration in SKCM were further assessed based on the TIMER database. We used the CIBERSORT package to measure the amount of 22 immune fractions between low and high TMB groups, and Wilcoxon's rank-sum amounts estimated the significant difference. In addition, the Cox regression model and survival analysis were used to determine the prognostic importance of immune cells. Finally, we estabilished a multivaried Cox results Tumor Mutation Burden Prognostic Index (TMBPI) and built a Receiver Operating Characteristic (ROC) curve to check the predictive accuracy.

RESULTS

Single nucleotide polymorphism (SNP) was more frequent than insertion or deletion and C > T was SKCM's most frequently single nucleotide variants (SNV). Higher TMB levels provided poor survival outcomes, associated with tumor stage, age, and gender. In addition, 224 differentially expressed genes were obtained and Venn diagram established the top 25 immune-related genes. GSEA observed that patients in high TMB groups associated with nucleotide excision repair, pyrimidine metabolism, basal transcription factors, spliceosome, RNA polymerase, and RNA degradation in cancers. 10 hub TMB-related immune genes were also established and 10 signature mutants were correlated with lower immune infiltrates. In addition, the infiltration levels of macrophages M1 and macrophages M2 in the low-TMB group were lower. Eventually, the TMBPI was developed and the AUC of ROC curve was 0.604.

CONCLUSIONS

High TMB contributed to low survival outcomes and may prevent SKCM immune infiltration. The 10 hub immune signature TMB-related mutants conferred lower immune cell infiltration that required further confirmation.