Fig. 3

The correlation between RNF8 and tumor-infiltrating immune cells. A Schematic representation of expression-based grouping method. Pancancer-normalized expression dataset were trisected based on RNF8 expression in different cancers. Differential gene expression analysis of RNF8 high-expression group compared to low-expression group and subsequent functional analysis including batch Gene set enrichment analysis were used to identify functional profile of RNF8 (B, C). Batch Gene set enrichment analysis reveal the general functions in cancers. D Six immune cell infiltration algorithms including Cibersort, xCell, EPIC, MCP-counter, quanTIseq, TIMER,were utilized to calculate the immune cells-infiltration score in pancancer dataset. Scores of different immune cells were clustered and visualized as heatmap. E violin plot showing the immune cells scores in different cancer. F tSNE dimension reducation were visualized based on single cell transcriptome data (GSM5354530) of breast cancer tissues. G, H Ten single cell transcriptome data were analyzed using R package Seurat to reveal the correlation between RNF8 expression (the mean expression of RNF8 in all cells). and the immune cell percentage (in non-tumor cells) in BRCA tissues. tSNE dimension reduction were performed and cell type annotation were match to each cell