Fig. 4: RPA bioinformatics in breast cancer.

A Correlation matrix showing the correlation between levels of RPA1, RPA2 and RPA3 protein expressions and other DNA repair biomarkers. B Correlation matrix showing the correlation between levels of RPA1, RPA2 and RPA3 protein expressions and other endocrine-resistant biomarkers. C Comparison of RPA1 gene expression to copy number variation in TCGA-BRCA Pan cancer cohort (n = 994). GISTIC analysis is shown for changes in RPA1 mRNA levels in tumours with copy number variations for TCGA-BRCA Pan cancer cohort (n = 994). The expression data was from normalized illumina HiSeq RNA-Seq data. The copy number variations are deep deletions (>2 copies deleted), shallow deletion (few copies altered), diploid, gains (few copies gained), amplification (>2 copies gained). D DNA methylation correlations with RPA1 gene expression were performed using SMART App. The beta-values (Illumina HumanMethylation450K) and expression data were from UCSC Xena tools. The CpG correlations shown are for CpG within CpG island in promoter for RPA1 (see methods sections for more details). The percentage of RNA gene types (Ensembl MART) are shown for non-coding RNAs (lncRNA, pseudogenes, miRNAs and other RNA which include snoRNA, tRNA and MT-RNA) plus protein-coding genes are shown for (E) RNAs expressed higher in low RPA1 tumours (n = 10284 confirmed gene types) and F RNAs expressed lower in low RPA1 tumours (n = 565 confirmed gene types). G Comparison of the differential changes that showed higher expression in low RPA1, RPA2 and RPA3. The RPA components had 46% similarity of the differential changes, with the majority of RPA2 changes like RPA1.