Abstract
Background: DNA methylation played essential roles in regulating gene expression. The impact of DNA methylation status on the occurrence and development of cancers has been well demonstrated. However, little is known about its prognostic role in breast cancer (BC). Materials: The Illumina Human Methylation450 array (450k array) data of BC was downloaded from the UCSC xena database. Transcriptomic data of BC was downloaded from the Cancer Genome Atlas (TCGA) database. Firstly, we used univariate and multivariate Cox regression analysis to screen out independent prognostic CpGs, and then we identified methylation-associated prognosis subgroups by consensus clustering. Next, a methylation prognostic model was developed using multivariate Cox analysis and was validated with the Illumina Human Methylation27 array (27k array) dataset of BC. We then screened out differentially expressed genes (DEGs) between methylation high-risk and low-risk groups and constructed a methylation-based gene prognostic signature. Further, we validated the gene signature with three subgroups of the TCGA-BRCA dataset and an external dataset GSE146558 from the Gene Expression Omnibus (GEO) database. Results: We established a methylation prognostic signature and a methylation-based gene prognostic signature, and there was a close positive correlation between them. The gene prognostic signature involved six genes: IRF2, KCNJ11, ZDHHC9, LRP11, PCMT1, and TMEM70. We verified their expression in mRNA and protein levels in BC. Both methylation and methylation-based gene prognostic signatures showed good prognostic stratification ability. The AUC values of 3-years, 5-years overall survival (OS) were 0.737, 0.744 in the methylation signature and 0.725, 0.715 in the gene signature, respectively. In the validation groups, high-risk patients were confirmed to have poorer OS. The AUC values of 3 years were 0.757, 0.735, 0.733 in the three subgroups of TCGA dataset and 0.635 in GSE146558 dataset. Conclusion: This study revealed the DNA methylation landscape and established promising methylation and methylation-based gene prognostic signatures that could serve as potential prognostic biomarkers and therapeutic targets.
Highlights
Breast cancer (BC) is the most prevalent cancer and the leading cause of cancer mortality in women worldwide (Freddie et al, 2020)
This study revealed the DNA methylation landscape and established promising methylation and methylation-based gene prognostic signatures that could serve as potential prognostic biomarkers and therapeutic targets
Pathological staging and TNM staging as covariates, 66 CpG loci (49 CpG loci associated with favorable prognosis, 17 CpG loci associated with poor prognosis) with p < 0.001 by multivariate Cox analysis were further selected and used as the methylation classification features (Figure 2)
Summary
Breast cancer (BC) is the most prevalent cancer and the leading cause of cancer mortality in women worldwide (Freddie et al, 2020). Gene expression analysis of these receptors further recognizes four subsets of BC: luminal A, luminal B, HER2-enriched (HER2-E) and Basal-like (Parker et al, 2009). These classification systems help predict the prognosis of BC patients, and guide treatment choices. Endocrine therapy for hormone receptor-positive BC and anti-HER2 treatment for HER2 expressing BC have greatly improved the prognosis of patients. A methylation prognostic model was developed using multivariate Cox analysis and was validated with the Illumina Human Methylation array (27k array) dataset of BC. We validated the gene signature with three subgroups of the TCGA-BRCA dataset and an external dataset GSE146558 from the Gene Expression Omnibus (GEO) database
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