Coupled Genome-Wide DNA Methylation and Transcription Analysis Identified Rich Biomarkers and Drug Targets in Triple-Negative Breast Cancer

Abstract

Triple-negative breast cancer (TNBC) has poor clinical prognosis. Lack of TNBC-specific biomarkers prevents active clinical intervention. We reasoned that TNBC must have its specific signature due to the lack of three key receptors to distinguish TNBC from other types of breast cancer. We also reasoned that coupling methylation and gene expression as a single unit may increase the specificity for the detected TNBC signatures. We further reasoned that choosing the proper controls may be critical to increasing the sensitivity to identify TNBC-specific signatures. Furthermore, we also considered that specific drugs could target the detected TNBC-specific signatures. We developed a system to identify potential TNBC signatures. It consisted of (1) coupling methylation and expression changes in TNBC to identify the methylation-regulated signature genes for TNBC; (2) using TPBC (triple-positive breast cancer) as the control to detect TNBC-specific signature genes; (3) searching in the drug database to identify those targeting TNBC signature genes. Using this system, we identified 114 genes with both altered methylation and expression, and 356 existing drugs targeting 10 of the 114 genes. Through docking and molecular dynamics simulation, we determined the structural basis between sapropterin, a drug used in the treatment of tetrahydrobiopterin deficiency, and PTGS2, a TNBC signature gene involved in the conversion of arachidonic acid to prostaglandins. Our study reveals the existence of rich TNBC-specific signatures, and many can be drug target and biomarker candidates for clinical applications.

DNA methylation profiling identifies two distinct subgroups in breast cancers with low hormone receptor expression, mainly associated with HER2 amplification status

“…Differential DNA methylation analysis revealed almost no difference between LowHR TNBC-like and true TNBC samples, strongly supporting the assumption that these subtypes do not only show similar clinical outcome, but are also identical on the molecular level. Our investigation for differentially methylated genes between LowHR HRpos-like and TNBC specimens revealed a considerable overlap with cancer-relevant genes that have previously been described to be differentially methylated and expressed in hormone receptor positive tumors and TNBCs [ 26 ]. This indicates that despite the low expression of hormone receptors, most HER2 amplified tumors are still very similar to hormone receptor positive breast cancers.…”

“…Abbreviations: ER estrogen receptor, PR progesterone receptor Similar results were obtained for DMRs. The DMRs identified when comparing LowHR HRpos-like samples and TNBCs included cancer-relevant genes such as EN1, TFF3 or IRX1 which have previously been described to be differentially methylated and expressed in TNBCs and hormone receptor positive breast cancers [26][27][28]. All DMRs are listed in Additional files 5-7: Tables S3-S5.…”

Epigenetic Regulation and Dietary Control of Triple Negative Breast Cancer

“…Interestingly, the sequestration of BRCA1 in the cytoplasm through its interaction with the ACC enzyme provides a link between dysregulation of lipid metabolism and BRCA1 function in the development of TNBC. Investigations on fatty acid synthesis and their regulation are essential for understanding TNBC development and for identification of targets of treatment connected with inflammation and metabolic pathways, including arachidonic acid and prostaglandins ( 122 ). Of particular interest is the central role of the AhR, both as a modulator of BRCA1 function and as a possible target for development of therapeutic strategies against TNBC.…”

Clinical Implications of Necroptosis Genes Expression for Cancer Immunity and Prognosis: A Pan-Cancer Analysis

“…The abnormal expression of tumor genes is mainly due to the abnormal methylation of regulatory regions ( 15 ).To examine methylation data for tumor and normal tissue pairs, we collected methylation data from the TCGA database ( n =10,129). Only 14 cancer types had paired data; hence, the differential methylation analysis is based on these 14 cancer types.…”