Breast cancer is the most frequent cancer among women worldwide. Biomarkers for early detection and prognosis of these patients are needed. We hypothesized that deafness, autosomal dominant 5 (DFNA5) may be a valuable biomarker, based upon strong indications for its role as tumor suppressor gene and its function in regulated cell death. In this study, we aimed to analyze DFNA5 methylation and expression in the largest breast cancer cohort to date using publicly available data from TCGA, in order to further unravel the role of DFNA5 as detection and/or prognostic marker in breast cancer. We analyzed Infinium HumanMethylation450k data, covering 22 different CpGs in the DFNA5 gene (668 breast adenocarcinomas and 85 normal breast samples) and DFNA5 expression (Agilent 244K Custom Gene Expression: 476 breast adenocarcinomas and 56 normal breast samples; RNA-sequencing: 666 breast adenocarcinomas and 71 normal breast samples). DFNA5 methylation and expression were significantly different between breast cancer and normal breast samples. Overall, breast cancer samples showed higher DFNA5 methylation in the putative gene promoter compared to normal breast samples, whereas in the gene body and upstream of the putative gene promoter, the opposite is true. Furthermore, DFNA5 methylation, in 10 out of 22 CpGs, and expression were significantly higher in lobular compared to ductal breast cancers. An important result of this study was the identification of a combination of one CpG in the gene promoter (CpG07504598) and one CpG in the gene body (CpG12922093) of DFNA5, which was able to discriminate between breast cancer and normal breast samples (AUC = 0.93). This model was externally validated in three independent datasets. Moreover, we showed that estrogen receptor state is associated with DFNA5 methylation and expression. Finally, we were able to find a significant effect of DFNA5 gene body methylation on a 5-year overall survival time. We conclude that DFNA5 methylation shows strong potential as detection and prognostic biomarker for breast cancer.

In addition to its implication in hereditary hearing loss, the Gasdermin E (GSDME) gene is also a tumor suppressor involved in cancer progression through programmed cell death. GSDME epigenetic silencing through methylation has been shown in some cancer types, but studies are yet to fully explore its diagnostic/prognostic potential in colorectal cancer on a large‐scale. We used public data from The Cancer Genome Atlas (TCGA) to investigate differences in GSDME methylation and expression between colorectal cancer and normal colorectal tissue, and between left‐ and right‐sided colorectal cancers in 432 samples. We also explored GSDME's diagnostic capacity as a biomarker for colorectal cancer. We observed differential methylation in all 22 GSDME CpGs between tumor and normal tissues, and in 18 CpGs between the left‐ and right‐sided groups. In the cancer tissue, putative promoter probes were hypermethylated and gene body probes were hypomethylated, while this pattern was inversed in normal tissues. Both putative promoter and gene body CpGs correlated well together but formed distinct methylation patterns with the putative promoter exhibiting the most pronounced methylation differences between tumor and normal tissues. Clinicopathological parameters, excluding age, did not show any effect on CpG methylation. Although the methylation of 5 distinct probes was a good predictor of gene expression, we could not identify an association between GSDME methylation and expression in general. Survival analysis showed no association between GSDME methylation and expression on 5‐year patient survival. Through logistic regression, we identified a combination of 2 CpGs, that can discriminate between cancer and normal tissue with high accuracy (AUC = 0.95) irrespective of age and tumor stage. We also validated our model in 3 external methylation datasets, from the Gene Expression Omnibus database, and similar results were reached. Our results suggest that GSDME is a promising biomarker for the detection of colorectal cancer.

DNA methylation alterations have already been linked to cancer, and their usefulness for therapy and diagnosis has encouraged research into the human epigenome. Several biomarker studies have focused on identifying cancer types individually, yet common cancer and multicancer markers are still underexplored. We used The Cancer Genome Atlas (TCGA) to investigate genome‐wide methylation profiles of 14 different cancer types and developed a three‐step computational approach to select candidate biomarker CpG sites. In total, 1991 pan‐cancer and between 75 and 1803 cancer‐specific differentially methylated CpG sites were discovered. Differentially methylated blocks and regions were also discovered for the first time on such a large scale. Through a three‐step computational approach, a combination of four pan‐cancer CpG markers was identified from these sites and externally validated (AUC = 0.90), maintaining comparable performance across tumor stages. Additionally, 20 tumor‐specific CpG markers were identified and made up the final type‐specific prediction model, which could accurately differentiate tumor types (AUC = 0.87–0.99). Our study highlights the power of the methylome as a rich source of cancer biomarkers, and the signatures we identified provide a new resource for understanding cancer mechanisms on the wider genomic scale with strong applicability in the context of new minimally invasive cancer detection assays.

Due to the elevated rates of incidence and mortality of cancer, early and accurate detection is crucial for achieving optimal treatment. Molecular biomarkers remain important screening and detection tools, especially in light of novel blood-based assays. DNA methylation in cancer has been linked to tumorigenesis, but its value as a biomarker has not been fully explored. In this study, we have investigated the methylation patterns of the Gasdermin E gene across 14 different tumor types using The Cancer Genome Atlas (TCGA) methylation data (N = 6502). We were able to identify six CpG sites that could effectively distinguish tumors from normal samples in a pan-cancer setting (AUC = 0.86). This combination of pan-cancer biomarkers was validated in six independent datasets (AUC = 0.84–0.97). Moreover, we tested 74,613 different combinations of six CpG probes, where we identified tumor-specific signatures that could differentiate one tumor type versus all the others (AUC = 0.79–0.98). In all, methylation patterns exhibited great variation between cancer and normal tissues, but were also tumor specific. Our analyses highlight that a Gasdermin E methylation biomarker assay, not only has the potential for being a methylation-specific pan-cancer detection marker, but it also possesses the capacity to discriminate between different types of tumors.