A six‐CpG‐based methylation markers for the diagnosis of ovarian cancer in blood

Abstract

DNA methylation markers in the peripheral blood are able to be applied to treat epithelial cancer. Nevertheless, the diagnostic potential value of it for ovarian cancer (OV) has not been studied. The study aimed to explore the difference of DNA methylation in peripheral blood between OV patients and healthy women. Firstly, the whole blood of DNA methylation data was provided by the Gene Expression Omnibus (GEO) database. The linear model was applied to the identification of significantly differentially expressed methylated CpG sites (differentially methylation sites [DMP]), and the further screen of co‐expression CpG sites (Co‐DMP). A total of 2812 DMPs were identified, and weighted gene co‐expression network analysis helped to obtain seven co‐expression modules. Among them, the yellow module was the most related to OV. Co‐DMPs (167) in the yellow module were mainly distributed near the transcription start sites. However, most of them were not in the CpG island. Least absolute shrinkage and selection operator (LASSO) regression analysis was applied to the identification of stable OV‐related blood biomarkers that six Co‐DMPs (cg00134539, cg00226923, cg25268718, cg25697314, cg25839227, cg26574610) with the highest frequency were found as potential biomarkers. Finally, the diagnostic classifier was established using the support vector machine (SVM) with the accuracy rate of 87.1% and 74.5% in training data set and validation data set, respectively. To sum up, a new feature was provided here for the diagnosis of OV, which is helpful for the diagnosis and individualized treatments of early OV.

Host methylation predicts SARS-CoV-2 infection and clinical outcome

“…Our results support the hypothesis that the host epigenome, as measured in peripheral blood, is modified by infection from SARS-CoV-2 and can be used to identify novel biology and it is useful for clinical diagnosis, prognosis, and triage. Despite being a heterogeneous tissue, we relied on peripheral blood as the target tissue because it has proven to be a reliable source for generating epigenetic signatures and disease classifiers in other settings [50][51][52][53][54][55][56] . In this study, we observed many methylation changes that are, on average, >10% differentially methylated in the SARS-CoV-2+ group, including IRF7 and MX1 interferon-related genes.…”

Host methylation predicts SARS-CoV-2 infection and clinical outcome

“…Namely, we anticipate these changes to be time-sensitive, as the infection will need to have spread enough to induce methylation changes. Similarly, our case-control variables were defined by RT-PCR, which can carry a high false negative rate depending on the stage of infection and timing of sample collection 47 , and may have reduced the classification accuracy. However, we have follow-up EHR information for the patients in this cohort which minimizes the risk of misclassification bias.…”

Diagnostic value of CA125, HE4, and systemic immune-inflammation index in the preoperative investigation of ovarian masses

“…[3] As the early manifestations of OC are rather hidden, the disease may have developed to a middle or advanced stage at diagnosis, resulting in missed optimal treatment timing and increased mortality. [4][5][6] The 5-year survival rate of OC can be as high as 90% when detected early and treated with standard surgery and adjuvant therapy. [7] Differentiating malignant from benign ovarian tumors (BOTs) is imperative.…”