Dataset Information

ACTB Methylation in Blood as a Potential Marker for the Pre-clinical Detection of Stroke: A Prospective Nested Case-Control Study.

ABSTRACT:

Background

Stroke is the second leading cause of death worldwide. If risk of stroke could be evaluated early or even at a preclinical stage, the mortality rate could be reduced dramatically. However, the identified genetic factors only account for 5-10% of the risk of stroke. Studies on the risk factors of stroke are urgently needed. We investigated the correlation between blood-based β-actin (ACTB) methylation and the risk of stroke in a prospective nested case-control study.

Methods

The methylation level of ACTB was quantitatively determined by mass spectrometry in 139 stroke cases who developed stroke within 2 years after recruitment and 147 age- and sex-matched controls who remained stroke-free in a median follow-up of 2.71 years.

Results

We observed a highly significant correlation between hypomethylation of one CpG site of ACTB and increased risk of stroke in an onset-time-dependent manner (for onset time ≤ 1.5 years: odds ratio (OR) per + 10% methylation = 0.76, P = 0.001; for onset time ≤ 1.32 years: OR per + 10% methylation = 0.59, P = 7.82 × 10^-7; for onset time ≤ 1 year: OR per + 10% methylation = 0.43, P = 3.00 × 10^-6), and the increased cumulative incidence of stroke (log-rank P = 3.13 × 10^-7). Neighboring CpG sites showed an inverse correlation with age and drinking status in controls (P < 0.05) but not in stroke cases.

Conclusion

We firstly reported the blood-based ACTB methylation as a marker for the risk evaluation and preclinical detection of stroke, which can be further modified by age and drinking.

SUBMITTER: Liu C

PROVIDER: S-EPMC8160447 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundWe hypothesised that host-response biomarkers of viral infections might contribute to early identification of individuals infected with SARS-CoV-2, which is critical to breaking the chains of transmission. We aimed to evaluate the diagnostic accuracy of existing candidate whole-blood transcriptomic signatures for viral infection to predict positivity of nasopharyngeal SARS-CoV-2 PCR testing.MethodsWe did a nested case-control diagnostic accuracy study among a prospective cohort of health-care workers (aged ≥18 years) at St Bartholomew's Hospital (London, UK) undergoing weekly blood and nasopharyngeal swab sampling for whole-blood RNA sequencing and SARS-CoV-2 PCR testing, when fit to attend work. We identified candidate blood transcriptomic signatures for viral infection through a systematic literature search. We searched MEDLINE for articles published between database inception and Oct 12, 2020, using comprehensive MeSH and keyword terms for "viral infection", "transcriptome", "biomarker", and "blood". We reconstructed signature scores in blood RNA sequencing data and evaluated their diagnostic accuracy for contemporaneous SARS-CoV-2 infection, compared with the gold standard of SARS-CoV-2 PCR testing, by quantifying the area under the receiver operating characteristic curve (AUROC), sensitivities, and specificities at a standardised Z score of at least 2 based on the distribution of signature scores in test-negative controls. We used pairwise DeLong tests compared with the most discriminating signature to identify the subset of best performing biomarkers. We evaluated associations between signature expression, viral load (using PCR cycle thresholds), and symptom status visually and using Spearman rank correlation. The primary outcome was the AUROC for discriminating between samples from participants who tested negative throughout the study (test-negative controls) and samples from participants with PCR-confirmed SARS-CoV-2 infection (test-positive participants) during their first week of PCR positivity.FindingsWe identified 20 candidate blood transcriptomic signatures of viral infection from 18 studies and evaluated their accuracy among 169 blood RNA samples from 96 participants over 24 weeks. Participants were recruited between March 23 and March 31, 2020. 114 samples were from 41 participants with SARS-CoV-2 infection, and 55 samples were from 55 test-negative controls. The median age of participants was 36 years (IQR 27-47) and 69 (72%) of 96 were women. Signatures had little overlap of component genes, but were mostly correlated as components of type I interferon responses. A single blood transcript for IFI27 provided the highest accuracy for discriminating between test-negative controls and test-positive individuals at the time of their first positive SARS-CoV-2 PCR result, with AUROC of 0·95 (95% CI 0·91-0·99), sensitivity 0·84 (0·70-0·93), and specificity 0·95 (0·85-0·98) at a predefined threshold (Z score >2). The transcript performed equally well in individuals with and without symptoms. Three other candidate signatures (including two to 48 transcripts) had statistically equivalent discrimination to IFI27 (AUROCs 0·91-0·95).InterpretationOur findings support further urgent evaluation and development of blood IFI27 transcripts as a biomarker for early phase SARS-CoV-2 infection for screening individuals at high risk of infection, such as contacts of index cases, to facilitate early case isolation and early use of antiviral treatments as they emerge.FundingBarts Charity, Wellcome Trust, and National Institute of Health Research.

Project description:BackgroundPeripheral blood DNA methylation may be associated with breast cancer, but studies of candidate genes and global and genome-wide DNA methylation have been inconsistent.MethodsWe performed an epigenome-wide study using Infinium HumanMethylation450 BeadChips with prospectively collected blood DNA samples from the Sister Study (1552 cases, 1224 subcohort). Differentially methylated cytosine-phosphate-guanine sites (dmCpGs) were identified using case-cohort proportional hazard models and replicated using deposited data from European Prospective Investigation into Cancer and Nutrition in Italy (EPIC-Italy) (n = 329). The correlation between methylation and time to diagnosis was examined using robust linear regression. Causal or consequential relationships of methylation to breast cancer were examined by Mendelian randomization using OncoArray 500 K single-nucleotide polymorphism data. All statistical tests were two-sided.ResultsWe identified 9601 CpG markers associated with invasive breast cancer (false discovery rate = q < 0.01), with 510 meeting a strict Bonferroni correction threshold (10-7). A total of 2095 of these CpGs replicated in the independent EPIC-Italy dataset, including 144 meeting the Bonferroni threshold. Sister Study women who developed ductal carcinoma in situ had methylation similar to noncases. Most (1501, 71.6%) dmCpGs showed lower methylation in invasive cases. In case-only analysis, methylation was statistically significantly associated (false discovery rate = q < 0.05) with time to diagnosis for 892 (42.6%) of the dmCpGs. Analyses based on genetic association suggest that methylation differences are likely a consequence rather than a cause of breast cancer. Pathway analysis shows enrichment of breast cancer-related gene pathways, and dmCpGs are overrepresented in known breast cancer susceptibility genes.ConclusionsOur findings suggest that the DNA methylation profile of blood starts to change in response to invasive breast cancer years before the tumor is clinically detected.