Epigenetic clock as a correlate of anxiety

DNA methylation changes consistently throughout life and age-dependent alterations in DNA methylation can be used to estimate one’s epigenetic age. Post-mortem studies revealed higher epigenetic age in brains of patients with major depressive disorder, as compared with controls. Since MDD is highly correlated with anxiety, we hypothesized that symptoms of anxiety, as well as lower volume of grey matter (GM) in depression-related cortical regions, will be associated with faster epigenetic clock in a community-based sample of young adults. Participants included 88 young adults (53% men; 23–24 years of age) from the European Longitudinal Study of Pregnancy and Childhood (ELSPAC) who participated in its neuroimaging follow-up and provided saliva samples for epigenetic analysis. Epigenetic age was calculated according to Horvath (Horvath, 2013). Women had slower epigenetic clock than men (Cohen’s d = 0.48). In women (but not men), slower epigenetic clock was associated with less symptoms of anxiety. In the brain, women (but not men) with slower epigenetic clock had greater GM volume in the cerebral cortex (brain size-corrected; R2 = 0.07). Lobe-specific analyses showed that in women (but not men), slower epigenetic clock was associated with greater GM volume in frontal lobe (R2 = 0.16), and that GM volume in frontal lobe mediated the relationship between the speed of epigenetic clock and anxiety trait (ab = 0.15, SE = 0.15, 95% CI [0.007; 0.369]). These findings were not replicated, however, in a community-based sample of adolescents (n = 129; 49% men; 12–19 years of age), possibly due to the different method of tissue collection (blood vs. saliva) or additional sources of variability in the cohort of adolescents (puberty stages, socioeconomic status, prenatal exposure to maternal smoking during pregnancy)

Cross-platform Data Analysis Reveals a Generic Gene Expression Signature for Microsatellite Instability in Colorectal Cancer

The dysfunction of the DNA mismatch repair system results in microsatellite instability (MSI). MSI plays a central role in the development of multiple human cancers. In colon cancer, despite being associated with resistance to 5-fluorouracil treatment, MSI is a favourable prognostic marker. In gastric and endometrial cancers, its prognostic value is not so well established. Nevertheless, recognising the MSI tumours may be important for predicting the therapeutic effect of immune checkpoint inhibitors. Several gene expression signatures were trained on microarray data sets to understand the regulatory mechanisms underlying microsatellite instability in colorectal cancer. A wealth of expression data already exists in the form of microarray data sets. However, the RNA-seq has become a routine for transcriptome analysis. A new MSI gene expression signature presented here is the first to be valid across two different platforms, microarrays and RNA-seq. In the case of colon cancer, its estimated performance was (i) AUC = 0.94, 95% CI = (0.90 – 0.97) on RNA-seq and (ii) AUC = 0.95, 95% CI = (0.92 – 0.97) on microarray. The 25-gene expression signature was also validated in two independent microarray colon cancer data sets. Despite being derived from colorectal cancer, the signature maintained good performance on RNA-seq and microarray gastric cancer data sets (AUC = 0.90, 95% CI = (0.85 – 0.94) and AUC = 0.83, 95% CI = (0.69 – 0.97), respectively). Furthermore, this classifier retained high concordance even when classifying RNA-seq endometrial cancers (AUC = 0.71, 95% CI = (0.62 – 0.81). These results indicate that the new signature was able to remove the platform-specific differences while preserving the underlying biological differences between MSI/MSS phenotypes in colon cancer samples