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.
Highlights In young adult women, slower epigenetic clock predicted less symptoms of anxiety. In young adult women, slower epigenetic clock predicted greater cortical GM volume. This effect of epigenetic clock in young adult women was largest in frontal lobe. The link of epigenetic clock and anxiety was mediated by GM volume in frontal lobe. No similar relationships were found in young adult men or adolescents.
Background Integration of multi-omics data can provide a more complex view of the biological system consisting of different interconnected molecular components, the crucial aspect for developing novel personalised therapeutic strategies for complex diseases. Various tools have been developed to integrate multi-omics data. However, an efficient multi-omics framework for regulatory network inference at the genome level that incorporates prior knowledge is still to emerge. Results We present IntOMICS, an efficient integrative framework based on Bayesian networks. IntOMICS systematically analyses gene expression, DNA methylation, copy number variation and biological prior knowledge to infer regulatory networks. IntOMICS complements the missing biological prior knowledge by so-called empirical biological knowledge, estimated from the available experimental data. Regulatory networks derived from IntOMICS provide deeper insights into the complex flow of genetic information on top of the increasing accuracy trend compared to a published algorithm designed exclusively for gene expression data. The ability to capture relevant crosstalks between multi-omics modalities is verified using known associations in microsatellite stable/instable colon cancer samples. Additionally, IntOMICS performance is compared with two algorithms for multi-omics regulatory network inference that can also incorporate prior knowledge in the inference framework. IntOMICS is also applied to detect potential predictive biomarkers in microsatellite stable stage III colon cancer samples. Conclusions We provide IntOMICS, a framework for multi-omics data integration using a novel approach to biological knowledge discovery. IntOMICS is a powerful resource for exploratory systems biology and can provide valuable insights into the complex mechanisms of biological processes that have a vital role in personalised medicine.
Following the publication of the original article [1], the authors identified that the funding note was incorrect. The correct funding note is given below.
Background: Integration of multi-omics data can provide a more complex view of the biological system consisting of different interconnected molecular components, the crucial aspect for developing novel personalised therapeutic strategies for complex diseases. Various tools have been developed to integrate multi-omics data. However, an efficient multi-omics framework for regulatory network inference at the genome level that incorporates prior knowledge is still to emerge. Results: We present IntOMICS, an efficient integrative framework based on Bayesian networks. IntOMICS systematically analyses gene expression, DNA methylation, copy number variation and biological prior knowledge to infer regulatory networks. IntOMICS complements the missing biological prior knowledge by so-called empirical biological knowledge, estimated from the available experimental data. Regulatory networks derived from IntOMICS provide deeper insights into the complex flow of genetic information on top of the increasing accuracy trend compared to a published algorithm designed exclusively for gene expression data. The ability to capture relevant crosstalks between multi-omics modalities is verified using known associations in microsatellite stable/instable colon cancer samples. IntOMICS is also applied to detect potential predictive biomarkers in microsatellite stable stage III colon cancer samples. Conclusions: We provide IntOMICS, a framework for multi-omics data integration using a novel approach to biological knowledge discovery. IntOMICS is a powerful resource for exploratory systems biology and can provide valuable insights into the complex mechanisms of biological processes that has a vital role in personalised medicine.Availability: Source code and data used for producing the presented results are available at: https://gitlab.ics.muni.cz/bias/intomics.
Spatiotemporal control over developmental programs is vital to all organisms. Here we show that cytokinin (signaling) deficiency leads to early secondary cell wall (SCW) formation in Arabidopsis inflorescence stem that associates with precocious upregulation of a SCW transcriptional cascade controlled by NAC TFs (NSTs). We demonstrate that cytokinin signaling through the AHK2/3 and the ARR1/10/12 suppresses the expression of several NSTs and SCW formation in the apical portions of stems. Exogenous cytokinin application reconstituted both proper development and apical-basal gradient of NST1 and NST3 in a cytokinin biosynthesis-deficient mutant. We show that AHK2 and AHK3 required functional NST1 or NST3 to control SCW initiation in the interfascicular fibers, further evidencing that cytokinins act upstream of NSTs transcription factors. The premature onset of a rigid SCW biosynthesis and altered expression of NST1/3 and VND6/7 due to cytokinin deficiency led to the formation of smaller tracheary elements (TEs) and impaired hydraulic conductivity. We conclude that cytokinins downregulate NSTs to inhibit premature SCW formation in the apical part of the inflorescence stem, facilitating thus the development of fully functional TEs and interfascicular fibers.
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