ArticleBMC genomics2017
Exploring the nature of prediagnostic blood transcriptome markers of chronic lymphocytic leukemia by assessing their overlap with the transcriptome at the clinical stage.
Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
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Authors and funding
11 authors at 5 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe recently identified 700 genes whose expression levels were predictive of chronic lymphocytic leukemia (CLL) in a genome-wide gene expression analysis of prediagnostic blood from future cases and matched controls. We hypothesized that a large fraction of these markers were likely related to early disease manifestations. Here we aim to gain a better understanding of the natural history of the identified markers by comparing results from our prediagnostic analysis, the only prediagnostic analysis to date, to results obtained from a meta-analysis of a series of publically available transcriptomics profiles obtained in incident CLL cases and controls.
resultsWe observed considerable overlap between the results from our prediagnostic study and the clinical CLL signals (p-value for overlap Bonferroni significant markers 0.01; p-value for overlap nominal significant markers < 2.20e-16). We observed similar patterns with time to diagnosis and similar functional annotations for the markers that were identified in both settings compared to the markers that were only identified in the prediagnostic study. These results suggest that both gene sets operate in similar pathways.
conclusionAn overlap exists between expression levels of genes predictive of CLL identified in prediagnostic blood and expression levels of genes associated to CLL at the clinical stage. Our analysis provides insight in a set of genes for which expression levels can be used to follow the time-course of the disease; providing an opportunity to study CLL progression in more detail in future studies.
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