Article in Epigenetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 37% of its field
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literature
Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
19 authors at 4 institutions in 3 countries.
Lucie PoppovaDepartment of Internal Medicine - Hematology and Oncology, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic.
Sarka PavlovaDepartment of Internal Medicine - Hematology and Oncology, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic.ORCID 0000-0003-1528-9743
Beatriz GonzalezIGTP-PMPPC: Program of Predictive and Personalized Medicine of Cancer, Germans Trias I Pujol Research Institute, Badalona, Spain.ORCID 0000-0002-3699-9392
Jana KotaskovaDepartment of Internal Medicine - Hematology and Oncology, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic.ORCID 0000-0003-1672-7346
Karla PlevovaDepartment of Internal Medicine - Hematology and Oncology, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic.ORCID 0000-0002-6148-8877
Gabrijela DumbovicIGTP-PMPPC: Program of Predictive and Personalized Medicine of Cancer, Germans Trias I Pujol Research Institute, Badalona, Spain.ORCID 0000-0002-1278-0273
Pavlina JanovskaDepartment of Experimental Biology, Faculty of Science, Masaryk University, Brno, Czech Republic.ORCID 0000-0001-9871-0876
Vojtech BystryCentral European Institute of Technology (CEITEC), Masaryk University, Brno, Czech Republic.ORCID 0000-0001-5278-0801
Anna PanovskaDepartment of Internal Medicine - Hematology and Oncology, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic.ORCID 0000-0003-4955-3039
Lucie BezdekovaDepartment of Internal Medicine - Hematology and Oncology, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic.ORCID 0000-0002-2123-5226
Stanislava MaslejovaDepartment of Internal Medicine - Hematology and Oncology, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic.
Yvona BrychtovaDepartment of Internal Medicine - Hematology and Oncology, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic.ORCID 0000-0003-1038-870X
Michael DoubekDepartment of Internal Medicine - Hematology and Oncology, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic.ORCID 0000-0002-1269-6282
Marcela KrzyzankovaDepartment of Internal Medicine - Hematology and Oncology, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic.
Marek BorskyDepartment of Internal Medicine - Hematology and Oncology, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic.ORCID 0000-0001-5803-5941
Jiri MayerDepartment of Internal Medicine - Hematology and Oncology, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic.ORCID 0000-0003-0567-9887
Vitezslav BryjaDepartment of Experimental Biology, Faculty of Science, Masaryk University, Brno, Czech Republic.ORCID 0000-0002-9136-5085
Sergio AlonsoIGTP-PMPPC: Program of Predictive and Personalized Medicine of Cancer, Germans Trias I Pujol Research Institute, Badalona, Spain.ORCID 0000-0001-6497-892X
Sarka PospisilovaDepartment of Internal Medicine - Hematology and Oncology, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic.ORCID 0000-0001-7136-2680
Central European Institute of Technology · CZMasaryk University · CZInstitute of Predictive and Personalized Medicine of Cancer · ESGoethe University Frankfurt · DE
Funding
No grant is acknowledged in the PubMed record.
6 · The paper itself
Abstract
Genome methylation profiles define naïve-like (n-CLL), memory-like (m-CLL), and intermediate (i-CLL) subsets of chronic lymphocytic leukaemia (CLL). The profiles can be easily determined by the analysis of the five-CpG signature. m-CLL, i-CLL, and n-CLL with the good, intermediate, and poor prognoses, respectively, differ by the somatic hypermutation status of the immunoglobulin heavy chain variable gene (IGHV), a widely used prognostic predictor in CLL. We have previously shown that the expression of
Indexed as
Leukemia, Lymphocytic, Chronic, B-CellDNA MethylationHumansImmunoglobulin Heavy ChainsLigandsMutationPrognosisPromoter Regions, GeneticWnt-5a ProteinImmunoglobulin Heavy ChainsLigandsWnt-5a ProteinWNT5A protein, humanchronic lymphocytic leukaemiai-CLLm-CLLmethylationWNT5A
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Memory B-cell like chronic lymphocytic leukaemia is associated with specific methylation profile of · full record | OpenQuestion