Evidence map›Paper›PMID 39605637›Full record

ArticlebioRxiv : the preprint server for biology2024

Unveiling epigenetic regulatory elements associated with breast cancer development.

Marta Jardanowska-Kotuniak, Michał Dramiński, Michał Własnowolski, Marcin Łapiński, Kaustav Sengupta, Abhishek Agarwal, Adam Filip, Nimisha Ghosh, Vera Pancaldi, Marcin Grynberg and 3 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Marta Jardanowska-KotuniakComputational Biology Group, Institute of Computer Science of the Polish Academy of Sciences, Warsaw, Poland.ORCID 0000-0003-0181-6266
Michał DramińskiComputational Biology Group, Institute of Computer Science of the Polish Academy of Sciences, Warsaw, Poland.ORCID 0000-0002-0354-5917
Michał WłasnowolskiLaboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland.
Marcin ŁapińskiComputational Biology Group, Institute of Computer Science of the Polish Academy of Sciences, Warsaw, Poland.ORCID 0000-0003-0857-0713
Kaustav SenguptaLaboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland.ORCID 0000-0003-0603-0916
Abhishek AgarwalLaboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Warsaw, Poland.ORCID 0000-0003-4981-8746
Adam FilipComputational Biology Group, Institute of Computer Science of the Polish Academy of Sciences, Warsaw, Poland.
Nimisha GhoshDepartment of Computer Science and Information Technology, Institute of Technical Education and Research, Siksha O Anusandhan University, Bhubaneswar, Odisha, 751030, India.ORCID 0000-0002-0697-6368
Vera PancaldiCRCT, Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Centre de Recherches en Cancérologie de Toulouse, Toulouse, France.ORCID 0000-0002-7433-624X
Marcin GrynbergInstitute of Biochemistry and Biophysics of the Polish Academy of Sciences, Warsaw, Poland.ORCID 0000-0003-1887-7209
Indrajit SahaDepartment of Computer Science and Engineering, National Institute of Technical Teachers' Training and Research, Kolkata 700106, India.ORCID 0000-0001-9513-9707
Dariusz PlewczynskiLaboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland.ORCID 0000-0002-3840-7610
Michał J DąbrowskiComputational Biology Group, Institute of Computer Science of the Polish Academy of Sciences, Warsaw, Poland.ORCID 0000-0003-1269-6722

Funding

Nucleome Positioning System for Spatiotemporal Genome Organization and RegulationU54DK107967 · NIDDK · JACKSON LABORATORY · PI WEI, CHIA-LIN · 2015 to 2019
$3.6M
NIDDK NIH HHS U54 DK107967
6 · The paper itself

Abstract

Breast cancer is the most common cancer in women and the 2nd most common cancer worldwide, yearly impacting over 2 million females and causing 650 thousand deaths. It has been widely studied, but its epigenetic variation is not entirely unveiled. We aimed to identify epigenetic mechanisms impacting the expression of breast cancer related genes to detect new potential biomarkers and therapeutic targets. We considered The Cancer Genome Atlas database with over 800 samples and several omics datasets such as mRNA, miRNA, DNA methylation, which we used to select 2701 features that were statistically significant to differ between cancer and control samples using the Monte Carlo Feature Selection and Interdependency Discovery algorithm, from an initial total of 417,486. Their biological impact on cancerogenesis was confirmed using: statistical analysis, natural language processing, linear and machine learning models as well as: transcription factors identification, drugs and 3D chromatin structure analyses. Classification of cancer vs control samples on the selected features returned high classification weighted Accuracy from 0.91 to 0.98 depending on feature-type: mRNA, miRNA, DNA methylation, and classification algorithm. In general, cancer samples showed lower expression of differentially expressed genes and increased

Indexed as

breast cancerchromatin structuredifferentially methylated sitesepigenetic regulationMCFS-IDMonte Carlo Feature SelectionMXI1NKAPLNLPNRF1PITX1transcription factor

Identifiers

PMID39605637
PMCPMC11601335

What OpenQuestion holds

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Registered trials

None linked

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.