Article in Cells, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.8field-weighted citation impact, top 28% 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
1 citing paper in PubMed, 5 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
20 authors at 2 institutions in 2 countries.
Galina ZakharovaWorld-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow 119991, Russia.ORCID 0000-0002-2548-8511
Alexander ModestovLaboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia.
Polina PugachevaLaboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia.ORCID 0009-0007-0367-5774
Rijalda MekicLaboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia.ORCID 0009-0001-9945-4015
Ekaterina SavinaLaboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia.ORCID 0009-0004-9874-8873
Anastasia GuryanovaLaboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia.
Anastasia RachkovaLaboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia.ORCID 0000-0002-5485-4541
Semyon YakushovLaboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia.ORCID 0000-0002-8910-6404
Andrei AlimovLaboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia.ORCID 0000-0002-8495-7728
Elizaveta KulaevaLaboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia.ORCID 0000-0001-5886-7975
Elena FedoseevaLaboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia.ORCID 0000-0003-1134-1401
Artem KleymanLaboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia.
Kirill VasinLaboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia.
Victor TkachevOncobox Ltd., Moscow 121205, Russia.
Andrew GarazhaOmicsway Corp., Walnut, CA 91789, USA.
Marina SekachevaWorld-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow 119991, Russia.
Maria SuntsovaWorld-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow 119991, Russia.
Maksim SorokinLaboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia.ORCID 0000-0001-7685-3446
Anton BuzdinWorld-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow 119991, Russia.
Marianna A ZolotovskaiaLaboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia.ORCID 0000-0003-0668-2156
Sechenov University · RUMoscow Institute of Physics and Technology · RU
Funding
No grant is acknowledged in the PubMed record.
6 · The paper itself
Abstract
The evolution of protein-coding genes has both structural and regulatory components. The first can be assessed by measuring the ratio of non-synonymous to synonymous nucleotide substitutions. The second component can be measured as the normalized proportion of transposable elements that are used as regulatory elements. For the first time, we characterized in parallel the regulatory and structural evolutionary profiles for 10,890 human genes and 2972 molecular pathways. We observed a ~0.1 correlation between the structural and regulatory metrics at the gene level, which appeared much higher (~0.4) at the pathway level. We deposited the data in the publicly available database RetroSpect. We also analyzed the evolutionary dynamics of six cancer pathways of two major axes: Notch/WNT/Hedgehog and AKT/mTOR/EGFR. The Hedgehog pathway had both components slower, whereas the Akt pathway had clearly accelerated structural evolution. In particular, the major hub nodes Akt and beta-catenin showed both components strongly decreased, whereas two major regulators of Akt TCL1 and CTMP had outstandingly high evolutionary rates. We also noticed structural conservation of serine/threonine kinases and the genes related to guanosine metabolism in cancer signaling: GPCRs, G proteins, and small regulatory GTPases (Src, Rac, Ras); however, this was compensated by the accelerated regulatory evolution.
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.