Evidence map›Paper›PMID 37755705›Full record

ArticleProteomes2023

Algorithmically Reconstructed Molecular Pathways as the New Generation of Prognostic Molecular Biomarkers in Human Solid Cancers.

Marianna Zolotovskaia, Maks Kovalenko, Polina Pugacheva, Victor Tkachev, Alexander Simonov, Maxim Sorokin, Alexander Seryakov, Andrew Garazha, Nurshat Gaifullin, Marina Sekacheva and 2 more

Open access · goldAbstract read
In one paragraph

Article in Proteomes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.2field-weighted citation impact, top 12% 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

4 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Article
  3. Observational
  4. Article
4 · The record

Corrections and comments

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

12 authors at 3 institutions in 2 countries.

Marianna ZolotovskaiaLaboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia.ORCID 0000-0003-0668-2156
Maks KovalenkoLaboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia.ORCID 0000-0001-8987-1889
Polina PugachevaLaboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia.ORCID 0009-0007-0367-5774
Victor TkachevOmicsway Corp., Walnut, CA 91789, USA.
Alexander SimonovLaboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia.
Maxim SorokinLaboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia.ORCID 0000-0001-7685-3446
Alexander SeryakovMedical Holding SM-Clinic, 105120 Moscow, Russia.
Andrew GarazhaOmicsway Corp., Walnut, CA 91789, USA.
Nurshat GaifullinDepartment of Pathology, Faculty of Medicine, Lomonosov Moscow State University, 119991 Moscow, Russia.
Marina SekachevaLaboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia.
Galina ZakharovaLaboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia.
Anton A BuzdinLaboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia.
Moscow Institute of Physics and Technology · RUSechenov University · RULomonosov Moscow State University · RU

Funding

Russian Science Foundation 22-74-10031
6 · The paper itself

Abstract

Individual gene expression and molecular pathway activation profiles were shown to be effective biomarkers in many cancers. Here, we used the human interactome model to algorithmically build 7470 molecular pathways centered around individual gene products. We assessed their associations with tumor type and survival in comparison with the previous generation of molecular pathway biomarkers (3022 "classical" pathways) and with the RNA transcripts or proteomic profiles of individual genes, for 8141 and 1117 samples, respectively. For all analytes in RNA and proteomic data, respectively, we found a total of 7441 and 7343 potential biomarker associations for gene-centric pathways, 3020 and 2950 for classical pathways, and 24,349 and 6742 for individual genes. Overall, the percentage of RNA biomarkers was statistically significantly higher for both types of pathways than for individual genes (

Indexed as

cancergene expressionhuman interactomemolecular pathwayprognostic biomarkerproteomic dataRNA sequencingsurvival biomarker

Identifiers

PMID37755705
PMCPMC10535530
OpenAlexW4386161712

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.