Evidence map›Paper›PMID 32094374›Full record

ArticleScientific reports2020

Pan-cancer mapping of differential protein-protein interactions.

Gizem Gulfidan, Beste Turanli, Hande Beklen, Raghu Sinha, Kazim Yalcin Arga

Abstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Regulatory Effects ofMicroorganisms · 2025
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  20. Proteomics and its applications in breast cancer.American journal of cancer research · 2021
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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

5 authors.

Gizem GulfidanDepartment of Bioengineering, Marmara University, 34722, Istanbul, Turkey.
Beste TuranliDepartment of Bioengineering, Marmara University, 34722, Istanbul, Turkey.
Hande BeklenDepartment of Bioengineering, Marmara University, 34722, Istanbul, Turkey.
Raghu SinhaDepartment of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, 17033, Pennsylvania, United States.
Kazim Yalcin ArgaDepartment of Bioengineering, Marmara University, 34722, Istanbul, Turkey. kazim.arga@marmara.edu.tr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deciphering the variations in the protein interactome is required to reach a systems-level understanding of tumorigenesis. To accomplish this task, we have considered the clinical and transcriptome data on >6000 samples from The Cancer Genome Atlas for 12 different cancers. Utilizing the gene expression levels as a proxy, we have identified the differential protein-protein interactions in each cancer type and presented a differential view of human protein interactome among the cancers. We clearly demonstrate that a certain fraction of proteins differentially interacts in the cancers, but there was no general protein interactome profile that applied to all cancers. The analysis also provided the characterization of differentially interacting proteins (DIPs) representing significant changes in their interaction patterns during tumorigenesis. In addition, DIP-centered protein modules with high diagnostic and prognostic performances were generated, which might potentially be valuable in not only understanding tumorigenesis, but also developing effective diagnosis, prognosis, and treatment strategies.

Indexed as

Protein Interaction MapsAlgorithmsBiomarkers, TumorCell Line, TumorGene Expression ProfilingGene Expression Regulation, NeoplasticGenome, HumanHumansKaplan-Meier EstimateNeoplasm MetastasisNeoplasmsPhenotypePrincipal Component AnalysisPrognosisProtein Interaction MappingTranscriptomeBiomarkers, Tumor

Identifiers

PMID32094374
PMCPMC7039988

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Registered trials

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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.