Evidence map›Paper›PMID 37986165›Full record

SynthesisCell communication and signaling : CCS2023

Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumor-specificity and predictive potential of extracellular vesicles for cell invasion and proliferation - A meta-analysis.

Matyas Bukva, Gabriella Dobra, Edina Gyukity-Sebestyen, Timea Boroczky, Marietta Margareta Korsos, David G Meckes, Peter Horvath, Krisztina Buzas, Maria Harmati

Abstract readVideo-Audio MediaMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Cell communication and signaling : CCS, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Review
  6. Extracellular Vesicle Preparation and Analysis: A State-of-the-Art Review.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Review
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

9 authors.

Matyas BukvaDepartment of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary.
Gabriella DobraDepartment of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary.
Edina Gyukity-SebestyenDepartment of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary.
Timea BoroczkyDepartment of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary.
Marietta Margareta KorsosDepartment of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary.
David G MeckesDepartment of Biomedical Sciences, Florida State University College of Medicine, Tallahassee, FL, 32306, USA.
Peter HorvathLaboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network (HUN-REN), Szeged, 6726, Hungary.
Krisztina BuzasDepartment of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary.
Maria HarmatiDepartment of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary. harmatimarcsi@gmail.com.

Funding

National Research, Developement and Innovation Office TKP2021-EGA-09National Research, Development and Innovation Office OTKA-K143255New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund ÚNKP22-3University of Szeged Albert Szent-Györgyi Research Fund
6 · The paper itself

Abstract

backgroundAlthough interest in the role of extracellular vesicles (EV) in oncology is growing, not all potential aspects have been investigated. In this meta-analysis, data regarding (i) the EV proteome and (ii) the invasion and proliferation capacity of the NCI-60 tumor cell lines (60 cell lines from nine different tumor types) were analyzed using machine learning methods.

methodsOn the basis of the entire proteome or the proteins shared by all EV samples, 60 cell lines were classified into the nine tumor types using multiple logistic regression. Then, utilizing the Least Absolute Shrinkage and Selection Operator, we constructed a discriminative protein panel, upon which the samples were reclassified and pathway analyses were performed. These panels were validated using clinical data (n = 4,665) from Human Protein Atlas.

resultsClassification models based on the entire proteome, shared proteins, and discriminative protein panel were able to distinguish the nine tumor types with 49.15%, 69.10%, and 91.68% accuracy, respectively. Invasion and proliferation capacity of the 60 cell lines were predicted with R

conclusionIntegrating in vitro EV proteomic data, cell physiological characteristics, and clinical data of various tumor types illuminates the diagnostic, prognostic, and therapeutic potential of EVs. Video Abstract.

Indexed as

Extracellular VesiclesNeoplasmsCell ProliferationHumansProteomeProteomicsProteomeClassificationExtracellular vesiclesInvasionMachine learningNCI-60PredictionProliferation

Identifiers

PMID37986165
PMCPMC10658864

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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.