Evidence map›Paper›PMID 36232544›Full record

ArticleInternational journal of molecular sciences2022

Plasma Proteomics Enable Differentiation of Lung Adenocarcinoma from Chronic Obstructive Pulmonary Disease (COPD).

Thilo Bracht, Daniel Kleefisch, Karin Schork, Kathrin E Witzke, Weiqiang Chen, Malte Bayer, Jan Hovanec, Georg Johnen, Swetlana Meier, Yon-Dschun Ko and 8 more

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.3field-weighted citation impact, top 22% 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

12 citing papers in PubMed, 13 citations in OpenAlex.

  1. A Potential Novel Molecular Interaction in Bronchiolitis Obliterans Syndrome in Lung Transplantation Patients: The Role of SERPINA3 and Osteoprotegerin.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
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  7. Plasma proteomics identifies molecular subtypes in sepsis.Critical care (London, England) · 2025
    Article
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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

18 authors at 5 institutions in 1 country.

Thilo BrachtClinic for Anesthesiology, Intensive Care and Pain Therapy, University Medical Center Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany.ORCID 0000-0002-1194-6614
Daniel KleefischMedizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany.
Karin SchorkMedizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany.ORCID 0000-0003-3756-4347
Kathrin E WitzkeMedizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany.ORCID 0000-0002-0662-3394
Weiqiang ChenMedizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany.
Malte BayerClinic for Anesthesiology, Intensive Care and Pain Therapy, University Medical Center Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany.
Jan HovanecInstitute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), 44789 Bochum, Germany.
Georg JohnenInstitute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), 44789 Bochum, Germany.ORCID 0000-0002-3773-5946
Swetlana MeierInstitute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), 44789 Bochum, Germany.
Yon-Dschun KoDepartment of Internal Medicine, Johanniter-Kliniken Bonn GmbH, Johanniter Krankenhaus, 53113 Bonn, Germany.
Thomas BehrensInstitute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), 44789 Bochum, Germany.
Thomas BrüningInstitute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), 44789 Bochum, Germany.
Jana FassunkeInstitute of Pathology, Medical Faculty and Center for Molecular Medicine (CMMC), University of Cologne, 50924 Cologne, Germany.ORCID 0000-0002-8391-5577
Reinhard BuettnerInstitute of Pathology, Medical Faculty and Center for Molecular Medicine (CMMC), University of Cologne, 50924 Cologne, Germany.ORCID 0000-0001-8806-4786
Julian UszkoreitMedizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany.ORCID 0000-0001-7522-4007
Michael AdamzikClinic for Anesthesiology, Intensive Care and Pain Therapy, University Medical Center Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany.
Martin EisenacherMedizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany.ORCID 0000-0003-2687-7444
Barbara SitekClinic for Anesthesiology, Intensive Care and Pain Therapy, University Medical Center Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany.
Ruhr University Bochum · DEInstitute for Prevention and Occupational Medicine · DEUniversitätsklinikum Knappschaftskrankenhaus Bochum · DEUniversity of Cologne · DEJohanniter-Krankenhaus Bonn · DE

Funding

Federal Ministry of Education and Research FKZ 031 A 534 AGerman Social Accident Insurance (DGUV) FP-0259
6 · The paper itself

Abstract

Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD; thus, the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, a biomarker panel for AC detection and differentiation from COPD is still missing. Plasma samples from 176 patients with AC with or without underlying COPD, COPD patients, and hospital controls were analyzed using mass-spectrometry-based proteomics. We performed univariate statistics and additionally evaluated machine learning algorithms regarding the differentiation of AC vs. COPD and AC with COPD vs. COPD. Univariate statistics revealed significantly regulated proteins that were significantly regulated between the patient groups. Furthermore, random forest classification yielded the best performance for differentiation of AC vs. COPD (area under the curve (AUC) 0.935) and AC with COPD vs. COPD (AUC 0.916). The most influential proteins were identified by permutation feature importance and compared to those identified by univariate testing. We demonstrate the great potential of machine learning for differentiation of highly similar disease entities and present a panel of biomarker candidates that should be considered for the development of a future biomarker panel.

Indexed as

Adenocarcinoma of LungLung NeoplasmsPulmonary Disease, Chronic ObstructiveBiomarkersHumansProteomicsBiomarkersartificial intelligenceIg kappa light chainlung cancermachine learningplasma proteomicsrandom forestSAA1SERPINA3

Identifiers

PMID36232544
PMCPMC9569607
OpenAlexW4297236045

What OpenQuestion 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 OpenQuestion graph.