Evidence map›Paper›PMID 25992884›Full record

ArticlePloS one2015

Serum calprotectin, CD26 and EGF to establish a panel for the diagnosis of lung cancer.

Sonia Blanco-Prieto, Lorena Vázquez-Iglesias, Mar Rodríguez-Girondo, Leticia Barcia-Castro, Alberto Fernández-Villar, María Isabel Botana-Rial, Francisco Javier Rodríguez-Berrocal, María Páez de la Cadena

Abstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–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

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Biomarkers Suitable for Early Detection of Intrathoracic Cancers in Primary Care: A Systematic Review.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025
    Pooled it
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  6. Review
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  13. DPPIV/CD26: a tumor suppressor or a marker of malignancy?Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2016
    Review
  14. Diagnostic value of α-enolase expression and serum α-enolase autoantibody levels in lung cancer.Jornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia
    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

8 authors.

Sonia Blanco-PrietoDepartment of Biochemistry, Genetics and Immunology, Facultad de Biología, Universidad de Vigo, Vigo, Spain.
Lorena Vázquez-IglesiasDepartment of Biochemistry, Genetics and Immunology, Facultad de Biología, Universidad de Vigo, Vigo, Spain.
Mar Rodríguez-GirondoSiDOR (Statistical Inference, Decision and Operations Research) Research Group & Centro de Investigaciones Biomédicas (CINBIO), Facultad de Ciencias Económicas y Empresariales, Universidad de Vigo, Vigo, Spain.
Leticia Barcia-CastroDepartment of Biochemistry, Genetics and Immunology, Facultad de Biología, Universidad de Vigo, Vigo, Spain.
Alberto Fernández-VillarUnit of Interventional Broncopleural Pathology, Pneumology Department of Complejo Hospitalario Universitario de Vigo (CHUVI), Vigo, Spain.
María Isabel Botana-RialUnit of Interventional Broncopleural Pathology, Pneumology Department of Complejo Hospitalario Universitario de Vigo (CHUVI), Vigo, Spain.
Francisco Javier Rodríguez-BerrocalDepartment of Biochemistry, Genetics and Immunology, Facultad de Biología, Universidad de Vigo, Vigo, Spain.
María Páez de la CadenaDepartment of Biochemistry, Genetics and Immunology, Facultad de Biología, Universidad de Vigo, Vigo, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer is the most lethal neoplasia, and an early diagnosis is the best way for improving survival. Symptomatic patients attending Pulmonary Services could be diagnosed with lung cancer earlier if high-risk individuals are promptly separated from healthy individuals and patients with benign respiratory pathologies. We searched for a convenient non-invasive serum test to define which patients should have more immediate clinical tests. Six cancer-associated molecules (HB-EGF, EGF, EGFR, sCD26, VEGF, and Calprotectin) were investigated in this study. Markers were measured in serum by specific ELISAs, in an unselected population that included 72 lung cancer patients of different histological types and 56 control subjects (healthy individuals and patients with benign pulmonary pathologies). Boosted regression and random forests analysis were conducted for the selection of the best candidate biomarkers. A remarkable discriminatory capacity was observed for EGF, sCD26, and especially for Calprotectin, these three molecules constituting a marker panel boasting a sensitivity of 83% and specificity of 87%, resulting in an associated misclassification rate of 15%. Finally, an algorithm derived by logistic regression and a nomogram allowed generating classification scores in terms of the risk of a patient of suffering lung cancer. In conclusion, we propose a non-invasive test to identify patients at high-risk for lung cancer from a non-selected population attending a Pulmonary Service. The efficacy of this three-marker panel must be tested in a larger population for lung cancer.

Indexed as

AgedAged, 80 and overAlgorithmsBiomarkers, TumorCarcinoma, Non-Small-Cell LungDipeptidyl Peptidase 4Early Detection of CancerEpidermal Growth FactorFemaleHumansLeukocyte L1 Antigen ComplexLogistic ModelsLung NeoplasmsMaleMiddle AgedProspective StudiesBiomarkers, TumorDipeptidyl Peptidase 4DPP4 protein, humanEpidermal Growth FactorLeukocyte L1 Antigen Complex

Identifiers

PMID25992884
PMCPMC4436352

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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.