ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2020
Verification of a Blood-Based Targeted Proteomics Signature for Malignant Pleural Mesothelioma.
Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed, 8 citations in OpenAlex.
- Validation of a Proteomic Signature of Lung Cancer Risk from Bronchial Specimens of Risk-Stratified Individuals.Cancers · 2023Article
- Sample multiplexing-based targeted pathway proteomics with real-time analytics reveals the impact of genetic variation on protein expression.Nature communications · 2023Article
- Assessment of a Large-Scale Unbiased Malignant Pleural Effusion Proteomics Study of a Real-Life Cohort.Cancers · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 5 institutions in 3 countries.
Funding
Abstract
backgroundWe have verified a mass spectrometry (MS)-based targeted proteomics signature for the detection of malignant pleural mesothelioma (MPM) from the blood.
methodsA seven-peptide biomarker MPM signature by targeted proteomics in serum was identified in a previous independent study. Here, we have verified the predictive accuracy of a reduced version of that signature, now composed of six-peptide biomarkers. We have applied liquid chromatography-selected reaction monitoring (LC-SRM), also known as multiple-reaction monitoring (MRM), for the investigation of 402 serum samples from 213 patients with MPM and 189 cancer-free asbestos-exposed donors from the United States, Australia, and Europe.
resultsEach of the biomarkers composing the signature was independently informative, with no apparent functional or physical relation to each other. The multiplexing possibility offered by MS proteomics allowed their integration into a single signature with a higher discriminating capacity than that of the single biomarkers alone. The strategy allowed in this way to increase their potential utility for clinical decisions. The signature discriminated patients with MPM and asbestos-exposed donors with AUC of 0.738. For early-stage MPM, AUC was 0.765. This signature was also prognostic, and Kaplan-Meier analysis showed a significant difference between high- and low-risk groups with an HR of 1.659 (95% CI, 1.075-2.562;
conclusionsTargeted proteomics allowed the development of a multianalyte signature with diagnostic and prognostic potential for MPM from the blood. IMPACT: The proteomic signature represents an additional diagnostic approach for informing clinical decisions for patients at risk for MPM.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.