Plasma proteomics enable differentiation of lung adenocarcinoma from chronic obstructive pulmonary disease (COPD)
- 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.
Author: | Thilo BrachtORCiDGND, Daniel KleefischGND, Karin SchorkORCiDGND, Kathrin E. WitzkeORCiDGND, Weiqiang ChenGND, Malte BayerORCiDGND, Jan HovanecORCiDGND, Georg JohnenORCiDGND, Swetlana MeierGND, Yon-Dschun KoORCiDGND, Thomas BehrensORCiDGND, Thomas BrüningORCiDGND, Jana FassunkeORCiDGND, Reinhard BüttnerORCiDGND, Julian UszkoreitORCiDGND, Michael AdamzikORCiDGND, Martin EisenacherORCiDGND, Barbara SitekGND |
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URN: | urn:nbn:de:hbz:294-103104 |
DOI: | https://doi.org/10.3390/ijms231911242 |
Parent Title (English): | International journal of molecular sciences |
Publisher: | MDPI |
Place of publication: | Basel, Schweiz |
Document Type: | Article |
Language: | English |
Date of Publication (online): | 2023/10/27 |
Date of first Publication: | 2022/09/24 |
Publishing Institution: | Ruhr-Universität Bochum, Universitätsbibliothek |
Tag: | Open Access Fonds Ig kappa light chain; SAA1; SERPINA3; artificial intelligence; lung cancer; machine learning; plasma proteomics; random forest |
Volume: | 23 |
Issue: | 19, Article 11242 |
First Page: | 11242-1 |
Last Page: | 11242-16 |
Note: | Article Processing Charge funded by the Deutsche Forschungsgemeinschaft (DFG) and the Open Access Publication Fund of Ruhr-Universität Bochum. |
Institutes/Facilities: | Medizinisches Proteom-Center |
Knappschaftskrankenhaus Bochum, Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie | |
Dewey Decimal Classification: | Technik, Medizin, angewandte Wissenschaften / Medizin, Gesundheit |
open_access (DINI-Set): | open_access |
Licence (English): | Creative Commons - CC BY 4.0 - Attribution 4.0 International |