Bracht, Thilo ORCID: 0000-0002-1194-6614, Kleefisch, Daniel, Schork, Karin ORCID: 0000-0003-3756-4347, Witzke, Kathrin E., Chen, Weiqiang, Bayer, Malte, Hovanec, Jan, Johnen, Georg ORCID: 0000-0002-3773-5946, Meier, Swetlana, Ko, Yon-Dschun, Behrens, Thomas, Bruning, Thomas, Fassunke, Jana ORCID: 0000-0002-8391-5577, Buettner, Reinhard, Uszkoreit, Julian ORCID: 0000-0001-7522-4007, Adamzik, Michael, Eisenacher, Martin ORCID: 0000-0003-2687-7444 and Sitek, Barbara (2022). Plasma Proteomics Enable Differentiation of Lung Adenocarcinoma from Chronic Obstructive Pulmonary Disease (COPD). Int. J. Mol. Sci., 23 (19). BASEL: MDPI. ISSN 1422-0067

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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.

Item Type: Journal Article
Creators:
CreatorsEmailORCIDORCID Put Code
Bracht, ThiloUNSPECIFIEDorcid.org/0000-0002-1194-6614UNSPECIFIED
Kleefisch, DanielUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Schork, KarinUNSPECIFIEDorcid.org/0000-0003-3756-4347UNSPECIFIED
Witzke, Kathrin E.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Chen, WeiqiangUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Bayer, MalteUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Hovanec, JanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Johnen, GeorgUNSPECIFIEDorcid.org/0000-0002-3773-5946UNSPECIFIED
Meier, SwetlanaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Ko, Yon-DschunUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Behrens, ThomasUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Bruning, ThomasUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Fassunke, JanaUNSPECIFIEDorcid.org/0000-0002-8391-5577UNSPECIFIED
Buettner, ReinhardUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Uszkoreit, JulianUNSPECIFIEDorcid.org/0000-0001-7522-4007UNSPECIFIED
Adamzik, MichaelUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Eisenacher, MartinUNSPECIFIEDorcid.org/0000-0003-2687-7444UNSPECIFIED
Sitek, BarbaraUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
URN: urn:nbn:de:hbz:38-674999
DOI: 10.3390/ijms231911242
Journal or Publication Title: Int. J. Mol. Sci.
Volume: 23
Number: 19
Date: 2022
Publisher: MDPI
Place of Publication: BASEL
ISSN: 1422-0067
Language: English
Faculty: Unspecified
Divisions: Unspecified
Subjects: no entry
Uncontrolled Keywords:
KeywordsLanguage
FREE LIGHT-CHAINS; POTENTIAL BIOMARKER; CANCER; QUANTIFICATION; DISCOVERY; PATHOGENESIS; INFLAMMATION; ASSOCIATION; PANELMultiple languages
Biochemistry & Molecular Biology; Chemistry, MultidisciplinaryMultiple languages
URI: http://kups.ub.uni-koeln.de/id/eprint/67499

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