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Chronic obstructive pulmonary disease

ABSTRACT: Diaphragm muscles in Chronic Obstructive Pulmonary Disease (COPD) patients undergo an adaptive fast to slow transformation that includes cellular adaptations. This project studies the signaling mechanisms responsible for this transformation. Keywords: other

ORGANISM(S): Homo sapiens

PROVIDER: GSE475 | GEO | 2003/07/16

SECONDARY ACCESSION(S): PRJNA85155

REPOSITORIES: GEO

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Transcription profiling of human diaphragm Chronic Obstructive Pulmonary Disease (COPD) patients vs. controls to study diaphragm muscle fast to slow adaptive transformation.

Project description:Diaphragm muscles in Chronic Obstructive Pulmonary Disease (COPD) patients undergo an adaptive fast to slow transformation that includes cellular adaptations. This project studies the signaling mechanisms responsible for this transformation.

2007-10-25 | E-GEOD-475 | biostudies-arrayexpress

A preliminary study on the screening and mechanism of miRNA as a biomarker for patients with different progressive chronic obstructive pulmonary disease (COPD)

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Project description:Background: 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. Methods: 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. Results: 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. Conclusion: 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.

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