Novel biomarker genes which distinguish between smokers and chronic obstructive pulmonary disease patients with machine learning approach

Abstract

Background: Chronic obstructive pulmonary disease (COPD) is combination of progressive lung diseases. The diagnosis of COPD is generally based on the pulmonary function testing, however, difficulties underlie in prognosis of smokers or early stage of COPD patients due to the complexity and heterogeneity of the pathogenesis. Computational analyses of omics technologies are expected as one of the solutions to resolve such complexities. Methods: We obtained transcriptomic data by in vitro testing with exposures of human bronchial epithelial cells to the inducers for early events of COPD to identify the potential descriptive marker genes. With the identified genes, the machine learning technique was employed with the publicly available transcriptome data obtained from the lung specimens of COPD and non-COPD patients to develop the model that can reflect the risk continuum across smoking and COPD.Results: The expression levels of 15 genes were commonly altered among in vitro tissues exposed to known inducible factors for earlier events of COPD (exposure to cigarette smoke, DNA damage, oxidative stress, and inflammation), and 10 of these genes and their corresponding proteins have not previously reported as COPD biomarkers. Although these genes were able to predict each group with 65% accuracy, the accuracy with which they were able to discriminate COPD subjects from smokers was only 29%. Furthermore, logistic regression enabled the conversion of gene expression levels to a numerical index, which we named the "potential risk factor (PRF)" index. The highest significant index value was recorded in COPD subjects (0.56 at the median), followed by smokers (0.30) and non-smokers (0.02). In vitro tissues exposed to cigarette smoke displayed dose-dependent increases of PRF, suggesting its utility for prospective risk estimation of tobacco products. Conclusions: Our experimental-based transcriptomic analysis identified novel genes associated with COPD, and the 15 genes could distinguish smokers and COPD subjects from non-smokers via machine-learning classification with remarkable accuracy. We also suggested a PRF index that can quantitatively reflect the risk continuum across smoking and COPD pathogenesis, and we believe it will provide an improved understanding of smoking effects and new insights into COPD.

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

“…To reduce the dependence on lung function tests for early diagnosis of COPD, ML has also been used to mine and analyze transcriptomic data extracted from human bronchial epithelial cells, leading to the identification of abnormal expression of 15 genes in the disease, 10 of which had not previously been reported as COPD biomarkers. The different gene combinations were then analyzed by the random forest algorithm to distinguish non-smokers from smokers and COPD patients 74 ( Table 3 ). Despite the remarkable diagnostic accuracy of each subgroup (65%), further studies are required to improve the model performance in distinguishing COPD patients from smokers without COPD.…”

“…The size of trees and the number of variables usually determine the performance of model. [27,28,30], [47,48,59], [65][66][67]74,79], [90,99,101], [104,107,109] Support vector machine Support vector machine is usually used for classification and regression. It learns the optimal hyperplane to classify data.…”

Fully integrating pathophysiological insights in COPD: an updated working disease model to broaden therapeutic vision

“…Paradoxically, in terms of current medications for asthma/COPD, a risk factor for both COPD and lung cancer is upregulation of the ADRB2 gene for sympathetic β2 receptors, with downregulation of this gene activity associated with better squamous-cell cancer survival [76]. Using 3D epithelial cell culture, MATSUMURA et al [77] exposed cells to oxidant stress and found 15 genes were perturbed, with those for cell proliferation and redox homeostasis being downregulated; however, EMT-related genes (including for epidermal growth factor receptor (EGFR)) were upregulated and this was especially marked in cells from smokers and COPD patients.…”

Artificial Intelligence in COPD: New Venues to Study a Complex Disease

“…Machine learning approaches can also exploit the richness of gene expression data to assist in COPD diagnostic and prognostic tasks. In recent work, a random forest was used to classify airway transcriptomic data from 15 pre-selected candidate genes to define a COPD risk score 65 . Although the ability to discriminate COPD subjects was very limited, active research in exploiting omics data with machine learning may offer new insights about disease pathogenesis.…”