Identification of TRPC6 as a Novel Diagnostic Biomarker of PM-Induced Chronic Obstructive Pulmonary Disease Using Machine Learning Models

Abstract

Chronic obstructive pulmonary disease (COPD) was the third most prevalent cause of mortality worldwide in 2010; it results from a progressive and fatal deterioration of lung function because of cigarette smoking and particulate matter (PM). Therefore, it is important to identify molecular biomarkers that can diagnose the COPD phenotype to plan therapeutic efficacy. To identify potential novel biomarkers of COPD, we first obtained COPD and the normal lung tissue gene expression dataset GSE151052 from the NCBI Gene Expression Omnibus (GEO). A total of 250 differentially expressed genes (DEGs) were investigated and analyzed using GEO2R, gene ontology (GO) functional annotation, and Kyoto Encyclopedia of Genes and Genomes (KEGG) identification. The GEO2R analysis revealed that TRPC6 was the sixth most highly expressed gene in patients with COPD. The GO analysis indicated that the upregulated DEGs were mainly concentrated in the plasma membrane, transcription, and DNA binding. The KEGG pathway analysis indicated that the upregulated DEGs were mainly involved in pathways related to cancer and axon guidance. TRPC6, one of the most abundant genes among the top 10 differentially expressed total RNAs (fold change ≥ 1.5) between the COPD and normal groups, was selected as a novel COPD biomarker based on the results of the GEO dataset and analysis using machine learning models. The upregulation of TRPC6 was verified in PM-stimulated RAW264.7 cells, which mimicked COPD conditions, compared to untreated RAW264.7 cells by a quantitative reverse transcription polymerase chain reaction. In conclusion, our study suggests that TRPC6 can be regarded as a potential novel biomarker for COPD pathogenesis.

Keywords: TRPC6; chronic obstructive pulmonary disease (COPD); machine learning; particulate matter.

Figure 1

Overview of the experimental workflow.

Figure 2

A brief schema of decision tree.

Figure 3

Pseudocode of C4.5 algorithm [26].

Figure 4

Chart visualizing the performance of the three decision tree models and their structures.

Figure 5

Confusion matrix for results shown in Figure 4. As a result of the experiment using machine learning models, the COPD and control groups of the dataset were classified only according to the expression level of TRPC6, regardless of the values of the other genes, and the accuracy of the best model (J48) was over 99%. This result shows that the classifiers that the decision tree algorithm generated classified COPD patients and controls with simple criteria but high accuracy.

Figure 6

Chart for visualizing the performance of the three decision tree models with the GSE57148 dataset [30] and their structure.

Figure 7

Confusion matrices for results showed in Figure 6.

Figure 8

Determination of the TRPC6 mRNA expression in RAW 264.7 cells. Data are expressed as mean ± standard deviation (SD) of 3 independent experiments (n ≥ 3). The mRNA band intensities were converted to the numerical data using Image-Studio software (LI COR, Lincoln, NE, USA). One-way ANOVA was used for comparison of group means, followed by Dunnett’s t-test (* p < 0.05 vs. untreated control).