Identification of common microRNA between COPD and non-small cell lung cancer through pathway enrichment analysis

Abstract

Background: Different factors have been introduced which influence the pathogenesis of chronic obstructive pulmonary disease (COPD) and non-small cell lung cancer (NSCLC). COPD as an independent factor is involved in the development of lung cancer. Moreover, there are certain resemblances between NSCLC and COPD, such as growth factors, activation of intracellular pathways, as well as epigenetic factors. One of the best approaches to understand the possible shared pathogenesis routes between COPD and NSCLC is to study the biological pathways that are activated. MicroRNAs (miRNAs) are critical biomolecules that implicate the regulation of several biological and cellular processes. As such, the main goal of this study was to use a systems biology approach to discover common dysregulated miRNAs between COPD and NSCLC, one that targets most genes within common enriched pathways.

Results: To reconstruct the miRNA-pathways for each disease, we used the microarray miRNA expression data. Then, we employed "miRNA set enrichment analysis" (MiRSEA) to identify the most significant joint miRNAs between COPD and NSCLC based on the enrichment scores. Overall, our study revealed the involvement of the targets of miRNAs (such as has-miR-15b, hsa-miR-106a, has-miR-17, has-miR-103, and has-miR-107) in the most important common biological pathways.

Conclusions: According to the promising results of the pathway analysis, the identified miRNAs can be utilized as the new potential signatures for therapy through understanding the molecular mechanisms of both diseases.

Keywords: COPD; Non-small cell lung Cancer; Pathway analysis; miRNA.

Fig. 1

The workflow of steps performed in this study. This scheme shows that after collecting miRNA expression profiles, pre-processing was individually performed for each dataset, and then, the enrichment miRNA-pathways were utilized to discover dysregulated pathways though miRNA sets. Those common miRNAs that had the most effects on the enriched pathways on the basis of enrichment scores were selected, and the target genes were extracted from target prediction databases for common miRNAs between COPD and NSCLC. At the end, the pathways analysis was performed

Fig. 2

The network of common pathways. Each node represents the pathway, the size and the color depth of each node indicate the number of common core miRNAs between COPD and NSCLC in that pathway; also, the thickness of an edge in this network represents the number of shared miRNAs between the two pathways. P53 signaling, cell cycle, and non-small cell lung cancer pathways have the highest number of common miRNAs between COPD and NSCLC, in which the number of core miRNAs in p53 signaling, cell cycle, and non-small cell lung cancer pathways are 15, 15, and 10, respectively

Fig. 3

Results of miRNA set enrichment analysis in COPD and NSCLC for non-small cell lung cancer, cell cycle, and p53 signaling pathways. MiRSEA performs differential expression analysis for miRNAs based on differential weighted scores (a); integrates the differential expression level of miRNAs and miRNA-pathway weights, calculates miRScore, and creates a ranked list of miRNAs. Then, it maps miRNAs in the pathway to the ranked list and calculates the miRNA enrichment score for each pathway and miRNA correlation profiles (b); after calculating the enrichment score, MiRSEA prioritizes a pathway by FDR and the running miRNAs enrichment score for the pathway results (c)

Fig. 4

Significant core miRNAs among three enriched pathways based on average enrichment scores

Fig. 5

Converted non-small cell lung cancer (a) cell cycle (b) and p53 signaling (c) pathways in KEGG. The target genes of has-miR-15b, hsa-miR-106a, has-miR-17, has-miR-103 and has-miR-107 as the most significant core miRNAs are identified and then these miRNAs are mapped to the pathways