Chronic obstructive pulmonary disease candidate gene prioritization based on metabolic networks and functional information

CYP1B1, VEGFA, BCL2, and CDKN1A Affect the Development of Chronic Obstructive Pulmonary Disease

Purpose

Chronic obstructive pulmonary disease (COPD) is a progressive lung disease characterized by poor airflow. The purpose of this study was to explore the mechanisms involved in the development of COPD.

Patients and Methods

The mRNA expression profile GSE100281, consisting of 79 COPD and 16 healthy samples, was acquired from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between COPD samples and healthy samples were analyzed using the limma package. Functional enrichment analysis for the DEGs was carried out using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) tool. Furthermore, DEG-compound pairs were predicted using the Comparative Toxicogenomics Database. The KEGG metabolite IDs corresponding to the compounds were also obtained through the MetaboAnalyst pipeline. Based on the diffusion algorithm, the metabolite network was constructed. Finally, the expression levels of key genes were determined using quantitative PCR (qPCR).

Results

There were 594 DEGs identified between the COPD and healthy samples, including 242 upregulated and 352 downregulated genes. A total of 696 DEG-compound pairs, such as BCL2-C00469 (ethanol) and BCL2-C00389 (quercetin) pairs, were predicted. CYP1B1, VEGFA, BCL2, and CDKN1A were included in the top 10 DEG-compound pairs. Additionally, 57 metabolites were obtained. In particular, hsa04750 (inflammatory mediator regulation of TRP channels)-C00469 (ethanol) and hsa04152 (AMPK signaling pathway)-C00389 (quercetin) pairs were found in the metabolite network. The results of qPCR showed that the expression of CYP1B1, VEGFA, BCL2, and CDKN1A was consistent with that predicted using bioinformatic analysis.

Conclusion

CYP1B1, VEGFA, BCL2, and CDKN1A may play important functions in the development and progression of COPD.

Network-based integration method for potential breast cancer gene identification

Breast cancer is the most common female death-causing cancer worldwide. A network-based integration method was proposed to identify potential breast cancer genes. First, genes were prioritized using a gene prioritization algorithm by the strategy of disease risks transferred between genes in a network with weighted vertexes and edges. Our prioritization algorithm was effectives and robust for top-ranked seed gene number and higher area under the curve values compared to ToppGene and ToppNet. Then, 20 potential breast cancer genes were identified as common genes of the top 50 candidate genes for their robustness in multiple prioritizations. These genes could accurately classify tumor and normal samples of all and paired sample sets and three independent datasets. Of potential breast cancer genes, 18 were verified by literature and 2 were novel genes that need further study. This study would contribute to the understanding of the genetic architecture for the diagnosis and treatment of breast cancer.

Eosinophilic COPD Patients Display a Distinctive Serum miRNA Profile From Asthma and Non-eosinophilic COPD

BACKGROUND

Asthma and chronic obstructive pulmonary disease (COPD) are common chronic airway diseases that may overlap in some individuals. Asthma COPD overlap (ACO) is a heterogeneous conditions that includes smoking-asthma (SA) and COPD with eosinophilia (COPDe). MicroRNAs (miRNA) are regulators of gene expression with a great potential as biomarkers.

OBJECTIVES

The objective of this study was to identify distinctive miRNA signatures in patients from the whole spectrum of chronic obstructive bronchial disease (SA, COPDe, non-smoking asthmatics (NSA), and COPD) that could serve as diagnostic biomarkers or describe differential molecular mechanisms with potential therapeutic implications.

METHODS

From a previously characterized cohort of ACO, COPD and asthma patients, we selected a discovery group of 40 patients for miRNA expression profiling by means of microarray technology. Differential expression of miRNAs were validated by quantitative PCR in the complete cohort (n=274).

RESULTS

Thirty differentially expressed miRNAs (eBAYES p<0.05, fold change ≥2) were found among the different groups of patients regarding COPDe: 19 COPD-vs-COPDe, 13 NSA-vs-COPDe, 11 SA-vs-COPDe. A characteristic down-regulated miRNA expression pattern was identified in COPDe patients. Differential expression of miR-619-5p and miR-4486 in COPDe patients were validated in the complete cohort (n=274).

CONCLUSIONS

We postulate that COPDe patients show a characteristic expression profile of miRNAs distinctive from asthma and COPD. Also that SA and COPDe patients, which have been typically clustered in the ACO group, display distinct molecular events.

A Pathway Analysis Based on Genome-Wide DNA Methylation of Chinese Patients with Graves' Orbitopathy

Background

The pathogenesis Graves' Orbitopathy (GO) is not yet fully understood. Here, we conducted a pathway analysis based on genome-wide DNA methylation data of Chinese GO patients to explore GO-related pathways and potential feature genes.

Methods

Six GO patients and 6 age-matched control individuals were recruited, and a genome-scale screen of DNA methylation was measured using their peripheral blood sample. After extracting the differentially methylated regions (DMRs), we classified DMRs into three clusters with respect to median absolute deviation (MAD) for GO and control group, respectively. Then the extract tests were performed to identify significant pathways by comparing the counts of genes in each cluster between GO and control group in a pathway. For each significant pathway, we calculated the Methylation-based Inference of Regulatory Activity (MIRA) score to infer the regulatory activity of genes involved in the pathway. Furthermore, we took the significant pathways as the subsets and applied Random forests (RF) method to extract GO-related feature genes.

Results

We identified four potential significant pathways associated with the occurrence and development of GO disease. There were Toxoplasmosis, Axon guidance, Focal adhesion, and Proteoglycans in cancer (p<0.001 or p=0.007). The identified genes involved in the significant pathways, such as LDLR (p=0.019), CDK5 (p=0.036), and PIK3CB (p=0.020), were found to be correlated with GO phenotype.

Conclusion

Our study suggested pathway analyses can help understand the potential relationships between the DNA methylation level of some certain genes and their regulation in Chinese GO patients.

Prioritizing chronic obstructive pulmonary disease (COPD) candidate genes in COPD-related networks

Chronic obstructive pulmonary disease (COPD) is a multi-factor disease, which could be caused by many factors, including disturbances of metabolism and protein-protein interactions (PPIs). In this paper, a weighted COPD-related metabolic network and a weighted COPD-related PPI network were constructed base on COPD disease genes and functional information. Candidate genes in these weighted COPD-related networks were prioritized by making use of a gene prioritization method, respectively. Literature review and functional enrichment analysis of the top 100 genes in these two networks suggested the correlation of COPD and these genes. The performance of our gene prioritization method was superior to that of ToppGene and ToppNet for genes from the COPD-related metabolic network or the COPD-related PPI network after assessing using leave-one-out cross-validation, literature validation and functional enrichment analysis. The top-ranked genes prioritized from COPD-related metabolic and PPI networks could promote the better understanding about the molecular mechanism of this disease from different perspectives. The top 100 genes in COPD-related metabolic network or COPD-related PPI network might be potential markers for the diagnosis and treatment of COPD.