Co-expression network analysis identified hub genes critical to triglyceride and free fatty acid metabolism as key regulators of age-related vascular dysfunction in mice

High expression of MCM10 is predictive of poor outcomes in lung adenocarcinoma

Backgrounds

Lung adenocarcinoma is a complex disease that results in over 1.8 million deaths a year. Recent advancements in treating and managing lung adenocarcinoma have led to modest decreases in associated mortality rates, owing in part to the multifactorial etiology of the disease. Novel prognostic biomarkers are needed to accurately stage the disease and act as the basis of adjuvant treatments.

Material and Methods

The microarray datasets GSE75037, GSE31210 and GSE32863 were downloaded from the Gene Expression Omnibus (GEO) database to identify prognostic biomarkers for lung adenocarcinoma and therapy. The differentially expressed genes (DEGs) were identified by GEO2R. Functional and pathway enrichment analysis were performed by Kyoto Encyclopedia of Genes and Genomes and Gene Ontology (GO). Validation was performed based on 72 pairs of lung adenocarcinoma and adjacent normal lung tissues.

Results

Results showed that the DEGs were mainly focused on cell cycle and DNA replication initiation. Forty-one hub genes were identified and further analyzed by CytoScape. Here, we provide evidence which suggests MCM10 is a potential target with prognostic, diagnostic and therapeutic value. We base this on an integrated approach of comprehensive bioinformatics analysis and in vitro validation using the A549 lung adenocarcinoma cell line. We show that MCM10 overexpression correlates with a poor prognosis, while silencing of this gene decreases aberrant growth by 2-fold. Finally, evaluation of 72 clinical biopsy samples suggests that overexpression of MCM10 in the lung adenocarcinoma highly correlates with larger tumor size. Together, this work suggests that MCM10 may be a clinically relevant gene with both predictive and therapeutic value in lung adenocarcinoma.

An edge-based statistical analysis of long non-coding RNA expression profiles reveals a negative association between Parkinson's disease and colon cancer

BACKGROUND

Colon cancer (CC) is one of the most common malignant tumors, while Parkinson's disease (PD) is the second most common neurodegenerative disorder. Recent accumulating evidence indicates that these two diseases are associated with each other. Also, from the perspective of long non-coding RNAs, some well-known genes such as H19 and PVT1 can link these two diseases together. Several studies have shown that patients with PD had a decreased risk of developing CC compared with patients without PD. However, controversies surround the relationship between PD and CC, and to date, no concordant conclusion has been drawn.

METHODS

In this study, we aimed to assess the association between these two diseases based on lncRNA-to-lncRNA interactions. Motivated by the weighted gene co-expression network analysis method, a customized procedure was proposed and used to identify differentially correlated edges (DCEs) in the respective interaction networks for PD and CC and explore how these two diseases are linked.

RESULTS

Of the two sets of DCEs for PD and CC, 16 pairs overlapped. Among them, 15 edges had opposite signs, with positive signs for CC indicating a gain of connectivity, whereas negative signs for PD indicating a loss of connectivity.

CONCLUSIONS

By using the lncRNA expression profiles, and a customized procedure, an answer to the question about how PD and CC are associated is provided.

Exploring biomarkers and therapeutic targets for pressure overload induced heart failure based on microarray data

Background

Heart failure (HF) is an end stage heart condition with poor prognosis which brings about tremendous social medical cost. Along decades, mechanism and treatments of HF have been under restless research.

Methods

In the present study, we first established pressure overload induced HF model using transaortic arch constriction (TAC) method in mice. The global expression profiles of long noncoding RNA (lncRNA), microRNA (miRNA) and messenger RNA (mRNA) were obtained by microarray probes, which were further confirmed by quantitative PCR (qPCR). Bioinformatics analysis was performed using multiple methods including volcano plotting, heatmapping and hierarchical clustering, Gene Ontology (GO) and pathway enrichment analysis, and competing endogenous RNA (ceRNA) regulatory network construction.

Results

Totally, 1,139 differentially expressed mRNAs (DEmRNAs), 3,830 lncRNAs (DElncRNAs) and 13 miRNAs (DEmiRNAs) were identified in HF group compared to control group, which could distinctly differentiate HF from normal control and were potential candidate biomarkers for HF. GO and pathway enrichment analysis revealed that multiple significant biological processes and pathways were involved in HF pathogenesis, such as extracellular matrix structural constituent, proteinaceous extracellular matrix, positive regulation of apoptotic process and integrin signaling pathway. Nine DElncRNAs, 3 DEmiRNAs and 25 DEmRNAs were filtrated out to construct a ceRNA network, which visually displayed their regulatory roles with therapeutic target potential.

Conclusions

The present study identified differentially expressed RNAs that might be involved in the pathogenesis and progression of HF. The outcomes shed lights into the underlying mechanisms for HF and provided candidate biomarkers and intervention targets for further research.

Biomarker discovery in attention deficit hyperactivity disorder: RNA sequencing of whole blood in discordant twin and case-controlled cohorts

Background

A variety of DNA-based methods have been applied to identify genetic markers of attention deficit hyperactivity disorder (ADHD), but the connection to RNA-based gene expression has not been fully exploited.

Methods

Using well defined cohorts of discordant, monozygotic twins from the Michigan State University Twin Registry, and case-controlled ADHD cases in adolescents, the present studies utilized advanced single molecule RNA sequencing to identify expressed changes in whole blood RNA in ADHD. Multiple analytical strategies were employed to narrow differentially expressed RNA targets to a small set of potential biomarkers of ADHD.

Results

RNA markers common to both the discordant twin study and case-controlled subjects further narrowed the putative targets, some of which had been previously associated with ADHD at the DNA level. The potential role of several differentially expressed genes, including ABCB5, RGS2, GAK, GIT1 and 3 members of the galactose metabolism pathway (GALE, GALT, GALK1) are substantiated by prior associations to ADHD and by established mechanistic connections to molecular pathways relevant to ADHD and behavioral control.

Conclusions

The convergence of DNA, RNA, and metabolic data suggests these may be promising targets for diagnostics and therapeutics in ADHD.