Differential gene expression analysis and network construction of recurrent cardiovascular events

A Mass Spectrometry-Based Proteome Study of Twin Pairs Discordant for Incident Acute Myocardial Infarction within Three Years after Blood Sampling Suggests Novel Biomarkers

Acute myocardial infarction (AMI) is a major cause of mortality and morbidity worldwide, yet biomarkers for AMI in the short- or medium-term are lacking. We apply the discordant twin pair design, reducing genetic and environmental confounding, by linking nationwide registry data on AMI diagnoses to a survey of 12,349 twins, thereby identifying 39 twin pairs (48-79 years) discordant for their first-ever AMI within three years after blood sampling. Mass spectrometry of blood plasma identified 715 proteins. Among 363 proteins with a call rate > 50%, imputation and stratified Cox regression analysis revealed seven significant proteins (FDR < 0.05): FGD6, MCAM, and PIK3CB reflected an increased level in AMI twins relative to their non-AMI co-twins (HR > 1), while LBP, IGHV3-15, C1RL, and APOC4 reflected a decreased level in AMI twins relative to their non-AMI co-twins (HR < 1). Additional 50 proteins were nominally significant (p < 0.05), and bioinformatics analyses of all 57 proteins revealed biology within hemostasis, coagulation cascades, the immune system, and the extracellular matrix. A protein-protein-interaction network revealed Fibronectin 1 as a central hub. Finally, technical validation confirmed MCAM, LBP, C1RL, and APOC3. We put forward novel biomarkers for incident AMI, a part of the proteome field where markers are surprisingly rare and where additional studies are highly needed.

Bioinformatics analysis identifies ferroptosis‑related genes in the regulatory mechanism of myocardial infarction

Since ferroptosis is considered to be a notable cause of cardiomyocyte death, inhibiting ferroptosis has become a novel strategy in reducing cardiac cell death and improving cardiopathic conditions. Therefore, the aim of the present study was to search for ferroptosis-related hub genes and determine their diagnostic value in myocardial infarction (MI) to aid in the diagnosis and treatment of the disease. A total of 10,286 DEGs were identified, including 6,822 upregulated and 3.464 downregulated genes in patients with MI compared with healthy controls. After overlapping with ferroptosis-related genes, 128 ferroptosis-related DEGs were obtained. WGCNA successfully identified a further eight functional modules, from which the blue module had the strongest correlation with MI. Blue module genes and ferroptosis-related differentially expressed genes were overlapped to obtain 20 ferroptosis-related genes associated with MI. Go and KEGG analysis showed that these genes were mainly enriched in cellular response to chemical stress, trans complex, transferring, phosphorus-containing groups, protein serine/threonine kinase activity, FoxO signaling pathway. Hub genes were obtained from 20 ferroptosis-related genes through the PPI network. The expression of hub genes was found to be down-regulated in the MI group. Finally, the miRNAs-hub genes and TFs-hub genes networks were constructed. The GSE141512 dataset and the use of RT-qPCR assays on patient blood samples were used to confirm these results. The results showed that ATM, PIK3CA, MAPK8, KRAS and SIRT1 may play key roles in the development of MI, and could therefore be novel markers or targets for the diagnosis or treatment of MI.

Mechanism of CREB1 in cardiac function of rats with heart failure via regulating the microRNA-376a-3p/TRAF6 axis

Heart failure (HF) is a complicated disease resulting from impaired heart function. CREB1 is a candidate target in heart-concerning diseases. This paper attempts to explore the role of CREB1 in HF. Initially, the HF rat model was established by constricted abdominal aortic surgery and the cardiac function of HF rats was assessed by ultrasonic cardiogram. Levels of CK-MB and LDH and activity of Caspase-3 and Caspase-9 in HF rats were determined. Subsequently, myocardium pathological injury and myocardium apoptosis were detected. Additionally, the interactions between CREB1 and miR-376a-3p and between miR-376a-3p and TRAF6 were verified. The roles of CREB1, miR-376a-3p, and TRAF6 in HF were evaluated. In HF rats, CREB1 and miR-376a-3p were both downregulated while TRAF6 was upregulated. Besides, HF rats had decreased values of EF and FS, elevated levels of CK-MB and LDH, inflammatory infiltration, promoted cardiomyocyte apoptosis, and elevated activity of Caspase-3 and Caspase-9, which were all reversed by CREB1. Additionally, CREB1 activated miR-376a-3p expression, and miR-376a-3p targeted TRAF6 transcription. Both miR-376a-3p knockdown and TRAF6 overexpression annulled the protective role of CREB1 overexpression in cardiac function of HF rats. CREB1 activated miR-376a-3p expression to suppress TRAF6, thereby promoting the cardiac function of HF rats.

Molecular heterogeneity of guanine nucleotide binding-protein γ subunit 4 in left- and right-sided colon cancer

Molecular heterogeneity determines the differences in the pathological features, prognosis and survival after relapse when comparing left-sided colon cancer (LCC) and right-sided colon cancer (RCC). At present, the discrepancy in the underlying molecular events between the two types of colon cancer has not been thoroughly investigated. The present study aimed to explore novel targets to predict the disease stage and prognosis of LCC and RCC. Expression analysis of guanine nucleotide binding-protein γ subunit 4 (GNG4) was performed using the Gene Expression Profiling Interactive Analysis (GEPIA) and Oncomine databases. Survival and association analyses were performed using GEPIA and the colon adenocarcinoma dataset from The Cancer Genome Atlas database. GNG4-positive cells in a tissue microarray were examined using immunohistochemistry. According to the GNG4 expression data from Caucasian patients included in the TCGA dataset, GNG4 was highly expressed and positively associated with pathological stage and overall survival (OS) rates in colon cancer. GNG4 expression was higher in LCC than in RCC. Patients with LCC with high GNG4 expression exhibited higher pathological stage and lower survival rates, whereas this was not observed in patients with RCC. In addition, the clinical tissues used in the microarray showed that GNG4 expression was increased in Chinese patients with LCC compared with that in patients with RCC. Consistently, GNG4 expression was negatively associated with OS in patients with LCC, but not in patients with RCC. However, no association was observed between GNG4 expression and the disease stage of colon cancer in both patients with LCC and RCC. Overall, the molecular heterogeneity of GNG4 in LCC and RCC suggests that GNG4 may be used as a diagnostic and prognostic biomarker in patients with LCC.

Identification of crucial miRNAs and lncRNAs for ossification of ligamentum flavum

The present study aimed to screen crucial micro (mi)RNAs and long non-coding (lnc)RNAs involved in the development of ossification of ligamentum flavum (OLF) based on the miRNA-mRNA and lncRNA-miRNA-mRNA competing endogenous (ce)RNA regulatory network analyses, which are rarely reported. The differentially expressed genes (DEGs), differentially expressed lncRNAs (DELs) and differentially expressed miRNAs (DEMs) between 4 OLF and 4 healthy controls were identified using two microarray datasets GSE106253 and GSE106256 collected from the Gene Expression Omnibus database. A protein-protein interaction (PPI) network was constructed, followed by calculation of topological characteristics and sub-module analysis in order to obtain hub DEGs. The miRNA-mRNA and lncRNA-miRNA networks that were established based on their interaction pairs, obtained from miRwalk and starBase databases, respectively, were integrated to form the ceRNA network. The underlying functions of mRNAs were predicted using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The present study screened 828 DEGs, 119 DELs and 81 DEMs between OLF and controls. PPI network and module analyses identified interleukin (IL)10, adenylate cyclase (ADCY)5, suppressor of cytokine signaling (SOCS)3, G protein subunit gamma (GNG) 4, collagen type II α 1 chain (COL2A1) and collagen type XIII α 1 chain (COL13A1) as hub genes. The miRNA-mRNA network analysis demonstrated IL10 could be regulated by miR-210-3p, while COL13A1 and COL2A1 could be modulated by miR-329-3p and miR-222-5p, respectively. lncRNA-miRNA-mRNA ceRNA network analysis identified that small nucleolar RNA host gene 16-hsa-miR-196a-5p-SOCS3, ankyrin repeat and SOCS box containing 16-AS1-hsa-miR-379-5p-GNG4, nuclear enriched abundant transcript 1-has-miR-181b-5p-ADCY5, rhophilin 1-AS1-hsa-miR-299-3p-WNT7B interaction axes may be crucial. DAVID analysis predicted IL10, ADCY5, GNG4 and SOCS3 were involved in ‘adaptive immune response’, ‘Chemokine signaling pathway’ and ‘regulation of apoptosis’ processes, while COL2A1, COL13A1 and WNT7B may be ossification related. In conclusion, the identification of these crucial miRNAs and lncRNAs may be conducive for explaining the pathogenesis of OLF and provide certain natural, endogenous and nontoxic drug targets for the treatment of OLF.

Identification of Transcription Factor-Gene Regulatory Network in Acute Myocardial Infarction

BACKGROUND

Acute myocardial infarction (AMI) is a common disease with serious mortality and morbidity, worldwide. The present study aimed to identify differentially expressed genes (DEGs) and construct a transcription factor-gene regulatory network to further study the early diagnosis of AMI.

METHODS

The integrated analysis of publicly available Gene Expression Omnibus datasets of AMI was performed. Differentially expressed genes were identified between AMI and normal blood samples. Gene Ontology enrichment analyses, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and the transcription factor-gene regulatory network were used to obtain insights into the functions of DEGs. Quantitative real-time polymerase chain reactions (qRT-PCR) were performed to validate the expression level of DEGs.

RESULTS

A total of 2,502 DEGs, including 917 up-regulated genes and 1,585 down-regulated genes, were identified between AMI and normal blood samples by integrating four expression profiles of AMI. Differentially expressed genes were significantly enriched in pathways including complement and coagulation cascades, Staphylococcus aureus infection, and cell adhesion molecules. Transcription factors were screened and performed to construct the regulatory network. The transcription factor-gene regulatory network consisted of 871 interactions between 80 transcription factors and 716 DEGs. ETS homologous factor (EHF) was one of transcription factors that had high connectivity with DEGs and regulated CACNB4 in the network. Verification by qRT-PCR revealed that EHF, KRT6A and DSG3 were significantly up-regulated, while CACNG4 was significantly down-regulated in AMI. Furthermore, CACNG6, CACNB4 and CLDN18 had a tendency to be down-regulated, and CALML3 had a tendency to be up-regulated in AMI.

CONCLUSIONS

The identification of important differentially expressed transcription factors and genes in the development of AMI would be the groundwork for the early diagnosis and early intervention of AMI.