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BackgroundHypercholesterolemia arising from abnormal lipid metabolism is one of the critical risk factors for coronary artery disease (CAD), however the roles of genetic variants in lipid metabolism-related genes on premature CAD (≤60 years old) development still require further investigation. We herein genotyped four single nucleotide polymorphisms (SNPs) in lipid metabolism-related genes (rs1132899 and rs5167 in APOC4, rs1801693 and rs7765781 in LPA), aimed to shed light on the influence of these SNPs on individual susceptibility to early-onset CAD.MethodsGenotyping of the four SNPs (rs1132899, rs5167, rs1801693 and rs7765781) was performed in 224 premature CAD cases and 297 control subjects (≤60 years old) using polymerase chain reaction-ligation detection reaction (PCR–LDR) method. The association of these SNPs with premature CAD was performed with SPSS software.ResultsMultivariate logistic regression analysis showed that C allele (OR = 1.50, P = 0.027) and CC genotype (OR = 2.84, P = 0.022) of APOC4 rs1132899 were associated with increased premature CAD risk, while the other three SNPs had no significant effect. Further stratified analysis uncovered a more evident association with the risk of premature CAD among male subjects (C allele, OR = 1.65, and CC genotype, OR = 3.33).ConclusionsOur data provides the first evidence that APOC4 rs1132899 polymorphism was associated with an increased risk of premature CAD in Chinese subjects, and the association was more significant among male subjects.Electronic supplementary materialThe online version of this article (doi:10.1186/s12944-015-0065-7) contains supplementary material, which is available to authorized users.

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This study aimed to screen key biomarkers and investigate immune infiltration in pulmonary arterial hypertension (PAH) based on integrated bioinformatics analysis. The Gene Expression Omnibus (GEO) database was used to download three mRNA expression profiles comprising 91 PAH lung specimens and 49 normal lung specimens. Three mRNA expression datasets were combined, and differentially expressed genes (DEGs) were obtained. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and the protein-protein interaction (PPI) network of DEGs were performed using the STRING and DAVID databases, respectively. The diagnostic value of hub gene expression in PAH was also analyzed. Finally, the infiltration of immune cells in PAH was analyzed using the CIBERSORT algorithm. Total 182 DEGs (117 upregulated and 65 downregulated) were identified, and 15 hub genes were screened. These 15 hub genes were significantly associated with immune system functions such as myeloid leukocyte migration, neutrophil migration, cell chemotaxis, Toll-like receptor signaling pathway, and NF-κB signaling pathway. A 7-gene-based model was constructed and had a better diagnostic value in identifying PAH tissues compared with normal controls. The immune infiltration profiles of the PAH and normal control samples were significantly different. High proportions of resting NK cells, activated mast cells, monocytes, and neutrophils were found in PAH samples, while high proportions of resting T cells CD4 memory and Macrophages M1 cell were found in normal control samples. Functional enrichment of DEGs and immune infiltration analysis between PAH and normal control samples might help to understand the pathogenesis of PAH.

Background. Pulmonary arterial hypertension (PAH) is a disease or pathophysiological syndrome which has a low survival rate with abnormally elevated pulmonary artery pressure caused by known or unknown reasons. In addition, the pathogenesis of PAH is not fully understood. Therefore, it has become an urgent matter to search for clinical molecular markers of PAH, study the pathogenesis of PAH, and contribute to the development of new science-based PAH diagnosis and targeted treatment methods. Methods. In this study, the Gene Expression Omnibus (GEO) database was used to downloaded a microarray dataset about PAH, and the differentially expressed genes (DEGs) between PAH and normal control were screened out. Moreover, we performed the functional enrichment analyses and protein-protein interaction (PPI) network analyses of the DEGs. In addition, the prediction of miRNA and transcriptional factor (TF) of hub genes and construction miRNA-TF-hub gene network were performed. Besides, the ROC curve was used to evaluate the diagnostic value of hub genes. Finally, the potential drug targets for the 5 identified hub genes were screened out. Results. 69 DEGs were identified between PAH samples and normal samples. GO and KEGG pathway analyses revealed that these DEGs were mostly enriched in the inflammatory response and cytokine-cytokine receptor interaction, respectively. The miRNA-hub genes network was conducted subsequently with 131 miRNAs, 7 TFs, and 5 hub genes (CCL5, CXCL12, VCAM1, CXCR1, and SPP1) which screened out via constructing the PPI network. 17 drugs interacted with 5 hub genes were identified. Conclusions. Through bioinformatic analysis of microarray data sets, 5 hub genes (CCL5, CXCL12, VCAM1, CXCR1, and SPP1) were identified from DEGs between control samples and PAH samples. Studies showed that the five hub genes might play an important role in the development of PAH. These 5 hub genes might be potential biomarkers for diagnosis or targets for the treatment of PAH. In addition, our work also indicated that paying more attention on studies based on these 5 hub genes might help to understand the molecular mechanism of the development of PAH.

Background In December 2019, coronavirus disease 2019 (COVID-19) caused by a novel coronavirus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2; previously known as 2019-nCoV) emerged in Wuhan, China, and caused many infections and deaths. At present, there are no specific drugs for the etiology and treatment of COVID-19. A combination of traditional Chinese and western medicine is proposed to treat COVID-19, in which Huang Lian Jie Du decoction (HLJDD) is recommended for the treatment of COVID-19 in many provinces in China and has been widely used in the clinic. This study explored the potential targets of HLJDD in the treatment of COVID-19 based on network pharmacology. Methods First, the chemical composition and targets of HLJDD and COVID-19-related targets were obtained through the TCMSP, UniProt, GeneCards and OMIM databases. Second, HLJDD target and HLJDD-COVID-19 target networks were constructed via the STRING database and Cytoscape software. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the HLJDD-COVID-19 targets was applied via the DAVID database. Results Our study identified a total of 67 active ingredients of HLJDD and 204 targets of HLJDD. A total of 502 COVID-19-related targets were obtained, of which 47 were intersecting targets of HLJDD and COVID-19. A total of 179 GO terms and 77 KEGG terms, including the TNF signaling pathway, NF-κB signaling pathway and HIF-1 signaling pathway, were identified. Conclusion The present study explored the potential targets and signaling pathways of HLJDD during the treatment of COVID-19, which may provide a basis for the research and development of drugs for the treatment of COVID-19.

Background: Lung cancer is an intractable disease and the second leading cause of cancer-related deaths and morbidity in the world. This study conducted a bioinformatics analysis to identify critical genes associated with poor prognosis in non-small cell lung cancer (NSCLC).Methods: We downloaded three datasets (GSE33532, GSE27262, and GSE18842) from the gene expression omnibus (GEO), and used the GEO2R online tools to identify the differentially expressed genes (DEGs). We then used the Search Tool for Retrieval of Interacting Genes (STRING) database to establish a protein-protein interaction (PPI) network and used the Cytoscape software to perform a module analysis of the PPI network. A Kaplan-Meier plotter was used to perform the overall survival (OS) analysis, and the Gene Expression Profiling Interactive Analysis (GEPIA) database was used for expression level analysis of hub genes. Further, the UALCAN database was used to validate the relationship between the gene expression level of each hub gene and clinical characteristics.Results: We identified 254 DEGs, which were composed of 66 up-regulated genes and 188 downregulated genes. Out of these, five DEGs were identified as hub genes (CDC20, BUB1, CCNB2, CCNB1, UBE2C) by constructing a PPI network. The use of a Kaplan-Meier plotter to generate patient survival curves suggested a strong relationship between the five hub genes with worse OS. Validation of the above results using the GEPIA database showed that all the hub genes were highly expressed in NSCLC tissues.Using the UALACN data mining platform, we found that the five hub genes are correlated with tumor stage and the status of node metastasis in NSCLC patients. Conclusions:We identified five hub DEGs that might provide perspectives in the explorations of pathogenesis and treatments for NSCLC.