ObjectiveTo identify the core genes involved in the great saphenous varicose veins (GSVVs) through bioinformatics method. MethodsThe transcriptional data of GSVVs and normal great saphenous vein tissues (control tissues) were downloaded from the gene expression omnibus database. The single sample gene set enrichment analysis (ssGSEA) was used to calculate the Hallmark score. The weighted gene co-expression network analysis (WGCNA) combined with machine learning algorithms was used to screen the key genes relevant GSVVs. The protein-protein interaction (PPI) analysis was performed using the String database, and the receiver operating characteristic (ROC) curve was used to reflect the discrimination ability of the target genes for GSVVs. ResultsCompared with the control tissues, there were 548 up-regulated genes and 706 down-regulated genes in the GSVVs tissues, the Hallmark points of KRAS signaling and apical junction were down-regulated, while which of peroxisomes, coagulation, reactive oxygen species pathways, etc. were up-regulated in the GSVVs tissues. A total of 639 differentially expressed genes relevant GSVVs were obtained and 165 interaction relations between proteins encoded by 372 genes, and the top 10 genes with the highest betweeness values, ADAM10, APP, NCBP2, SP1, ASB6, ADCY4, HP, UBE2C, QSOX1, and CXCL1, were located at the center of the interaction relation. And the core genes were mainly related to copper ion homeostasis, neutrophil degranulation G protein coupled receptor signaling, response to oxidative stress, and regulation of amide metabolism processes. The SP1 and QSOX1 were both Hub genes. The expressions of the SP1 and QSOX1 in the GSVVs tissues were significantly up-regulated as compared with the control tissues. The areas under the ROC curves of SP1 and QSOX1 in distinguishing GSVVs tissues from normal tissues were 0.972 and 1.000, respectively. ConclusionsSP1 and QSOX1 are core genes in the occurrence and development of GSVVs. Regulation of SP1 or QSOX1 gene is expected to achieve precise treatment of GSVVs.
Objective To explore the aberrantly expressed genes in hepatocellular carcinoma (HCC) and their relationship with prognosis of HCC through bioinformatics analysis. Methods Five datasets related to HCC were selected from the GeneExpression Omnibus database to explore differentially expressed genes (DEGs), followed by further gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The co-upregulated genes CNIH4 and TOMM40 were selected to explore the differences in their expressions in HCC tissues and normal tissues, and to explore the relationship between their expressions and the 5-year survival of patients by using TCGA database. Tissues and paraneoplastic tissues of eight cases of HCC who underwent surgery at the Guangdong Second Provincial General Hospital were collected to verify the expression differences of CNIH4 and TOMM40L mRNA. Results A total of 25 up-regulated genes and 21 down-regulated genes were identified in this study. The results of GO analysis and KEGG analysis indicated that DEGs were mainly related to catabolism, cell division, DNA replication and repair. The results of TCGA database analysis showed that the expression of up-regulated genes CNIH4 mRNA and TOMM40L mRNA were up-regulated in HCC tissues as compared with normal tissues (P<0.05) and that the 5-year survival of patients in the high expression group was worse than that in the low expression group (P<0.05). The results of clinical samples showed that CNIH4 mRNA and TOMM40L mRNA were up-regulated in HCC tissues as compared with paraneoplastic tissues. Conclusion CNIH4 and TOMM40L genes are up-regulated in HCC tissues, and their high expressions are associated with poor prognosis, and may be potential biomarkers and prognostic indicators for HCC.
ObjectiveTo investigate differentially expressed genes (DEGs) and potential molecular mechanisms between hepatitis C-related hepatocellular carcinoma (HCV-HCC) and hepatitis B-related HCC (HBV-HCC). MethodsThe data of HCV-HCC and HBV-HCC gene expressions were downloaded and integrated from the public gene expression database, and the limma package was used to investigate the DEGs between the HCV-HCC and HBV-HCC samples. The gene set enrichment analysis (GSEA) was used to explore the differences in suppressed or activated gene sets between the HCV-HCC and HBV-HCC samples, and the MCODE was used to explore the key molecular modules, and then the potential biological processes and molecular pathways of the key molecular modules were analyzed. The effect of key genes on survival of the HCC patients was analyzed by the Kaplan-Meier-Plotter database.ResultsIn this study, 119 HBV-HCC samples and 163 HCV-HCC samples were obtained, and the 199 DEGs were screened out. Compared with HBV-HCC, the activated gene sets of HCV-HCC were mainly enriched in the gene sets of inflammation, complement, up-regulation of genes in response to interferon, up-regulation of genes in response to KRAS, genes regulated by the nuclear factor- κB-tumor necrosis factor pathway, and apoptosis. However, the cell cycle-related gene sets were obviously suppressed. Eight key molecular modules enriched by DEGs were found, which included 18 key genes (IFI27, DDX60, MX1, IRF9, OAS3, OAS1, RSAD2, GBP4, HERC6, ISG15, IFIT1, CMPK2, EPSTI1, IFI44, IFI44L, HERC5, IFITM1, CXCL10). GO analysis showed that the biological process was mainly concentrated in the body response related to virus infection, the molecular component was mainly in the host cells, and the molecular function was mainly enriched in the biological combination. KEGG analysis showed that the key genes were mainly involved in the molecular signaling pathway related to virus infection. The survival analysis showed that the 9 key genes (CXCL10, HERC6, DDX60, IFITM1, IFI27, GBP4, IFI44L, IFI44, MX1) were closely related to better prognosis of patients with HCC (HR<1, P<0.05). ConclusionsThere is an essential difference between HBV-HCC and HCV-HCC. Occurrence of HCV-HCC is mainly related to virus infection and immune response induced by the virus. Therefore, for HCV infection, active antiviral treatment is necessary for avoiding hepatitis turning into chronic viral infection and preventing or blocking HCV infection converting to HCC.
Objective To detect the expression and clinical significance of POLD1 gene in non-small cell lung cancer (NSCLC) via bioinformatics method. Methods The expression difference of POLD1 in NSCLC tissue and normal lung tissue was investigated by TIMER database. UALCAN database was used to further verify different expression of POLD1 as well as the relationship between POLD1 expression and clinicopathological characteristics of NSCLC. The correlation between POLD1 gene and prognosis of NSCLC patients was detected by GEPIA and TIMER database. cBioPortal database was used to analyze frequencies of POLD1 gene mutation. POLD1-related protein-protein interaction network was constructed by STRING database. The relationship between POLD1 and immune infiltration was based on TISIDB database. Results The expression of POLD1 gene in lung adenocarcinoma and lung squamous cell carcinoma was significantly higher than that in normal lung tissue. In lung adenocarcinoma, patients with lower POLD1 level showed better prognosis. 1.2% of lung adenocarcinoma patients and 1.8% of lung squamous cell carcinoma patients carried mutated POLD1 gene, mainly missense mutations. POLD1 may interact with POLD2, POLD3, POLD4, POLE, RPA1, PCNA, MSH6, MSH2 and FEN1. The biological processes include DNA replication, mismatch repair, etc. Besides, the expression of POLD1 in NSCLC was correlated with the number of different immune cells. Conclusions The POLD1 gene is highly expressed in NSCLC patients, and negatively related with survival prognosis in patients of lung adenocarcinoma. POLD1 gene may be a potential diagnostic target and prognostic marker in NSCLC.
ObjectiveTo explore the mechanism of DDX46 regulation of esophageal squamous cell carcinoma.MethodsPicture signals of fluorescence in gene array were scanned and differential expression of gene in two groups (a DDX46-shRNA-LV group and a control-LV group) were compared by GCOSvL.4 software. These differential expressed genes were analyzed by bioinformatics methods finally, and validated by quantitative real time polymerase chain reaction (qRT-PCR) analysis.ResultsAccording to the screening criteria of fold change ≥2 and P<0.05, 1 006 genes were differentially expressed after DDX46 knockdown, including 362 up-regulated and 644 down-regulated genes. Bioinformatics analysis and gene co-expression network building identified that these differentially expressed genes were mainly involved in cell cycle, proliferation, apoptosis, adhesion, energy metabolism, immune response, etc. Phosphatidylinositol 3-kinase (PI3K) was the key molecule in the network. The results of RT-qPCR were completely consistent with the results of gene microarra.ConclusionBioinformatics can effectively exploit the microarray data of esophageal squamous cell carcinoma after DDX46 knockdown, which provides a valuable clue for further exploration of DDX46 tumorigenesis mechanism and helps to find potential drug therapy.
Objective To investigate the relationship between miR-3187-5p in peripheral blood and pericardial drainage after coronary artery bypass grafting (CABG) and postoperative atrial fibrillation (POAF). Methods Patients who underwent CABG in the Heart Center of Beijing Chao-Yang Hospital from March to May 2022 were enrolled. Peripheral blood and pericardial drainage were collected at 0 h after surgery (immediate time for patients to return to ICU from operating room) to detect miR-3187-5p, and perioperative confounding factors were also collected. The miR-3187-5p was measured by quantitative real-time PCR and its regulated target genes were analyzed by bioinformatics. Results A total of 15 patients were enrolled, including 9 males and 6 females with an average age of 65.6±8.2 years. The incidence rate of POAF was 40.0%. miR-3187-5p in pericardial drainage at 0 h after surgery was an independent predictor for POAF. A total of 1 642 target genes of miR-3187-5p were predicted. GO function enrichment analysis and KEGG signal pathway enrichment analysis showed that target genes of miR-3187-5p were enriched in TGF-β, MAPK, Wnt and other classical collagen metabolic signal pathways, which might activate collagen metabolism by negatively regulating SMAD6 and other inhibitors of the pathways. Conclusion This study is the first to find that miR-3187-5p in pericardial drainage at 0 h after surgery is a potential, novel, and predictive factor for POAF, which may be related to the regulation of myocardial fibrosis signal pathways like TGF-β, MAPK and Wnt pathways, promoting the early collagen metabolism imbalance after CABG, increasing the collagen deposition in the atrium, and then promoting the early structural reconstruction after CABG and leading to the occurrence of POAF. The result provides a research basis for the accurate prediction and prevention of clinical POAF.
Objective To explore the key genes, pathways and immune cell infiltration of bicuspid aortic valve (BAV) with ascending aortic dilation by bioinformatics analysis. Methods The data set GSE83675 was downloaded from the Gene Expression Omnibus database (up to May 12th, 2022). Differentially expressed genes (DEGs) were analyzed and gene set enrichment analysis (GSEA) was conducted using R language. STRING database and Cytoscape software were used to construct protein-protein interaction (PPI) network and identify hub genes. The proportion of immune cells infiltration was calculated by CIBERSORT deconvolution algorithm. Results There were 199 DEGs identified, including 19 up-regulated DEGs and 180 down-regulated DEGs. GSEA showed that the main enrichment pathways were cytokine-cytokine receptor interaction, pathways in cancer, regulation of actin cytoskeleton, chemokine signaling pathway and mitogen-activated protein kinase signaling pathway. Ten hub genes (EGFR, RIMS3, DLGAP2, RAPH1, CCNB3, CD3E, PIK3R5, TP73, PAK3, and AGAP2) were identified in PPI network. CIBERSORT analysis showed that activated natural killer cells were significantly higher in dilated aorta with BAV. Conclusions These identified key genes and pathways provide new insights into BAV aortopathy. Activated natural killer cells may participate in the dilation of ascending aorta with BAV.
ObjectiveAlthough evidence links idiopathic pulmonary fibrosis (IPF) and diabetes mellitus (DM), the exact underlying common mechanism of its occurrence is unclear. This study aims to explore further the molecular mechanism between these two diseases. MethodsThe microarray data of idiopathic pulmonary fibrosis and diabetes mellitus in the Gene Expression Omnibus (GEO) database were downloaded. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to identify co-expression genes related to idiopathic pulmonary fibrosis and diabetes mellitus. Subsequently, differentially expressed genes (DEGs) analysis and three public databases were employed to analyze and screen the gene targets related to idiopathic pulmonary fibrosis and diabetes mellitus. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Metascape. In addition, common microRNAs (miRNAs), common in idiopathic pulmonary fibrosis and diabetes mellitus, were obtained from the Human microRNA Disease Database (HMDD), and their target genes were predicted by miRTarbase. Finally, we constructed a common miRNAs-mRNAs network by using the overlapping genes of the target gene and the shared gene. ResultsThe results of common gene analysis suggested that remodeling of the extracellular matrix might be a key factor in the interconnection of DM and IPF. Finally, hub genes (MMP1, IL1R1, SPP1) were further screened. miRNA-gene network suggested that has-let-19a-3p may play a key role in the common molecular mechanism between IPF and DM. ConclusionsThis study provides new insights into the potential pathogenic mechanisms between idiopathic pulmonary fibrosis and diabetes mellitus. These common pathways and hub genes may provide new ideas for further experimental studies.
ObjectiveTo explore the mechanism of paucigranulocytic asthma and to find therapeutic target for paucigranulocytic asthma.MethodsGSE143303 data and platform information were downloaded from GEO. Gene Set Enrichment Analysis were performed to construct positive and negative gene-gene interaction network correlation with paucigranulocytic asthma. Differential expression analysis, pathway commonality analysis were performed with R language.ResultsGSE143303 data set contained 47 endobronchial biopsies from adult (16 cases of paucigranulocytic asthma, 13 cases of healthy control). Compared with control group, the paucigranulocytic asthma group had 115 differential genes set (37 positive and 78 negative). The results of pathway commonality analysis showed that the crosslink existed within the negative gene-gene interaction network correlation with paucigranulocytic asthma. Among these, most of the genes belonged to the protein HLA gene family. Differential expression analysis show that HLA-DQB1, HLA-DRB5 were differential genes and TNFRSF13B was significantly downregulated genes in the intersect genes.ConclusionTNFRSF13B, HLA-DQB1, HLA-DRB5 and regulatory networks associated with them are the crucial factors contributing to paucigranulocytic asthma.
ObjectiveTo explore the mechanism of dihydrouridine synthase 4-like (DUS4L) on the development of lung adenocarcinoma (LUAD).MethodsThe RNA-seq expression data of LUAD was downloaded from The Cancer Genome Atlas (TCGA), and the relationship between its clinical pathological characteristics and DUS4L mRNA expression was evaluated. The effect of DUS4L knockdown on the proliferation of A549 cells was detected by EDU proliferation assay. The gene expression profile of lung adenocarcinoma A549 cells in the DUS4L knockdown group (KD group) and control group (NC group) was detected by transcriptome sequencing technique. The differential genes were screened by DESeq2. ClusterProfiler was used to perform GO functional enrichment analysis of differential genes.ResultsThe expression of DUS4L mRNA in LUAD tissues was higher than that in normal tissues, and the up-regulation of DUS4L was related to the clinical pathological characteristics of LUAD patients. EDU proliferation assay suggested that knocking down DUS4L could inhibit the proliferation of A549 cells. A total of 456 differential genes were screened, including 289 up-regulated genes and 167 down-regulated genes [|log2(fold change)|>1 and Padj<0.05]. STC2 and TRIB3 were significantly down-regulated (P<0.05). Differential genes were mainly involved in the production of interleukin-8, angiogenesis, vascular endothelial cell proliferation and other biological pathways.ConclusionDUS4L can widely regulate the gene expression of LUAD cells, which provides a new idea for further studying the function and role of DUS4L in the occurrence and development of LUAD and finding new therapeutic targets for LUAD.