ObjectiveTo explore the differentially expressed genes (DEGs) in venous leg ulcer (VLU) by bioinformatics, and further explore the molecular mechanism of the disease, predict early diagnostic markers and treatment targets.MethodsThe expression profiles of VLU were downloaded from the gene expression omnibus (GEO) database, the DEGs of VLU and inflammatory phase of normal skin healing were identified by R software and used to perform gene ontology (GO) and KEGG pathway enrichment analysis, obtaining the key genes of the pathway. We analyzed the proteins of protein interaction (PPI) network by STRING database and Cytoscape 3.2.1 software to obtain hub genes.ResultsA total of 409 DEGs were obtained, including 173 upregulted genes and 236 downregulted genes. The GO analysis showed that the upregulated DEGs mainly distributed in collagen-containing extracellular matrix (ECM), cornified envelope and collagen trimer, involved in biological processes such as skin development, keratinocyte differentiation and cornification, which mediated molecular functions such as ECM structural constituent, ECM structural constituent conferring tensile strength and integrin binding. The downregulated DEGs mainly distributed in tertiary granule, secretory granule membrane and tertiary granule membrane cornification, involved in biological processes such as response to chemokine, leukocyte migration and neutrophil chemotaxis, which mediated molecular functions such as chemokine activity, chemokine receptor binding and cytokine activity. KEGG pathway enrichment analysis results showed that the upregulated DEGs were mainly enriched in ECM-receptor interaction and protein digestion and absorption pathways, collagen type Ⅰ alpha1 chain (COL1A1), collagen type Ⅰ alpha2 chain (COL1A2), and collagen type Ⅵ alpha 6 chain (COL6A6) were the key genes of pathway; the downregulated DEGs were mainly enriched in Staphylococcus aureus infection, Toll-like receptor signaling pathway and leukocyte transendothelial migration pathways, interleukin (IL)-1β, C-X-C motif chemokine ligand 8 (CXCL8), IL-10, matrix metalloproteinase (MMP)1, and MMP9 were the key genes of pathway. The hub core genes of the PPI network were formyl peptide receptor (FPR)1, FPR2, IL-1β, IL-10, and CXCL8.ConclusionsThe results of this study indicate that the genes and signaling pathways involved in COL1A1, COL1A2, COL6A6, IL-1β, CXCL8, IL-10, MMP1, and MMP9 affect the healing of VLU. FPR1, FPR2, IL-1β, IL-10, and CXCL8 can be used as potential therapeutic targets.
ObjectiveTo understand the molecular mechanisms underlying the carcinogenesis and progression of papillary thyroid carcinoma (PTC), and provide candidate targets for diagnosis and treatment of PTC.MethodsTo identify the differentially expressed genes (DEGs) in the carcinogenesis and progression of PTC, the study was carried by analyzing the microarray datasets downloaded from gene expression omnibus (GEO) database, and making a series of studies including the protein-protein interaction network, KEGG and GO enrichment analyses of DEGs.ResultsA total of 339 DEGs and 10 hub genes were identified. While the expression of KIT was downregulated in the samples of PTC, the figures for FN1, CCND1, TIMP1, ICAM1, APOE, MET, RUNX2, KRT19 and SERPINA1 were upregulated. After the hypothesis test was corrected by multiple tests, the results showed that the changes of APOE [false discovery rate (FDR)=0.047 5] and KIT (FDR=0.042 0) gene expression had a certain impact on recurrence free survival (RFS) of PTC patients, which was statistically significant.ConclusionsSurvival analysis showed that FN1, ICAM1, APOE, MET, KRT19, KIT and SERPINA1 may be involved in the carcinogenesis or prognosis of PTC. However, further studies are needed to elucidate the biological function of these genes in PTC.
ObjectiveTo study the abnormal biological pathways of intrahepatic cholangiocarcinoma (ICC) from the transcriptomics level and identify genes associated with the prognosis of ICC.MethodsThe differentially expressed genes were screened by t test and fold change method, then KEGG functional enrichment analysis was performed on related genes. The STRING database was applied to construct protein interaction network and find the hub nodes of the network by calculating the degree, betweenness, and closeness of each node. Kaplan-Meier survival analysis was performed using log-rank test to identify prognostic genes related to ICC.ResultsAll of 1 134 differentially expressed genes were overlapped in 3 datasets, which were mainly involved in 15 pathways, including DNA replication, cell cycle, drug metabolism, RNA transport, etc. signaling pathways and amino acid synthesis. According to protein interaction network analysis, TAF1, GRB2, E2F4, HNF4A, MYC, and TP53 genes were hub nodes. As GRB2 and TP53 genes were also the death related genes of ICC, it was found that patients with lower GRB2 gene expression had a better overall survival than those with higher GRB2 gene expression (P=0.040 9), while patients with lower TP53 had a worse overall survival than those with higher TP53 gene expression (P=0.027 3), which were also verified in the TCGA database.ConclusionsThe abnormal cell metabolism is notably related to the tumorigenesis of ICC. TAF1, GRB2, E2F4, HNF4A, MYC, and TP53 are the key genes in the carcinogenesis and progression of ICC. Expressions of GRB2 and TP53 genes are associated with the prognosis of ICC.
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 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.
Objective To explore the role of cyclin B1 (CCNB1), cyclin B2 (CCNB2) and cyclin dependent kinase 1 (CDK1) in lung adenocarcinoma (LUAD) using bioinformatic data. Methods First, RNA expression data were downloaded from two datasets in Gene Expression Omnibus (GEO), and DESeq2 software was used to identify deferentially expressed genes (DEGs). Subsequent analyses were conducted based on the results of these DEGs: protein-protein interaction (PPI) network was constructed with STRING database; the modules in PPI network were analyzed by Molecular Complex Detection software, and the most significant modules were selected, the genes included in these modules were the hub genes; high-throughput RNA sequencing data from other databases were used to verify the expression of these hub genes to confirm whether they were DEGs; survival curve analyses of the confirmed DEGs were conducted to select genes that had significant influence on the survival of LUAD; the expression of these hub genes in different stages of LUAD were also analyzed. Then, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed for these selected hub genes using KOBAS database. MuTarget tool was used to analyze the correlations between the expression of these selected hub genes and gene mutation status in LUAD. The potential value of these hub genes in the treatment of LUAD was explored based on the drug information in GDSC database. Finally, immunohistochemical data from Human Protein Atlas (HPA) database were used to verify the expression of these hub genes in LUAD again. Results According to the expression data in GEO, 594 up-regulated genes and 651 down-regulated genes were identified (P<0.05), among which 30 hub genes were selected for subsequent analyses. The RNA high-throughput sequencing data of other databases verified that 18 genes were DEGs, among which 8 hub genes had significant impact on disease-free survival in LUAD (P<0.05). Moreover, the 8 genes were differentially expressed in different stages of LUAD, which were higher in the middle and late stage of LUAD. Among the 8 genes. CCNB1, CCNB2 and CDK1 were significantly enriched in the cell cycle pathway. The expression of CCNB1, CCNB2 and CDK1 in LUAD was closely related to the TP53 mutation status. In addition, CDK1 was associated with four drugs, revealing the potential value of CDK1 in the treatment of LUAD. Finally, immunohistochemical data from HPA database verified that CCNB1, CCNB2 and CDK1 were highly expressed in LUAD in the protein level. Conclusion Overexpression of CCNB1, CCNB2 and CDK1 are associated with poor prognosis of LUAD, indicating that the three genes may be prognostic biomarkers of LUAD and CDK1 is a potential therapeutic target for LUAD.
Objective To reveal the differences in gene expression levels between Th2-driven classical asthma (CA) and Th2-driven cough variant asthma (CVA) in order to investigate the pathogenesis of asthma further. Methods Clinical data were collected from asthmatic patients in the Department of Respiratory and Critical Care of Sichuan Provincial People's Hospital from June 1, 2018, to December 31, 2019. The healthy control (HC) group was healthy adults from the physical examination center. Gene expression of peripheral blood mononuclear cells (PBMCs) in the CA group, CVA group, and HC group was determined by full-length transcriptome sequencing. Differential genes were screened by GO, KEGG analysis, and protein-protein interaction (PPI) network analysis. The results of interaction network analysis were visualized by Cytoscape. Finally, the candidate genes were verified by real-time quantitative polymerase chain reaction (RT-PCR). ResultsA total of 31 patients with asthma were included in the study, including 20 patients in the CA group and 11 patients in the CVA group. According to serum total IgE > 60 IU/mL and fractional exhaled nitric oxide (FeNO) > 40 ppb as the screening condition, 9 cases of Th2-driven CA and 5 cases of Th2-driven CVA were screened for analysis. Gene expression analysis showed 300 differentially expressed genes between the Th2-driven CA group and the Th2-driven CVA group, among which 155 genes were up-regulated, and 145 were down-regulated. GO enrichment analysis showed that differential genes were mainly enriched in drug response, nitrogen compound biosynthesis, cytoplasmic matrix, protein binding, ATP binding, etc. KEGG pathway analysis showed that differential genes were mainly concentrated in 2-oxy-carboxylic acid metabolism and cytotoxic signaling pathways mediated by natural killer cells. PPI analysis revealed extensive protein interactions between different genes. Ten candidate genes were screened for RT-PCR verification and finally found that CLU, GZMB, PPBP, PRF1, PTGS1, and TMSB4X were significantly differentially expressed between the Th2-driven CA group and the Th2-driven CVA group. Conclusions Asthma's occurrence results from the interaction of many genes and pathways. CLU, GZMB, PPBP, PRF1, PTGS1, and TMSB4X genes may be essential in developing Th2-driven CVA to Th2-driven CA.