The purpose of this paper is to present the research on the molecular biological characteristics of proto-oncogene pim-2 and to analyze the related mechanism. Proto-oncogene pim-2 was studied and analyzed by the bioinformatics method and technology. With an online server, the chromosomal localization of pim-2 gene was analyzed, and the exon, open reading frame, CpG island and miRNAs complementary fragments and the like were predicted. With bioinformatics software, the physicochemical property of transcription protein of proto-oncogene pim-2 and various modification sites of protein sequence, such as ubiquitination and glycosylation, were predicted, the antigenic index was calculated, and the spatial structural was modeled. The research findings showed that the proto-oncogene pim-2 comprised six exons, the CDS (coding sequence) transcribed a section of peptide chain including 311 amino acids, a gene promoter has a CpG island, and the 3'UTR region contains an miRNA gene. The molecular weight of the Pim-2 protein was 34, 188.47, the isoelectric point was 5.78, the instability index was 45.87, and the extinction coefficient was 279nm. A plurality of covalent modification sites, two ubiquitination sites, four glycosylation sites, an SUMO sumoylation site, a nitrosation site, two palmitoylation sites and sixteen regions with higher antigenic index were distributed in the protein sequence. This research showed that the related regions and modification sites distributed on the sequence of proto-oncogene pim-2 were closely related to the carcinogenic effect thereof.
To screen new tuberculosis diagnostic antigens and vaccine candidates, we predicted the epitopes of Mycobacterium tuberculosis latent infection-associated protein Rv2004c by means of bioinformatics. The homology between Rv2004c protein and human protein sequences was analyzed with BLAST method. The second structures, hydrophilicity, antigenicity, flexibility and surface probability of the protein were analyzed to predict B cell epitopes and T cell epitopes by Protean software of DNAStar software package. The Th epitopes were predicted by RANKPEP and SYFPEITHI supermotif method, the CTL epitopes were predicted by means of combination analyses of SYFPEITHI supermotif method, BIMAS quantitative motif method and NetCTL prediction method. The peptide sequences with higher scores were chosen as the candidate epitopes. Blast analysis showed that Rv2004c protein had low homology with human protein. This protein had abundant secondary structures through analysis of DNAStar software, the peptide segments with high index of hydrophilicity, antigenicity, surface probability and flexibility were widely distributed and were consistent with segments having beta turn or irregular coil. Ten candidates of B cell epitopes were predicted. The Th epitopes of Rv2004c protein were located after the 200th amino acid. Of 37 Th cell epitopes predicted, there were more epitopes of HLA-DRB1*0401 and HLA-DRB1*0701 phenotypes, and the MHC restrictive types of some Th cell epitopes exist cross overlap. Of 10 CTL epitopes predicted, there were more number and higher score of HLA-A2 restricted epitopes. Therefore Mycobacterium tuberculosis Rv2004c protein is a protein antigen with T cell and B cell epitopes, and is expected to be a new target protein candidate for tuberculosis diagnosis and vaccine.
ObjectiveTo observe the expressions of miR-143-3p in gastric cancer cells and gastric carcinoma tissues with its clinical significance, and to analyze the target genes with enriched pathway by using bioinformatics methods.MethodsThe expressions of miR-143-3p in different differentiation gastric cancer cells and normal gastric mucosa cell line, and the expressions in gastric cancer tissues and adjacent tissues were detected by real-time fluorescent quantitative PCR. In addition, OncomiR and YM500 databases were used to analyze the expression of miR-143-3p in gastric cancer tissues compared with adjacent tissues. Furthermore, the targets of miR-143-3p were predicted by using the software of miRecords website database, and at least three software-supported target genes were chosen to analyze the enriched the signal pathways in which the target gene was involved with DAVID 6.7 software.ResultsThe expressions of miR-143-3p in the different differentiation degree of gastric cancer cells compared with normal gastric mucosa cell line were downregulated (P<0.001), and the expression of miR-143-3p in gastric cancer tissues compared with adjacent tissues was also downregulated (downregulated in 36 cases, upregulated in 18 cases, and no alteration in 4 cases). The expression of miR-143-3p in gastric cancer tissues was associated with lymph node metastasis and invasion depth (P<0.05). Bioinformatics analysis results showed that the target genes of miR-143-3p were enriched in 38 signaling pathways associated with cancer.ConclusionMiR-143-3p is a down-regulated molecular marker in gastric cancer and a potentially clinically related tumor suppressor gene, which may be involved in the cancerous phenotype in carcinogenesis and development of gastric cancer.
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.
ObjectiveTo analyze the expression and clinical significance of cyclin-dependent kinase 1 (CDK1) in lung adenocarcinoma by bioinformatics.MethodsBased on the gene expression data of lung adenocarcinoma patients in The Cancer Genome Atlas (TCGA), the differential expression of CDK1 in lung adenocarcinoma tissues and normal lung tissues was analyzed. The expression of CDK1 gene in lung adenocarcinoma was analyzed by UALCAN at different angles. Survival analysis of different levels of CDK1 gene expression in lung adenocarcinoma was performed using Kaplan-Meier Plotter. Correlation Cox analysis of CDK1 expression and overall survival was based on clinical data of lung adenocarcinoma in TCGA. Gene set enrichment analysis was performed on gene sequences related to CDK1 expression in clinical cases. The protein interaction network of CDK1 from Homo sapiens was obtained by STRING. CDK1-related gene proteins were obtained and analyzed by the web server Gene Expression Profiling Interactive Analysis (GEPIA).ResultsBased on the analysis of TCGA gene expression data, CDK1 expression in lung adenocarcinoma was higher than that in normal lung tissues. UALCAN analysis showed that high CDK1 expression may be associated with smoking. Survival analysis indicated that when CDK1 gene was highly expressed, patients with lung adenocarcinoma had a poor prognosis. Univariate and multivariate Cox regression analysis of CDK1 expression and overall survival showed that high CDK1 expression was an independent risk factor for survival of patients with lung adenocarcinoma. Gene set enrichment analysis revealed that high CDK1 expression was closely related to DNA replication, cell cycle, cancer pathway and p53 signaling pathway.ConclusionCDK1 may be a potential molecular marker for prognosis of lung adenocarcinoma. In addition, CDK1 regulation may play an important role in DNA replication, cell cycle, cancer pathway and p53 signaling pathway in lung adenocarcinoma.
ObjectiveTo screen differential expression of genes in hepatocellular carcinoma (HCC) by bioinformatics method, and analyze its clinical significance and its possible molecular mechanism in HCC.MethodsThe HCC gene expression profile GSE101728 was picked out to analyze the differential expression genes. The hub genes were identified by STRING and Cytoscape. GO and KEGG analysis were carried out by using DAVID and PPI network were constructed by STRING. The relationship among the hub genes were analyzed by using GEPIA.ResultsA total of 1 082 DEGs were captured (354 up-regulated genes and 728 down-regulated genes). Meantime, 10 hub genes ［cyclin dependent kinase 1 (CDK1), cyclin B1 (CCNB1), cyclin A2 (CCNA2), polo-like kinase 1 (PLK1), laser kinase B (AURKB), cyclin of cell division 20 (CDC20), centromere protein A (CENPA), mitotic arrest defective protein 2 (MAD2L1), cyclin B2 (CCNB2), and kinesin family 2C (KIF2C)］ were identified, and its expression and clinical significance were verified by GEPIA. GO and KEGG analysis showed 10 hub genes were mainly enriched in cell division and cell cycle. Expressions of AURKB, CCNB1, and MAD2L1 were obviously positively correlated (P<0.05).ConclusionThis study analyzes the hub genes in the development of HCC by bioinformatics methods and provides valuable information for further research on the mechanism of HCC.
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 explore the clinical significance and possible potential mechanism of hepatocellular carcinoma through the screening of key genes in hepatocellular carcinoma.MethodsHepatocellular carcinoma gene chip was obtained from GEO database, differentially expressed genes (DEGs) were screened by GEO2R online tools and Venn map, GO analysis and KEGG pathway analysis were performed in DAVID database, core genes were screened by STRING and Cytscape software, core genes were analyzed in Kaplan-Meier Plotter for survival analysis, and expression was analyzed by GEPIA database. The core genes related to prognosis and highly expressed in hepatocellular carcinoma were analyzed by Metascape online tool for function and pathway enrichment analysis. Finally, the key genes were verified in hepatocellular carcinoma and paracancerous tissues.ResultsA total of 94 DEGs were screened from three gene chips GSE14520, GSE60502, and GSE102079, obtained from GEO. After the selected DEGs was analyzed by GO function analysis, KEGG pathway enrichment analysis, STRING and Cytscape software by DAVID, 19 core DEGs were screened. After 19 core DEGs were analyzed by Kaplan-Meier Plotter website, 9 genes [ribonucleotide reductase M2 (RRM2), polycomb repressive complex 1 (PRC1), topoisomerase Ⅱ alpha (TOP2A), aurora kinase A (AURKA), nucleolar spindle-associated protein 1 (NUSAP1), Rac-GTPase activating protein 1 (RACGAP1), abnormal spindle-like microcephaly-associated (ASPM), cyclin dependent kinase 1 (CDK1) and GINS complex subunit 1 (GINS1)] were found to be associated with the prognosis of hepatocellular carcinoma. The expressions of these 9 genes were analyzed by GEPIA, and the results showed that all 9 genes were highly expressed in hepatocellular carcinoma tissues. The functions and pathways of 9 highly expressed genes were analyzed by metascape website. Finally, RRM2 was selected for verification in hepatocellular carcinoma tissues and adjacent tissues, and it was found that the staining score of RRM2 in hepatocellular carcinoma tissues was (10.9±1.5) points, which was significantly higher than its staining score in adjacent tissues [(4.5±1.2) points], P<0.001.ConclusionThe nine genes identified by bioinformatics analysis may be the key genes in the occurrence and development of hepatocellular carcinoma, which can provide reference for further study on the pathogenesis, diagnosis and treatment of hepatocellular carcinoma.
ObjectiveTo explore the significance of mesenchymal epithelial transition factor (MET) as a clinical prognostic evaluation index for patients with pancreatic cancer based on bioinformatics analysis.MethodsThe GSE28735 and GSE62452 gene chips from GEO database were downloaded and the difference of MET gene expression between cancer and adjacent cancerous tissues were analyzed by bioinformatics. We downloaded pancreatic cancer gene chip from TCGA database to analyze the correlation between MET gene expression and clinicopathological features of pancreatic cancer patients and prognosis risk. Finally, the possible molecular mechanism of MET involved in pancreatic carcinogenesis was analyzed by GO and KEGG enrichment analysis.ResultsThe expression level of MET gene in pancreatic cancer tissues was significantly higher than that in adjacent cancerous tissues (P<0.001). The overall survival and disease-free survival of pancreatic cancer patients in the high MET gene expression group were lower than those in the low expression group (P<0.001). The expression level of MET gene was related to the age of pancreatic cancer patients, T stage, and histological grading of tumors (P<0.05), and high MET gene expression, age >65 years, and N1 stage were independent risk factors affecting the prognosis of pancreatic cancer patients. KEGG enrichment analysis showed that MET was mainly related to PI3K/AKT signaling pathway, FAK signaling pathway, and cancer transcription dysregulation and so on.ConclusionMET may be a valuable tumor marker for pancreatic cancer and can predict the poor prognosis of patients with pancreatic cancer.
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.