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.
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 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.
ObjectiveTo analyze the expression of cold-induced RNA-binding protein (CIRBP) in lung adenocarcinoma and its clinical significance based on bioinformatics, in order to provide a new direction for the study of therapeutic targets for lung adenocarcinoma.MethodsThe CIRBP gene expression data and patient clinical information data in lung adenocarcinoma tissues and adjacent tissues were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The expression of CIRBP in lung adenocarcinoma was analyzed. Furthermore, its relationship with clinicopathological features and prognosis in patients with lung adenocarcinoma was analyzed. GO and KEGG enrichment analysis were carried out for the screened genes. The CIRBP protein interaction network was constructed by STRING, and the correlation analysis was carried out using the GEPIA online website.ResultsThe expression level of CIRBP gene in lung adenocarcinoma tissues was significantly lower than that in adjacent tissues (P<0.01), and its expression level was correlated with T stage and N stage in clinicopathological features. The prognosis of patients with high CIRBP expression in lung adenocarcinoma was significantly better than that with low CIRBP expression. Univariate and multivariate Cox regression analysis showed that CIRBP was an independent prognostic factor in patients with lung adenocarcinoma. GO functional annotation showed its enrichment in organelle fission, nuclear fission, chromosome separation, and DNA replication, etc. KEGG analysis showed that it was mainly involved in cell cycle and DNA replication. Protein interaction network and GEPIA online analysis showed that the expression level of CIRBP was negatively correlated with the expression level of cyclin B2.ConclusionCIRBP gene is down-regulated in lung adenocarcinoma tissues, and its expression level is closely related to patient prognosis. CIRBP gene may be a potential therapeutic target and prognostic marker for lung adenocarcinoma.
Objective A series of bioinformatics methods were used to identify ferroptosis related biomarkers in lupus nephritis (LN). Methods We retrieved sequencing data of GSE112943 from the GEO (Gene Expression Omnibus) database and screened LN differentially expressed genes. We searched for ferroptosis-related gene (FRG) through FerrDb database, and screened LN-FRG. We conducted enrichment analysis on the LN-FRGs using David online bioinformatics database and screened the core LN-FRG using cytoHubba. We used external data sets to verify the core LN-FRGs, constructed competing endogenous RNA networks, and conducted molecular docking analysis. Results A total of 37 LN-FRGs were selected through screening. These genes are mainly enriched in inflammation, immune regulation and ferroptosis related signaling pathways. Through the cytoHubba and external dataset validation, the key core LN-FRG of ATF3 (activating transcription factor 3) was ultimately identified, and its expression was significantly increased in LN (P<0.05). Molecular docking analysis showed that ATF3 was closely bound to SLC7A11 and NRF2, and may participate in the occurrence and development of LN through the microRNA-27-ATF3 regulation axis. Conclusion The pivotal gene ATF3 may participate in the inflammation and immune injury of LN through ferroptosis.
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.
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 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 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.
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.