ObjectiveTo investigate the relation between disulfidptosis-related genes (DRGs) and prognosis or immunotherapy response of patients with pancreatic cancer (PC). MethodsThe transcriptome data, somatic mutation data, and corresponding clinical information of the patients with PC in The Cancer Genome Atlas (TCGA) were downloaded. The DRGs mutated in the PC were screened out from the 15 known DRGs. The DRGs subtypes were identified by consensus clustering algorithm, and then the relation between the identified DRGs subtypes and the prognosis of patients with PC, immune cell infiltration or functional enrichment pathway was analyzed. Further, a risk score was calculated according to the DRGs gene expression level, and the patients were categorized into high-risk and low-risk groups based on the mean value of the risk score. The risk score and overall survival of the patients with high-risk and low-risk were compared. Finally, the relation between the risk score and (or) tumor mutation burden (TMB) and the prognosis of patients with PC was assessed. ResultsThe transcriptome data and corresponding clinical information of the 177 patients with PC were downloaded from TCGA, including 161 patients with somatic mutation data. A total of 10 mutated DRGs were screened out. Two DRGs subtypes were identified, namely subtype A and subtype B. The overall survival of PC patients with subtype A was better than that of patients with subtype B (χ2=8.316, P=0.003). The abundance of immune cell infiltration in the PC patients with subtype A was higher and mainly enriched in the metabolic and conduction related pathways as compaired with the patients with subtype B. The mean risk score of 177 patients with PC was 1.921, including 157 cases in the high-risk group and 20 cases in the low-risk group. The risk score of patients with subtype B was higher than that of patients with subtype A (t=14.031, P<0.001). The overall survival of the low-risk group was better than that of the high-risk group (χ2=17.058, P<0.001), and the TMB value of the PC patients with high-risk was higher than that of the PC patients with low-risk (t=5.642, P=0.014). The mean TMB of 161 patients with somatic mutation data was 2.767, including 128 cases in the high-TMB group and 33 cases in the low-TMB group. The overall survival of patients in the high-TMB group was worse than that of patients in the low-TMB group (χ2=7.425, P=0.006). ConclusionDRGs are closely related to the prognosis and immunotherapy response of patients with PC, and targeted treatment of DRGs might potentially provide a new idea for the diagnosis and treatment of PC.
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.
Objective To investigate the relationship between the expression of mast cell expressed membrane protein 1 (MCEMP1) in gastric cancer and its relationship with prognosis and tumor immune infiltration. Methods Transcriptome expression profile data and clinical data information of gastric cancer and normal samples were downloaded from TCGA database, and differentially expressed genes in gastric cancer tumor microenvironment were extracted using R 4.0.5 software. Protein-protein interaction network of differentially expressed genes was constructed by using STRING online website, protein-protein interaction network and univariate Cox proportional hazards regression analysis were used for cross-tabulation analysis to obtain key genes. Kruskal-Wallis rank sum test was used to investigate the correlation between key genes and clinicopathological features. The possible signaling pathways involved in key genes were predicted by gene set enrichment analysis. We further analyzed the relationship between expression of key gene and the level of immune infiltration and immune molecules in gastric cancer by TISIDB online database and CIBERSORT algorithm. Results A total of 760 differentially expressed genes in gastric cancer were found and a key gene of MCEMP1 was derived from cross-tabulation analysis based on the results of protein-protein interaction network and univariate Cox proportional hazards regression analysis. Expression of MCEMP1 was significantly upregulated in gastric cancer tissues (P<0.001), and survival analysis showed that the overall survival rate of the group with high expression level of MCEMP1 was lower than that of low expression [HR=1.176, 95%CI (1.066, 1.297）, P=0.046]. Expression of MCEMP1 also correlated with age, T-stage, and clinical stage of gastric cancer (P<0.05) , and expression of MCEMP1 was significantly associated with a variety kinds of immune cells and expression of immune molecules (P<0.05). Conclusion MCEMP1 is a potential prognostic marker for gastric cancer and is associated with immune infiltration in gastric cancer.
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 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.
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.
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.
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 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.