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Objective To explore the expression, correlation with clinicopathologic parameters, and clinical significance of MIS18 binding protein 1 (MIS18BP1) in bladder cancer. Methods TCGA and GEO databases were used to analyze the mRNA expression of MIS18BP1 in tumors and controls, and the results were verified via qRT-PCR. UALCAN online database was utilized in the analysis of the expression of MIS18BP1 and its correlation with clinicopathological parameters and the degree of immune cell infiltration. Immunohistochemistry was employed to analyze the expression of MIS18BP1 in bladder cancer and its relationship with clinicopathological features. The ROC curve was applied to evaluate the diagnostic value of MIS18BP1 mRNA in bladder cancer. Results Bioinformatics analysis and qRT-PCR results revealed the increased expression of MIS18BP1 mRNA in bladder cancer compared with that in the control group (P<0.05). Immunohistochemistry unveiled the significantly high positive rate of MIS18BP1 protein in bladder cancer (P<0.05) and its correlation with the clinical stage of tumors, depth of invasion, and lymph node metastasis (P<0.05). The immune infiltration analysis showed the association of MIS18BP1 with immune cell infiltration in bladder cancer. Conclusion The increased expression level of MIS18BP1 gene and protein in bladder cancer may regulate the development of bladder cancer by influencing immune cell infiltration.
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【Objective】 To screen the differentially expressed immune genes between responders （Rs） and non-responders （NRs） in chronic hepatitis B patients receiving interferon alpha （IFN-α） treatment and to explore the molecular basis of IFN-α treatment failure. 【Methods】 The gene expression profile GSE27555 which contained 6 Rs and 7 NRs was obtained from the Gene Expression Omnibus （GEO） database； then differentially expressed genes between liver tissues of Rs and NRs were selected by the R software. The iconic immune gene set consisting of 1793 genes was downloaded from the immunology database and analysis portal （ImmPort）. The immune genes were extracted from the differentially expressed genes to obtain the differentially expressed immune genes. Subsequently， Gene Ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analyses of the differentially expressed immune genes were performed by the R software. Protein-protein interaction （PPI） network of the differentially expressed immune genes was constructed using the STRING online tool. The plugin CytoHubba of the Cytoscape software was applied to identify the top 10 genes by using Degree， MCC， MNC， and Closeness algorithms； then the intersection was taken to obtain the hub genes. 【Results】 A total of 88 differentially expressed immune genes， consisting of 13 upregulated and 75 downregulated genes， were identified between Rs and NRs. GO analysis showed that the differentially expressed immune genes were significantly enriched in T cell activation， cell chemotaxis， regulation of cell-cell adhesion， antigen processing and presentation. KEGG pathway analysis suggested that the differentially expressed immune genes were significantly enriched in cytokine-cytokine receptor interactions， Th cell differentiation， antigen processing and presentation， interactions between viral proteins and cytokines and cytokine receptors， chemokine signaling pathways， T cell receptor signaling pathway， IL-17 signaling pathway， natural killer cell-mediated cytotoxicity， Toll-like receptor signaling pathway， and other immune response signaling pathways. The top 7 hub genes， identified by the plugin cytoHubba of the Cytoscape software by using Degree， MCC， MNC and Closeness algorithms， were CD8A， IFNG， CCL2， CCL5， CXCL10， CCL4， and FCGR3A. 【Conclusion】 This study made a comprehensive analysis of the differentially expressed immune genes and signal pathways between Rs and NRs by bioinformatics， and identified 7 Hub genes related to the ineffectiveness of IFN-α treatment in CHB patients. These hub genes may serve as potential biomarkers for predicting the response of IFN-α treatment in CHB patients.
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Objective:To analyze the data of ultra-high dose rate (FLASH) radiotherapy in GEO (Gene Expression Omnibus) database by bioinformatics method, in order to find the hub genes involved in flash radiotherapy induced acute T-lymphoblastic leukemia.Methods:The gene expression profiles of malignant tumors receiving FLASH radiotherapy were downloaded from GEO database. The R software was used to screen the differential expressed genes (DEGs) and analyze their biological functions and signal pathways. The protein-protein interaction (PPI) network of DEGs was analyzed by online tool of STRING, and Hub genes were screened by Cytoscape plug-in. The expressions of screened Hub genes in acute T lymphoblastic leukemia were identified with TCGA (The Cancer Genome Atlas) and GTEx (Genotype-Tissue Expression) database.Results:Based on the analysis of GSE100718 microarray dataset of GEO database, a total of 12 800 genes were found to be associated with radiosensitivity of acute T lymphoblastic leukemia, of which 61 significantly altered DEGs were selected for further analysis. It was found that these genes were involved in the biological processes of metabolism, stress response, and immune response through the pathways of oxidative phosphorylation, unfolded protein response, fatty acid metabolism, and so on. PPI analysis indicated that HSPA5 and SCD belonged to the Hub genes involved in the regulation of FLASH radiosensitivity, and they were significantly highly expressed in acute T lymphoblastic leukemia combined with TRD/LMO2-fusion gene.Conclusions:Through bioinformatics analysis, the Hub genes involved in regulating the sensitivity of FLASH radiotherapy and conventional radiotherapy can be effectively screened, and thus the gene expression profiles can be used to guide the stratification of cancer patients to achieve a precise radiotherapy.
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【Objective】 To make bioinformatics analysis of inflammatory cardiomyopathy so as to screen out hub genes related to etiology and therapeutic targets. 【Methods】 Differential expression analysis of inflammatory cardiomyopathy gene chip data from Gene Expression Omnibus （GEO） Database was carried out via GEO2R tool. Protein-protein interaction（PPI）network and hub genes identification were realized by String database and CytoHubba. GO and KEGG enrichment analysis for functional annotation and pathway analysis of hub genes were conducted by R language. Web-based enrichment analysis platform Enrichr and Drug Signatures database were applied to screen out candidate drugs targeting hub genes for inflammatory cardiomyopathy. 【Results】 The 149 DEGs were statistically significant， among which 44 were upregulated and 105 were downregulated. To identify hub genes， PPI network consisting of 37 nodes and 116 edges was constructed， and 16 hub genes were NDUFB7， POLR2L， NDUFS7， UQCR11， NDUFA13， NDUFA2， PHPT1， NDUFB10， UBA52， ATP5D， NDUFA3， COX6B1， POLR2J， COX4I2， AURKAIP1 and MRPL41. Hub genes were enriched to 113 different GO terms， and the most significant terms were mitochondrial ATP synthesis coupled electron transport， respiratory electron transport chain， oxidative phosphorylation， respiratory chain， mitochondrial inner membrane， NADH dehydrogenase activity and oxidoreductase activity. DEGs were enriched to 13 different signal pathways， including oxidative phosphorylation， non-alcoholic fatty liver disease， diabetic cardiomyopathy， and cardiac muscle contraction. We screened out candidate drugs targeting hub genes， namely， metformin hydrochloride， clindamycin， and hydralazine. 【Conclusion】 Hub genes screened out by decoding the expression profiles are convolved in the etiology and mechanism of inflammatory cardiomyopathy， which might serve as latent therapeutic targets and benefit patients with inflammatory cardiomyopathy.
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@#[Abstract] Objective: Bioinformatics combined with Gene Expression Omnibus (GEO) was used to screen key genes involved in the development of gastric cancer in order to obtain molecular markers for diagnosis, target selection and prognosis prediction of gastric cancer. Methods: The chip data sets related to gastric cancer (GC) from the GEO database were downloaded, and differentially expressed genes (DEG) were screened. Functional enrichment analysis on DEG was performed, and protein-protein interaction network (PPI) was constructed to screen key genes. Then, co-expression networks were further constructed, and survival curves were drawn and hierarchical clustering analysis was performed. Results: A total of 261 GC-related DEGs were selected, and 14 key genes were obtained through analysis, which were PLOD1, PLOD3, COL1A1, COL1A2, COL2A1, COL3A1, COL4A1, COL4A2, COL8A1, COL12A1, COL15A1, ITGA2, LUM and SERPINH1. Key genes are mainly involved in biological processes such as generation of collagen fiber tissues, extracellular matrix tissues, extracellular structure tissues, skin morphogenesis, collagen biosynthesis and vascular development. Survival curve analysis showed that the change in the expression of COL3A1 (P=0.0241) significantly reduced the overall survival rate of patients with gastric cancer; the change in the expression of ITGA2 (P=0.0679) also showed a correlation with the reduction of disease-free survival in gastric cancer patients. Compared with normal gastric tissues, hierarchical cluster analysis showed that the expressions of genes PLOD1, PLOD3, COL3A1, ITGA2, COL1A2, COL1A1, COL4A1, LUM, COL12A1, SERPINH1 and COL8A1 in GC tissues were up-regulated. Conclusion: The key genes obtained after screening can be used as potential molecular markers for early diagnosis, treatment target selection and prognosis judgment of gastric cancer, which provide reference for subsequent research.
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ObjectiveMicroRNAs (miRNA) play an important role in the development and regression of osteoporosis. This study aims to screen for miRNAs and genes closely related to osteoporosis, and to complete the construction of a miRNA-mRNA regulatory network of osteoporosis.MethodsThe gene chip expression profile of osteoporosis was obtained through the GEO database, and the differentially expressed genes (DEGs) and miRNAs were analyzed and screened via GEO2R. We used volcanic maps to display differential genes and miRNAs, and completed the GO and KEGG pathway analysis through the David online database. The String online database is used to complete PPI protein network analysis. The TargetScan, miRTarBase and miRDB were used to predict the targeted genes. Finally, the PPI and miRNA-mRNA regulatory networks were visualized by Cytoscape software.ResultsWe obtained 15 differential miRNAs and 174 differentially expressed genes through screening. The GO enrichment analysis mainly focused on drug response, angiogenesis, ion transport, regulation of small GTPase mediated signal ransduction, adaptive immune response, etc. NF-κB signaling pathway and HIF-1 signal were obtained through KEGG enrichment analysis. We obtained 10 hub genes through the cytohubba plug-in. We also obtained 3065 targeted genes by processing of seven miRNAs, and then intersected them with 174 DEGs to obtain 44 intersection genes. Finally, we successfully constructed a regulation map of miRNA-mRNA network. The miR-194-5p is significantly up-regulated in osteoporosis.ConclusionThe miR-194-5p might play an important role in osteoporosis by regulating its target gene CDH2, which provides candidate targets for the diagnosis and treatment of osteoporosis.
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@#[Abstract] Objective: To screen the key genes associated with esophageal adenocarcinoma by using TCGA and GEO databases, and to analyze their biological functions, relevant signaling pathways and clinical significance. Methods: The esophageal adenocarcinoma data downloaded from TCGA database and GSE92396 microarray data from GEO database were integrated. The analysis of differentially expressed genes (DEGs) were performed by using DEseq2 and Limma packages of R software to obtain the co-differentially expressed genes, which were then chosen for the GO function enrichment analysis and KEGG pathway analysis with clusterProfiler package of R software. The key node genes that regulate the protein expressions in esophageal adenocarcinoma were screened out by protein-protein interaction (PPI) network analysis using the string website and Cytoscape 3.7.2 software. The correlation between key node genes and the survival of patients was further analyzed by combining with TCGA database. Results: By analyzing the chip data of 90 cases of adenocarcinoma tissues and 18 cases of normal esophageal tissues from databases, a total of 521 co-differentially expressed genes were obtained, including 356 upregulated genes and 165 downregulated genes. These genes were closely related to the metabolic-associated functions mainly involving epidermis development, epidermal cell differentiation and signaling pathways involving cytokine-cytokine receptor interaction, etc. The PPI network analysis revealed 15 key node genes. The survival time for patients with low CXCL8 and CCL20 expression was significantly longer compared with the patients with high expression level (median survival: 32.4 vs 19.7 months, P<0.05; 32.4 vs 13.9 months, P<0.05). Conclusion: These results show that CXCL8 and CCL20 may play an important role in the occurrence, development and prognosis of esophageal adenocarcinoma, and may be used as potential indicators to judge the prognosis of patients.
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BACKGROUND: Fat infiltration is a key factor in the failure of rotator cuff repair. However, the pathological mechanism of fatty infiltration after rotator cuff injury is unclear. OBJECTIVE: To explore the differences in the expression of key genes after rotator cuff injury, to determine their functions and mechanism pathways, and to provide a theoretical basis for the pathological mechanism of fatty infiltration after rotator cuff injury. METHODS: GSE93661 was obtained through GEO database to screen differentially expressed genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to analyze the underlying mechanism of fatty infiltration. The protein-protein interaction network was constructed to obtain the pivot genes and analyze the potential pathogenic targets. RESULTS AND CONCLUSION: A total of 471 differentially expressed genes were identified. GO and KEGG analysis showed that neuroactive ligand-receptor interactions and cell adhesion molecular pathways were potential mechanisms of fat infiltration in rotator cuff tears. Leukotriene B4 receptor, as a pivot gene in the protein-protein interaction network, may be a key target for fat infiltration in rotator cuff tears. We have discovered potential key genes and pathways in the pathological development of fatty infiltration, providing a reference direction for future treatment.
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Objective: To investigate the anti-hepatoma active components of Rhei Radix et Rhizoma and their molecular mechanisms through GEO database, integrative pharmacology platform and molecular docking technology. Method: The active ingredients of Rhei Radix et Rhizoma were screened by TCMIP and the corresponding targets of these components were predicted through TCMIP and Swisstarget databases. The hepatoma gene chip database was downloaded from GEO databases, and the differentially expressed genes between hepatocellular carcinoma (HCC) and normal liver tissue were analyzed by GEO2R. Based on the matching results of potential targets of Rhei Radix et Rhizoma and the targets of hepatoma, the key targets of Rhei Radix et Rhizoma against hepatoma were screened, and GO function enrichment and KEGG pathway enrichment analysis of the key targets were performed. Main components and core targets of Rhei Radix et Rhizoma against hepatoma were analyzed and screened by constructing PPI network, component-target network and traditional Chinese medicine-component-target-pathway network. Furthermore, the molecular docking between the core targets and the main active components was performed by Schrodinger-Maestro software to virtually verify their binding ability and analyze their binding mode. Result: A total of 20 anti-hepatoma active components of Rhei Radix et Rhizoma were collected and related 86 targets were obtained, including CDK1, AKR1C3, PTGS2, AR and CCNB1, etc. The results of GO functional enrichment mainly focused on the cell cycle, G2/M transition of mitotic cell cycle, oxidation-reduction process, drug reactions and steroid metabolism processes, etc. The results of KEGG pathway enrichment mainly involved cell cycle, cell senescence, complement system, arachidonic acid metabolism and bile metabolism, and these metabolic pathways were related to cell apoptosis, metastasis, inflammation and immunity. The results of molecular docking showed that 92.2% of the active components had good binding ability with the 10 core proteins, and the main combination forms mainly were hydrogen bonds, hydrophobic bonds, π-π bonds and cation-π. Conclusion: The active components of Rhei Radix et Rhizoma including rhein, emodin, chrysophanol-8-O-β-D-glucopyranoside, chrysophanol-1-O-β-D-glucoside and rhapontigenin can act on multiple targets such as CDK1, CCNB1, CYP2C9, MMP9 and PTGS2, by regulating signaling pathways related to cell apoptosis, metastasis, inflammation and immunity to play an anti-hepatoma effect.
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Objective To perform bioinformatics analysis of the genetic chip data of rheumatoid arthritis (RA) in order to search for the characteristic gene expression profiles. Methods Differential expression analysis of RA Gene chip data in GEO database was performed using GEO2R, and GO and KEGG enrichment analysis of functional annotation and pathway analysis of differentially expressed genes (DEGs) were conducted by DAVID6.8 and R language. Protein-protein interaction (PPI) and target genes acquisition were realized by String-database and software Cytoscape3.7.1. Results The 1 184 DEGs in synovial tissues isolated from the knee joints of RA patients were statistically significant. Among them 664 were up-regulated and 520 were down-regulated. DEGs were enriched to 70 different GOterms, and the most significant terms were signal transduction, plasma membrane and protein binding. DEGs were enriched to 62 different signal pathways, including cytokine-cytokine receptor interaction, osteoclast differentiation, rheumatoid arthritis, Th17 cell differentiation, and IL17 signal pathway. PPI analysis screened out 19 pivotal target genes, namely, NKG7, BCL6, SEMA4D, NFIL3, RAC2, MLIP, SEL1L3, GUSBP11, IGLV1-44, IGLJ3, IGLC1, IGKV1OR2-118, IGKV1OR2-108, IGKC, IGHV4-31, IGHV3-23, IGHM, IGHD and CYAT1. Conclusion Partial DEGs screened out by analyzing the expression profiles are involved in the key links affecting the development of synovial inflammation in RA, which may provide an important theoretical basis for early diagnosis and treatment of this disease and development of targeted drugs.
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Objective@#To identify the pivotal differentially expressed genes in cervical cancer based on data mining from GEO database. @*Methods@#The cervical cancer-related gene expression data (GSE9750 and GSE63514) was downloaded from GEO database and the differentially expressed genes were screened and identified by GEO2R tool. GO and KEGG enrichment pathways were analyzed by using DAVID online tool. The results were verified using the data from GSE7803, GSE39001 and TCGA. @*Results@#A total of 176 differentially expressed cervical cancer-related genes were screened, including 41 up-regulated genes and 135 down-regulated genes. Among them, INHBA was highly expressed in cervical cancer tissues and negatively correlated with overall survival ( HR =2.5, P ＜ 0.01 ) and disease-free survival ( HR =2.7, P ＜0.01). @*Conclusion@#We obtained multiple genes related to the pathogenesis of cervical cancer by data mining of the gene chip in GEO database. INHBA could be used as a new potential molecular marker for tumor diagnosis and prognosis evaluation.
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Objective MicroRNAs (miRNA) play an important role in the development and progression of intervertebral disc degeneration (IDD), but the underlying mechanisms remain unclear. This study was to search for differentially expressed miRNAs and predict their target genes in the degenerative intervertebral disc tissue. Methods Data on the miRNA expression profile in the nucleus pulposus of the intervertebral disc were downloaded from the GEO database, involving nucleus pulposus samples from 3 cases of IDD and normal nucleus pulposus samples from another 3 patients with new traumatic lumbar fracture. Differentially expressed microRNAs were identified in the nucleus pulposus tissues of the IDD and normal control groups with the R Software, and the target genes significantly differentially expressed in the miRNAs were predicted using the miRWalk Software. The above target genes were enriched in the clusterProfiler package by GO biological function analysis and KEGG pathway analysis. Meanwhile, a protein-protein interaction network of the target genes was constructed with the STRING database and Cytoscape software, and the hub genes were identified. Based on the Pfirrmann grading of IDD, the subjects involved in the GSE23130 data were divided into a control group (≤grade 3, n = 15) and an IDD group (>grade 3, n = 8) followed by analysis of the expression levels of the hub genes. Results A total of 374 differentially expressed miRNAs were identified, 189 up-regulated and 185 down-regulated, with hsa-let-7b-5p most significantly down-regulated. Prediction of the 5 most significantly up- or down-regulated miRNAs showed the highest number of target genes in hsa-let-7b-5p, 85 in all, including GPAT4, E2F2, and PAK1. GO enrichment analysis manifested that these target genes were mainly involved in the biological processes of cell cycle G1/S phase transition and positive regulation of membrane-targeted proteins. The signaling pathways enriched in the target genes mainly included prolactin, insulin, p53 signaling pathways. Ten hub genes were identified by analysis of the PPI network, including CCND2, NRAS, E2F2, E2F6, STX3, CDCA8, RRM2, PPP2R2A, TXLNG and AKT2. The expression levels of CCND2, NRAS, E2F2, E2F6, STX3, CDCA8, RRM2, PPP2R2A and AKT2 in the degenerative intervertebral disc tissue were significantly higher than those in the control group (P < 0.05). Conclusion Significantly reduced expression of hsa-let-7b-5p in the nucleus pulposus tissue of IDD patients may play an important role in IDD by regulating its target genes CCND2, NRAS, etc.
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Objective: The potential biological targets for anti-lung adenocarcinoma of Solanum nigrum were scored using the weighted co-expression network analysis (WGCNA) method. Methods: A database of chemical components of S. nigrum was established through oral bioavailability (OB), drug-likeness (DL) based on Traditional Chinese Medicine Systems Pharmacology (TCMSP) and literature retrieval. The targets of active ingredients of S. nigrum were predicted based on reverse docking with DRAR-CPI server, and combined with WGCNA to mine GSE10072 dataset in Gene Expression Omnibus (GEO) database to obtain coexpression gene module. Furthermore, the potential anti-lung adenocarcinoma targets of S. nigrum were confirmed under intersected with predicted targets and coexpression genes. The GO terms of biological processes and KEGG pathway enrichment analysis of predicted targets and anti-lung adenocarcinoma targets were performed by Metascape database, respectively. Using the targets-pathways networks to study the mechanisms of S. nigrum in the fight against cancer. The transcriptional level expression of key String database combined with Cytoscape software to draw the proteins-proteins interactions (PPI), and active ingredients-targets-pathways networks to study the mechanisms of S. nigrum in the fight against cancer. The transcriptional level expression of key genes in lung adenocarcinoma cancer tissues and normal lung tissues was assessed based on UALCAN dataset. And the correlation between key genes and prognosis of lung cancer patients was calculated by KM plotter analysis. Results: This study collected nine active components of S. nigrum, including medioresinol, sitosterol, diosgenin, solanocapsine, quercetin, α-chaconine, solasonin, solamargine, and solasodine. Totally 271 targets were predicted, and 41 potential anticancer targets were confirmed. The potential regulatory pathways included pathway in cancer, PI3K-Akt signaling pathway, chemical carcinogenesis, central carbon metabolism in cancer and so on. From the PPI network, we found that hub genes EGFR, CASP8, HPGDS, FYN, and high expression of EGFR and CASP8 were related to the poor overall survival in patient with lung adenocarcinoma. Oncontrary, lower expression of HPGDS and FYN were also associated with poor overall survival. Conclusion: This study reflects the multi-component, multi-target and multi-pathway features of S. nigrum, and provides a scientific basis for anticancer substance and elucidating the mechanisms of action of S. nigrum, as well as a reference for the study of mechanisms.
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Objective To make a parallel mining the data of expression differences of a crucial gene XPA involved in nucleotide excision repair pathway of human skin microarrays by bioinformatics from the system level.Methods Using the ScanGEO, the data of microarrays which included the significant differences expression level of XPA were screened and analyzed from 59 human skin samples in the GEO database. Results There were 7 samples with the down-regulated expression of XPA: cutaneous malignant melanoma, epidermal injury model, DNA damage and UV radiation, foreskin fibroblast response to Toxoplasma gondii RH type 1 (ROP5) mutant infection, interleukin-20 subfamily cytokines effect on epidermal keratinocytes, Egr-1 overexpression effect on skin fibroblasts in vitro: time course, in vitro model for inflammatory dendritic cells.Present expression down. Conclusion Based on the GEO database and ScanGEO, high-throughput shared data can be screened and analyzed efficiently.
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Objective To analyse the expression and clinical significance of 12 kinds of microRNAs (miR) in patients with ovarian cancer using public gene expression databases. Methods The microRNA expression data were screened in dataset GSE14407 and TCGA database, then 12 kinds of microRNAs were obtained including miR-10B, miR-1244, miR-622, miR-21, miR-503, Let-7D, miR-155, miR-30C, miR-17, miR-101-1, miR-186 and miR-770. The expression data of these 12 kinds of microRNAs were compared and identified to find the differential ones between normal tissue and tumors. Data of 505 ovary cancer patients were divided into two groups by age, tumor grade, clinical stage, disease location, tumor residual and microRNA expression. Kaplan-Meier survival analysis and Cox multivariate analysis were used to compare the overall survival of ovary cancer patients between two groups. Results Compared with ovary cancer, the expression levels of Let-7D and miR-101-1 were higher, but the expression levels of miR-155 and miR-770 were decreased, in adjacent tissue of ovary tumor (P<0.05). The Kaplan-Meier survival analysis result showed that lower survival rates were found in patients with age≥59 years, clinical stage (Ⅲ+Ⅳ) and lower Let-7D expression (P<0.05). The multivariate Cox regression analysis showed that the decreased expression level of Let-7D was the independent risk factor for the prognosis of ovarian cancer. Conclusion The expression of Let-7D is correlated with the prognosis of ovarian cancer, which is the independent biomarker to predict prognosis of ovarian cancer.