ObjectiveTo conduct a bioinformatics analysis of gene expression profiles in frontal lobe of patients with Parkinson disease (PD), in order to explore the potential mechanism related to depression in PD.MethodsAll the bioinformatics data before March 20th 2019 were acquired from Gene Expression Omnibus (GEO) database, using " Parkinson disease” as the key word. The species was limited to human (Homo sapiens), and the detective method was limited to expression profiling by array. ImgGEO (Integrative Gene Expression Meta-Analysis from GEO database), DAVID (the Database for Annotation, Visualization and Integrated Discovery), STRING and Cytoscape 3.6.1 software were utilized for data analysis.ResultsTotally, 45 samples (24 PD cases and 21 healthy controls) were obtained from 2 datasets. We identified 236 differentially expressed genes (DEGs) in the post-mortem frontal lobe between PD cases and healthy controls, in which 146 genes were up-regulated and 90 genes were down-regulated. Based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis, the DEGs were mainly enriched in the structures of postsynaptic membrane, cell membrane component, postsynaptic membrane dense area, and myelin sheath, and were involved in the occurrence of PD, depression, and other diseases. These genes were involved in the biological processes of dopaminergic, glutamate-nergic, GABA-nergic synapses, and some other synapses, as well as several signaling pathways (e.g. mitogen- activated protein kinase signal pathway, p53 signal pathway, and Wnt signal pathway), which were associated with PD and depression pathogenesis. Besides, we found that NFKBIA, NRXN1, and RPL35A were the Hub proteins.ConclusionsGene expression in frontal lobe of patients with PD is associated with the pathogenesis of PD. This study provides a theoretical basis for understanding the mechanism of PD occurrence and progression, as well as the potential mechanism of depression in PD.
Objective To validate the different expressions of human fxyd6 gene between normal bile duct tissues and malignant tumor tissues, and to observe the subcellular localization of human fxyd6 gene in human cholangiocarcinoma cells. MethodsThe different expressions between normal bile duct tissues and malignant tumor tissues were identified by RT-PCR. In situ polymerase chain reaction （IS-RT-PCR） was applied to detect the subcellular localization of fxyd6 gene in paraffin sections of human cholangiocarcinoma cells. Image analysis software was used to semiquantitatively determine the difference between normal and malignant tissues. ResultsHuman fxyd6 gene was highly expressed in cholangiocarcinoma tissues and lowly expressed in normal ones. There was a significant difference between the expressions of carcinoma cells and normal cells （P＜0.05）. IS-RT-PCR showed that fxyd6 gene localized in the kytoplasma of epithelial cells of human cholangiocarcinoma. ConclusionHuman fxyd6 gene may act as an essential component of the malignant transformation process in human cholangiocarcinoma.
Lung cancer is one of the malignant tumors with the greatest threat to human health, and studies have shown that some genes play an important regulatory role in the occurrence and development of lung cancer. In this paper, a LightGBM ensemble learning method is proposed to construct a prognostic model based on immune relate gene (IRG) profile data and clinical data to predict the prognostic survival rate of lung adenocarcinoma patients. First, this method used the Limma package for differential gene expression, used CoxPH regression analysis to screen the IRG to prognosis, and then used XGBoost algorithm to score the importance of the IRG features. Finally, the LASSO regression analysis was used to select IRG that could be used to construct a prognostic model, and a total of 17 IRG features were obtained that could be used to construct model. LightGBM was trained according to the IRG screened. The K-means algorithm was used to divide the patients into three groups, and the area under curve (AUC) of receiver operating characteristic (ROC) of the model output showed that the accuracy of the model in predicting the survival rates of the three groups of patients was 96%, 98% and 96%, respectively. The experimental results show that the model proposed in this paper can divide patients with lung adenocarcinoma into three groups [5-year survival rate higher than 65% (group 1), lower than 65% but higher than 30% (group 2) and lower than 30% (group 3)] and can accurately predict the 5-year survival rate of lung adenocarcinoma patients.
ObjectiveThe role of ferroptosis-related genes in the occurrence and development of lung injury caused by sepsis was investigated by bioinformatics methods, and the closely related genes were predicted. MethodsThe Dataset GSE154653 was downloaded from the gene expression database (GEO), and a total of 8 cases of microarray gene set were included in normal group and lipopolysaccharide (LPS)-induced sepsis lung tissue. The differential expression genes (DEGs) were screened out under conditions of |log2 FC|>1 and P.adj<0.05. Meanwhile, the selected DEGs were combined with the driver and suppressor genes of ferroptosis downloaded from the ferroptosis database (FerrDb) to obtain the differential genes associated with ferroptosis in sepsis (Fe-DEGs). These Fe-DEGs were further analyzed using R language, DAVID, and STRING online tools to identify GO-KEGG functions and pathways, and the construction of PPI network. Results The Bioinformatics approach screened out 3533 DEGs and intersected 53 key genes related to ferroptosis. The further biological process (BP) of GO enrichment analysis mainly involves the positive regulation of transcription, the positive regulation of RNA polymerase II promoter transcription, the cytokine mediated signaling pathway, and the positive regulation of angiogenesis. The molecular function (MF) mainly involves the same protein binding, transcriptional activation activity and REDOX enzyme activity. The pathways are enriched in iron death, HIF-1 signaling pathway and AGE-RAGE signaling pathway. Five key Fe-DEGs genes were screened by constructing PPI network, including CYBB, LCN2, HMOX1, TIMP1 and CDKN1A. Conclusion CYBB、LCN2、HMOX1、TIMP1 and CDKNIA genes may be key genes involved in ferroptosis of lung tissue caused by sepsis.
ObjectiveTo explore the pathogenesis of tuberculosis and provide new ideas for its early diagnosis and treatment.MethodsGSE54992 gene expression profile was obtained from the gene expression database. Differentially expressed genes (DEGs) were screened using National Center forBiotechnology Information platform, and GO enrichment analysis, pathway analysis, pathway network analysis, gene network analysis, and co-expression analysis were performed to analyze the DEGs.ResultsCompared with the control group, a total of 3 492 genes were differentially expressed in tuberculosis. Among them, 1 686 genes were up-regulated and 1 806 genes were down-regulated. DEGs mainly involved small molecule metabolic processes, signal transduction, immune response, inflammatory response, and innate immune response. Pathway analysis revealed chemokine signaling pathway, tuberculosis, NF-Kappa B signaling pathway, cytokine-cytokine receptor interaction, and so on; gene signal network analysis found that the core genes were AKT3, PLCB1, MAPK8, and NFKB1; co-expression network analysis speculated that the core genes were PYCARD, TNFSF13, PHPT1, COMT, and GSTK1.ConclusionsAKT3, PYCARD, IRG1, CD36 and other genes and their related biological processes may be important participants in the occurrence and development of tuberculosis. Bioinformatics can help us to comprehensively study the mechanism of disease occurrence, which can provide potential targets for the diagnosis and treatment of tuberculosis.
Objective To investigate specific changes of T cell repertoire in convalescent patients infected by influenza A (H7N9) virus. Methods Peripheral blood samples from 8 convalescent patients infected by H7N9 virus and 10 healthy donors were collected. After extracting whole DNA from these samples, arm-PCR were performed and the products were submitted to Illumina HiSeq2000 platform to produce deep sequencing data of the nucleotide sequences of complementary determining region 3 of T cell receptor β chain (TRB). Differences were compared in TRB diversity and V-D-J gene usage and similarities of sequences between the patients and the healthy donors. Results Frequency of V-D-J gene usage was different between the H7N9 patient group and the healthy group, such as TRBV30, TRBV27, and TRBV18 (Student's t test, P < 05). Main component analysis showed V-J pairing pattern was significantly different between two groups, which may have potential in identifying patients from healthy people. A considerable number of shared CDR3s were found in patient-patient pairs and normal-normal pairs, while seldom were found in patient-normal pairs. The similarity between patients was also confirmed by overlap distance analysis. Indexes for assessing diversity of immune repertoires, Shannon-Weiner index and Simpson index, were both lower in the patients (Student's t test, P < 05), suggesting that the immune system of the patients had not recovered 6 months after H7N9 infection. Compared with the healthy donors, the number of hyper-expression clones increased in the patient group, and some of them showed similarity among patients. Conclusions TRB repertoires are less diverse in patients with increased hyper-expressed clones and identifiable V-J usage pattern, which is identifiable from normal population. These results suggest that there are H7N9-specific changes in TRB repertoires of H7N9 infected patients in convalescent phase, which have potential implication in diagnosis and therapeutic T cell development.
ObjectiveTo bioinformatically analyze the gene chip data of chondrocytes from osteoarthritis patients from the Gene Expression Omnibus (GEO) database, and explore the molecular mechanisms of osteoarthritis.MethodsWe searched the GEO database (up to April 23rd, 2021) for data of chondrocytes and gene expression profiling in human knee osteoarthritis via the key words of “osteoarthritis OR cartilage OR chondrocyte*”. Then, we selected the samples by our inclusion criteria. The data were normalized before analysis. After differentially expressed genes were identified, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Search Tool for the Retrival of Interacting Genes/Proteinsm, R language, Perl language, Cytoscape software, and DAVID database were used to perform differentially expressed gene analysis, functional annotation, and enrichment analysis.ResultsThe differentially expressed genes were mostly enriched in cell components and some extracellular regions, which participated in cell division, mitosis, cell proliferation and inflammatory response mainly via the regulation of protein kinase activity. The differentially expressed genes were mainly involved in the cell proliferation signaling pathway, mitogen-activated protein kinase signaling pathway, oocyte meiosis, cell cycle and so on.ConclusionsMultiple signaling pathways are involved in the changes of chondrocytes in human knee osteoarthritis, mainly about cell cycle and protein metabolism genes/pathways. Inflammatory factors and cytokines may be the most important links in the pathogenesis of osteoarthritis.
Objective To investigate the expression levels of fatty acid metabolism-related genes in acute myeloid leukemia (AML) and construct a prognostic risk regression model for AML. Methods Gene expression data from control groups and AML patients were downloaded from the GTEx database and The Cancer Genome Atlas (TCGA) database, followed by screening for differentially expressed genes (DEGs) between AML patients and controls. Fatty acid metabolism-related genes were obtained from the MSigDB database. The intersection of DEGs and fatty acid metabolism-related genes yielded fatty acid metabolism-associated DEGs. A protein-protein interaction network was constructed using the STRING database. Hub genes were analyzed via random forest, Kaplan-Meier survival, and Cox proportional hazards regression based on TCGA clinical data to establish a prognostic model and evaluate their diagnostic and prognostic significance. Immune cell infiltration differences between high- and low-risk groups were assessed using CIBERSORT algorithms to explore immune microenvironment variations and correlations with risk scores. Results A total of 60 fatty acid metabolism-related DEGs were identified. Further screening revealed 15 hub genes, among which four genes (HPGDS, CYP4F2, ACSL1, and EHHADH) were selected via integrated random forest, Cox regression, and Kaplan-Meier analyses to construct an AML prognostic lipid metabolism gene signature. Heatmaps demonstrated statistically significant differences in tumor-infiltrating immune cell proportions between risk groups (P<0.05). Conclusion The constructed lipid metabolism gene prognostic model may serve as a biomarker for overall survival in AML patients and provide new insights for immunotherapy drug development.
ObjectiveTo investigate the significant genes in Mesio-temporal lobe epilepsy (MTLE) and explore the molecular mechanism of MTLE.MethodsThe microarray data of MTLE were downloaded from the Gene Expression Omnibus (GEO) database and analyzed by bioinformatics methods using GEO2R tool, Venny2.1.0, FUNRICH and Cytoscape software, DAVID and String databases.ResultsOf all the 331 differentially expressed genes(DEGs), 46 genes were down-regulated and 285 genes were up-regulated in dataset GSE88992; Furthermore, the core module genes were identified from those DEGs, which were expressed mostly in plasma membrane and extracellular space; The major molecular funtion were chemokine activity, cytokine activity and chemokine receptor binding; The main biological pathways involved neutrophil chemotaxis, inflammatory response and positive regulation of ERK1 and ERK2 cascade; The KEGG analysis showed DEGs enriched in Chemokine signaling pathway, Cytokine-cytokine receptor interaction and Complement and coagulation cascades. In addition, ten hub genes (Il6, Fos, Stat3, Ptgs2, Ccl2, Timp1, Cd44, Icam1, Atf3, Cxcl1) were found to significantly express in the MTLE.ConclusionThe pathogenesis of MTLE involves multiple genes, and multiple cell signaling pathways. Thus investigations of these genes may provide valuable insights into the mechanism of MTLE.
ObjectiveTo investigate the molecular pathogenesis of pulmonary fibrosis induced by bleomycin in a murine model,and provide novel insights for clinical diagnosis and treatment. MethodsFrom Gene Expression Omnibus,we downloaded microarray data extracted from experiments of bleomycin induced pulmonary fibrosis in wild-type mice. With BRB-Array Tools,differentially expressed genes at different time points during disease development were screened,selected and analyzed by DAVID software. ResultsBRB array analysis identified 45101 differentially expressed genes. After induction by bleomycin on 7th day,1164 genes and 735 genes were significantly up-regulated and down-regulated (P<0.05,fold change>2),respectively. On 14th day,731 genes and 390 genes were significantly up-regulated and down-regulated (P<0.05,fold change>2),respectively. DAVID analysis revealed that the up-regulated genes were significantly enriched in cell cycle,p53 signaling and chemokine signaling pathway,damaging reaction and collagen metabolism gene sets. While the down-regulated genes were enriched in the drug metabolism pathway gene set. ConclusionsBioinformatics methodologies are able to efficiently analyze microarray data and extract its underlying information,provide novel insights for major molecular events and shift of cell signaling pathway during pulmonary fibrosis progression,and furthermore,finding molecular markers for early diagnosis and therapeutic targets.