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.
ObjectiveTo investigate the expression of Yes-associated protein (YAP) screened by bioinformatics in rats with myocardial-ischemia reperfusion injury and establish the base for further research.
MethodsThe difference of gene spectrum of rats with myocardial-ischemia reperfusion injury was analyzed by bioinformatics technique. The related signaling pathways and key genes were screened by KOBAS2.0 and KEGG. Eighteen Sprague Dawley rats were randomly divided into three groups: normal group (n=6), sham operation group (n=6) and myocardial-ischemia reperfusion injury group (n=6). The expression of target gene was detected by immunochemistry, quantitive reverse transcription polymerase chain reaction and western blotting.
ResultsA total of 345 differentially expressed genes were found by bioinformatics, among which 181 were up-regulated and 164 were down-regulated. The differential genes were mainly enriched in Wnt, HIPPO, MAPK, Jak-STAT and other signaling pathways. We focused on HIPPO pathway and found that the expression of YAP increased significantly in myocardial-ischemia reperfusion injury group, compared with the normal group and sham operation group (P＜0.05).
ConclusionsThe expression of YAP of HIPPO signal pathway is increased in rats with myocardial-ischemia reperfusion injury.
Objective To explore the pathogenesis of acute respiratory disease syndrome (ARDS) by bioinformatics analysis of neutrophil gene expression profile in order to find new therapeutic targets. Methods The gene expression chips include ARDS patients and healthy volunteers were screened from the Gene Expression Omnibus (GEO) database. The differentially expressed genes were carried out through GEO2R, OmicsBean, STRING, and Cytoscape, then enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathways was conducted to investigate the biological processes involved in ARDS via DAVID website. Results Bioinformatics analysis showed 86 differential genes achieved through the GEO2R website. Eighty-one genes were included in the STRING website for protein interaction analysis. The results of the interaction were further analyzed by Cytoscape software to obtain 11 hub genes: AHSP, ALAS2, CD177, CLEC4D, EPB42, GPR84, HBD, HVCN1, KLF1, SLC4A1, and STOM. GO analysis showed that the differential gene was enriched in the cellular component, especially the integrity of the plasma membrane. KEGG analysis showed that multiple pathways especially the cytokine receptor pathway involved in the pathogenesis of ARDS. Conclusions A variety of genes and pathways have been involved in the pathogenesis of ARDS. Eleven hub genes are screened, which may be involved in the pathogenesis of ARDS and can be used in subsequent studies.
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 establish and validate the diagnostic model of ferroptosis genes for acute myocardial infarction (AMI) based on bioinformatics. MethodsFive AMI gene expression data were obtained from Gene Expression Omnibus (GEO), namely GSE66360, GSE48060, GSE60993, GSE83500, GSE34198. Among them, GSE66360 was used as the training set to perform differential analysis, and intersection of differential genes and ferroptosis genes was taken to obtain differentially expressed ferroptosis genes in AMI. GO and KEGG enrichment analysis was performed using Metascape website. Subsequently, random forest (RF) algorithm was used to screen out key genes with high classification performance according to the Keeny coefficient score, and artificial neural network (ANN) diagnostic model of AMI ferroptosis feature gene was constructed by model group GSE83500. The area under the receiver operating characteristic curve (AUC) of 10-fold cross-validation was used to evaluate the performance and generalization ability of the model, and 3 external independent datasets were used to verify the diagnostic performance of this model. The single sample gene setenrichment analysis was used to explore the difference in immune cell infiltration between infarcted myocardium and normal myocardium after AMI. In addition, correlation analysis between immune cells and key genes was also conducted. Finally, potential drugs that would prevent and treat AMI by regulating ferroptosis were screened out from the Coremin Medical platform. ResultsA total of 16 differentially expressed ferroptosis genes were obtained in the training set, GO enrichment analysis showed that they mainly participated in biological functions such as cellular response to biological stimuli and chemical stress, regulation of interleukin 17, etc. KEGG enrichment analysis showed that these genes were significantly enriched in NOD-like receptor signaling pathway, programmed cell necrosis, Leishmaniasis and other pathways. Four genes with good classification performance were screened out using RF algorithm, namely EPAS1, SLC7A5, FTH1, and ZFP36. The results of 10-fold cross-validation showed that the minimum AUC value was 0.746, the maximum value was 0.906, and the average value was 0.805. The AUC of the ANN model was 0.859, and the AUC values of the three independent validation sets were 0.763 (GSE48060), 0.673 (GSE60993), 0.698 (GSE34198). Immune cell infiltration found that macrophages, mast cells and monocytes were significantly active after AMI. Correlation analysis found that there were positive correlations between 4 key genes and activated dendritic cells, eosinophils and γδT cells. A total of 20 potential western medicines were predicted which could prevent and treat AMI by regulating ferroptosis, and the predicted potential Chinese medicine was mainly heat-clearing and detoxifying and blood-activating and removing blood stasis drugs. ConclusionThe identified AMI ferroptosis genes by bioinformatics method have certain diagnostic significance, which provides a reference for disease diagnosis and treatment.
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.
Circular RNA are one kind of non-coding RNA, charactered by covalently closed rings. They can influence biological functions such as cell transduction and protein synthesis. They are associated with pathogenesis of many diseases and become a novel family of biomarkers. Now we try to introduce the origin, structure, function of circular RNA and the involved research methodology. Furthermore, we primarily discuss their application in the tuberculosis research.
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.
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.
Objective To explore the mode and role of differential expression of circular RNAs (circRNAs) in myelodysplastic syndrome (MDS). Methods We preprocessed and analyzed the circRNA expression profile datasets GSE163386, GSE94591, and GSE81173 in the GEO (Gene Expression Omnibus) database. By using the circBank database and the ENCORI, miRDB, and miRWalk databases to predict microRNAs (miRNAs) that interacted with differentially expressed circRNAs and messenger RNAs (mRNAs), the circRNA-miRNA-mRNA axis was constructed. We retrieved miRNAs related to MDS in PubMed and further obtained competing endogenous RNA (ceRNA) networks related to MDS by taking intersections. Results Through analysis, 128 differentially expressed circRNAs were identified, 48 highly expressed, and 80 low expressed. Among differentially expressed circRNAs with multiple differences>10, 3 were upregulated and 11 were downregulated. Through analysis, 101 differentially expressed mRNA were identified, with 9 upregulated and 92 downregulated. Intersecting with the MDS related miRNAs retrieved by PubMed, we further obtained the MDS related ceRNA network, namely circRNA (has_circ_0061137)-miRNA (has-miR-16-5p)-mRNA (RUBCNL, TBC1D9, SLC16A6) and circRNA (has_circ_0061137)-miRNA (has-miR-125b-5p)-mRNA (CCR5, SLC16A6, IRF4), all of which were downregulated. Conclusion The ceRNA networks revealed in this study may help elucidate the circRNA mechanism in MDS.
