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.
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.
Objective To study the expression of 4 circular RNA (circRNA) in peripheral blood mononuclear cells (PBMC) of patients with epilepsy and to predict its function by bioinformatics, so as to provide basis for exploring the pathogenesis of epilepsy. Methods From May 2020 to May 2021, 22 epilepsy patients were treated in the Department of Neurology of the First Affiliated Hospital of Baotou Medical College of Inner Mongolia University of Science and Technology, and 22 control group were selected. There were 13 males and 8 females in the epilepsy group, with an average age of (36.41±8.39)years. There were 11 males and 11 females in the control group, with an average age of (34.41±8.68) years. The expression levels of circRNA EFCAB2, C14orf159, PARG and TMEM39 in PBMC were detected by real-time fluorescence quantitative PCR, and their functions were predicted by bioinformatics. Results Compared with the control group, the relative expression of EFCAB2 and C14orf159 in PBMC of epileptic patients was 1.42±0.06 (t=29.41) and 1.31±0.03 (t=25.27), PARG and TMEM39 were not detected in peripheral blood PBMC. Bioinformatics analysis showed that three mirnas obtained by EFCAB2 were miR-6873-3p, miR-6739-3p and miR-7110-3p. Three mirnas were obtained by C14orf159: miR-1180-3p, miR-6501-3p, and miR-3622b-5p. The seizure-related genes were predicted by TargetScan database. EFCAB2: miR-6873-3p met the requirements of 11 downstream genes. A total of 7 downstream genes of miR-6739-3p met the requirements.A total of 14 downstream genes were eligible for miR-7110-3p and a total of 9 downstream genes were eligible for miR-6501-3p. A total of 14 downstream genes were eligible for miR-3622B-5p.miR-1180-3p has a total of 1 downstream genes that meet the requirements. Conclusions Studies have shown that two circrnas, EFCAB2 and C14orf159, may be important biological markers of epilepsy. Through bioinformatics analysis, these two circrnas may act as "molecular sponges" to regulate epilepsy. EFCAB2 has the potential to act as a "molecular sponge" for miR-6873-3p and miR-7110-3p, and it was found that miR-6873-3p and miR-7110-3 share a common downstream target gene MAP1B-which plays a role in epilepsy by regulating voltage-gated sodium channels. C14orf159 can act as a molecular sponge for miR-6501-3p to regulate the expression of CCL3 and play a role in epilepsy.
Objective To explore the expression of yes-associated protein 1 (YAP1), as a key protein of Hippo signal pathway, in rats with brain injury. Methods A total of 18 Sprague Dawley rats were randomly divided into three groups: normal group, sham operation group and brain injury group. The expression of YAP1 in rats with brain injury was detected by immunochemistry, quantitative polymerase chainreaction and Western blotting. Result Seventy-two hours after the brain injury, the expression level of YAP1 in protein and gene increased significantly in brain injury group, compared with those in the normal and sham operation group (P<0.05). Conclusion The expression of YAP1 increases in rats with brain injury, which maybe a new target for therapy.
现已认识到免疫反应、转录因子核因子κB( NF-κB) 的激活、细胞因子、中性粒细胞的激活和肺泡渗入、凝血级联反应、肾素-血管紧张素系统等多种因素构成的复杂网络参与急性肺损伤/急性呼吸窘迫综合征( ALI/ARDS) 的发病过程[ 1-5] 。虽然脓毒症、创伤、肺炎等ALI/ARDS诱发因素很常见, 但仅有部分病人发生ALI/ARDS, 并且具有相似临床特征的ALI/ARDS病人可有截然不同的结果, 这种异质性引起研究者对影响ALI/ARDS 易感性和预后的遗传因子进行鉴别的浓厚兴趣[ 6] 。由于数量庞大的表现型变异, 不完全的基因外显率、复杂的基因-环境相互作用及高度可能的基因座不均一性而使ALI 遗传学的研究受到挑战[ 7] 。近年来基因组学技术被应用于ALI/ARDS 发病机制的研究, 加深了人们对ALI/ARDS的认识并有可能发展出新的治疗策略以降低其发病率和病死率。