RseA基因影响耻垢分枝杆菌药物敏感性的生物信息学分析与初步验证

目的 筛选RseA在耻垢分枝杆菌中可能调控药物敏感性的基因或通路,并进行初步验证。方法 利用分子克隆技术构建RseA基因过表达耻垢分枝杆菌组和空质粒对照组;利用转录组测序技术和生物信息学方法筛选2组细菌间的差异表达基因(DEGs),分析其基因本体论(GO)及京都基因和基因组百科全书(KEGG)富集情况;并构建蛋白质-蛋白质相互作用(PPI)网络。利用结核分枝杆菌复苏促进因子E(RpfE)促使非复制持留菌复苏,测定复苏指数(RI)对筛选出的关键靶向基因或通路进行初步验证。结果 RseA基因过表达后,筛选出2 403个DEGs,其中2 335个表达上调,68个表达下调。GO分析结果表明,DEGs主要参与氮化合物代谢过程的调控、RNA代谢过程的调控、转录因子活性和序列特异性DNA结合过程调控等。KEGG分析结果显示,DEGs主要参与萜类骨架的生物合成、果糖和甘露糖代谢和同源重组等通路。从PPI网络MCODE模块中筛选出前8个hub基因rpsG、rplM、rpsO、rpsT、rpmH、rplS、rpsJ、rplT,并从这些基因的分析结果得知,其主要参与了核糖体通路。使用RpfE促进复苏后,RseA组复苏指数明显低于对照组。 RseA调控了多个靶向基因,参与核糖体的结构组成和核糖体通路,增强耻垢分枝杆菌对抗结核药物的敏感性,为进一步的机制研究奠定了基础。     

锯齿状息肉病综合征的关键基因与潜在治疗药物的生物信息学筛选

目的 基于生物信息学方法利用GEO芯片数据库分析锯齿状息肉病综合征(serrated polyposis syndrome,SPS)的关键基因,探索SPS的分子调控机制及潜在的治疗药物.方法 从GEO数据库下载GSE19963数据集,利用GEO2R分析SPS组织样本和正常结肠黏膜组织样本的表达数据,利用DAVID数据库...     

Identification of key biomarkers and functional pathways in osteosarcomas with lung metastasis: Evidence from bioinformatics analysis

Background:In osteosarcoma, the lung is the most common metastatic organ. Intensive work has been made to illuminate the pathogeny, but the specific metastatic mechanism remains unclear. Thus, we conducted the study to seek to find the key genes and critical functional pathways associated with progression and treatment in lung metastasis originating from osteosarcoma.Methods:Two independent datasets (GSE14359 and GSE85537) were screened out from the Gene Expression Omnibus (GEO) database and the overlapping differentially expressed genes (DEGs) were identified using GEO2R online platform. Subsequently, the Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were conducted using DAVID. Meanwhile, the protein-protein interaction (PPI) network constructed by STRING was visualized using Cytoscape. Afterwards, the key module and hub genes were extracted from the PPI network using the MCODE and cytoHubba plugin. Moreover, the raw data obtained from GSE73166 and GSE21257 were applied to verify the expression differences and conduct the survival analyses of hub genes, respectively. Finally, the interaction network of miRNAs and hub genes constructed by ENCORI was visualized using Cytoscape.Results:A total of 364 DEGs were identified, comprising 96 downregulated genes and 268 upregulated genes, which were mainly involved in cancer-associated pathways, adherens junction, ECM-receptor interaction, focal adhesion, MAPK signaling pathway. Subsequently, 10 hub genes were obtained and survival analysis demonstrated SKP2 and ASPM were closely related to poor prognosis of patients with osteosarcoma. Finally, hsa-miR-340-5p, has-miR-495-3p, and hsa-miR-96-5p were found to be most closely associated with these hub genes according to the interaction network of miRNAs and hub genes.Conclusion:The key genes and functional pathways identified in the study may contribute to understanding the molecular mechanisms involved in the carcinogenesis and progression of lung metastasis originating from osteosarcoma, and provide potential diagnostic and therapeutic targets.     

综合基因表达谱分析与人类肝胰岛素抵抗相关蛋白分子表达研究*

目的 分析糖尿病患者与正常人肝组织之间的差异表达基因（DEGs）,筛选出肝组织中与可能产生胰岛素抵抗相关作用的蛋白分子。方法 从GEO数据库下载基因芯片数据集GSE23343（包括10例2型糖尿病和7例正常人肝组织的基因表达值）,通过DEGs表达谱分析和功能通路富集分析,构建DEGs对应的蛋白质-蛋白质相互作用网络。结果 分析得到928个显著上调的DEGs（P<0.01）,发现DEGs主要富集在细胞和代谢生物过程中,KEGG通路富集显示DEGs主要集中于信号转导和肿瘤相关通路。经蛋白质相互作用网络构建,筛选出5个关键蛋白分子MDM2、PCNA、CAV1、PIK3R1、NR3C1。结论 系统地筛选出人类肝组织中可能与胰岛素抵抗形成相关的蛋白分子,为进一步实验研究肝胰岛素抵抗产生机制和新的降血糖药物作用靶点提供基础。     

DGKZ基因沉默条件下人骨肉瘤细胞芯片的生物信息学分析

目的基于生物信息学方法分析二酰甘油激酶ζ(diacylglycerol kinase zeta,DGKZ)基因沉默的人骨肉瘤细胞芯片并探寻其分子机制。方法从GEO数据库中获取人骨肉瘤细胞芯片,在R软件中筛选DGKZ沉默处理组与对照组的差异基因(differential expression genes,DEGs);DEGs提交至DAVID数据库进行基因功能与通路富集分析;利用STRING数据库和Cytoscape软件构建蛋白互作网络,寻找网络结构中的核心基因并预测其转录因子。结果共筛选出368个DEGs,其中包含105个上调的基因与263个下调的基因。GO富集分析结果表明DEGs主要富集的生物学过程有成骨细胞分化的负调控、血管生成、RNA聚合酶Ⅱ启动子转录的负调控、脂肪细胞分化的正调控、细胞周期和Wnt信号通路;KEGG通路富集分析结果表明DEGs主要涉及的通路有TGF-β信号转导通路、癌症通路、胰腺癌、致癌病毒、MAPK信号转导通路以及代谢通路。经STRING数据库与Cytoscape软件找出度值前10的核心基因有SIRT1、EP300、PTGS2、BMP2、HIF1A、RB1、HIST1H2BO、NT5E、H1F0、HIST1H2AC,核心基因通过iRegulon插件预测的转录因子中标准化富集分数最高的是YY1。结论在DGKZ基因沉默条件下,转录因子YY1及相关靶基因能在骨肉瘤中发挥重要作用。     

基因表达数据库对冠心病关键调控基因的生物信息学分析

目的：心血管疾病仍是目前世界范围内重大的公共卫生问题之一,亟需进一步探索冠心病（CHD）关键的调控基因。本文通过生物信息学分析,应用基因表达数据库（GEO）对冠心病患者相关基因进行研究分析,探索可能的新的冠心病调控机制。方法：检索GEO数据库选择冠心病患者和对照者的相关基因表达谱数据。导入处理后的冠心病患者的GSE71226数据集,利用GEO2R分析鉴定冠心病和健康人的差异表达基因（DEGs）,通过分析确定了CHD患者和对照组的差异基因。结果：共筛选出575个DEGs。进一步构建了一个蛋白质-蛋白质相互作用（PPI）网络,以可视化DEGs和识别中心基因。构建蛋白质-蛋白质相互作用（PPI）网络旨在识别中心基因。通过GO通路富集分析,上调和下调的各排名前15信号通路,参与了胰岛素分泌、心脏传导系统、生长激素分泌等相关的生物过程, KEGG通路富集分析显示,主要与序列特异性DNA复制、cAMP、脂肪消化吸收酶等相关的信号通路等有关。进一步构建了PPI网络互作图,显示五个中心基因（SST、BBS10、CCK、POMC、HSPA9）,其中SST与冠心病的预后密切相关。结论：CHD的关键调控基因包括SST、BBS10、CCK、POMC、HSPA9,可能通过双链DNA结合、cAMP、脂肪消化吸收酶等相关的信号通路参与冠心病发病。     

慢性日本血吸虫病肝纤维化差异表达基因的生物信息学分析

目的 筛选慢性日本血吸虫病肝纤维化差异表达基因（differentially expressed genes, DEGs）,并对其功能进行分析。方法 从基因表达综合（Gene Expression Omnibus, GEO）数据库下载慢性日本血吸虫病肝纤维化患者测序表达谱数据集,利用R语言进行DEGs筛选,对其生物学功能进行基因本体论（Gene Ontology, GO）和京都基因和基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）富集分析并构建蛋白质-蛋白质相互作用（protein-protein interaction,PPI）网络,以筛选关键基因。结果 共鉴定出62个DEGs,其中12个下调表达基因、50个上调表达基因。GO富集分析显示,DEGs主要富集在脂肪酸、硫化合物、酰基辅酶A、硫酯代谢等116种生物学过程,线粒体基质、线粒体外膜、细胞器外膜等19种细胞组分及胰岛素样生长因子结合、氧化还原酶活性等7种分子功能。KEGG通路富集分析显示,DEGs与磷脂酰肌醇-3-激酶/丝苏氨酸蛋白激酶（phosphatidylinos...     

Bioinformatics study on different gene expression profiles of fibroblasts and vascular endothelial cells in keloids

Keloid is a benign fibroproliferative skin tumor. The respective functions of fibroblasts and vascular endothelial cells in keloid have not been fully studied. The purpose of this study is to identify the respective roles and key genes of fibroblasts and vascular endothelial cells in keloids, which can be used as new targets for diagnosis or treatment.The microarray datasets of keloid fibroblasts and vascular endothelial cells were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened out. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for functional enrichment analysis. The search tool for retrieval of interacting genes and Cytoscape were used to construct protein-protein interaction (PPI) networks and analyze gene modules. The hub genes were screened out, and the relevant interaction networks and biological process analysis were carried out.In fibroblasts, the DEGs were significantly enriched in collagen fibril organization, extracellular matrix organization and ECM-receptor interaction. The PPI network was constructed, and the most significant module was selected, which is mainly enriched in ECM-receptor interaction. In vascular endothelial cells, the DEGs were significantly enriched in cytokine activity, growth factor activity and transforming growth factor-β (TGF-β) signaling pathway. Module analysis was mainly enriched in TGF-β signaling pathway. Hub genes were screened out separately.In summary, the DEGs and hub genes discovered in this study may help us understand the molecular mechanisms of keloid, and provide potential targets for diagnosis and treatment.     

Screening and identification of potential biomarkers for obstructive sleep apnea via microarray analysis

Obstructive sleep apnea (OSA) is a common chronic disease and increases the risk of cardiovascular disease, metabolic and neuropsychiatric disorders, resulting in a considerable socioeconomic burden. This study aimed to identify potential key genes influence the mechanisms and consequences of OSA.Gene expression profiles related to OSA were obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in subcutaneous adipose tissues from OSA compared with normal tissues were screened using R software, followed by gene ontology (GO) and pathway enrichment analyses. Subsequently, a protein-protein interaction (PPI) network for these DEGs was constructed by STRING, and key hub genes were extracted from the network with plugins in Cytoscape. The hub genes were further validated in another GEO dataset and assessed by receiver operating characteristic (ROC) analysis and Pearson correlation analysis.There were 373 DEGs in OSA samples in relative to normal controls, which were mainly associated with olfactory receptor activity and olfactory transduction. Upon analyses of the PPI network, GDNF, SLC2A2, PRL, and SST were identified as key hub genes. Decreased expression of the hub genes was association with OSA occurrence, and exhibited good performance in distinguishing OSA from normal samples based on ROC analysis. Besides, the Pearson method revealed a strong correlation between hub genes, which indicates that they may act in synergy, contributing to OSA and related disorders.This bioinformatics research identified 4 hub genes, including GDNF, SLC2A2, PRL, and SST which may be new potential biomarkers for OSA and related disorders.     

乙型肝炎病毒相关肝细胞癌核心差异表达基因生物信息学分析

目的 探索与乙型肝炎病毒（HBV）相关肝细胞癌（HCC）发生发展相关的核心基因,为进一步揭示HBV相关HCC发病机制提供参考。方法 从高通量基因表达数据库（GEO）中下载GSE55092、GSE121248两个数据集,采用R语言筛选HCC组织和癌旁组织间差异表达基因（DEGs）,并绘制可视化火山图。对DEGs基因进行基因本体论（GO）和京都基因和基因组百科全书（KEGG）富集分析,构建蛋白质相互作用（PPI）网络,并用Cytoscape 3.9.0开源平台中分子复合物检测（MCODE）和cytoHubba插件筛选核心DEGs。利用UALCAN和Kaplan Meier-plotter数据库中临床样本数据对筛选出的核心DEGs进行差异表达和生存分析验证。结果 从GSE55092数据集和GSE121248数据集中分别筛选出1 148个和686个DEGs,其中下调表达基因分别为703个和477个、上调表达基因分别为445个和209个;两个数据集共筛选出557个共同表达的DEGs,其中下调表达基因384个、上调表达基因173个。GO富集分析显示,DEGs主要参与细胞分裂、细胞增殖、氧化还原、免...     

Network Clusters Analysis Based on Protein–Protein Interaction Network Constructed in Phosgene-Induced Acute Lung Injury

Purpose

Acute lung injury (ALI) is characterized by impairment in gas exchange and/or lung mechanics that leads to hypoxemia with the presence of diffuse pulmonary infiltrate. Assessments of lung injury play important roles in the development of rational medical countermeasures. The purpose of this study is to investigate the molecular mechanisms of phosgene-induced lung injury.

Methods

We downloaded the gene expression profile of lung tissue from mice exposed to air or phosgene from gene expression omnibus database and identified differentially expressed genes (DEGs) in ALI. Furthermore, we constructed a protein–protein interaction (PPI) network and identified network clusters.

Results

In total, 582 DEGs were found and 4 network clusters were identified in the constructed PPI network. Gene set enrichment analysis found that DEGs were mainly involved in mitochondrion organization and biogenesis, mRNA metabolic process, negative regulation of transferase activity or catalytic activity, and coenzyme metabolic process. Pathways of spliceosome, glutathione metabolism, and cell cycle were dysregulated in phosgene-induced ALI. Besides, we identified four genes, including F3, Meis1, Pvf, and Cdc6 in network clusters, which may be used as biomarkers of phosgene-induced ALI.

Conclusions

Our results revealed biological processes and pathways involved in phosgene-induced ALI and may expand understandings of phosgene-induced ALI. However, further experiments are needed to confirm our findings.     

基于癌症基因组图谱数据库构建肝细胞癌相关miRNA-mRNA调控网络

目的 探讨肝细胞癌(HCC)发病机制,为HCC的诊断和临床治疗提供依据。方法 基于癌症基因组图谱数据库,鉴定HCC样本与癌旁组织样本之间差异表达的miRNA与mRNA。通过miRDB、TargetScan数据库预测差异表达miRNA的靶基因,并与差异mRNA取交集确定目标基因。使用FunRich软件预测在HCC差异表达miRNA中的潜在转录因子。利用Bioconductor中的clusterProfiler包进行功能注释和途径富集分析。通过STRING数据库构建了蛋白质-蛋白质相互作用网络,对前20个枢纽基因进行表达验证和生存分析。结果 对375例HCC样本、50例癌旁样本进行差异miRNA分析,共鉴定出300个差异表达的miRNA。对374例HCC样本、50例癌旁样本进行差异mRNA分析,共鉴定出1 106个差异表达的mRNA。分别在上调的和下调的miRNA中选择20个差异表达最显著的miRNA预测其靶基因。对131个候选基因进行功能富集分析,结果显示主要与缬氨酸、亮氨酸和异亮氨酸降解,碳代谢以及催乳素信号通路等途径有关。结论 在肝细胞癌中异常表达的miRNA-mRNA通路可能成为肝...     

Dermatomyositis: immunological landscape,biomarkers, and potential candidate drugs

Introduction

Dermatomyositis (DM) is a rare inflammatory disease characterized by the invasion of the skin and muscles. Environmental, genetic, and immunological factors contribute to disease pathology. To date, no bioinformatics studies have been conducted on the potential pathogenic genes and immune cell infiltration in DM. Therefore, we aimed to identify differentially expressed genes (DEGs) and immune cells, as well as potential pathogenic genes and immune characteristics, which may be useful for the diagnosis and treatment of DM.

Method

GSE1551, GSE5370, GSE39454, and GSE48280 from Gene Expression Omnibus were included in our study. Limma, ClusterProfiler, and Kyoto Encyclopedia of Genes and Genomes were used to identify DEGs, Gene Ontology (GO), and perform pathway analyses, respectively. Cytoscape was used to construct the protein-protein interaction (PPI) network. Small-molecule drugs were identified using a connectivity map (CMap), and the TIMER database was used to identify infiltrating cells.

Results

DEG analysis identified 12 downregulated and 163 upregulated genes. GO analysis showed that DEGs were enriched in immune-related pathways. Ten hub genes were identified from the PPI network. Additionally, CMap analysis showed that caffeic acid, sulfaphenazole, molindone, tiabendazole, and bacitracin were potential small-molecule drugs with therapeutic significance. We identified eight immune cells with differential infiltration in patients with DM and controls. Finally, we constructed a powerful diagnostic model based on memory B cells, M1, and M2 macrophages.

Conclusions

This study explored the potential molecular mechanism and immunological landscape of DM and may guide future research and treatment of DM.

Key Points

? We explored the molecular mechanism and immunological landscape of dermatomyositis.

? GO analysis showed that DEGs were enriched in immune-related pathways.

? We predicted small-molecular drugs with potential therapeutic significance based on bioanalytical techniques.

? We identified six immune cells with differential infiltration in patients with DM and controls.

     

Analysis of T lymphocyte-related biomarkers in pancreatic cancer

BackgroundPancreatic cancer remains one of the most lethal cancers.ObjectiveThis study aimed to analyze T cell-related biomarkers and their molecular network in pancreatic cancer.MethodsRNAseq sequencing data and clinical data of pancreatic cancer were obtained from TCGA database. The STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) algorithm was used to screen the DEGs related to the tumor immune cells. The pearson correlation analysis were used to analyze the relationships between DEGs and T cells. Additionally, the T cell-related DEGs were subjected to protein-protein interaction, competing endogenous RNA (ceRNA), and chemical small molecule-target network construction. Furthermore, the prognosis-associated DEGs were screened.ResultsA total of 412 stromal score-associated and 312 immune score-associated DEGs were obtained. From these DEGs, 50 CD4⁺ T cell-related genes and 13 CD8⁺ T cell-related genes were selected. The PPI networks associated with immune cell-related genes were constructed and found that CD22, SELL, and OLR1 had higher degrees in the PPI network. The number of ceRNA regulatory relation pairs obtained from CD4⁺ T cells and CD8⁺ T cells were 59 and 48, respectively. Additionally, both CD4⁺ T cell- and CD8⁺ T cell-related genes predicted 29 small molecules. CXCL9 and GIMAP7 were screened out from CD4⁺ T cell-related genes, which were related with the survival of pancreatic cancer.ConclusionWe mapped T cell-related gene profile in pancreatic cancer and constructed their potential regulatory network.     

基于生物信息学的糖尿病心肌病生物标志物及关键通路的筛选

目的通过对基因表达(GEO)数据库中糖尿病心肌病(DCM)相关的基因芯片进行生物信息学分析,获得DCM的生物标志物及其调控的关键通路。方法从GEO数据库获取DCM的基因表达芯片(GSE26887),并借助DAVID在线分析平台对这些基因进行基因本体论(GO)富集分析和京都基因与基因组百科全书(KEGG)信号通路分析,同时利用生物信息学软件STRING 10.0构建这些基因的蛋白-蛋白相互作用(PPI)网络。结果本研究中所采用的芯片GSE26887共包含7例DCM患者及5名健康对照。共筛选出差异表达基因(DEGs)236个,包括134个上调基因及102个下调基因。其中,差异最大的5个上调基因依次为NPPA、SFRP4、DSC1、NEB及FRZB;差异最大的5个下调基因依次为SERPINE1、SERPINA3、ANKRD2、XRCC4及S100A8。GO和KEGG结果表明,DCM发展过程中的DEGs主要富集在炎症、免疫紊乱、代谢紊乱、线粒体功能障碍等方面。PPI网络揭示连接度最高的15个hub基因依次为IL-6、MYC、ACTA2、SERPINE1、ASPN、SPP1、KIT、TFRC、FMOD、PDE5A、MYH6、FPR1、C3、CDKN1A及SOCS3。结论 DCM患者的DEGs与炎症、免疫紊乱及能量代谢密切相关,本研究所筛选出的差异最大的5个上调基因和5个下调基因有望成为DCM诊断的标志分子,15个hub基因有望成为DCM治疗的靶点。