基于Hubness的类别均衡的时间序列实例选择算法

针对实例选择算法INSIGHT存在选出的实例类别分布不均衡和得分相等的实例的重要性无法区分两个问题,分别提出了改进算法。改进算法B-INSIGHT1基于分治思想,通过筛选出训练集各类中最具有代表性的实例,来确保选出的实例类别分布尽可能均衡。改进算法B-INSIGHT2将改进算法B-INSIGHT1的单重排序改进成了双重排序,以便更有效地衡量实例的重要性。实验结果表明,在时间复杂度基本不变的前提下,所提算法在分类准确率上均优于INSIGHT算法。

基于Hubness现象的高维数据混合聚类算法

高维数据聚类是聚类分析中的难点。K-hubs聚类算法是在K-means方法基础上,结合高维数据空间的Hubness现象对数据进行聚类。针对K-hubs聚类算法需要随机确定初始聚类中心,不适用于非超球状簇等问题,本文提出了基于多阶段层次聚类和划分聚类的高维数据混合聚类算法。该算法将数据点按其Hub值分为Hub点,Midhub点和Antihub点三类,然后对Hub点和Midhub点分别采用层次聚类,接着进一步采用层次聚类合并簇,最后,对Antihub点利用划分聚类合并到最近的簇。在UCI数据集上的实验结果表明,与其它最新的聚类算法相比,本文提出的算法在高维数据集上得到了较好的聚类结果。

基于Hubness与类加权的k最近邻分类算法

针对高维不平衡数据中维数灾难和类不平衡分布问题,提出一种改进k最近邻(kNN)分类算法HWNN。将样本的k发生分布作为其在预测时对各个类的支持度,以此减少高维数据中hubs对kNN分类带来的潜在负面影响。通过类加权的方式增加少数类在所有样本k发生中的分布比例,以提升对少数类样本的预测精度。在16个不平衡UCI数据集上的实验结果表明,该算法在高维不平衡数据中的分类结果优于典型kNN方法,且在普通维度的不平衡数据中优势同样明显。

面向高维数据的PCA-Hubness聚类方法

hub聚类算法可以解决传统聚类算法无法处理高维数据的问题。然而,由于它未考虑数据中的冗余和噪声特征,从而降低聚类性能。因此,提出PCA-Hubness聚类方法用于提高高维数据的聚类性能。PCA-Hubness聚类方法利用逆近邻数的偏度和本征维度的相互关系,以偏度的变化率为降维依据,保证在对高维数据降维时不会损失过多的有价值信息,有利于提高聚类效果。此算法在UCI数据集上进行实验,相比hub聚类算法,轮廓系数平均提高15%。

Philips iU22型及iE33型超声诊断设备故障维修案例分析

总结Philips iU22和iE33型超声诊断设备的结构及常见故障,分析其6例故障原因,确定故障维修方案更换相应元器件或重新安装系统软件等方法维修技术路线,为医院同行的日常维修工作提供一些参考。

脓毒症潜在相关基因的生物信息学分析及功能预测

目的 通过生物信息学方法筛选并分析与脓毒症相关的差异表达基因及关键(Hub)基因,以期为临床诊疗提供潜在靶点和生物标志物。方法 使用基因表达综合数据库(GEO)筛选脓毒症基因表达数据集,运用GEO自带在线分析工具(GEO2R)分析差异表达基因(DEGs);并结合STRING 12.0和DAVID数据库对DEGs进行富集分析;再通过Cytoscape 3.9.1软件筛选出Hub基因,并对其进行功能分析。结果 筛选出328个DEGs,其中上调基因2个,下调基因326个。富集结果显示,DEGs的功能主要与蛋白质修饰、分解代谢、蛋白酶复合物、线粒体及酶活性等有关。通过CytoHubba插件筛选出了核因子κB亚基1(NFKB1)、NEDD8、人NADH-泛醌氧化还原酶A8(NDUFA8)、POLR2F、延伸蛋白B(ELOB)、PSMB6、SEC61A1、COX4I1、人NADH-泛醌氧化还原酶B8(NDUFB8)及ATP5PD 10个Hub基因,经生物过程(BP)可视化发现这些Hub基因在生物过程中相互作用。结论 NFKB1、NEDD8、NDUFA8、POLR2F、ELOB、PSMB6、SEC61A1、COX4I1、NDUFB8及ATP5PD等10个Hub基因可能是脓毒症诊疗的潜在靶点和新型生物标志物,但部分基因对脓毒症发生发展的作用机制研究相对不足,仍需后续进一步验证。

基于HubGLasso注意力机制的脑网络分类研究

脑网络分类有助于脑疾病的早期诊断,也有益于理解脑疾病发病机理,具有重要的研究与应用价值。其中,卷积神经网络应用广泛,可以提取脑网络的拓扑特征,是脑网络分类中的一个前沿热点。然而,现有方法未考虑脑网络中Hub节点对脑功能的重要贡献,这可能会导致特征提取不充分,限制了它们的分类性能。为此,该文提出了一种基于HubGLasso注意力机制的卷积神经网络模型,用于进行脑网络分类任务。该方法包含了一种新的卷积层结构,首先利用GLasso模型去除脑网络中的冗余信息,然后引入Hub约束与注意力机制,使其能够提取与异常Hub结构相关的重要特征,并用于脑疾病诊断。实验结果表明,该方法在包含1112个被试的真实自闭症数据集上取得了68.67%的准确率,显著优于目前已有方法,证明了其应用价值。更进一步,通过对训练后的模型进行特征分析,能够得到与脑疾病相关的脑区信息与Hub节点结构信息,为脑疾病病理机制的研究提供了全新的视角。

Inhibitory effect of saffron on head and neck squamous cell carcinoma via targeting of ESR1 and CCND1 by its active compound crocetin

Background:Traditional Chinese medicine is promising for managing challenging and complex disorders,including cancer,and in particular,saffron is applied in treating various cancer types.However,its potential therapeutic efficacy and active components in managing squamous cell carcinoma of the head and neck(HNSCC)remain unclear yet.Methods:Using network pharmacology approaches,active ingredients of saffron,their target genes,and HNSCC-related genes were identified.Enrichment analyses were conducted for determining molecular functions and pathways enriched by genes that overlapped between the saffron target gene set and the HNSCC gene set.Among the four known active ingredients of saffron,crocetin was found to have the strongest inhibitory impact on HNSCC,based on the findings of cell viability and migration assays.Therefore,the potential target genes of crocetin in HNSCC cells were examined using molecular docking experiments and were confirmed by qPCR.Result s:Four active ingredients of saffron and 184 of their target genes were identified.Further,a total of 34 overlapping saffron-/HNSCC-associated targets related to the four active ingredients were screened,and crocetin was chosen for further investigation because it had the strongest inhibitory effect on HNSCC cells.Molecular docking experiments indicated that ESR1 and CCND1 were the target genes of crocetin.These results were confirmed through qPCR analysis,in which crocetin was found to lower the expression of the ESR1 and CCND1 genes in AMC-HN-8 and FaDu cells.Conclusion:According to our results,crocetin is a primary active anti-cancer component of saffron that may have potential in the development of novel HNSCC-treating medications.However,more thorough molecular research is necessary for confirming these results and elucidating the anti-cancer mechanism underlying saffron.

通过生物信息学分析肾移植后慢性排斥反应差异表达基因

目的:通过利用生物信息学技术分析肾移植后慢性排斥反应的差异表达基因,可以筛选出与该疾病发展相关的潜在致病靶点,为寻找新的治疗靶点提供了理论依据。方法:从基因表达谱综合数据库下载基因微阵列数据,并进行交叉计算以确定差异表达基因(DEGs)。将DEGs与基因本体(GO)分析是用来研究基因在不同条件下的表达差异以及其功能和相互关系的方法,而京都基因和基因组百科全书(KEGG)富集分析则是用来探索基因在特定生物过程中的功能和通路的工具。通过对免疫细胞浸润的分布进行计算,可以将排斥组的免疫浸润结果作为性状,在加权基因共表达网络分析(WGCNA)中进行分析,以获得与排斥相关的基因。然后,利用STRING数据库和Cytoscape软件构建蛋白质-蛋白质相互作用网络(PPI),以识别枢纽基因标记。结果:从3个数据集(GSE7392、GSE181757、GSE222889)共获得60个整合后的DEGs。通过GO及KEGG分析,GEDs主要集中在免疫应答的调节、防御反应、免疫系统过程的调节、刺激反应等。通路主要富集在抗原处理和呈递、EB病毒感染、移植物抗宿主、同种异体移植排斥、自然杀伤细胞介导的细胞毒性等。再利用WGCNA和PPI网络筛选后,HLA-A、HLA-B、HLA-F、TYROBP被鉴定为枢纽基因(Hub基因)。选择带有临床信息的数据GSE21374构建4个枢纽基因的诊断效能及风险预测模型图,结果认为4个Hub基因均具有良好诊断价值(曲线下面积在0.794-0.819)。从推理上可以得出结论,HLA-A、HLA-B、HLA-F和TYROBP这4种基因可能在肾移植后慢性排斥反应的发生和进展中具有重要作用。结论:DEGs在研究肾移植后慢性排斥反应的发病机制中起到重要作用,可以通过富集分析和枢纽基因筛选,以及相关诊断效能和疾病风险预测的推断分析,为进一步研究肾移植后慢性排斥反应的发病机制和发现新的治疗靶点提供理论支持。

基于加权基因共表达网络和癌症基因组图谱临床数据分析并鉴定肝细胞癌的Hub基因研究

背景 肝细胞癌(HCC)是全球常见的癌症相关死亡的第三大原因,约占所有原发性肝癌病例的90%,其复发率和死亡率较高,目前发生的分子机制仍不清楚。目的 探索HCC潜在的分子机制,发掘新的生物标志物。方法 从TCGA数据库下载RNA-seq表达数据和临床相关信息,通过差异基因表达分析正常肝脏组织与HCC组织的差异基因;对差异表达基因进行富集分析;基于TCGA中HCC的基因表达数据概况,使用WGCNA R包建立共表达网络,进行加权基因共表达网络分析(WGCNA),选择具有临床意义的模块,并筛选候选Hub基因;进一步分析候选Hub基因在HCC组织和正常肝脏组织显著差异表达、与HCC患者总体生存期和无病生存期是否显著相关,最终确定Hub基因;通过人类蛋白质图谱数据库对Hub基因蛋白表达进行验证。结果 本研究的基因表达数据来自50个正常肝脏组织样本和373个HCC组织样本。通过差异基因表达分析发现7 230个在HCC和正常肝脏组织之间差异表达的基因(HCC中3 691个上调基因和3 539个下调基因)。富集分析表明,上调的差异表达基因主要参与细胞周期调控和有丝分裂过程;下调的差异表达基因主要参与小分子代谢和有机酸代谢等过程。WGCNA确定了19个与HCC患者临床特征相关基因模块,通过分析模块与临床特征之间的关系,筛选出青色模块和紫色模块。青色模块基因中同时与患者总生存期和无病生存期强烈相关的前两个基因为VPS45和FAM189B;紫色模块基因中同时与患者总生存期和无病生存期强烈相关的前两个基因分别为CLEC1B和FCN3,因此将VPS45、FAM189B、CLEC1B和FCN3确定为最终的Hub基因。人类蛋白质图谱数据库免疫组织化学染色显示:VPS45和FAM189B在HCC组织中的表达高于正常肝脏组织,FCN3在HCC组织中的表达低于正常肝脏组织,CLEC1B在HCC组织和正常肝脏组织中表达差异不明显。结论 初步确定VPS45、FAM189B、CLEC1B和FCN3可能是HCC的新型潜在生物标志物,这些Hub基因可能为HCC的靶向治疗提供理论基础。

Identification of hub genes associated with Helicobacter pylori infection and type 2 diabetes mellitus:A pilot bioinformatics study

BACKGROUND Helicobacter pylori(H.pylori)infection is related to various extragastric diseases including type 2 diabetes mellitus(T2DM).However,the possible mechanisms connecting H.pylori infection and T2DM remain unknown.AIM To explore potential molecular connections between H.pylori infection and T2DM.METHODS We extracted gene expression arrays from three online datasets(GSE60427,GSE27411 and GSE115601).Differentially expressed genes(DEGs)commonly present in patients with H.pylori infection and T2DM were identified.Hub genes were validated using human gastric biopsy samples.Correlations between hub genes and immune cell infiltration,miRNAs,and transcription factors(TFs)were further analyzed.RESULTS A total of 67 DEGs were commonly presented in patients with H.pylori infection and T2DM.Five significantly upregulated hub genes,including TLR4,ITGAM,C5AR1,FCER1G,and FCGR2A,were finally identified,all of which are closely related to immune cell infiltration.The gene-miRNA analysis detected 13 miRNAs with at least two gene cross-links.TF-gene interaction networks showed that TLR4 was coregulated by 26 TFs,the largest number of TFs among the 5 hub genes.CONCLUSION We identified five hub genes that may have molecular connections between H.pylori infection and T2DM.This study provides new insights into the pathogenesis of H.pylori-induced onset of T2DM.

基于生物信息学分析溃疡性结肠炎的差异表达基因和miRNA

目的通过生物信息学分析方法寻找溃疡性结肠炎(UC)的差异表达基因(DEGs)及其相应的microRNA(miRNA),筛选参与UC发生发展相关的潜在致病靶点,为寻找UC诊断标志物以及新的治疗靶点提供理论依据。方法从基因表达数据库(GEO)获取数据集,通过GEO2R对数据集进行分组和筛选DEGs并取交集,再进行PPI、GO、KEGG分析和miRNA预测。结果GO分析显示其主要集中在中性粒细胞迁移、脂多糖应答、细胞外泌体、CXCR趋化因子受体结合等,KEGG分析显示其主要富集在补体途径凝血通路、IL-17信号通路、百日咳、类风湿性关节炎通路、肿瘤坏死因子信号通路等方面。通过Cytoscape筛选出MCC值前十的hub基因CXCL1、IL1B、TIMP1、CXCL8、IL6、MMP1、SERPINE1、PTGS2、SPP1、MMP2。通过NetworkAnalyst3.0在线网站进行可视化展示,可推断hsa-mir-204-5p、hsa-mir-146a-5p、hsa-mir-335-5p、hsa-mir-1-3p、hsa-mir-21-5p等5种miRNA在疾病发展中起关键作用。结论在UC发病机制相关研究中,DEGs与疾病的发生发展密切相关,可通过对基因的富集分析、以及hub基因、关键miRNA筛选为更深入研究UC的发病机制及寻找治疗新靶点提供研究思路和理论依据。

Transcriptome analysis reveals steroid hormones biosynthesis pathway involved in abdominal fat deposition in broilers

Excessive abdominal fat deposition reduces the feed efficiency and increase the cost of production in broilers.Therefore,it is an important task for poultry breeders to breed broilers with low abdominal fat.Abdominal fat deposition is a highly complex biological process,and its molecular basis remains elusive.In this study,we performed transcriptome analysis to compare gene expression profiles at different stages of abdominal fat deposition to identify the key genes and pathways involved in abdominal fat accumulation.We found that abdominal fat weight(AFW)increased gradually from day 35(D35)to 91(D91),and then decreased at day 119(D119).Accordingly,after detecting differentially expressed genes(DEGs)by comparing gene expression profiles at D35 vs.D63 and D35 vs.D91,and identifying gene modules associated with fat deposition by weighted gene co-expression network analysis(WGCNA),we performed intersection analysis of the detected DEGs and WGCNA gene modules and identified 394 and 435 intersecting genes,respectively.The results of the Gene Ontology(GO)functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses showed that the steroid hormone biosynthesis and insulin signaling pathways were co-enriched in all intersecting genes,steroid hormones have been shown that regulated insulin signaling pathway,indicating the importance of the steroid hormone biosynthesis pathway in the development of broiler abdominal fat.We then identified 6 hub genes(ACTB,SOX9,RHOBTB2,PDLIM3,NEDD9,and DOCK4)related to abdominal fat deposition.Further analysis also revealed that there were direct interactions between 6 hub genes.SOX9 has been shown to bind to proteins required for steroid hormone receptor binding,and RHOBTB2 indirectly regulates the steroid hormones biosynthesis through cyclin factor,and ultimately affect fat deposition.Our results suggest that the genes RHOBTB2 and SOX9 play an important role in fat deposition in broilers,by regulating steroid hormone synthesis.These findings provide new targets and directions for further studies on the mechanisms of fat deposition in chicken.

Key genes and regulatory networks for diabetic retinopathy based on hypoxia-related genes:a bioinformatics analysis

AIM:To prevent neovascularization in diabetic retinopathy(DR)patients and partially control disease progression.METHODS:Hypoxia-related differentially expressed genes(DEGs)were identified from the GSE60436 and GSE102485 datasets,followed by gene ontology(GO)functional annotation and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis.Potential candidate drugs were screened using the CMap database.Subsequently,a protein-protein interaction(PPI)network was constructed to identify hypoxia-related hub genes.A nomogram was generated using the rms R package,and the correlation of hub genes was analyzed using the Hmisc R package.The clinical significance of hub genes was validated by comparing their expression levels between disease and normal groups and constructing receiver operating characteristic curve(ROC)curves.Finally,a hypoxia-related miRNA-transcription factor(TF)-Hub gene network was constructed using the NetworkAnalyst online tool.RESULTS:Totally 48 hypoxia-related DEGs and screened 10 potential candidate drugs with interaction relationships to upregulated hypoxia-related genes were identified,such as ruxolitinib,meprylcaine,and deferiprone.In addition,8 hub genes were also identified:glycogen phosphorylase muscle associated(PYGM),glyceraldehyde-3-phosphate dehydrogenase spermatogenic(GAPDHS),enolase 3(ENO3),aldolase fructose-bisphosphate C(ALDOC),phosphoglucomutase 2(PGM2),enolase 2(ENO2),phosphoglycerate mutase 2(PGAM2),and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3(PFKFB3).Based on hub gene predictions,the miRNA-TF-Hub gene network revealed complex interactions between 163 miRNAs,77 TFs,and hub genes.The results of ROC showed that the except for GAPDHS,the area under curve(AUC)values of the other 7 hub genes were greater than 0.758,indicating their favorable diagnostic performance.CONCLUSION:PYGM,GAPDHS,ENO3,ALDOC,PGM2,ENO2,PGAM2,and PFKFB3 are hub genes in DR,and hypoxia-related hub genes exhibited favorable diagnostic performance.

Single-cell transcriptomics reveals T-cell heterogeneity and immunomodulatory role of CD4^(+) T native cells in Candida albicans infection

Objective:Candida albicans is a common fungal pathogen that triggers complex host defense mechanisms,including coordinated innate and adaptive immune responses,to neutralize invading fungi effectively.Exploring the immune microenvironment has the potential to inform the development of therapeutic strategies for fungal infections.Methods:The study analyzed individual immune cell profiles in peripheral blood mononuclear cells from Candida albicans-infected mice and healthy control mice using single-cell transcriptomics,fluorescence quantitative PCR,and Western blotting.We investigated intergroup differences in the dynamics of immune cell subpopulation infiltration,pathway enrichment,and differentiation during Candida albicans infection.Results:Our findings indicate that infiltration of CD4^(+)naive cells,regulatory T(Treg)cells,and Microtubules(MT)-associated cells increased after infection,along with impaired T cell activity.Notably,CD4^(+) T cells and plasma cells were enhanced after infection,suggesting that antibody production is dependent on T cells.In addition,we screened 6 hub genes,transcription factor forkhead box protein 3(Foxp3),cytotoxic T-lymphocyte associated protein 4(CTLA4),Interleukin 2 Receptor Subunit Beta(Il2rb),Cd28,C-C Motif Chemokine Ligand 5(Ccl5),and Cd27 for alterations associated with CD4^(+) T cell differentiation.Conclusions:These results provide a comprehensive immunological landscape of the mechanisms of Candida albicans infection and greatly advance our understanding of adaptive immunity in fungal infections.

Identification of differentially expressed mRNAs as novel predictive biomarkers for gastric cancer diagnosis and prognosis

BACKGROUND Gastric cancer(GC)has a high mortality rate worldwide.Despite significant progress in GC diagnosis and treatment,the prognosis for affected patients still remains unfavorable.AIM To identify important candidate genes related to the development of GC and iden-tify potential pathogenic mechanisms through comprehensive bioinformatics analysis.METHODS The Gene Expression Omnibus database was used to obtain the GSE183136 dataset,which includes a total of 135 GC samples.The limma package in R software was employed to identify differentially expressed genes(DEGs).Thereafter,enrichment analyses of Gene Ontology(GO)terms and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathways were performed for the gene modules using the clusterProfile package in R software.The protein-protein interaction(PPI)networks of target genes were constructed using STRING and visualized by Cytoscape software.The common hub genes that emerged in the cohort of DEGs that was retrieved from the GEPIA database were then screened using a Venn Diagram.The expression levels of these overlapping genes in stomach adenocarcinoma samples and non-tumor samples and their association with prognosis in GC patients were also obtained from the GEPIA database and Kaplan-Meier curves.Moreover,real-time quantitative polymerase chain reaction(RT-qPCR)and western blotting were performed to determine the mRNA and protein levels of glutamic-pyruvic transaminase(GPT)in GC and normal immortalized cell lines.In addition,cell viability,cell cycle distribution,migration and invasion were evaluated by cell counting kit-8,flow cytometry and transwell assays.Furthermore,we also conducted a retrospective analysis on 70 GC patients diagnosed and surgically treated in Wenzhou Central Hospital,Dingli Clinical College of Wenzhou Medical University,The Second Affiliated Hospital of Shanghai University between January 2017 to December 2020.The tumor and adjacent normal samples were collected from the patients to determine the potential association between the expression level of GPT and the clinical as well as pathological features of GC patients.RESULTS We selected 19214 genes from the GSE183136 dataset,among which there were 250 downregulated genes and 401 upregulated genes in the tumor samples of stage III-IV in comparison to those in tumor samples of stage I-II with a P-value<0.05.In addition,GO and KEGG results revealed that the various upregulated DEGs were mainly enriched in plasma membrane and neuroactive ligand-receptor interaction,whereas the downregulated DEGs were primarily enriched in cytosol and pancreatic secretion,vascular smooth muscle contraction and biosynthesis of the different cofactors.Furthermore,PPI networks were constructed based on the various upregulated and downregulated genes,and there were a total 15 upregulated and 10 downregulated hub genes.After a comprehensive analysis,several hub genes,including runt-related transcription factor 2(RUNX2),salmonella pathogenicity island 1(SPI1),lysyl oxidase(LOX),fibrillin 1(FBN1)and GPT,displayed prognostic values.Interestingly,it was observed that GPT was downregulated in GC cells and its upregulation could suppress the malignant phenotypes of GC cells.Furthermore,the expression level of GPT was found to be associated with age,lymph node metastasis,pathological staging and distant metastasis(P<0.05).CONCLUSION RUNX2,SPI1,LOX,FBN1 and GPT were identified key hub genes in GC by bioinformatics analysis.GPT was significantly associated with the prognosis of GC,and its upregulation can effectively inhibit the proliferative,migrative and invasive capabilities of GC cells.

Comprehensive analysis of the potential pathogenesis of COVID-19 infection and liver cancer

BACKGROUND A growing number of clinical examples suggest that coronavirus disease 2019(COVID-19)appears to have an impact on the treatment of patients with liver cancer compared to the normal population,and the prevalence of COVID-19 is significantly higher in patients with liver cancer.However,this mechanism of action has not been clarified.Gene sets for COVID-19(GSE180226)and liver cancer(GSE87630)were obtained from the Gene Expression Omnibus database.After identifying the common differentially expressed genes(DEGs)of COVID-19 and liver cancer,functional enrichment analysis,protein-protein interaction network construction and scree-ning and analysis of hub genes were performed.Subsequently,the validation of the differential expression of hub genes in the disease was performed and the regulatory network of transcription factors and hub genes was constructed.RESULTS Of 518 common DEGs were obtained by screening for functional analysis.Fifteen hub genes including aurora kinase B,cyclin B2,cell division cycle 20,cell division cycle associated 8,nucleolar and spindle associated protein 1,etc.,were further identified from DEGs using the“cytoHubba”plugin.Functional enrichment analysis of hub genes showed that these hub genes are associated with P53 signalling pathway regulation,cell cycle and other functions,and they may serve as potential molecular markers for COVID-19 and liver cancer.Finally,we selected 10 of the hub genes for in vitro expression validation in liver cancer cells.CONCLUSION Our study reveals a common pathogenesis of liver cancer and COVID-19.These common pathways and key genes may provide new ideas for further mechanistic studies.

The role of exercise in modulating the HP pathway to reduce glioma-induced epilepsy

Background:Glioma-induced refractory epilepsy can be alleviated through conventional exercise,providing a potential therapeutic approach to manage this condition.This study aims to investigate the underlying mechanisms.Methods:Bioinformatics methodologies were employed to scrutinize gene expression data from public repositories such as GEO,with a specific focus on mobility-related genes in epilepsy.Through differential and enrichment analyses,differentially expressed genes(DEGs)were identified,while protein-protein interaction networks elucidated pivotal hub genes.Results:Our analysis revealed 32 DEGs,comprising 23 upregulated and 9 downregulated genes.Enrichment analysis underscored significant alterations in immune pathways in epilepsy.Two central hub genes,haptoglobin(HP)and prostaglandin-endoperoxide synthase 2(PTGS2),were found to be modulated by Arginase 1(ARG1)and Chemokine(C-X-C motif)ligand 8(CXCL8).GSVA analysis associated elevated PTGS2 expression with metabolic pathways,while increased HP expression was correlated with angiogenesis and inflammation.Subsequent experiments validated HP’s role in tumor cell proliferation,emphasizing its potential as a therapeutic target.Conclusion:This study highlights the crucial involvement of HP and PTGS2 genes in the etiology of epilepsy,linked to discrepancies in the immune system.These findings offer fresh perspectives on the management of epilepsy,emphasizing the neuroprotective possibilities of targeting specific gene pathway.

Functional investigation and two-sample Mendelian randomization study of primary biliary cholangitis hub genes

BACKGROUND The identification of specific gene expression patterns is crucial for understanding the mechanisms underlying primary biliary cholangitis(PBC)and finding relevant biomarkers for diagnosis and therapeutic evaluation.AIM To determine PBC-associated hub genes and assess their clinical utility for disease prediction.METHODS PBC expression data were obtained from the Gene Expression Omnibus database.Overlapping genes from differential expression analysis and weighted gene coexpression network analysis(WGCNA)were identified as key genes for PBC.Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were performed to explore the potential roles of key genes.Hub genes were identified in protein-protein interaction(PPI)networks using the Degree algorithm in Cytoscape software.The relationship between hub genes and immune cells was investigated.Finally,a Mendelian randomization study was conducted to determine the causal effects of hub genes on PBC.RESULTS We identified 71 overlapping key genes using differential expression analysis and WGCNA.These genes were primarily enriched in pathways related to cytokinecytokine receptor interaction,and Th1,Th2,and Th17 cell differentiation.We utilized Cytoscape software and identified five hub genes(CD247,IL10,CCL5,CCL3,and STAT3)in PPI networks.These hub genes showed a strong correlation with immune cell infiltration in PBC.However,inverse variance weighting analysis did not indicate the causal effects of hub genes on PBC risk.CONCLUSION Hub genes can potentially serve as valuable biomarkers for PBC prediction and treatment,thereby offering significant clinical utility.

To analyze the differentially expressed genes in chronic rejection after renal transplantation by bioinformatics

Objective: To use bioinformatics technology to analyse differentially expressed genes in chronic rejection after renal transplantation, we can screen out potential pathogenic targets associated with the development of this disease, providing a theoretical basis for finding new therapeutic targets. Methods: Gene microarray data were downloaded from the Gene Expression Profiling Integrated Database (GEO) and cross-calculated to identify differentially expressed genes (DEGs). Analysis of differentially expressed genes (DEGs) with gene ontology (GO) is a method used to study the differences in gene expression under different conditions as well as their functions and interrelationships, while Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis is a tool used to explore the functions and pathways of genes in specific biological processes. By calculating the distribution of immune cell infiltration, the result of immune infiltration in the rejection group can be analysed as a trait in Weighted Gene Co-Expression Network Analysis (WGCNA) for genes associated with rejection. Then, protein-protein interaction networks (PPI) were constructed using the STRING database and Cytoscape software to identify hub gene markers. Results: A total of 60 integrated DEGs were obtained from 3 datasets (GSE7392, GSE181757, GSE222889). By GO and KEGG analysis, the GEDs were mainly concentrated in the regulation of immune response, defence response, regulation of immune system processes, and stimulation response. The pathways were mainly enriched in antigen processing and presentation, EBV infection, graft-versus-host, allograft rejection, and natural killer cell-mediated cytotoxicity. After further screening using WGCNA and PPI networks, HLA-A, HLA-B, HLA-F, and TYROBP were identified as hub genes (Hub genes). The data GSE21374 with clinical information was selected to construct the diagnostic efficacy and risk prediction model plots of the four hub genes, and the results concluded that all four Hub genes had good diagnostic value (area under the curve in the range of 0.794-0.819). From the inference, it can be concluded that the four genes, HLA-A, HLA-B, HLA-F and TYROBP, may have an important role in the development and progression of chronic rejection after renal transplantation. Conclusion: DEGs play an important role in the study of the pathogenesis of chronic rejection after renal transplantation, and can provide theoretical support for further research on the pathogenesis of chronic rejection after renal transplantation and the discovery of new therapeutic targets through enrichment analysis and pivotal gene screening, as well as inferential analyses of related diagnostic efficacy and disease risk prediction.