基于转录组学数据分析痛风合并动脉粥样硬化的发病机制

目的]采用GEO数据库探讨痛风合并动脉粥样硬化(As)的共同发病机制。 [方法]从GEO数据库中下载痛风(GSE160170)和As(GSE100927)的基因表达矩阵,分析痛风和As的差异表达基因(DEG),并分别进行富集分析。在分析共同差异表达基因(CDEG)后,对其进行功能富集分析、蛋白质-蛋白质相互作用(PPI)网络分析和核心基因(HG)鉴定,并对HG进行共表达分析及验证。最后,分析痛风、As的免疫细胞浸润,同时探索HG与浸润免疫细胞(IIC)之间的相关性。 [结果]GSE160170数据集中获得了1 606个DEG,GSE100927数据集中获得了481个DEG。其中的22个CDEG富集分析结果表明,细胞因子的调控作用可能是痛风合并As的关键机制。通过使用CytoHubba插件识别了6个HG(CCR2、CD2、FCGR3A、FGD3、IL10RA、SIGLEC1),结果显示这些HG尚且可靠。共表达网络显示这些HG可以影响肿瘤坏死因子超家族细胞因子产生的调节作用。免疫细胞浸润分析表明,痛风中的NK细胞表达显著增加,且与CCR2基因呈显著相关;As中的活化肥大细胞表达显著增加,且与CD2基因呈显著相关。 [结论]肿瘤坏死因子超家族细胞因子产生的调节作用很可能是痛风合并As的核心因素。     

心肌梗死相关miRNA-mRNA调控网络及其作用

目的 探讨心肌梗死相关的微小RNA(miRNA)-信使RNA(mRNA)调控网络,并分析其对心肌梗死发生发展的调控作用。方法 从基因表达数据库下载微阵列数据（GSE61741）,用R语言进行差异分析,并对获得的差异表达miRNA进行上游转录因子及下游靶基因的预测;从GEO中获取GSE66360数据集,用R语言进行差异分析,将差异表达基因与靶基因取交集获得目标基因;对目标基因进行基因本体（GO）生物学功能注释和京都基因与基因组百科全书（KEGG）通路富集分析,构建蛋白质相互作用（PPI）网络并筛选出Hub基因及核心miRNA。结果 共得到17个差异表达miRNA,其中4个表达上调、13个表达下调,其主要转录因子为2级POU结构域转录因子1、特异性蛋白1、早期生长反应1。差异表达miRNA靶基因与差异表达mRNA取交集共得到165个目标基因。GO富集分析发现其主要与细菌来源分子的反应、三级颗粒、细胞因子活性、趋化因子活性等生物过程和分子功能相关;KEGG通路富集分析显示目标基因主要聚集在TNF、IL-17、NF-κB等信号通路。miRNA-mRNA网络筛选出7个核心基因（JUN、CXCL8...     

食管鳞状细胞癌与胃腺癌共同致病机制的网络分析

目的 探讨食管鳞状细胞癌与胃腺癌共同致病机制。方法 我们使用基因表达综合数据库(Gene Expression Omnibus, GEO)鉴定出食管鳞状细胞癌和胃腺癌的共同差异表达基因,并通过功能富集分析,蛋白互作网络(PPI网络)及转录因子调控网络的构建进一步揭示共同致病基因的生物学特征。结果 89个共同上调差异基因和27个共同下调差异基因构成一个PPI网络,然后筛选前15个核心基因。在删除验证组中无统计学意义的基因后得到12个核心基因,分别是：UBE2C,CXCL8,SPARC,COL1A1,COL1A2,KIF20A,COL3A1,TPX2,THBS1,COL5A1,COL4A1,SERPINH1。本研究继续对基因的转录因子进行富集分析并验证,然后构建转录因子靶基因调控网络。3个转录因子(HDAC2,NFKB1,RELA)调控3个核心基因(CXCL8,COL1A2,COL1A1)的网络分析进一步阐明了食管鳞状细胞癌与胃腺癌的共同致病途径。结论 本研究发现3个转录因子调控3个核心基因的共同致病基因网络。这与食管鳞状细胞癌和胃腺癌共同致病机制高度有关。其中HDAC2转录因子通过调控C...     

非酒精性脂肪性肝病及动脉粥样硬化的基因串扰综合分析

目的]使用生物信息学的方法寻找非酒精性脂肪性肝病(NAFLD)和动脉粥样硬化(As)的共同转录特征,通过两种疾病的基因串扰分析,挖掘NAFLD相关As新的潜在机制和关键靶点,并进一步在动物组织和人血清样本中验证关键靶点的表达水平。 [方法]GEO数据库下载NAFLD(数据集GSE89632)和As(数据集GSE43292)的基因表达谱,进行差异基因分析和加权基因共表达网络分析,筛选两种疾病的共享基因。通过String数据库、蛋白质互作分析和R软件等工具对共享基因进行富集分析。利用Cytoscape软件计算、外部数据集(GSE100927)验证及机器学习方法(LASSO回归)筛选出核心基因。最后,通过构建高脂饮食非酒精脂肪肝和As小鼠模型以及收集NAFLD合并冠心病患者的外周血清,验证重要的核心基因。 [结果]识别出两种疾病的75个共享基因,发现共享基因的主要富集通路,包括细胞因子-细胞因子受体相互作用、IL-17信号通路、脂质和As、NF-κB信号通路等。综合多种生物信息学方法,最终筛选出2个重要的核心基因(MMP-9和CCL3)。动物实验验证结果表明,高脂饮食组小鼠肝脏和主动脉窦组织的MMP-9和CCL3含量都明显升高,高脂饮食组小鼠肝脏组织的MMP-9和CCL3含量分别为对照组的2.43倍(P＜0.001)和1.35倍(P＜0.01),高脂饮食组小鼠主动脉窦组织的MMP-9和CCL3含量分别为对照组的2.10倍(P＜0.001)和1.58倍(P＜0.01)。人血清样本验证结果表明,NAFLD合并冠心病患者血清中的MMP-9和CCL3含量分别为单纯冠心病患者的1.21倍(P＜0.01)和1.29倍(P＜0.01)。 [结论]本研究基于生物信息学分析发现MMP-9和CCL3可能是NAFLD相关As中发挥关键作用的核心基因,为研究NAFLD相关As提供一定的靶点参考。     

系统性红斑狼疮初发患者Th细胞异常分化相关分子基因表达研究

目的：研究系统性红斑狼疮（SLE）患者存在Th1/Th2失衡与其调控细胞因子及其受体密切相关的基础，探讨SLE患者在调控Th1/Th2分化的其他环节上也存在有基因表达的异常。方法：采用TaqMan Real Time PCR的方法研究38例初发SLE患者调控Th类细胞分化的细胞转录调控水平（IkB,IRF-1,STAT4,GATA3,IL-4R)，细胞趋化，粘附和迁移有关的基因（CCR1、CCR2、CCR4、CCR5）、与细胞凋亡相关的基因（caspase1)、参与信号传导（CD38）基因的表达，并以28名正常人和50例类风湿关节炎（RA）患者作为对照。结果：（1）与正常人比初发SLE患者调控向Th1分化的白细胞介素（IL）－12／IL－2Rβ／STAT4通路中STAT4的表达水平并示见显著降低，反倒升高（P＜0．01）；IRF－1的水平无明显改变（P＞0．05），调控向Th2分化的IL－4／IL－R／GATA3通路中GATA3的表达降低（P＜0．01）。（2）以Th1占优势的RA患者中，调控向Th2分化的IL－4R表达降低（P＜0．01），但GATA3的表达却升高（P＜0．01），较初发SLE患者STAT4升高（P＜0．01）,IkB,IRF-1的基因表达水平降低（P＜0．01）；GATA3，IL－4R也升高（P＜0．01）。结论：在SLE患者中IL－12、IL－18通过和IL－12R在发挥调控Th1/Th2中的异常可能还通过其他的调控途径或STAT4，GATA3磷酸化及转位存在缺陷引起；RA和SLF患者在调控Th1/Th2分化的信号转导因子表达水平中未出现明显的以Th1或Th2占优势异常的格局；以及疾病的不同阶段，其调控基因的表达有差异；CCR1、CCR2、CCR4、CCR5的升高在RA的发病中起着重要的作用。     

基于生物信息学技术筛选缺血性脑卒中相关转录因子及中药活性成分研究

目的 基于生物信息学技术筛选缺血性脑卒中（IS）相关转录因子（TF）及中药活性成分。方法 在基因表达综合数据库（GEO）中选择GSE16561、GSE58294、GSE162955芯片数据集，使用R 4.0.5软件对GSE16561、GSE58294数据集进行预处理并筛选差异表达基因（DEGs），然后进行基因集富集分析（GSEA）。通过STRING平台构建DEG的蛋白质-蛋白质相互作用（PPI）网络并筛选关键DEG，通过GSE162955数据集绘制ROC曲线，以评估关键DEG对IS的诊断价值；然后通过TRRUST平台构建TF-miRNA-核心DEG调控网络，筛选枢纽TF、主要miRNA及核心DEG。通过Coremine Medical及Uniprot平台预测并筛选IS相关中药活性成分。结果 韦恩图结果显示，GSE16561数据集和GSE58294数据集的交集DGEs共220个，包括130个上调DGEs和90个下调DEGs。GSEA结果显示，KEGG通路主要富集在MAPK信号通路、神经营养因子信号通路和趋化因子信号通路；GO功能生物过程主要富集在中心粒细胞外渗，细胞成分主要富集在细胞器内...     

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。     

转录因子KLF2和KLF4调控人血管内皮细胞血管稳态相关基因表达的特征

[目的] Krüppel样因子(KLF)2和4是与血管稳态密切相关的两个核心转录因子,具有抗炎、抗钙化、抗血栓等多重保护效应。本研究旨在在内皮细胞中阐明并验证KLF2和KLF4共同调控的血管稳态相关基因谱。[方法]使用腺病毒(Ad-KLF2或Ad-KLF4)及对照病毒(Ad-NC)处理人脐静脉内皮细胞(HUVEC)24 h后提取RNA并进行转录组测序分析。过表达KLF2和KLF4的测序结果与已报道的KLF2/KLF4双基因敲除鼠测序结果进行叠加。筛选出的差异表达基因通过实时荧光定量PCR在Ad-KLF2或Ad-KLF4处理的HUVEC以及在阿托伐他汀或白藜芦醇处理的HUVEC中进行验证。[结果]转录组学叠加发现,KLF2和KLF4上调的差异基因有256个,KEGG通路富集分析显示这些差异基因主要富集于肥厚型心肌病、扩张型心肌病、ECM-受体交互以及黏着斑、致心律失常性右心室心肌病等;KLF2和KLF4下调的差异基因有145个,KEGG通路富集分析显示这些差异基因主要富集于癌症中的microRNA、糖胺聚糖生物合成-硫酸软骨素/硫酸皮聚糖、矿物质吸收、p53信号通路以及氨基酸生物合成等。...     

热休克因子1调控的细胞凋亡相关基因的筛选与鉴定

[目的] 观察热休克因子1在热休克反应时对细胞凋亡相关基因表达的调控，同时筛选和初步鉴定热休克因子1调控的下游凋亡相关基因。方法 采用热休克因子1基因敲除小鼠热休克反应模型，抽提热休克因子1基因敲除小鼠和野生型小鼠心肌和肺组织的总RNA进行基因芯片实验，观察凋亡相关基因表达的情况；同时采用生物信息学技术对上述差异表达基因的启动子区进行分析，筛选含有热休克元件的基因；通过逆转录一聚合酶链反应进一步验证热休克因子1对上述基因表达的调控。结果 热休克反应后，野生型小鼠与热休克因子1基因敲除小鼠组织中差异表达的细胞凋亡相关基因有40个。经Genomatix和TESS软件分析发现其中Fasl、Fas、Bim、Bak等8个基因的启动子区含有热休克因子1结合位点。经逆转录一聚合酶链反应证实，热休克反应使Fasl在热休克因子1基因敲除小鼠的表达较野生型小鼠明显增高，而在稳定转染热休克因子1的Raw264.7细胞，Fasl的表达较转空载体细胞明显降低。结论 热休克因子1可调控FasL等多个细胞凋亡相关基因的表达。     

干扰素γ促进胰岛β细胞中白细胞介素18和白细胞介素1β转换酶的表达

目的：探讨白细胞介素18（IL－18）和IL－1β转换酶（caspase-1 )在胰岛β细胞的表达状况及其调控机制。方法：大鼠胰岛素瘤（RIN）细胞，FACS纯化的大鼠胰岛β细胞，大鼠离体胰岛和干扰素调节因子1（IRF－1）基因敲除小鼠的离体胰岛与细胞因子孵育后，用逆转录－聚合酶链反应（RT－PCR）方法检测IL－18和caspase-1 mRNA的表达，用Western blot方法检测IL－18蛋白的表达。结果：（1）干扰素γ（IFN-γ）可增强IL－18mRNA在RIN细胞，纯化大鼠β细胞和大离体胰岛中的表达，该效应在IRF－1基因敲除小鼠胰岛中未见明显变化，（2）IFN-γ显著增强caspase-1mRNA在RIN细胞和大鼠离体胰岛中的表达，该效应在IRF－1基因敲除小鼠胰岛中完全消失；（3）在RIN细胞的培养上清液和细胞裂解物中未能检测到IL－18蛋白。结论：IFN-γ上调IL－18和caspase-1在胰岛β细胞中的表达，IL－18的表达不受转录因子IRF－1的影响，caspse-1的表达呈IRF－1依赖性。     

冠状病毒感染相关心肌损伤机制的组学分析及治疗药物的预测

目的研究冠状病毒感染相关心肌损伤机制并预测可能有效的治疗药物。方法在基因表达数据库检索并筛选得到GSE59185数据集,根据不同的亚型分为wt组、ΔE组、Δ3组、Δ5组和对照组。用R语言Limma程序包对各组进行差异表达基因分析,将各组上调、下调表达基因分别取交集,作为共同差异表达基因,在DAVID数据库进行基因本体学(GO)和京都基因与基因组百科全书(KEGG)通路富集分析。采用自主研发的表观精准治疗预测平台(EpiMed)进行治疗药物预测。用STRING数据库对共同差异表达基因构建蛋白质互作网络并筛选核心基因。结果各组差异表达基因分析,共交集上调基因191个,下调基因18个,共同差异表达基因共209个。GO富集分析发现,共同差异基因主要富集在病毒反应、病毒防御反应、Ⅰ型干扰素反应、γ干扰素调节、γ干扰素介导的信号通路、先天免疫反应调节等;KEGG通路富集主要与细胞因子与受体相互作用、病毒蛋白与细胞因子和细胞因子受体相互作用、TNF、Toll样受体、缺氧诱导因子1及白细胞介素17信号通路等有关。通过EpiMed预测的药物主要为白藜芦醇、利托那韦、维甲酸、连翘、鱼腥草等。网络分析筛选得到干扰素调节因子7、干扰素刺激基因15、抗粘液病毒基因1、β型蛋白酶体亚基8、干扰素调节因子9、 2′,5′-寡聚腺苷酸合成酶(OAS)1、OAS2、OAS3、含基本S腺苷蛋氨酸域2、2′,5′-寡聚腺苷酸合成酶样等核心基因。结论多个炎症通路的异常活化可能是冠状病毒感染后患者发生心肌损伤的原因。白藜芦醇、利托那韦、维甲酸、连翘、鱼腥草可能对此具有治疗作用。     

急性胰腺炎基因表达谱芯片的生物信息学分析及药物筛选

目的应用生物信息学方法筛选急性胰腺炎(AP)差异表达基因(DEGs)及相应的候选治疗药物。方法从基因表达数据库(GEO)中下载小鼠AP相关的高通量芯片数据集(GSE109227和GSE65146),使用GEO2R筛选DEGs。利用DAVID数据库对DEGs进行基因本体功能富集和通路富集分析。在String数据库中建立蛋白-蛋白相互作用关系(PPI)并利用Cytoscape软件进行可视化,筛选出子网络模块和关键基因。预测关键基因相关的miRNAs并通过比较毒物遗传学数据库(CTD)针对关键基因进行治疗药物的筛选。结果从高通量芯片数据集GSE109227和GSE65146中共筛选到130个上调基因和16个下调基因。DEGs主要参与炎症反应、中性粒细胞趋化、TNF介导的细胞反应、正调控基因表达等生物学过程,且参与细胞外基质受体相互作用、肌动蛋白细胞骨架的调控、白细胞内皮迁移、Focal adhesion等信号通路。在PPI网络中,共筛选出12个关键基因和6个子网络模块。miR-199a-5p、miR-1-3p等miRNAs可能作用于关键基因转录后调控。CTD数据库中筛选到染料木黄酮、白藜芦醇、槲皮素可降低关键基因表达水平。结论利用生物信息学方法筛选的相关基因可能在AP发生中具有重要作用,并可作为药物的筛选依据。     

基于GEO数据库对胆管癌潜在分子靶点的筛选及生物信息学分析

背景胆管癌恶性程度高,预后差.靶向治疗是胆管癌的重要研究方向,探索新的分子靶点对于胆管癌靶向治疗至关重要.目的用生物信息学分析方法挖掘胆管癌的枢纽基因,为胆管癌的靶向治疗提供潜在分子靶点.方法从GEO数据库中下载2组胆管癌表达谱芯片数据,采用GEO2R在线分析工具筛选胆管癌肿瘤组织与正常组织差异表达基因,对差异表达基因作GO富集分析、KEGG通路分析、蛋白质相互作用网络分析,利用Cytoscape软件筛选枢纽基因.使用GEPIA数据库对枢纽基因在胆管癌组织中的表达量进行验证.结果共得到共同差异表达基因158个.GO富集分析结果显示,差异基因主要参与细胞对锌离子反应、细胞增殖与粘附、代谢以及蛋白质聚合等生物学过程,主要存在外泌体、胞外区、弹性纤维等区域,主要分子功能与结合肝素、半胱氨酸型内肽酶抑制剂活性、蛋白质同源二聚化、受体结合及磷酸吡哆醛结合等相关.KEGG通路分析结果显示,差异基因主要参与矿物质吸收、代谢、PPAR信号通路及脂肪酸降解等过程.基于String数据库构建蛋白质相互作用网络图,Cytoscape软件CytoHubba插件筛选枢纽基因,皆为上调基因.GEPIA数据库验证枢纽基因在胆管癌组织中表达量显著高于正常组织.结论本研究获取了8个与胆管癌相关的枢纽基因,分别是NUSAP1,TOP2A,RAD51AP1,MCM4,KIAA0101,CDCA5,TYMS,ZWINT.这些基因为深入研究胆管癌的靶向治疗提供了新思路,有望成为新的分子治疗靶点.     

Analysis of the autophagy gene expression profile of pancreatic cancer based on autophagy-related protein microtubule-associated protein 1A/1B-light chain 3

BACKGROUND Pancreatic cancer is a highly invasive malignant tumor. Expression levels of the autophagy-related protein microtubule-associated protein 1 A/1 B-light chain 3(LC3) and perineural invasion(PNI) are closely related to its occurrence and development. Our previous results showed that the high expression of LC3 was positively correlated with PNI in the patients with pancreatic cancer. In this study, we further searched for differential genes involved in autophagy of pancreatic cancer by gene expression profiling and analyzed their biological functions in pancreatic cancer, which provides a theoretical basis for elucidating the pathophysiological mechanism of autophagy in pancreatic cancer and PNI.AIM To identify differentially expressed genes involved in pancreatic cancer autophagy and explore the pathogenesis at the molecular level.METHODS Two sets of gene expression profiles of pancreatic cancer/normal tissue(GSE16515 and GSE15471) were collected from the Gene Expression Omnibus.Significance analysis of microarrays algorithm was used to screen differentially expressed genes related to pancreatic cancer. Gene Ontology(GO) analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis were used to analyze the functional enrichment of the differentially expressed genes. Protein interaction data containing only differentially expressed genes was downloaded from String database and screened. Module mining was carried out by Cytoscape software and ClusterOne plug-in. The interaction relationship between the modules was analyzed and the pivot nodes between the functional modules were determined according to the information of the functional modules and the data of reliable protein interaction network.RESULTS Based on the above two data sets of pancreatic tissue total gene expression, 6098 and 12928 differentially expressed genes were obtained by analysis of genes with higher phenotypic correlation. After extracting the intersection of the two differential gene sets, 4870 genes were determined. GO analysis showed that 14 significant functional items including negative regulation of protein ubiquitination were closely related to autophagy. A total of 986 differentially expressed genes were enriched in these functional items. After eliminating the autophagy related genes of human cancer cells which had been defined, 347 differentially expressed genes were obtained. KEGG pathway analysis showed that the pathways hsa04144 and hsa04020 were related to autophagy. In addition,65 clustering modules were screened after the protein interaction network was constructed based on String database, and module 32 contains the LC3 gene,which interacts with multiple autophagy-related genes. Moreover, ubiquitin C acts as a pivot node in functional modules to connect multiple modules related to pancreatic cancer and autophagy.CONCLUSION Three hundred and forty-seven genes associated with autophagy in human pancreatic cancer were concentrated, and a key gene ubiquitin C which is closely related to the occurrence of PNI was determined, suggesting that LC3 may influence the PNI and prognosis of pancreatic cancer through ubiquitin C.     

基于GEO和TCGA数据库食管鳞状细胞癌预后模型构建与验证

目的探讨食管鳞状细胞癌(esophageal squamous cell carcinoma,ESCC)组织与正常组织之间的差异表达基因,构建ESCC的预后相关模型并验证其临床应用价值。方法首先,基于GSE20347、GSE23400、GSE26886、GSE45168、GSE77861数据集确定ESCC的差异表达基因。其次,经通路富集后使用STRING和Cytoscape软件筛选关键基因。随后,Kaplan-Meier Plotter和单变量Cox回归用于生存分析,多因素Cox回归应用于构建预后模型。实时荧光定量PCR用于验证预后相关基因的差异表达情况。此外,我们通过Kaplan-Meier曲线、ROC曲线、一致性指数、灵敏度和特异度验证模型的预后价值。最后,利用基因集富集分析进一步探讨ESCC预后机制。结果本研究发现98个差异表达基因和15个关键基因,其中6个关键基因与预后相关。此外,基于VCAN、ALOX12和ACPP的预后模型经验证临床应用价值较好。结论基于VCAN、ALOX12和ACPP的预后模型可独立预测ESCC的预后。     

Bioinformatics analysis of aberrantly methylated-differentially expressed genes and pathways in hepatocellular carcinoma

AIM To discover methylated-differentially expressed genes(MDEGs) in hepatocellular carcinoma(HCC) and to explore relevant hub genes and potential pathways. METHODS The data of expression profiling GSE25097 and methylation profiling GSE57956 were gained from GEO Datasets. We analyzed the differentially methylated genes and differentially expressed genes online using GEO2 R. Functional and enrichment analyses of MDEGs were conducted using the DAVID database. A protein-protein interaction(PPI) network was performed by STRING and then visualized in Cytoscape. Hub genes were ranked by cytoH ubba, and a module analysis of the PPI network was conducted by MCODE in Cytoscape software. RESULTS In total, we categorized 266 genes as hypermethylated, lowly expressed genes(Hyper-LGs) referring to endogenous and hormone stimulus, cell surface receptor linked signal transduction and behavior. In addition, 161 genes were labelled as hypomethylated, highly expressed genes(Hypo-HGs) referring to DNA replication and metabolic process, cell cycle and division. Pathway analysis illustrated that Hyper-LGs were enriched in cancer, Wnt, and chemokine signalling pathways, while Hypo-HGs were related to cell cycle and steroid hormone biosynthesis pathways. Based on PPI networks, PTGS2, PIK3 CD, CXCL1, ESR1, and MMP2 were identified as hub genes for Hyper-LGs, and CDC45, DTL, AURKB, CDKN3, MCM2, and MCM10 were hub genes for Hypo-HGs by combining six ranked methods of cytoH ubba. CONCLUSION In the study, we disclose numerous novel genetic and epigenetic regulations and offer a vital molecular groundwork to understand the pathogenesis of HCC. Hub genes, including PTGS2, PIK3 CD, CXCL1, ESR1, MMP2, CDC45, DTL, AURKB, CDKN3, MCM2, and MCM10, can be used as biomarkers based on aberrant methylation for the accurate diagnosis and treatment of HCC.     

Identifying the hub genes and immune infiltration related to pyroptosis in rheumatoid arthritis

Rheumatoid arthritis (RA) is one of the most common autoimmune joint disorders globally, but its pathophysiological mechanisms have not been thoroughly investigated. Pyroptosis significantly correlates with programmed cell death. However, targeting pyroptosis has not been considered as a therapeutic strategy in RA due to a lack of systematic studies on validated biomarkers. The present study aimed to identify hub pyroptosis biomarkers and immune infiltration in RA. The gene expression profiles of synovial tissues were obtained from the Gene Expression Omnibus (GEO) database to identify differentially expressed pyroptosis genes (DEPGs). Meanwhile, the CIBERSORT algorithm was used to explore the association between immune infiltration and RA. Consequently, two hub DEPGs (EGFR and JUN) were identified as critical genes in RA. Through gene ontology and pathway enrichment analysis. EGFR and JUN were found to be primarily involved in the ErbB signaling pathway, PD-1 checkpoint pathway, GnRH signaling pathway, etc. Furthermore, for immune infiltration analysis, the pyroptosis genes EGFR and JUN were closely connected with four and one immune cell types, respectively. Overall, this study presents a novel method to identify hub DEPGs and their correlation with immune infiltration, which may provide novel perspectives into the diagnosis and treatment of patients with RA.     

Identification of hub genes and candidate drugs in hepatocellular carcinoma by integrated bioinformatics analysis

Background:Hepatocellular carcinoma (HCC) is the third cancer-related cause of death in the world. Until now, the involved mechanisms during the development of HCC are largely unknown. This study aims to explore the driven genes and potential drugs in HCC.Methods:Three mRNA expression datasets were used to analyze the differentially expressed genes (DEGs) in HCC. The bioinformatics approaches include identification of DEGs and hub genes, Gene Ontology terms analysis and Kyoto encyclopedia of genes and genomes enrichment analysis, construction of protein–protein interaction network. The expression levels of hub genes were validated based on The Cancer Genome Atlas, Gene Expression Profiling Interactive Analysis, and the Human Protein Atlas. Moreover, overall survival and disease-free survival analysis of HCC patients were further conducted by Kaplan–Meier plotter and Gene Expression Profiling Interactive Analysis. DGIdb database was performed to search the candidate drugs for HCC.Results:A total of 197 DEGs were identified. The protein–protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes software, 10 genes were selected by Cytoscape plugin cytoHubba and served as hub genes. These 10 genes were all closely related to the survival of HCC patients. DGIdb database predicted 29 small molecules as the possible drugs for treating HCC.Conclusion:Our study provides some new insights into HCC pathogenesis and treatments. The candidate drugs may improve the efficiency of HCC therapy in the future.     

Mining immune-related genes with prognostic value in the tumor microenvironment of breast invasive ductal carcinoma

The tumor microenvironment (TME) plays an important role in the development of breast cancer. Due to limitations in experimental conditions, the molecular mechanism of TME in breast cancer has not yet been elucidated. With the development of bioinformatics, the study of TME has become convenient and reliable.Gene expression and clinical feature data were downloaded from The Cancer Genome Atlas database and the Molecular Taxonomy of Breast Cancer International Consortium database. Immune scores and stromal scores were calculated using the Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data algorithm. The interaction of genes was examined with protein-protein interaction and co-expression analysis. The function of genes was analyzed by gene ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes analysis and gene set enrichment analysis. The clinical significance of genes was assessed with Kaplan-Meier analysis and univariate/multivariate Cox regression analysis.Our results showed that the immune scores and stromal scores of breast invasive ductal carcinoma (IDC) were significantly lower than those of invasive lobular carcinoma. The immune scores were significantly related to overall survival of breast IDC patients and both the immune and stromal scores were significantly related to clinical features of these patients. According to the level of immune/stromal scores, 179 common differentially expressed genes and 5 hub genes with prognostic value were identified. In addition, the clinical significance of the hub genes was validated with data from the molecular taxonomy of breast cancer international consortium database, and gene set enrichment analysis analysis showed that these hub genes were mainly enriched in signaling pathways of the immune system and breast cancer.We identified five immune-related hub genes with prognostic value in the TME of breast IDC, which may partly determine the prognosis of breast cancer and provide some direction for development of targeted treatments in the future.