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.     

Identification of potential biomarkers of peripheral blood mononuclear cell in hepatocellular carcinoma using bioinformatic analysis: A protocol for systematic review and meta-analysis

Background:Hepatocellular carcinoma (HCC) is the cause of an overwhelming number of cancer-related deaths across the world. Developing precise and noninvasive biomarkers is critical for diagnosing HCC. Our research was designed to explore potentially useful biomarkers of host peripheral blood mononuclear cell (PBMC) in HCC by integrating comprehensive bioinformatic analysis.Methods:Gene expression data of PBMC in both healthy individuals and patients with HCC were extracted from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs). The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were applied to annotate the function of DEGs. Protein-protein interaction analysis was performed to screen the hub genes from DEGs. cBioportal database analysis was performed to assess the prognostic significance of hub genes. The Cancer Cell Line Encyclopedia (CCLE) and The Human Protein Atlas (HPA) database analyses were performed to confirm the expression levels of the hub genes in HCC cells and tissue.Results:A total of 95 DEGs were screened. Results of the GO analysis revealed that DEGs were primarily involved in platelet degranulation, cytoplasm, and protein binding. Results of the KEGG analysis indicated that DEGs were primarily enriched in focal adhesion. Five genes, namely, myosin light chain kinase (MYLK), interleukin 1 beta (IL1B), phospholipase D1 (PLD1), cortactin (CTTN), and moesin (MSN), were identified as hub genes. A search in the CCLE and HPA database showed that the expression levels of these hub genes were remarkably increased in the HCC samples. Survival analysis revealed that the overexpression of MYLK, IL1B, and PLD1 may have a significant effect on HCC survival. The aberrant high expression levels of MYLK, IL1B, and PLD1 strongly indicated worse prognosis in patients with HCC.Conclusions:The identified hub genes may be closely linked with HCC tumorigenicity and may act as potentially useful biomarkers for the prognostic prediction of HCC in PBMC samples.     

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：借助公共数据平台,通过挖掘HCC潜在的关键基因及生物学功能,深入研究肝细胞癌的发病机制,构建miRNAs-mRNA网络。 方法：基于TCGA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miRNAs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miRNAs。对374例HCC样本、50 例癌旁样本进行差异mRNA分析,共鉴定出1106个差异表达的 mRNAs。分别在上调的和下调的miRNAs中选择20个差异表达最显著的miRNAs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通路可能成为肝细胞癌诊断的新型生物标志物,为诊断和临床治疗提供依据。     

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、免疫应答、蛋白质水解等生物学过程,细胞核、细胞质、胞外囊泡、内质网膜等细胞成分,与钙离子、蛋白激酶、DNA、血红素等结合分子功能;KEGG通路分析显示,DEGs主要参与细胞周期、卵母细胞减数分裂、代谢途径、抗生素生物合成、p53信号通路等通路。PPI网络分析发现10个核心DEGs,包括CDK1、CCNB1、CCNA2、TOP2A、AURKA、CCNB2、KIF11、CDC20、KIF20A、BUB1B;经临床样本数据验证,CDK1、KIF11、KIF20A这3个DEGs在HBV相关HCC患者中差异表达,且与患者预后不良相关。结论　CDK1、KIF11、KIF20A可能在HBV相关HCC发生发展中发挥重要作用,有望成为HBV相关HCC潜在诊断标志物和治疗靶标。     

基于基因芯片的miRNA表达差异在结核性脑膜炎与病毒性脑膜炎诊断中的价值

目的: 评价基于基因芯片的miRNA表达差异在结核性脑膜炎(tuberculous meningitis,TBM)与病毒性脑膜炎(viral meningitis,VM)诊断中的价值。方法: 选取2017年12月至2019年9月在首都医科大学附属北京胸科医院和首都医科大学附属北京天坛医院治疗的TBM患者28例和VM患者27例作为验证样本进行实时荧光定量逆转录PCR(qRT-PCR)验证。选取前期收集的8例TBM患者和5例VM患者作为基因芯片样本进行全基因组miRNA微阵列分析,通过预测差异性miRNA的靶基因、基因本体(GO)富集分析、京都基因与基因组百科全书(KEGG)信号通路分析、构建蛋白质-蛋白质相互作用(PPI)网络筛选出两病间前10个差异性miRNA枢纽基因。再采用qRT-PCR对患者间表达差异明显的miR-21-5p进行验证,并通过受试者工作特征曲线(ROC)下面积(AUC)分析miRNA的诊断价值。结果: 通过miRNA微阵列分析初步筛选出26个差异表达的miRNA(14个表达上调,12个表达下调)和对应的靶基因3217个。经GO富集分析、KEGG信号通路分析筛选出190个相关靶基因,对其进行PPI网络分析,筛选出前10个靶基因作为核心靶基因。对基因芯片检测样本的hsa-miR-21-5p相对表达量进行qRT-PCR验证,发现TBM患者hsa-miR-21-5p的表达水平[15.890(3.423,25.581)]较VM患者[0.807(0.614,0.955)]明显上调(Z=-2.355,P=0.019),差异倍数(TBM/VM)在基因芯片和qRT-PCR中分别是4.817和4.660,两种检测方法结果基本一致;对28例TBM和27例VM患者的hsa-miR-21-5p进行qRT-PCR结果比较,发现hsa-miR-21-5p在TBM患者的相对表达量为4.825(2.433,21.362),明显高于VM患者[0.204(0.112,0.711)],差异有统计学意义(Z=-5.000,P=0.000),且ROC曲线区分TBM与VM患者的AUC为0.893(0.806~0.980),敏感度为85.7%,特异度为88.9%。结论: 相较于VM患者,作为基因芯片检测中表达差异明显的miR-21-5p在TBM患者中表达明显上调,可作为鉴别TBM与VM的潜在生物标志物。     

Bioinformatics-Based Identification of Methylated-Differentially Expressed Genes and Related Pathways in Gastric Cancer

Background and Aims

The aim of the study was to identify methylated-differentially expressed genes (MDEGs) in gastric cancer and investigate their potential pathways.

Methods

Expression profiling (GSE13911 and GSE29272) and methylation profiling (GSE25869 and GSE30601) data were obtained from GEO DataSets. Differentially expressed genes and differentially methylated genes were identified using GEO2R. Gene ontology and pathway enrichment analyses were performed for the MDEGs. Protein–protein interaction (PPI) networks were established by STRING and Cytoscape. Analysis of modules in the PPI networks was performed using MCODE. Further, the hub genes derived from the PPI networks were verified by The Cancer Genome Atlas (TCGA) database and human tissues, with methylation-specific PCR for genes methylation and real-time qPCR for genes expression.

Results

A total of 445 genes were identified as hypermethylated, lowly expressed genes (Hyper-LGs), which were enriched in the regulation of system process and channel activity. A total of 129 genes were identified as hypomethylated, highly expressed genes (Hypo-HGs), which were involved in cell adhesion, cell proliferation, and protein binding. Pathway analysis showed that Hyper-LGs were associated with neuroactive ligand–receptor interaction and calcium signaling pathway, while Hypo-HGs were enriched in pathways in cancer. In the PPI networks, after verification by TCGA analysis and human tissue detection, CASR, CXCL12, and SST were identified as significantly different hub genes.

Conclusions

MDEG analysis helps to understand the epigenetic regulation mechanisms involved in the development and progression of gastric cancer. The hub genes have predictive and prognostic value as methylation-based biomarkers for the precise diagnosis and treatment of gastric cancer.

     

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.     

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.     

动脉性肺动脉高压相关基因的生物信息学分析

目的通过GEO数据库转录组和miRNA基因芯片数据筛选和分析动脉性肺动脉高压相关基因。方法通过GEO数据库收集转录组数据集(GSE113439、GSE53408)和miRNA数据集(GSE21284、GSE55427)。使用R3.5.2软件进行差异分析,筛选差异表达基因和miRNA,通过miRNet数据库筛选差异miRNA的靶基因,利用FunRich 3.13软件分析共同差异基因。最后用Cytoscape 3.7.2软件进行动脉性肺动脉高压相关生物过程和通路分析,筛选与动脉性肺动脉高压致病机制相关的潜在基因。结果最终分析确定188个上调基因、50个miRNA、20个生物过程和通路。其中与细胞增殖、迁移、凋亡及免疫紧密相关的GO生物过程和Reactome通路有5个:GO:0097581细胞板状伪足相关、GO:0030866皮质肌动蛋白细胞骨架相关、GO:0002886骨髓来源的白细胞介导的免疫调节相关、GO:0031122细胞质微管组织和生物发生相关、R-HSA:400253昼夜节律相关。参与调控的主要相关基因为:ANLN、BIRC3、CHD9、CLOCK、CXCL8、DST、FER、HIF1A、KIF23、PCM1、PLS1、ROCK2、TWF1。miRNA为hsa-miR-129-5p、hsa-miR-485-3p、hsalet-7b-5p、hsa-miR-215、hsa-miR-519a-3p、hsa-miR-519c-3p、hsa-miR-98-5p、hsa-miR-380-3p、hsa-miR-155-5p、hsa-miR-23a-3p、hsa-miR-642-5p。结论筛选出的基因ANLN、BIRC3、CLOCK、DST、FER、KIF23、PCM1、PLS1、TWF1可能关系到血管重构,这为动脉性肺动脉高压的治疗提供新的思路,为发展新的抗血管重构治疗提供更多可能的手段。     

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。     

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。     

Identification of Hub Genes and Analysis of Prognostic Values in Hepatocellular Carcinoma by Bioinformatics Analysis

BackgroundHepatocellular carcinoma (HCC) is one of the most frequent cancers in the world. In this study, differentially expressed genes (DEGs) between tumor tissues and normal tissues were identified using the comprehensive analysis method in bioinformatics.Materials and MethodsWe downloaded 3 mRNA expression profiles from the Gene Expression Omnibus database to identify DEGs between tumor tissues and adjacent normal tissues. The Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway analysis, protein-protein interaction network was performed to understand the function of DEGs. OncoLnc, which was linked to The Cancer Genome Atlas survival data, was used to investigate the prognostic values of hub genes. The expression of selected hub genes was validated by the quantitative real-time polymerase chain reaction.ResultsA total of 235 DEGs, consisting of 36 upregulated and 199 downregulated genes, were identified between tumor tissue and normal tissue. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis results showed the upregulated DEGs to be significantly enriched in cell division, mid-body, ATP binding and oocyte meiosis pathways. The downregulated DEGs were mainly involved in epoxygenase P450 pathway, extracellular region, oxidoreductase activity and metabolic pathways. Ten hub genes, including Aurora kinase A, Cell division cycle 20, formiminotransferase cyclodeaminase, UBE2C, Cyclin B2, pituitary tumor-transforming gene 1, CDKN3, CKS1B, Topoisomerase-II alpha and KIF20A, were identified as the key genes in HCC. Survival analysis found the expression of hub genes to be significantly correlated with the survival of patients with HCC.ConclusionsThe present study identified hub genes and pathways in HCC that may be potential targets for diagnosis, treatment and prognostic prediction.     

Several critical genes and microRNAs associated with the development of polycystic ovary syndrome

BackgroundWe aimed to identify key genes and microRNAs (miRNAs) associated with the development of polycystic ovary syndrome (PCOS).MethodsGSE84376 mRNA microarray data (15 PCOS granulosa cells and 13 control granulosa cells) and GSE34526 mRNA microarray data (7 PCOS granulosa cells and 3 control granulosa cells) were downloaded from the Gene Expression Omnibus (GEO) database. First, differentially expressed gene (DEG) analysis, gene set enrichment analysis (GSEA) for differentially expressed mRNAs, and protein–protein interaction (PPI) network analysis were conducted. Next, miRNA-target genes were analyzed and functions predicted, and a competing endogenous RNA (ceRNA) network was constructed. Finally, the relationship between miR-486-5p and PRELID2 was experimentally validated.ResultsSpleen tyrosine kinase (SYK), major histocompatibility complex, class II, DR alpha (HLA-DRA), and interleukin 10 (IL-10) were important nodes in the PPI network. Interestingly, HLA-DRA was significantly enriched in phagosomes mediated by Staphylococcus aureus infection, and in IL-10 enriched during S. aureus infection. One miRNA (miR-486-5p) and a single target gene (PRELID2) were obtained from the ceRNA network. Further experiments showed that miR-486-5p is upregulated and PRELID2 is downregulated in PCOS patient granulosa cells, and that miR-486-5p targets the PRELID2 3′UTR. Topological property analysis showed that hsa-miR-4687-5p downregulation and hsa-miR-4651 upregulation determined the levels of most mRNAs. Levels of the hsa-miR-4651 target gene were significantly enriched in the leukocyte transendothelial migration pathway.ConclusionsOur results suggest that HLA-DRA and IL-10 may contribute to PCOS progression via phagosome enriched by S. aureus infection, while miR-486-5p may be implicated in follicular development in PCOS by targeting PRELID2. Besides, miR-4651 may be involved in inflammation via leukocyte transendothelial migration, by regulating its target gene. These findings may indicate new directions and constitute a breakthrough in studying the pathophysiology of PCOS.     

Integrated analysis of ceRNA network in hepatocellular carcinoma using bioinformatics analysis

Background:Long noncoding RNAs (lncRNAs) can work as microRNA (miRNA) sponges through a competitive endogenous RNA (ceRNA) mechanism. LncRNAs and miRNAs are important components of competitive endogenous binding, and their expression imbalance in hepatocellular carcinoma (HCC) is closely related to tumor development, diagnosis, and prognosis. This study explored the potential impact of the ceRNA regulatory network in HCC on the prognosis of HCC patients.Methods:We thoroughly researched the differential expression profiles of lncRNAs, miRNAs, and mRNAs from 2 HCC Gene Expression Omnibus datasets ({"type":"entrez-geo","attrs":{"text":"GSE98269","term_id":"98269"}}GSE98269 and {"type":"entrez-geo","attrs":{"text":"GSE60502","term_id":"60502"}}GSE60502). Then, a dysregulated ceRNA network was constructed by bioinformatics. In addition, hub genes in the ceRNA network were screened by Cytoscape, these hub genes functional analysis was performed by gene set enrichment analysis, and the expression of these hub genes in tumors and their correlation with patient prognosis were verified with Gene Expression Profiling Interactive Analysis.Results:A ceRNA network was successfully constructed in this study including 4 differentially expressed (DE) lncRNAs, 7 DEmiRNAs, and 166 DEmRNAs. Importantly, 4 core genes (CCNA2, CHEK1, FOXM1, and MCM2) that were significantly associated with HCC prognosis were identified.Conclusions:Our study provides comprehensive and meaningful insights into HCC tumorigenesis and the underlying molecular mechanisms of ceRNA. Furthermore, the specific ceRNAs can be further used as potential therapeutic targets and prognostic biomarkers for HCC.     

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。     

Gene expression profiling reveals key genes and pathways related to the development of non-alcoholic fatty liver disease

Background. This study aims to identify key genes and pathways involved in non-alcoholic fatty liver disease (NAFLD).Material and methods. The dataset GSE48452 was downloaded from Gene Expression Omnibus, including 14 control liver samples, 27 healthy obese samples, 14 steatosis samples and 18 nonalcoholic steatohepatitis (NASH) samples. Differentially expressed genes (DEGs) between controls and other samples were screened through LIMMA package. Then pathway enrichment analysis for DEGs was performed by using DAVID, and alterations of enriched pathways were determined. Furthermore, protein-protein interaction (PPI) networks were constructed based on the PPI information from HPRD database, and then, networks were visualized through Cytoscape. Additionally, interactions between microRNAs (miRNAs) and pathways were analyzed via Fisher’s exact test.Results. A total of 505, 814 and 783 DEGs were identified for healthy obese, steatosis and NASH samples in comparison with controls, respectively. DEGs were enriched in ribosome (RPL36A, RPL14, etc.), ubiquitin mediated proteolysis (UBE2A, UBA7, etc.), focal adhesion (PRKCA, EGFR, CDC42, VEGFA, etc.), FcγR-mediated phagocytosis (PRKCA, CDC42, etc.), and so on. The 27 enriched pathways gradually deviated from baseline (namely, controls) along with the changes of obese-steatosis-NASH. In PPI networks, PRKCA interacted with EGFR and CDC42. Besides, hsa-miR-330-3p and hsa-miR-126 modulated focal adhesion through targeting VEGFAand CDC42.Conclusions. The identified DEGs (PRKCA, EGFR, CDC42, VEGFA), disturbed pathways (ribosome, ubi-quitin mediated proteolysis, focal adhesion, FcγR-mediated phagocytosis, etc.) and miRNAs (hsa-miR-330-3p, hsa-miR-126, etc.) might be closely related to NAFLD progression. These results might contribute to understanding NAFLD mechanism, conducting experimental researches, and designing clinical practices.     

Identification of potential serum exosomal microRNAs involved in acinar-ductal metaplasia that is a precursor of pancreatic cancer associated with chronic pancreatitis

Backgrounds:Due to difficulty in early diagnosis of chronic pancreatitis (CP), it is urgent to find novel biomarkers to detect CP. Exosomal microRNAs (Exo-miRNAs) located in the serum may be potential diagnostic and therapeutic targets for CP.Objective:To identify differentially expressed Exo-miRNAs (DE-Exo-miRNAs) in the serum of CP patients, we performed a bioinformatics analysis.Methods:The dataset {"type":"entrez-geo","attrs":{"text":"GSE128508","term_id":"128508"}}GSE128508 was downloaded from the Gene Expression Omnibus (GEO) database. The analysis was carried out using BRB-ArrayTools and significance analysis of microarrays (SAM). The target genes of DE-S-Exo-miRNAs were predicted by miRWalk databases. Further gene ontology (GO) term and Kyoto Encyclopedia of Genomes (KEGG) pathway analyses were performed with plug-in ClueGO in Cytoscape software 3.7.0. Subsequently, the interaction regulatory network between encoded proteins of target genes was performed with the Search Tool for the Retrieval of Interacting Genes (STRING) database and analyzed using plug-in Molecular Complex Detection (MCODE) and cytoHubba in Cytoscape software 3.7.0.Results:We identified 227 DE-Exo-miRNAs in the serum. Further analysis using the miRWalk database identified 5164 target genes of these miRNAs. The protein–protein interaction (PPI) regulatory network of 1912 potential target genes for hub 10 up-regulated miRNAs with high degrees and one down-regulated miRNAs were constructed using the STRING database and Cytoscape software. The functional analysis using Cytoscape software tool highlighted that target genes involved in pancreatic cancer. Acinar-ductal metaplasia (ADM) in the inflammatory environment of CP is a precursor of pancreatic cancer. Subsequently, we constructed a network of target genes associated with ADM and their miRNAs.Conclusions:Exo-miRNAs in the serum as well as their target genes may be promising targets for the early diagnosis and treatment of CP. In addition, we identified potential Exo-miRNAs involved in ADM that is a precursor of pancreatic cancer associated with CP.