本研究通过多种生物信息学手段寻找参与肺腺癌发生和预后的相关性基因,为探索肺腺癌的分子机制提供理论依据。首先从基因表达汇编(gene expression omnibus,GEO)数据库中下载Ⅰ期肺腺癌的基因表达芯片和基因甲基化芯片,利用GEO2R工具分...本研究通过多种生物信息学手段寻找参与肺腺癌发生和预后的相关性基因,为探索肺腺癌的分子机制提供理论依据。首先从基因表达汇编(gene expression omnibus,GEO)数据库中下载Ⅰ期肺腺癌的基因表达芯片和基因甲基化芯片,利用GEO2R工具分析肺腺癌组织与正常肺组织之间的差异基因,鉴定出199个低甲基化高表达基因和403个高甲基化低表达基因,使用注释、可视化和集成发现(the database for annotation,visualization and integrated discovery,DAVID)数据库分析其功能。然后,通过STRING构建选定基因的蛋白质间相互作用(PPI)网络,并在Cytoscape软件中可视化,通过插件MCODE和cytoHubba进一步对PPI网络进行分析,筛选出7个关键基因(FGF2、VWF、CDH5、CD34、KITLG、BMP2和TEK),通过癌症基因组图谱(the cancer genome atlas,TCGA)数据库和人类蛋白质图谱(HPA)进行验证,并截取了免疫组化代表性图像。最后,使用Kaplan-Meier Plotter在线工具验证每个关键基因在肺腺癌患者中的独立预后价值,发现这7个关键基因均与患者总生存时间密切相关。本研究通过结合基因表达谱和甲基化谱芯片数据的分析获得了7个与早期肺腺癌相关的关键基因,有助于更全面地了解肺腺癌的分子机制,可作为肺腺癌基于甲基化的异常生物标志物及潜在的治疗分子靶标。展开更多
Background: More effective biomarkers for use intuberculosis prevention,diagnosis, and treatmentare urgently needed. The?potential of miRNAsfor use as biomarkers of human disease has received much attention;however, s...Background: More effective biomarkers for use intuberculosis prevention,diagnosis, and treatmentare urgently needed. The?potential of miRNAsfor use as biomarkers of human disease has received much attention;however, suitable miRNA biomarkers for use in tuberculosis (TB) diagnosis and treatment have not yet been identified. Methods: We used human miRNA arrays to identify miRNAs in Peripheral Blood Mononuclear Cells (PBMCs) that are differentially expressed in subjects with active disease, those with latent TB infections (LTBI) and healthy individuals. The relationship between differentially-expressed miRNAs and mRNAs was examined using Tar- getScanS, Pic-Tar and miRanda. The expression profiles of selected miRNAs in subjects with active disease, those with LTBI and healthy individuals were validated by qRT-PCR. Results: miRNA array analysis of PBMCs from subjects with active disease, those with LTBI and healthy individuals identified 26 differentially-expressed miRNAs. Analysis of gene expression levels in THP-1 cells using mRNA arrays identified 87 differentially-expressed genes, 80 of which were up-regulated (ratio >2) and 7 of which were down-regulated (ratio In silico miRNA target prediction identified target mRNAs for 15of the 26 differentially-expressed miRNAs. Differentially-expressed miRNAs were identified for 90 of the 178 differentially-expressed genes. has-miR-21* and has-miR-26b had the highestnumbers of differentially-expressed target mRNAs.PCR validation of has-miR-21* and has-miR- 15b* demonstrated the fidelity of our microarray results. Conclusion: Whole-genome transcriptional profiling identified differentially-expressed mRNAs and miRNAs. Differentially-expressed miRNAs?combined with predicted differentially-expressed mRNAs from the same whole-genome transcriptional profiling may be used as the new ways to better understand?TB disease.This discovery of differentially-expressed?miRNAsand mRNAs provides a resource for further studies on the role?of miRNAs in tuberculosis.展开更多
文摘本研究通过多种生物信息学手段寻找参与肺腺癌发生和预后的相关性基因,为探索肺腺癌的分子机制提供理论依据。首先从基因表达汇编(gene expression omnibus,GEO)数据库中下载Ⅰ期肺腺癌的基因表达芯片和基因甲基化芯片,利用GEO2R工具分析肺腺癌组织与正常肺组织之间的差异基因,鉴定出199个低甲基化高表达基因和403个高甲基化低表达基因,使用注释、可视化和集成发现(the database for annotation,visualization and integrated discovery,DAVID)数据库分析其功能。然后,通过STRING构建选定基因的蛋白质间相互作用(PPI)网络,并在Cytoscape软件中可视化,通过插件MCODE和cytoHubba进一步对PPI网络进行分析,筛选出7个关键基因(FGF2、VWF、CDH5、CD34、KITLG、BMP2和TEK),通过癌症基因组图谱(the cancer genome atlas,TCGA)数据库和人类蛋白质图谱(HPA)进行验证,并截取了免疫组化代表性图像。最后,使用Kaplan-Meier Plotter在线工具验证每个关键基因在肺腺癌患者中的独立预后价值,发现这7个关键基因均与患者总生存时间密切相关。本研究通过结合基因表达谱和甲基化谱芯片数据的分析获得了7个与早期肺腺癌相关的关键基因,有助于更全面地了解肺腺癌的分子机制,可作为肺腺癌基于甲基化的异常生物标志物及潜在的治疗分子靶标。
文摘Background: More effective biomarkers for use intuberculosis prevention,diagnosis, and treatmentare urgently needed. The?potential of miRNAsfor use as biomarkers of human disease has received much attention;however, suitable miRNA biomarkers for use in tuberculosis (TB) diagnosis and treatment have not yet been identified. Methods: We used human miRNA arrays to identify miRNAs in Peripheral Blood Mononuclear Cells (PBMCs) that are differentially expressed in subjects with active disease, those with latent TB infections (LTBI) and healthy individuals. The relationship between differentially-expressed miRNAs and mRNAs was examined using Tar- getScanS, Pic-Tar and miRanda. The expression profiles of selected miRNAs in subjects with active disease, those with LTBI and healthy individuals were validated by qRT-PCR. Results: miRNA array analysis of PBMCs from subjects with active disease, those with LTBI and healthy individuals identified 26 differentially-expressed miRNAs. Analysis of gene expression levels in THP-1 cells using mRNA arrays identified 87 differentially-expressed genes, 80 of which were up-regulated (ratio >2) and 7 of which were down-regulated (ratio In silico miRNA target prediction identified target mRNAs for 15of the 26 differentially-expressed miRNAs. Differentially-expressed miRNAs were identified for 90 of the 178 differentially-expressed genes. has-miR-21* and has-miR-26b had the highestnumbers of differentially-expressed target mRNAs.PCR validation of has-miR-21* and has-miR- 15b* demonstrated the fidelity of our microarray results. Conclusion: Whole-genome transcriptional profiling identified differentially-expressed mRNAs and miRNAs. Differentially-expressed miRNAs?combined with predicted differentially-expressed mRNAs from the same whole-genome transcriptional profiling may be used as the new ways to better understand?TB disease.This discovery of differentially-expressed?miRNAsand mRNAs provides a resource for further studies on the role?of miRNAs in tuberculosis.
