Analysis of the microRNA Profile of Coal-Burning Endemic Fluorosis Using Deep Sequencing and Bioinformatic Approaches
Analysis of the microRNA Profile of Coal-Burning Endemic Fluorosis Using Deep Sequencing and Bioinformatic Approaches
复制标题
利用深度测序和生物信息学方法分析燃煤地方性氟中毒的 microRNA 谱
DOI：
10.1007/s00128-019-02660-8
复制
发表时间：
2019-06
影响因子：
2.7
通讯作者：
Xueli Pan
中科院分区：
文献类型：
--
作者：
Fei Wang;Chen Li;Yu Qin;Xue Han;Jiayu Gao;Aihua Zhang;Peng Luo;Xueli Pan
使用microRNA测序（miRNA-seq）鉴定血浆中差异表达的microRNA（miRNAs），并选择五种miRNAs进行验证。使用miRWalk3.0数据库预测这5种miRNA的潜在靶基因，并使用基因本体（GO）和京都基因和基因组百科全书（KEGG）数据库分析重叠部分。病例组和对照组的比较显示了127个已知的差异表达的miRNAs。分别有44个和83个miRNAs表达上调和下调。通过对5个miRNAs的靶基因预测，共获得1360个靶基因。GO富集分析显示，这些异常表达的miRNAs的靶基因与分泌、蛋白结合和细胞生长有关。KEGG通路分析表明，癌症、钙信号传导和大鼠肉瘤（Ras）信号传导等通路可能受到这5种miRNAs的调控。这些发现突出了燃煤型地方性氟中毒中miRNAs的不同表达模式。
MicroRNAs (miRNAs) differentially expressed in plasma were identified using microRNA sequencing (miRNA-seq), and five miRNAs were selected for validation. Potential target genes of these five miRNAs were predicted using the miRWalk3.0 database, and the overlapping portions were analyzed using the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Comparison of the cases and controls revealed 127 known differentially expressed miRNAs. A total of 44 and 83 miRNAs were upregulated and downregulated, respectively. Through target gene prediction of five miRNAs, we obtained 1360 target genes. GO enrichment analysis showed that the target genes of these dysregulated miRNAs were related with secretion, protein binding, and cell growth. The KEGG pathway analysis showed that pathways in cancer, calcium signaling, and rat sarcoma (Ras) signaling, etc. were likely regulated by these five miRNAs. These findings highlight the distinct expression patterns of miRNAs in coal-burning endemic fluorosis.
登录
查看更多内容
影响因子：
1.4
作者：
Bin Fan;Yan-ni Yu;Ying Zhang
通讯作者：
Bin Fan;Yan-ni Yu;Ying Zhang
影响因子：
3.7
作者：
Sticht C;De La Torre C;Parveen A;Gretz N
通讯作者：
Gretz N
影响因子：
4.6
作者：
Mandourah AY;Ranganath L;Barraclough R;Vinjamuri S;Hof RV;Hamill S;Czanner G;Dera AA;Wang D;Barraclough DL
通讯作者：
Barraclough DL
影响因子：
4.5
作者：
Shouli Wu;Weimin Yan;B. Qiu;Yongfang Liao;J. Gu;Shaofeng Wei;A. Zhang;Xueli Pan
通讯作者：
Shouli Wu;Weimin Yan;B. Qiu;Yongfang Liao;J. Gu;Shaofeng Wei;A. Zhang;Xueli Pan
影响因子：
2.1
作者：
Browne, Brendan M.;Stensland, Kristian D.;Reiger-Christ, Kimberly M.
通讯作者：
Reiger-Christ, Kimberly M.