Bioinformatics analysis of leptin regulating gallbladder contraction and secretion in mice
10.3760/cma.j.issn.1673-4203.2019.10.009
- VernacularTitle: 瘦素调控小鼠胆囊收缩与分泌的生物信息学分析
- Author:
Jidong BAI
1
;
Rongquan XUE
2
;
Lan YU
3
;
Yijun XIA
2
;
Yongle BAI
4
;
Longfu XI
2
;
Xiaoyue HAN
5
;
Libo HAN
1
Author Information
1. Graduate School of Inner Mongolia Medical University, Hohhot 010059, China
2. Department of Hepatobiliary Pancreatic and Splenic Surgery, People's Hospital of Inner Mongolia Autonomous Region, Hohhot 010017, China
3. Clinical Medical Research Center of People′s Hospital of Inner Mongolia Autonomous Region, Hohhot 010017, China
4. Department of General Surgery, Xinzhou Modern Hospital of Shanxi Province, Xinzhou 034000, China
5. Department of Emergency Medical, People′s Hospital of Inner Mongolia Autonomous Region, Hohhot 010017, China
- Publication Type:Journal Article
- Keywords:
Leptin;
Gallbladder;
Mice;
Bioinformatics
- From:
International Journal of Surgery
2019;46(10):682-686
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To identify the key pathogenic genes of leptin regulating gallbladder contraction and secretion in mice and to reveal the potential molecular mechanism by comprehensive bioinformatics.
Methods:The expression profile of GSE3293 was downloaded from Gene Expression Omnibus (GEO) database. The data contained 8 samples, including 4 leptin-treated gallbladder samples and 4 saline-treated gallbladder samples. The most valuable 250 differentially expressed genes (DEGs) were obtained by grouping analysis of GEO online GEO 2 R-TOP 250 software or tools, and further analyzed by bioinformatics. The GO function and KEGG pathway enrichment of DEGs were analyzed by DAVID online software. The protein-protein interaction (PPI) network of DEGs was constructed from STRING database.
Results:A total of 250 differentially expressed genes were identified from the GSE3293 dataset, of which 197 genes were up-regulated and 53 genes were down-regulated. GO analysis showed that the biological functions of DEGs were mainly concentrated on MHC class II protein complexes, plasma membrane, extracellular exosome. KEGG pathway analysis showed that these DEGs were mainly involved in tuberculosis, leishmaniasis, cell adhesion molecules, bacteriophages, infection and other signaling pathways. PPI network showed that these DEGs coded proteins interacted strongly, and the first five pairs of DEGs with the strongest correlation were screened out.
Conclusions:The molecular mechanism of cholelithiasis is predicted from gene level by bioinformatics analysis of function enrichment and PPI of DEGs in mouse gallbladder. However, the function of DEGs still needs a lot of clinical and molecular biological experiments to confirm.
