|
|
|
| Data mining and bioinformatics analysis of expression profile chip for embryonic and mature mice liver |
| LYU Xing1 YANG Shuhong2 |
1.Department of General Surgery, Taikang Tongji (Wuhan) Hospital, Hubei Province, Wuhan 430050, China;
2.Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei Province, Wuhan, 430030, China |
|
|
|
Abstract Objective To depth analysis of liver differential expression genes in embryonic and mature mice. Methods Download gene expression profile data numbered GSE65063 from the National Center for Biotechnology Information (NCBI) GEO database, microchips were extracted at 14 embryonic days and 56 postnatal days. Online analysis tool GEO2R was used to complete differentially expressed genes (DEGs) screening. DAVID was used to analyze the gene ontology and Kyoto Encyclopedia of Genes and Genomes biological pathway enrichment of DEGs, STRING was used to analyze the interaction between proteins encoded by DEGs, visualization with Cytoscape software. MCODE loaded in Cytoscape software was used to build functional modules, CytoHubba was used to screen the hub genes and analyze them to construct PPI network map. Results A total of 2969 DEGs were screened in mature liver compared with embryonic liver, of which 1620 were up-regulated and 1349 were down-regulated. Upregulated DEGs were mainly concentrated in metabolism-related biological processes, organelle components and metabolic pathways, while down-regulated DEGs were mainly concentrated in cell cycle, DNA repair and other cell proliferation-related organelles, biological processes and signaling pathways. Modular analysis showed that the top gene modules were all down-regulated genes, which were mainly enriched in signaling pathways related to cell cycle and cell senescence. Module two was mainly related to complement system, cholesterol metabolism and PARP signaling pathway. Module third was mainly related to retinol metabolism steroid and somatic hormone synthesis. A total of 16 hub genes were screened out, among which 14 were down-regulated and two were up-regulated. Conclusion There are significant differences in gene expression profiles between matare and embryonic liver maturation and embryonic stage. Liver development and maturation are regulated by specific gene groups in different time and space. Metabolism-related genes are dominant in mature liver, while cell proliferation and division related genes are mainly concentrated in embryonic liver. The screened hub genes, especially the down-regulated genes, may be used as early diagnostic and prognostic indicators for liver abnormalities such as liver cancer, which is of great clinical significance.
|
|
|
|
|
|
[1] Cascio S，Zaret KS. Hepatocyte differentiation initiates during endodermal-mesenchymal interactions prior to liver formation [J]. Development，1991，113（1）：217-225.
[2] Gordillo M，Evans T，Gouon-Evans V. Orchestrating liver development [J]. Development，2015，142（12）：2094-2108.
[3] Jochheim A，Cieslak A，Hillemann T，et al. Multi-stage analysis of differential gene expression in BALB/C mouse liver development by high-density microarrays [J]. Differentiation，2003，71（1）：62-72.
[4] Knebel B，Goddeke S，Hartwig S，et al. Alteration of Liver Peroxisomal and Mitochondrial Functionality in the NZO Mouse Model of Metabolic Syndrome [J]. Proteomics Clin Appl，2018，12：1700128.
[5] Ferdinandusse S，Denis S，van Roermund CWT，et al. A novel case of ACOX2 deficiency leads to recognition of a third human peroxisomal acyl-CoA oxidase [J]. Biochim Biophys Acta Mol Basis Dis，2018，1864（3）：952-958.
[6] Szoka PR，Gallagher JF，Held WA. In vitro synthesis and characterization of precursors to the mouse major urinary proteins [J]. J Biol Chem，1980，255（4）：1367-1373.
[7] Hurst JL，Robertson DHL，Tolladay U，et al. Proteins in urine scent marks of male house mice extend the longevity of olfactory signals [J]. Anim Behav，1998，55（5）：1289-1297.
[8] Pallauf K，Gunther I，Chin D，et al. In Contrast to Dietary Restriction，Application of Resveratrol in Mice Does not Alter Mouse Major Urinary Protein Expression [J]. Nutrients，2020，12，815.
[9] Yang LV，Nicholson RH，Kaplan J，et al. Hemogen is a novel nuclear factor specifically expressed in mouse hem-atopoietic development and its human homologue EDAG maps to chromosome 9q22，a region containing breakpoints of hematological neoplasms [J]. Mech Dev，2001， 104（1/2）：105-111.
[10] Xu J，Li Z，Su Q，et al. Embryonic develop-associated gene 1 is overexpressed and acts as a tumor promoter in thyroid carcinoma，[J]. Biomed Pharmacother，2016，81：86-92.
[11] Karim S，Mirza Z，Chaudhary AG，et al. Assessment of Radiation Induced Therapeutic Effect and Cytotoxicity in Cancer Patients Based on Transcriptomic Profiling [J]. Int J Mol Sci，2016，17（2）：250.
[12] Yook SH，Oltvai ZN，Barabasi AL. Functional and topological characterization of protein interaction networks [J]. Proteomics，2004，4（4）：928-942.
[13] Szklarczyk D，Gable AL，Lyon D，et al. STRING v11：protein-protein association networks with increased coverage，supporting functional discovery in genome-wide experimental datasets [J]. Nucleic Acids Res，2019，47（D1）：D607-D613.
[14] Horvath S，Dong J. Geometric interpretation of gene coexpression network analysis [J]. PLoS Comput Biol，2008， 4（8）：e1000117.
[15] Yin L，Wang Y，Lin Y，et al. Explorative analysis of the gene expression profile during liver regeneration of mouse： a microarray-based study [J]. Artif Cells Nanomed Biotechnol，2019，47（1）：1113-1121.
[16] Yang WX，Pan YY，You CG. CDK1，CCNB1，CDC20，BUB1，MAD2L1，MCM3，BUB1B，MCM2，and RFC4 May Be Potential Therapeutic Targets for Hepatocellular Carcinoma Using Integrated Bioinformatic Analysis [J]. Biomed Res Int，2019，2019：1245072.
[17] Gao X，Wang X，Zhang S. Bioinformatics identification of crucial genes and pathways associated with hepatocellular carcinoma [J]. Biosci Rep，2018，38（6）：BSR20181441.
[18] Zhang L，Huang Y，Ling J，et al. Screening and function analysis of hub genes and pathways in hepatocellular carcinoma via bioinformatics approaches [J]. Cancer Biomark，2018，22（3）：511-521.
[19] Wu M，Liu Z，Li X，et al. Analysis of potential key genes in very early hepatocellular carcinoma [J]. World J Surg Oncol，2019，17（1）：77.
[20] Chen W，Li G，Peng J，et al. Transcriptomic analysis reveals that heat shock protein 90alpha is a potential diagnostic and prognostic biomarker for cancer [J]. Eur J Cancer Prev，2020，29（4）：357-364.
[21] Liu W，Li J，Zhang P，et al. A novel pan-cancer biomarker plasma heat shock protein 90alpha and its diagnosis determinants in clinic [J]. Cancer Sci，2019，110（9）：2941-2959.
[22] Bracht T，Hagemann S，Loscha M，et al. Proteome analysis of a hepatocyte-specific BIRC5 （survivin）-knockout mouse model during liver regeneration [J]. J Proteome Res，2014，13（6）：2771-2782.
[23] Chaudhary K，Poirion OB，Lu L，et al. Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer [J]. Clin Cancer Res，2018，24（6）：1248-1259.
[24] Berasain C，Avila MA. The EGFR signalling system in the liver：from hepatoprotection to hepatocarcinogenesis [J]. J Gastroenterol，2014，49（1）：9-23.
[25] Whitfield AJ，Barrett PH，van Bockxmeer FM，et al. Lipid disorders and mutations in the APOB gene [J]. Clin Chem，2004，50（10）：1725-1732. |
|
|
|
