目的 通過(guò)分析基因?qū)用娴牟町惐磉_(dá),以探討與腸梗阻相關(guān)的發(fā)病基因和治療靶基因。
方法 在基因表達(dá)公共數(shù)據(jù)庫(kù)(gene expression omnibus,GEO) 中收集到10個(gè)樣品的基因表達(dá)數(shù)據(jù),其中小鼠粘連小腸組織術(shù)后1、3、7、和14d時(shí)間點(diǎn)的基因芯片數(shù)據(jù)共8個(gè),小鼠正常小腸組織芯片數(shù)據(jù)2個(gè),應(yīng)用基因本體論(gene ontology,
GO) 富集分析和微陣列顯著性分析(significance analysis of microarray,SAM)方法,分析10個(gè)樣品的基因表達(dá)情況,并對(duì)差異表達(dá)的基因進(jìn)行功能注釋和生物學(xué)分析。
結(jié)果 腸粘連的發(fā)生伴隨著大量應(yīng)答刺激的基因表達(dá)上調(diào),且這些基因產(chǎn)物都分布在細(xì)胞膜上。分析術(shù)后不同時(shí)間點(diǎn)的基因表達(dá)情況發(fā)現(xiàn),Hmgcs 2基因的表達(dá)上調(diào)出現(xiàn)在術(shù)后3~14d,Stxbp5基因的表達(dá)上調(diào)出現(xiàn)在術(shù)后14d。
結(jié)論 粘連的發(fā)生可能與應(yīng)答刺激的基因及其分布于細(xì)胞膜上的基因產(chǎn)物有關(guān),Hmgcs 2和Stxbp 5基因可能與因粘連而導(dǎo)致的其他并發(fā)疾病的發(fā)生有關(guān)。這為發(fā)現(xiàn)腸梗阻潛在的治療靶點(diǎn)提供了依據(jù)。
引用本文: 顏祿斌,崔鴻斌,李斌德,李來(lái)元,李剛,邴志桐,沈陽(yáng). 腸粘連引起腸梗阻的基因水平研究△. 中國(guó)普外基礎(chǔ)與臨床雜志, 2013, 20(12): 1369-1373. doi: 復(fù)制
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- 1. Menzies D, Ellis H. Intestinal obstruction from adhesions—how big is the problem?[J]. Ann R Coll Surg Engl, 1990, 72(1): 60-63.
- 2. 許龍?zhí)茫?鄭樟棟, 曾天定, 等. 小腸腫瘤并發(fā)癥的診斷和治療[J]. 中華普通外科雜志, 2003, 18(1):28-30.
- 3. 王李, 童衛(wèi)東. 術(shù)后腸梗阻的發(fā)生機(jī)制研究進(jìn)展[J]. 中國(guó)普外基礎(chǔ)與臨床雜志, 2011, 18(10): 1114-1117.
- 4. 王甘露, 侍立志, 賀德, 等. 術(shù)后粘連性不全性腸梗阻的非手術(shù)治療[J]. 中國(guó)普外基礎(chǔ)與臨床雜志, 2011, 18(3):286-289.
- 5. 胡孔旺, 朱化剛, 耿小平, 等. 粘連性腸梗阻手術(shù)指征多因素分析[J]. 中國(guó)普外基礎(chǔ)與臨床雜志, 2010, 17(9): 939-943.
- 6. Yu SL, Singh S, Chen HW, et al. Intra-abdominal adhesion formation induces anti-oxidative injury, enhances cell proliferation, and prevents complement-mediated lysis[J]. Wound Repair Regen, 2008, 16(3):388-398.
- 7. Hu J, Xu J. Density based pruning for identification of differentially expressed genes from microarray data[J]. BMC Genomics,2010, 11(Suppl 2):S3.
- 8. Rajski M, Vogel B, Baty F, et al. Global gene expression analysis of the interaction between cancer cells and osteoblasts to predict bone metastasis in breast cancer[J]. PLoS One, 2012, 7(1):e29743.
- 9. Seita J, Sahoo D, Rossi DJ, et al. Gene Expression Commons:an open platform for absolute gene expression profiling[J]. PLoS One, 2012, 7(7):e40321.
- 10. Huang DW, Sherman BT, Lempicki RA. Systematic and integra-tive analysis of large gene lists using DAVID bioinformatics reso-urces[J]. Nat Protocols, 2008, 4(1):44-57.
- 11. Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium[J]. Nat Genet, 2000, 25(1):25-29.
- 12. Harris MA, Clark J, Ireland A, et al. The Gene Ontology (GO) database and informatics resource[J]. Nucleic Acids Res, 2004, 32(Database issue):D258-D261.
- 13. Wu G, Feng X, Stein L. A human functional protein interaction network and its application to cancer data analysis[J]. Genome Biology, 2010, 11(5):R53.
- 14. Wang F, Bing Z, Zhang Y, et al. Quantitative proteomic analysisfor radiation-induced cell cycle suspension in 92-1 melanoma cell line[J]. J Radiat Res, 2013, 54(4):649-662.
- 15. Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus:NCBI gene expression and hybridization array data repository[J].Nucleic Acids Res, 2002, 30(1):207-210.
- 16. Barrett T, Troup DB, Wilhite SE, et al. NCBI GEO:mining tens of millions of expression profiles—database and tools update[J]. Nucleic Acids Res, 2007, 35(Database issue):D760-D765.
- 17. Davis S, Meltzer PS. GEOquery: a bridge between the Gene Exp-ression Omnibus (GEO) and BioConductor[J]. Bioinformatics, 2007, 23(14):1846-1847.
- 18. Zang S, Guo R, Zhang L, et al. Integration of statistical inferencemethods and a novel control measure to improve sensitivity and specificity of data analysis in expression profiling studies[J]. J Biomed Inform, 2007, 40(5):552-560.
- 19. Saeed AI, Sharov V, White J, et al. TM4: a free, open-source system for microarray data management and analysis[J]. Biotec-hniques, 2003, 34(2):374-378.
- 20. Huang da W, Sherman BT, Lempicki RA. Bioinformatics enric-hment tools:paths toward the comprehensive functional analysis of large gene lists[J]. Nucleic Acids Res, 2009, 37(1):1-13.
- 21. Udeshi ND, Mani DR, Eisenhaure T, et al. Methods for quanti-fication of in vivo changes in protein ubiquitination following prote-asome and deubiquitinase inhibition[J]. Mol Cell Proteomics, 2012, 11(5): 148-159.
- 22. Cao R, Chen K, Song Q, et al. Quantitative proteomic analysis of membrane proteins involved in astroglial differentiation ofneural stem cells by SILAC labeling coupled with LC-MS/MS[J]. J Proteome Res, 2011, 11(2):829-838.
- 23. Zhang S. A comprehensive evaluation of SAM, the SAM R-package and a simple modification to improve its performance[J]. BMC Bioinformatics, 2007, 8:230.
- 24. Camarero N, Mascaró C, Mayordomo C, et al. Ketogenic HMGCS2 is a c-Myc target gene expressed in differentiated cells of human colonic epithelium and down-regulated in colon cancer[J]. Mol Cancer Res, 2006, 4(9):645-653.
- 25. Maraqa L, Cummings M, Peter M, et al. P3 evaluating the expression of HMGCS2 in human breast cancer relapses[J]. Breast, 2007, 16 Suppl 1:S13.
- 26. Widberg CH, Bryant NJ, Girotti M, et al. Tomosyn interacts with the t-SNAREs syntaxin4 and SNAP23 and plays a role ininsulin-stimulated GLUT4 translocation[J]. J Biol Chem, 2003,278(37):35093-35101.