肿瘤是一种严重影响人类健康和生命的复杂疾病。某些生物学通路在肿瘤的发生、发展和转移的过程中发挥了关键作用, 如何发现和研究肿瘤相关通路是人们面临的一大挑战。随着以基因芯片数据为代表的海量实验数据的产出, 很多研究小组提出了一系列算法和模型通过整合和分析实验数据, 鉴定和模拟肿瘤相关的生物学通路, 发现了很多重要的生物学结论。文章对这些研究工作进行了综述, 给出了一些常用的算法、软件和数据库资源, 并讨论了该领域存在的问题和以后的发展方向。
Cancer is a complex disease which greatly affects the human health. It has been widely reported that certain biologcial pathways play important roles in the process of tumorigenesis, tumor progression and metastasis. Identification and simulation of these pathways can help to understand the underlying mechanisms. With the deposition of huge amount of microarray data, many groups have developed a series of algorithms and models to analyze the microarray datasets for the identification and simulation of tumor related pathways. In this paper, firstly we review the recent development of these algorithms and models; then list the related software and data sources; and finally discuss the existence problems and per-spectives in this field.
[1] Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA, 1998, 95(25): 14863-14868.
[2] D'Haeseleer P. How does gene expression clustering work? Nat Biotechnol, 2005, 23(12): 1499-1501.
[3] Janes KA, Yaffe MB. Data-driven modelling of signal-transduction networks. Nat Rev Mol Cell Biol, 2006, 7(11): 820-828.
[4] Sherlock G. Analysis of large-scale gene expression data. Curr Opin Immunol, 2000, 12(2): 201-205.
[5] Tjaden B. An approach for clustering gene expression data with error information. BMC Bioinformatics, 2006, 7: 17.
[6] Wilkin GA, Huang XZ. A practical comparison of two K-Means clustering algorithms. BMC Bioinformatics, 2008, 9(Suppl. 6): S19.
[7] Blackhall FH, Wigle DA, Jurisica I, Pintilie M, Liu N, Darling G, Johnston MR, Keshavjee S, Waddell T, Winton T, Shepherd FA, Tsao MS. Validating the prognostic value of marker genes derived from a non-small cell lung cancer microarray study. Lung Cancer, 2004, 46(2): 197-204.
[8] Dembélé D, Kastner P. Fuzzy C-means method for clustering microarray data. Bioinformatics, 2003, 19(8): 973-980.
[9] Kluger Y, Basri R, Chang JT, Gerstein M. Spectral biclustering of microarray data: coclustering genes and conditions. Genome Research, 2003, 13(4): 703-716.
[10] Lapointe J, Li CD, Higgins JP, van de Rijn M, Bair E, Montgomery K, Ferrari M, Egevad L, Rayford W, Bergerheim U, Ekman P, DeMarzo AM, Tibshirani R, Botstein D, Brown PO, Brooks JD, Pollack JR. Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc Natl Acad Sci USA, 2004, 101(3): 811-816.
[11] Segal E, Shapira M, Regev A, Pe'er D, Botstein D, Koller D, Friedman N. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet, 2003, 34(2): 166-176.
[12] Koumakpayi IH, Le Page C, Mes-Masson AM, Saad F. Hierarchical clustering of immunohistochemical analysis of the activated ErbB/PI3K/Akt/NF-κB signalling pathway and prognostic significance in prostate cancer. Br J Cancer, 2010, 102(7): 1163-1173.
[13] Ma SG, Kosorok MR. Identification of differential gene pathways with principal component analysis. Bioinformatics, 2009, 25(7): 882-889.
[14] Huang DS, Zheng CH. Independent component analysis-based penalized discriminant method for tumor classification using gene expression data. Bioinformatics, 2006, 22(15): 1855-1862.
[15] Sandberg R, Ernberg I. Assessment of tumor characteristic gene expression in cell lines using a tissue similarity index (TSI). Proc Natl Acad Sci USA, 2005, 102(6): 2052-2057.
[16] Janes KA, Albeck JG, Gaudet S, Sorger PK, Lauffenburger DA, Yaffe MB. A systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis. Science, 2005, 310(5754): 1646-1653.
[17] Bild AH, Yao G, Chang JT, Wang QL, Potti A, Chasse D, Joshi MB, Harpole D, Lancaster JM, Berchuck A, Olson JA Jr, Marks JR, Dressman HK, West M, Nevins JR. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature, 2006, 439(7074): 353-357.
[18] Ivakhno S, Armstrong JD. Non-linear dimensionality reduction of signaling networks. BMC Syst Biol, 2007, 1: 27.
[19] Huang D W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucl Acids Res, 2009, 37(1): 1-13.
[20] Curtis RK, Oreši? M, Vidal-Puig A. Pathways to the analysis of microarray data. Trends Biotechnol, 2005, 23(8): 429-435.
[21] Dr?ghici S, Khatri P, Martins RP, Ostermeier GC, Krawetz SA. Global functional profiling of gene expression. Genomics, 2003, 81(2): 98-104.
[22] Rivals I, Personnaz L, T
