title: Discovery of significant pathways in breast cancer metastasis via module extraction and comparison.
abstract: Discovering significant pathways rather than single genes or small gene sets involved in metastasis is becoming more and more important in the study of breast cancer. Many researches have shed light on this problem. However, most of the existing works are relying on some priori biological information, which may bring bias to the models. The authors propose a new method that detects metastasis-related pathways by identifying and comparing modules in metastasis and non-metastasis gene co-expression networks. The gene co-expression networks are built by Pearson correlation coefficients, and then the modules inferred in these two networks are compared. In metastasis and non-metastasis networks, 36 and 41 significant modules are identified. Also, 27.8% (metastasis) and 29.3% (non-metastasis) of the modules are enriched significantly for one or several pathways with p-value <0.05. Many breast cancer genes including RB1, CCND1 and TP53 are included in these identified pathways. Five significant pathways are discovered only in metastasis network: glycolysis pathway, cell adhesion molecules, focal adhesion, stathmin and breast cancer resistance to antimicrotubule agents, and cytosolic DNA-sensing pathway. The first three pathways have been proved to be closely associated with metastasis. The rest two can be taken as a guide for future research in breast cancer metastasis.
authors.affiliation: School of Mathematical Sciences, Fudan University, Shanghai 200433, People's Republic of China.
authors.forename: Xiaochen
authors.initials: X
authors.lastname: Wang
authors.affiliation: School of Mathematical Sciences, Fudan University, Shanghai 200433, People's Republic of China.
authors.forename: Huajie
authors.initials: H
authors.lastname: Qian
authors.affiliation: Center for Computational Systems Biology, School of Mathematical Sciences, Fudan University Shanghai, Shanghai 200433, People's Republic of China. zhangs@fudan.edu.cn.
authors.forename: Shuqin
authors.initials: S
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000737:chemistry; D001943:Breast Neoplasms / Q000473:pathology*; D002448:Cell Adhesion; D019295:Computational Biology / Q000379:methods; D003600:Cytosol / Q000378:metabolism; D004247:DNA / Q000378:metabolism; D019008:Drug Resistance, Neoplasm; D005260:Female; D022001:Focal Adhesions; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006019:Glycolysis; D006801:Humans; D008870:Microtubules / Q000737:chemistry; D009362:Neoplasm Metastasis; D015398:Signal Transduction; D050777:Stathmin / Q000737:chemistry
chemical_list: D000970:Antineoplastic Agents; D050777:Stathmin; D004247:DNA