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1.  Regulation of adipose oestrogen output by mechanical stress 
Nature communications  2013;4:1821.
Adipose stromal cells are the primary source of local estrogens in adipose tissue, aberrant production of which promotes oestrogen receptor-positive breast cancer. Here we show that extracellular matrix compliance and cell contractility are two opposing determinants for oestrogen output of adipose stromal cells. Using synthetic extracellular matrix and elastomeric micropost arrays with tunable rigidity, we find that increasing matrix compliance induces transcription of aromatase, a rate-limiting enzyme in oestrogen biosynthesis. This mechanical cue is transduced sequentially by discoidin domain receptor 1, c-Jun N-terminal kinase 1, and phosphorylated JunB, which binds to and activates two breast cancer-associated aromatase promoters. In contrast, elevated cell contractility due to actin stress fibre formation dampens aromatase transcription. Mechanically stimulated stromal oestrogen production enhances oestrogen-dependent transcription in oestrogen receptor-positive tumour cells and promotes their growth. This novel mechanotransduction pathway underlies communications between extracellular matrix, stromal hormone output, and cancer cell growth within the same microenvironment.
doi:10.1038/ncomms2794
PMCID: PMC3921626  PMID: 23652009
2.  A Steiner tree-based method for biomarker discovery and classification in breast cancer metastasis 
BMC Genomics  2012;13(Suppl 6):S8.
Background
Metastatic breast cancer is a leading cause of cancer-related deaths in women worldwide. DNA microarray has become an important tool to help identify biomarker genes for improving the prognosis of breast cancer. Recently, it was shown that pathway-level relationships between genes can be incorporated to build more robust classification models and to obtain more useful biological insight from such models. Due to the unavailability of complete pathways, protein-protein interaction (PPI) network is becoming more popular to researcher and opens a new way to investigate the developmental process of breast cancer.
Methods
In this study, a network-based method is proposed to combine microarray gene expression profiles and PPI network for biomarker discovery for breast cancer metastasis. The key idea in our approach is to identify a small number of genes to connect differentially expressed genes into a single component in a PPI network; these intermediate genes contain important information about the pathways involved in metastasis and have a high probability of being biomarkers.
Results
We applied this approach on two breast cancer microarray datasets, and for both cases we identified significant numbers of well-known biomarker genes for breast cancer metastasis. Those selected genes are significantly enriched with biological processes and pathways related to cancer carcinogenic process, and, importantly, have much higher stability across different datasets than in previous studies. Furthermore, our selected genes significantly increased cross-data classification accuracy of breast cancer metastasis.
Conclusions
The randomized Steiner tree based approach described in this study is a new way to discover biomarker genes for breast cancer, and improves the prediction accuracy of metastasis. Though the analysis is limited here only to breast cancer, it can be easily applied to other diseases.
doi:10.1186/1471-2164-13-S6-S8
PMCID: PMC3481447  PMID: 23134806

Results 1-2 (2)