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Articles by J Meng
Total Records ( 3 ) for J Meng
  J Meng , W Xie , L Cao , C Hu and Z. Zhen
 

Hepatoma-derived growth factor (HDGF), a nuclear protein with both mitogenic and angiogenic activity, has been reported to be mainly involved in tumorigenesis and the progression of non-small cell lung cancer. In this study, the HDGF expression was knocked down by specific-shRNA with lentivirus expression vector targeting HDGF in lung squamous cell carcinoma 520 cells. HDGF knocked down by shRNA suppressed the cell proliferation significantly both in vitro and in vivo as indicated by MTT, plate clone and transplanted tumor model assays. In addition, the knocked-down expression of HDGF also inhibited cell migration and invasion as shown in transwell and Boyden experiments. We concluded that HDGF acts as an oncogene participating in the pathogenesis of squamous cell lung cancer, and HDGF may be a key therapeutic target for non-small cell lung cancer.

  J Meng , S. J Gao and Y. Huang
 

Motivation: Clustering is a popular data exploration technique widely used in microarray data analysis. When dealing with time-series data, most conventional clustering algorithms, however, either use one-way clustering methods, which fail to consider the heterogeneity of temporary domain, or use two-way clustering methods that do not take into account the time dependency between samples, thus producing less informative results. Furthermore, enrichment analysis is often performed independent of and after clustering and such practice, though capable of revealing biological significant clusters, cannot guide the clustering to produce biologically significant result.

Result:We present a new enrichment constrained framework (ECF) coupled with a time-dependent iterative signature algorithm (TDISA), which, by applying a sliding time window to incorporate the time dependency of samples and imposing an enrichment constraint to parameters of clustering, allows supervised identification of temporal transcription modules (TTMs) that are biologically meaningful. Rigorous mathematical definitions of TTM as well as the enrichment constraint framework are also provided that serve as objective functions for retrieving biologically significant modules. We applied the enrichment constrained time-dependent iterative signature algorithm (ECTDISA) to human gene expression time-series data of Kaposi's sarcoma-associated herpesvirus (KSHV) infection of human primary endothelial cells; the result not only confirms known biological facts, but also reveals new insight into the molecular mechanism of KSHV infection.

Availability: Data and Matlab code are available at http://engineering.utsa.edu/~yfhuang/ECTDISA.html

Contact: yufei.huang@utsa.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

 
 
 
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