Biomedical subjects

Yizheng Li

Publications and source records attributed to Yizheng Li.

3 recordsLinked to original sources

PubMed2006

Detecting and profiling tissue-selective genes.

The widespread use of DNA microarray technologies has generated large amounts of data from various tissue and/or cell types. These data set the stage to answer the question of tissue specificity of human transcriptome in a comprehensive manner. Our focus is to uncover the tissue-gene relationship by identifying genes that are preferentially expressed in a small number of tissue types. The tissue selectivity would shed light on the potential physiological functions of these genes and provides an indispensable reference to compare against disease pathophysiology and to identify or validate tissue-specific drug targets. Here we describe a systematic computational and statistical approach to profile gene expression data to identify tissue-selective genes with the use of a more extensive data set and a well-established multiple-comparison procedure with error rate control. Expression data of 35,152 probe sets in 97 normal human tissue types were analyzed, and 3,919 genes were identified to be selective to one or a few tissue types. We presented results of these tissue-selective genes and compared them to those identified by other studies.

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PubMed2004

Identification and validation of novel androgen-regulated genes in prostate cancer.

Androgen-regulated genes (ARGs) are essential for the development of the prostate. Ironically, ARGs are also responsible for the pathogenesis of prostate cancer. We used oligonucleotide array technology to study the expression profiles of ARGs in LNCaP prostate cancer cells and identified 692 dihydrotestosterone-regulated genes. Representative clusters containing genes with similar expression patterns to prostate-specific antigen and other known ARGs are discussed. Based on functional information, we categorized several candidate targets for prostate cancer therapy and diagnosis. Although many of these candidate targets are known to play an important role in cancer development, several are novel genes to the field of prostate cancer. A cross-comparison study of our results with those that have been previously published from three other array experiments using a similar LNCaP model validated 13 of these candidate targets as androgen-regulated. FKBP51 (FK506-binding immunophilin 51) was found in the same cluster as prostate-specific antigen and its protein expression was increased in LNCaP cells treated with either dihydrotestosterone or synthetic androgen R1881. Results from mining the Gene Logic BioExpress database showed that FKBP51 expression is significantly higher in the prostate cancer group than in the normal and normal adjacent group. Additionally, the androgen-independent prostate tumor xenograft, CWR22R, had higher FKBP51 protein levels than that of the androgen-dependent prostate tumor xenograft, CWR22. A tissue microarray study further revealed that FKBP51 protein expression was higher in prostate cancer specimens than in benign prostate tumor samples. These results suggest the potential value of FKBP51 as a novel diagnostic marker or target for prostate cancer therapy.

Androgens↗

PubMed2002

Gene expression changes during mouse skeletal myoblast differentiation revealed by transcriptional profiling.

Studies described here utilize high-density oligonucleotide arrays to characterize changes in global mRNA expression patterns during proliferation, cell cycle withdrawal, and terminal differentiation in mouse C2C12 myoblasts. Statistical analyses revealed 629 sequences differentially regulated between proliferating and differentiating myoblasts. These genes were clustered using self-organizing maps to identify sets of coregulated genes and were assigned to functional categories that were analyzed for distribution across expression clusters. Clusters were identified with statistically significant enrichment of functional categories including muscle contraction, cell adhesion, extracellular matrix function, cellular metabolism, mitochondrial transport, DNA replication, cell cycle control, mRNA transcription, and unexpectedly, immune regulation. In addition, functional category enrichment data can be used to predict gene function for numerous differentially regulated expressed sequence tags. The results provide new insight into how genes involved in these cellular processes may play a role in skeletal muscle growth and differentiation.

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