Biomedical subjects

Li-Li Hsiao

Publications and source records attributed to Li-Li Hsiao.

7 recordsLinked to original sources

PubMed2002

Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma.

The pathological distinction between malignant pleural mesothelioma (MPM)and adenocarcinoma (ADCA) of the lung can be cumbersome using established methods. We propose that a simple technique, based on the expression levels of a small number of genes, can be useful in the early and accurate diagnosis of MPM and lung cancer. This method is designed to accurately distinguish between genetically disparate tissues using gene expression ratios and rationally chosen thresholds. Here we have tested the fidelity of ratio-based diagnosis in differentiating between MPM and lung cancer in 181 tissue samples (31 MPM and 150 ADCA). A training set of 32 samples (16 MPM and 16 ADCA) was used to identify pairs of genes with highly significant, inversely correlated expression levels to form a total of 15 diagnostic ratios using expression profiling data. Any single ratio of the 15 examined was at least 90% accurate in predicting diagnosis for the remaining 149 samples (e.g., test set). We then examined (in the test set) the accuracy of multiple ratios combined to form a simple diagnostic tool. Using two and three expression ratios, we found that the differential diagnoses of MPM and lung ADCA were 95% and 99% accurate, respectively. We propose that using gene expression ratios is an accurate and inexpensive technique with direct clinical applicability for distinguishing between MPM and lung cancer. Furthermore, we provide evidence suggesting that this technique can be equally accurate in other clinical scenarios.

Adenocarcinoma↗

PubMed2002

Global analysis of gene expression in renal ischemia-reperfusion in the mouse.

Ischemia-induced acute renal failure (ARF) is a relatively common disorder with major morbidity and mortality. To study global gene expression during ARF, 6-week-old C57BL/6 male mice underwent 30 min of bilateral renal ischemia followed by reperfusion [I/R] or sham operation. Oligonucleotide microarrays [Affymetrix] with approximately 10,000 genes, 6,643 of which were present in mouse kidney, were used to analyze mRNA expression for up to 4 days following I/R. Fifty-two genes at day 1 and 40 at day 4 were up-regulated more than 4-fold [400%]. Seventy genes at day 1 and 30 genes at day 4 were down-regulated to under 0.25-fold from baseline [25%]. Real-time quantitative RT-PCR confirmed changes in expression for 8 genes of interest. Most of the induced transcripts are involved in cell structure, extracellular matrix, intracellular calcium binding, and cell division/differentiation. Our data identified several novel genes that may be important in renal repair after ischemia.

ADP-Ribosylation Factor 6↗

PubMed2002

HugeIndex: a database with visualization tools for high-density oligonucleotide array data from normal human tissues.

High-density oligonucleotide arrays are a powerful tool for uncovering changes in global gene expression in various disease states. To this end, it is essential to first characterize the variations of gene expression in normal physiological processes. We established the Human Gene Expression (HuGE) Index database (www.HugeIndex.org) to serve as a public repository for gene expression data on normal human tissues using high-density oligonucleotide arrays. This resource currently contains the results of 59 gene expression experiments on 19 human tissues. We provide interactive tools for researchers to query and visualize our data over the Internet. To facilitate data analysis, we cross-reference each gene on the array with its annotation in the LocusLink database at NCBI.

Computer Graphics↗

PubMed2002

Interactive exploration of microarray gene expression patterns in a reduced dimensional space.

The very high dimensional space of gene expression measurements obtained by DNA microarrays impedes the detection of underlying patterns in gene expression data and the identification of discriminatory genes. In this paper we show the use of projection methods such as principal components analysis (PCA) to obtain a direct link between patterns in the genes and patterns in samples. This feature is useful in the initial interactive pattern exploration of gene expression data and data-driven learning of the nature and types of samples. Using oligonucleotide microarray measurements of 40 samples from different normal human tissues, we show that distinct patterns are obtained when the genes are projected on a two-dimensional plane spanned by the loadings of the two major principal components. These patterns define the particular genes associated with a sample class (i.e., tissue). When used separately from the other genes, these class-specific (i.e., tissue-specific) genes in turn define distinct tissue patterns in the projection space spanned by the scores of the two major principal components. In this study, PCA projection facilitated discriminatory gene selection for different tissues and identified tissue-specific gene expression signatures for liver, skeletal muscle, and brain samples. Furthermore, it allowed the classification of nine new samples belonging to these three types using the linear combination of the expression levels of the tissue-specific genes determined from the first set of samples. The application of the technique to other published data sets is also discussed.

Gene Expression Profiling↗

PubMed2002

Global analysis of gene expression: methods, interpretation, and pitfalls.

Over the past 15 years, global analysis of mRNA expression has emerged as a powerful strategy for biological discovery. Using the power of parallel processing, robotics, and computer-based informatics, a number of high-throughput methods have been devised. These include DNA microarrays, serial analysis of gene expression, quantitative RT-PCR, differential-display RT-PCR, and massively parallel signature sequencing. Each of these methods has inherent advantages and disadvantages, often related to expense, technical difficulty, specificity, and reliability. Further, the ability to generate large data sets of gene expression has led to new challenges in bioinformatics. Nonetheless, this technological revolution is transforming disease classification, gene discovery, and our understanding of regulatory gene networks.

Computational Biology↗

PubMed2001

DNA microarray analysis of complex biologic processes.

DNA microarrays, or gene chips, allow surveys of gene expression, (i.e., mRNA expression) in a highly parallel and comprehensive manner. The pattern of gene expression produced, known as the expression profile, depicts the subset of gene transcripts expressed in a cell or tissue. At its most fundamental level, the expression profile can address qualitatively which genes are expressed in disease states. However, with the aid of bioinformatics tools such as cluster analysis, self-organizing maps, and principle component analysis, more sophisticated questions can be answered. Microarrays can be used to characterize the functions of novel genes, identify genes in a biologic pathway, analyze genetic variation, and identify therapeutic drug targets. Moreover, the expression profile can be used as a tissue or disease "fingerprint." This review details the fabrication of arrays, data management tools, and applications of microarrays to the field of renal research and the future of clinical practice.

Drug Design↗

PubMedDate not supplied

DNA microarrays: applicability to renal physiology and disease.

The utilization of DNA microarray is increasing in all areas of medical research as evidenced by the exponential increase in the number of publications on MedLine. Improvements in DNA microarray technology and the bioinformatic techniques for data analysis continue to take place and result in improved reliability, reproducibility, and volume of data. Replicate analysis remains important in order to allow statistical examination of significance. Expression of key genes should also be confirmed by alternative techniques. Single microarray experiments yield such vast amounts of data that shortage of manpower is the limiting factor to progression of full scientific discovery. Sharing of microarray data on the internet and its distribution to a broader scientific community will help overcome this manpower shortage. Currently, DNA microarray technology serves as a research tool. Ultimately, microarray technology may be used widely in the clinical setting as fingerprinting of cells, tissue, and disease states, though more targeted strategies using a limited repertoire of genes may be most valuable.

Animals↗