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Yuh-Pyng Sher

Publications and source records attributed to Yuh-Pyng Sher.

3 recordsLinked to original sources

Human kallikrein 8 protease confers a favorable clinical outcome in non-small cell lung cancer by suppressing tumor cell invasiveness.

The human kallikrein 8 (KLK8) gene, a member of the human tissue kallikrein gene family, encodes a serine protease. The KLK8 protein (hK8) is known to be a favorable prognostic marker in ovarian cancer, but the biological basis of this is not understood. We found that overexpressing the KLK8 gene in highly invasive lung cancer cell lines suppresses their invasiveness. This role in invasiveness was further confirmed by the fact that inhibition of endogenous KLK8 expression with a specific short hairpin RNA reduced cancer cell invasiveness. In situ degradation and cell adhesion assays showed that proteins produced from KLK8 splice variants modify the extracellular microenvironment by cleaving fibronectin. DNA microarray experiments and staining of cells for actin filaments revealed that the degradation of fibronectin by hK8 suppresses integrin signaling and retards cancer cell motility by inhibiting actin polymerization. In addition, studies in a mouse model coupled with the detection of circulating tumor cells by quantitative PCR for the human Alu sequence showed that KLK8 suppresses tumor growth and invasion in vivo. Finally, studies of clinical specimens from patients with non-small cell lung cancer showed that the time to postoperative recurrence was longer for early-stage patients (stages I and II) with high KLK8 expression (mean, 49.9 months) than for patients with low KLK8 expression (mean, 22.9 months). Collectively, these findings show that KLK8 expression confers a favorable clinical outcome in non-small cell lung cancer by suppressing tumor cell invasiveness.

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Prognosis of non-small cell lung cancer patients by detecting circulating cancer cells in the peripheral blood with multiple marker genes.

PURPOSE: Current lung cancer staging and prognosis methods are based on imaging methods, which may not be sensitive enough for early and accurate detection of metastasis. This study aims to validate the use of a panel of markers for circulating cancer cell detection to improve the accuracy of cancer staging, prognosis, and as a rapid assessment of therapeutic response. EXPERIMENTAL DESIGN: We analyzed the National Cancer Institute-Cancer Genome Anatomy Project database to identify potential marker genes for the detection of circulating cancer cells in peripheral blood. Nested real-time quantitative PCR and a scoring method using cancer cell load Lc were employed to correlate the amount of circulating cancer cells with clinical outcomes in 54 non-small cell lung cancer (NSCLC) patients. The Kaplan-Meier method was employed for analysis of prognostic variables. RESULTS: A panel of four marker genes was identified and experimentally validated. With these marker genes, we achieved an overall positive detection rate of 72% for circulating cancer cells in the peripheral blood of NSCLC patients. Patients who had higher Lc values had worse outcomes and shorter survival times. Patients with poor therapeutic response were revealed by positive detection of circulating cancer cells after therapy. The results correlated well with the patients' survival time. CONCLUSION: Circulating cancer cell detection by a panel of markers and the Lc scoring method can supplement the current tumor, node, metastasis staging method for improved prognosis and for rapid assessment of therapeutic response. Together, they may facilitate the design of better therapeutic strategies for the treatment of NSCLC patients.

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Matching gene activity with physiological functions.

Matching the activity of the genes with biomechanics and physiology is an effective way to use cDNA microarray technology. Required are data on the change of activities of genes associated with specific physiological functions with respect to a continuous variable such as time. For each pair of data (gene and physiological function) as functions of time, we can compute a coefficient of correlation, R. The correlation is perfect if R is +1 or -1; it is nonexistent if R = 0. By evaluating R for every gene in a microarray, we can arrange the genes in the order of the number R, thus learning which genes are best correlated with the mechanical or physiological function. We illustrate this procedure by studying the blood vessels in the lung in response to pulmonary hypoxic hypertension, including the remodeling of vascular morphometry, the elastic moduli, and the zero-stress state of the vessel wall. For each physiological function, we identify the top genes that correlate the best. We found that different genes correlate best with a given function in large and small arteries, and that the genes in pulmonary veins which respond to arterial functions are different from those in pulmonary arteries. We found one set of genes matching the remodeling of arterial wall thickness, but another set of genes whose integral of activity over time best fit the wall thickness change. Our method can be used to study other thought-provoking problems.

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