Objective： To investigate the mechanism of resistance to docetaxel in human lung cancer. Methods： Human lung carcinoma SPC-A1/Docetaxel cells were derived from parental SPC-A1 cells by continuous exposure to increa...Objective： To investigate the mechanism of resistance to docetaxel in human lung cancer. Methods： Human lung carcinoma SPC-A1/Docetaxel cells were derived from parental SPC-A1 cells by continuous exposure to increasing concentration of docetaxel. The drug sensitivity was measured by MTT assay in vitro. The cDNA microarray identified a set of differentially expressed genes, and some genes were confirmed by RT-PCR. P-glycoprotein level was measured by flow cytometry analysis. Results： The results of drug sensitivity measured by MTT assay showed that SPC-A1/Docetaxel cells were 13.2-fold resistant to docetaxel and cross-resistant at varying levels to other drugs. The cDNA microarray results identified a set of differentially expressed genes, which showed 428 genes that were up-regulated and 506 genes that were down-regulated in SPC-A1/Docetaxel ceils, and some genes were confirmed by RT-PCR. Flow cytometry analysis suggests expression of P-glycoprotein （P-gp） was more abundant in SPC-A1/Docetaxel cells than in the parental cells and docetaxel selection reduces the apoptotic response. Conclusion： The results suggest that docetaxel selection led to changes in gene expression that contribute to the multidrug resistance phenotype.展开更多
A major focus of systems biology is to characterize interactions between cellular compo- nents, in order to develop an accurate picture of the intricate networks within biological systems. Over the past decade, protei...A major focus of systems biology is to characterize interactions between cellular compo- nents, in order to develop an accurate picture of the intricate networks within biological systems. Over the past decade, protein microarrays have greatly contributed to advances in proteomics and are becoming an important platform for systems biology. Protein microarrays are highly flex- ible, ranging from large-scale proteome microarrays to smaller customizable microarrays, making the technology amenable for detection of a broad spectrum of biochemical properties of proteins. In this article, we will focus on the numerous studies that have utilized protein microarrays to recon- struct biological networks including protein-DNA interactions, posttranslational protein modifica- tions （PTMs）, lectin glycan recognition, pathogen-host interactions and hierarchical signaling cascades. The diversity in applications allows for integration of interaction data from numerous molecular classes and cellular states, providing insight into the structure of complex biological sys- tems. We will also discuss emerging applications and future directions of protein microarray tech- nology in the global frontier.展开更多
Understanding tumor diversity has been a long-lasting and challenging question for researchers in the field of cancer heterogeneity or tumor evolution. Studies have reported that com- pared to normal cells, there is a...Understanding tumor diversity has been a long-lasting and challenging question for researchers in the field of cancer heterogeneity or tumor evolution. Studies have reported that com- pared to normal cells, there is a higher genetic diversity in tumor cells, while higher genetic diversity is associated with higher progression risks of tumor. We thus hypothesized that tumor diversity also holds true at the gene expression level. To test this hypothesis, we used t-test to compare the means of Simpson＇s diversity index for gene expression （SDIG） between tumor and non-tumor samples. We found that the mean SDIG in tumor tissues is significantly higher than that in the non-tumor or normal tissues （P 〈 0.05） for most datasets. We also combined microarrays and next-generation sequencing data for validation. This cross-platform and cross-experimental validation greatly increased the reliability of our results.展开更多
基金supported by a grant from the Postdoctor Research Project of Jiangsu Province (No.0602031B)
文摘Objective： To investigate the mechanism of resistance to docetaxel in human lung cancer. Methods： Human lung carcinoma SPC-A1/Docetaxel cells were derived from parental SPC-A1 cells by continuous exposure to increasing concentration of docetaxel. The drug sensitivity was measured by MTT assay in vitro. The cDNA microarray identified a set of differentially expressed genes, and some genes were confirmed by RT-PCR. P-glycoprotein level was measured by flow cytometry analysis. Results： The results of drug sensitivity measured by MTT assay showed that SPC-A1/Docetaxel cells were 13.2-fold resistant to docetaxel and cross-resistant at varying levels to other drugs. The cDNA microarray results identified a set of differentially expressed genes, which showed 428 genes that were up-regulated and 506 genes that were down-regulated in SPC-A1/Docetaxel ceils, and some genes were confirmed by RT-PCR. Flow cytometry analysis suggests expression of P-glycoprotein （P-gp） was more abundant in SPC-A1/Docetaxel cells than in the parental cells and docetaxel selection reduces the apoptotic response. Conclusion： The results suggest that docetaxel selection led to changes in gene expression that contribute to the multidrug resistance phenotype.
基金the Grants awarded to HZ (Grant No. RR020839, DK082840,RO1GM076102, CA125807, CA160036 and HG006434)an F31 NRSA Predoctoral Fellowship to IU (Grant No.5F31GM096716)
文摘A major focus of systems biology is to characterize interactions between cellular compo- nents, in order to develop an accurate picture of the intricate networks within biological systems. Over the past decade, protein microarrays have greatly contributed to advances in proteomics and are becoming an important platform for systems biology. Protein microarrays are highly flex- ible, ranging from large-scale proteome microarrays to smaller customizable microarrays, making the technology amenable for detection of a broad spectrum of biochemical properties of proteins. In this article, we will focus on the numerous studies that have utilized protein microarrays to recon- struct biological networks including protein-DNA interactions, posttranslational protein modifica- tions （PTMs）, lectin glycan recognition, pathogen-host interactions and hierarchical signaling cascades. The diversity in applications allows for integration of interaction data from numerous molecular classes and cellular states, providing insight into the structure of complex biological sys- tems. We will also discuss emerging applications and future directions of protein microarray tech- nology in the global frontier.
基金supported by University of California, Los AngelesUniversity of North Texas Health Science Center of the United States
文摘Understanding tumor diversity has been a long-lasting and challenging question for researchers in the field of cancer heterogeneity or tumor evolution. Studies have reported that com- pared to normal cells, there is a higher genetic diversity in tumor cells, while higher genetic diversity is associated with higher progression risks of tumor. We thus hypothesized that tumor diversity also holds true at the gene expression level. To test this hypothesis, we used t-test to compare the means of Simpson＇s diversity index for gene expression （SDIG） between tumor and non-tumor samples. We found that the mean SDIG in tumor tissues is significantly higher than that in the non-tumor or normal tissues （P 〈 0.05） for most datasets. We also combined microarrays and next-generation sequencing data for validation. This cross-platform and cross-experimental validation greatly increased the reliability of our results.
