靶向沉默核干因子对前列腺癌PC-3细胞全基因组表达谱变化的影响

目的 探讨核干因子(NS)基因在前列腺癌恶性增殖中的作用机制.方法 采用表达谱基因芯片技术,分析前列腺癌PC-3细胞NS基因表达下调后全基因组表达谱的变化,筛选与NS相互作用的差异表达基因及信号通路.采用实时荧光定量聚合酶链反应对重要的差异表达基因进行验证.结果 共筛选出219个差异表达的基因,这些基因涉及到细胞周期、细胞增殖、信号转导、细胞凋亡和细胞分化等多个方面.NS基因表达下调后主要引起周期素依赖激酶4抑制因子(INK4)家族基因(p15、p16和p18)的上调,以及cyclin D1和HDACI基因的下调,其主要作用点位于CDK4/6-cyclinD和pRb-E2F1复合体上.结论 在前列腺癌PC-3细胞中,NS基因可能主要通过抑制p15、p16和p18等INK4家族的肿瘤抑制基因的表达,从而促进肿瘤的发生和发展;NS基因是细胞周期G1/S期检查点的重要调控因子.     

MicroRNA expression profiling in prostate cancer

MicroRNAs (miRNA) are small, endogenously expressed noncoding RNAs that negatively regulate expression of protein-coding genes at the translational level. Accumulating evidence, such as aberrant expression of miRNAs, suggests that they are involved in the development of cancer. They have been identified in various tumor types, showing that different sets of miRNAs are usually deregulated in different cancers. To identify the miRNA signature specific for prostate cancer, miRNA expression profiling of 6 prostate cancer cell lines, 9 prostate cancer xenografts samples, 4 benign prostatic hyperplasia (BPH), and 9 prostate carcinoma samples was carried out by using an oligonucleotide array hybridization method. Differential expression of 51 individual miRNAs between benign tumors and carcinoma tumors was detected, 37 of them showing down-regulation and 14 up-regulation in carcinoma samples, thus identifying those miRNAs that could be significant in prostate cancer development and/or growth. There was a significant trend (P=0.029) between the expression of miRNAs and miRNA locus copy number determined by array comparative genomic hybridization, indicating that genetic aberrations may target miRNAs. Hierarchical clustering of the tumor samples by their miRNA expression accurately separated the carcinomas from the BPH samples and also further classified the carcinoma tumors according to their androgen dependence (hormone naive versus hormone refractory), indicating the potential of miRNAs as a novel diagnostic and prognostic tool for prostate cancer.     

Gene expression profiling reveals novel biomarkers in nonsmall cell lung cancer

The development of reliable gene expression profiling technology is having an increasing impact on our understanding of lung cancer biology. Our study aimed to determine any correlation between the phenotypic heterogeneity and genetic diversity of lung cancer. Microarray analysis was performed on a set of 46 tumor samples and 45 paired nontumor samples of nonsmall cell lung cancer (NSCLC) samples to establish gene signatures in primary adenocarcinomas and squamous-cell carcinomas, determine differentially expressed gene sequences at different stages of the disease and identify sequences with biological significance for tumor progression. After the microarray analysis, the expression level of 92 selected genes was validated by qPCR and the robust Bonferroni test in an independent set of 70 samples composed of 48 tumor samples and 22 nontumor samples. Gene sequences were differentially expressed as a function of tumor type, stage and differentiation grade. High upregulation was observed for KRT15 and PKP1, which may be good markers to distinguish squamous-cell carcinoma samples. High downregulation was observed for DSG3 in stage IA adenocarcinomas.     

Gene expression profiling in cancer research

Gene expression profiling is increasingly used in cancer research. For each patient, the expression of thousands of genes in the tumour can be measured simultaneously on a microarray. Microarray studies aim at classifying patients based on two types of classification schemes: unsupervised classification, which uses clustering in order to identify homogeneous subtypes of a disease on the basis of gene expression, or supervised classification, which principally aims at the identification of genes or set of genes differentially expressed between tumours with different characteristics (molecular signature), for instance between a group of patients with bad and good prognosis. The data consists of a small number of patients and a large number of variables, raising serious methodological problems. We will use published results on breast cancer in order both to study the power of the experiments and to illustrate the problems in interpretation and validity of their results. We recommend rigorous evaluation of this new technology.     

Gene expression profiling in breast cancer

PURPOSE OF REVIEW: Gene expression profiling has highlighted the biologic heterogeneity of breast cancer and has begun to influence the ability of the medical community to individualize patient therapy. The review is intended to highlight the most important advances in the field over recent years with an emphasis on those most relevant to the practicing oncologist. RECENT FINDINGS: Two prognostic profiling assays, the Mammaprint and Oncotype Dx, are in phase III clinical trials designed to evaluate their contribution to therapeutic decision making. Predictive profiles for both chemotherapy and targeted therapy are also in development. In addition, application of genetic profiling techniques to a variety of tumor types is starting to identify those processes, like proliferation, that are integral to carcinogenesis as a whole. SUMMARY: The biologic heterogeneity of breast cancer has become clearer through genome-wide profiling technologies. Validation of the clinical utility of prognostic profiles may enable oncologists to better identify those patients whose prognosis justifies more intensive therapy, while predictive profiles may soon be able to determine which type of chemotherapy a patient should receive. In addition, profiling is starting to identify new therapeutic targets which will point the field of breast cancer oncology in new directions.     

Gene expression profiling revealed survivin as a target of 3,3'-diindolylmethane-induced cell growth inhibition and apoptosis in breast cancer cells

The phytochemical indole-3-carbinol (I3C), found in cruciferous vegetables, and its major acid-catalyzed reaction product 3,3'-diindolylmethane (DIM) showed anticancer activity mediated by its pleiotropic effects on cell cycle progression, apoptosis, carcinogen bioactivation, and DNA repair. To further elucidate the molecular mechanism(s) by which 3,3'-diindolylmethane exerts its effects on breast cancer cells, we have used microarray gene expression profiling analysis. We found a total of 1,238 genes altered in 3,3'-diindolylmethane-treated cells, among which 550 genes were down-regulated and 688 genes were up-regulated. Clustering analysis showed significant alterations in some genes that are critically involved in the regulation of cell growth, cell cycle, apoptosis, and signal transduction, including down-regulation of survivin. Previous studies have shown that antiapoptotic protein survivin is overexpressed in many human cancers, including breast cancer. However, very little or no information is available regarding the consequence of down-regulation of survivin for cancer therapy. We, therefore, hypothesized that down-regulation of survivin as observed by 3,3'-diindolylmethane could be an important approach for the treatment of breast cancer. We have tested our hypothesis using multiple molecular approaches and found that 3,3'-diindolylmethane inhibited cell growth and induced apoptosis in MDA-MB-231 breast cancer cells by down-regulating survivin, Bcl-2, and cdc25A expression and also caused up-regulation of p21(WAF1) expression, which could be responsible for cell cycle arrest. Down-regulation of survivin by small interfering RNA before 3,3'-diindolylmethane treatment resulted in enhanced cell growth inhibition and apoptosis, whereas overexpression of survivin by cDNA transfection abrogated 3,3'-diindolylmethane-induced cell growth inhibition and apoptosis. These results suggest that targeting survivin by 3,3'-diindolylmethane could be a new and novel approach for the prevention and/or treatment of breast cancer.     

Gene expression profiling of the androgen independent prostate cancer cells demonstrates complex mechanisms mediating resistance to docetaxel

The molecular mechanisms conferring resistance to docetaxel in prostate cancer patients remain partially understood. We generated docetaxel resistant derivatives of the androgen independent prostate cancer cell lines PC-3 and DU-145. Docetaxel rapidly induces DU-145 cell death via apoptosis and the drug resistant cells were produced by periodically exposing proliferating DU-145 cultures to small doses of docetaxel. In PC-3 cells docetaxel induces delayed cell death via mitotic catastrophe evident by profound multinucleation and formation of giant cells. Mononucleated progeny of the giant PC-3 cells shows significant resistance to docetaxel. Gene expression profiling of these docetaxel resistant PC-3 cells revealed sets of docetaxel inducible and constitutively expressed genes associated with major cancer pathways. A contradictory overlap with DU-145 docetaxel resistant cells was also found. Analyses suggested significant changes associated with apoptotic function, DNA repair, cell growth, survival and proliferation, metabolism, maintenance of cytoskeleton and extracellular matrix formation. These cellular processes often contribute to drug resistance and our study identified a set of genes managing this phenotype. Additional analyses of the drug resistant PC-3 cells using shRNA constructs determined direct relevance of Cyclin G2 to docetaxel resistance as well as prevention of multinucleation, whereas the knockdown of upregulated CYP1B1 showed no effect on either of these processes. Downregulated GBP1 was explored by ectopic overexpression and even though GBP1 has a potential to mediate resistance to docetaxel, it was not utilized in PC-3 cells. The results suggest complex combination of gene expression pattern changes that enables resistance to docetaxel while preventing death via multinucleation.     

Gene expression profiling of breast cancer

Numerous genes are controlled by complex regulatory networks and involved in the development and progression of breast cancer, and these genes are the key factors determining each characteristic of the tumor. Gene expression profiling, a large scale analysis of gene expression, has created new possibilities for the molecular characterization of cancer. Systematic analysis of expression patterns of thousands of genes in tumor cells using DNA microarrays and correlation of these patterns to specific features of phenotypic variation may provide the basis for an improved taxonomy of cancer. These profiles have the potential to explain the genetic heterogeneity of breast cancer and allow treatment strategies to be planned in accordance with their probability of success in individual patients.     

Gene expression profiling of primary breast cancer

Gene expression profiling is a method to measure the expression of a large number of genes in tissue specimens simultaneously. This analytical technique is actively explored as an emerging diagnostic tool for breast cancer. An important assumption behind this research is that the constellation of multiple genes will be more predictive of clinical outcome than any single gene alone. Gene expression signatures were shown to predict prognosis of breast cancer as well as response to particular chemotherapy regimens. The first multigene predictor of prognosis after tamoxifen therapy is already commercially available in the United States. This article reviews recent advances in the clinical application of this technique to breast cancer.     

Gene expression profiling of advanced lung cancer

Lung cancer is a highly aggressive neoplasm with 85% mortality. To identify new tumor-associated genes, we compared the expression profile of a primary metastasizing adenocarcinoma with normal airway epithelial cells. Two cDNA libraries of up- and downregulated genes were generated, comprising 253 and 299 clones, respectively. The sequence analysis revealed 205 different known genes and 314 cDNA fragments of unknown functions. Northern-blot analysis of 167 clones confirmed differential expression in 58%, and indicated a similar expression pattern in additional lung-cancer cell lines for selected clones, strengthening the value of this model for the identification of new candidate genes in lung carcinogenesis.     

Gene expression profiling in clinically localized prostate cancer: a four-gene expression model predicts clinical behavior.

PURPOSE: New diagnostic and prognostic molecular markers are required for prostate cancer, one of the most common male malignancies in Western countries. Gene expression profiling may help to identify genes involved in prostate carcinogenesis, yield clinical biomarkers, and improve tumor classification. EXPERIMENTAL DESIGN: To identify fundamental differences between normal and neoplastic prostate tissue, we used real-time quantitative RT-PCR assays to quantify the mRNA expression of 291 selected genes in samples of normal prostate and of well-documented primary, clinically localized prostate tumors. RESULTS: Forty-six genes showed significantly different expression in tumors relative to normal prostate. The dysregulated genes belong notably to the extracellular membrane and extracellular membrane remodeling categories and are involved in angiogenesis. Furthermore, we obtained a four-gene (XLKD1/LYVE1, CGA, F2R/PAR1, and BCL-G) model that discriminated between the seven patients with and the seven patients without relapse, independently of stage and grade. CONCLUSIONS: Some dysregulated genes are good candidates for use as molecular markers and/or therapeutic targets. Furthermore, differential gene expression profiling of clinically localized prostate tumors from relapsing and nonrelapsing patients identified a set of four genes with a pattern of expression that defines a molecular signature that could predict the clinical behavior of this disease.     

Gene expression profiling of tumor-stromal interactions between pancreatic cancer cells and stromal fibroblasts

The interactions between cancer cells and surrounding stroma play a critical role in tumor progression, but their molecular basis is largely unknown. Global gene expression profiling was performed using oligonucleotide microarrays to determine changes in the gene expression of pancreatic cancer cells (CFPAC1) and stromal fibroblasts induced by coculture. This analysis identified multiple genes as differentially expressed in pancreatic cancer cells and in fibroblasts as a consequence of their mutual interactions, including those that encode for proteins associated with tumor invasion, metastasis, and angiogenesis. Among the genes identified, the cyclooxygenase-2 (COX-2)/PTGS2 gene was of particular interest because COX-2 expression was markedly augmented in both cell types (cancer cells and fibroblasts) in response to coculture. Coculture with fibroblasts also induced COX-2 expression in additional pancreatic cancer cells with an unmethylated COX-2 promoter, but not in those with a methylated COX-2 promoter. Using an in vitro invasion assay, we found an increase in the invasive potential of CFPAC1 cells when they were cocultured with fibroblasts, an effect blocked partially by the addition of a selective COX-2 inhibitor, NS-398, or by COX-2 knockdown with small interfering RNA. Thus, COX-2 inhibitors can decrease the invasive properties of pancreatic cancer cells acquired through tumor-stromal interactions.     

Gene expression profiling: Decoding breast cancer

Gene expression assays that are used in daily clinical practice for treating early breast cancer patients have been introduced in the clinic only recently. This review discusses the development of these arrays, summarizes the validation of those that are commercially available and indicates how the information provided by these assays can help in the care of patients. The review also provides an extensive overview of commercially available assays focusing on MammaPrint, the first and only assay for breast cancer management that has been cleared by the FDA.