Retinoic acid and ascorbic acid act synergistically in inhibiting human breast cancer cell proliferation

BACKGROUND: Breast cancer is an increasingly common malignancy. Several vitamins such as retinoic acid (RA), ascorbic acid (AA), vitamin D and vitamin E are known to prevent the development and progression of breast cancer. OBJECTIVE: We sought to determine whether RA and AA together (RA+AA) acted synergistically in blocking the proliferation of human breast cancer cells. To elucidate the mechanism by which RA+AA inhibited breast carcinoma proliferation, we then evaluated the gene expression profiles of the treated and untreated cells by radioactive cDNA microarray analysis. METHODS: We cultured the human breast cancer cell line MCF-7 for 3 days with 100 nM RA and/or 1 mM AA, counted the cell numbers and harvested the total RNAs for cDNA microarray analysis. RESULTS: RA, AA and RA+AA reduced MCF-7 cell proliferation by 20.7%, 23.3% and 75.7% relative to the untreated cell proliferation, respectively. The synergistic ratio of RA and AA was 1.72. The MCF-7 gene expression profiles showed that 29 genes were up-regulated and 38 genes were down-regulated after RA+AA treatment. The nature of these genes suggests that the mechanism by which RA and AA act synergistically in inhibiting human breast cancer cell proliferation may involve the expression of genes that induce differentiation and block proliferation, and the up-regulation of antioxidant enzymes and proteins involved in apoptosis, cell cycle regulation and DNA repair. CONCLUSION: Combined treatment with RA and AA inhibits the proliferation of human breast cancer cells by altering their gene expression related to antioxidation processes as well as the proliferation inhibitory pathway.     

Differential gene expression in retinoic acid-induced differentiation of acute promyelocytic leukemia cells, NB4 and HL-60 cells

Acute promyelocytic leukemia (APL) is characterized by a specific chromosome translocation t(15;17), which results in the fusion of the promyelocytic leukemia gene (PML) and retinoic acid receptor alpha gene (RARalpha). APL can be effectively treated with the cell differentiation inducer all-trans retinoic acid (ATRA). NB4 cells, an acute promyelocytic leukemia cell line, have the t(15;17) translocation and differentiate in response to ATRA, whereas HL-60 cells lack this chromosomal translocation, even after differentiation by ATRA. To identify changes in the gene expression patterns of promyelocytic leukemia cells during differentiation, we compared the gene expression profiles in NB4 and HL-60 cells with and without ATRA treatment using a cDNA microarray containing 10,000 human genes. NB4 and HL-60 cells were treated with ATRA (10(-6)M) and total RNA was extracted at various time points (3, 8, 12, 24, and 48h). Cell differentiation was evaluated for cell morphology changes and CD11b expression. PML/RARalpha degradation was studied by indirect immunofluoresence with polyclonal PML antibodies. Typical morphologic and immunophenotypic changes after ATRA treatment were observed both in NB4 and HL-60 cells. The cDNA microarray identified 119 genes that were up-regulated and 17 genes that were down-regulated in NB4 cells, while 35 genes were up-regulated and 36 genes were down-regulated in HL60 cells. Interestingly, we did not find any common gene expression profiles regulated by ATRA in NB4 and HL-60 cells, even though the granulocytic differentiation induced by ATRA was observed in both cell lines. These findings suggest that the molecular mechanisms and genes involved in ATRA-induced differentiation of APL cells may be different and cell type specific. Further studies will be needed to define the important molecular pathways involved in granulocytic differentiation by ATRA in APL cells.     

Differential Gene Expression Profile Associated with the Abnormality of Bone Marrow Mesenchymal Stem Cells in Aplastic Anemia

Aplastic anemia (AA) is generally considered as an immune-mediated bone marrow failure syndrome with defective hematopoietic stem cells (HSCs) and marrow microenvironment. Previous studies have demonstrated the defective HSCs and aberrant T cellular-immunity in AA using a microarray approach. However, little is known about the overall specialty of bone marrow mesenchymal stem cells (BM-MSCs). In the present study, we comprehensively compared the biological features and gene expression profile of BM-MSCs between AA patients and healthy volunteers. In comparison with healthy controls, BM-MSCs from AA patients showed aberrant morphology, decreased proliferation and clonogenic potential and increased apoptosis. BM-MSCs from AA patients were susceptible to be induced to differentiate into adipocytes but more difficult to differentiate into osteoblasts. Consistent with abnormal biological features, a large number of genes implicated in cell cycle, cell division, proliferation, chemotaxis and hematopoietic cell lineage showed markedly decreased expression in BM-MSCs from AA patients. Conversely, more related genes with apoptosis, adipogenesis and immune response showed increased expression in BM-MSCs from AA patients. The gene expression profile of BM-MSCs further confirmed the abnormal biological properties and provided significant evidence for the possible mechanism of the destruction of the bone marrow microenvironment in AA.     

An integrated cross-platform prognosis study on neuroblastoma patients

There have been several reports about the potential for predicting prognosis of neuroblastoma patients using microarray gene expression profiling of the tumors. However these studies have revealed an apparent diversity in the identity of the genes in their predictive signatures. To test the contribution of the platform to this discrepancy we applied the z-scoring method to minimize the impact of platform and combine gene expression profiles of neuroblastoma (NB) tumors from two different platforms, cDNA and Affymetrix. A total of 12442 genes were common to both cDNA and Affymetrix arrays in our data set. Two-way ANOVA analysis was applied to the combined data set for assessing the relative effect of prognosis and platform on gene expression. We found that 26.6% (3307) of the genes had significant impact on survival. There was no significant impact of microarray platform on expression after application of z-scoring standardization procedure. Artificial neural network (ANN) analysis of the combined data set in a leave-one-out prediction strategy correctly predicted the outcome for 90% of the samples. Hierarchical clustering analysis using the top-ranked 160 genes showed the great separation of two clusters, and the majority of matched samples from the different platforms were clustered next to each other. The ANN classifier trained with our combined cross-platform data for these 160 genes could predict the prognosis of 102 independent test samples with 71% accuracy. Furthermore it correctly predicted the outcome for 85/102 (83%) NB patients through the leave-one-out cross-validation approach. Our study showed that gene expression studies performed in different platforms could be integrated for prognosis analysis after removing variation resulting from different platforms.     

Comparison of microarray and sage techniques in gene expression analysis of human glioblastoma

To enhance glioblastoma (GB) marker discovery we compared gene expression in GB with human normal brain (NB) by accessing SAGE Genie web site and compared obtained results with published data. Nine GB and five NB SAGE-libraries were analyzed using the Digital Gene Expression Displayer (DGED), the results of DGED were tested by Northern blot analysis and RT-PCR of arbitrary selected genes. Review of available data from the articles on gene expression profiling by microarray-based hybridization showed as few as 35 overlapped genes with increased expression in GB. Some of them were identified in four articles, but most genes in three or even in two investigations. There was found also some differences between SAGE results of GB analysis. Digital Gene Expression Displayer approach revealed 676 genes differentially expressed in GB vs. NB with cut-off ratio: twofold change and P < or = 0.05. Differential expression of selectedgenes obtained by DGED was confirmed by Northern analysis and RT-PCR. Altogether, only 105 of 955 genes presented in published investigations were among the genes obtained by DGED. Comparison of the results obtained by microarrays and SAGE is very complicated because authors present only the most prominent differentially expressed genes. However, even available data give quite poor overlapping of genes revealed by microarrays. Some differences between results obtained by SAGE in different investigations can be explained by high dependence on the statistical methods used. As for now, the best solution to search for molecular tumor markers is to compare all available results and to select only those genes, which significant expression in tumor combined with very low expression in normal tissues was reproduced in several articles. 105 differentially expressed genes, common to both methods, can be included in the list of candidates for the molecular typing of GBs. Some genes, encoded cell surface or extra-cellular proteins may be useful for targeting gliomas with antibody-based therapy.     

Cancer Association Study of Aminoacyl-tRNA Synthetase Signaling Network in Glioblastoma

Aminoacyl-tRNA synthetases (ARSs) and ARS-interacting multifunctional proteins (AIMPs) exhibit remarkable functional versatility beyond their catalytic activities in protein synthesis. Their non-canonical functions have been pathologically linked to cancers. Here we described our integrative genome-wide analysis of ARSs to show cancer-associated activities in glioblastoma multiforme (GBM), the most aggressive malignant primary brain tumor. We first selected 23 ARS/AIMPs (together referred to as ARSN), 124 cancer-associated druggable target genes (DTGs) and 404 protein-protein interactors (PPIs) of ARSs using NCI's cancer gene index. 254 GBM affymetrix microarray data in The Cancer Genome Atlas (TCGA) were used to identify the probe sets whose expression were most strongly correlated with survival (Kaplan-Meier plots versus survival times, log-rank t-test <0.05). The analysis identified 122 probe sets as survival signatures, including 5 of ARSN (VARS, QARS, CARS, NARS, FARS), and 115 of DTGs and PPIs (PARD3, RXRB, ATP5C1, HSP90AA1, CD44, THRA, TRAF2, KRT10, MED12, etc). Of note, 61 survival-related probes were differentially expressed in three different prognosis subgroups in GBM patients and showed correlation with established prognosis markers such as age and phenotypic molecular signatures. CARS and FARS also showed significantly higher association with different molecular networks in GBM patients. Taken together, our findings demonstrate evidence for an ARSN biology-dominant contribution in the biology of GBM.     

Identification of UGP2 as a progression marker that promotes cell growth and motility in human glioma

Glioblastoma (GBM) is one of the most common highly malignant primary brain tumor with poor prognosis. This study aimed to explore the possible mechanism by bioinformatics method and detect potential function of UGP2 of GBM. Gene expression microarray data of GSE4412 and messenger RNA-sequencing data of GBM with samples clinical information were downloaded from the Gene Expression Omnibus database and The Cancer Genome Atlas database, respectively. Differentially expressed genes (DEGs) analysis using the Kyoto Encyclopedia of Genes and Genomes and Gene Ontology based on R language. A total of 1000 common DEGs were identified in GBM samples, including 353 upregulated and 647 downregulated genes. Based on the random survival forest model, we identified UDP-glucose pyrophosphorylase 2 (UGP2) (upregulated gene) had a significant effect on GBM prognosis. Functional enrichment showed that UGP2 was enriched in the biological progresses of cell proliferation, migration, and invasion. Furthermore, UGP2 expression is aberrantly overexpressed in human glioma and positively correlated with pathologic grade. A loss-of-function study showed that knockdown of UGP2 decreases U251 cell growth, migration, and invasion in vivo and vitro. We proposed the development and progression of human glioma were associated with survival based on bioinformatics analysis. We also found that UGP2 might function as prognostic markers in the pathogenesis of GBM.     

Simultaneous measurement of gene expression for hepatotoxicity in thioacetamide-administered rats by DNA microarrays

DNA microarray technology was developed as a tool for simultaneously measuring a number of gene expression changes, and has been applied for investigations of toxicity assessments of chemicals. In this study, we used a typical hepatotoxicant, thioacetamide (TA), to find correlations between the extent of hepatotoxicity and certain gene expression patterns or specific gene expression profiles. TA was intraperitoneally administered at high (400 mg/kg), medium (150 mg/kg) or low (50 mg/kg) dose (four rats per group) and then the serum and liver were collected at the indicated time (6, 12, 24, 36 and 48 h). Serum biochemical markers were measured and hepatic mRNA expression profiles were analyzed by a DNA microarray. Serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) were increased by TA-administration in a dose-dependent manner and reached the maximum at 24h. Hierarchical clustering analysis of all dosage groups revealed in 2 major clusters, distinguished by an early (6 and 12h) and a late (24, 36 and 48 h) phase. The early and late phase clusters were sorted in time- and dose-dependent manners. The major gene expression profile obtained by quality-threshold (QT) clustering analysis showed the same maximal toxic time as that estimated by the serum biochemical markers. The individual expression profiles of the candidate genes selected in our previous studies and the simultaneous gene expression patterns measured by five typical hepatotoxicants including TA also reflected the hepatotoxicity of TA. These findings suggest that the potential toxic effects appearing as gene expression changes are independent of the dosage of TA. This study suggested that the major gene expression profile estimated by QT clustering would be a sensitive marker of hepatotoxicity.     

Determination of stromal signatures in breast carcinoma

Many soft tissue tumors recapitulate features of normal connective tissue. We hypothesize that different types of fibroblastic tumors are representative of different populations of fibroblastic cells or different activation states of these cells. We examined two tumors with fibroblastic features, solitary fibrous tumor (SFT) and desmoid-type fibromatosis (DTF), by DNA microarray analysis and found that they have very different expression profiles, including significant differences in their patterns of expression of extracellular matrix genes and growth factors. Using immunohistochemistry and in situ hybridization on a tissue microarray, we found that genes specific for these two tumors have mutually specific expression in the stroma of nonneoplastic tissues. We defined a set of 786 gene spots whose pattern of expression distinguishes SFT from DTF. In an analysis of DNA microarray gene expression data from 295 previously published breast carcinomas, we found that expression of this gene set defined two groups of breast carcinomas with significant differences in overall survival. One of the groups had a favorable outcome and was defined by the expression of DTF genes. The other group of tumors had a poor prognosis and showed variable expression of genes enriched for SFT type. Our findings suggest that the host stromal response varies significantly among carcinomas and that gene expression patterns characteristic of soft tissue tumors can be used to discover new markers for normal connective tissue cells.     

Comparison of microarray and SAGE techniques in gene expression analysis of human glioblastoma

To enhance glioblastoma (GB) marker discovery, we compared gene expression in GB with human normal brain (NB) by accessing the SAGE Genie web site and compared the results with published data. Nine GB and five NB SAGE libraries were analyzed using the Digital Gene Expression Displayer (DGED); the results of DGED were tested by Northern blot analysis and RT-PCR of arbitrarily selected genes. Review of available data from the articles on gene expression profiling by microarray-based hybridization showed as few as 35 overlapped genes with increased expression in GB. Some of them were identified in four articles, but most genes were identified in three or even in two investigations. Some differences were also found between SAGE results of GB analysis. The Digital Gene Expression Displayer approach revealed 676 genes differentially expressed in GB vs. NB with cutoff ratio: twofold change and P ≤ 05. Differential expression of selected genes obtained by DGED was confirmed by Northern analysis and RT-PCR. Altogether, only 105 of 955 genes presented in published investigations were among the genes obtained by DGED. Comparison of the results obtained by microarrays and SAGE is very complicated because the authors present only the most prominent differentially expressed genes. However, even available data give quite poor overlapping of genes revealed by microarrays. Some differences between results obtained by SAGE in different investigations can be explained by high dependence on the statistical methods used. As for now, the best solution to search for molecular tumor markers is to compare all available results and to select only those genes where significant expression in tumors combined with very low expression in normal tissues was reproduced in several articles. One hundred five differentially expressed genes, common to both methods, can be included in the list of candidates for the molecular typing of GBs. Some genes, encoded cell surface or extracellular proteins may be useful for targeting gliomas with antibody-based therapy. The text was submitted by the authors in English.     

Comparative analyses identify molecular signature of MRI-classified SVZ-associated glioblastoma

Glioblastoma (GBM) is a highly aggressive brain cancer with limited therapeutic options. While efforts to identify genes responsible for GBM have revealed mutations and aberrant gene expression associated with distinct types of GBM, patients with GBM are often diagnosed and classified based on MRI features. Therefore, we seek to identify molecular representatives in parallel with MRI classification for group I and group II primary GBM associated with the subventricular zone (SVZ). As group I and II GBM contain stem-like signature, we compared gene expression profiles between these 2 groups of primary GBM and endogenous neural stem progenitor cells to reveal dysregulation of cell cycle, chromatin status, cellular morphogenesis, and signaling pathways in these 2 types of MRI-classified GBM. In the absence of IDH mutation, several genes associated with metabolism are differentially expressed in these subtypes of primary GBM, implicating metabolic reprogramming occurs in tumor microenvironment. Furthermore, histone lysine methyltransferase EZH2 was upregulated while histone lysine demethylases KDM2 and KDM4 were downregulated in both group I and II primary GBM. Lastly, we identified 9 common genes across large data sets of gene expression profiles among MRI-classified group I/II GBM, a large cohort of GBM subtypes from TCGA, and glioma stem cells by unsupervised clustering comparison. These commonly upregulated genes have known functions in cell cycle, centromere assembly, chromosome segregation, and mitotic progression. Our findings highlight altered expression of genes important in chromosome integrity across all GBM, suggesting a common mechanism of disrupted fidelity of chromosome structure in GBM.     

cDNA microarray analyses reveal candidate marker genes for the detection of ascidian disease in Korea

A serious disease of the ascidian Halocynthia roretzi has been spread extensively among Korean aquaculture sites. To reveal the cause of the disease and establish a monitoring system for it, we constructed a cDNA microarray spotted with 2,688 cDNAs derived from H. roretzi hemocyte cDNA libraries to detect genes differentially expressed in hemocytes between diseased and non-diseased ascidians. We detected 21 genes showing increased expression and 16 genes showing decreased expression in hemocytes from diseased ascidians compared with those from non-diseased ascidians. RT-PCR analyses confirmed that the expression levels of genes encoding astacin, lysozyme, ribosomal protein PO, and ubiquitin-ribosomal protein L40e fusion protein were increased in hemocytes from diseased ascidians, while those of genes encoding HSP40, HSP70, fibronectin, carboxypeptidase and lactate dehydrogenase were decreased. These genes were expressed not only in hemocytes but also in various other tissues in ascidians. Furthermore, the expression of glutathione-S transferase omega, which is known to be up-regulated in H. roretzi hemocytes during inflammatory responses, was strongly increased in hemocytes from diseased ascidians. These gene expression profiles suggest that immune and inflammatory reactions occur in the hemocytes of diseased ascidians. These genes will be good markers for detecting and monitoring this disease of ascidians in Korean aquaculture sites.     

基于生物信息学技术筛选影响胶质母细胞瘤化疗敏感性相关基因的研究

目的：应用生物信息学技术筛选影响胶质母细胞瘤（GBM）化疗敏感性的相关基因。方法：对2批胶质瘤患者BIOSTAR基因芯片进行分析。通过随访完善临床资料,筛选芯片中胶质母细胞瘤患者生存期长、短两组间的差异基因,明确差异基因参与的功能和通路,并构建与烷化剂相关基因的信号传导网络,结合芯片数据、患者预后和信号传导网络,筛选GBM化疗敏感性的相关基因。结果：两组芯片中间差异基因有503条。2批芯片的差异基因主要参与62项基因功能,主要参与31条信号传导通路。通过对差异基因功能、通路,烷化剂信号转导网络的分析,得到影响胶质母细胞瘤化疗敏感性的核心的差异基因IFNGR2、IL8、ITGA5、TNFRSF1B。结论：通过严谨的实验设计和科学的统计学判别,结合患者完整的生存资料,本研究成功地应用生物信息学技术对基因芯片的大量数据进行挖掘和分析,并筛选出了可能影响GBM患者预后和化疗药物敏感性的基因,为进一步功能实验和患者个体化治疗奠定了基础。     

A systems biology-based classifier for hepatocellular carcinoma diagnosis

Aim

The diagnosis of hepatocellular carcinoma (HCC) in the early stage is crucial to the application of curative treatments which are the only hope for increasing the life expectancy of patients. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with HCC progression. However, those marker sets shared few genes in common and were poorly validated using independent data. Therefore, we developed a systems biology based classifier by combining the differential gene expression with topological features of human protein interaction networks to enhance the ability of HCC diagnosis.

Methods and Results

In the Oncomine platform, genes differentially expressed in HCC tissues relative to their corresponding normal tissues were filtered by a corrected Q value cut-off and Concept filters. The identified genes that are common to different microarray datasets were chosen as the candidate markers. Then, their networks were analyzed by GeneGO Meta-Core software and the hub genes were chosen. After that, an HCC diagnostic classifier was constructed by Partial Least Squares modeling based on the microarray gene expression data of the hub genes. Validations of diagnostic performance showed that this classifier had high predictive accuracy (85.88∼92.71%) and area under ROC curve (approximating 1.0), and that the network topological features integrated into this classifier contribute greatly to improving the predictive performance. Furthermore, it has been demonstrated that this modeling strategy is not only applicable to HCC, but also to other cancers.

Conclusion

Our analysis suggests that the systems biology-based classifier that combines the differential gene expression and topological features of human protein interaction network may enhance the diagnostic performance of HCC classifier.