Gene expression patterns in human liver cancers

Hepatocellular carcinoma (HCC) is a leading cause of death worldwide. Using cDNA microarrays to characterize patterns of gene expression in HCC, we found consistent differences between the expression patterns in HCC compared with those seen in nontumor liver tissues. The expression patterns in HCC were also readily distinguished from those associated with tumors metastatic to liver. The global gene expression patterns intrinsic to each tumor were sufficiently distinctive that multiple tumor nodules from the same patient could usually be recognized and distinguished from all the others in the large sample set on the basis of their gene expression patterns alone. The distinctive gene expression patterns are characteristic of the tumors and not the patient; the expression programs seen in clonally independent tumor nodules in the same patient were no more similar than those in tumors from different patients. Moreover, clonally related tumor masses that showed distinct expression profiles were also distinguished by genotypic differences. Some features of the gene expression patterns were associated with specific phenotypic and genotypic characteristics of the tumors, including growth rate, vascular invasion, and p53 overexpression.     

Variation of human mucin gene expression in gastric cancer cell lines and gastric mucous cell primary cultures

Human gastric mucous cells - gastric cancer cell lines mucin gene expression - TNFalpha - RT-PCR immunocytochemistry Little is known on the expression pattern of mucin genes in human gastric cancer cell lines in relation to mucin expression in normal gastric epithelial cells. Thus, the aim of this study was to compare gastric cancer cell lines and non-transformed epithelial cells in their expression of the different mucin genes, in order to use these cells as models for physiological MUC expression in human stomach. Human gastric mucous cell primary cultures which were obtained from surgical specimen by collagenase/pronase treatment and a panel of six human gastric cancer cells were screened for mRNA expression of the mucin genes MUC1, MUC2, MUC5AC, MUC5B, and MUC6. Mucin gene expression was analyzed by semi-quantitative RT-PCR, and by Western blotting and immunocytochemistry. Primary cultured human gastric mucous cells retained the stomach-specific pattern of mRNA expression found in gastric mucosal biopsies (MUC1, MUC5AC, MUC6), whereas any gastric cancer cell line exhibited an aberrant mucin gene expression. Mucin gene expression showed large variations in levels and patterns from cell line to cell line, but MUC2 was aberrantly expressed in all cancer cells. Immunocytochemistry confirmed aberrant MUC2 protein expression in cancer cells. The expression of the secretory mucin genes MUC2 and MUC5AC varied in relation to the length of cultivation of the cancer cell lines. Treatment of the gastric cancer cells with TNFalpha resulted in an enhanced mRNA expression of MUC1, MUC2, and MUC5AC (2-fold increase within 3 hours; p <0.05). In contrast, immunocytochemistry disclosed a decrease in MUC2 and MUC5AC staining intensity. Our results indicate that primary cultured human gastric mucous cells provide a physiological in vitro system for investigations of gastric mucin gene regulation. In gastric cancer cells marked changes in the mucin gene expression pattern are found with coexpression of non-gastric type mucins. Gastric mucin gene expression may be regulated by proinflammatory cytokines which could have implications in gastritis.     

Simple decision rules for classifying human cancers from gene expression profiles

MOTIVATION: Various studies have shown that cancer tissue samples can be successfully detected and classified by their gene expression patterns using machine learning approaches. One of the challenges in applying these techniques for classifying gene expression data is to extract accurate, readily interpretable rules providing biological insight as to how classification is performed. Current methods generate classifiers that are accurate but difficult to interpret. This is the trade-off between credibility and comprehensibility of the classifiers. Here, we introduce a new classifier in order to address these problems. It is referred to as k-TSP (k-Top Scoring Pairs) and is based on the concept of 'relative expression reversals'. This method generates simple and accurate decision rules that only involve a small number of gene-to-gene expression comparisons, thereby facilitating follow-up studies. RESULTS: In this study, we have compared our approach to other machine learning techniques for class prediction in 19 binary and multi-class gene expression datasets involving human cancers. The k-TSP classifier performs as efficiently as Prediction Analysis of Microarray and support vector machine, and outperforms other learning methods (decision trees, k-nearest neighbour and na?ve Bayes). Our approach is easy to interpret as the classifier involves only a small number of informative genes. For these reasons, we consider the k-TSP method to be a useful tool for cancer classification from microarray gene expression data. AVAILABILITY: The software and datasets are available at http://www.ccbm.jhu.edu CONTACT: actan@jhu.edu.     

Viral gene expression patterns in human herpesvirus 6B-infected T cells

Herpesvirus gene expression is divided into immediate-early (IE) or alpha genes, early (E) or beta genes, and late (L) or gamma genes on the basis of temporal expression and dependency on other gene products. By using real-time PCR, we have investigated the expression of 35 human herpesvirus 6B (HHV-6B) genes in T cells infected by strain PL-1. Kinetic analysis and dependency on de novo protein synthesis and viral DNA polymerase activity suggest that the HHV-6B genes segregate into six separate kinetic groups. The genes expressed early (groups I and II) and late (groups V and VI) corresponded well with IE and L genes, whereas the intermediate groups III and IV contained E and L genes. Although HHV-6B has characteristics similar to those of other roseoloviruses in its overall gene regulation, we detected three B-variant-specific IE genes. Moreover, genes that were independent of de novo protein synthesis clustered in an area of the viral genome that has the lowest identity to the HHV-6A variant. The organization of IE genes in an area of the genome that differs from that of HHV-6A underscores the distinct differences between HHV-6B and HHV-6A and may provide a basis for further molecular and immunological analyses to elucidate their different biological behaviors.     

Differential expression of CHL1 gene during development of major human cancers

Background

CHL1 gene (also known as CALL) on 3p26.3 encodes a one-pass trans-membrane cell adhesion molecule (CAM). Previously CAMs of this type, including L1, were shown to be involved in cancer growth and metastasis.

Methodology/Principal Findings

We used Clontech Cancer Profiling Arrays (19 different types of cancers, 395 samples) to analyze expression of the CHL1 gene. The results were further validated by RT-qPCR for breast, renal and lung cancer. Cancer Profiling Arrays revealed differential expression of the gene: down-regulation/silencing in a majority of primary tumors and up-regulation associated with invasive/metastatic growth. Frequent down-regulation (>40% of cases) was detected in 11 types of cancer (breast, kidney, rectum, colon, thyroid, stomach, skin, small intestine, bladder, vulva and pancreatic cancer) and frequent up-regulation (>40% of cases) – in 5 types (lung, ovary, uterus, liver and trachea) of cancer. Using real-time quantitative PCR (RT-qPCR) we found that CHL1 expression was decreased in 61% of breast, 60% of lung, 87% of clear cell and 89% papillary renal cancer specimens (P<0.03 for all the cases). There was a higher frequency of CHL1 mRNA decrease in lung squamous cell carcinoma compared to adenocarcinoma (81% vs. 38%, P = 0.02) without association with tumor progression.

Conclusions/Significance

Our results suggested that CHL1 is involved in the development of different human cancers. Initially, during the primary tumor growth CHL1 could act as a putative tumor suppressor and is silenced to facilitate in situ tumor growth for 11 cancer types. We also suggested that re-expression of the gene on the edge of tumor mass might promote local invasive growth and enable further metastatic spread in ovary, colon and breast cancer. Our data also supported the role of CHL1 as a potentially novel specific biomarker in the early pathogenesis of two major histological types of renal cancer.     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, “RFE_Relief algorithm” was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, "RFE_Relief algorithm" was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

人睾丸与脑基因表达谱的相似性

人类的物种形成与进化问题一直是研究的一个焦点。近年来,对于人和灵长类以及果蝇等其他一些动物多种组织基因表达谱的研究表明,在人的进化过程中脑基因表达的改变最为显著,并且脑中许多基因的表达呈显著上调。信息学分析显示,在多种组织当中,人的脑与睾丸可能存在最为相似的基因表达谱。这些结果提示睾丸可能与脑类似,也在人的物种形成和进化历程中起着重要作用。本文对人睾丸和脑基因表达谱的研究进行了回顾,并提出了该研究方向今后的一些研究设想。     

Large scale comparison of global gene expression patterns in human and mouse

Background  

It is widely accepted that orthologous genes between species are conserved at the sequence level and perform similar functions in different organisms. However, the level of conservation of gene expression patterns of the orthologous genes in different species has been unclear. To address the issue, we compared gene expression of orthologous genes based on 2,557 human and 1,267 mouse samples with high quality gene expression data, selected from experiments stored in the public microarray repository ArrayExpress.     

VDR-mediated gene expression patterns in resting human coronary artery smooth muscle cells

Vitamin D analogs such as paricalcitol and calcitriol that activate the vitamin D receptor (VDR) provide survival benefit for Stage 5 chronic kidney disease (CKD) patients, possibly associated with a decrease in cardiovascular (CV)-related incidents. Phenotypic changes of smooth muscle cells play an important role in CV disease. The role of vitamin D analogs in modulating gene expression in smooth muscle cells is still not well understood. In this study, DNA microarray analysis of approximately 22,000 different human genes was used to characterize the VDR-mediated gene expression profile in human coronary artery smooth muscle cells (CASMC) at rest. Cells in serum free medium were treated with 0.1 microM calcitriol (1alpha,25-dihydroxyvitamin D(3)) or paricalcitol (19-nor-1alpha,25-(OH)(2)D(2)) for 30 h. A total of 181 target genes were identified, with 103 genes upregulated and 78 downregulated (>two fold changes in either drug treatment group with P < 0.01). No significant difference was observed between calcitriol and paricalcitol. Target genes fell into various categories with the top five in cellular process, cell communication, signal transduction, development, and morphogenesis. Twenty-two selected genes linked to the CV system were also impacted. Real-time RT-PCR and/or Western blotting analysis were employed to confirm the expression patterns of selected genes such as 25-hydroxyvitamin D-24-hydroxylase, Wilms' tumor gene 1, transforming growth factorbeta3, plasminogen activator inhibitor-1, thrombospondin-1 (THBS1), and thrombomodulin (TM). This study provides insight into understanding the role of VDR in regulating gene expression in resting smooth muscle cells.     

Variation in enzymatic transient gene expression assays

We examined causes for high variability in data from enzymatic transient gene expression assays. Our results strongly suggest that variation in transfection efficiency is the major cause of data variation and can seriously compromise valid interpretation of data. We compared averaging data from multiple transfections and cotransfection of a second reporter gene as methods for correcting for variation in transfection efficiency. We found that transfection efficiency can be so highly variable that neither method necessarily overcomes the resulting bias in data. Depending upon the degree in variation in transfection efficiency, a combination of the two methods may be advisable. The need to normalize data for transfection efficiency is dependent upon the difference in strengths of promoters being tested and the relative variability of the transfection method used. We also show that the level of reporter gene expression between transfection experiments performed on different days can vary by more than 10-fold.     

Clustering gene expression patterns.

Recent advances in biotechnology allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. Analysis of data produced by such experiments offers potential insight into gene function and regulatory mechanisms. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. The corresponding algorithmic problem is to cluster multicondition gene expression patterns. In this paper we describe a novel clustering algorithm that was developed for analysis of gene expression data. We define an appropriate stochastic error model on the input, and prove that under the conditions of the model, the algorithm recovers the cluster structure with high probability. The running time of the algorithm on an n-gene dataset is O[n2[log(n)]c]. We also present a practical heuristic based on the same algorithmic ideas. The heuristic was implemented and its performance is demonstrated on simulated data and on real gene expression data, with very promising results.     

Microarray analysis of piceatannol-induced changes in gene expression in human gastric cancer cells

Piceatannol, isolated from the roots of Rheum undulatum, prevented oxygen radical formation and inhibited lipid peroxidation; the effective concentrations were 25 M for a radical scavenging assay and 2.1 M for a microsome assay. Using cDNA microarray analysis to determine which genes were modulated by piceatannol, 33 genes were up-regulated while 157 genes were down-regulated in the human gastric cancer cell line.     

Specific gene expression patterns in liver cirrhosis

Liver cirrhosis (LC) is a complex disease that can develop into hepatocellular carcinoma (HCC). In an effort to investigate genetic differences between LC and HCC, we used cDNA microarray analysis to characterize the gene expression profiles in LC and HCC tissues. Consistent differences were observed among the expression patterns in LC, HCC, and normal liver tissues. Interestingly, the expression patterns of LC without tumor association (LCT) were also readily distinguished from those of LC tissues near hepatic tumor tissues (near-tumor tissue, NTT). Moreover, 25 cirrhosis-specific genes could be used to divide the NTT samples into two groups: inflammatory active cirrhosis (NTTa) and inflammatory inactive cirrhosis (NTTi). We found that NTTa samples showed gene expression patterns similar to those of the LCT and HCC groups, whereas the expression patterns of the NTTi group were significantly different from those of the LCT, NTTa, and HCC groups. Finally, we selected two of the 25 LC-specific genes and showed that these markers could be used to successfully discriminate among the different LC subtypes. Collectively, these novel results allow the identification of new genetic subgroups of LC and provide new candidate genes for use as early markers for active cirrhosis and HCC.