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.     

Variation in gene expression patterns in human gastric cancers

Gastric cancer is the world's second most common cause of cancer death. We analyzed gene expression patterns in 90 primary gastric cancers, 14 metastatic gastric cancers, and 22 nonneoplastic gastric tissues, using cDNA microarrays representing approximately 30,300 genes. Gastric cancers were distinguished from nonneoplastic gastric tissues by characteristic differences in their gene expression patterns. We found a diversity of gene expression patterns in gastric cancer, reflecting variation in intrinsic properties of tumor and normal cells and variation in the cellular composition of these complex tissues. We identified several genes whose expression levels were significantly correlated with patient survival. The variations in gene expression patterns among cancers in different patients suggest differences in pathogenetic pathways and potential therapeutic strategies.     

Tissue differences revealed by gene expression profiles of various cell lines

Mechanisms through which tissues are formed and maintained remain unknown but are fundamental aspects in biology. Tissue-specific gene expression is a valuable tool to study such mechanisms. But in many biomedical studies, cell lines, rather than human body tissues, are used to investigate biological mechanisms Whether or not cell lines maintain their tissue-specific characteristics after they are isolated and cultured outside the human body remains to be explored. In this study, we applied a novel computational method to identify core genes that contribute to the differentiation of cell lines from various tissues. Several advanced computational techniques, such as Monte Carlo feature selection method, incremental feature selection method, and support vector machine (SVM) algorithm, were incorporated in the proposed method, which extensively analyzed the gene expression profiles of cell lines from different tissues. As a result, we extracted a group of functional genes that can indicate the differences of cell lines in different tissues and built an optimal SVM classifier for identifying cell lines in different tissues. In addition, a set of rules for classifying cell lines were also reported, which can give a clearer picture of cell lines in different issues although its performance was not better than the optimal SVM classifier. Finally, we compared such genes with the tissue-specific genes identified by the Genotype-tissue Expression project. Results showed that most expression patterns between tissues remained in the derived cell lines despite some uniqueness that some genes show tissue specificity.     

Robust prediction of gene regulation in colorectal cancer tissues from DNA methylation profiles

DNA methylation is recognized as one of several epigenetic regulators of gene expression and as potential driver of carcinogenesis through gene-silencing of tumor suppressors and activation of oncogenes. However, abnormal methylation, even of promoter regions, does not necessarily alter gene expression levels, especially if the gene is already silenced, leaving the exact mechanisms of methylation unanswered. Using a large cohort of matching DNA methylation and gene expression samples of colorectal cancer (CRC; n = 77) and normal adjacent mucosa tissues (n = 108), we investigated the regulatory role of methylation on gene expression. We show that on a subset of genes enriched in common cancer pathways, methylation is significantly associated with gene regulation through gene-specific mechanisms. We built two classification models to infer gene regulation in CRC from methylation differences of tumor and normal tissues, taking into account both gene-silencing and gene-activation effects through hyper- and hypo-methylation of CpGs. The classification models result in high prediction performances in both training and independent CRC testing cohorts (0.92<AUC<0.97) as well as in individual patient data (average AUC = 0.82), suggesting a robust interplay between methylation and gene regulation. Validation analysis in other cancerous tissues resulted in lower prediction performances (0.69<AUC<0.90); however, it identified genes that share robust dependencies across cancerous tissues. In conclusion, we present a robust classification approach that predicts the gene-specific regulation through DNA methylation in CRC tissues with possible transition to different cancer entities. Furthermore, we present HMGA1 as consistently associated with methylation across cancers, suggesting a potential candidate for DNA methylation targeting cancer therapy.     

Comparative microarray data analysis for the expression of genes in the pathway of glioma

Our present work focuses on the set of genes, which are involved in primary brain tumors - the glioma pathway. These gliomas are mostly malignant (cancerous) in nature and are difficult to be cured and that's why they attract the attention of all the workers. To understand the relative functionality of these genes, we analyzed the expression pattern of all genes, using gene expression data, at genomic level, and then to check their universality in all other cancers, we compared their expression levels and patterns in all other types of cancers by using gene expression graphs, and observed their expression levels in all these cancers, whether they are over or under expressed. We found that every gene has its own unique expression pattern and level and on that basis it can be classified. We also found that oncogenes and tumor suppressor genes that were involved in the glioma pathway were showing similar expression patterns in other cancers too but their expression level is low.     

葡萄Actin基因家族的鉴定及进化和表达分析

采用生物信息学方法从葡萄(Vitis vinifera Linn.)全基因组中鉴定Actin基因家族,并对各基因的染色体定位和结构特征,编码蛋白质的理化性质、亚细胞定位、二级结构、三级结构和系统进化,以及不同组织的基因表达进行研究.结果表明:葡萄Actin基因家族16个基因分布在12条染色体上.16个基因的结构特征及其编码蛋白质的理化性质差异较大.16个基因的长度及其内含子总长度的变化范围较大,编码序列(CDS)和外显子总长度的变化范围较小.除登录号GSVIVG01008254001和GSVIVG01014035001的基因外,其他14个基因的GC含量均低于其CDS的GC含量.除登录号GSVIVG01008254001的基因外,其他15个基因编码的蛋白质的理论相对分子质量为12534.54~82612.33,理论等电点为pI 4.92~pI 9.13.16个基因编码蛋白质的消光系数为14105~73645,脂肪族氨基酸指数为65.54~92.06,其中9个为稳定蛋白,7个为不稳定蛋白.除登录号GSVIVG01014035001的基因外,其他15个基因编码的蛋白质均为亲水性蛋白.登录号GSVIVG01016517001的基因编码的蛋白质定位于细胞质和细胞核,其他15个基因编码的蛋白质定位于细胞质.二级结构和三级结构显示:葡萄Actin基因家族16个基因编码的蛋白质均由α螺旋、无规则卷曲和延伸链构成,且总体以无规则卷曲为主.系统进化分析和不同组织的基因表达分析结果显示:与拟南芥〔Arabidopsis thaliana(Linn.)Heynh.〕相似,葡萄Actin基因家族16个基因编码的蛋白质分为3个亚家族,ClassⅡ亚家族(营养型)包括登录号GSVIVG01003099001和GSVIVG01026580001的基因编码的蛋白质,这2个基因在所有组织中的表达均较高;ClassⅢ亚家族(生殖型)包括登录号GSVIVG01033494001、GSVIVG01024980001和GSVIVG01016550001的基因编码的蛋白质,这3个基因在花粉、雄蕊和花中的表达均较高;ClassⅠ亚家族包括其他11个基因编码的蛋白质,这11个基因在各组织中的表达总体上较低.研究结果显示:葡萄Actin基因家族的表达具有组织特异性.     

Variation in tissue-specific gene expression among natural populations

Background

Variation in gene expression is extensive among tissues, individuals, strains, populations and species. The interactions among these sources of variation are relevant for physiological studies such as disease or toxic stress; for example, it is common for pathologies such as cancer, heart failure and metabolic disease to be associated with changes in tissue-specific gene expression or changes in metabolic gene expression. But how conserved these differences are among outbred individuals and among populations has not been well documented. To address this we examined the expression of a selected suite of 192 metabolic genes in brain, heart and liver in three populations of the teleost fish Fundulus heteroclitus using a highly replicated experimental design.

Results

Half of the genes (48%) were differentially expressed among individuals within a population-tissue group and 76% were differentially expressed among tissues. Differences among tissues reflected well established tissue-specific metabolic requirements, suggesting that these measures of gene expression accurately reflect changes in proteins and their phenotypic effects. Remarkably, only a small subset (31%) of tissue-specific differences was consistent in all three populations.

Conclusions

These data indicate that many tissue-specific differences in gene expression are unique to one population and thus are unlikely to contribute to fundamental differences between tissue types. We suggest that those subsets of treatment-specific gene expression patterns that are conserved between taxa are most likely to be functionally related to the physiological state in question.     

Usefulness of CDK5RAP3, CCNB2, and RAGE genes for the diagnosis of lung adenocarcinoma

We used oligonucleotide microarrays with probe sets to 22,283 genes to analyze the gene expression profile of lung adenocarcinoma. Cancerous and noncancerous tissue samples were obtained from 23 patients with stage I or II lung cancer; 18 tissue pairs and 5 cancerous tissues. A list of 2065 genes that differentiate between cancerous and noncancerous tissues was generated using Winsorized paired t-tests. We analyzed CDK5RAP3 and CCNB2, which are involved in cell cycle progression, and RAGE. The first 2 of these 3 genes proved to be overexpressed in tumor tissue, whereas the RAGE gene was suppressed in tumor tissue. When CDK5RAP3 and CCNB2 were examined in individual patients we found that in cases where one of these genes was only slightly overexpressed the other was highly overexpressed. The combined expression of the 2 cell cycle genes was found to be statistically significant for differentiating between cancerous and noncancerous tissues. Inclusion of the data for the RAGE gene made the differentiation more powerful. The gene expression ratio gave a clear result: when CDK5RAP3 was expressed more than RAGE, the tissue was carcinomatous, and vice versa. We therefore conclude that these 3 genes may be used as a very reliable biomarker of lung adenocarcinoma.     

A correlation between BCL-2 modifying factor,p53 and livin gene expressions in cancer colon patients

Accumulating evidence has revealed that livin gene and BCL-2 modifying factor (BMF) gene are closely associated with the initiation and progression of colon carcinoma by activating or suppressing multiple malignant processes. Those genes that can detect colon - cancer are a promising approach for cancer screening and diagnosis. This study aimed to evaluate correlation between livin, BMF and p53 genes expression in colon cancer tissues of patients included in the study, and their relationship with clinicopathological features and survival outcome in those patients. In this study, 50 pathologically diagnosed early cancer colon patients included and their tissue biopsy with 50 matched adjacent normal tissue, and 50 adenoma tissue specimens were analyzed for livin gene and BMF gene expressions using real time PCR. The relationship of those genes expressions with clinicopathological features, tumor markers, Time to Progression and overall survival for those patients were correlated in cancer colon group. In this study, there was a significant a reciprocal relationship between over expression of livin gene and down regulation of BMF and p53 genes in colon cancer cells. Livin mRNA was significantly higher, while BMF and p53 mRNA were significantly lower in colorectal cancer tissue compared to benign and normal colon tissue specimens (P < 0.001), however, this finding was absent between colon adenomas and normal mucosa. There was a significant association between up regulation of livin and down regulation of BMF and p53 expressions with more aggressive tumor (advanced TNM stage), rapid progression with metastasis and decreased overall survival in cancer colon patients, hence these genes can serve as significant prognostic markers of poor outcome in colon cancer patients. This work highlights the role of livin, BMF and p53 genes in colorectal tumorigenesis and the applicability of using those genes as a diagnostic and prognostic markers in patients with colon carcinoma and as a good target for cancer colon treatment in the future.     

癌症基因表达谱挖掘中的特征基因选择算法GA/WV

鉴定癌症表达谱的特征基因集合可以促进癌症类型分类的研究,这也可能使病人获得更好的临床诊断?虽然一些方法在基因表达谱分析上取得了成功,但是用基因表达谱数据进行癌症分类研究依然是一个巨大的挑战,其主要原因在于缺少通用而可靠的基因重要性评估方法。GA/WV是一种新的用复杂的生物表达数据评估基因分类重要性的方法,通过联合遗传算法(GA)和加权投票分类算法(WV)得到的特征基因集合不但适用于WV分类器,也适用于其它分类器?将GA/WV方法用癌症基因表达谱数据集的验证,结果表明本方法是一种成功可靠的特征基因选择方法。     

Use of SMART-generated cDNA for gene expression studies in multiple human tumors

We demonstrate here that SMART PCR-amplified cDNAs arrayed on a nylon membrane are suitable for high-throughput tissue expression profiling when starting biological materials are limited. We show that SMART cDNA accurately reflects gene expression patterns found in total RNA by comparing the expression level of several target genes in SMART PCR-amplified cDNAs and their corresponding total RNAs. We also arrayed cDNAs from 68 matched tumor and normal samples on a nylon membrane to determine whether SMART PCR-amplified cDNA could be used for detecting differentially expressed genes in these tissues. These arrays containing normalized tumor and normal cDNAs were hybridized with probes for glutathione peroxidase and gelsolin. The hybridization results revealed cancer-related and patient-specific gene expression differences between tumor and normal tissues for these genes. These studies show that SMART PCR-amplified cDNAs maintain the complexity of the original mRNA population and are thus suitable for high-throughput studies to compare the relative abundance of target genes and to detect differentially expressed genes in a wide variety of tissues simultaneously.     

The delayed effect of mustard gas on housekeeping gene expression in lung biopsy of chemical injuries

ObjectiveSulfur mustard (SM) was used as a chemical weapon in Iraq-Iran war. Exposed people have major complications in important organs such as pulmonary system. Some studies have shown that SM could affect the expression of endogenous genes and non-housekeeping genes, time dependently. To understand the accurate molecular mechanism of the delayed effect of SM, the identification of the gene expression pattern in these patients is essential. Hence, we have evaluated mRNA expression of four common housekeeping genes (ACTIN, PGK1, β2m, GAPDH) in SM-exposed and non-exposed (control) formalin-fixed, paraffin-embedded (FFPE) human lung tissues.MethodParaffin block of lung biopsy of SM-exposed people (11 cases) and people without exposure to SM as control group (9 cases) have been selected. The mRNA expression of four endogenous control genes has been evaluated by qRT-PCR. The stability value of each gene was calculated by different methods.ResultIt was found that ACTIN mRNA has the highest expression (30.26±2.87) and PGK1 has the lowest standard deviation (SD) (30.885±2.215) between pooled groups. The best correlation was between ACTIN and PGK1 expressions. The M value has shown that ACTIN and then PGK1 are the most stable housekeeping genes among. The results obtained from the GeNorm and NormFinder have indicated that the pair ACTIN- PGK1 is the most suitable choice for endogenous control genes.ConclusionACTIN and PGK1 genes are stable in studied lung tissues and are the better than two other housekeeping genes. In addition, mustard gas does not affect their expression in long term.     

Investigation of the association between ATP2B4 and ATP5B genes with colorectal cancer

Colorectal cancer (CRC) develops as a multi-step process which results from gradual accumulation of mutations in proto-oncogenes, tumor suppressor, and DNA repair genes. Mortality rate of CRC is very high. Therefore, development of alternative diagnostic methods which can be used in the early diagnosis is crucial. ATP2B4 gene encodes one of the four isoforms of p-type ATPase PMCA enzyme and bears critical importance in maintaining the balance of intracellular calcium homeostasis by providing the export of calcium ions out of the cell. ATP5B encodes a subunit of the mitochondrial ATP synthase which is an f-type ATPase. In this study, the relationship between ATP2B4 and ATP5B genes and CRC regarding gene expression was investigated. Study groups were constructed from a number of 50 patients (25 males, 25 females) with the mean age of 55.68 ± 9.4 and the gene expression levels in the healthy and cancerous tissues of the patients were compared by using semi-quantitative PCR and Real-Time PCR methods. As a result, in patients with rectum tumors, there was a significant relationship between ATP2B4 gene expression and the tumor location and in patients younger than 45 years, ATP5B gene expressions were detected significantly higher in tumor tissues by using RT-PCR. However, no significant relationship was detected in terms of expression differences of ATP2B4 and ATP5B genes between cancerous and healthy tissues of the CRC patients. ATP2B4 and ATP5B genes might have indirect associations in CRC pathogenesis and the investigation of their interactions with DNA repair and other related genes may help in understanding of CRC formation.     

Low-volume jet injection for efficient nonviral in vivo gene transfer

The transfer of naked deoxyribonucleic acid (DNA) represents an alternative to viral and liposomal gene transfer technologies for gene therapy applications. Various procedures are employed to deliver naked DNA into the desired cells or tissues in vitro and in vivo, such as by simple needle injection, particle bombardment, in vivo electroporation or jet injection. Among the various nonviral gene delivery technologies jet injection is gaining increasing acceptance because it allows gene transfer into different tissues with deeper penetration of the applied naked DNA. The versatile hand-held Swiss jet injector uses pressurized air to force small volumes of 3 to 10 μL of naked DNA into targeted tissues. The β-galactosidase (LacZ) reporter gene construct and tumor necrosis factor α gene-expressing vectors were successfully jet injected at a pressure of 3.0 bar into xenotransplanted human tumor models of colon carcinoma. Qualitative and quantitative expression analysis of jet injected tumor tissues revealed the efficient expression of these genes in the tumors. Using this Swiss jet-injector prototype repeated jet injections of low volumes (3–10 μL) into one target tissue can easily be performed. The key parameters of in vivo jet injection such as jet injection volume, pressure, jet penetration into the tumor tissue, DNA stability have been defined for optimized nonviral gene therapy. These studies demonstrate the applicability of the jet injection technology for the efficient and simultaneous in vivo gene transfer of two different plasmid DNAs into tumors. It can be employed for nonviral gene therapy of cancer using minimal amounts of naked DNA.     

Influence of the transposable element neighborhood on human gene expression in normal and tumor tissues

Transposable elements (TEs) are genomic sequences able to replicate themselves, and to move from one chromosomal position to another within the genome. Many TEs contain their own regulatory regions, which means that they may influence the expression of neighboring genes. TEs may also be activated and transcribed in various cancers. We therefore tested whether gene expression in normal and tumor tissues is influenced by the neighboring TEs. To do this, we associated all human genes to the nearest TEs. We analyzed the expression of these genes in normal and tumor tissues using SAGE and EST data, and related this to the presence and type of TEs in their vicinity. We confirmed that TEs tend to be located in antisense orientation relative to their hosting genes. We found that the average number of tissues where a gene is expressed varies depending on the type of TEs located near the gene, and that the difference in expression level between normal and tumor tissues is greatest for genes that host SINE elements. This deregulation increases with the number of SINE copies in the gene vicinity. This suggests that SINE elements might contribute to the cascade of gene deregulation in cancer cells.