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Transcriptomic analysis using microarray techniques has operated on the premise of providing system biological understanding of cells and tissues in response to a given variable. Microarray technology has ability to enable the genetic diseases like cancer to be studied in unprecedented detail, at the both genomic and transcriptomic levels. To understand the relationship between quantitative transcriptome of a sample and its phenotype undergoes complex mechanism that gives rise to the measured mRNA levels (Teschendorff et al, 2007). The mRNA is not the ultimate product of the gene, transcription is the first step in gene regulation and information about the transcript levels in needed for understanding gene regulatory networks. Transcriptomics or global analysis of gene expression, also called genome-wide expression profiling, is one of the tools that is used to get an understanding of genes and pathways involved in biological process. Common meta-analysis of Transcriptomics data's are generated using microarray techniques such as cDNA microarrays and oligo-microarrays. Global gene expression profiling (transcriptomic analysis) using high-density DNA array chips has operated on the premise of providing system biological understanding of cells and tissues in response to a given variable. However, the transcriptomic approach alone is stymied by the lack of functional connectivity among
disparate gene expression events and the large gaps in our global understanding of gene functions in most genomes. In addition, biochemical regulation occurs not only at the transcriptional level but also at the translational, posttranslational, and metabolic levels (Fan et al, 2005).
Meta analysis for diseases like cancer can be done with information collected universally about the results from the disease using microarray technique. This meta-analysis helps to understand the genes that are responsible for progression of diseases. In the field of microarray data, meta-analysis is difficult by distinct experimental platforms. So the expressions of genes are not directly comparable. The genes responsible for cancer can be studied using meta-analysis of cancer microarray data, by comparing normal genes with cancer infected genes. This helps to identify the cancer types and the genes responsible for causing cancer. The results obtained were represented using statistical analysis. Many different studies were done based on meta-analysis of cancer and other diseases. The results were obtained. A study on meta-analysis of cancer is very important to know about the distinct biological functions, disease progression and response to treatment.
To identify the genes, that produces cancer is very important to cure the disease. Scientists have represented similarities of global gene expression outlines of cancer tissue and equivalent normal tissue. This analysis differentiates hundreds of genes expressed in cancer relative to normal tissue and it need a much effort to differentiate the genes that occupies a role which is censorious in the neoplastic phenotype from which that are spuriously differentially expressed. The cancer samples were compared based on degree of progression. To understand this progression, the worldwide gene representation profiles of undifferentiated and fully differentiated cancers from the same origin. The cancer verses normal studies analysis produce hundreds of expressed genes depending on a difference. Thus it remains a censorious problem to elucidate the necessary transcriptional characteristic part of thing of neoplastic transformation and progress towards both extending future systematic investigation and to give the meaning of therapeutic agents. To identify the cancer type-specific gene expression of neoplastic transformation and progression depends on cancer class-independent, and probably indispensable, transcriptional characteristic part of a thing of important processes (Rhodes et al, 2004).
The research paper entitled "Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression" by Rhodes et al, in year 2004. In this paper Rhodes et al, shows beneficial DNA microarray to recognize the gene expression of human cancer, the clinical features of these are frequently large signatures that are unmanageable and remain elusive. To know about this they developed statistical systematic procedure, relative meta-profiling, which recognize and estimate the intersection of several gene expression from a varied accumulation of microarray data sets, compared 38 million gene expression measurement from 3,700 cancer samples. From this they made an ordinary transcriptional profile that is globally marked by action in greatest cancer types relative to the typical tissues from which they induce, such as reflecting necessary transcriptional characteristic of neoplastic transformation. In addition they have characterized a transcriptional profile that is activated in several types of undifferentiated cancer, proposing general molecular mechanisms by which cancer cells forward towards the destination and avoid differentiation. Finally these transcriptional profiles were validated on independent data sets.
This paper concludes several details on 152 cancer microarrays. They collected the information by using many literatures. The data sets are mostly of two formats either single channel intensity data or dual channel ratio data. Eventhough there are many sophisticated analytical and statistical methods have been used to differential expression analysis they sort single approach that would be simple and useful in this application. They discuss that by studying the samples profiled in each data sets they defined potential differential expression analysis.
COMPARATIVE META-PROFILING METHOD
This method is usually accepted that microarray data from separate experimental platforms, frequently used different reference samples, they ate not directly comparable. The method is developed for statistical measures across data sets. This is used to identify multiple differential expression signatures, called meta-signatures, which is used by automated method. Comparative meta-profiling (figure1). A lay of analogous differential representation analysis were chosen for meta-profiling, a direction and significance threshold set which can be used to define differential expression and genes are sorted depending on number of signatures according to the set they are present. In statistical measures, the minimum meta-false discovered rate is used to estimate the degree of intersection of gene expression signature.
Figure 1: comparative meta-profiling flow diagram (Rhodes et al, 2004).
META-SIGNATURE OF NEOPLASTIC TRANSFORMATION
Meta-signature of neoplastic transformation has begun by meta-profiling 36 neoplastic transformations from 221 data sets which spam several tissue types which includes prostrate, colon lung, liver, breast, B lymphocytes. In the fact meta-signature, the genes in the signature has reflected essential transcriptional features of cancer, in random stimulation such that genes were made assigned to signatures, none of the genes were available in 10 or more signatures, at least 10 signatures were present in the present in 183 genes, represented a statistically significant multi cancer-type meta-signature depicts the 67 genes present in atleast 12 cancer versus usual signatures, most of these genes has coming before in the time been associated with the cancer, the arrangements have only been done with only one particular type of cancer or else in cell lines, and not including with cancer.
META-SIGNATURE OF UNDIFFERENTIATED CANCER
In meta-signature of undifferentiated cancers they sought to recognize meta-signatures which characterize cancer progression. Undifferentiated cancers of various sorts of all fail to recapitulate their typical tissue architecture. Instead maintaining the confused state of greater cellular proliferation and invasion. The un-differentiated cancer was associated with aggressive behaviour and poor patient outcomes. Thus undifferentiated meta-signature existed. It proposes ordinary transcriptional mechanisms by which cancer cell types avoid differentiation. The meta-profiling was performed on seven undifferentiated verses well differentiated; by chance sixty-nine genes were present in atleast four of seven signatures. Twenty-four genes are available in six of seven signatures.
Figure 2: Meta-signature of undifferentiated cancer (Rhodes et al, 2004).
INDEPENDENT DATA SET VALIDATION OF META-SIGNATURES
The data set validation of meta-signature are independent and comply with the rules of the biological relevance of meta-signature that tested discriminative power of 12 independent data sets. They also identified meta-signatures of cancer that share common transcription residue which includes over expression of specific chromatin remodelling and transcription memory genes play a major role in cancer cell to avoid differentiation. Finally this paper provides simple, scalable framework for comparing and assessing intersection of multiple gene expression signatures from different data set.
Concluding this paper, collection of public microarray data combined with the comparative meta-profiling frame work resulted in a platform that is useful for drawing conclusions that span multiple microarray data sets and importantly, multiple cancer types. The integrated microarray data and analysis from number of cancer types; we can characterise a meta- signature of neoplastic transformation. The genes of the universal activation suggest that they may be essential to carcinogenesis, and likely represent the convergence of a number of transforming mechanisms in different cellular contexts. Meta-signature for cancer progression is also identified, representing that different types of high-grade cancer share common transcriptional features, which includes the over expression of specific chromatin remodelling and transcriptional memory genes which plays a vital role in cancer cells. Finally this paper provides simple, scalable framework for comparing and assessing intersection of multiple gene expression signatures from different data set (Rhodes et al, 2004).
The identification of ordinary transcriptional programs of neoplastic transformation and progression to a broad range of cancer types is discussed in this paper. In order to establish this meta-analysis of microarrays were adopt and thereby modified. Utilization of this method avoids many of the dangers that compare the complex of disparate microarray data sets by expressing likely in statistical measures of differential expression produced independently from each data set rather than real gene expression measurements. Similar methods were used in this discussion. The term comparative meta-profiling is not used in validating analogous data sets but, by comparing and also by estimating the intersection of several gene expression data sets which is cancer type-specific, with the destination of recognizing necessary, transcriptional profiles of neoplastic transformation and progression. In initial data analysis for each 40 microarray data sets being in the database, and the sample were profiled and reviewed for further process. This microarray meta-profiling is very efficient and successful method for neoplastic transformation and progression of cancer types. Here 34 studies had at least four small representative parts equivalent to both classes of one analysis concerned and were further analyzed. The analysis of interest added, cancer versus respective normal tissue, high grade cancer versus low grade, poor outcome cancer verses good outcome cancer, metastasis versus primary cancer and sub1 versus sub2. After the assessment of samples, each gene was assessed for differential expression to access statistics, and the results were obtained. Examinations were conducted on both expression were differential expression analysis for two-sided and over expression analysis for one-sided. For the narration of multiple hypotheses testing Q values are calculated by using estimated number of false positives by number of positives at a given P value. This is an efficient method to get results of multiple hypothesis testing.
Meta-profiling is usually performed to mention the hypothesis which chooses a set of differential expression signatures shares intersection of genes, thus inferring about the related biological occurrence. The method was automated and gives a set of S same differential expression analysis .This same differential representation analysis are chosen for meta-profiling an over expression direction and a significance threshold (T).These direction and threshold are chosen to give the meaning of differential expression signatures from the choosed analysis (TDEFAULT=0.10)and the genes are sorted by the various values of signatures in which they are being in the place and also to determine the number of genes being in every possible number of signature which is failed ( namely N0,N1..Ns).In addition to that, random permutations are performed in such a way that the actual values are made responded to genes per study. To such an extent that the genes signature remains same and maintained through the study. The stimulation is helpful us to produces a score of the number of genes (i.e) genes being in every possible number of made signatures (such as E0,E1....Es). The signature of interaction with the real signatures is estimated by the least possible meta-false discovery value (i.e) mFDRMIN= MINIMUM ([Ei=1]/Ni]) for i=0 to S. If another condition exists such that mFDRMIN<0.10, then the meta signature gives the meaning as those genes that are importantly differentially represented (Q<T) in at least j of S detailed examination, where j is same as that of i when mFDRMIN was defined.Also another cases occur such that if there is no meta-signature is mentioned or the value of genes is two or more signature which extends to 0, in which the instance of occurring is negative. These convince that a meta-signature is not left because of an excessively liberal Q utility threshold. The results can be predicted by knowing about the classification exactness of the meta- signatures, a formal expression of leave-one-out was applied. To identify the class of particular sample, the sample are to be removed from the data set first, and the samples that are remaining were used to calculate the two class means of each gene in the signature. The remaining quantity gene expression utility were fit to be compared to the class means. The class mean is the one, in which left out samples value was closest to receive the vote. The obtained votes were reckonend, and the prediction was defined as the grades with the greatest votes. A fisher's extract examination was beneficial to estimate the significance of the different categories.
In my point of view, this meta-analysis for identifying cancer types is really useful and efficient. Here ordinary transcriptional events of neoplastic transformation and progression to a broad range of cancer types were identified. To establish this meta-analysis of microarrays were adopted and modified. This method avoids many of the dangers that complex the comparison of essentially different microarray data sets by expressing similarities in a statistical quantity of differential expression produced independently from every known fact set rather than real gene expression measurements. In this paper similar method were used, termed comparative meta-profiling, not used to validating analogous known fact sets, but at comparing and estimating the intersection of several cancer type-defined gene representation data sets, with the destination of identifying necessary, transcriptional profiles of neoplastic transformation and onward movement towards a destination.
The general character of meta-signature is different, since all sorts of cancer share the common characteristics of unregulated cell proliferation and also invasion, and this would help the genes that are necessary to these course of action that would highly represented in multiple cancer sorts. The vast array transforming structures that are known to originate the cancer and different types of tissue types represented in this analysis. These genes may correspond to the convergence on necessary transcriptional characteristic of neoplastic transformation. The agents aiming at the proteasome complex, of which three groups were established in the meta-signature. This agent in clinical trials shows to include apoptosis and sensitize cancer cells to traditional tumoral agents. The wide spread action of genes that put into code successfully aimed at proteins propose that other genes in the meta- signature may occupy equally censorious role in carcinogenesis and may do service as novel therapeutic targets.
The results obtained were more efficient, systematic collection of public microarray combined with the comparative meta-profiling frame work gives a useful platform for drawing conclusions that span various microarray data sets and importantly, multiple cancer types meta-signature of neoplastic transformation program is always activated in cancer. Universal overexpressioin suggests that the genes may serve as attractive therapeutic targets. The meta-signature for cancer progression demonstrates various types of high-grade cancer which share common transcriptional features. this work provides a simple, scalable frame work for comparing and assessing the inter section of multiple gene expression signatures from disparate data sets. This is a successful approach, this is increasingly useful as the mass of published transcriptome data continues to grow (Rhodes et al, 2004).
The DNA micro array technique for cancer transcription was discussed with different studies based on DNA microarray is applied to study human cancer, by this we can know about the disease progression and response to treatment. Integrative computational and analytical approaches, including meta-analysis, transcriptional network analysis, interactome analysis and integrative model system analysis. Were used to study human cancer. This is also an successful paper by these methods we can analyse the cancer transcriptome (Rhodes.R.D, Chinnaiyan.M.A, 2005). To evaluate the utility of transcript profiling for prediction of proteins in expression levels, they compared profiles across NCI-60 transcript profile data sets based on affymetrix matrix one new protein profile data set, based on reverse-phase protein lysate arrays. Using the new transcript data combines with previously published cDNA array and affymetrix data sets, developed a consensus set of transcript profiles based on the four different microarray platforms (Shankavaram et al, 2007). In this meta-analysis of cancer using microarray, sometimes we get negative results, activation in transformed cells of normal stem cells, self-renewal pathways might contribute to the survival life cycle of cancer stem cells and promote tumor progression. The DNA microarray foe cancer samples were made and the results were obtained. Kaplan-meier analysis represented that stem cell-like expression profile of 11-gene expressed in primary tumors is an efficient predictor of a interval to disease recurrence and death after therapy in cancer patients were identified with 11 distinct types of cancer. The data represents BMI-1 conserved driven pathway, in normal stem cells and highly malignant subset of human cancers that is diagnosed in a wide range of organs and exhibiting metastatic dissemination as well as high probability of unfavourable therapy outcome (Glinsky et al, 2005).
This systematic collection of public microarray data combines with the comparitive meta-profiling frame work which generates a useful platform for identifying the types of cancer. This universal activation suggests that genes serve as attractive therapeutic agent. Finally this paper provides simple, scalable framework for comparing and assesing intersection of multiple gene expression signatures from different data set. The validation of independent microarray data confirms that meta-signature represent common gene expression which may be important to process of neoplastic transformation and progression of cancer cells. In addition they characteised transcriptional profile, that is commonly activated in various types of undifferentiation. And they validate these transcriptional profiles on data sets. Cancer profiling is a growing field, so this can be coupled with advancements in other high-throughput. Molecular approaches such as promoter arrays, array genome hybridization, SNP arrays, metabolomics and proteomics, to obtain maximum biological insight from the collective cancer genomics.