Breast cancer starts from breast tissue, usually in the inner lining of the milk ducts or lobules which supply ducts with milk (Sariego J., 2010). It is becoming increasingly difficult to ignore the effect of 1/8 women in the world. So far, breast cancer has been known as one of most famous cancer disease, with an incidence rate higher than twice that of colorectal cancer or cervical cancer, and its incidence rate just lower than lung cancer. (Norazizah Shafee, et al, 2008) Like other cancer disease, the pathogenic mechanism of breast cancer usually associates with environment and genetics risk factors. It is a very complex cancer disease, but still keeps poorly understood about its pathogenesis. Several studies have produced some evidences shown that it is about 100 times between the incidences rates of women than men, but survival rates are equal in both sexes (American Cancer Society, 2008).Nobody knew why women could get breast cancer. However, there is increasing concern that women have a large number of risk factors to get it.
The treatment of breast cancer is usually determined by the size, stage, rate of growth and characteristics of cancer. The multistep process of breast cancer development could be obvious defined as the sequence of pathologically stages. According to the patients’ clinical behavior, the breast cancer is really difficult to accurately classify by these predictors for metastases, such as lymph node status and histological grade (McGuire, 1991, Goldhirsch A, 1998 and Eifel P et al, 2001). As the recently research, people thought the primary breast cancer usually starts from the premalignant stage of atypical ductal hyperplasia (ADH), develops into ductal carcinoma in situ (DCIS) which is the preinvasive stage of breast cancer, and terminates in the potentially lethal stage which is invasive ductal carcinoma (IDC). This linear model of breast cancer progression has been used for diagnosing and treating breast cancer at earlier clinical stages (Tabar, L et al., 2000). However, the breast cancer in stages of DCIS and IDC are related to heterogeneous with mitotic activity and cellular differentiation. Therefore, breast cancer can be divided into three cancer grades in which grade I, II, and III lesions according to subtype the stages of DCIS and IDC with well, moderately, and poorly differentiated breast cancer (Dalton, L. W, 2000 and Holland, R., 1994). However, the cancer genesis of breast cancer is still understood poorly, such as limited identified the biologically relevant genes related to the different pathological stages. Chemotherapy or hormonal therapy usually could reduce approximately 1/3 risk distant metastases, but 70–80% of patients by this treatment would not have the chance of survival (Hedenfalk I, 2001 and Ahr A, 2001). Therefore, the research of breast cancer need technology to outperform all currently used various clinical and pathological factors in predicting outcome of cancer and adjuvant therapy methods. Gene expression profiling technologies may be provided researchers with a very good opportunity to carry out exhaustive genetic profiling of breast cancer and establish a molecular profile for breast cancers (Ahr A,2001) .
Over the last 20 years, gene expression profiling technologies have the advances lead to the ability to examine plenty of genes expression at the same time and provide good methods to analysis of multiple markers quickly. To recognize the trails of gene expression is also very useful to identify tumor markers. Until now, gene expression profiling technologies have developed to identify hundreds and even thousands of genes expression at the same time. Gene expression array, such as DNA microarray, tissue microarray, is one of most widely applied gene expression profiling technologies for genome-wide gene-expression studies which received a big deal of attention on the global level.
The gene expression array based on oligonucleotide-microarrays is popular (.Hardiman, G, 2006 and Perou, C. M, 2000). There are two major methods to construct oligonucleotide arrays: 1) microarrays are composed of 25 bases short oligonucleotides which are synthesized directly onto a solid matrix with photolithographic technology (Affymetrix); 2) microarrays are composed of 55–70 bases long oligonucleotides which located on an ink-jet printing process (Agilent) or spotted by a robotic printing persist in a solid medium, such as the fibrous mesh membranes (CodeLink) or glass slides ‘‘chip’’. Through DNA microarray technique, thousands of DNA spots which located single gene onto the single slide or chip (Eisen MB, 1998 and Khan J, 1998). The Affymetrix microarray hybridizes one sample per chip by a single-color detection scheme, but Agilent technology could hybridize the same array with two different samples by a two-color scheme.
High-throughput screening techniques have been fundamentally developing biomedical research now. It is one of best option to prioritize and select the best targets from thousands of candidate genes and proteins. Analysis of the molecular targets in situ at the cellular level, assessment of the molecular targets expression could be analyzed in all tissues and diseases and be used to evaluate their clinical significance with important to target selection through additional information. Compared with the high-throughput techniques of genomics and proteomics, most tissue-based molecular analyses are slow, cumbersome and require extensive manual interaction.
Opposed to the classical northern-blotting analysis, the target is hybridized in parallel to a great number of DNA sequences, immobilized on a solid surface by the microarray experiment principle (Bast RC Jr, 2001). It can detect and quantify thousands of transcript species at the same time. Recently, the technology of gene expression array has been developed rapidly. More powerful robots for arraying, novel surface technology for glass slides, and novel protocol of labeling and dyes, could be advanced together. They can extend the quality and complexity of microarray experiments to increase different organisms’ genome-sequence information including human. The high-density cDNA microarray technology (LMC) offers a novel chance for high-throughput breast cancer genetic analysis (Schena, M., 1995). It has the capacity to develop monitoring of plenty of genes. Most current microarray studies have the ability to perform array-based expression analysis with in vivo-derived genetic material originating from morphologically distinct cellular subpopulations within neoplastic tissue. It can be used to analyze gene expression in a clinical cancer specimen.
Especially in the construction of tumor markers for purposes of diagnose, the single gene identification express in cancer cells with great pharmacological interest, but not in normal cells (EllisM, 2002). The protein can be used to design the target strategies for chemotherapy or immunotherapy, which could be identified on cancer cell surface (Paik S, 2004). The transcriptional profiling of solid tumor is very complex. In fact, they may include a variable great number of infiltrating tissue, for example endothelial, stroma and lymphoid cells. RNA isolated from purified cancer cells by laser-capture micro dissection needs amplification of substantial target and significant bias (Sauter G, 2002). Recent developments of gene expression profiling technologies prove that the identified potential tumor markers using whole solid tumors are very useful. Through this tissue microarray technique, cancer specimens which are up to 1,000, can be marked in correlative for fluorescence or immunohistochemistry to resemble gene expression with the rearrangement of chromosomal in situ hybridization (Sallinen, S.,2000). Gene expression profiling technologies also have direct contribution for the annotation of the human genome sequence by the regulation of gene transcription. (Sauter G, 2002)
It is also one of best prognostic or predictive context of prospective clinical method to assay novel gene factors (Hayes DF, 1996 and 1998). It can be directly designed as the objective of trial and the function of targets. However, these prognostic factors can only be considered as one possible therapy method in fact. If the prognosis of patients is poor, these factors will be useless. For this reason, the clinical utility of these prognostic factors is really complicated to identify without treatment factors, and these factors also need to layout severe clinical trial. However, the evidence of research on most tumor marker is not easy to confirm, and have more chance to advance novel clinical treatment hypotheses.
It is also used to identify the gene target or pathways with unknown function drug, and have the possibly chance to offer a quick way to the description of novel drugs. Loss of the gene encoded the desired drug target should stop all the gene expression changes which caused by the novel drug. It might result in some needless correlation with other protein, which called “off-target” side-effects (Buyse, M., 2006). Plenty of large DNA microarray data sets of gene expression profiling technologies were located in the public domain, such as specific pathways activation, treatment of novel drug, and cataloguing gene expression changes which regard to mitogens, etc. To a certain extent, these techniques could be able to analysis original investigated data with more significant sense of the expression trails compared with other data sets.
Several research reports revealed that the same RNA samples hybridized on distinct microarray programme might lead to contradictory results by application of gene expression profiling technologies (Shi, L. et al., 2006). Therefore some skepticism about the reproducibility and the reliability of the techniques appeared. However, in fact, these different results reflect that data analyses by high throughput technologies were very complex, without any significant meaning on the techniques are inferior or unreliable. Definitely, most the differences were considered as illogical sequence correctness and explication, low specific spotted DNA/RNA and tissue microarray, different isoforms lack of specific probe, or differences in hybridization and measurement of fluorescence (Kothapalli, R., 2002, Tan, P. K., 2003, Baum, M., 2003, Barczak, A., 2003 and Hardiman, G, 2006). Gene expression profiling technologies have also been used throughout on cancer-derived cell lines and to organize distinct cancer cell-line panels from cancer origin (Loi, S., 2007 and Dai, H., 2005).
Gene expression profiling technologies could be used to identify breast cancer into classification from different gene expression trails (Colozza, M.,2006). According to the analysis of cluster, breast cancer can be classified into different synthesized expression profiling which can be characterized by the upregulated expression of several genes targets, such as predominantly oestrogen receptor (ER)-negative, progesterone receptor (PR)-negative and ERBB2-negative(Perou, C. M.,2000, Sorlie, T., 2001 and 2003, and Sotiriou, C., 2003). Even more important, the novel molecular subgroups have obvious clinical application outcomes and therapy responses. In fact, the PR-negative and ER-negative could be recognized by different characterization of breast cancer. The expression trail of gene targets revealed the active hormone-mediated transcription activity associated with the androgen receptor (AR) pathway. It points out the latent target therapeutic pathway in breast cancer. Breast cancer sensitive in luminal A tumor with low proliferation and grade (Doane, A. S., 2006 and Farmer, P., 2005).
Stupendously, the clinical regard parameters have dissimilar gene-expression trails, such as nodal status tumor size and menopausal status (Sorlie, T., 2003). On the contrary, these significant prognostic parameters gain more tumor stage, but not the intrinsic biological possession of the breast cancer. Therefore, it has already begun to change the clinical therapeutic method and identify standard clinico-pathological parameters. The main challenge on the world is to detect novel potential gene targets for new therapeutic application development by gene expression profiling technologies.
To sum up, data of gene expression profiling associated with analysis of genomic changes, such as the relative genomic hybridized microarray analysis, whole genomic DNA microarray hybridization. These technologies could be used to recognize gain or loss of gene expression which lead to advance breast cancer (Teschendorff, A. E., 2006). It is also expected to recognize gene expression signatures which suggest breast cancer .clinical behavior.
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