Typically defined as the tool for measuring the gene expression ability and, therefore, the tool for fast and efficient diagnosing of cancerous cells (Haferlach et al. 2010), the technology of gene expression profiling is widely used in contemporary medicine. GEP is traditionally performed in thirty-nine steps (Imebaud & Auffray 2005), which include the identification of the experimental design, the collection of genes, identification of samples, array preparation, provision of a targeted synthesis, hybridization, transformation of the key data, acquisition of knowledge and storage of data (Imebaud & Auffray 2005).
A closer look at GEP will show that it basically needs four key stages to be carried out. Particularly, the process requires RNA expression, promoter analysis, protein expression and post-translational modification (Li et al. 2013). It should be borne in mind, though, that GEP has different methods of pattern prognoses (Greaves et al. 2013, p. 261); therefore, some of the GEP process stages may vary depending on the type of prognostic pattern used in the course of the profiling. Therefore, the overall process of GEP includes the following stages: RNA expression (commonly in the form of northern blotting involving the analysis of the mRNA rates, promoter analysis, promoter expression and posttranslational modification analysis (Yoshida et al. 2012). It also should be noted that due to the specific genetic structure of the hyperproliferative tumor, for which aneuploidy is typical, it is rather difficult to “establish correlations between genetic abnormalities and clinical outcomes” (Zhan et al. 2015, p. 1746).
In order to carry out the GEP analysis successfully, one will have to carry out a biopsy with a specimen of the malignant mass obtained. In the case of myeloma, the specimen in question may constitute a sample of the patient’s bone marrow (Lin et al. 2014); in other instances, a biopsy is carried out to retrieve the required specimen and carry out the evaluation of the disease progress.
As a result of the above-mentioned analysis, the information concerning the type of multiple myeloma developed will be made available to researchers. However, in the most successful instances of a GEP analysis, not only the identification of a specific tumor that the oncologist has to deal with but also the classification of myeloma, in general, can be facilitated. Additionally, the above-mentioned analysis allows for locating a more or less identifiable clinical outcome in patients; particularly, the application of GEP in the instances of breast cancer deserves to be mentioned as a tool for defining clinical outcomes and the “previously unknown mediators of the metastatic steps of invasion and dissemination in human breast tumors in vivo” (Patsialou et al., 2012).
Many researchers point out the complexity and the relative costliness of gene expression profiling to be introduced into clinical practice. The problem is complicated by the heterogeneity of multiple myeloma and by the numerous arrays of gene expressions. Different researchers distinguish between five, seven, eight, or even ten subgroups in myeloma molecular classification.
At the same time, it may be recognized that the use of the gene expression profile, which is based on the evaluation of genes, contributing to the processes of proliferation, apoptosis, and cellular differentiation adequately reflects the biological heterogeneity of multiple myeloma. In such a way, it may be used for prognosis or risk stratification. Prognostication, therefore, is carried out based on the outcomes of GEN as the further avenues for addressing the problem are defined. Gene expression profiling may provide the development of new and more veracious approaches to prognosis and improve the efficiency of multiple myeloma diagnostics and, therefore, either merit or demerit the further investigations.
Reference List
Greaves, P, Clear, A, Coutinho, R, Wilson, A, Matthews, J, Owen, A, Shanyinde, T et al. 2013, ‘Expression of FOXP3, CD68, and CD20 at diagnosis in the microenvironment of classical Hodgkin lymphoma is predictive of outcome,’ Journal of Clinical Oncology, vol. 31, no. 2, pp. 256–262.
Haferlach, T, Kohlmann, A, Wieczorek, L, Basso, G., Kronnie, Gd T, Béné, M-C, Vos, J D et al. 2010 ‘Clinical utility of microarray-based gene expression profiling in the diagnosis and subclassification of leukemia: report from the International Microarray Innovations in Leukemia Study Group,’ Journal of Clinical Oncology, vol. 28, n. 18, pp. 2529–2537.
Imebaud, S & Auffray, C 2005, ‘‘The 39 steps’ in gene expression profiling: critical issues and proposed best practices for microarray experiments,’ Drug Discovery Today, vol. 10, no. 17, pp. 1175–1182.
Li, S, Liu, Q, Wang, Y, Gu, Y, Liu, D, Wang, C, Ding, G, et al. 2013, ‘Differential gene expression profiling and biological process analysis in proximal nerve segments after sciatic nerve transection,’ PlosOne, vol. 8, no, 2, pp. 1–10.
Lin, X S, Hu, L, Kirley, S, Correll, M, Quackenbush, J, Wu, C-L, & McDougal, W S 2014, ‘Differentiating progressive from nonprogressive T1 bladder cancer by gene expression profiling: Applying RNA-sequencing analysis on archived specimens,’ Urologic Oncology: Seminars and Original Investigations, vol. 32, no. 3, pp. 327–336.
Patsialou, A, Wang, Y, Lin, J, Whitney, K, Goswami, S, Kenny, P A & Condeelis, J S 2012, ‘Selective gene-expression profiling of migratory tumor cells in vivo predicts clinical outcome in breast cancer patients,’ Breast Cancer Research, vol. 14, no. 5, pp. 139–157.
Yoshida, S, Ishikawa, K, Arima, M, Asato, R, Sassa, Y. & Ishibashi, T 2012, Fibrovascular membranes associated with PDR: development of molecular targets by global gene expression profiling, Web.
Zhan, F, Hardin, J, Kordsmeier, B, Bumm, K, Zheng, M, Tian, E, Sanderson, et al. 2015, ‘Global gene expression profiling of multiple myeloma, monoclonal gammopathy of undetermined significance, and normal bone marrow plasma cells’, The American Society of Hematology, vol. 99, no. 5, pp. 1745–1757, viewed 30 July 2015, via Informaworld database.