Microarray Technology and Cancer Gene Profiling: 593 (Advances in Experimental Medicine and Biology)
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Instead, the interpretation of a common clustering may be crucial. However, sometimes it seems that authors feel the urge to present their microarray results in the form of a cluster diagram, although such clusters are not always meaningful. Because the conventional diagnosis of cancer is based on the morphological appearance of stained tissues whose analysis requires highly trained pathologists, the introduction of microarrays offered the hope that classification and prediction of cancer by means of their gene expression profiles could be more objective and accurate.
Nevertheless, the problem of classification by microarrays is not simple; the genes that make it possible to predict the classes have to be identified from a large number of genes in a relatively small number of samples. Moreover, it is important to identify which genes contribute most to the classification. For this purpose, several new computational methods have been developed. In addition to algorithms suggested by Golub et al. At the moment, the reference method for classification problems in microarray studies are support vector machine SVM algorithms, and new promising supervised gene selection algorithms based on the SVM technique 25 [such as recursive feature elimination 26 and RFR 18 , 19 ] have been introduced.
An increasing number of studies have used microarrays in the field of thyroidology. Both platforms, spotted cDNA and oligonucleotide microarrays with a two-color or radioactive detection, and in situ photolithographically synthesized oligonucleotide arrays with a one-color detection, have been used to analyze the gene expression profiles of malignant thyroid tumors [ i.
Contributions of microarray studies to unravel the molecular etiology or patho physiology of thyroid tumors. The first array investigation of PTC was performed by Huang et al. In their study of eight PTC tumors, which were compared with normal surrounding tissue from the same eight individuals, the authors specified 50 genes with the most distinct gene expression changes. They confirmed a decreased expression of several thyroid-specific genes [ e. Moreover, they identified a number of additional PTC-specific genes, many of them associated with cell cycle or mitogenic control [ e.
In subsequent studies, many of the genes specified in this study e. Also, the findings of Jarzab et al. Thus, the involvement of adhesion-related genes appears a characteristic feature of PTC, which is not so distinct in many other cancers. For the adhesion-controlling PTC genes, it is well known that they participate in both invasion and metastasis and are involved in inflammation. Borrello et al. This conclusion remains in some conflict with clinical data showing better outcome in PTC cases with concomitant lymphocytic infiltration In the PTC gene expression profile, a very distinct expression pattern of immune response genes was observed, which follows in terms of intensity the pattern that is characteristic for the difference between tumor and normal tissue The molecular background of PTC was comprehensively investigated in a recent study of Melillo et al.
Using a broad methodology, they showed that the oncogenic proteins i. This finding is supported by the gene expression study of Frattini et al. However, in a recent reanalysis of the data sets of Huang et al. Although gene expression profiles of PTC with different genotypes i. Melillo et al. Therefore, the three oncoproteins, which are part of a single signaling pathway, are each able to trigger specific signals in addition to the common ones. This conclusion is supported and widened by the gene expression profiling of Giordano et al.
Distinct genes are from Ref. If this holds true, these observations would also be in line with the observation of a less favorable prognosis of BRAF mutant PTC 64 , 69 , Differences in the gene expression profile of PTC variants were indicated by Giordano et al. The difference between classic and tall cell variant was also described by Wreesmann et al. On the other hand, the differences between the classic and follicular PTC variant are less visible 40 , although some authors published lists of genes differentiating both variants This issue will be addressed below.
Gene expression profiles of FTC were analyzed in several microarray studies, with the aim to improve the molecular differentiation between FTC and follicular adenomas and to elucidate the molecular etiology of FTC further 27 — In an early investigation, Barden et al. An alternative approach of microarray analysis that aims at identifying the minimal number of discriminating genes appears more promising for diagnostic purposes and could also lead to further elucidation of the molecular etiology. Weber et al. Cerutti et al. Although these genes were primarily selected for diagnostic purposes with the aim to differentiate between follicular adenomas and carcinomas , they should also be considered for their significance regarding the biology of FTC.
By a combination of microarray data and previously published loss of heterozygosity data of FTC and normal thyroid samples, Aldred et al. Although both caveolins are down-regulated in FTC, their molecular mechanisms of down-regulation remain unclear. Loss of heterozygosity was found in a subgroup of tumors, but the authors could not identify mutations in either gene, and furthermore, the methylation status of the caveolin-1 promoter did not correlate with the expression pattern Therefore, these genes most likely do not make it possible to distinguish between benign and malignant thyroid tumors.
In their study, Lui et al. Moreover, this conclusion is in line with other findings. French et al. Nikiforova et al.
A gene expression profiling study of Lacroix et al. Recently, Giordano et al. Interestingly, in their study Giordano et al. Kroll et al. In a recent study, we confirmed these findings. It has been suggested that a down-regulation of lymphocyte and macrophage-specific genes 52 , 53 might reflect a different cellular composition of the AFTNs with a lack of lymphocytes and discussed by Wattel et al. Interestingly, two independent studies using different microarray platforms i. The stringent and prominent SIAT1 expression pattern that we found in our microarray study of AFTNs 52 prompted us to investigate one of its possible functional relevances further.
Subsequent studies characterized a new aspect of posttranslational modification of the TSHR. In cell culture experiments, we demonstrated for the first time that the transfer of sialic acid directly affects TSHR signaling because it improves and prolongs the cell-surface expression of the TSHR Furthermore, microarray investigations of AFTNs and TSH-stimulated primary thyroid cells illustrate a remarkable induction or activation of negative feedback mechanisms such as an up-regulation of phosphodiesterases 11 , 51 , 53 , which is in line with previous findings of Persani et al.
Interestingly, whereas microarray studies of TSH-stimulated primary thyroid cell cultures reveal an increased expression of the regulator of G protein signaling RGS 2 11 , 51 , which has been shown to reduce the TSHR signaling via inositolphosphate 87 , RGS2 is characterized by a decreased expression in AFTNs 11 , 51 , Such a difference can most likely be explained by defects in the RGS regulation pathway or by additional counter-regulatory mechanisms that occur only in the chronically proliferating AFTNs. These findings demonstrate that the additional investigation of a compatible cell model e.
However, it is obvious that the gene expression profile of the cell model itself can only depict a part of the complex situation present in the tissue samples. Several microarray studies, which were performed with the aim to identify novel diagnostic and clinical markers for differentiated thyroid tumors, used benign thyroid nodules, histologically classified as follicular adenoma or hyperplastic nodules, for comparison with PTC and FTC 29 , 30 , 32 , 33 , 35 , 37 , 40 , 42 , 49 , However, with respect to their function, histologically benign thyroid nodules can be further distinguished as AFTNs or CTNs or less often as so-called warm nodules, which do not show detectable differences to the surrounding thyroid tissue by scintigraphy.
C , the current knowledge concerning their molecular etiology is very limited. Nevertheless, there is only one study that investigated the array-based gene expression profiles of CTNs classified by scintigraphy In an early study we could show a down-regulation of several signal transducing components both in AFTNs and CTNs, which seemed to reflect a disturbed signaling system 8. These similarities in the gene expression patterns of AFTNs and CTNs might be attributable to a common property of both benign tumor entities, e. To gain a higher resolution that might help to identify specific signaling cascades involved in nodular development, we subsequently compared gene expression profiles of 22 CTNs to their normal surrounding tissue using the U95A Affymetrix GeneChip On the basis of the high number of investigated genes approximately 10, full-length genes and an improved statistical analysis that made it possible to analyze the significance of differential gene expression within gene sets e.
Furthermore, these expression data also revealed that contrary to PTCs, altered expression of components belonging to the RAS-MAPK cascade is of minor importance for the development of CTNs because gene sets representing this pathway did not show differential expression in comparison to the surrounding normal tissue.
This is in line with findings of Esapa et al. Moreover, these results are supported by findings of Krohn et al. This expression pattern is especially interesting because it has been shown that thyroid cells undergoing a long-term PKC stimulation are characterized by a general loss of thyroid-specific functions e. The up-regulation of cell cycle genes in the gene expression profile of CTNs 54 is so distinct that it may be used for the differential molecular diagnosis of these tumors, as shown by RFR algorithm Mainly genes related to proliferation and growth processes were included into a gene molecular classifier e.
Furthermore, the CTN classifier contains genes considered as cancer specific, such as the fibroblast growth factor receptor 1 FGFR1 found down-regulated by Chevillard et al. In contrast to AFTNs, the CTN multigene signature was also found in some PTC, which could be explained by the partly dedifferentiated and proliferating phenotype that is common to both entities.
Several studies provide evidence that differentiated functions of thyrocytes and of iodide metabolism can be reinduced by retinoic acid — CRABP1 encodes a high-affinity cellular retinoic acid binding protein that regulates the availability of retinoic acid for its nuclear receptors and is also involved in retinoic acid catabolism Our recent findings showing an increased expression of CRABP1 in AFTNs in comparison to their normal surrounding tissue support this assumption unpublished observation. Overall, in addition to the interesting finding of decreased CRABP1 expression, gene expression profiling of CTNs allowed identification of the molecular pattern of their increased proliferation that is characterized by a differential expression of several cell cycle-associated genes.
Despite the fact that microarray studies revealed very distinct changes in the expression of certain genes, none of the several genes identified by array studies as differentially regulated was proven to be an ideal single marker of PTC — For example, DPP4 dipeptidyl-peptidase 4 , which was indicated by Huang et al. Also, oncofibronectin, galectin 3, and other proposed markers did not work properly in a single gene context — Moreover, the different multigene classifiers proposed by various authors showed only a minor overlap of the respective markers included.
In fact, such a comparison requires a systematic bioinformatic approach. Such analysis has been performed in some types of cancer but is not available for thyroid cancer. The discriminating gene set contained both known cancer-associated genes e. However, such large gene lists are not applicable for diagnostic purposes. Thus, approaches to limit the number of genes in an identifier have been undertaken. The aim of the study of Jarzab et al. This gene set was selected not by univariate approaches but by complementation of the genes, by a RFR algorithm.
Interestingly, the classifier does not contain many known genes found in other approaches like FN1 or TIMP , whereas some other genes previously known for their up-regulation in PTC e. Within the classifier there were some new genes, previously not described in PTC [ e. The goal of this approach was not to obtain 20 genes showing a correlated change in expression between tumor and benign tissue but to generate complementary information from a selected set of genes.
The idea of using gene interactions to improve classification accuracy can be explained with the simplest example of a two-gene linear interaction Fig. However, two genes cannot classify all samples, and it is necessary to base the classification on a larger number of genes, usually five to The gene PTC classifier obtained by RFR [the name RFR refers to the number of genes included in the classifier 44 ] performs very well to differentiate between tumors red bar and normal tissues green bar.
The intensity of the color refers to the intensity of gene expression; red refers to the up-regulation, and green refers to the down-regulation. However, only some genes e. Other included genes e. Taken together, these two markers make it possible to classify both samples properly.
Taking into account the known heterogeneity of malignant tumors, RFR algorithm helped to obtain a robust molecular classifier able to recognize a wide range of PTC tumors. Fujarewicz, M. Jarzab, M. Eszlinger, K. Krohn, R. Paschke, M. Oczko-Wojciechowska, M. Wiench, A.
Kukulska, B. Jarzab, and A. Swierniak, manuscript in preparation. In this context, it is advisable to proceed with molecular classifiers composed of at least six genes. It is more than a simple coincidence that Mazzanti et al. Tumors and tissues often consist of more than one type of cells that might contribute differently to the measured expression of a given gene The resulting problem of data interpretation in such a complex situation can be solved in different ways.
Before the microarray experiment is performed, specific cell types can be isolated by microdissection. At the moment, there are some related bioinformatic research reports of GSE in glioma. Some studies have shown that different enrichment pathway analyses of DEGs can be classified according to their degrees of differential expression during tumor progression in order to explore the deterioration of low into high grade glioma [ 34 ]. Some research finds that the DEGs are regulated by transcription factors in glioblastoma [ 35 ] and microarray technology has been used to identify the DEGs and their functions in the development of three types of glioma astrocytoma, glioblastoma, and oligodendroglioma [ 36 ].
Different from these, our study selects the node of the highest score from each module as hub genes in MCODE after comparing nontumor samples with glioma samples. These hub nodes are the key genes of interaction, in the PPI network, that may play important roles in the occurrence and development of glioma. Moreover, hub gene identification is more persuasive, since we validate the association of hub genes and glioma by using survival analysis in two independent datasets to identify four genes that may be cancer biomarkers for glioma. Though not all hub genes associated with the survival of glioma patients, but some hub genes play important roles in immune or inflammation.
For example, WNT10B plays an important role in regulating asthmatic airway inflammation through modification of the T cell response [ 37 ]. In conclusion, we presumed these key genes identified by a series of bioinformatics analyses on DEGs between tumor samples and normal samples, probably related to the development of glioma. These hub genes could also affect the survival time of glioma patients as validated from two independent datasets.
These identified genes and pathways provide a more detailed molecular mechanism for underlying glioma initiation and development. However, further molecular and biological experiments are required to confirm the functions of the key genes in glioma. All authors declare that they have no conflict of interests to state.
Microarray Technology and Cancer Gene Profiling
This may be because the snippet appears in a figure legend, contains special characters or spans different sections of the article. J Immunol Res. Published online Dec 6. PMID: Corresponding author. Haixiong Xu: moc. Received Jul 30; Accepted Oct This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This article has been cited by other articles in PMC. Abstract Glioma is the most common malignant tumor in the central nervous system. Introduction Among the various histological subtypes of brain tumor, glioma is the most common malignant tumor in the central nervous system [ 1 ]. Materials and Methods 2. Results 3. Open in a separate window. Figure 1. Table 1 Gene ontology analysis of downregulated genes associated with glioma. Table 2 KEGG pathway analysis of downregulation genes associated with glioma.
Figure 2. Table 3 The enriched pathways for genes in the highest module. Identification of Biomarkers In order to identify biomarkers, we calculated the survival rate for two groups of 12 hub genes in the generation dataset GSE and compared the result with the validation dataset GSE through Kaplan-Meier analysis and log-rank test.
Microarray technology and cancer gene profiling
Figure 3. Figure 4. Table 4 Cox multivariate analyses of biomarkers associated with OS in the generation and validation datasets. Discussion In the present study, we identified DEGs between glioma and normal samples and used a series of bioinformatics analyses to screen key gene and pathways associated with cancer. Conflicts of Interest All authors declare that they have no conflict of interests to state.
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