Occupational Cancers by Unknown

Author:Unknown
Language: eng
Format: epub
ISBN: 9783030307660
Publisher: Springer International Publishing

Gene Expression Profiling

In MM, different microarray approaches have revealed specific gene expression profiles in comparison with lung cancer or with different reference samples, such as in comparison with benign mesothelial cells or mesothelial cell lines. However, the large-scale use of gene expression profiles as differential diagnostic markers may be partly limited by the unstable nature of mRNA. Array-based experiments on MM have been reviewed by Gray et al. (2009), Melaiu et al. (2012), and Gueugnon et al. (2011) [100–102].

In particular, if one wishes to devise diagnostic or prognostic tests, then several studies have identified either single genes or gene sets or the gene expression ratios which are claimed to distinguish tumor entities such as MM and lung adenocarcinoma or which may have some prognostic value in MM [103, 104]. Molecular diagnostic tests have also been developed to be performed on cells from pleural effusions [104]. Certain gene pair ratios or gene expression levels for use in prognostications of MM patients have been postulated [105–110]. Fine needle biopsy specimens of MM studied using a panel of 6 genes CALB2, CLDN7, ANXA8, EPCAM, CD200, and NKX2-1 by RT-PCR as well as the calculation of expression ratios were considered suitable as a MPM diagnostic and prognostic test [111] and were shown to have 100% sensitivity and 90% specificity in distinguishing MPM from lung adenocarcinoma.

Serial analysis of gene expression (SAGE) has also been applied to reveal novel players in MM, with intelectin being one of the identified genes. Intelectin has also been shown to be induced in human primary mesothelial cells by exposure to crocidolite asbestos and SV40 infection [112]. Recently, higher expression of DAB2 and intelectin-1 at the mRNA level as well as at the protein level in epithelioid mesothelioma compared to lung adenocarcinoma is postulated as a potential future IHC marker for differentiating epithelioid mesothelioma from pulmonary adenocarcinoma [113].

Prognostic mRNA markers, as presented in the current literature, have only few overlapping genes [108]. In epithelioid MM, many genes have been implicated as being upregulated, e.g., those encoding matriptase, ITGB4 (integrin beta 4), and P-cadherin [107, 114, 115]. In contrast, specifically in sarcomatoid MM, only a few genes have been identified as being upregulated; these include those encoding MMP9 (matrix metallopeptidase 9), tissue-type plasminogen activator, and some growth factors or receptors (basic fibroblast growth factor [FGF], platelet-derived growth factor receptor beta [PDGFR-β], FGF receptor 1 [FGFR-1], transforming growth factor beta [TGF-β], and insulin-like growth factor-binding protein [IGFBP] 6 and 7). Some of the genes such as aurora kinase A (AURKA) were also classified as unfavorable genes in the prognosis of the patient [108, 114–116].

Gene expression arrays and subsequent data mining procedures may be advantageous in the search for potential therapeutic molecular targets. In a data-driven approach, SIM2s was revealed as a novel MM-associated gene [117]. CHEK1, RAD21, FANCD2, and RAN have been proposed as new co-targets in MM [118]. When CHEK1 siRNA was transfected into MM cell lines, the cells displayed enhanced apoptotic processes [119]. Furthermore, UBE1L, a component of the ubiquitin-proteasome pathway showed differential expression in MM cells compared to normal cells [119].

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