A review of transcriptome studies combined with data mining reveals novel potential markers of malignant pleural mesothelioma

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• 作者：Ombretta Melaiu^a ; ^{ombretta.melaiu@for.unipi.it} ; Alfonso Cristaudo^b ; ^{a.cristaudo@med.unipi.it} ; Erika Melissari^c ; ^{erika.melissari@for.unipi.it} ; Manuela Di Russo^c ; ^{manuela.dirusso@for.unipi.it} ; Alessandra Bonotti^b ; ^{abonotti@yahoo.it} ; Rossella Bruno^b ; ^{rossella.b1985@libero.it} ; Rudy Foddis^b ; ^{r.foddis@ao-pisa.toscana.it} ; Federica Gemignani^a ; ^{fgemignani@biologia.unipi.it} ; Silvia Pellegrini^c ; ^{silvia.pellegrini@med.unipi.it} ; Stefano Landi^a ; ^{slandi@biologia.unipi.it}
• 关键词：MPM ; malignant pleural mesothelioma ; MG ; mesothelioma gene ; U ; up-regulated ; D ; down-regulated ; RTS ; review of transcriptomic studies ; DM ; data mining
• 刊名：Mutation Research/Reviews in Mutation Research
• 出版年：2012
• 期刊代码：118_13835742
• 类别：bio
• 出版时间：April-June, 2012
• 卷：750
• 期：2
• 页码：132-140
• 文件大小：305 K

摘要

Malignant pleural mesothelioma (MPM), a cancer of the serosal pleural cavities, is one of the most aggressive human tumors. In order to identify genes crucial for the onset and progression of MPM, we performed an extensive literature review focused on transcriptome studies (RTS). In this kind of studies a great number of transcripts are analyzed without formulating any a priori hypothesis, thus preventing any bias coming from previously established knowledge that could lead to an over-representation of specific genes.

Each study was thoroughly analyzed paying particular attention to: (i) the employed microarray platform, (ii) the number and type of samples, (iii) the fold-change, and (iv) the statistical significance of deregulated genes. We also performed data mining (DM) on MPM using three different tools (Coremine, SNPs3D, and GeneProspector). Results from RTS and DM were compared in order to restrict the number of genes potentially deregulated in MPM. Our main requirement for a gene to be a 鈥渕esothelioma gene鈥?(MG) is to be reproducibly deregulated among independent studies and confirmed by DM. A list of MGs was thus produced, including PTGS2, BIRC5, ASS1, JUNB, MCM2, AURKA, FGF2, MKI67, CAV1, SFRP1, CCNB1, CDK4, and MSLN that might represent potential novel biomarkers or therapeutic targets for MPM. Moreover, it was found a sub-group of MGs including ASS1, JUNB, PTGS2, EEF2, SULF1, TOP2A, AURKA, BIRC5, CAV1, IFITM1, PCNA, and PKM2 that could explain, at least in part, the mechanisms of resistance to cisplatin, one first-line chemotherapeutic drug used for the disease. Finally, the pathway analysis showed that co-regulation networks related to the cross-talk between MPM and its micro-environment, in particular involving the adhesion molecules, integrins, and cytokines, might have an important role in MPM. Future studies are warranted to better characterize the role played by these genes in MPM.