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.