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

Our plain-English summary of A review of transcriptome studies combined with data mining reveals novel potential markers of malignant pleural mesothelioma, by O. Melaiu, A. Cristaudo, E. Melissari, et al (Mutation Research, 2012).

The study

• Authors: O. Melaiu, A. Cristaudo, E. Melissari, M. Di Russo, A. Bonotti, R. Bruno, R. Foddis, F. Gemignani, S. Pellegrini, S. Landi
• Journal: Mutation Research
• Published: 2012;750(2):132-140
• Type of article: Review
• Cited by: 44 articles, as counted by NIH iCite
• Read it: PubMed · Publisher

Abstract

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 "mesothelioma gene" (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.

Copyright © 2011 Elsevier B.V. All rights reserved.

In plain English

This article was a review, not a new experiment. The authors searched PubMed for microarray studies published until 2011 that compared gene activity in malignant pleural mesothelioma cells (cell lines and tumor tissue) with normal mesothelial cells. They selected nine papers. They ranked genes reported as abnormally switched up or down in more than one independent study. They then checked these genes against three literature-mining tools (Coremine, SNPs3D and GeneProspector), which together retrieved 3236 genes. When the 78 top-ranked genes from the review were compared with the genes found by all three tools, PTGS2 and BIRC5 emerged as the two most promising candidates. Others included ASS1, JUNB, MCM2, AURKA, FGF2, MKI67, CAV1, SFRP1, CCNB1, CDK4 and MSLN. The authors noted that 12 of the genes are known from studies of other cancers to be linked to resistance to the drug cisplatin. The authors concluded that these genes are potential biomarkers or treatment targets. They said that only four of the nine studies had complete, retrievable data, so a meta-analysis was not feasible. The article did not measure asbestos exposure or its risks, although asbestos-related terms were among the keywords used for the literature mining.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: review of published microarray (transcriptome) studies, combined with automated literature data mining and pathway analysis
• Who was studied: nine published microarray studies, found through a PubMed search of English-language papers up to 2011, that compared mesothelioma cell lines or tumor tissue with normal mesothelial cells
• Exposure: No exposure was measured. Asbestos-related terms (Asbestos Exposure, asbestos-exposed, asbestos) were among the keywords used to search the literature-mining tools.
• Main results:
  • Of 968 genes identified across the reviewed studies, 931 were statistically significant (P-value less than 0.05) by the authors' scoring method
  • The data-mining tools collected 3236 genes, of which 56 were found by all three tools
  • Data mining retrieved about 43% of the genes collected by the review of transcriptome studies
  • Comparing the 78 most significant review genes with the genes found by all three mining tools, PTGS2 and BIRC5 emerged as the two most promising candidate genes
  • Twelve genes (ASS1, JUNB, PTGS2, EEF2, SULF1, TOP2A, AURKA, BIRC5, CAV1, IFITM1, PCNA, PKM2) were highlighted because other studies have linked them to cisplatin resistance in various human cancers or cancer cell lines
  • In pathway analysis of 397 genes shared by the review and the data mining, genes mapped to 103 significant KEGG pathways; four of the eight most relevant concerned cell-cell and cell-matrix interactions
  • The reviewed transcriptome studies did not report down-regulation of genes commonly inactivated in mesothelioma, such as CDKN2A, NF2 and BAP1
• The authors' conclusion: The authors concluded that the literature review combined with data mining detected several potentially interesting genes that could be used as therapeutic targets or biomarkers for malignant pleural mesothelioma, and that more studies are needed
• Limitations the authors state:
  • Few studies met the criteria, and raw data were unavailable for half of them, so a meta-analysis was not feasible
  • The studies used no uniform criteria for describing methods or presenting results
  • Most studies did not report tumor histology, or merged all mesotheliomas into one type
  • The authors stated that more studies are needed to determine which genes are important

Topics

The National Library of Medicine indexes this study under subjects our site covers: Mesothelioma.

Medical terms in this study, explained in our Lexicon: Pleural Mesothelioma, Statistical Significance, Mesothelioma, Biomarker, Cisplatin, Cytokine.

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