Abstract

Malignant mesothelioma (MESO) is a highly aggressive cancer with poor prognosis. Epithelial–mesenchymal transition (EMT) is a critical process in malignancies involved in tumor angiogenesis, progression, invasion and metastasis, immunosuppressive microenvironment and therapy resistance. However, there is a lack of specific biomarkers to identify EMT in MESO. Biphasic MESO with dual phenotypes could be an optimal model to study EMT process. Using a powerful EMTome to investigate EMT gene signature, we identified a panel of EMT genes COL5A2, ITGAV, SPARC and ACTA2 in MESO. In combination with TCGA database, Timer2.0 and other resources, we observed that overexpression of these emerging genes is positively correlated with immunosuppressive infiltration, and an unfavorable factor to patient survival in MESO. The expression of these genes was confirmed in our patients and human cell lines. Our findings suggest that these genes may be novel targets for therapeutics and prognosis in MESO and other types of cancers.

Highlights

  • Malignant mesothelioma (MESO) is a rare cancer associated with poor prognosis

  • We screened all up-regulated genes in the mesothelioma microenvironment using peritoneal lavage

  • The up- or down-regulated genes with significant changes over time were analyzed using microarray from single cells collected from peritoneal lavage of RN5-bearing mice

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Summary

Introduction

Malignant mesothelioma (MESO) is a rare cancer associated with poor prognosis. Clonal structure in mesothelioma may be a critical prognostic i­ndicator[1]. Considerable evidence has shown that biphasic and sarcomatoid subtypes are associated with worse prognosis than the epithelioid s­ ubtype[2], most likely indicating that MESO with dual phenotypes such as biphasic mesothelioma could be an optimal model to study the epithelial-mesenchymal transition (EMT) process, a process through which epithelial cells adopt mesenchymal f­eatures[3,4]. Single cells or RNA from peritoneal lavage of naive mice were used as controls

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