Abstract
Malignant pleural mesothelioma (MPM), predominantly caused by asbestos exposure, is a highly aggressive cancer with poor prognosis. The staging systems currently used in clinics is inadequate in evaluating the prognosis of MPM. In this study, a five-gene signature was developed and enrolled into a prognostic risk score model by LASSO Cox regression analysis based on two expression profiling datasets (GSE2549 and GSE51024) from Gene Expression Omnibus (GEO). The five-gene signature was further validated using the Cancer Genome Atlas (TCGA) MPM dataset. Univariate and multivariate Cox analyses proved that the five-gene signature was an independent prognostic factor for MPM. The signature remained statistically significant upon stratification by Brigham stage, AJCC stage, gender, tumor size, and lymph node status. Time-dependent receiver operating characteristic (ROC) curve indicated good performance of our model in predicting 1- and 2-years overall survival in MPM patients. The C-index was 0.784 for GSE2549 and 0.753 for the TCGA dataset showing moderate predictive accuracy of our model. Furthermore, Gene Set Enrichment Analysis suggested that the five-gene signature was related to pathways resulting in MPM tumor progression. Together, we have established a five-gene signature significantly associated with prognosis in MPM patients. Hence, the five-genes signature may serve as a potentially useful prognostic tool for MPM patients.
Highlights
Malignant pleural mesothelioma (MPM), the most common form of malignant mesothelioma, is a highly aggressive neoplasm arising from the pleural mesothelial tissues covering the lung and is predominantly associated with occupational and environmental exposure to asbestos fibers (Wagner et al, 1960; Walker et al, 1983)
To obtain the differentially expressed genes (DEGs) between human MPM tumor and normal tissues, two expression datasets GSE2549 and GSE51024 were enrolled as discovery datasets (Figure 1)
We found the genes selected by the three methods overlapped with the five-gene signature which confirmed the validity of the genes selected by least absolute shrinkage and selection operator (LASSO) Cox regression method (Supplementary Tables 1, 2 and Supplementary Figure 1)
Summary
Malignant pleural mesothelioma (MPM), the most common form of malignant mesothelioma, is a highly aggressive neoplasm arising from the pleural mesothelial tissues covering the lung and is predominantly associated with occupational and environmental exposure to asbestos fibers (Wagner et al, 1960; Walker et al, 1983). Non-tissuebased biomarkers have been characterized, including soluble mesothelin-related protein (SMRP), osteopontin and fibulin-3 (Hollevoet et al, 2012; Kindler et al, 2018) None of these biomarkers being evaluated at this time for MPM have demonstrated sufficiently rigorous prospective or blinded validation to recommend their use (Kindler et al, 2018). The study took inspiration from previous research and concerning the difficulty to apply the MPM staging system into clinical work to make accurate evaluation of prognosis for MPM patients (Rusch et al, 2012), it is of great value to use bioinformatics methods to discover prognostic genes between MPM tumor and normal tissues as possible biomarkers and construct a risk-score model to clarify MPM patients into high- and low-risk subgroups, leveraging an objective approach in MPM patients’ prognosis evaluation. In this study, based on the gene expression profiling data from GEO and TCGA dataset, we developed and validated a reliable five-gene signature model independent of clinicopathological factors that improved the risk stratification for MPM patients
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