Identification and validation of a Prognostic Signature in Malignant Pleural Mesothelioma

Date 24 November 2018
Event ESMO Asia 2018 Congress
Session Poster display - Cocktail
Topics Mesothelioma
Translational Research
Presenter Jian-Guo Zhou
Citation Annals of Oncology (2018) 29 (suppl_9): ix113-ix120. 10.1093/annonc/mdy441
Authors J. Zhou, H. Ma
  • Department Of Oncology, Zunyi Medical College Affiliated Hospital, 563003 - Zunyi/CN

Abstract

Background

Dysregulated genes as critical role in the development and progression of cancer, implying their potentials as novel independent biomarkers for cancer diagnosis and prognosis. Prognostic model-based gene expression profiles were not widely utilized clinically. We investigated the prognostic significance of expression profile-based gene signature for outcome prediction in patients with malignant pleural mesothelioma (MPM).

Methods

Gene expression profiles of a large cohort of patients with MPM were obtained and analyzed by repurposing the publically available microarray data. A gene-focus risk score model was developed from the training dataset, and then validated in the TCGA-MESO (mesothelioma) datasets. The time-dependent receiver operating characteristic (ROC) curve was used to evaluate the prognostic performance for survival prediction. The biological function of prognostic genes was predicted using bioinformatics analysis.

Results

Three genes were identified to be significantly associated with overall survival (OS) of patients with MPM in the training dataset (GSE2549), and were combined to develop a three-genes prognostic signature to stratify patients into low-risk and high-risk groups. MPM patients of training dataset in the low-risk group exhibited longer OS than those in the high-risk group (HR = 0.25, 95% CI = 0.11 to 0.56, p < 0.001). The similar prognostic values of three-genes signature were observed in the validated TCGA-MESO cohort (HR = 0.53 95% CI = 0.33 - 0.85, p = 0.008). ROC analysis also demonstrated the better performance for predicting 3-year OS in GEO and TCGA cohorts (KM-AUC for GEO = 0.989, KM-AUC for TCGA = 0.618). The C-statistic for 3-genes model was 0.761. Validation on TCGA-MESO confirmed the model’s ability to discriminate between risk groups in an alternative data set with fair performance (C-statistic 0.68). Functional enrichment analysis suggested that these three mRNAs may be involved in known genetic and epigenetic events linked to MPM.

Conclusions

This study has identified and validated a novel 3-gene model to reliably discriminate patients at high and lower risk of death from unselected populations of patients with MPM. Further larger, prospective multi-institutional cohort studies are necessary to validate these models.

Editorial acknowledgement

Clinical trial identification

Legal entity responsible for the study

Affiliated Hospital of Zunyi Medical University.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No.81360351, 81660512), Special Fund for Traditional Chinese Medicine and Ethnic Medicine supported by The Administration of Traditional Chinese Medicine of Guizhou Province (Grant No.QZYY2017-113), Project ZY-201751044 supported by Zunyi Medical University Training Program of Innovation and Entrepreneurship for Undergraduates, Project ZYKY-20173830 supported by Zunyi Medical University School of medicine and Science Training Program of Innovation and Entrepreneurship for Undergraduates, the Open Project Program of the Special Key Laboratory of Oral Diseases Research, Higher Education Institution in Guizhou Province, and the Master Scientific Research Foundation of Zunyi Medical University.

Disclosure

All authors have declared no conflicts of interest.