Research Paper Advance Articles

Development and validation of a nomogram with an epigenetic signature for predicting survival in patients with lung adenocarcinoma

Jiao Wang1, *, , Li He2, *, , Yunliang Tang3, , Dan Li4, , Yuting Yang5, , Zhenguo Zeng5, ,

  • 1 Department of Endocrinology and Metabolism, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
  • 2 Department of Pathology, Jingdezhen First People's Hospital, Jingdezhen 333000, Jiangxi, China
  • 3 Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
  • 4 Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
  • 5 Department of Critical Care Medicine, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
* Equal contribution

Received: June 18, 2020       Accepted: August 25, 2020       Published: November 18, 2020      

https://doi.org/10.18632/aging.104090
How to Cite

Copyright: © 2020 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Epigenetic factors play crucial roles in carcinogenesis by modifying chromatin architecture. Here, we established an epigenetic biosignature-based model for examining survival in patients with lung adenocarcinoma (LUAD). We retrieved gene-expression profiles and clinical data from The Cancer Genome Atlas and Gene Expression Omnibus and clustered the data into training (n = 490) and Validation (n = 226) datasets, respectively. To establish an epigenetic model, we identified prognostic epigenetic regulation-related genes by LASSO and Cox regression analyses, and established a novel 11-gene signature, including EPC1, GADD45A, HCFC2, RCOR1, SMARCAL1, TLE2, TRIM28, and ZNF516, for predicting LUAD overall survival (OS). The biosignature performed optimally in both the training and validation sets according to receiver operating characteristic and calibration plots. Moreover, the biosignature classified patients into high- and low-risk clusters with distinct survival times, with Cox regression analysis revealing the biosignature as an independent LUAD prognostic index. Furthermore, the generated nomogram integrating the prognostic gene biosignature and clinical indices predicted LUAD OS with high efficiency and outperformed tumor-node-metastasis staging in LUAD survival prediction. These results demonstrated the efficacy of the epigenetic signature prognostic nomogram for reliably predicting LUAD OS and its potential application for informing clinical decision making and individualized treatment.

Abbreviations

ALK: anaplastic lymphoma kinase; DCA: decision curve analysis; EGFR: epidermal growth factor receptor; ERG: epigenetic regulation-related gene; ERK: extracellular-signal-regulated kinase; GSEA: gene set enrichment analysis; GEO: Gene Expression Omnibus; GO: Gene Ontology; GSVA: gene set variation analysis; Kyoto Encyclopedia of Genes and Genomes; LUAD: lung adenocarcinoma; OS: overall survival; RFS: relapse-free survival; TCGA: The Cancer Genome Atlas; TNM: tumor-node-metastasis.