Research Paper|Volume 13, Issue 14|pp 18701—18717

Development and validation of a novel epigenetic-related prognostic signature and candidate drugs for patients with lung adenocarcinoma

Zhihao Wang¹, Kidane Siele Embaye¹, Qing Yang², Lingzhi Qin¹, Chao Zhang¹, Liwei Liu¹, Xiaoqian Zhan¹, Fengdi Zhang³, Xi Wang¹, Shenghui Qin¹

¹Institute of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
²Department of Pharmacy, Hiser Medical Center of Qingdao, Qingdao 266033, China
³Department of Pathology, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan 430030, China

Received: November 9, 2020Accepted: May 11, 2021Published: July 20, 2021

https://doi.org/10.18632/aging.203315

Copyright: © 2021 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

Background: Epigenetic dysregulation has been increasingly proposed as a hallmark of cancer. Here, the aim of this study is to establish an epigenetic-related signature for predicting the prognosis of lung adenocarcinoma (LUAD) patients.

Results: Five epigenetic-related genes (ERGs) (ARRB1, PARP1, PKM, TFDP1, and YWHAZ) were identified as prognostic hub genes and used to establish a prognostic signature. According our risk score system, LUAD patients were stratified into high and low risk groups, and patients in the high risk group had a worse prognosis. ROC analysis indicated that the signature was precise in predicting the prognosis. A new nomogram was constructed based on the five hub genes, which can predict the OS of every LUAD patients. The calibration curves showed that the nomogram had better accuracy in prediction. Finally, candidate drugs that aimed at hub ERGs were identified, which included 47 compounds.

Conclusions: Our epigenetic-related signature nomogram can effectively and reliably predict OS of LUAD patients, also we provide precise targeted chemotherapeutic drugs.

Methods: The genomic data and clinical data of LUAD cohort were downloaded from the TCGA database and ERGs were obtained from the EpiFactors database. GSE31210 and GSE50081 microarray datasets were included as independent external datasets. Univariate Cox, LASSO regression, and multivariate Cox analyses were applied to construct the epigenetic-related signature.