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
- 1 Institute of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- 2 Department of Pharmacy, Hiser Medical Center of Qingdao, Qingdao 266033, China
- 3 Department of Pathology, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan 430030, China
Received: November 9, 2020 Accepted: May 11, 2021 Published: July 20, 2021https://doi.org/10.18632/aging.203315
How to Cite
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.
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.