ArticleAging2021
Development and validation of a novel epigenetic-related prognostic signature and candidate drugs for patients with lung adenocarcinoma.
Article in Aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Machine learning-based coagulation-related signature for predicting the prognosis and therapy benefits in lung adenocarcinoma.Discover oncology · 2025Article
- Comprehensive Analysis Reveals the Potential Roles of Transcription Factor Dp-1 in Lung Adenocarcinoma.World journal of oncology · 2023Article
- Article
- Identification of N7-methylguanosine related signature for prognosis and immunotherapy efficacy prediction in lung adenocarcinoma.Frontiers in medicine · 2022Article
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEpigenetic 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.
resultsFive 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.
conclusionsOur epigenetic-related signature nomogram can effectively and reliably predict OS of LUAD patients, also we provide precise targeted chemotherapeutic drugs.
methodsThe 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.
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