English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The aim of the present study was to identify candidate prognostic DNA methylation biomarkers for lung adenocarcinoma (LUAD), since the modern precise medicine for the treatment of LUAD requires more biomarkers and novel therapeutic targets of interest. DNA methylation profiling data of LUAD were downloaded from The Cancer Gene Atlas portal. Differentially methylated genes (DMGs) were screened to differentiate between samples designated as good and bad prognosis. LUAD-associated methylation modules were obtained with the weighted correlation network analysis (WGCNA) package, followed by function enrichment analysis. Optimal prognostic DMGs were selected using the LASSO estimation-based Cox-PH approach and were used to construct a prognostic risk scoring system. The training set was dichotomized by risk score, into high- and low-risk groups. The differences in overall survival (OS) time or recurrence-free survival (RFS) time between the two groups were evaluated using a Kaplan-Meier curve. A total of 742 DMG samples were screened for good and bad prognosis. WGCNA identified three LUAD-associated modules, which were primarily associated with cytoskeleton organization, transcription and apoptosis. A nine-gene prognostic methylation signature was determined, which included C20orf56, BTG2, C13orf16, DNASE1L1, ZDHHC3, FHDC1, ARF6, ITGB3 and ICAM4 . A risk score-based methylation signature classified the patients in the training set into high- and low-risk groups with significantly different OS or RFS times. The prognostic value of the methylation signature was successfully verified in a validation set. In conclusion, the present study identified a nine-gene methylation signature for the prediction of survival and recurrence in patients with LUAD and improved the understanding of the alterations in DNA methylation in LUAD.

DNA methylation profiling analysis identifies a DNA methylation signature for predicting prognosis and recurrence of lung adenocarcinoma