Deep learning reveals cuproptosis features assist in predict prognosis and guide immunotherapy in lung adenocarcinoma.

ABSTRACT:

Background

Cuproptosis is a recently found non-apoptotic cell death type that holds promise as an emerging therapeutic modality in lung adenocarcinoma (LUAD) patients who develop resistance to radiotherapy and chemotherapy. However, the Cuproptosis' role in the onset and progression of LUAD remains unclear.

Methods

Cuproptosis-related genes (CRGs) were identified by a co-expression network approach based on LUAD cell line data from radiotherapy, and a robust risk model was developed using deep learning techniques based on prognostic CRGs and explored the value of deep learning models systematically for clinical applications, functional enrichment analysis, immune infiltration analysis, and genomic variation analysis.

Results

A three-layer artificial neural network ris

SUBMITTER: Li G 

PROVIDER: S-EPMC9437348 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature