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

BackgroundCuproptosis 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. MethodsCuproptosis-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. ResultsA three-layer artificial neural network risk model was constructed based on 15 independent prognostic radiotherapy-related CRGs. The risk model was observed as a robust independent prognostic factor for LUAD in the training as well as three external validation cohorts. The patients present in the low-risk group were found to have immune hot tumors exhibiting anticancer activity, whereas the high-risk group patients had immune cold tumors with active metabolism and proliferation. The high-risk group patients were more sensitive to chemotherapy whereas the low-risk group patients were more sensitive to immunotherapy. Genomic variants did not vary considerably among both groups of patients. ConclusionOur findings advance the understanding of cuproptosis and offer fresh perspectives on the clinical management and precision therapy of LUAD.

Automated summary

In this study, a risk model of cuproptosis-related genes was constructed using deep learning techniques and demonstrated good independent prognostic performance in LUAD. Additionally, the study found differences in immune status and chemotherapy sensitivity among different risk groups of patients.