A Deep Learning Approach for Prognostic Evaluation of Lung Adenocarcinoma Based on Cuproptosis-Related Genes

Abstract

Lung adenocarcinoma represents a significant global health challenge. Despite advances in diagnosis and treatment, the prognosis remains poor for many patients. In this study, we aimed to identify cuproptosis-related genes and to develop a deep neural network model to predict the prognosis of lung adenocarcinoma. We screened differentially expressed genes from The Cancer Genome Atlas data through differential analysis of cuproptosis-related genes. We then used this information to establish a prognostic model using a deep neural network, which we validated using data from the Gene Expression Omnibus. Our deep neural network model incorporated nine cuproptosis-related genes and achieved an area under the curve of 0.732 in the training set and 0.646 in the validation set. The model effectively distinguished between distinct risk groups, as evidenced by significant differences in survival curves (p < 0.001), and demonstrated significant independence as a standalone prognostic predictor (p < 0.001). Functional analysis revealed differences in cellular pathways, the immune microenvironment, and tumor mutation burden between the risk groups. Furthermore, our model provided personalized survival probability predictions with a concordance index of 0.795 and identified the drug candidate BMS-754807 as a potentially sensitive treatment option for lung adenocarcinoma. In summary, we presented a deep neural network prognostic model for lung adenocarcinoma, based on nine cuproptosis-related genes, which offers independent prognostic capabilities. This model can be used for personalized predictions of patient survival and the identification of potential therapeutic agents for lung adenocarcinoma, which may ultimately improve patient outcomes.

Surrogate Biomarker Prediction from Whole-Slide Images for Evaluating Overall Survival in Lung Adenocarcinoma

“…Gene expression signatures have been widely used in research to evaluate OS in LUAD [ 15 , 16 , 17 , 18 , 19 , 20 , 28 ]. For example, Zhou et al established a three-gene signature that achieved a C-index of 0.638 in TCGA-LUAD [ 28 ].…”

“…Extensive research has been dedicated to identifying prognostic gene expression signatures from transcriptomic data in LUAD. Various methods such as Cox regression, random survival forests, and deep neural networks have been implemented by researchers with the aim of stratifying patients into low- and high-risk groups [ 15 , 16 , 17 , 18 , 19 , 20 ]. Researchers have also explored the use of multimodal artificial intelligence (AI) models aimed at evaluating survival, integrating clinical, genomic, and histopathological information [ 21 , 22 ].…”

Identification of New Prognostic Genes and Construction of a Prognostic Model for Lung Adenocarcinoma

“…Lung adenocarcinoma (LUAD) is the major mortality cause of lung cancer and creates a heavy burden on the global healthcare system [1,2]. In past decades, standard treatments including surgical resection, radiotherapy, chemotherapy, and immunotherapy have advanced considerably [3].…”