Liang Pengchen, Chen Jianguo, Yao Lei, Hao Zezhou, Chang Qing
Shanghai Key Laboratory of Gastric Neoplasms, Department of Surgery, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200020, China.
School of Microelectronics, Shanghai University, Shanghai 201800, China.
Biomedicines. 2023 May 19;11(5):1479. doi: 10.3390/biomedicines11051479.
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 ( < 0.001), and demonstrated significant independence as a standalone prognostic predictor ( < 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.
肺腺癌是一项重大的全球健康挑战。尽管在诊断和治疗方面取得了进展，但许多患者的预后仍然很差。在本研究中，我们旨在识别与铜死亡相关的基因，并开发一个深度神经网络模型来预测肺腺癌的预后。我们通过对与铜死亡相关基因的差异分析，从癌症基因组图谱数据中筛选出差异表达基因。然后，我们利用这些信息，使用深度神经网络建立了一个预后模型，并使用基因表达综合数据库的数据对其进行了验证。我们的深度神经网络模型纳入了9个与铜死亡相关的基因，在训练集中的曲线下面积为0.732，在验证集中为0.646。该模型有效地将不同风险组区分开来，生存曲线存在显著差异（<0.001）证明了这一点，并且作为一个独立的预后预测指标显示出显著的独立性（<0.001）。功能分析揭示了风险组之间细胞通路、免疫微环境和肿瘤突变负担的差异。此外，我们的模型提供了个性化的生存概率预测，一致性指数为0.795，并确定候选药物BMS-754807为肺腺癌潜在的敏感治疗选择。总之，我们提出了一个基于9个与铜死亡相关基因的肺腺癌深度神经网络预后模型，该模型具有独立的预后能力。该模型可用于患者生存的个性化预测以及肺腺癌潜在治疗药物的识别，这最终可能改善患者的预后。
