Department of Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Cancer Med. 2024 Jan;13(1):e6763. doi: 10.1002/cam4.6763. Epub 2023 Dec 22.
Kidney renal clear cell carcinoma (KIRC), as a common case in renal cell carcinoma (RCC), has the risk of postoperative recurrence, thus its prognosis is poor and its prognostic markers are usually based on imaging methods, which have the problem of low specificity. In addition, cuproptosis, as a novel mode of cell death, has been used as a biomarker to predict disease in many cancers in recent years, which also provides an important basis for prognostic prediction in KIRC. For postoperative patients with KIRC, an important means of preventing disease recurrence is pharmacological treatment, and thus matching the appropriate drug to the specific patient's target is also particularly important. With the development of neural networks, their predictive performance in the field of medical big data has surpassed that of traditional methods, and this also applies to the field of prognosis prediction and drug-target prediction.
The purpose of this study is to screen for cuproptosis genes related to the prognosis of KIRC and to establish a deep neural network (DNN) model for patient risk prediction, while also developing a personalized nomogram model for predicting patient survival. In addition, sensitivity drugs for KIRC were screened, and a graph neural network (GNN) model was established to predict the targets of the drugs, in order to discover potential drug action sites and provide new treatment ideas for KIRC.
We used the Cancer Genome Atlas (TCGA) database, International Cancer Genome Consortium (ICGC) database, and DrugBank database for our study. Differentially expressed genes (DEGs) were screened using TCGA data, and then a DNN-based risk prediction model was built and validated using ICGC data. Subsequently, the differences between high- and low-risk groups were analyzed and KIRC-sensitive drugs were screened, and finally a GNN model was trained using DrugBank data to predict the relevant targets of these drugs.
A prognostic model was built by screening 10 significantly different cuproptosis-related genes, the model had an AUC of 0.739 on the training set (TCGA data) and an AUC of 0.707 on the validation set (ICGC data), which demonstrated a good predictive performance. Based on the prognostic model in this paper, patients were also classified into high- and low-risk groups, and functional analyses were performed. In addition, 251 drugs were screened for sensitivity, and four drugs were ultimately found to have high sensitivity, with 5-Fluorouracil having the best inhibitory effect, and subsequently their corresponding targets were also predicted by GraphSAGE, with the most prominent targets including Cytochrome P450 2D6, UDP-glucuronosyltransferase 1A, and Proto-oncogene tyrosine-protein kinase receptor Ret. Notably, the average accuracy of GraphSAGE was 0.817 ± 0.013, which was higher than that of GAT and GTN.
Our KIRC risk prediction model, constructed using 10 cuproptosis-related genes, had good independent prognostic ability. In addition, we screened four highly sensitive drugs and predicted relevant targets for these four drugs that might treat KIRC. Finally, literature research revealed that four drug-target interactions have been demonstrated in previous studies and the remaining targets are potential sites of drug action for future research.
肾透明细胞癌（KIRC）作为肾细胞癌（RCC）的常见病例，存在术后复发的风险，因此预后较差，其预后标志物通常基于影像学方法，特异性低。此外，铜死亡作为一种新的细胞死亡模式，近年来已被用作许多癌症的疾病生物标志物，这也为 KIRC 的预后预测提供了重要依据。对于术后 KIRC 患者，预防疾病复发的重要手段是药物治疗，因此将合适的药物与特定患者的靶标相匹配也尤为重要。随着神经网络的发展，其在医学大数据领域的预测性能已经超过了传统方法，这同样适用于预后预测和药物靶点预测领域。
本研究旨在筛选与 KIRC 预后相关的铜死亡基因，并建立用于患者风险预测的深度神经网络（DNN）模型，同时开发用于预测患者生存的个性化列线图模型。此外，筛选 KIRC 的敏感药物，并建立图神经网络（GNN）模型预测药物的靶点，以发现潜在的药物作用部位，为 KIRC 提供新的治疗思路。
我们使用癌症基因组图谱（TCGA）数据库、国际癌症基因组联盟（ICGC）数据库和药物银行数据库进行研究。使用 TCGA 数据筛选差异表达基因（DEGs），然后使用 ICGC 数据构建基于 DNN 的风险预测模型并进行验证。随后，分析高低风险组之间的差异，筛选 KIRC 敏感药物，最后使用 DrugBank 数据训练 GNN 模型，预测这些药物的相关靶点。
通过筛选 10 个显著不同的铜死亡相关基因，构建了一个预后模型，该模型在训练集（TCGA 数据）中的 AUC 为 0.739，在验证集（ICGC 数据）中的 AUC 为 0.707，具有良好的预测性能。基于本文的预后模型，还将患者分为高风险组和低风险组，并进行功能分析。此外，筛选出 251 种敏感药物，最终发现 4 种药物具有较高的敏感性，其中 5-氟尿嘧啶的抑制效果最好，随后通过 GraphSAGE 预测了它们对应的靶点，其中最突出的靶点包括细胞色素 P450 2D6、UDP-葡糖醛酸基转移酶 1A 和原癌基因酪氨酸蛋白激酶受体 Ret。值得注意的是，GraphSAGE 的平均准确率为 0.817±0.013，高于 GAT 和 GTN。
本研究构建的基于 10 个铜死亡相关基因的 KIRC 风险预测模型具有良好的独立预后能力。此外，我们筛选出 4 种高敏感药物，并预测了这 4 种药物的相关靶点，这些靶点可能用于治疗 KIRC。最后，文献研究表明，其中 4 个药物-靶点相互作用已在之前的研究中得到证实，其余靶点是未来研究中药物作用的潜在部位。
