研究表明长非编码RNA (long non-coding RNA, lncRNA)影响着许多疾病的生物学过程,例如疾病的发生、传播、治愈等。因此,预测潜在lncRNA-疾病关联(lncRNA-disease associations, LDAs)对疾病的诊疗和治疗有着重要意义。本文提出一种新的深度学习方法预测LDAs,称为TCNLDA。首先,分别构建了lncRNA的功能相似性矩阵、高斯相似性矩阵和序列相似性矩阵,以及疾病的语义相似性矩阵和高斯相似性矩阵,并将其进行矩阵融合处理。然后,构建lncRNA-疾病对,并对其使用自编码器(Autoencoder, AE)进行特征提取。最后,将提取好的特征输入到时序卷积网络(Temporal Convolutional Network, TCN)中进行训练输出预测得分。两个数据集中,TCNLDA与其他模型进行了比较,结果显示TCNLDA优于其他LDAs预测方法。消融实验验证了TCNLDA中各部分的不可缺少性。案例研究进一步表明,TCNLDA在预测新型LDAs方面有着很好的实用性。Studies have shown that long non-coding RNA (lncRNA) influences the biological processes of many diseases, such as disease onset, spread, and cure. Therefore, predicting potential lncRNA-disease associations (LDAs) is important for disease diagnosis and treatment. In this paper, we propose a new deep learning method to predict LDAs, called TCNLDA. Firstly, the functional similarity matrix, Gaussian similarity matrix and sequence similarity matrix of lncRNAs, and the semantic similarity matrix and Gaussian similarity matrix of diseases are constructed and processed for matrix fusion, respectively. Then, the lncRNA-disease pairs are constructed and feature extraction is performed on them using autoencoder (AE). Finally, the extracted features were input into Temporal Convolutional Network (TCN) for training to output the prediction scores. In both datasets, TCNLDA was compared with other models, and the results showed that TCNLDA outperformed other LDAs prediction methods. Ablation experiments verified the indispensability of the c
在这项研究中,我们构建了一个基于昼夜节律相关基因(Circadian Genes, CRGs)的风险评估模型,用于预测乳腺癌(Breast Cancer, BRCA)患者的预后和免疫治疗效果。通过分析TCGA数据库中的基因表达数据和临床信息,我们识别出与预后相关的八个基因。通过多因素Cox回归分析,我们筛选出这些基因并构建了预后标志物。利用Kaplan-Meier分析和受试者工作特征(Receiver Operation Characteristic, ROC)曲线评估了该标志物的预后价值,并在GSE42568数据集中进行了验证。结果表明,高风险评分患者的预后较差,1年、3年和5年生存率的曲线下面积(Area under Curve, AUC)分别为0.667、0.703和0.713。通过基因集富集分析(Gene Set Enrichment Analysis, GSEA)和免疫景观分析,我们发现高风险组显著富集于细胞周期通路和半胱氨酸及甲硫氨酸代谢通路,而低风险组则富集于免疫相关通路。此外,高风险组中免疫检查点基因的表达较低,与较差的预后一致。我们的研究揭示了CRGs在乳腺癌预后和免疫治疗中的重要性,并为未来的个性化治疗策略提供了新的方向。In this study, we constructed a risk assessment model based on circadian rhythm-related genes (Circadian Genes, CRGs) to predict the prognosis and immunotherapy outcomes of breast cancer (Breast Cancer, BRCA) patients. By analyzing gene expression data and clinical information from the TCGA database, we identified eight genes associated with prognosis. Through multivariate Cox regression analysis, we screened these genes and constructed a prognostic biomarker. The prognostic value of this biomarker was evaluated using Kaplan-Meier analysis and Receiver Operating Characteristic (ROC) curves, and it was validated in the GSE42568 dataset. The results showed that patients with high-risk scores had poorer prognoses, with area under the curve (AUC) values for 1-year, 3-year, and 5-year survival rates being 0.667, 0.703, and 0.713, respectively. Through Gene Set E
