Department of Information Management, Tainan University of Technology, Tainan, Taiwan; Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan; Ph. D. Program in Biomedical Engineering, Kaohsiung Medical University, Kaohsiung, Taiwan; School of Dentistry, Kaohsiung Medical University, Kaohsiung, Taiwan; Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung, Taiwan.
Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
Comput Biol Med. 2023 May;157:106706. doi: 10.1016/j.compbiomed.2023.106706. Epub 2023 Mar 17.
Colorectal cancer is a leading cause of cancer mortality worldwide, with an increasing incidence rate in developing countries. Integration of genetic information with cancer therapy guidance has shown promise in cancer treatment, indicating its potential as an essential tool in translation oncology. However, the high-throughput analysis and variability of genomic data poses a major challenge to conventional analytic approaches. In this study, we propose an advanced analytic approach, named "Fuzzy-based RNNCoxPH," incorporated fuzzy logic, recurrent neural networks (RNNs), and Cox proportional hazards regression (CoxPH) for detecting missense variants associated with high-risk of all-cause mortality in rectum adenocarcinoma. The test data set was downloaded from "Rectum adenocarcinoma, TCGA-READ" the Genomic Data Commons (GDC) portal. In this study, four model-based risk score models were derived using RNN, CoxPH, RNNCoxPH, and RNNCoxPH. The RNNCoxPH and RNNCoxPH models were obtained as the sum and product of the RNN risk degree matrix and the CoxPH risk degree matrix, respectively. Moreover, the fuzzy logic system was used to calculate the survival risk values of missense variants and classified their membership grade to improve the identification of high-risk gene variation locations associated with cancer mortality. The four models were integrated to develop an advanced risk estimation model. There were 20 028 variants associated with survival status, amongst 17 638 variants were associated with survival and 2390 variants associated with mortality. The proposed Fuzzy-based RNNCoxPH model obtained a balanced accuracy of 93.7%, which was significantly higher than that of the other four test methods. In particular, the CoxPH model is commonly used in medical researches and the XGBoost model is famous for its high accuracy in machine learning. The results suggest that the Fuzzy-based RNNCoxPH model exhibits a higher efficacy in identifying and classifying the missense variants related to mortality risk in rectum adenocarcinoma.
结直肠癌是全球癌症死亡率的主要原因，发展中国家的发病率呈上升趋势。将遗传信息与癌症治疗指导相结合，在癌症治疗中显示出了前景，表明其作为转化肿瘤学的重要工具具有潜力。然而，基因组数据的高通量分析和可变性对传统分析方法构成了重大挑战。在这项研究中，我们提出了一种名为“基于模糊的 RNNCoxPH”的先进分析方法，该方法结合了模糊逻辑、递归神经网络 (RNN) 和 Cox 比例风险回归 (CoxPH)，用于检测与直肠腺癌全因死亡率高风险相关的错义变体。测试数据集从基因组数据公共库 (GDC) 门户的“直肠腺癌，TCGA-READ”下载。在这项研究中，使用 RNN、CoxPH、RNNCoxPH 和 RNNCoxPH 衍生了四个基于模型的风险评分模型。RNNCoxPH 和 RNNCoxPH 模型分别是 RNN 风险度矩阵和 CoxPH 风险度矩阵的和与积。此外，模糊逻辑系统用于计算错义变体的生存风险值，并对其隶属度等级进行分类，以提高对与癌症死亡率相关的高风险基因变异位置的识别能力。将这四个模型整合在一起，开发了一个先进的风险估计模型。有 20028 个变体与生存状态相关，其中 17638 个变体与生存相关，2390 个变体与死亡相关。所提出的基于模糊的 RNNCoxPH 模型获得了 93.7%的平衡准确率，明显高于其他四种测试方法。特别是，CoxPH 模型在医学研究中常用，XGBoost 模型因其在机器学习中的高精度而闻名。结果表明，基于模糊的 RNNCoxPH 模型在识别和分类与直肠腺癌死亡率相关的错义变体方面具有更高的功效。
