Medical Biochemistry and Molecular Biology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha City, Egypt.
Lipids Health Dis. 2024 Aug 24;23(1):266. doi: 10.1186/s12944-024-02231-9.
Nonalcoholic Steatohepatitis (NASH) results from complex liver conditions involving metabolic, inflammatory, and fibrogenic processes. Despite its burden, there has been a lack of any approved food-and-drug administration therapy up till now.
Utilizing machine learning (ML) algorithms, the study aims to identify reliable potential genes to accurately predict the treatment response in the NASH animal model using biochemical and molecular markers retrieved using bioinformatics techniques.
The NASH-induced rat models were administered various microbiome-targeted therapies and herbal drugs for 12 weeks, these drugs resulted in reducing hepatic lipid accumulation, liver inflammation, and histopathological changes. The ML model was trained and tested based on the Histopathological NASH score (HPS); while (0-4) HPS considered Improved NASH and (5-8) considered non-improved, confirmed through rats' liver histopathological examination, incorporates 34 features comprising 20 molecular markers (mRNAs-microRNAs-Long non-coding-RNAs) and 14 biochemical markers that are highly enriched in NASH pathogenesis. Six different ML models were used in the proposed model for the prediction of NASH improvement, with Gradient Boosting demonstrating the highest accuracy of 98% in predicting NASH drug response.
Following a gradual reduction in features, the outcomes demonstrated superior performance when employing the Random Forest classifier, yielding an accuracy of 98.4%. The principal selected molecular features included YAP1, LATS1, NF2, SRD5A3-AS1, FOXA2, TEAD2, miR-650, MMP14, ITGB1, and miR-6881-5P, while the biochemical markers comprised triglycerides (TG), ALT, ALP, total bilirubin (T. Bilirubin), alpha-fetoprotein (AFP), and low-density lipoprotein cholesterol (LDL-C).
This study introduced an ML model incorporating 16 noninvasive features, including molecular and biochemical signatures, which achieved high performance and accuracy in detecting NASH improvement. This model could potentially be used as diagnostic tools and to identify target therapies.
非酒精性脂肪性肝炎（NASH）是一种复杂的肝脏疾病，涉及代谢、炎症和纤维化过程。尽管它的负担很重，但到目前为止，还没有任何经过食品和药物管理局批准的治疗方法。
利用机器学习（ML）算法，本研究旨在利用生物信息学技术检索生化和分子标志物，识别可靠的潜在基因，以准确预测 NASH 动物模型的治疗反应。
NASH 诱导的大鼠模型接受了各种微生物组靶向治疗和草药药物治疗 12 周，这些药物导致肝脂质积累、肝脏炎症和组织病理学变化减少。ML 模型基于组织病理学 NASH 评分（HPS）进行训练和测试；（0-4）HPS 被认为是改善的 NASH，（5-8）被认为是非改善的，通过大鼠肝脏组织病理学检查证实，包括 34 个特征，包含 20 个分子标志物（mRNA-microRNA-长非编码-RNA）和 14 个生化标志物，这些标志物在 NASH 发病机制中高度富集。所提出的模型中使用了六种不同的 ML 模型来预测 NASH 的改善，梯度提升在预测 NASH 药物反应方面表现出最高的准确性，达到 98%。
在特征逐渐减少的情况下，当使用随机森林分类器时，结果表现出更好的性能，准确率为 98.4%。主要选择的分子特征包括 YAP1、LATS1、NF2、SRD5A3-AS1、FOXA2、TEAD2、miR-650、MMP14、ITGB1 和 miR-6881-5P，而生化标志物包括甘油三酯（TG）、ALT、ALP、总胆红素（T.胆红素）、甲胎蛋白（AFP）和低密度脂蛋白胆固醇（LDL-C）。
本研究介绍了一种 ML 模型，该模型包含 16 个非侵入性特征，包括分子和生化特征，在检测 NASH 改善方面具有较高的性能和准确性。该模型可能被用作诊断工具，并识别靶向治疗方法。
