Kucukakcali Zeynep, Colak Cemil, Gozukara Bag Harika Gozde, Balikci Cicek Ipek, Ozhan Onural, Yildiz Azibe, Danis Nefsun, Koc Ahmet, Parlakpinar Hakan, Akbulut Sami
Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280 Malatya, Turkey.
Department of Pharmacology, Faculty of Medicine, Inonu University, 44280 Malatya, Turkey.
Diagnostics (Basel). 2023 Apr 28;13(9):1583. doi: 10.3390/diagnostics13091583.
The first aim of this study is to perform bioinformatic analysis of lncRNAs obtained from liver tissue samples from rats treated with cisplatin hepatotoxicity and without pathology. Another aim is to identify possible biomarkers for the diagnosis/early diagnosis of hepatotoxicity by modeling the data obtained from bioinformatics analysis with ensemble learning methods.
In the study, 20 female Sprague-Dawley rats were divided into a control group and a hepatotoxicity group. Liver samples were taken from rats, and transcriptomic and histopathological analyses were performed. The dataset achieved from the transcriptomic analysis was modeled with ensemble learning methods (stacking, bagging, and boosting). Modeling results were evaluated with accuracy (Acc), balanced accuracy (B-Acc), sensitivity (Se), specificity (Sp), positive predictive value (Ppv), negative predictive value (Npv), and F1 score performance metrics. As a result of the modeling, lncRNAs that could be biomarkers were evaluated with variable importance values.
According to histopathological and immunohistochemical analyses, a significant increase was observed in the sinusoidal dilatation and Hsp60 immunoreactivity values in the hepatotoxicity group compared to the control group ( < 0.0001). According to the results of the bioinformatics analysis, 589 lncRNAs showed different expressions in the groups. The stacking model had the best classification performance among the applied ensemble learning models. The Acc, B-Acc, Se, Sp, Ppv, Npv, and F1-score values obtained from this model were 90%, 90%, 80%, 100%, 100%, 83.3%, and 88.9%, respectively. lncRNAs with id rna-XR_005492522.1, rna-XR_005492536.1, and rna-XR_005505831.1 with the highest three values according to the variable importance obtained as a result of stacking modeling can be used as predictive biomarker candidates for hepatotoxicity.
Among the ensemble algorithms, the stacking technique yielded higher performance results as compared to the bagging and boosting methods on the transcriptomic data. More comprehensive studies can support the possible biomarkers determined due to the research and the decisive results for the diagnosis of drug-induced hepatotoxicity.
本研究的首要目的是对从顺铂肝毒性处理及无病理状态的大鼠肝脏组织样本中获取的长链非编码RNA（lncRNA）进行生物信息学分析。另一个目的是通过使用集成学习方法对从生物信息学分析中获得的数据进行建模，以识别肝毒性诊断/早期诊断的潜在生物标志物。
在本研究中，将20只雌性Sprague-Dawley大鼠分为对照组和肝毒性组。采集大鼠肝脏样本，并进行转录组学和组织病理学分析。利用集成学习方法（堆叠、装袋和提升）对转录组分析得到的数据集进行建模。使用准确率（Acc）、平衡准确率（B-Acc）、灵敏度（Se）、特异性（Sp）、阳性预测值（Ppv）、阴性预测值（Npv）和F1分数性能指标对建模结果进行评估。作为建模的结果，对可能作为生物标志物的lncRNA进行了可变重要性值评估。
根据组织病理学和免疫组织化学分析，与对照组相比，肝毒性组的窦状隙扩张和热休克蛋白60（Hsp60）免疫反应性值显著增加（<0.0001）。根据生物信息学分析结果，589个lncRNA在各组中表现出不同的表达。在所应用的集成学习模型中，堆叠模型具有最佳的分类性能。该模型获得的Acc、B-Acc、Se、Sp、Ppv、Npv和F1分数值分别为90%、90%、80%、100%、100%、83.3%和88.9%。根据堆叠建模得到的可变重要性，具有最高三个值的lncRNA（id为rna-XR_005492522.1、rna-XR_005492536.1和rna-XR_005505831.1）可作为肝毒性的预测生物标志物候选物。
在集成算法中，与装袋和提升方法相比，堆叠技术在转录组数据上产生了更高的性能结果。更全面的研究可以支持因本研究确定的潜在生物标志物以及药物性肝毒性诊断的决定性结果。
