School of Life Sciences, Shanghai University, Shanghai, 200444, China.
Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Mol Genet Genomics. 2022 Sep;297(5):1301-1313. doi: 10.1007/s00438-022-01918-x. Epub 2022 Jul 3.
Lung is the most important organ in the human respiratory system, whose normal functions are quite essential for human beings. Under certain pathological conditions, the normal lung functions could no longer be maintained in patients, and lung transplantation is generally applied to ease patients' breathing and prolong their lives. However, several risk factors exist during and after lung transplantation, including bleeding, infection, and transplant rejections. In particular, transplant rejections are difficult to predict or prevent, leading to the most dangerous complications and severe status in patients undergoing lung transplantation. Given that most common monitoring and validation methods for lung transplantation rejections may take quite a long time and have low reproducibility, new technologies and methods are required to improve the efficacy and accuracy of rejection monitoring after lung transplantation. Recently, one previous study set up the gene expression profiles of patients who underwent lung transplantation. However, it did not provide a tool to predict lung transplantation responses. Here, a further deep investigation was conducted on such profiling data. A computational framework, incorporating several machine learning algorithms, such as feature selection methods and classification algorithms, was built to establish an effective prediction model distinguishing patient into different clinical subgroups, corresponding to different rejection responses after lung transplantation. Furthermore, the framework also screened essential genes with functional enrichments and create quantitative rules for the distinction of patients with different rejection responses to lung transplantation. The outcome of this contribution could provide guidelines for clinical treatment of each rejection subtype and contribute to the revealing of complicated rejection mechanisms of lung transplantation.
肺是人体呼吸系统中最重要的器官，其正常功能对人体至关重要。在某些病理条件下，患者的正常肺功能无法维持，通常会进行肺移植以缓解患者的呼吸困难并延长其生命。然而，肺移植过程中和之后存在多种风险因素，包括出血、感染和移植排斥反应。特别是，移植排斥反应难以预测或预防，导致肺移植患者出现最危险的并发症和严重状况。鉴于大多数常见的肺移植排斥反应监测和验证方法可能需要相当长的时间且重复性低，因此需要新技术和方法来提高肺移植排斥反应监测的效果和准确性。最近，一项先前的研究建立了接受肺移植患者的基因表达谱。然而，它没有提供预测肺移植反应的工具。在这里，对这些分析数据进行了进一步深入的调查。建立了一个包含多种机器学习算法（如特征选择方法和分类算法）的计算框架，以建立有效的预测模型，将患者分为不同的临床亚组，对应于肺移植后不同的排斥反应。此外，该框架还筛选出具有功能富集的关键基因，并为区分不同排斥反应的患者创建定量规则，以区分对肺移植的不同排斥反应。本研究的结果可为每个排斥亚型的临床治疗提供指导，并有助于揭示肺移植复杂的排斥机制。
