基于机器学习的算法识别非酒精性脂肪性肝炎中的关键线粒体相关基因。
Machine learning-based algorithm identifies key mitochondria-related genes in non-alcoholic steatohepatitis.
机构信息
Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Hefei, 230022, Anhui Province, China.
出版信息
Lipids Health Dis. 2024 May 8;23(1):137. doi: 10.1186/s12944-024-02122-z.
BACKGROUND
Evidence suggests that hepatocyte mitochondrial dysfunction leads to abnormal lipid metabolism, redox imbalance, and programmed cell death, driving the onset and progression of non-alcoholic steatohepatitis (NASH). Identifying hub mitochondrial genes linked to NASH may unveil potential therapeutic targets.
METHODS
Mitochondrial hub genes implicated in NASH were identified via analysis using 134 algorithms.
RESULTS
The Random Forest algorithm (RF), the most effective among the 134 algorithms, identified three genes: Aldo-keto reductase family 1 member B10 (AKR1B10), thymidylate synthase (TYMS), and triggering receptor expressed in myeloid cell 2 (TREM2). They were upregulated and positively associated with genes promoting inflammation, genes involved in lipid synthesis, fibrosis, and nonalcoholic steatohepatitis activity scores in patients with NASH. Moreover, using these three genes, patients with NASH were accurately categorized into cluster 1, exhibiting heightened disease severity, and cluster 2, distinguished by milder disease activity.
CONCLUSION
These three genes are pivotal mitochondrial genes implicated in NASH progression.
背景
有证据表明，肝细胞线粒体功能障碍导致脂质代谢异常、氧化还原失衡和程序性细胞死亡，从而引发和促进非酒精性脂肪性肝炎（NASH）的发生和发展。确定与 NASH 相关的线粒体枢纽基因可能揭示潜在的治疗靶点。
方法
通过使用 134 种算法对与 NASH 相关的线粒体枢纽基因进行分析。
结果
在 134 种算法中，随机森林算法（RF）最为有效，它确定了三个基因：醛酮还原酶家族 1 成员 B10（AKR1B10）、胸苷酸合成酶（TYMS）和髓系细胞表达的触发受体 2（TREM2）。这些基因上调，并与促进炎症的基因、参与脂质合成、纤维化和 NASH 患者的非酒精性肝炎活动评分的基因呈正相关。此外，使用这三个基因可以准确地将 NASH 患者分为 1 组，表现出更高的疾病严重程度，以及 2 组，其疾病活动较轻。
结论
这三个基因是与 NASH 进展相关的重要线粒体基因。
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