通过机器学习识别与进展相关的基因并构建慢性病的预后模型
Bingkun Zhou1, Hu Zhou2, Xiaodong Huang3
1Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
Frontiers in cell and developmental biology
|September 2, 2025
概括
这项研究开发了一种新的机器学习模型,使用转录学来预测慢性病 (CKD) 的进展. 该模型识别了关键基因,为早期CKD干预提供了新的诊断目标.
科学领域:
- 基因组学和生物信息学
- 计算生物学
- 肝脏病学
背景情况:
- 早期诊断和干预对于改善慢性病 (CKD) 的结果至关重要.
- 现有的CKD风险预测模型通常依赖于传统的指标,而基于转录学方法的方法存在差距.
- 机器学习与转录学数据的整合为增强CKD预测提供了有前途的途径.
研究的目的:
- 通过机器学习开发和验证基于转录学的慢性病风险预测模型.
- 确定与CKD发生和进展相关的核心基因组.
- 在独立数据集和小鼠模型中评估这些基因组的预测性能.
主要方法:
- 使用权重基因相关网络分析 (WGCNA) 和随机森林算法从转录组数据 (GSE137570) 中识别核心基因组.
- 使用GSE66494,GSE180394和GSE45980数据集进行了外部验证.
- 使用Cox,LASSO和逻辑回归构建预测模型,并通过小鼠单侧尿管阻塞 (UUO) 模型进行验证.
主要成果:
- 鉴定了三个基因组 (最小,中,最大),最大组在外部验证中显示出最佳的CKD分类性能 (AUC: 0. 767).
- 包括CCL2,SUCLG1和ACADM在内的最小基因组被用于构建考克斯回归风险模型.
- 观察到人类和小鼠的CKD在KEGG丰富度上的高度相似性,证实了小鼠核心基因的诊断价值.
结论:
- 使用机器学习成功开发了一种基于转录学的慢性病风险预测模型.
- 已识别的核心基因作为未来的实验研究和治疗策略的潜在目标.
- 这种方法提高了对慢性病的理解,并为早期检测和干预提供了工具.
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