Lin Chen, Zheng Meng, Wu Wensi, Wang Zhishan, Lu Guofeng, Feng Shaodan, Zhang Xinlan
Department of Emergency, The Third Affiliated People's Hospital, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
Hemodialysis Center, The Third Affiliated People's Hospital, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
Front Genet. 2025 Jul 22;16:1561331. doi: 10.3389/fgene.2025.1561331. eCollection 2025.
Sepsis frequently induces acute kidney injury (AKI), and the complex interplay between these two conditions worsens prognosis, prolongs hospitalization, and increases mortality. Despite therapeutic options such as antibiotics and supportive care, early diagnosis and treatment remain a challenge. Understanding the underlying molecular mechanisms linking sepsis and AKI is critical for the development of effective diagnostic tools and therapeutic strategies.
We used two sepsis (GSE57065 and GSE28750) and three AKI (GSE30718, GSE139061, and GSE67401) datasets from the NCBI Gene Expression Omnibus (GEO) for model development and validation, and performed batch effect mitigation, differential gene, and functional enrichment analysis using R software packages. We assessed 113 combinations of 12 different algorithms to develop an internally and externally validated machine-learning model for diagnosing AKI. Finally, we used functional enrichment analysis to identify potential therapeutic agents for AKI.
We identified 556 and 725 DEGs associated with sepsis and AKI, respectively, with 28 overlapping genes suggesting shared pathways. Functional enrichment analysis revealed important associations of AKI with immune responses and cell adhesion processes. The immune infiltration analysis showed significant differences in immune cell presence between sepsis and AKI patients compared with the control group. The machine-learning models identified eight key genes (, , , , , , , ) with potential for diagnosing AKI. The diagnostic performance of the model constructed in this way was excellent (area under the curve = 0.978), especially in the under 60 years and male patient subgroups. The diagnostic performance outperformed previous models in both the training and validation sets. In addition, cyclosporin A and nine other drugs were identified as potential agents for treating sepsis-associated AKI.
This study highlights the potential of integrating bioinformatics and machine-learning approaches to generate a new diagnostic model for sepsis-associated AKI using molecular crossovers with sepsis. The genes identified have potential to serve as biomarkers and therapeutic targets, providing avenues for future research aimed at enhancing sepsis-associated AKI diagnosis and treatment.
脓毒症常诱发急性肾损伤（AKI），这两种病症之间复杂的相互作用会使预后恶化、延长住院时间并增加死亡率。尽管有抗生素和支持性治疗等治疗选择，但早期诊断和治疗仍然是一项挑战。了解脓毒症与AKI之间潜在的分子机制对于开发有效的诊断工具和治疗策略至关重要。
我们使用来自NCBI基因表达综合数据库（GEO）的两个脓毒症数据集（GSE57065和GSE28750）以及三个AKI数据集（GSE30718、GSE139061和GSE67401）进行模型开发和验证，并使用R软件包进行批次效应缓解、差异基因和功能富集分析。我们评估了12种不同算法的113种组合，以开发一个经过内部和外部验证的用于诊断AKI的机器学习模型。最后，我们使用功能富集分析来识别AKI的潜在治疗药物。
我们分别鉴定出556个和725个与脓毒症和AKI相关的差异表达基因（DEG），其中28个重叠基因提示存在共同途径。功能富集分析揭示了AKI与免疫反应和细胞黏附过程的重要关联。免疫浸润分析显示，与对照组相比，脓毒症患者和AKI患者的免疫细胞存在情况存在显著差异。机器学习模型鉴定出八个具有诊断AKI潜力的关键基因（、、、、、、、）。以这种方式构建的模型的诊断性能极佳（曲线下面积 = 0.978），尤其是在60岁以下和男性患者亚组中。该诊断性能在训练集和验证集中均优于先前的模型。此外，环孢素A和其他九种药物被鉴定为治疗脓毒症相关AKI的潜在药物。
本研究强调了整合生物信息学和机器学习方法以利用与脓毒症的分子交叉生成一种用于脓毒症相关AKI的新诊断模型的潜力。所鉴定的基因有潜力作为生物标志物和治疗靶点，为未来旨在加强脓毒症相关AKI诊断和治疗的研究提供了途径。
