Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
Lipids Health Dis. 2023 Aug 9;22(1):124. doi: 10.1186/s12944-023-01878-0.
Nonalcoholic fatty liver disease (NAFLD) is now the major contributor to chronic liver disease. Disorders of lipid metabolism are a major element in the emergence of NAFLD. This research intended to explore lipid metabolism-related clusters in NAFLD and establish a prediction biomarker.
The expression mode of lipid metabolism-related genes (LMRGs) and immune characteristics in NAFLD were examined. The "ConsensusClusterPlus" package was utilized to investigate the lipid metabolism-related subgroup. The WGCNA was utilized to determine hub genes and perform functional enrichment analysis. After that, a model was constructed by machine learning techniques. To validate the predictive effectiveness, receiver operating characteristic curves, nomograms, decision curve analysis (DCA), and test sets were used. Lastly, gene set variation analysis (GSVA) was utilized to investigate the biological role of biomarkers in NAFLD.
Dysregulated LMRGs and immunological responses were identified between NAFLD and normal samples. Two LMRG-related clusters were identified in NAFLD. Immune infiltration analysis revealed that C2 had much more immune infiltration. GSVA also showed that these two subtypes have distinctly different biological features. Thirty cluster-specific genes were identified by two WGCNAs. Functional enrichment analysis indicated that cluster-specific genes are primarily engaged in adipogenesis, signalling by interleukins, and the JAK-STAT signalling pathway. Comparing several models, the random forest model exhibited good discrimination performance. Importantly, the final five-gene random forest model showed excellent predictive power in two test sets. In addition, the nomogram and DCA confirmed the precision of the model for NAFLD prediction. GSVA revealed that model genes were down-regulated in several immune and inflammatory-related routes. This suggests that these genes may inhibit the progression of NAFLD by inhibiting these pathways.
This research thoroughly emphasized the complex relationship between LMRGs and NAFLD and established a five-gene biomarker to evaluate the risk of the lipid metabolism phenotype and the pathologic results of NAFLD.
非酒精性脂肪性肝病（NAFLD）现已成为慢性肝病的主要病因。脂代谢紊乱是非酒精性脂肪性肝病发生的重要因素。本研究旨在探讨 NAFLD 中与脂代谢相关的聚类，并建立预测生物标志物。
检测 NAFLD 中脂代谢相关基因（LMRGs）的表达模式和免疫特征。利用“ConsensusClusterPlus”包对脂代谢相关亚群进行研究。利用 WGCNA 确定枢纽基因并进行功能富集分析。然后，通过机器学习技术构建模型。为了验证预测的有效性，使用了接收器工作特征曲线、列线图、决策曲线分析（DCA）和测试集。最后，利用基因集变异分析（GSVA）研究生物标志物在 NAFLD 中的生物学作用。
在 NAFLD 和正常样本之间鉴定出失调的 LMRGs 和免疫反应。在 NAFLD 中确定了两个 LMRG 相关的聚类。免疫浸润分析表明 C2 具有更多的免疫浸润。GSVA 还表明这两种亚型具有明显不同的生物学特征。通过两个 WGCNAs 确定了 30 个聚类特异性基因。功能富集分析表明，聚类特异性基因主要参与脂肪生成、白细胞介素信号和 JAK-STAT 信号通路。比较几种模型，随机森林模型表现出良好的判别性能。重要的是，最终的五个基因随机森林模型在两个测试集中表现出优异的预测能力。此外，列线图和 DCA 证实了模型对 NAFLD 预测的准确性。GSVA 显示模型基因在几个免疫和炎症相关途径中下调。这表明这些基因可能通过抑制这些途径来抑制 NAFLD 的进展。
本研究深入强调了 LMRGs 与 NAFLD 之间的复杂关系，并建立了一个五基因生物标志物来评估脂代谢表型和 NAFLD 病理结果的风险。
