Xu Xingyu, Dong Yaqin, Liu Jianjun, Zhang Peng, Yang Wenqi, Dai Longfei
Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People's Republic of China.
Department of Radiation Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People's Republic of China.
J Inflamm Res. 2024 Nov 28;17:9975-9986. doi: 10.2147/JIR.S480574. eCollection 2024.
Although immune cells play a critical role in lipid metabolism and inflammation regulation in patients with non-alcoholic steatohepatitis (NASH), the specific immune cells involved and associated genes remain unclear.
We identified differential immune cell profiles between normal liver and NASH specimens using the CIBERSORT algorithm. Next, we conducted a weighted gene co-expression network analysis (WGCNA) to identify genes highly correlated with these immune cells in NASH. Subsequently, core genes of immune cells were identified using machine learning algorithms.
The abundance of M1 macrophages significantly increased in patients with NASH. The Random Forest (RF) algorithm identified six M1 macrophage-related genes (, and ) crucial in NASH. These six genes positively correlated with five inflammatory genes (, and ), lipid synthesis gene (), collagen synthesis genes ( and ), liver fibrosis stage, NASH activity score (NAS), and aspartate aminotransferase (AST) levels. These were negatively correlated with the lipid transport gene (), beta fatty acid oxidation gene (), and M2 macrophage abundance. Moreover, a predictive model based on these six genes achieved a C-index of 0.902 for diagnosing NASH across four cohorts. The expression of these six genes accurately stratified patients with NASH into low disease activity cluster 1 and high disease activity cluster 2.
These six core genes of M1 macrophages contribute to NASH progression by regulating inflammation, lipid metabolism, and liver fibrosis.
尽管免疫细胞在非酒精性脂肪性肝炎(NASH)患者的脂质代谢和炎症调节中起关键作用,但涉及的特定免疫细胞及相关基因仍不清楚。
我们使用CIBERSORT算法确定正常肝脏和NASH标本之间的差异免疫细胞图谱。接下来,我们进行了加权基因共表达网络分析(WGCNA),以确定与NASH中这些免疫细胞高度相关的基因。随后,使用机器学习算法确定免疫细胞的核心基因。
NASH患者中M1巨噬细胞的丰度显著增加。随机森林(RF)算法确定了六个在NASH中起关键作用的与M1巨噬细胞相关的基因(……以及……)。这六个基因与五个炎症基因(……以及……)、脂质合成基因(……)、胶原蛋白合成基因(……以及……)、肝纤维化分期、NASH活动评分(NAS)和天冬氨酸转氨酶(AST)水平呈正相关。它们与脂质转运基因(……)、β脂肪酸氧化基因(……)和M2巨噬细胞丰度呈负相关。此外,基于这六个基因的预测模型在四个队列中诊断NASH的C指数达到0.902。这六个基因的表达将NASH患者准确地分层为低疾病活动簇1和高疾病活动簇2。
这六个M1巨噬细胞核心基因通过调节炎症、脂质代谢和肝纤维化促进NASH进展。