论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
NASH中M1型巨噬细胞核心基因对炎症、脂质代谢和肝纤维化的调节
Authors Xu X , Dong Y, Liu J, Zhang P, Yang W, Dai L
Received 18 August 2024
Accepted for publication 12 November 2024
Published 28 November 2024 Volume 2024:17 Pages 9975—9986
DOI https://doi.org/10.2147/JIR.S480574
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Professor Ning Quan
Xingyu Xu,1,* Yaqin Dong,2,* Jianjun Liu,1 Peng Zhang,1 Wenqi Yang,1 Longfei Dai1
1Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People’s Republic of China; 2Department of Radiation Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Longfei Dai, Email doctorlongfeidai@163.com
Background: 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.
Methods: 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.
Results: The abundance of M1 macrophages significantly increased in patients with NASH. The Random Forest (RF) algorithm identified six M1 macrophage-related genes (COL10A1, FAP, IL32, STMN2, SUSD2, and THY1) crucial in NASH. These six genes positively correlated with five inflammatory genes (CCL2, IL1B, TNF, CSF1, and IL15), lipid synthesis gene (FAS), collagen synthesis genes (COL1A1 and COL3A1), liver fibrosis stage, NASH activity score (NAS), and aspartate aminotransferase (AST) levels. These were negatively correlated with the lipid transport gene (CD36), beta fatty acid oxidation gene (PPARA), 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.
Conclusion: These six core genes of M1 macrophages contribute to NASH progression by regulating inflammation, lipid metabolism, and liver fibrosis.
Keywords: NASH, M1 macrophages, machine learning, diagnosis, cluster