Development and validation of biomarkers related to anoikis in liver cirrhosis based on bioinformatics analysis  

作　　者：Jiang-Yan Luo Sheng Zheng Juan Yang Chi Ma Xiao-Ying Ma Xing-Xing Wang Xin-Nian Fu Xiao-Zhou Mao 

机构地区：[1]Department of Gastroenterology,The Second Affiliated Hospital of Dali University,Kunming 650011,Yunnan Province,China [2]Department of Gastroenterology,The Third People's Hospital of Yunnan Province,Kunming 650011,Yunnan Province,China

出　　处：《World Journal of Hepatology》2024年第11期1306-1320,共15页世界肝病学杂志（英文）

基　　金：Supported by The Basic Research Joint Special General Project of Yunnan Provincial Local Universities(part),No.202301BA070001-029 and No.202301BA070001-044;Yunnan Province High-Level Scientific and Technological Talents and Innovation Team Selection Special-Young and Middle-aged Academic and Technical Leaders Reserve Talent Project,No.202405AC350067.

摘　　要：BACKGROUND According to study,anoikis-related genes(ARGs)have been demonstrated to play a significant impact in cirrhosis,a major disease threatening human health worldwide.AIM To investigate the relationship between ARGs and cirrhosis development to provide insights into the clinical treatment of cirrhosis.METHODS RNA-sequencing data related to cirrhosis were obtained from the Gene Expression Omnibus database.Differentially expressed genes(DEGs)between cirrhotic and normal tissues were intersected with ARGs to derive differentially expressed ARGs(DEARGs).The DEARGs were filtered using the least absolute shrinkage and selection operator,support vector machine recursive feature elimination,and random forest algorithms to identify biomarkers for cirrhosis.These biomarkers were used to create a nomogram for predicting the prognosis of cirrhosis.The proportions of diverse immune cell subsets in cirrhotic vs normal tissues were compared using the CIBERSORT computational method.In addition,the linkage between immune cells and biomarkers was assessed,and a regulatory network of mRNA,miRNA,and transcription factors was constructed relying on the biomarkers.RESULTS The comparison of cirrhotic and normal tissue samples led to the identification of 635 DEGs.Subsequent intersection of the DEGs with ARGs produced a set of 26 DEARGs.Subsequently,three DEARGs,namely,ACTG1,STAT1,and CCR7,were identified as biomarkers using three machine-learning algorithms.The proportions of M1 and M2 macrophages,resting CD4 memory T cells,resting mast cells,and plasma cells significantly differed between cirrhotic and normal tissue samples.The proportions of M1 and M2 macrophages,resting CD4 memory T cells,and resting mast cells were significantly correlated with the expression of the three biomarkers.The mRNA–miRNA–TF network showed that ACTG1,CCR7,and STAT1 were regulated by 28,42,and 35 miRNAs,respectively.Moreover,AR,MAX,EP300,and FOXA1 were found to regulate four miRNAs related to the biomarkers.CONCLUSION This study revealed ACTG1,STA

关 键 词：Anoikis-related genes CIRRHOSIS Machine learning BIOMARKER Therapeutic drugs BIOINFORMATICS Immune infiltration 

分 类 号：R575[医药卫生—消化系统]