Luo JY, Zheng S, Yang J, Ma C, Ma XY, Wang XX, Fu XN, Mao XZ. Development and validation of biomarkers related to anoikis in liver cirrhosis based on bioinformatics analysis. World J Hepatol 2024; 16(11): 1306-1320 [DOI: 10.4254/wjh.v16.i11.1306]
Corresponding Author of This Article
Sheng Zheng, Doctor, Associate Professor, Department of Gastroenterology, The Third People's Hospital of Yunnan Province, No. 292 Beijing Road, Guandu District, Kunming 650011, Yunnan Province, China. zheng_sheng523@163.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Basic Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Hepatol. Nov 27, 2024; 16(11): 1306-1320 Published online Nov 27, 2024. doi: 10.4254/wjh.v16.i11.1306
Development and validation of biomarkers related to anoikis in liver cirrhosis based on bioinformatics analysis
Jiang-Yan Luo, Sheng Zheng, Juan Yang, Chi Ma, Xiao-Ying Ma, Xing-Xing Wang, Xin-Nian Fu, Xiao-Zhou Mao
Jiang-Yan Luo, Chi Ma, Xiao-Ying Ma, Xing-Xing Wang, Xin-Nian Fu, Xiao-Zhou Mao, Department of Gastroenterology, The Second Affiliated Hospital of Dali University, Kunming 650011, Yunnan Province, China
Sheng Zheng, Juan Yang, Department of Gastroenterology, The Third People's Hospital of Yunnan Province, Kunming 650011, Yunnan Province, China
Author contributions: Conceptualization by Zheng S and Yang J; Luo JY contributed to software, resources, project administration; Ma XY, Wang XX and Fu XN contributed to data curation; formal analysis by Ma C; Mao XZ contributed to investigation; Zheng S and Luo JY writing—original draft preparation; Yang J, Ma C, Ma XY, Wang XX, Fu XN and Mao XZ contributed to visualization; Zheng S and Yang J contributed to funding acquisition. All authors have read and agreed to the published version of the manuscript.
Supported byThe 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.
Institutional review board statement: This study was reviewed and approved by the Ethics Review Committee of the Third People's Hospital of Yunnan Province (approval No. 2023KY052).
Conflict-of-interest statement: All other authors have nothing to disclose.
Data sharing statement: Data sharing statement: The data analyzed in this research were collected from Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) and previous literature.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Sheng Zheng, Doctor, Associate Professor, Department of Gastroenterology, The Third People's Hospital of Yunnan Province, No. 292 Beijing Road, Guandu District, Kunming 650011, Yunnan Province, China. zheng_sheng523@163.com
Received: June 14, 2024 Revised: September 29, 2024 Accepted: October 20, 2024 Published online: November 27, 2024 Processing time: 144 Days and 22.9 Hours
Abstract
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, STAT1, and CCR7 as biomarkers of cirrhosis, providing a reference for developing novel diagnostic and therapeutic strategies for cirrhosis.
Core Tip: Studies have highlighted the role of anoikis-related genes (ARGs) in cirrhosis. In this study, machine learning algorithms were used to identify differentially expressed ARGs (DEARGs) based on RNA-sequencing data. Three DEARGs were identified as biomarkers for cirrhosis (ACTG1, STAT1, and CCR7). The proportions of M1 and M2 macrophages, CD4 T cells, and mast cells were different between cirrhotic and normal tissues and were correlated with the expression of the three biomarkers. An mRNA–miRNA–TF network was constructed based on the three biomarkers. miRNAs and transcription factor regulating the biomarkers were identified. The findings of this study may facilitate the development of novel diagnostic and therapeutic strategies for cirrhosis.
