Yu Runzhi, Huang Yiqin, Hu Xiaona, Chen Jie
Department of Gastroenterology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.
Department of General Practice, Huadong Hospital Affiliated to Fudan University, Shanghai, China.
Heliyon. 2024 Jun 29;10(14):e32783. doi: 10.1016/j.heliyon.2024.e32783. eCollection 2024 Jul 30.
The prevalence of nonalcoholic fatty liver disease (NAFLD) is a major form of chronic liver disease. This study aimed to scrutinize the diagnostic biomarkers of NAFLD and their correlation with the immune microenvironment through bioinformatic analysis.
To identify genes associated with nonalcoholic fatty liver disease (NAFLD), we obtained microarray datasets (GSE63067 and GSE89632) from the Gene Expression Omnibus (GEO) database. Machine learning techniques such as Support Vector Machine (SVM), Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) were used to identify key genes. We performed gene ontology analysis to identify the driver pathways of NAFLD. External datasets (merging GSE48452, GSE66676 and GSE135251) were used to validate the identified genes and confirm protein levels by Western blotting. The CIBERSORT algorithm and immune-related techniques, such as ssGSEA, were used to assess the level of infiltration of different immune cell types and their functions. Finally, Spearman's analysis confirmed the relationship between pivotal genes and immune cells.
Hub genes (BBOX1, FOSB, NR4A2, RAB26 and SOCS2) were identified as potential biomarkers. This study demonstrates that these hub genes are significantly dysregulated in NAFLD, suggesting that they may be useful as diagnostic indicators and possible targets for treatment. Also covered are their possible effects on inflammation, immune cell activation, and liver damage in NAFLD. A better understanding of the intricate relationship between metabolic inefficiency, immunological response, and liver pathology in NAFLD may be gained from this work, which can lead to the development of new diagnostic tools and clinical treatments.
The current study identified BBOX1, FOSB, NR4A2, RAB26 and SOCS2 as important diagnostic biomarkers for NAFLD. The study highlights the important function of immune cell infiltration in developing NAFLD. Their findings provide valuable molecular biological insights into the development of NAFLD and may lead to novel therapeutic strategies for treating this disease.
非酒精性脂肪性肝病（NAFLD）的患病率是慢性肝病的主要形式。本研究旨在通过生物信息学分析，仔细研究NAFLD的诊断生物标志物及其与免疫微环境的相关性。
为了鉴定与非酒精性脂肪性肝病（NAFLD）相关的基因，我们从基因表达综合数据库（GEO）中获取了微阵列数据集（GSE63067和GSE89632）。使用支持向量机（SVM）、最小绝对收缩和选择算子（LASSO）以及随机森林（RF）等机器学习技术来识别关键基因。我们进行基因本体分析以识别NAFLD的驱动途径。使用外部数据集（合并GSE48452、GSE66676和GSE135251）来验证所鉴定的基因，并通过蛋白质印迹法确认蛋白质水平。使用CIBERSORT算法和免疫相关技术，如单样本基因集富集分析（ssGSEA），来评估不同免疫细胞类型的浸润水平及其功能。最后，Spearman分析证实了关键基因与免疫细胞之间的关系。
枢纽基因（BBOX1、FOSB、NR4A2、RAB26和SOCS2）被鉴定为潜在的生物标志物。本研究表明，这些枢纽基因在NAFLD中显著失调，这表明它们可能作为诊断指标和潜在的治疗靶点。还探讨了它们对NAFLD中炎症、免疫细胞激活和肝损伤的可能影响。通过这项工作，可能会更好地理解NAFLD中代谢低效、免疫反应和肝脏病理之间的复杂关系，这可能会导致开发新的诊断工具和临床治疗方法。
当前研究确定BBOX1、FOSB、NR4A2、RAB26和SOCS2为NAFLD重要的诊断生物标志物。该研究突出了免疫细胞浸润在NAFLD发生发展中的重要作用。他们的发现为NAFLD的发展提供了有价值的分子生物学见解，并可能导致治疗该疾病的新策略。
