A comprehensive analysis of m6A/m7G/m5C/m1A-related gene expression and immune infiltration in liver ischemia-reperfusion injury by integrating bioinformatics and machine learning algorithms

• Published in: European journal of medical research 2024-06, Vol.29 (1), p.326-19, Article 326
• Main Authors: Meng, Zhanzhi, Li, Xinglong, Lu, Shounan, Hua, Yongliang, Yin, Bing, Qian, Baolin, Li, Zhongyu, Zhou, Yongzhi, Sergeeva, Irina, Fu, Yao, Ma, Yong
• Format: Article
• Language: English
• Subjects: Algorithms; Bioinformatics; Cells; Computational biology; Computational Biology - methods; Data mining; DNA binding proteins; Gene Expression Profiling - methods; Genes; Genetic transcription; Genomes; Genomics; Health aspects; Humans; Immune infiltration; Immune response; Liver; Liver - metabolism; Liver - pathology; Liver diseases; Liver ischemia–reperfusion injury; Machine Learning; Medical research; Medicine, Experimental; Messenger RNA; Methylation; Methylation modification; Methylene blue; Methyltransferases; MicroRNA; Protein Interaction Maps - genetics; Protein-protein interactions; Reperfusion injury; Reperfusion Injury - genetics; Reperfusion Injury - immunology; Transplantation
• ISSN: 2047-783X; 0949-2321; 2047-783X
• DOI: 10.1186/s40001-024-01928-y

Liver ischemia-reperfusion injury (LIRI) is closely associated with immune infiltration, which commonly occurs after liver surgery, especially liver transplantation. Therefore, it is crucial to identify the genes responsible for LIRI and develop effective therapeutic strategies that target immune response. Methylation modifications in mRNA play various crucial roles in different diseases. This study aimed to identify potential methylation-related markers in patients with LIRI and evaluate the corresponding immune infiltration. Two Gene Expression Omnibus datasets containing human liver transplantation data (GSE12720 and GSE151648) were downloaded for integrated analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted to investigate the functional enrichment of differentially expressed genes (DEGs). Differentially expressed methylation-related genes (DEMRGs) were identified by overlapping DEG sets and 65 genes related to N6-methyladenosine (m6A), 7-methylguanine (m7G), 5-methylcytosine (m5C), and N1-methyladenosine (m1A). To evaluate the relationship between DEMRGs, a protein-protein interaction (PPI) network was utilized. The core DEMRGs were screened using three machine learning algorithms: least absolute shrinkage and selection operator, random forest, and support vector machine-recursive feature elimination. After verifying the diagnostic efficacy using the receiver operating characteristic curve, we validated the expression of the core DEMRGs in clinical samples and performed relative cell biology experiments. Additionally, the immune status of LIRI was comprehensively assessed using the single sample gene set enrichment analysis algorithm. The upstream microRNA and transcription factors of the core DEMRGs were also predicted. In total, 2165 upregulated and 3191 downregulated DEGs were identified, mainly enriched in LIRI-related pathways. The intersection of DEGs and methylation-related genes yielded 28 DEMRGs, showing high interaction in the PPI network. Additionally, the core DEMRGs YTHDC1, METTL3, WTAP, and NUDT3 demonstrated satisfactory diagnostic efficacy and significant differential expression and corresponding function based on cell biology experiments. Furthermore, immune infiltration analyses indicated that several immune cells correlated with all core DEMRGs in the LIRI process to varying extents. We identified core DEMRGs (YTHDC1, METTL3, WTAP, and NUDT3) associated with immune infiltrat