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

Meng, Zhanzhi; Li, **nglong; Lu, Shounan; Hua, Yongliang; Yin, Bing; Qian, Baolin; Li, Zhongyu; Zhou, Yongzhi; Sergeeva, Irina; Fu, Yao; Ma, Yong

doi:10.1186/s40001-024-01928-y

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

Research
Open access
Published: 13 June 2024

Volume 29, article number 326, (2024)
Cite this article

Download PDF

You have full access to this open access article

European Journal of Medical Research Aims and scope Submit manuscript

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

Download PDF

Zhanzhi Meng^1,2,
**nglong Li^1,2,
Shounan Lu^1,2,
Yongliang Hua^1,3,
Bing Yin^1,2,
Baolin Qian^1,2,
Zhongyu Li^1,2,
Yongzhi Zhou^1,2,
Irina Sergeeva^1,2,
Yao Fu⁴ &
…
Yong Ma^1,2

405 Accesses
Explore all metrics

Abstract

Background

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.

Methods

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 overlap** 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.

Results

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.

Conclusions

We identified core DEMRGs (YTHDC1, METTL3, WTAP, and NUDT3) associated with immune infiltration in LIRI through bioinformatics and validated them experimentally. This study may provide potential methylation-related gene targets for LIRI immunotherapy.

Key m6A regulators mediated methylation modification pattern and immune infiltration characterization in hepatic ischemia-reperfusion injury

Article Open access 04 December 2023

The m6A/m5C/m1A regulator genes signature reveals the prognosis and is related with immune microenvironment for hepatocellular carcinoma

Article Open access 11 May 2023

Identification of m7G regulator-mediated RNA methylation modification patterns and related immune microenvironment regulation characteristics in heart failure

Article Open access 13 February 2023

Background

Ischemia–reperfusion injury (IRI), a pathophysiological process, involves two stages: reduction or interruption of blood supply and subsequent reperfusion and reoxygenation of the target organs [1]. Several organs, including the liver, are known to be at high risk of develo** IRI [Full size image

Screening of core DEMRGs

Among the 28 DEMRGs, we identified the core DEMRGs using 3 machine learning algorithms. LASSO regression algorithm was applied to identify 13 genes (Fig. 4a), RF was used to select 10 genes in descending order of importance (Fig. 4b), and SVM-RFE was employed to select 4 genes among the original 28 DEMRGs (Fig. 4c). We used a Venn diagram to determine the overlap** region of three groups of genes analyzed using the machine learning algorithms (Fig. 4d). The three algorithms identified YTH N6-methyladenosine RNA binding protein C1 (YTHDC1), METTL3, Wilms tumor 1-associated protein (WTAP), and Nudix hydrolase 3 (NUDT3) as overlap** DEMRGs at the core. Correlation analysis of the four genes is displayed in the chord diagram, which was constructed using the “circlize” package. In this diagram, the red lines represented a positive correlation, and the genes connected by green lines represented a negative correlation. Moreover, the darker the color, the stronger the correlation (Fig. 4e). To assess the predictive ability of LIRI, we constructed the gene prediction model using the “pROC” package (Fig. 4f). A higher AUC value indicates superior diagnostic performance. The ROC curves for YTHDC1, METTL3, WTAP, and NUDT3 demonstrated a reasonably satisfactory prediction accuracy, with AUCs of 0.819, 0.835, 0.933, and 0.862, respectively. As shown in Additional file 6: Fig S3, following LIRI, a notable increase was observed in the expression of YTHDC1 and WTAP, whereas the expression of METTL3 and NUDT3 decreased significantly.

Validation of core DEMRG expression and cytological experiments

We examined the changes in the expression of core DEMRGs in 10 pairs of liver transplantation samples. The results were consistent with those of transcriptome analysis. Compared with liver tissue before transplantation, a significant increase was observed in the mRNA expression of YTHDC1 and WTAP post-transplantation, whereas the mRNA expression of METTL3 and NUDT3 decreased significantly (Fig. 5a). Subsequently, we isolated and cultured primary mouse hepatocytes and established a H/R model, with cells undergoing 6 h of hypoxia followed by 6 h of reoxygenation. After extracting RNA from cells, we compared the expression of the core DEMRGs before and after H/R stimulation, and the results indicated that the changes in the expression of the four DEMRGs were consistent with those observed in the clinical samples (Fig. 5b). Among the four types of methylation modifications identified in our previous analysis, only genes related to m6A or m7G showed significant changes. Therefore, we determined whether there were alterations at the level of m6A and m7G methylation in hepatocytes after LIRI. We performed dot blot assays to detect changes in m6A and m7G methylation in hepatocytes. The results revealed a significant increase in the overall levels of m6A and m7G methylation following H/R stimulation (Fig. 5c). Next, we performed functional validation of each core DEMRG. Three siRNAs were designed for each gene, and RT-qPCR was used to determine the siRNA that inhibited gene expression most effectively (Additional file 7: Fig S4). Next, we evaluated the effect of each core DEMRG on cell viability using the CCK-8 assay. We demonstrated that altering the expression of core DEMRGs within cells under normoxic conditions did not significantly affect cell viability. Subsequently, we subjected the cells to H/R stimulation and then performed CCK-8 assay. The results indicated that after reducing the expression of YTHDC1 or NUDT3, cell viability increased significantly in the siRNA group compared with that in the NC group. Conversely, decreasing the expression of METTL3 or WTAP resulted in significantly lower cell viability in the siRNA group than in the NC group (Fig. 5d).

Assessment of infiltration of immune cells

The ssGSEA method from the “GSVA” R package was used to assess the correlation among immune cells, with a darker red color representing a greater association between any two types of cells (Fig. 6a). We compared immune cell infiltration between pre- and post-transplant samples according to ssGSEA scores. As shown in the box plot, activated CD4 T cells, activated dendritic cells, mast cells, CD65dim natural killer cells, natural killer T cells, regulatory T cells, T helper 1 cells, T helper 2 cells, T helper 17 cells, eosinophils, and neutrophils were more abundant in the post-transplant group than in the pre-transplant group (Fig. 6b). We then investigated the potential association between the expression levels of each of the core DEMRGs and 23 types of immune cells (Fig. 6c). The lollipop diagrams indicated that the expression of WTAP and YTHDC1 was significantly positively correlated with the infiltration levels of activated CD4 T cells, mast cells, eosinophils, and CD65dim natural killer cells. In contrast, the expression of NUDT3 was negatively correlated with the infiltration levels of CD65dim natural killer cells, mast cells, eosinophils, and neutrophils. Additionally, the expression of METTL3 was negatively correlated with the infiltration levels of activated CD4 T cells, eosinophils, T helper 1 cells, mast cells, and activated dendritic cells.

Further research on core DEMRGs

To explore the possible function of core DEMRGs in LIRI, including the interrelated pathways, we performed a correlation analysis between each of the core DEMRGs and the rest of the genes. Then, we revealed the top 50 genes most positively associated with the core DEMRGs by constructing heatmaps (Fig. 7). GSEA based on REACTOME was performed to determine the significant pathways mainly associated with the mechanisms of LIRI. Ridgeline plots of the top 20 pathways obtained using the “clusterProfiler” R package are displayed in Fig. 8. With an adjusted P-value of 0.01823362, the enrichment of Interleukin-10 signaling, Interleukin-4, and Interleukin-13 signaling, Signaling by interleukins, and Cytokine signaling in the immune system was negatively associated with the expression of METTL3. Similarly, the enrichment of Toll-like receptor 2 cascade, Toll-like receptor 4 cascade, interleukin-10 signaling, interleukin-4, and interleukin-13 signaling was significantly negatively associated with the expression of NUDT3. In contrast, the enrichment of interleukin-10 signaling, interleukin-4, interleukin-13 signaling, cytokine signaling in the immune system, Toll-like receptor 4 cascade, and Toll-like receptor 3 cascade was significantly associated with the expression of WTAP. Furthermore, the enrichment of mitochondrial translation initiation, mitochondrial translation, mitochondrial translation termination, and respiratory electron elongation was significantly negatively associated with the expression of YTHDC1. These results suggest that the core DEMRGs are associated with many pathways in the progression of LIRI, especially immune-related and oxidative metabolism pathways. We then predicted the upstream miRNAs and transcription factors of the four core DEMRGs using the RegNetwork data repository and constructed the network using Cytoscape (Additional file 8: Fig S5). As shown in the network, activating the transcription factor family might be the crucial upstream transcription factor of YTHDC1 and NUDT3. Additionally, the E2F family showed potential as the upstream transcription factors of YTHDC1 and METTL3. Seven miRNAs, including hsa-miR-181a, hsa-miR-181b, hsa-miR-181c, hsa-miR-543, hsa-miR-141, hsa-miR-200a, and hsa-miR-451, showed the possibility of being present upstream of WTAP and YTHDC1. Among all miRNAs and transcription factors, MYC showed the best match as the predictive factor, which could be an upstream transcription factor of YTHDC1, NUDT3, and METTL3.

Discussion

In general, LIRI is caused by multiple liver surgeries or various forms of trauma. It inflicts liver function through intricate biological processes, including but not limited to oxidative stress, calcium overload, apoptosis, pyroptosis, and ferroptosis. However, despite complicated and specific mechanisms, LIRI always starts with activated Kupffer cells, followed by the process of immune infiltration [3, 11]. Studies have reported that RNA modification, particularly represented by m6A, m5C, m1A, and m7G, plays a significant role in various biological progresses [40]. Immune responses, including innate and adaptive immune responses, are regulated via RNA modification by altering the modified bases [41, 42]. Although the partial correlation between RNA modification and LIRI has been shown, it remains unclear whether it is affected by immune processes and specific targets [43]. To further illustrate how RNA methylation modifications participate in the immune modulatory mechanisms of LIRI, we performed a series of analyses systematically.

In this study, according to the etiology of liver ischemia and reperfusion, we found two applicable sets of liver transplantation samples from the GEO database and then merged them. We removed the batch effect for the following analyses. We identified DEGs with an adjusted P-value of < 0.05 and subsequently performed GO and KEGG enrichment analyses. The BP category in GO analysis showed that all DEGs were mainly associated with multiple immune cell differentiation, regulation of cytokine production, regulation of T cell activation, and regulation of autophagy, indicating immunological processes. In the CC category, most terms were related to the nuclear envelope, ribonucleoprotein granule, and organelle outer membrane, including the mitochondrial outer membrane, suggesting that DEGs participated in the transcription and translation processes within cells. In addition, the MF category demonstrated that the DEG functions were largely concentrated in DNA-binding transcription activator activity, ubiquitin-like protein transferase activity, and ubiquitin-like protein ligase activity, suggesting that the cell death forms derived from ubiquitination were mainly involved in LIRI. KEGG enrichment analysis revealed that DEGs possessed some correlation with Herpes simplex virus 1 infection, autophagy, apoptosis, and several canonical signaling pathways, including the Wnt signaling pathway, FoxO signaling pathway, IL-17 signaling pathway, and NF-kappa B signaling pathway. We obtained 28 DEMRGs after overlap** the DEG sets and 65 methylation-related genes and then constructed a PPI network of the core genes using the STRING database. Three types of machine learning techniques were applied to identify the core DEMRGs, each of which had a unique method for screening potential makers. Notably, YTHDC1, METTL3, WTAP, and NUDT3 were selected, and as all four AUC values were > 0.8, the predictive model showed satisfactory predictive performance. The ssGSEA method was used to analyze the immune infiltration process that occurs during LIRI.

As the pivotal core of the inflammatory cytotoxic cycle in LIRI, the immune infiltration process is undoubtedly characterized by sensitive and explorable changes. Neutrophils, being the primary line of defense against invading pathogens, are mobilized to the site of ischemia/reperfusion and considered a biomarker for LIRI [44]. CD4 T cells are considered essential inflammatory mediators in the LIRI process [45]. Activated CD4 T cells were shown to activate innate immunity and amplify the subsequent cascade reaction [46]. Furthermore, original CD4 T cells undergo differentiation into diverse T cell subtypes, such as regulatory T, T helper 1, T helper 2, and T helper 17 cells. These subtypes play different roles in modifying inflammatory responses [47]. Another type of CD4 T cells—natural killer T cells—recruit and activate natural killer cells directly and play a role in the response of pro- or anti-inflammation in LIRI according to different subsets [48, 64], pre-mRNA splicing [65], and translation [66]. m7G sites were also detected in transfer RNA [67] and ribosomal RNA [68] and were discovered internally within mRNA in 2019 [69]. Unlike the well-studied pattern of the modification, i.e., regulating the level of methylation with “writers” and “erasers” and executing with “readers,” methyltransferase-like 1 (METTL1) and WD repeat domain 4 (WDR4), together forming a functional methyltransferase complex, have been perceived to be the only two clear factors serving as the “writers” of m7G modification. However, in July 2023, Quaking proteins have been reported as the first “reader” of m7G modification [70, 71]. No prior studies have examined the potential relationship between m7G modification and LIRI. The current study revealed a significant elevation in total m7G levels in hepatocytes following H/R stimulation. This suggests dynamic alterations in m7G methylation and demethylation during LIRI, which warrants further investigation. Similarly, postischemic angiogenesis could be enhanced via METTL1 by promoting the translation of vascular endothelial growth factor A mRNA [24]. Wang et al. showed that METTL1 ablation in fibroblasts decreases the expression of m7G methylated fibrotic genes, thereby alleviating myocardial infarction-induced cardiac fibrosis [25]. The Nudix superfamily, a series of hydrolases that possess a conserved nucleoside diphosphate linked to another moiety X (Nudix), serving as divalent cation-regulated enzymes that hydrolyze various dinucleotides and inositol pyrophosphates (PP-InsPs), contains 8 of 22 Nudix proteins in the human genome, namely, Nudt2, Nudt3, Nudt12, Nudt15, Nudt16, Nudt17, Nudt19, and Dcp2 (Nudt20), and possesses the ability to decap RNA [72, 73]. Notably, Nudt3 was shown to play a crucial role in maintaining cell viability during oxidative stress, acting as a Zn²⁺-dependent polyphosphate hydrolase both in vitro and in vivo [74]. Furthermore, the study revealed the potential protective effect of downregulating NUDT3 in LIRI and its robust association with natural killer cells, neutrophils, and T helper 17 cells. m7G modification associated with NUDT3 may serve as a promising and innovative therapeutic target after additional validation.

This study has some limitations, despite the comprehensive analysis of methylation modification in LIRI and the relevant immune infiltration. First, it was a retrospective study based on public databases. A validation study with a larger clinical sample size is warranted to further demonstrate the potential of core DEMRGs in predicting LIRI progression. Second, it is necessary to further investigate whether the crucial genes related to m1A or m5C play roles in LIRI, as the current study only identified the central genes related to m6A and m7G.

Conclusions

This study revealed that the methylation-related genes YTHDC1, METTL3, WTAP, and NUDT3 emerge as core DEMRGs, indicating their distinct biological pivotal roles as diagnostic markers for LIRI. Furthermore, the analyses underscored the potential involvement of immune cells in the progression of LIRI, with YTHDC1, METTL3, WTAP, and NUDT3 showing numerous associations across a diverse array of immune cell types. These findings highlighted that immune cells tend to exert a significant influence on the advancement of LIRI. Thus, a comprehensive exploration of these immune cells could offer valuable insights into determining targets for immunotherapy and optimizing immunomodulatory strategies to improve post-liver transplantation recovery.

Availability of data and materials

The original contributions presented in the study are included in the article/Supplementary Information, further inquiries can be directed to the corresponding authors.

Abbreviations

ATF4:: Activating transcription factor 4
AUC:: Area under the ROC curve
BP:: Biological process
cDNA:: Complementary DNA
CC:: Cellular component
DEMRGs:: Differentially expressed methylation-related genes
DEGs:: Differentially expressed genes
FTO:: Fat mass and obesity-associated protein
FOXO1:: Forkhead box O1
GEO:: Gene Expression Omnibus
GO:: Gene ontology
GSEA:: Gene set enrichment analysis
HBSS:: Hanks Balanced Salt Solutions
H/R:: Hypoxia/reoxygenation
IRI:: Ischemia–reperfusion injury
KEGG:: Kyoto Encyclopedia of Genes and Genomes
LASSO:: Least absolute shrinkage and selection operator
LIRI:: Liver IRI
m1A:: N1-Methyladenosine
m5C:: 5-Methylcytosine
m6A:: N6-Methyladenosine
m7G:: 7-Methylguanine
METTL1:: Methyltransferase-like 1
METTL3:: Methyltransferase-like 3
MF:: Molecular function
MTC:: Methyltransferase complex
NC:: Negative control
NUDT3:: Nudix hydrolase 3
Nudix:: Nucleoside diphosphate linked to another moiety X
PPI:: Protein–protein interaction
PTEN:: Phosphatase and tensin homolog
QKIs:: Quaking proteins
RegNetwork:: Regulatory Network Repository of Transcription Factor and microRNA-Mediated Gene Regulations
RF:: Random forests
ROC:: Receiver operating characteristic
RT-qPCR:: Real-Time Quantitative Polymerase Chain Reaction
siRNA:: Small interfering RNA
ssGSEA:: Single-sample gene set enrichment analysis
SVM-RFE:: Support vector machine-recursive feature elimination
STRING:: Search Tool for the Retrieval of Interacting Genes
VEGFA:: Vascular endothelial growth factor A
WDR4:: WD repeat domain 4
WTAP:: Wilms tumor 1-associated protein
YTHDC1:: YTH N6-methyladenosine RNA-binding protein C1
YTHDF1:: YTH domain-containing family protein 1

References

Zhang M, Liu Q, Meng H, Duan H, Liu X, Wu J, et al. Ischemia–reperfusion injury: molecular mechanisms and therapeutic targets. Signal Transduct Target Ther. 2024;9(1):12.
Article CAS PubMed PubMed Central Google Scholar
**ang Q, Yi X, Zhu X-H, Wei X, Jiang D-S. Regulated cell death in myocardial ischemia–reperfusion injury. Trends Endocrinol Metab. 2023;35(3):219–34.
Article PubMed Google Scholar
Hirao H, Nakamura K, Kupiec-Weglinski JW. Liver ischaemia–reperfusion injury: a new understanding of the role of innate immunity. Nat Rev Gastroenterol Hepatol. 2022;19(4):239–56.
Article CAS PubMed Google Scholar
Schaller B, Graf R. Cerebral ischemia and reperfusion: the pathophysiologic concept as a basis for clinical therapy. J Cereb Blood Flow Metab. 2004;24(4):351–71.
Article PubMed Google Scholar
Malek M, Nematbakhsh M. Renal ischemia/reperfusion injury; from pathophysiology to treatment. J Renal Inj Prev. 2015;4(2):20–7.
CAS PubMed PubMed Central Google Scholar
Liu J, Man K. Mechanistic Insight and clinical implications of ischemia/reperfusion injury post liver transplantation. Cell Mol Gastroenterol Hepatol. 2023;15(6):1463–74.
Article CAS PubMed PubMed Central Google Scholar
Kim Y-I. Ischemia–reperfusion injury of the human liver during hepatic resection. J Hepatobiliary Pancreat Surg. 2003;10(3):195–9.
Article CAS PubMed Google Scholar
Wang C, Li Z, Zhao B, Wu Y, Fu Y, Kang K, et al. PGC-1α protects against hepatic ischemia reperfusion injury by activating PPARα and PPARγ and regulating ROS production. Oxid Med Cell Longev. 2021;2021:6677955.
PubMed PubMed Central Google Scholar
Abu-Amara M, Yang SY, Tapuria N, Fuller B, Davidson B, Seifalian A. Liver ischemia/reperfusion injury: processes in inflammatory networks—a review. Liver Transpl. 2010;16(9):1016–32.
Article PubMed Google Scholar
Land WG. The role of postischemic reperfusion injury and other nonantigen-dependent inflammatory pathways in transplantation. Transplantation. 2005;79(5):505–14.
Article PubMed Google Scholar
Zhai Y, Busuttil RW, Kupiec-Weglinski JW. Liver ischemia and reperfusion injury: new insights into mechanisms of innate-adaptive immune-mediated tissue inflammation. Am J Transplant. 2011;11(8):1563–9.
Article CAS PubMed PubMed Central Google Scholar
Waddington CH. The epigenotype. Int J Epidemiol. 2012;41(1):10–3.
Article CAS PubMed Google Scholar
Cohn WE. Pseudouridine, a carbon-carbon linked ribonucleoside in ribonucleic acids: isolation, structure, and chemical characteristics. J Biol Chem. 1960;235:1488–98.
Article CAS PubMed Google Scholar
He C. Grand challenge commentary: RNA epigenetics? Nat Chem Biol. 2010;6(12):863–5.
Article CAS PubMed Google Scholar
Saletore Y, Meyer K, Korlach J, Vilfan ID, Jaffrey S, Mason CE. The birth of the epitranscriptome: deciphering the function of RNA modifications. Genome Biol. 2012;13(10):175.
Article CAS PubMed PubMed Central Google Scholar
Tang J, Zhuang S. Histone acetylation and DNA methylation in ischemia/reperfusion injury. Clin Sci. 2019;133(4):597–609.
Article CAS Google Scholar
Yao W, Han X, Ge M, Chen C, **ao X, Li H, et al. N6-Methyladenosine (m6A) methylation in ischemia–reperfusion injury. Cell Death Dis. 2020;11(6):478.
Article CAS PubMed PubMed Central Google Scholar
Boccaletto P, Machnicka MA, Purta E, Piatkowski P, Baginski B, Wirecki TK, et al. MODOMICS: a database of RNA modification pathways. 2017 update. Nucleic Acids Res. 2018;46(D1):D303–7.
Article CAS PubMed Google Scholar
Li D, Li K, Zhang W, Yang K-W, Mu D-A, Jiang G-J, et al. The m6A/m5C/m1A regulated gene signature predicts the prognosis and correlates with the immune status of hepatocellular carcinoma. Front Immunol. 2022;13: 918140.
Article CAS PubMed PubMed Central Google Scholar
Zha L-F, Wang J-L, Cheng X. The effects of RNA methylation on immune cells development and function. FASEB J. 2022;36(10): e22552.
Article CAS PubMed Google Scholar
Wang J-N, Wang F, Ke J, Li Z, Xu C-H, Yang Q, et al. Inhibition of METTL3 attenuates renal injury and inflammation by alleviating TAB3 m6A modifications via IGF2BP2-dependent mechanisms. Sci Transl Med. 2022;14(640): eabk2709.
Article CAS PubMed Google Scholar
Yu Z, Zheng L, Geng Y, Zhang Y, Wang Y, You G, et al. FTO alleviates cerebral ischemia/reperfusion-induced neuroinflammation by decreasing cGAS mRNA stability in an m6A-dependent manner. Cell Signal. 2023;109: 110751.
Article CAS PubMed Google Scholar
Chen Z, Zhu W, Zhu S, Sun K, Liao J, Liu H, et al. METTL1 promotes hepatocarcinogenesis via m7G tRNA modification-dependent translation control. Clin Transl Med. 2021;11(12): e661.
Article CAS PubMed PubMed Central Google Scholar
Zhao Y, Kong L, Pei Z, Li F, Li C, Sun X, et al. m7G methyltransferase METTL1 promotes post-ischemic angiogenesis via promoting VEGFA mRNA translation. Front Cell Dev Biol. 2021;9: 642080.
Article PubMed PubMed Central Google Scholar
Wang L, Zhou J, Kong L, Ying G, Sha J, Yi D, et al. Fibroblast-specific knockout of METTL1 attenuates myocardial infarction-induced cardiac fibrosis. Life Sci. 2023;329: 121926.
Article CAS PubMed Google Scholar
Wang Y-Y, Tian Y, Li Y-Z, Liu Y-F, Zhao Y-Y, Chen L-H, et al. The role of m5C methyltransferases in cardiovascular diseases. Front Cardiovasc Med. 2023;10:1225014.
Article CAS PubMed PubMed Central Google Scholar
Chokkalla AK, Pajdzik K, Dou X, Dai Q, Mehta SL, Arruri V, et al. Dysregulation of the epitranscriptomic mark m1A in ischemic stroke. Transl Stroke Res. 2023;14(6):806–10.
Article CAS PubMed Google Scholar
Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res. 2013;41(Database issue):D991–5.
CAS PubMed Google Scholar
Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882–3.
Article CAS PubMed PubMed Central Google Scholar
Chen Z, Zhang Z, Ding W, Zhang J-H, Tan Z-L, Mei Y-R, et al. Expression and potential biomarkers of regulators for M7G RNA modification in gliomas. Front Neurol. 2022;13: 886246.
Article PubMed PubMed Central Google Scholar
Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7.
Article CAS PubMed PubMed Central Google Scholar
Fernández-Delgado M, Sirsat MS, Cernadas E, Alawadi S, Barro S, Febrero-Bande M. An extensive experimental survey of regression methods. Neural Netw. 2019;111:11–34.
Article PubMed Google Scholar
Ishwaran H, Kogalur UB. Consistency of random survival forests. Stat Probab Lett. 2010;80(13–14):1056–64.
Article PubMed PubMed Central Google Scholar
Huang S, Cai N, Pacheco PP, Narrandes S, Wang Y, Xu W. Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genom Proteom. 2018;15(1):41–51.
CAS Google Scholar
Engebretsen S, Bohlin J. Statistical predictions with glmnet. Clin Epigenet. 2019;11(1):123.
Article Google Scholar
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102(43):15545–50.
Article CAS PubMed PubMed Central Google Scholar
Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43(Database issue):D447–52.
Article CAS PubMed Google Scholar
Liu Z-P, Wu C, Miao H, Wu H. RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse. Database. 2015;2015: bav095.
Article PubMed PubMed Central Google Scholar
Liu J, Huang X, Werner M, Broering R, Yang D, Lu M. Advanced method for isolation of mouse hepatocytes, liver sinusoidal endothelial cells, and Kupffer cells. Methods Mol Biol. 2017;1540:249–58.
Article CAS PubMed Google Scholar
Wilkinson E, Cui Y-H, He Y-Y. Roles of RNA modifications in diverse cellular functions. Front Cell Dev Biol. 2022;10: 828683.
Article PubMed PubMed Central Google Scholar
Han D, Xu MM. RNA modification in the immune system. Annu Rev Immunol. 2023;41:73–98.
Article CAS PubMed Google Scholar
Shulman Z, Stern-Ginossar N. The RNA modification N6-methyladenosine as a novel regulator of the immune system. Nat Immunol. 2020;21(5):501–12.
Article CAS PubMed Google Scholar
Du YD, Guo WY, Han CH, Wang Y, Chen XS, Li DW, et al. N6-Methyladenosine demethylase FTO impairs hepatic ischemia–reperfusion injury via inhibiting Drp1-mediated mitochondrial fragmentation. Cell Death Dis. 2021;12(5):442.
Article CAS PubMed PubMed Central Google Scholar
Schofield ZV, Woodruff TM, Halai R, Wu MC-L, Cooper MA. Neutrophils—a key component of ischemia–reperfusion injury. Shock. 2013;40(6):463–70.
Article CAS PubMed Google Scholar
Rao J, Cheng F, Yang S, Zhai Y, Lu L. Ag-specific CD4 T cells promote innate immune responses in liver ischemia reperfusion injury. Cell Mol Immunol. 2019;16(1):98–100.
Article CAS PubMed Google Scholar
Kageyama S, Kadono K, Hirao H, Nakamura K, Ito T, Gjertson DW, et al. Ischemia–reperfusion injury in allogeneic liver transplantation: a role of CD4 T cells in early allograft injury. Transplantation. 2021;105(9):1989–97.
Article CAS PubMed PubMed Central Google Scholar
Ruterbusch M, Pruner KB, Shehata L, Pepper M. In vivo CD4+ T cell differentiation and function: revisiting the Th1/Th2 paradigm. Annu Rev Immunol. 2020;38:705–25.
Article CAS PubMed Google Scholar
Zimmerman MA, Martin A, Yee J, Schiller J, Hong JC. Natural killer T cells in liver ischemia–reperfusion injury. J Clin Med. 2017;6(4):41.
Article PubMed PubMed Central Google Scholar
Huang M, Cai H, Han B, **a Y, Kong X, Gu J. Natural killer cells in hepatic ischemia–reperfusion injury. Front Immunol. 2022;13: 870038.
Article CAS PubMed PubMed Central Google Scholar
Wang Y, Yang Y, Wang M, Wang S, Jeong J-M, Xu L, et al. Eosinophils attenuate hepatic ischemia–reperfusion injury in mice through ST2-dependent IL-13 production. Sci Transl Med. 2021;13(579): eabb6576.
Article CAS PubMed PubMed Central Google Scholar
Jiang X, Liu B, Nie Z, Duan L, **ong Q, ** Z, et al. The role of m6A modification in the biological functions and diseases. Signal Transduct Target Ther. 2021;6(1):74.
Article CAS PubMed PubMed Central Google Scholar
Li H-B, Tong J, Zhu S, Batista PJ, Duffy EE, Zhao J, et al. m6A mRNA methylation controls T cell homeostasis by targeting the IL-7/STAT5/SOCS pathways. Nature. 2017;548(7667):338–42.
Article CAS PubMed PubMed Central Google Scholar
Wang H, Hu X, Huang M, Liu J, Gu Y, Ma L, et al. Mettl3-mediated mRNA m6A methylation promotes dendritic cell activation. Nat Commun. 2019;10(1):1898.
Article PubMed PubMed Central Google Scholar
Zhang M, Ueki S, Kimura S, Yoshida O, Castellaneta A, Ozaki KS, et al. Roles of dendritic cells in murine hepatic warm and liver transplantation-induced cold ischemia/reperfusion injury. Hepatology. 2013;57(4):1585–96.
Article CAS PubMed Google Scholar
Leoni C, Bataclan M, Ito-Kureha T, Heissmeyer V, Monticelli S. The mRNA methyltransferase Mettl3 modulates cytokine mRNA stability and limits functional responses in mast cells. Nat Commun. 2023;14(1):3862.
Article CAS PubMed PubMed Central Google Scholar
He Z, Li Y, Ma S, Yang M, Ma Y, Ma C, et al. Degranulation of gastrointestinal mast cells contributes to hepatic ischemia–reperfusion injury in mice. Clin Sci. 2018;132(20):2241–59.
Article CAS Google Scholar
Wang J, Zhang J, Ma Y, Zeng Y, Lu C, Yang F, et al. WTAP promotes myocardial ischemia/reperfusion injury by increasing endoplasmic reticulum stress via regulating m6A modification of ATF4 mRNA. Aging. 2021;13(8):11135–49.
Article CAS PubMed PubMed Central Google Scholar
Wang Z, Qi Y, Feng Y, Xu H, Wang J, Zhang L, et al. The N6-methyladenosine writer WTAP contributes to the induction of immune tolerance post kidney transplantation by targeting regulatory T cells. Lab Invest. 2022;102(11):1268–79.
Article CAS PubMed Google Scholar
Ito-Kureha T, Leoni C, Borland K, Cantini G, Bataclan M, Metzger RN, et al. The function of Wtap in N6-adenosine methylation of mRNAs controls T cell receptor signaling and survival of T cells. Nat Immunol. 2022;23(8):1208–21.
Article CAS PubMed Google Scholar
Zheng P-F, Hong X-Q, Liu Z-Y, Zheng Z-F, Liu P, Chen L-Z. m6A regulator-mediated RNA methylation modification patterns are involved in the regulation of the immune microenvironment in ischaemic cardiomyopathy. Sci Rep. 2023;13(1):5904.
Article CAS PubMed PubMed Central Google Scholar
Zhang Z, Wang Q, Zhao X, Shao L, Liu G, Zheng X, et al. YTHDC1 mitigates ischemic stroke by promoting Akt phosphorylation through destabilizing PTEN mRNA. Cell Death Dis. 2020;11(11):977.
Article CAS PubMed PubMed Central Google Scholar
Liu Z, Wang T, She Y, Wu K, Gu S, Li L, et al. N6-Methyladenosine-modified circIGF2BP3 inhibits CD8+ T-cell responses to facilitate tumor immune evasion by promoting the deubiquitination of PD-L1 in non-small cell lung cancer. Mol Cancer. 2021;20(1):105.
Article CAS PubMed PubMed Central Google Scholar
Shimotohno K, Kodama Y, Hashimoto J, Miura KI. Importance of 5′-terminal blocking structure to stabilize mRNA in eukaryotic protein synthesis. Proc Natl Acad Sci USA. 1977;74(7):2734–8.
Article CAS PubMed PubMed Central Google Scholar
Pei Y, Shuman S. Interactions between fission yeast mRNA cap** enzymes and elongation factor Spt5. J Biol Chem. 2002;277(22):19639–48.
Article CAS PubMed Google Scholar
Lindstrom DL, Squazzo SL, Muster N, Burckin TA, Wachter KC, Emigh CA, et al. Dual roles for Spt5 in pre-mRNA processing and transcription elongation revealed by identification of Spt5-associated proteins. Mol Cell Biol. 2003;23(4):1368–78.
Article CAS PubMed PubMed Central Google Scholar
Muthukrishnan S, Both GW, Furuichi Y, Shatkin AJ. 5′-Terminal 7-methylguanosine in eukaryotic mRNA is required for translation. Nature. 1975;255(5503):33–7.
Article CAS PubMed Google Scholar
Guy MP, Phizicky EM. Two-subunit enzymes involved in eukaryotic post-transcriptional tRNA modification. RNA Biol. 2014;11(12):1608–18.
Article PubMed Google Scholar
Sloan KE, Warda AS, Sharma S, Entian K-D, Lafontaine DLJ, Bohnsack MT. Tuning the ribosome: the influence of rRNA modification on eukaryotic ribosome biogenesis and function. RNA Biol. 2017;14(9):1138–52.
Article PubMed Google Scholar
Zhang L-S, Liu C, Ma H, Dai Q, Sun H-L, Luo G, et al. Transcriptome-wide map** of internal N7-methylguanosine methylome in mammalian mRNA. Mol Cell. 2019;74(6):1304–16.
Article CAS PubMed PubMed Central Google Scholar
Alexandrov A, Martzen MR, Phizicky EM. Two proteins that form a complex are required for 7-methylguanosine modification of yeast tRNA. RNA. 2002;8(10):1253–66.
Article CAS PubMed PubMed Central Google Scholar
Zhao Z, Qing Y, Dong L, Han L, Wu D, Li Y, et al. QKI shuttles internal m7G-modified transcripts into stress granules and modulates mRNA metabolism. Cell. 2023;186(15):3208–26.
Article CAS PubMed Google Scholar
Bessman MJ, Frick DN, O’Handley SF. The MutT proteins or “Nudix” hydrolases, a family of versatile, widely distributed, “housecleaning” enzymes. J Biol Chem. 1996;271(41):25059–62.
Article CAS PubMed Google Scholar
Song M-G, Bail S, Kiledjian M. Multiple Nudix family proteins possess mRNA decap** activity. RNA. 2013;19(3):390–9.
Article CAS PubMed PubMed Central Google Scholar
Samper-Martín B, Sarrias A, Lázaro B, Pérez-Montero M, Rodríguez-Rodríguez R, Ribeiro MPC, et al. Polyphosphate degradation by Nudt3-Zn2+ mediates oxidative stress response. Cell Rep. 2021;37(7): 110004.
Article PubMed Google Scholar

Download references

Funding

This work was supported by the Research Fund of the National Natural Scientific Foundation of China (81100305, 81470876, 81270527, and 82370643), Natural Science Foundation of Heilongjiang Province of China (QC2013C094, LC2018037), Chen ** Foundation for the Development of Science and Technology of Hubei Province (CXPJJH11900001-2019349), and the Scientific Foundation of the First Affiliated Hospital of Harbin Medical University (2019L01, HYD2020JQ0007 and HYD2020JQ0012).

Author information

Authors and Affiliations

Department of Minimally Invasive Hepatic Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
Zhanzhi Meng, **nglong Li, Shounan Lu, Yongliang Hua, Bing Yin, Baolin Qian, Zhongyu Li, Yongzhi Zhou, Irina Sergeeva & Yong Ma
Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China
Zhanzhi Meng, **nglong Li, Shounan Lu, Bing Yin, Baolin Qian, Zhongyu Li, Yongzhi Zhou, Irina Sergeeva & Yong Ma
Department of Pediatric Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
Yongliang Hua
Department of Ultrasound, The First Affiliated Hospital of Harbin Medical University, Harbin, China
Yao Fu

Authors

Zhanzhi Meng
View author publications
You can also search for this author in PubMed Google Scholar
**nglong Li
View author publications
You can also search for this author in PubMed Google Scholar
Shounan Lu
View author publications
You can also search for this author in PubMed Google Scholar
Yongliang Hua
View author publications
You can also search for this author in PubMed Google Scholar
Bing Yin
View author publications
You can also search for this author in PubMed Google Scholar
Baolin Qian
View author publications
You can also search for this author in PubMed Google Scholar
Zhongyu Li
View author publications
You can also search for this author in PubMed Google Scholar
Yongzhi Zhou
View author publications
You can also search for this author in PubMed Google Scholar
Irina Sergeeva
View author publications
You can also search for this author in PubMed Google Scholar
Yao Fu
View author publications
You can also search for this author in PubMed Google Scholar
Yong Ma
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

ZM contributed to the conceptualization, visualization and writing—original draft. XL and YH contributed to methodology and formal analysis. SL contributed to the methodology and the validation. ZL contributed to the writing—review and editing. ZM, BY and BQ contributed to the writing—original draft. YZ and IS contributed to the validation. YF and YM contributed to the supervision. YM contributed to the conceptualization, project administration and funding acquisition. All authors contributed to the manuscript revision and read and approved the submitted version.

Corresponding author

Correspondence to Yong Ma.

Ethics declarations

Ethics approval and consent to participate

All procedures involving human samples were approved by the Ethics Committee of the First Affiliated Hospital of Harbin Medical University and patient informed consent was obtained (IRB-AF/SC-04/02.0). All the animal experiments were performed in accordance with the standard protocols of the Committee on the Use of Live Animals in Teaching and Research of Harbin Medical University, and approved by the Ethics Committee of the First Affiliated Hospital of Harbin Medical University (IACUC number: 2021087).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no known competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1. The details of the GEO datasets used to analysis.

Additional file 2: Table S2. siRNA sequences.

Additional file 3: Table S3. The primer sequences for RT-qPCR.

40001_2024_1928_MOESM4_ESM.tif

Additional file 4: Fig. S1. DEMRGs between the pre-transplant and post-transplant groups. a, b DEMRGs were shown in the volcano plot (a) and Heatmap (b). c Differential expression of DEMRGs in GEO gene sets. DEMRGs: differentially expressed methylation-related genes. The P-values were shown as: *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

40001_2024_1928_MOESM5_ESM.tif

Additional file 5: Fig. S2. The protein–protein interaction network of DEMRGs. DEMRGs: differentially expressed methylation-related genes.

40001_2024_1928_MOESM6_ESM.tif

Additional file 6: Fig. S3. The expression level of core DEMRGs in datasets. The P-values were shown as: ****P < 0.0001.

40001_2024_1928_MOESM7_ESM.tif

Additional file 7: Fig. S4. Efficiency verification of RT-qPCR for siRNAs targeting core DEMRGs. The P-values were shown as: *P < 0.05, **P < 0.01, ****P < 0.0001.

40001_2024_1928_MOESM8_ESM.tif

Additional file 8: Fig. S5. Construction of a network consisting of the potential upstream miRNAs and transcription factors of core DEMRGs.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article

Meng, Z., Li, X., Lu, S. et al. 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. Eur J Med Res 29, 326 (2024). https://doi.org/10.1186/s40001-024-01928-y

Download citation

Received: 28 October 2023
Accepted: 06 June 2024
Published: 13 June 2024
DOI: https://doi.org/10.1186/s40001-024-01928-y

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

Abstract

Background

Methods

Results

Conclusions

Similar content being viewed by others

Background

Screening of core DEMRGs

Validation of core DEMRG expression and cytological experiments

Assessment of infiltration of immune cells

Further research on core DEMRGs

Discussion

Conclusions

Availability of data and materials

Abbreviations

References

Funding

Author information

Authors and Affiliations

Contributions

Corresponding author

Ethics declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Additional information

Publisher's Note

Supplementary Information

Rights and permissions

About this article

Cite this article

Share this article

Keywords

Search

Navigation