Screening of key genes for m6A modification differences in childhood sepsis

Screening of key genes for m6A modification differences in childhood sepsis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Screening of key genes for m6A modification differences in childhood sepsis quxiang Hong, WenTao Wu, XiaoMin Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4182389/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Sepsis in children is a syndrome associated with organ dysfunction caused by immune dysregulation of inflammatory responses in children. According to the latest data, nearly50 million people have been diagnosed with sepsisand nearly10 million have died. M6A methylation has been reported to be associated with sepsis-associated inflammatory response [ 2 ] ,however, the molecular biological mechanism underlying the diagnosis and treatment of m6A related genes in children remains unclear. It provides a new way for clinical incidence prediction and molecular biology diagnosis, and further guides clinical treatment.The GEO database chip dataset GSE66099 was downloaded and annotated by platform files. The m6A related genes were extracted. The data were standardized by R language limma package.181 children with septic shock,18 children with sepsis were selected as sepsis group,47 normal childrenand30 children with common SIRS were selected as control group. The difference of m6A gene expression between control group and sepsis group was analyzed by correlation test. The importance score of m6A-related genes in sepsis was obtained by cross-validation error of random forest tree method, disease-related characteristic genes were screened, the influence of core difference genes on sepsis incidence was analyzed, and nomogram was drawn to predict patient incidence. The number of disease characteristic genes was determined by LASSO model, ROC curve was drawn, and related genes were selected for further analysis. Cluster analysis was performed on sepsis patients according to the expression of biomarkers, and difference and correlation analysis were performed on immune infiltration. Among the first 13 differentially expressed genes, DIGFBP1 and IGFBP2 were up-regulated in sepsis patients, while METTL3, MITTL14, MERTTL16, RBM15, RBM15B, CBLL1, YTHF2, HNRNPC, LRPPRC, ELAVL1 and FTO were down-regulated in sepsis patients. In addition, ROC curve analysis showed that HNRNPC, LRPPRC, FTO andELAVL1 were characteristic genes of the disease. We also identified two m6A genotypes and two differential genotypes. Based on differential gene expression, nine m6A gene expressions were statistically different in a 2-typing pattern, with differences associated with immune infiltration. m6A methylation modification may play a potentially important role in the diagnosis,immune infiltration and treatment of sepsis in children. HNRNPC may be one of the potential molecular markers for predicting sepsis in children. Typing based on m6A gene expression has potential implications for the treatment of sepsis in children. Biological sciences/Genetics/Gene regulation Biological sciences/Biochemistry/Dna sepsis m6A methylation modification differential gene bioinformatics. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Sepsis is a systemic inflammatory response syndrome caused by the invasion of bacteriaand pathogenic microorganisms into the bodyIn addition to systemic inflammatory response syndrome and primary infection, severe patients often have organ hypoperfusion. Mortality rates among children remain high throughout the year, with one study predicting about 1.2 million cases of sepsis among children worldwideand about 3 million cases [ 3 ] among newborns. Blood culture as the gold standard for sepsis diagnosis,detection time is long,specimen requirements are high. In recent years, N6-methyladenosine methylation has become a hot research topic, which is the most common internal modification of mRNA. Other modifications include N1-adenosine methylation (m1A) and cytosine hydroxylation (m5C). It plays an important role in many fundamental biological processes [ 4 ] , such as lipid transport [ 5 ] , autoimmune diseases, and tumor [ 6 ] mRNA regulation [ 7 ] .m6A-related genes are associated with morbidity, for example, METTL3 knockdown inhibits proliferation, invasion, migration and anoikis resistance of GC cells [ 8 ] ; m6A regulatory factor dysregulation mediates immune cell infiltration in TME [ 9 ] . Based on GEO database platform, this study explores the differential genes of m6A-related genes and discusses the expression content of m6A differential genes in immune cells for further exploring sepsis immune infiltration related monitoring, providing important basis for sepsis prediction at molecular biological level, and further guiding new molecular biological markers and targeted drug development at gene level for sepsis treatment. Materials and methods Data acquisition Sepsis is a systemic inflammatory response syndrome caused by external infection. Because of its rapid developmentand high mortality,sepsis has become one of the main causes of death in ICU, especially in children. The mortality of severe sepsis and septic shock children reached 17.7% and 50.8% respectively.The expression of m6A-related genes was significantly different in sepsis and non-sepsis patients, and some genes may be used as potential biological indicators for early diagnosis of sepsis and differentiation of other inflammatory diseases. However, the changes of these genes and immune-related regulatory mechanisms are still unclear, and the pathogenesis of sepsis still needs further study. The gene expression profilesof 23 children with septic shock,77 children with SIRS and 199 healthy children were selected from the gene expression database of National Center for Biotechnology Informationto extract m6A related gene expression. RNA gene expression profiles were further analyzed and compared. method Gene ChipGSE66099 Data Set Download Sepsis children downloaded gene chip GSE66099 dataset from GEO data platform of National Center for Biotechnology Information, labeled gene names according to the platform, standardized the data by limma package, extracted m6A-related gene expression, and divided into control group and sepsis group according to sample characteristics. Screening of differential genes and drawing of protein interaction mapand gene position schematic map ROC curve and boxplot were used to compare the accuracy of random forest tree and machine learning, and random forest tree was selected for gene expression analysis. Drawing nomogram, scoring characteristic genes, analyzing the primary and secondary relationship of characteristic genes in diseases, and predicting disease incidence through characteristic genes. At the same time, the number of related genes was determined by lasso, and ROC curves were drawn to further determine the importance of genes in diseases. Protein interaction network and core driver gene analysis of m6A differential genes, downloading human protein interaction data from protein interaction database (string). Based on previous studies on m6A methylation, 28 m6A-related genes can be divided into three categories: writers, readers and erasers. A total of 22 genes were extracted for further analysis. In addition, we mapped the location of m6A-related genes on chromosomes by perl script, and used limma, ggplot2, ggpubr and ggExtra packages for correlation analysis. Correlation cluster analysis based on different m6A gene expression ROC curves were drawn for the screened genes, and genes highly associated with diseases were selected and classified based on their expression levels. After deleting the control group, cluster typing was performed by Consensus ClusterPlus package, and experimental groups were classified by optimal K value of consensus matrix. Heat maps and box plots were drawn using m6A expression levels and PCA analysis was performed. Difference analysis was performed on relevant immune cells using R packages of limma, GSEAbase, GSVA and ggpubr.The proportion of immune cell infiltration in different m6A subtypes was evaluated and the results were correlated. The pairs of genes with the highest correlation were selected according to the heat maps. The sepsis children were divided into two groups: high and low gene expression group. Crossover genes of different m6 Acluster-type were obtained by R package of limma and VennDiagram, and GO and KEGG analysis were performed. At the same time, cluster analysis of differential genes was performed to evaluate the expression of differential genes. Immune cell correlation analysis The degree of immune cell infiltration in AB and AB was evaluated by immunocyte correlation analysis using R packages such as limma, GSEABase and GSVA. Correlation analysis and heat mapping were performed to select the differentially expressed genes with the highest correlation, and based on this, children with sepsis were divided into high and low expression groups for further analysis. m6A score and PCA analysis PCA analysis was performed using prcomp andpredict commandsto score m6A regulatorygene expression for differential analysis of m6Aclusters and geneClusters by R package of limma and ggpubr. In addition, these datasets were classified into high and low groups based on m6A scores. In addition, differential expression of m6Aclusters and gene Clusters on genes(IL2,IL4,IL5,IL6,IL10,IFNGR1,IFNGR2,TNF,LTA, andLTB) was analyzed by R-packages of limma, shape2, and ggpubr.GO and KEGG.The differential genes of different m6A genotypes were obtained and further GO and KEGG analyses were performed. Samples of crossover genes were classified similarly to those described above. Differential gene and immune infiltration analyses were performed to further assess differential gene expression in septic children. Data analysis The analysis was performed by R-4.1.1 software. All P values were two-sided and P0.05 was considered statistically significant. Results Screening of m6A differential genes and results after data processing A totalof 22 m6As were extracted from GS66099, including 9 writers( METTL3, METTL14, METTL16, WTAP, VIRMA, ZC3H13, RBM15, RBM 15B). CBLL1); 11readers (YTHDC2, YTHDF1, YTHDF2, YTHDF3, HNRNPC, LRPPRC, HNRNPA2B1, IGFBP1, IGFBP2, IGFBP3, ELAVL1) and 2 erasers (FTO, ALKBH5) m6A-related chromosomal locations are shown in (Fig. 1 A), and there is interaction between these m6A genes (Fig. 1DE). Correlation analysis of m6A-related genes showed that DIGFBP1, IGFBP2 were upregulated in sepsis patients, while METTL3, MITTL14, MERTTL16, RBM15, RBM15B, CBLL1, YTHF2, HNRNPC, LRPPRC, ELAVL1, FTO were downregulated in control group(Fig. 1BC). Expression of m6ARNA methylation modification genes is presented as a box plot(Fig. 1 A), indicating that m6A methylation modification gene expression levels differ significantly between septic and non-septic children.The expression of gene WTAP was negatively correlated with gene FTO andMETTL3 was positively correlated with gene FTO( Fig. 1DE). Screening of characteristic genes The differential genes related to m6A were extracted from the dataset, and the accuracy of characteristic genes was screened by residual analysis and ROC comparison between machine learning method and random forest tree method. It can be seen that the boxplot random forest tree is better than the machine learning method( Fig. 2 A), and the ROC curve area under the random forest tree curve is 1, and the machine learning method is0.866, i.e., the random forest tree method is more accurate( Fig. 2 B).The random forest tree method was used to cross-validate errors among control group( Fig. 2 C), experimental group and all samples to obtain differential genes and plot nomograms to predict the incidence of sepsis from the perspective of gene expression( Fig. 2 DE). The number of disease characteristic genes was further determined by intersection of LASSO model and random forest number( Fig. 2 FG), ROC curve was drawn, and the diagnostic value of characteristic genes was further evaluated by AUC. The maximum AUC of gene HNRNPC was 0.803 (95% CI, 0.745–0.816), and the four genes with the highest accuracy were screened (AUC > 0.7). HNRNPC was the most significant. LRPPRC, FTO and ELAVL1 are also significant, with AUC of 0.794, 0.743 and 0.734, respectively ( Fig. 3 ). Therefore, HNRNPC, LRPPRC, FTO and ELAVL1 are considered as important biomarkers of sepsis in children. m6Atyping and difference analysis of immune infiltration among typing K value (k = 2–9) was determined by m6A expression and cluster analysis map. K = 2 was considered as the optimal K value (Fig. 4 A-C). The four genes HNRNPC, LRPPRC, FTO andELAVL1 were up-regulated in m6A group(Fig. 4DE). PCA analysis shows that patients can be divided into two distinct groups based on these four genes(Fig. 4 F). Compared withm6A group, activated B cells, activated CD4 cells, activated CD8 cells,CD56 natural killer cells, γδ T cells, immune B cells, immature dendritic cells, mast cells, NKT cells, follicular helper T cells, Th1 cells and Th2 cells were down-regulated in group B, while activated dendritic cells, macrophages, NK cells and plasmacytoid dendritic cells were down-regulated in group B. According to the results of gene and immune cell correlation heat map, we further analyzed the relationship between four important genes and immune cells. (Figs. 5 A- 5 F) Analysis on the differenceof m6A scorebetween m6A typing and genotype The m6A score was statistically different between the m6A and genotype groups. (Fig. 6 A-C)The m6A score was lower in genotype and m6A group A. IL 6, IL 10, IFNGR 1, IFNGR 2, TNF, LTA and LTB were statistically different between the m6A and genotype groups(Fig. 6DE). GO enrichment and KEGG pathway analysis of differential genes For logFc = 1, p = 0.05 Differential gene screening was performed and GO enrichment analysis was performed for differential genes. results showed in that biological proces (BP), differential genes are mainly enrich in: cell surface receptor immune response regulatory signaling pathway, immune response activation cell surface receptor signaling pathway,immune response activation signal transmission, immune response activation (cell surface immune response related pathway), antigen receptor receive signaling pathway, T cell receptor signaling pathway, lymphocyte, T cell differentiation,(T cell, lymphocyte related regulatory pathway) leukocyte activation positive regulation. (Leukocyte Related Pathway) In the process of cell composition (CC), differential genes are mainly enriched inspecific granules,plasma outer membrane, immune synapse, specific granulevesicle cavity, cytoplasmic vesicle cavity, T cell receptor complex, secretory granulevesicle cavity,specific granule membrane,membrane raft.(cell membrane surface composition,vesicle composition) During molecular function (MF), differential genes are mainly enriched inMHC protein complex binding, immunoreceptor activity, non-membrane protein tyrosine kinase,MHC classIIprotein complexsurface,MHC proteinsurface, phosphatidylcholine bindingsurface, snoRNA bindingsurface, peptide antigen bindingsurface, ribosome small subunitsurface, protein tyrosine kinase.(MHC associated proteins,immune receptors,ribosomal surfaces).(Fig. 7AB) KEGG pathway analysis significantly focused on 13 signaling pathways: hematopoietic lineage, T cell receptor signaling pathway, primary immunodeficiency, Th17 cell differentiation, Th1 and Th2 cell differentiation, human T cell leukemia virus infection, EB virus infection, PD-L1 expression and PD-1 checkpoint pathway in cancer, natural killer cell-mediated cytotoxicity, malaria, nucleotide metabolism, purine metabolism, inflammatory bowel disease m6A regulatory genes. Differential analysis showed that METTL3/14/16, RBM15, YTHDF2, HNRNPC, LRPPRC,ELAVL1 and FTO expression in gene Cluster B was reduced compared with gene Cluster A, and no significant difference was observed. Furthermore, analysis of differences between immune cell infiltrations showed that expression of different immune cells was mainly reduced in gene Cluster B compared to gene Cluster A. (Fig. 7 ) Discussion Sepsis is one of the most important diseases leading to death in ICU. Early diagnosis and early application of antibiotics can reduce morbidity and mortality. Therefore, the screening and validation of reliable biomarkers and the improvement of the accuracy and efficiency of sepsis prediction have been the goals of experts and scholars at home and abroad. RNA modifications play a critical role in regulating molecular events and diseases, and miRNAs have been shown to be closely associated [10] not only with epigenetics [11] but also with sepsis heterogeneity and prognosis. m6A is an important methylation modification, and m6A methylation plays a role in transcription by affecting RNA splicing, export, translation, and stability. First, mRNA is spliced into mature transcripts and exported from the nucleus to the cytoplasm before translation. The splicing and export process is regulated by m6A [2] . Most studies on m6A methylation have been limited to tumor-related directions [12] , with the focus on single genes associated with single diseases or with specific immune cells. Further understanding of the relationship between m6A regulation and sepsis may provide a new way for clinical morbidity prediction and molecular diagnosis, and further guide clinical treatment. In this study, we isolated HNRNPC, LRPPRC, FTO and ELAVL1 genes associated with sepsis in children. Sepsis in children was divided into two subtypes. The two groups showed significantly different genetic status and immune status. HNRNPC, LRPPRC,ELAVL1 are m6A related readers, FTO are related erasers, which provide direction for further research on gene and sepsis related mechanism. These results suggest that HNRNPC has the best diagnostic value in childhood sepsis and may be a potentially important gene in early sepsis. HNRNPC plays a splicing role in m6A modification. Two HNRNPC proteins, HNRNPG and HNRNPC 11, do not bind directly to m6A but functionally regulate m6A RNA transcripts [13] . HNRNPC, i.e. heterogeneous nuclear ribonucleoprotein C1/C2, is a protein composed of 306 amino acids and localized in the nucleus [10] . HNRNPC is spliced at the early stages of spliceome assembly and pre-mRNA [11] , and is found to be upregulated in many tumors. It is considered to be one of the important genes regulating cancer-specific alternative lysis and polyadenylation, and can influence tumor metastasis and cell death in combination with other proteins. Furthermore, HNRNPC regulates the stability and translational level of binding molecules. More recently, it has been reported that m6A affects RNA secondary structure, while HNRNPC can regulate mRNA abundance and splicing of m6A after recognition, known as the "m6A switch." LRPPRC is a leucine-rich pentapeptide repeat protein, mainly distributed in mitochondria, belonging to a family of proteins containing PPR motifs. This family of proteins binds to RNA and regulates transcription. It plays an important role in RNA processing, splicing, stability, editing and translation, mainly manifested in strong inhibitionof autophagy [14] and complex interaction with apoptosis. LRPPRC has been shown to play an important regulatory role in many diseases. It is specifically overexpressed in lung adenocarcinoma [15] . It can also be associated with other proteins, thereby negatively regulating mitochondrial mediated antiviral immunity [16] . In an animal experiment, LRPPRC knockout resulted in impaired mitochondrial respiration, decreased ATP production, and increased hyperpolarization and mitochondrial reactive oxygen species production in mouse hearts, possibly due to LRPPRC inhibition of ATPIF1-related mRNA translation [17] , suggesting that LRPPRC expression may promote ATP production. At the same time, numerous studies have shown that mitochondrial dysfunction in cells with LRPPRC defects is impaired in their ability to maintain energy homeostasis, especially under conditions of inflammation and nutritional stress [18, 19] . This suggests that when sepsis patients are in a state of systemic inflammatory dysfunction and organ tissue ischemia and hypoxia, cells in LRPPRC deficient patients are more likely to be ATP deficient and thus more vulnerable to damage. ELAVL1: RNA-binding proteinHuR,also a protein that regulates post-transcription products, is overexpressed and overexpressed in most cancers.This dysregulation of HuR enables itto participate inthe translation of messenger RNA (mRNA) in many cancers andvarious disease pathogenesis. HuR can increase the stability of some transcription products and also promote the translation of some target mRNAs. In hypoxia environment, HuR can significantly increase the expression of VEGF and HIF-1α, which may be related to the repair and angiogenesis after hypoxia induced cell damage. By altering the pattern of protein expression,HuR can influence major cellular processes such as proliferation, differentiation, carcinogenesis, senescence, apoptosis, and responses to immune and environmental stress [20] . In this study, we foundthat differences amongthe three readers involved autophagy and apoptosis, infection, and cell damage and repair under stress conditions. However, ourstudy of m6A regulators was limited to bioinformatics analysis, and its underlying mechanisms need to be further explored. FTO: Originally reported as an in vitro demethylase of N3-methylformamide in single-stranded DNA83 and [21] N3-methyluridine in single-stranded RNA, FTO was first found to be associated with weight gain and obesity in humans [22, 23] . It is widely expressed in all adult and fetal tissues, with highest expression in the brain. FTO has been shown to play an oncogene role in leukemia [25] , glioblastoma, and renal clear cell carcinoma. [26] FTO expression is abnormally upregulated by oncoproteins in some subtypes of AML, with a corresponding decrease in fat mass and obesity-related protein (FTO, m6A demethylase) expression. These changes were related to the significant increase of IL-6, TNF-a and IL-1b expression and the decrease of left ventricular function [2] .METTL3/14/16,RBM15, WTAP,YTHDF2, HNRNPC, LRPPRC,ELAVL1 and FTO were significantly different among10The association of these genes with sepsis remains to be studied. Our study suggests that these genes may be key targets for m6A regulators, which may trigger new treatment strategies for sepsis. However, further research is needed to clarify their role in sepsis diagnosis and treatment. Conclusion This study initially screened out methylation modified differential genes in sepsis patients, GO analysis and KEGG pathway analysis of these differential genes, preliminary identification of disease-related signal pathways, in addition, through the construction of protein interaction network map,four core driver genes play an important role in the pathogenesis of disease, providing a direction for further exploration of disease-related protein interaction mechanism. Differences in gene expression, immune infiltration, and signaling between m6A subtypes of sepsis patients were also identified. At the same time, it has certain limitations. Retrospective studies based on GEO genetic data only have genetic data without more demographic data and clinical characteristics, and cannot be further studied. This paper only analyzes the data from the perspective of bioinformatics, but does not verify it in practice. The actual value of the results and the mechanism of deeper pairs need to be further studied. Declarations Conflict of interest The authors declare that there are no competing interests. Author Contribution Hong wrote the main manuscript text , prepared figures 4-7,Wu prepared figures 1-3.All authors reviewed the manuscript. Acknowledgements We express our sincere gratitude to the GEO database.The datasets analyzed during the current study are available in the GEO database, specifically the GSE66099 dataset repository, accessible via https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi. Data Availability The datasets analyzed during the current study are available in the GEO database, specifically the GSE66099 dataset repository, accessible via https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi. References Pei F, Yao R Q, Ren C, et al. Expert consensus on the monitoring and treatment of sepsis-induced immunosuppression[J]. Mil Med Res, 2022,9(1):74.DOI:10.1186/s40779-022-00430-y. Qian W, Cao Y. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4182389","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":311011400,"identity":"89100ccd-7711-4493-b793-b9afdac6d560","order_by":0,"name":"quxiang Hong","email":"","orcid":"","institution":"Xuzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"quxiang","middleName":"","lastName":"Hong","suffix":""},{"id":311011401,"identity":"0dee2d74-4b66-4de8-be65-ac931c5c8b04","order_by":1,"name":"WenTao Wu","email":"","orcid":"","institution":"Xuzhou Medical 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12:50:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4182389/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4182389/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58173489,"identity":"f8cb8871-38c3-4ebb-94a6-02b81e2f6bd0","added_by":"auto","created_at":"2024-06-12 04:05:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":324575,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Distribution of m6A gene (BC) Expression difference between septic and non-septic patients;(DE) Interaction relationship between genes\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4182389/v1/114eac1749840f1192f398f3.jpg"},{"id":58171872,"identity":"e336dcb4-aaaa-473e-92d7-6e34235a1f09","added_by":"auto","created_at":"2024-06-12 03:49:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":212998,"visible":true,"origin":"","legend":"\u003cp\u003eFigure Screening Feature Gene Model Selection (A) Specificity Screening Box Plot (B)ROC Curve Comparison Model Prediction Accuracy(C) Random Forest Tree Model(D) Highest Scoring Gene Score(E)Nomogram Predicted Gene Expression and Incidence(FG) Select Appropriate λ Value\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4182389/v1/04c72a6e46bfa9cbd822b2b0.jpg"},{"id":58171874,"identity":"11c53fe5-a9ba-49f4-9e86-6e48ef248c43","added_by":"auto","created_at":"2024-06-12 03:49:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":237133,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of screened genes\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4182389/v1/a80b91a57110a098ad74177e.jpg"},{"id":58171877,"identity":"e96e2956-1ae7-4e04-b4a9-ebbf90ecbcf8","added_by":"auto","created_at":"2024-06-12 03:49:53","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":194239,"visible":true,"origin":"","legend":"\u003cp\u003em6A typing (A-C) typing according to m6A expression (DE) difference in characteristic gene expression after m6A typing (F) PAC principal component analysis\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4182389/v1/6c7bf50bc4fb0df40bdcc3f1.jpg"},{"id":58173024,"identity":"afda1337-3801-4557-b17d-1e687e0fe553","added_by":"auto","created_at":"2024-06-12 03:57:53","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":505000,"visible":true,"origin":"","legend":"\u003cp\u003eImmunoinfiltration betweendifferent m6A subgroups (A) Differences and(B) Correlation analysis(C-F) Sample classification based on(C)ELAVL1 (D) FTO(E) HNRNPC(F) LRPPRC expression pairs\u003c/p\u003e","description":"","filename":"figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4182389/v1/faea4220ed1a7491c8f1956a.jpg"},{"id":58171876,"identity":"ba771e16-ce28-49ff-858d-e896d7ed930c","added_by":"auto","created_at":"2024-06-12 03:49:53","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":352083,"visible":true,"origin":"","legend":"\u003cp\u003eDifference analysis of m6A typing(A) Difference analysis between different gene analysis(B)Difference analysis between m6A typing(C)Difference analysis between m6A typing and m6A typing(D) Genotyping and(E)Relationship between m6A typing and common cytokine related gene expression\u003c/p\u003e","description":"","filename":"figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4182389/v1/156df644bc98dfb37013fd3a.jpg"},{"id":58171878,"identity":"d0489d61-e15a-409f-8b7d-4349238d3726","added_by":"auto","created_at":"2024-06-12 03:49:53","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":675564,"visible":true,"origin":"","legend":"\u003cp\u003e(AB)GO enrichment analysis of differential genes(CD) KEGG enrichment analysis of differential genes\u003c/p\u003e","description":"","filename":"figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4182389/v1/2bff9b18df1e2e4fed641d32.jpg"},{"id":60278016,"identity":"5ca1e050-2ee8-4f9a-9bd5-d09b524d2aed","added_by":"auto","created_at":"2024-07-15 05:44:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2957787,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4182389/v1/a4f9625f-b513-4117-8a4d-71b0fb72dba3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Screening of key genes for m6A modification differences in childhood sepsis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis is a systemic inflammatory response syndrome caused by the invasion of bacteriaand pathogenic microorganisms into the bodyIn addition to systemic inflammatory response syndrome and primary infection, severe patients often have organ hypoperfusion. Mortality rates among children remain high throughout the year, with one study predicting about 1.2\u0026nbsp;million cases of sepsis among children worldwideand about 3\u0026nbsp;million cases\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003eamong newborns. Blood culture as the gold standard for sepsis diagnosis,detection time is long,specimen requirements are high. In recent years, N6-methyladenosine methylation has become a hot research topic, which is the most common internal modification of mRNA. Other modifications include N1-adenosine methylation (m1A) and cytosine hydroxylation (m5C). It plays an important role in many fundamental biological processes\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, such as lipid transport\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, autoimmune diseases, and tumor\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e mRNA regulation\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.m6A-related genes are associated with morbidity, for example, METTL3 knockdown inhibits proliferation, invasion, migration and anoikis resistance of GC cells\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e; m6A regulatory factor dysregulation mediates immune cell infiltration in TME\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Based on GEO database platform, this study explores the differential genes of m6A-related genes and discusses the expression content of m6A differential genes in immune cells for further exploring sepsis immune infiltration related monitoring, providing important basis for sepsis prediction at molecular biological level, and further guiding new molecular biological markers and targeted drug development at gene level for sepsis treatment.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData acquisition\u003c/h2\u003e \u003cp\u003eSepsis is a systemic inflammatory response syndrome caused by external infection. Because of its rapid developmentand high mortality,sepsis has become one of the main causes of death in ICU, especially in children. The mortality of severe sepsis and septic shock children reached 17.7% and 50.8% respectively.The expression of m6A-related genes was significantly different in sepsis and non-sepsis patients, and some genes may be used as potential biological indicators for early diagnosis of sepsis and differentiation of other inflammatory diseases. However, the changes of these genes and immune-related regulatory mechanisms are still unclear, and the pathogenesis of sepsis still needs further study. The gene expression profilesof 23 children with septic shock,77 children with SIRS and 199 healthy children were selected from the gene expression database of National Center for Biotechnology Informationto extract m6A related gene expression. RNA gene expression profiles were further analyzed and compared.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003emethod\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eGene ChipGSE66099 Data Set Download\u003c/h2\u003e \u003cp\u003eSepsis children downloaded gene chip GSE66099 dataset from GEO data platform of National Center for Biotechnology Information, labeled gene names according to the platform, standardized the data by limma package, extracted m6A-related gene expression, and divided into control group and sepsis group according to sample characteristics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eScreening of differential genes and drawing of protein interaction mapand gene position schematic map\u003c/h2\u003e \u003cp\u003eROC curve and boxplot were used to compare the accuracy of random forest tree and machine learning, and random forest tree was selected for gene expression analysis. Drawing nomogram, scoring characteristic genes, analyzing the primary and secondary relationship of characteristic genes in diseases, and predicting disease incidence through characteristic genes. At the same time, the number of related genes was determined by lasso, and ROC curves were drawn to further determine the importance of genes in diseases. Protein interaction network and core driver gene analysis of m6A differential genes, downloading human protein interaction data from protein interaction database (string). Based on previous studies on m6A methylation, 28 m6A-related genes can be divided into three categories: writers, readers and erasers. A total of 22 genes were extracted for further analysis. In addition, we mapped the location of m6A-related genes on chromosomes by perl script, and used limma, ggplot2, ggpubr and ggExtra packages for correlation analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation cluster analysis based on different m6A gene expression\u003c/h2\u003e \u003cp\u003eROC curves were drawn for the screened genes, and genes highly associated with diseases were selected and classified based on their expression levels. After deleting the control group, cluster typing was performed by Consensus ClusterPlus package, and experimental groups were classified by optimal K value of consensus matrix. Heat maps and box plots were drawn using m6A expression levels and PCA analysis was performed. Difference analysis was performed on relevant immune cells using R packages of limma, GSEAbase, GSVA and ggpubr.The proportion of immune cell infiltration in different m6A subtypes was evaluated and the results were correlated. The pairs of genes with the highest correlation were selected according to the heat maps. The sepsis children were divided into two groups: high and low gene expression group. Crossover genes of different m6 Acluster-type were obtained by R package of limma and VennDiagram, and GO and KEGG analysis were performed. At the same time, cluster analysis of differential genes was performed to evaluate the expression of differential genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImmune cell correlation analysis\u003c/h2\u003e \u003cp\u003eThe degree of immune cell infiltration in AB and AB was evaluated by immunocyte correlation analysis using R packages such as limma, GSEABase and GSVA. Correlation analysis and heat mapping were performed to select the differentially expressed genes with the highest correlation, and based on this, children with sepsis were divided into high and low expression groups for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003em6A score and PCA analysis\u003c/h2\u003e \u003cp\u003ePCA analysis was performed using prcomp andpredict commandsto score m6A regulatorygene expression for differential analysis of m6Aclusters and geneClusters by R package of limma and ggpubr. In addition, these datasets were classified into high and low groups based on m6A scores.\u003c/p\u003e \u003cp\u003eIn addition, differential expression of m6Aclusters and gene Clusters on genes(IL2,IL4,IL5,IL6,IL10,IFNGR1,IFNGR2,TNF,LTA, andLTB) was analyzed by R-packages of limma, shape2, and ggpubr.GO and KEGG.The differential genes of different m6A genotypes were obtained and further GO and KEGG analyses were performed. Samples of crossover genes were classified similarly to those described above. Differential gene and immune infiltration analyses were performed to further assess differential gene expression in septic children.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe analysis was performed by R-4.1.1 software. All P values were two-sided and P0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eScreening of m6A differential genes and results after data processing\u003c/h2\u003e \u003cp\u003eA totalof 22 m6As were extracted from GS66099, including 9 writers( METTL3, METTL14, METTL16, WTAP, VIRMA, ZC3H13, RBM15, RBM 15B).\u003c/p\u003e \u003cp\u003eCBLL1); 11readers (YTHDC2, YTHDF1, YTHDF2, YTHDF3, HNRNPC, LRPPRC, HNRNPA2B1, IGFBP1, IGFBP2, IGFBP3, ELAVL1) and 2 erasers (FTO, ALKBH5) m6A-related chromosomal locations are shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), and there is interaction between these m6A genes (Fig.\u0026nbsp;1DE). Correlation analysis of m6A-related genes showed that DIGFBP1, IGFBP2 were upregulated in sepsis patients, while METTL3, MITTL14, MERTTL16, RBM15, RBM15B, CBLL1, YTHF2, HNRNPC, LRPPRC, ELAVL1, FTO were downregulated in control group(Fig.\u0026nbsp;1BC). Expression of m6ARNA methylation modification genes is presented as a box plot(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), indicating that m6A methylation modification gene expression levels differ significantly between septic and non-septic children.The expression of gene WTAP was negatively correlated with gene FTO andMETTL3 was positively correlated with gene FTO( Fig.\u0026nbsp;1DE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eScreening of characteristic genes\u003c/h2\u003e \u003cp\u003eThe differential genes related to m6A were extracted from the dataset, and the accuracy of characteristic genes was screened by residual analysis and ROC comparison between machine learning method and random forest tree method. It can be seen that the boxplot random forest tree is better than the machine learning method( Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), and the ROC curve area under the random forest tree curve is 1, and the machine learning method is0.866, i.e., the random forest tree method is more accurate( Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).The random forest tree method was used to cross-validate errors among control group( Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), experimental group and all samples to obtain differential genes and plot nomograms to predict the incidence of sepsis from the perspective of gene expression( Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e DE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe number of disease characteristic genes was further determined by intersection of LASSO model and random forest number( Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e FG), ROC curve was drawn, and the diagnostic value of characteristic genes was further evaluated by AUC. The maximum AUC of gene HNRNPC was 0.803 (95% CI, 0.745\u0026ndash;0.816), and the four genes with the highest accuracy were screened (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7). HNRNPC was the most significant. LRPPRC, FTO and ELAVL1 are also significant, with AUC of 0.794, 0.743 and 0.734, respectively ( Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Therefore, HNRNPC, LRPPRC, FTO and ELAVL1 are considered as important biomarkers of sepsis in children.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003em6Atyping and difference analysis of immune infiltration among typing\u003c/h2\u003e \u003cp\u003eK value (k\u0026thinsp;=\u0026thinsp;2\u0026ndash;9) was determined by m6A expression and cluster analysis map. K\u0026thinsp;=\u0026thinsp;2 was considered as the optimal K value (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C). The four genes HNRNPC, LRPPRC, FTO andELAVL1 were up-regulated in m6A group(Fig.\u0026nbsp;4DE). PCA analysis shows that patients can be divided into two distinct groups based on these four genes(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eCompared withm6A group, activated B cells, activated CD4 cells, activated CD8 cells,CD56 natural killer cells, γδ T cells, immune B cells, immature dendritic cells, mast cells, NKT cells, follicular helper T cells, Th1 cells and Th2 cells were down-regulated in group B, while activated dendritic cells, macrophages, NK cells and plasmacytoid dendritic cells were down-regulated in group B. According to the results of gene and immune cell correlation heat map, we further analyzed the relationship between four important genes and immune cells. (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis on the differenceof m6A scorebetween m6A typing and genotype\u003c/h2\u003e \u003cp\u003eThe m6A score was statistically different between the m6A and genotype groups. (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C)The m6A score was lower in genotype and m6A group A. IL 6, IL 10, IFNGR 1, IFNGR 2, TNF, LTA and LTB were statistically different between the m6A and genotype groups(Fig.\u0026nbsp;6DE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGO enrichment and KEGG pathway analysis of differential genes\u003c/h2\u003e \u003cp\u003eFor logFc\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;=\u0026thinsp;0.05 Differential gene screening was performed and GO enrichment analysis was performed for differential genes. results showed\u003c/p\u003e \u003cp\u003ein that biological proces (BP), differential genes are mainly enrich in: cell surface receptor immune response regulatory signaling pathway, immune response activation cell surface receptor signaling pathway,immune response activation signal transmission, immune response activation (cell surface immune response related pathway), antigen receptor receive signaling pathway, T cell receptor signaling pathway, lymphocyte, T cell differentiation,(T cell, lymphocyte related regulatory pathway) leukocyte activation positive regulation. (Leukocyte Related Pathway)\u003c/p\u003e \u003cp\u003eIn the process of cell composition (CC), differential genes are mainly enriched inspecific granules,plasma outer membrane, immune synapse, specific granulevesicle cavity, cytoplasmic vesicle cavity, T cell receptor complex, secretory granulevesicle cavity,specific granule membrane,membrane raft.(cell membrane surface composition,vesicle composition)\u003c/p\u003e \u003cp\u003eDuring molecular function (MF), differential genes are mainly enriched inMHC protein complex binding, immunoreceptor activity, non-membrane protein tyrosine kinase,MHC classIIprotein complexsurface,MHC proteinsurface, phosphatidylcholine bindingsurface, snoRNA bindingsurface, peptide antigen bindingsurface, ribosome small subunitsurface, protein tyrosine kinase.(MHC associated proteins,immune receptors,ribosomal surfaces).(Fig.\u0026nbsp;7AB)\u003c/p\u003e \u003cp\u003eKEGG pathway analysis significantly focused on 13 signaling pathways: hematopoietic lineage, T cell receptor signaling pathway, primary immunodeficiency, Th17 cell differentiation, Th1 and Th2 cell differentiation, human T cell leukemia virus infection, EB virus infection, PD-L1 expression and PD-1 checkpoint pathway in cancer, natural killer cell-mediated cytotoxicity, malaria, nucleotide metabolism, purine metabolism, inflammatory bowel disease m6A regulatory genes. Differential analysis showed that METTL3/14/16, RBM15, YTHDF2, HNRNPC, LRPPRC,ELAVL1 and FTO expression in gene Cluster B was reduced compared with gene Cluster A, and no significant difference was observed. Furthermore, analysis of differences between immune cell infiltrations showed that expression of different immune cells was mainly reduced in gene Cluster B compared to gene Cluster A. (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSepsis is one of the most important diseases leading to death in ICU. Early diagnosis and early application of antibiotics can reduce morbidity and mortality. Therefore, the screening and validation of reliable biomarkers and the improvement of the accuracy and efficiency of sepsis prediction have been the goals of experts and scholars at home and abroad. RNA modifications play a critical role in regulating molecular events and diseases, and miRNAs have been shown to be closely associated\u003csup\u003e[10]\u003c/sup\u003e not only with epigenetics\u003csup\u003e[11]\u003c/sup\u003e but also with sepsis heterogeneity and prognosis. m6A is an important methylation modification, and m6A methylation plays a role in transcription by affecting RNA splicing, export, translation, and stability. First, mRNA is spliced into mature transcripts and exported from the nucleus to the cytoplasm before translation. The splicing and export process is regulated by m6A\u003csup\u003e[2]\u003c/sup\u003e. Most studies on m6A methylation have been limited to tumor-related directions\u003csup\u003e[12]\u003c/sup\u003e, with the focus on single genes associated with single diseases or with specific immune cells. Further understanding of the relationship between m6A regulation and sepsis may provide a new way for clinical morbidity prediction and molecular diagnosis, and further guide clinical treatment. In this study, we isolated HNRNPC, LRPPRC, FTO and ELAVL1 genes associated with sepsis in children. Sepsis in children was divided into two subtypes. The two groups showed significantly different genetic status and immune status. HNRNPC, LRPPRC,ELAVL1 are m6A related readers, FTO are related erasers, which provide direction for further research on gene and sepsis related mechanism.\u003c/p\u003e\n\u003cp\u003eThese results suggest that HNRNPC has the best diagnostic value in childhood sepsis and may be a potentially important gene in early sepsis. HNRNPC plays a splicing role in m6A modification. Two HNRNPC proteins, HNRNPG and HNRNPC 11, do not bind directly to m6A but functionally regulate m6A RNA transcripts\u003csup\u003e[13]\u003c/sup\u003e. HNRNPC, i.e. heterogeneous nuclear ribonucleoprotein C1/C2, is a protein composed of 306 amino acids and localized in the nucleus\u003csup\u003e[10]\u003c/sup\u003e. HNRNPC is spliced at the early stages of spliceome assembly and pre-mRNA\u003csup\u003e[11]\u003c/sup\u003e, and is found to be upregulated in many tumors. It is considered to be one of the important genes regulating cancer-specific alternative lysis and polyadenylation, and can influence tumor metastasis and cell death in combination with other proteins. Furthermore, HNRNPC regulates the stability and translational level of binding molecules. More recently, it has been reported that m6A affects RNA secondary structure, while HNRNPC can regulate mRNA abundance and splicing of m6A after recognition, known as the \u0026quot;m6A switch.\u0026quot;\u003c/p\u003e\n\u003cp\u003eLRPPRC is a leucine-rich pentapeptide repeat protein, mainly distributed in mitochondria, belonging to a family of proteins containing PPR motifs. This family of proteins binds to RNA and regulates transcription. It plays an important role in RNA processing, splicing, stability, editing and translation, mainly manifested in strong inhibitionof autophagy\u0026nbsp;\u003csup\u003e[14]\u003c/sup\u003eand complex interaction with apoptosis. LRPPRC has been shown to play an important regulatory role in many diseases. It is specifically overexpressed in lung adenocarcinoma\u003csup\u003e[15]\u003c/sup\u003e. It can also be associated with other proteins, thereby negatively regulating mitochondrial mediated antiviral immunity\u003csup\u003e[16]\u003c/sup\u003e. In an animal experiment, LRPPRC knockout resulted in impaired mitochondrial respiration, decreased ATP production, and increased hyperpolarization and mitochondrial reactive oxygen species production in mouse hearts, possibly due to LRPPRC inhibition of ATPIF1-related mRNA translation\u003csup\u003e[17]\u003c/sup\u003e, suggesting that LRPPRC expression may promote ATP production. At the same time, numerous studies have shown that mitochondrial dysfunction in cells with LRPPRC defects is impaired in their ability to maintain energy homeostasis, especially under conditions of inflammation and nutritional stress\u003csup\u003e[18, 19]\u003c/sup\u003e. This suggests that when sepsis patients are in a state of systemic inflammatory dysfunction and organ tissue ischemia and hypoxia, cells in LRPPRC deficient patients are more likely to be ATP deficient and thus more vulnerable to damage.\u003c/p\u003e\n\u003cp\u003eELAVL1:\u0026nbsp;RNA-binding proteinHuR,also a protein that regulates post-transcription products, is overexpressed and overexpressed in most cancers.This dysregulation of HuR enables itto participate inthe translation of messenger RNA (mRNA) in many cancers andvarious disease pathogenesis. HuR can increase the stability of some transcription products and also promote the translation of some target mRNAs. In hypoxia environment, HuR can significantly increase the expression of VEGF and HIF-1\u0026alpha;, which may be related to the repair and angiogenesis after hypoxia induced cell damage. By altering the pattern of protein expression,HuR can influence major cellular processes such as proliferation, differentiation, carcinogenesis, senescence, apoptosis, and responses to immune and environmental stress\u003csup\u003e[20]\u003c/sup\u003e. In this study, we foundthat differences amongthe three readers involved autophagy and apoptosis, infection, and cell damage and repair under stress conditions. However, ourstudy of m6A regulators was limited to bioinformatics analysis, and its underlying mechanisms need to be further explored.\u003c/p\u003e\n\u003cp\u003eFTO: Originally reported as an in vitro demethylase of N3-methylformamide in single-stranded DNA83 and\u003csup\u003e[21]\u003c/sup\u003e N3-methyluridine in single-stranded RNA, FTO was first found to be associated with weight gain and obesity in humans\u003csup\u003e[22, 23]\u003c/sup\u003e. It is widely expressed in all adult and fetal tissues, with highest expression in the brain. FTO has been shown to play an oncogene role in leukemia\u003csup\u003e[25]\u003c/sup\u003e, glioblastoma, and renal clear cell carcinoma.\u003csup\u003e[26]\u003c/sup\u003e FTO expression is abnormally upregulated by oncoproteins in some subtypes of AML, with a corresponding decrease in fat mass and obesity-related protein (FTO, m6A demethylase) expression. These changes were related to the significant increase of IL-6, TNF-a and IL-1b expression and the decrease of left ventricular function\u003csup\u003e[2]\u003c/sup\u003e.METTL3/14/16,RBM15, WTAP,YTHDF2, HNRNPC, LRPPRC,ELAVL1 and FTO were significantly different among10The association of these genes with sepsis remains to be studied. Our study suggests that these genes may be key targets for m6A regulators, which may trigger new treatment strategies for sepsis. However, further research is needed to clarify their role in sepsis diagnosis and treatment.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study initially screened out methylation modified differential genes in sepsis patients, GO analysis and KEGG pathway analysis of these differential genes, preliminary identification of disease-related signal pathways, in addition, through the construction of protein interaction network map,four core driver genes play an important role in the pathogenesis of disease, providing a direction for further exploration of disease-related protein interaction mechanism. Differences in gene expression, immune infiltration, and signaling between m6A subtypes of sepsis patients were also identified.\u003c/p\u003e \u003cp\u003eAt the same time, it has certain limitations. Retrospective studies based on GEO genetic data only have genetic data without more demographic data and clinical characteristics, and cannot be further studied. This paper only analyzes the data from the perspective of bioinformatics, but does not verify it in practice. The actual value of the results and the mechanism of deeper pairs need to be further studied.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eHong wrote the main manuscript text , prepared figures 4-7,Wu prepared figures 1-3.All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe express our sincere gratitude to the GEO database.The datasets analyzed during the current study are available in the GEO database, specifically the GSE66099 dataset repository, accessible via https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available in the GEO database, specifically the GSE66099 dataset repository, accessible via https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePei F, Yao R Q, Ren C, et al. 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Cell Rep, 2017,18(11):2622-2634.DOI:10.1016/j.celrep.2017.02.059.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"sepsis, m6A methylation modification, differential gene, bioinformatics.","lastPublishedDoi":"10.21203/rs.3.rs-4182389/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4182389/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSepsis in children is a syndrome associated with organ dysfunction caused by immune dysregulation of inflammatory responses in children. According to the latest data, nearly50\u0026nbsp;million people have been diagnosed with sepsisand nearly10\u0026nbsp;million have died. M6A methylation has been reported to be associated with sepsis-associated inflammatory response\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e,however, the molecular biological mechanism underlying the diagnosis and treatment of m6A related genes in children remains unclear. It provides a new way for clinical incidence prediction and molecular biology diagnosis, and further guides clinical treatment.The GEO database chip dataset GSE66099 was downloaded and annotated by platform files. The m6A related genes were extracted. The data were standardized by R language limma package.181 children with septic shock,18 children with sepsis were selected as sepsis group,47 normal childrenand30 children with common SIRS were selected as control group. The difference of m6A gene expression between control group and sepsis group was analyzed by correlation test. The importance score of m6A-related genes in sepsis was obtained by cross-validation error of random forest tree method, disease-related characteristic genes were screened, the influence of core difference genes on sepsis incidence was analyzed, and nomogram was drawn to predict patient incidence. The number of disease characteristic genes was determined by LASSO model, ROC curve was drawn, and related genes were selected for further analysis. Cluster analysis was performed on sepsis patients according to the expression of biomarkers, and difference and correlation analysis were performed on immune infiltration. Among the first 13 differentially expressed genes, DIGFBP1 and IGFBP2 were up-regulated in sepsis patients, while METTL3, MITTL14, MERTTL16, RBM15, RBM15B, CBLL1, YTHF2, HNRNPC, LRPPRC, ELAVL1 and FTO were down-regulated in sepsis patients. In addition, ROC curve analysis showed that HNRNPC, LRPPRC, FTO andELAVL1 were characteristic genes of the disease. We also identified two m6A genotypes and two differential genotypes. Based on differential gene expression, nine m6A gene expressions were statistically different in a 2-typing pattern, with differences associated with immune infiltration. m6A methylation modification may play a potentially important role in the diagnosis,immune infiltration and treatment of sepsis in children. HNRNPC may be one of the potential molecular markers for predicting sepsis in children. Typing based on m6A gene expression has potential implications for the treatment of sepsis in children.\u003c/p\u003e","manuscriptTitle":"Screening of key genes for m6A modification differences in childhood sepsis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-12 03:49:48","doi":"10.21203/rs.3.rs-4182389/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c9900179-9417-4d3c-9131-f3536db71a40","owner":[],"postedDate":"June 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32870548,"name":"Biological sciences/Genetics/Gene regulation"},{"id":32870549,"name":"Biological sciences/Biochemistry/Dna"}],"tags":[],"updatedAt":"2024-07-15T05:36:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-12 03:49:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4182389","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4182389","identity":"rs-4182389","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}