Bioinformatic Analysis of m6A Regulator-Mediated RNA Methylation Modification Patterns and Immune Microenvironment Characterization in Endometriosis

Epigenetic regulation plays an essential role in immunity and inflammation in endometriosis. In this study, we aimed to explore differences in m6A regulators between endometriosis patients and normal women and analyze the effect of m6A modification on immune and inflammatory microenvironment. The samples for analysis were downloaded from the Gene Expression Omnibus database, including ectopic endometrium (EC), eutopic endometrium (EU), and normal eutopic endometrium (NM) samples from non-endometriosis women. The validation process involved utilizing our previous RNA-sequencing data. Subsequently, a correlation analysis was performed to ascertain the relationship between m6A and the inflammatory microenvironment profile, encompassing infiltrating immunocytes, immune-inflammation reaction gene sets, and human leukocyte antigen genes. LASSO analyses were used to develop risk signature. The findings of this study indicate that the m6A regulators FTO were observed to be significantly up-regulated, while YTHDF2, CBLL1, and METTL3 were down-regulated in endometriosis tissues. The CIBERSORT analysis revealed that the local inflammatory microenvironment of ectopic lesions plays a crucial role in the development of endometriosis. Notably, M2 macrophages exhibited a significant difference between the EC and NM groups. Moreover, M2 macrophages demonstrated a positive correlation with FTO (0.39) and a negative correlation with CBLL1 (− 0.35). Furthermore, consistent clustering of EC and EU samples resulted in the identification of three distinct cell subtypes. Among different cell subtypes, significant differences were in immunoinfiltrating cells, plasma cells, naive CD4 T cells, memory activated CD4 T cells, gamma delta T cells, resting NK cells and activated NK cells but not in macrophages. Furthermore, the identification of various compounds capable of targeting these m6A genes was achieved. In conclusions, our integrated bioinformatics analysis results demonstrated that m6A-related genes METTL3, CBLL1 and YTHDF2 may be useful biomarkers for endometriosis in ectopic endometrium. The potential therapeutic approach of targeting m6A regulators holds promise for the treatment of endometriosis. Similar content being viewed by others Data Availability Publicly available datasets from GEO datasets were analyzed in this study. All data are contained within the manuscript and additional files. And the entire RNA-seq dataset is available at the sequencing Read Archive (SRA) database (https://www.ncbi.nlm.nih.gov/sra/PRJNA769152). Abbreviations - CBLL1: - Casitas B-lineage proto-oncogene like 1 - CCK-8: - Cell counting kit-8 - ELAVL1: - ELAV like RNA binding protein 1 - EMT: - Epithelial-to-mesenchymal transition - FTO: - Fat mass and obesity-associated protein - HLAs: - Human leukocyte antigen gene - LRPPRC: - Leucine-rich pentatricopeptide repeat containing - LASSO: - Least absolute shrinkage and selection operator - METTL3/14: - Methyltransferase-like 3/14 - m6A: - N6-methyladenosine - OS: - Overall survival - ROC: - Receiver operating characteristic curve - RPMI-1640: - Roswell park memorial institute 1640 - YTHDC1, 2: - YT521-B homology domain-containing protein 1 1/2 - YTHDF1/2/3: - YTH N6-methyladenosine RNA binding protein F1/2/3 - WTAP: - Wilms’ tumor-associated protein

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The authors thank the whole team for assistance. Funding This study is supported by the medical research fund of Guangdong Province (A2022266), Guangzhou municipal science and technology bureau (202201020263), Guangdong basic and applied basic research foundation (2023A1515011109) and Guangdong basic and applied basic research foundation (2022A1515110697). The funders (Weilin Zheng and Lixing Cao) provided important supports during design of the study and collection, analysis, and interpretation of data and in writing the manuscript. Author information Authors and Affiliations Contributions WLZ wrote the main manuscript text. WLZ and ZYF contributed to the data analysis and experimental verification. XT contributed to the pathological analysis. XFL and LXC revised the article critically. LXC and WLZ designed the study. All authors have read and agreed to the published version of the manuscript. Corresponding author Ethics declarations Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethical Approval The medical ethics committee at the Guangdong Second Provincial General Hospital (Approval No. 2022-KY-KZ-252-02) approved this study. Additional information Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary Information Below is the link to the electronic supplementary material. 10528_2024_10725_MOESM1_ESM.tif (download TIF ) Supplementary file1 (TIF 2061 kb)—Figure S1: Workflow for the m6A regulators methylation modification patterns involved in immume microenvironment. Exploration of potential therapeutic agents in endometriosis by using data downloaded from the GEO database. Identification of the macrophage M2-related biomarkers through the CIBERSORT and WGCNA algorithms. 10528_2024_10725_MOESM2_ESM.tif (download TIF ) Supplementary file2 (TIF 5036 kb)—Figure S2: Expressions of m6A phenotype-related genes in ectopic and eutopic endometrium and normal endometrium. (A, B) The m6A genes based on ANOVA analysis in box plot and heat map of m6A genes for EC, EU and NM groups difference analysis, respectively. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001. METTL3, p = 0.0095; RBM15, p = 0.0073; CBLL1, p = 0.00019; YTHDF2, p = 1.44E-06; YTHDC1, p = 0.0051; ELAVL1, p = 9.39E-07; LRPPRC, p = 0.00177; FTO, p = 0.01077. 10528_2024_10725_MOESM3_ESM.tif (download TIF ) Supplementary file3 (TIF 701 kb)—Figure S3: Verification of m6A expressions in EC, EU and NM. A–C shows the m6A genes based on Wilcox’s test in box plot of EC versus EU, EC versus NM, and EU versus NM, respectively. Ns denotes that statistical differences are not significant. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001. 10528_2024_10725_MOESM4_ESM.tif (download TIF ) Supplementary file4 (TIF 6372 kb)—Figure S4: Spearman correlation analysis of the m6A modifification regulators in EC, EU and NM, respectively. The darker the color in the figure, the stronger the correlation, and the blank indicates that the statistical p-value is greater than or equal to 0.05, which does not reach the significant level. 10528_2024_10725_MOESM5_ESM.tif (download TIF ) Supplementary file5 (TIF 653 kb)—Figure S5: The forest map drawn by the multivariate logistic model established for EU and NM samples and three related genes in GSE141549. Figure S5B was showed the riskScore of EU in the training, and the Figure S4C showed the AUC of the classifier was 0.658, and the effect was poor. 10528_2024_10725_MOESM6_ESM.tif (download TIF ) Supplementary file6 (TIF 3275 kb)—Figure S6: The m6A genes associated with the immune microenvironment. (A) The relationship between gene METTL3 expression and B cell memory is depicted in A. The x-axis represents the expression level of gene METTL3, while the y-axis represents the proportion of immune cells in B cell memory. The figure illustrates a negative correlation between B cell memory content and METTL3 gene expression, as evidenced by a correlation coefficient of − 0.24 and a statistically significant p-value of 0.047 (p＜0.05). These findings suggest a potential association between low METTL3 expression and high B cell memory expression. Similar results were observed in (B)–(L), further supporting the potential relationship between these factors. (B)METTL3 and B cell naive; (C) CBLL1 and T cells memory resting; (D) CBLL1 and T cells regulatory(Tregs); (E) CBLL1 and Mast cells resting; (F) YTHDF2 and T cells CD4 memory resting; (G) YTHDF2 and T cells regulatory (Tregs); (H) YTHDF2 and T cells tollicular helper; (I) YTHDF2 and NK cells resting; (J) FTO and T cells CD4 memory resting; (K) FTO and T cells regulatory(Tregs); (L) FTO and Dendritic cells activated. 10528_2024_10725_MOESM7_ESM.tif (download TIF ) Supplementary file7 (TIF 2412 kb)—Figure S7: (A) The boxplot of immune-related genes. (B) The correlation diagram of immune-related genes and m6A genes. *p < 0.05; **p < 0.01; ***p < 0.001, ns, no significant. 10528_2024_10725_MOESM8_ESM.tif (download TIF ) Supplementary file8 (TIF 2643 kb)—Figure S8: (A) The boxplot of HLA related genes. (B) The correlation diagram of HLA genes and m6A genes. *p < 0.05; **p < 0.01; ***p < 0.001, ns, no significant. 10528_2024_10725_MOESM9_ESM.tif (download TIF ) Supplementary file9 (TIF 1653 kb)—Figure S9: A(a-d) Consistency clustering of EC and EU samples. B–D are boxplots of immune-related genes, HLA-related genes and inflammatory genes in different subtypes, respectively. 10528_2024_10725_MOESM11_ESM.xlsx (download XLSX ) Supplementary file11 (XLSX 15 kb)—Table S2: Clinical and histopathological characteristics of patients included in the study. DIE, Deep infiltrating endometriosis. Rights and permissions Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. About this article Cite this article Zheng, W., Fu, Z., Tan, X. et al. Bioinformatic Analysis of m6A Regulator-Mediated RNA Methylation Modification Patterns and Immune Microenvironment Characterization in Endometriosis. Biochem Genet 63, 433–464 (2025). https://doi.org/10.1007/s10528-024-10725-5 Received: Accepted: Published: Version of record: Issue date: DOI: https://doi.org/10.1007/s10528-024-10725-5