Exploration of m7G Modification Level and Immune Infiltration Characteristics in Patients with Primary Sclerosing Cholangitis

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Exploration of m7G Modification Level and Immune Infiltration Characteristics in Patients with Primary Sclerosing Cholangitis

https://doi.org/10.21203/rs.3.rs-5199555/v1

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Primary Sclerosing Cholangitis (PSC) is closely associated with immune disorders influenced by both genetic and environmental factors. The role of post-translational modifications in PSC remains to be explored. In this study, we analyzed 20 differentially expressed m7G-related genes between 220 PSC patients and 320 healthy individuals. Through three clustering strategies, we identified that m7G Cluster A and Gene Cluster B exhibited similar biological processes and immune cell infiltration patterns, primarily involving macrophages, T lymphocytes, and pathways related to infection, tumor transformation, and myeloid-derived suppressor cells. Notably, these clusters were linked to higher m7G scores, indicating a potential relationship between m7G RNA and the pathogenesis and progression of PSC. Furthermore, we identified eight key m7G-related genes and constructed a nomogram model to predict the risk of PSC, providing insights into its underlying mechanisms and potential clinical applications.

Health sciences/Diseases/Gastrointestinal diseases/Liver diseases/Primary sclerosing cholangitis

Biological sciences/Computational biology and bioinformatics/Genome informatics

Primary Sclerosing Cholangitis

N7-methylguanosine

Immune Cells

Gene-Clustering Approach

Disease Risk Model

Primary sclerosing cholangitis (PSC) is a relatively rare chronic cholestatic liver disease characterized by inflammation and fibrosis of intrahepatic and/or extrahepatic bile ducts, resulting in multifocal bile duct strictures and advanced liver disease. The global incidence of PSC ranges between approximately 0.78 and 31.7 per 100,000 individuals¹. Patients with PSC have a poor prognosis, often complicated by inflammatory bowel disease, with a high risk of developing cholangiocarcinoma and colorectal cancer. There is no effective treatment available, and the majority of patients ultimately require liver transplantation, which is prone to recurrence². The pathogenesis of PSC remains unclear, but it is associated with genetic and environmental factors such as diet, dysbiosis of the gut microbiota, and infections, leading to immune dysregulation³.

Post-transcriptional modifications have been recognized to play crucial roles in various physiological and pathological processes, attracting considerable attention⁴. Among them, N7-methylguanosine (m7G) is one of the most prevalent modifications, achieved through methylation of the seventh N of RNA guanosine, introducing positively charged or amphipathic ions into the nucleobase. Depending on the location of methylated guanosine, m7G modification can be classified into m7G-caps modification and internal m7G modification, found at the 5' cap end of eukaryotic mRNA and internally in mRNA, tRNA, rRNA, and miRNA, mediating almost the entire process of RNA metabolism, including pre-mRNA splicing, mRNA structural stability, transcription, translation, and nuclear export⁵.

With advances in RNA methylation detection methods and high-throughput technologies, an increasing number of diseases have been found to be associated with disruption of m7G methylation, including infection, inflammatory, autoimmune diseases, metabolic disease and various types of cancers^6-8. Particularly in cancer diseases, m7G modification and its regulatory factors play critical roles in the immunobiology of tumor immune microenvironment. METTL1-mediated m7G tRNA modification recruits bone marrow-derived suppressive cells (PMN-MDSC), promoting the progression of cholangiocarcinoma⁹.

The role of genetic susceptibility in the pathogenesis and progression of PSC is relatively well established¹⁰, but less attention has been paid to the role of RNA modifications. Currently, it is clear that post-transcriptional modifications play a role in other autoimmune liver diseases, such as miRNA-506 being associated with the stability of biliary epithelial cells in primary biliary cholangitis (PBC)¹¹, and lncRNA AKO53349 in CD8+ T cells being correlated with the onset and prognosis of PBC¹². However, the relationship between m7G modification and PSC has not yet been reported, and its functions and mechanisms remain to be explored. This study evaluated the expression of 20 key regulators of m7G RNA modification in patients with PSC. By employing three different strategies to classify disease subtypes, we performed immune infiltration analysis, functional enrichment analysis, and constructed a PSC risk model to investigate the role of m7G RNA modification in the onset and progression of PSC.

2.1. The m7G-clusters

This study included 220 patients with PSC and 320 healthy individuals. The expression levels of m7G-related genes between the two groups are illustrated in Figure 1A. Out of the 27 m7G-related genes, 20 showed differential expression. We designate these 20 differentially expressed genes as m7G-DEGs. Additionally, a distinct transcription profile was derived from the expression levels of m7G-DEGs between PSC and healthy patients (Figure 1B). The PAM algorithm was employed to ascertain the optimal consensus matrix. The optimal consensus matrix (k=2) was achieved (Figure 1C). Through clustering, PSC patients were segregated into two distinct groups: m7G-cluster A and Cluster B. Cluster A displays elevated expression levels of DCP2, NUDT16, NUDT4, AGO2, EIF4E3, EIF4G3, IFIT5, and LSM1. Meanwhile, cluster B showcases heightened expression of METTL1, NUDT3, EIF4E2, GEMIN5, EIF3D, EIF4A1, and SNUPN (Figure 1D). Different transcription profiles were generated based on the expression levels of the 20 m7G-DEGs between Cluster A and Cluster B (Figure 1E).

PCA elucidates discernible boundaries between groups (Figure 2A). Following that, we examined the expression of immune cells within the two groups. The analysis of immune infiltration revealed distinct profiles between cluster A and B. In cluster B, there was a significant presence of activated B cells, activated CD8+ T cells, CD56dim natural killer cells, and immature B cells. Conversely, cluster A displayed a higher abundance of activated dendritic cells, gamma delta T cells, myeloid-derived suppressor cells (MDSCs), macrophages, neutrophils, plasmacytoid dendritic cells, and type 2 T helper cells (Figure 2B). Additionally, the investigation examined the connections between 20 m7G-DEGs and immune cell populations (Figure 2C). EIF4E3 exhibited a stronger positive relationship with immune cells than the remaining genes under consideration. Patients were categorized into high and low EIF4E3 expression groups based on the gene expression levels of EIF4E3. Subsequently, the profiles of immune cells were compared between these two groups to analyze the differences. Patients with low EIF4E3 expression showed increased infiltration of activated B cells and immature B cells. In contrast, those with high EIF4E3 expression had a significant increase in the infiltration of activated dendritic cells, CD56bright natural killer cells, gamma delta T cells, MDSCs, macrophages, mast cells, monocytes, neutrophils, and plasmacytoid dendritic cells (Figure 2D).

We conducted an analysis of the DEGs between m7G cluster A and B, identifying a total of 4,535 genes that exhibited differential expression. Subsequently, we carried out gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for the DEGs identified in our study. The results of the GO enrichment analysis indicated that the DEGs were primarily associated with processes such as ribosome biogenesis, immune response-regulating signaling pathways, immune response-activating signaling pathways, mononuclear cell differentiation, rRNA metabolic processes, and immune response-regulating cell surface receptor signaling pathways (Figure 2E). The KEGG pathway analysis indicated that the enriched pathways were primarily within the domain of human infectious diseases, Coronavirus disease, the NF-kappa B signaling pathway, osteoclast differentiation, Th17 cell differentiation, as well as pathways related to PD-L1 expression, the PD-1 checkpoint pathway in cancer, and the hematopoietic cell lineage (Figure 2F).

2.2 The gene clusters

Then, 220 patients with PSC were re-grouped according to the expression level of DEGs, and the PAM algorithm was used to determine the two groups (Figure 3A). The patients were segregated into two clusters, designated as Gene Cluster A and Gene Cluster B, and a heat map was constructed to illustrate the expression patterns of the transcriptional profiles within these clusters (Figure 3B). The analysis of the expression of 20 m7G-DEGs in the two clusters revealed that METTL1, NUDT3, EIF4E2, EIF3D, and EIF4A1 were upregulated in gene cluster A. Conversely, NSUN2, NUDT4, AGO2, EIF4E3, and IFIT5 showed higher expression levels in gene cluster B (Figure 3C). Following this, the immune infiltration profiles of the two groups were also assessed. Gene cluster A displayed a higher level of immune infiltration by activated B cells, immature B cells, and regulatory T cells. In comparison, and gene cluster B had increased immune infiltration by CD56bright natural killer cells, MDSCs, and monocytes (Figure 3D).

2.3 Development of m7G-score

Furthermore, we developed the m7G-SCORE, a scoring system that calculates a composite score of m7G gene expression for PSC patients, assigning an individual m7G score to each patient. In the m7G-cluster grouping, cluster A is associated with a higher m7G score (p<0.001) (Figure 3E). Conversely, in the gene-cluster grouping, cluster B exhibits a higher m7G score (p<0.05) (Figure 3F). Based on the median m7G score, the 220 PSC patients were categorized into a high-score group and a low-score group. To enhance the differentiation of these three subtypes, we analyzed the demographics of the three patient groups. This comparison is presented in Figure 3G.

2.4 Construction of disease risk model

Prior to developing a nomogram model for assessing disease risk, it is necessary to identify characteristic genes using machine learning techniques. We aimed to determine whether RF or SVM is the more appropriate algorithm for gene selection. The selection is based on a comparison of the residual and the reverse cumulative distribution of the residuals. The findings indicate that RF machine learning outperforms SVM in screening for PSC-specific characteristic genes (Figure 4A-B). RF demonstrated a superior performance with an AUC of 1, while SVM achieved an AUC of 0.964 (Figure 4C). Consequently, we selected RF machine learning as the preferred method for identifying PSC-associated genetic signature genes. According to Figure 4D, we chose the ntree value that gives the lowest error rate (ntree = 428) (Figure 4D).

Subsequently, we determined the importance scores for m7G-related genes. Based on a threshold of an importance score greater than 10, we identified 8 genes: NSUN2, EIF4G3, EIF4E3, EIF3D, AGO2, LSM1, DCPS, and DCP2, as being significant in the screening process (Figure 5A). Utilizing these eight genes, we constructed a nomogram model designed to predict the risk of PSC disease (Figure 5B). In order to calibrate the nomogram, a calibration curve was used. The calibration curve showed satisfactory results (Figure 5C). The results of the CICA indicated that the high-risk PSC patients identified by the nomogram model closely aligned with the actual positive cases (Figure 5D). The DCA result demonstrated that nomogram model for predicting PSC risk provides substantial net benefit (Figure 5E).

In this study, we identified 20 differentially expressed m7G-related genes in patients with PSC. Using m7G clustering, gene clustering, and m7G score clustering analyses for different subtyping, we observed distinct immune cell infiltration patterns between m7G clusters A and B. Functional enrichment analysis of the differentially expressed genes revealed that they are primarily associated with processes and signaling pathways related to infection, immune response, and immune cell differentiation. Gene clustering analysis suggested that the m7G-DEGs in gene cluster B are related to tumors and infections. The types of immune cell infiltration in m7G cluster A and gene cluster B are similar to the immune cells infiltrating in PSC liver biopsy pathology observed clinically. These results indicate that m7G modification may play a role in the onset and progression of PSC.

The pathogenesis of PSC involves a combination of genetic, environmental, immune, bile acid metabolism, and gut microbiota factors¹³. Due to its close association with inflammatory bowel disease, researchers have proposed the "microbiome hypothesis" and the "gut-associated lymphocyte homing hypothesis"¹⁴^,¹⁵. According to these theories, microorganisms/metabolic molecules from the gut and T cells activated in gut-associated lymphoid tissues migrate to the liver, mediating immune-mediated damage. Pathologically, PSC patients often exhibit reactive T cells, macrophages, and neutrophils around the bile ducts, with T lymphocytes being the predominant cell type. The infiltration of immune cells is closely related to the progression of PSC.

In this study, the immune cell types with higher abundance in m7G cluster A are consistent with the immune cells infiltrating the bile ducts described above. It has been reported that T cell infiltration (high Th2 cells, low CD8, Th1 cells) is closely associated with high-risk scores for PSC, fibrosis, and cancer progression¹⁶. During the course of PSC, depletion of resident Kupffer cells (KC) in the liver sinusoids leads to the recruitment of monocyte-derived macrophages, which can further attract other inflammatory cells and participate in chronic cholestatic liver damage¹⁷. Additionally, MDSCs have been identified in these patients, and existing studies suggest that gut microbiota contribute to the development of an immunosuppressive environment in the liver by increasing MDSCs, thereby promoting the onset of liver cancer¹⁸. These findings indicate that patients with PSC in the m7G cluster A group may have a worse prognosis.

Among the m7G DEGs, EIF4E3 shows a significant positive correlation with immune cell infiltration. High expression of EIF4E3 in PSC is associated with diverse immune cell infiltration, which is consistent with previous reports. EIF4E3 regulates m7G modification by interacting with the 5'-cap structure of mRNA in a manner distinct from other members of the EIF4E family, thereby affecting mRNA export and translation¹⁹. The expression level of EIF4E3 correlates positively with the level of immune cell infiltration and influences immune infiltration and response to immunotherapy in patients with liver cancer and colorectal cancer²⁰^,²¹. EIF4E3 has been clearly identified as a tumor suppressor factor²².

In the gene grouping based on DEG expression levels, the DEGs involved in gene cluster B (NSUN2, NUDT4, AGO2, EIF4E3, and IFIT5) are primarily associated with tumors and infections¹⁹^,^23-26. In these patients, immune cell infiltration predominantly includes CD56 bright natural killer cells, MDSCs, and monocytes. This finding is consistent with the results observed in m7G cluster A. In the m7G-SCORE system we developed, both m7G cluster A and gene cluster B are associated with higher m7G scores, indicating a stronger correlation with m7G regulation for these patient groups. The close relationship between m7G-DEGs in these groups and infection, tumors, immune cell infiltration (including MDSCs and KC) further underscores the role of m7G RNA modification in the pathogenesis and progression of PSC.

We developed a predictive model for PSC risk based on eight m7G-related genes (NSUN2, EIF4G3, EIF4E3, EIF3D, AGO2, LSM1, DCPS, and DCP2). These genes are primarily associated with infection, immune cell activation, and tumors. For example, eIF3D²⁷, AGO2²⁸, and DCP2²⁹ can inhibit viral infection or replication, while LSM1 promotes viral RNA translation and replication³⁰. NSUN2, eIF3D, and AGO2 are linked to tumor cell proliferation, metastasis, and drug resistance^31-33. EIF4G3, eIF3D, AGO2, and DCPS are associated with T cell antigen recognition, activation, and IFN secretion²⁷^,^34-36. These results indicate that the development and progression of PSC are closely related to infection, immune cell activation and infiltration, and tumor transformation. The predictive model based on these genes will aid in further assessing the risk of the disease.

In summary, we have, for the first time, explored the changes and significance of m7G RNA modification in patients with PSC and developed a related disease risk prediction model. However, this study has certain limitations. Detailed clinical information was not available, making the clinical significance of different groups less clear. Additionally, the inability to obtain pathological results for these patients means that the types of immune cell infiltration analyzed may not accurately reflect the actual immune cell infiltration in PSC patients. There is also a lack of PSC biopsy samples to further validate our experimental results.

4.1. The RNA-Seq of patients with PSC

In our study, we utilized transcriptome data from the GSE177044 database to explore the expression profiles of m7G genes in PSC. This dataset consisted of RNA-Seq profiles obtained from 220 patients diagnosed with PSC and 320 healthy controls. R software was used for data processing and analysis. P-values less than 0.05 were considered statistically significant.

4.2. Grouping based on the m7G-related genes

Current studies primarily employ three strategies for classifying disease subtypes: m7G-clustering, gene-clustering, and m7G-score clustering. Our initial investigation focused on comparing the expression of m7G genes in PSC patients and healthy controls, allowing us to pinpoint differentially expressed genes (DEGs). By analyzing the expression profiles of these m7G-DEGs, we were able to classify 220 PSC patients into two distinct m7G-clusters, labeled as A and B.

The gene-clustering approach involved identifying DEGs between the m7G-cluster A and B patients. Furthermore, we calculated an m7G score through principal component analysis (PCA) based on the expression data of the 20 m7G-DEGs. This score, a composite of the first two principal components (PC1 and PC2), was used to divide patients into two groups: "m7G-score high" and "m7G-score low," using the median score as the threshold for separation.

4.3. Establishment of disease risk model for PSC

Our approach commenced with the application of machine learning algorithms to discern the unique genetic markers of PSC patients. Subsequently, we integrated these genetic markers into a nomogram model, which is intended to estimate the probability of disease occurrence in individuals with PSC. We utilized machine learning approaches, such as random forests (RF) and support vector machines (SVM), in our analysis. To select the better suitable machine learning method, we examined the residuals，ROC and the reverse cumulative distribution of the residuals. Additionally, we employed calibration curves, as well as decision curve analysis (DCA) and integrated calibration index (CICA), to validate the accuracy and performance of the calibration model.

DCA, decision curve analysis; DEGs, differentially expressed genes; KC, Kupffer cells; m7G, N7-methylguanosine; MDSC, marrow-derived suppressive cells; PCA, principal component analysis; PSC, Primary sclerosing cholangitis; RF, random forests; SVM, support vector machines

Authors' contributions

ZSL: Conceptualization, Project administration; HCW: Writing - Original Draft, Funding acquisition; LLL: Data Curation, Funding acquisition; YJG: Writing - Original Draft; YC Writing - Review & Editing; JF: Writing - Review & Editing, Supervision. All authors contributed to study supervision, read, and approved the final version of this manuscript.

Funding

This study was supported by grants from Natural Science Foundation of Fujian Province of China (Grant Number: 2021J011263) and the Joint Funds for the innovation of science and Technology, Fujian province (Grant Number:2023Y9160).

Ethics approval and consent to participate

All the data are obtained from the GEO databases, so that the informed consent can be guaranteed.

Availability of data and materials

PRO-Seq data were deposited into the Gene Expression Omnibus database under accession number GSE177044 and are available at the following URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE177044.

Competing interests

The authors declared that there is no conflict of interests.

Consent for publication

Not applicable.

Acknowledgements

Not applicable.

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Exploration of m7G Modification Level and Immune Infiltration Characteristics in Patients with Primary Sclerosing Cholangitis

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1. Introduction

2. Results

3. Discussion

4. Material and Methods

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