Identification of Key Genes and Exploration of Immune Activation Pathways in T-cell Mediated Rejection through Integrated Bulk and Single-Cell RNA-Seq Analysis with Machine Learning

Identification of Key Genes and Exploration of Immune Activation Pathways in T-cell Mediated Rejection through Integrated Bulk and Single-Cell RNA-Seq Analysis with Machine Learning | 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 Identification of Key Genes and Exploration of Immune Activation Pathways in T-cell Mediated Rejection through Integrated Bulk and Single-Cell RNA-Seq Analysis with Machine Learning Wenhao Shao, Huaxing Ding, Yan Wang, Zhiyong Shi, Hezhao Zhang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4583452/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Liver transplantation is the definitive treatment for end-stage liver disease, yet T-cell mediated rejection (TCMR) remains a major challenge. This study aims to identify key genes associated with TCMR and their potential biological processes and mechanisms. The GSE145780 dataset was subjected to differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms to pinpoint key genes associated with TCMR. Gene Set Enrichment Analysis (GSEA), immune infiltration analysis were conducted, along with constructing regulatory networks were constructed to ascertain the biological relevance of these genes. Expression validation was performed using single-cell RNA-seq (scRNA-seq) data and liver biopsy tissues from patients. We identified 5 key genes ( ITGB2, FCER1G, IL-18, GBP1, and CD53) that are associated with immunological functions, such as chemotactic activity, antigen processing, and T cell differentiation. GSEA highlighted enrichment in chemokine signaling and antigen presentation pathways. A lncRNA-miRNA-mRNA network was delineated, and drug target prediction yielded 26 potential drugs. Evaluation of expression levels in non-rejection (NR) and TCMR groups exhibited significant disparities in T cells and myeloid cells. Tissue analyses from patients corroborated the upregulation of GBP1, IL-18, CD53, and FCER1G in TCMR cases. Through comprehensive analysis, this research has identified 4 genes intimately connected with TCMR following liver transplantation, shedding light on the underlying immune activation pathways and suggesting putative targets for therapeutic intervention. Biological sciences/Immunology/Transplant immunology Biological sciences/Immunology/Transplant immunology/Allograft Biological sciences/Biological techniques/Bioinformatics T-cell mediated rejection Liver transplant rejection Single-cell RNA sequencing Enrichment analysis Immune analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction In 1963, Dr. Starzl pioneered human liver transplantation, marking a significant milestone in medical history 1 . While advancements in surgical techniques and perioperative care were made, early postoperative rejection resulted in one-year survival rates of only about 30% before the 1980s 2 . The introduction of immunosuppressive drugs, particularly cyclosporin A, revolutionized the field by significantly improving patient outcomes 3 . Subsequent drugs like tacrolimus and mycophenolate further enhanced survival rates. However, 15%-35% of liver transplant recipients still face T-cell-mediated rejection (TCMR) within two years post-transplant, highlighting the need for further molecular research to improve early diagnosis and treatment 4,5 . Recent advancements in next-generation sequencing, machine learning, and bioinformatics tools have dramatically enhanced our ability to analyze complex post-transplant data. These technologies allow for a detailed examination of immune cell diversity and gene expression changes associated with transplant rejection. Integrating omics research with clinical findings provides a deeper understanding of liver transplant rejection mechanisms and facilitates the development of personalized treatment strategies 6 . High-throughput sequencing technologies have enabled extensive studies on gene and protein expression alterations in both human and animal models, shedding light on the immune processes involved in rejection 7–10 . Research has identified specific transcriptional markers in peripheral blood as predictive indicators of rejection. For instance, proteomic analysis has highlighted Heme Oxygenase-1 (HO-1) as a potential biomarker for predicting acute rejection after liver transplantation 11 . Additionally, RNA sequencing (RNA-seq) has revealed significant increases in CXCL8 in liver tissues of pediatric patients with subclinical rejection post-transplantation. Clinical studies have shown that serum CXCL8 levels can diagnose subclinical rejection with high accuracy 12 . In conclusion, identifying novel biomarkers for liver transplant rejection and elucidating their molecular mechanisms is crucial for early diagnosis and effective treatment. This study analyzes liver biopsy transcriptome data from post-liver transplant patients using publicly available databases. By integrating bulk and single-cell RNA sequencing (scRNA-seq) with machine learning algorithms, we identified five key genes associated with TCMR. We further explored the pathways and biological processes enriched by these genes, providing insights into the molecular mechanisms of TCMR and potential therapeutic targets in liver transplantation. Results Research Workflow We identified LTR-DEGs by intersecting DEGs between the TCMR and NR groups with TCMR-related genes elucidated in the WGCNA. Following that, GO and KEGG analyses were conducted to explore the functions and pathways of these LTR-DEGs. A PPI network and machine learning algorithms were subsequently employed to pinpoint 5 key genes: ITGB2, FCER1G, IL-18, GBP1, and CD53. Single-gene GSEA analysis further revealed their biological function. Additionally, immune infiltration analysis was conducted to assess fluctuations in immune cells during TCMR and to examine the associations between these key genes and immune cells. Furthermore, We also constructed a lncRNA-miRNA-mRNA network pertinent to key genes, and predicted targeted drugs. The scRNA-seq data validated the differential expression of these key genes in distinct cell types. Lastly, we confirmed the expression levels of these genes in liver biopsy samples from post-transplantation patients (Fig. 1 A). Identification of DEGs and Key Module Genes A total of 180 DEGs were obtained between the TCMR and NR groups, including 171 upregulated genes and 9 downregulated genes (Fig. 1 B-C, Supplementary Table 1). To further identify genes associated with TCMR, WGCNA was conducted. Sample clustering results showed the presence of an outlier sample (GSM4332835), which was removed and the samples were re-clustered (Fig. 1 D-E). When the soft threshold was set to meet a scale-free distribution (Fig. 1 F), a total of 10 modules were obtained through the dynamic tree cutting algorithm (Fig. 1 G). Among them, the MEblue module (cor = 0.79, p = 5e-36) showed the highest correlation with TCMR. (Fig. 1 H). Therefore, this module was considered the key module, and the 5,498 genes within this module were defined as key module genes for subsequent analysis. Acquisition and functional enrichment of LTR-DEGs Based on the intersection of DEGs and key module genes, 119 LTR-DEGs were identified (Fig. 2 A). Enrichment analysis revealed that these LTR-DEGs are involved in 525 GO terms and 38 KEGG pathways. The GO analysis showed that the biological processes (BP) predominantly relate to cytokine-mediated signaling pathways, granulocyte chemotaxis, myeloid cell migration, and antigen processing and presentation. The cellular components (CC) are mainly associated with the secretory granule membrane, MHC protein complex, and MHC class II protein complex. The molecular functions (MF) are chiefly related to chemokine activity, chemokine receptor binding, MHC protein complex binding, and G protein-coupled receptor binding (Fig. 2 B-C, Supplementary Table 2). KEGG enrichment analysis included that pathways such as antigen processing and presentation, chemokine signaling pathways, and cytokine-cytokine receptor interaction (Fig. 2 D-E, Supplementary Table 3). This suggests that these LTR-DEGs are primarily involved in activating and regulating immune cells, as well as processing and presenting antigens, thereby initiating specific immune responses. Screening and expression analysis of key genes A PPI network constructed from the 119 LTR-DEGs consisted of 114 nodes and 735 edges (Fig. 3 A). Hub gene networks obtained through four algorithms are shown (Fig. 3 B). Ultimately, 10 candidate genes (CYBB, ITGB2, FCER1G, IL-18, CXCL11, GBP1, PLEK, CD53, LAPTM5, and C3AR1) were selected for further analysis (Fig. 3 C). LASSO regression analysis then selected 5 LASSO feature genes (ITGB2, FCER1G, IL-18, GBP1, and CD53) (Fig. 3 D-E). The SVM model had the lowest error rate with six genes (GBP1, FCER1G, ITGB2, CYBB, IL-18, and CD53) (Fig. 3 F). Overlapping LASSO and SVM-RFE feature genes, five key genes were identified (ITGB2, FCER1G, IL-18, GBP1, and CD53) (Fig. 3 G). Expression analysis showed significantly higher expression of these genes in the TCMR group than in the NR group (Supplementary Fig. 1). These genes, as novel biomarkers and therapeutic targets, provide deeper insight into TCMR mechanisms and may guide future diagnostic and therapeutic strategies. Single-gene GSEA Analysis Single-gene GSEA analysis identified that key genes are mainly enriched in antigen processing and presentation, cell cycle, T cell receptor pathway, chemokine signaling pathway, NK cell-mediated cytotoxicity, Toll-like receptor signaling pathway, etc. (Supplementary Fig. 2, Supplementary Tables 4–8). These findings indicate complex functions of key genes in processes like antigen recognition, activation of immune cells, and regulation of immune responses. Immune-related analysis of key genes Bar graphs displayed the proportions of 28 immune cells in each sample (Fig. 4 A). Significant differences in 27 immune cells were observed between the TCMR and NR groups, with a greater degree of differential immune cell infiltration in the TCMR group (Fig. 4 B). Correlation analysis showed that CD53 had the strongest positive correlation with Th1 cells. There was a significant positive correlation between key genes and all differential immune cells (Fig. 4 C, Supplementary Tables 9–10). Drug prediction for key genes and construction of lncRNA-miRNA-mRNA network We have identified 26 therapeutic drugs targeting three central genes by the DGIdb database (Fig. 5 A). The network included 19 drugs targeting ITGB2 (ERLIZUMAB, LIFITEGRAST, cyclophosphamide, EFALIZUMAB, etc.), 7 drugs targeting IL-18 (IBOCTADEKIN, THYROXINE, etc.), and one drug targeting FCER1G (ASPIRIN). To identify the lncRNA-miRNA-mRNA network of key genes in TCMR, several online databases (mirTarbase and starBase) were used. The network comprised 4 mRNAs (ITGB2, FCER1G, IL-18, and GBP1), 8 miRNAs (hsa-miR-26b-5p, hsa-miR-1225-3p, hsa-miR-335-5p, etc.), and 43 lncRNAs (AC008040.1, HAGLR, AL158206.1, FGD5-AS1, AC069281.2, etc.) (Fig. 5 B, Supplementary Tables 11–12). Expression analysis of key genes in different cell clusters After quality control, scRNA-seq data were available for subsequent analysis (Supplementary Fig. 3A). Cell screening and standard data processing yielded 2000 highly variable genes (Fig. 6 A). Subsequently, we further analyzed the top 20 PCs of the inflection point through principal component analysis (PCA) (Supplementary Fig. 3B). To assess the effectiveness of the clustering results, cell flow Sankey diagrams at different resolutions were drawn (Supplementary Fig. 3C). Unsupervised clustering divided these cells into 11 cell clusters, visualized using t-SNE (Figs. 6 B). Based on the expression patterns of marker genes in each cell cluster, these cell clusters were divided into 8 cell subtypes (T cells, B cells, myeloid cells, etc.) (Fig. 6 C-D). The clustering results for the NR and TCMR groups were presented separately (Fig. 6 E-F), with the highest proportion of T cells in both the TCMR and NR groups (Fig. 6 G). The proportions of endothelial cells, hepatocytes, and neutrophils were significantly higher in the NR group than in the TCMR group. Expression validation results showed key genes were primarily expressed in T cells, myeloid cells, and NK cells (Fig. 7 A-E). Significant differences in the expression of key genes between T cells and myeloid cells were observed between NR and TCMR groups. Key genes expression validation Immunohistochemical staining confirmed the expression levels of five key genes in TCMR patients and controls. Results showed FCER1G, IL-18, GBP1, and CD53 were upregulated in TCMR liver tissues compared to controls, while ITGB2 expression did not differ significantly (Fig. 8 ). These findings indicate FCER1G, IL-18, GBP1, and CD53 have potential as predictive indicators of immune rejection post-liver transplantation and may play significant roles in immune activation during rejection. Discussion Liver transplantation is a proven treatment for end-stage liver disease. However, TCMR substantially risks graft failure and elevates patient mortality, highlighting the imperative for investigating its molecular determinants 13 . Such inquiries are fundamental to enhancing early detection and therapeutic interventions. While the original study utilized the dataset to develop a diagnostic system for rejection, this study delves deeper into the molecular mechanisms of TCMR and identifies potential drug targets, thus providing a valuable complement to the original research 14 . In the initial phase of our research, We identified DEGs between the TCMR and NR groups from the GSE145780 dataset and combined a WGCNA network to select LTR-DEGs. GO and KEGG enrichment analyses connected these LTR-DEGs with immune functions such as cytokine activation and antigen processing. Following this, we constructed a PPI network and used 4 algorithms to further identify the candidate genes. Machine learning further validated the reliability of our findings, pinpointing five key genes (ITGB2, FCER1G, IL-18, GBP1, and CD53) linked to TCMR. Further interrogation using scRNA-seq confirmed that the five key genes were upregulated in both T cells and myeloid cells within the TCMR group. ITGB2/CD18 encodes an integrin beta chain protein, which is specifically expressed by leukocytes and can form corresponding β2 integrin heterodimers with four different known alpha chains, participating in cell adhesion and cell surface-mediated signal transduction 15 . Mac-1 (CD11b/CD18) and LFA-1 (CD11a/CD18), as members of the β2 integrin family, play critical roles in mediating leukocyte adhesion to target structures and other immune cells 16 . Research found that their interaction with ICAM-1 facilitates adhesion, crucial for lymphocyte regulation and inflammation control during early liver transplant rejection 17 ; this finding is consistent with our research results. Additionally, in mucosal biopsies from lung transplant recipients, ITGB2 has been identified as a key gene associated with lung transplant rejection 18 . And in a rabbit model of heterotopic heart transplantation, antibody therapy targeting ITGB2 has shown significant efficacy in preventing transplant rejection 19 . Leukocyte surface antigen CD53, a member of the tetraspanin family, is a transmembrane signaling and pro-inflammatory protein widely expressed in various types of immune cells 20,21 . Literature reports that CD53 can induce homotypic cell adhesion by activating β2 integrin LFA-1 in NK, T, and B cells, promoting adhesion and migration of immune cells 22,23 . Additionally, studies have shown that CD53 stimulation enhances T cell proliferation. It drives the transition of naive T cells to effector/memory phenotypes. These effects have been observed in vitro in human T cells and in genetically modified mice. Further investigation revealed CD53's role in regulating migration rate and stability of CD45RO on T cell surfaces. This regulation impacts TCR signal transduction. The absence of CD53 results in a loss of CD45RO expression on T cells. Consequently, it alters the expression of CD45 isoforms and diminishes T cell activation 24 . In a transcriptomic analysis of post-kidney transplant recipients, CD53 was identified as a biomarker for acute rejection in kidney transplantation 25 . FCER1G encodes the γ subunit of the high-affinity immunoglobulin E (IgE) Fc receptor, widely expressed in various types of immune cells. The FCER1G gene is involved in various biological processes, including neutrophil activation, T cell differentiation, immunoglobulin-mediated immune responses, and Fc receptor-mediated signaling pathways 26–28 . Similar to this study, FCER1G has been proven to be an upregulated gene highly representative of acute rejection in heart, liver, and kidney transplants in a transcriptome data analysis of various solid organ transplants 29 . GBP1 is a GTPase of the dynamin superfamily, involved in the regulation of membrane, cytoskeleton, and cell cycle progression dynamics 30 . Furthermore, GBP1 is key to host cell immunity and antibacterial protection, recognizing infection and inhibiting bacterial proliferation by activating inflammasomes and regulating pyroptosis 31 . GBP1 is highly expressed in macrophages, endothelial cells, and epithelial cells, with continued high expression levels in T cells after IFN-γ stimulation induced by interferon-γ (IFN-γ) 32 . In several studies of renal transplant rejection, researchers have confirmed through transcriptomic analysis and tissue biopsies that GBP1 may serve as a biological characteristic and predictive model for acute rejection 33–35 . Recent research have found that GBP1 binds to lipopolysaccharide (LPS) during bacterial infection, mediating the recruitment and activation of inflammatory caspase-4 through cleavage of GSDMD to induce pyroptosis 36, 37 . IL-18 is a member of the IL-1 family of pro-inflammatory cytokines, originally identified in the cytoplasm of macrophages as an inactive precursor (pro-IL-18). This precursor is transformed into an active mature form by proteolytic cleavage 38 . Recent proteomic studies have revealed IL-18 plays a significant biological role in regulating innate and adaptive immunity 39 . It also effectively induces IFN-γ production, exerting various immunoregulatory functions in the presence of different cytokines. These functions include regulating the transformation of T cells to regulatory T cells, modulating Th1 and Th2 responses, and participating in Th17 responses 40–42 . It has been documented in existing literature that IL-18 plays a significant role in the rejection reactions of various solid organs. In a rat orthotopic liver transplantation model, blocking the binding of IL-18 to its receptor significantly alleviated graft rejection 43 . Elevated levels of IL-18 have also been observed in acute rejection reactions following kidney and heart transplantation 44,45 .Historically, the maturation of IL-18 has been ascribed primarily to Caspase-1-mediated cleavage within the NLRP3 inflammasome complex 46,47 . However, recent studies indicate that in Caspase-4, as part of the non-canonical pyroptosis pathway, can recognize and cleave the same site on pro-IL-18, leading to its activation. This interaction establishes a Caspase-4–IL-18 axis that links non-canonical pyroptosis to adaptive immunity 48 . Our findings lend support to the hypothesis that both GBP1 and IL-18 levels increase substantially in liver tissues following TCMR. This suggests that post-transplantation dysbiosis and the aberrant translocation of lipopolysaccharide (LPS) into the liver through the gut-liver axis might provoke non-canonical pyroptotic pathways in liver macrophages and other cells, mediated by GBP1. This cascade potentially triggers TCMR via cytokine release subsequent to cell death. TCMR is characterized by a complex, multifactorial immune response, with extant experimental research underscoring the pivotal role of various immune cells in this process 49 . In our study, immune infiltration analysis of two groups of data revealed a significant increase in the majority of immune cells in TCMR tissues, with scRNA-seq clustering analysis further showing the most significant upregulation in T and myeloid cells. Additionally, correlation analysis of the five selected key genes with 28 types of immune cells showed strong correlations with T cells, B cells, myeloid cells, etc. scRNA-seq revealed that the expression of the five key genes in the TCMR group was significantly increased in T and myeloid cell clusters compared to the NR group. Integrating bulk RNA-seq and scRNA-seq analyses, these key genes appear to be pivotal in immune activation during TCMR, significantly influencing antigen presentation and lymphocyte activation between Antigen-Presenting Cell (APC) and T cells. To deepen the understanding of the specific mechanisms of TCMR, we explored differences in miRNA and lncRNA levels based on mRNA differences and constructed an entire ceRNA network, which may be involved in important biological pathways related to TCMR. Currently, some studies have found that these miRNAs affect the progression of certain immune-related diseases by regulating corresponding mRNAs. This holds important guiding significance for transplant rejection, which also involves aberrant activation and dysfunction of immune cells. Research has found that miR-346 expression is present in synovial cells activated by LPS induction of pattern recognition receptors (PRRs) in rheumatoid arthritis patients. This miRNA negatively regulates the IL-18 response in fibroblast-like synoviocytes by inhibiting the transcription of Bruton's tyrosine kinase. This mechanism has been validated in THP-1 cells, demonstrating miR-346's inhibitory effect on IL-18 50 . Researchers have found through dual-luciferase reporter analysis that miR-130a negatively regulates IL-18, thereby participating in the regulation of primary immune thrombocytopenia 51 . The miR-124-3p, which regulates GBP1, acts as a suppressor in various cancers and also plays a role in cell apoptosis and inflammatory lesions 52 . This approach potentially offers a new perspective for researching TCMR mechanisms, discovering novel therapeutic targets, and developing biomarkers. Moreover, drug prediction for the five key genes identified 26 therapeutic drugs corresponding to three key genes, with tacrolimus, mycophenolate mofetil, methylprednisolone, and cyclosporine already widely used in the treatment of liver transplant rejection 49,53,54 . Previous research has confirmed that colchicine inhibits T cell proliferation by blocking the expression of the IL-2R gene, damaging the antigen recognition process 55 . Lifitegrast, an integrin antagonist, has been proven in dry eye disease research to block the antigen transfer process from APC to T cells 56 . These targeted drugs may provide new insights for the prevention and treatment of TCMR after liver transplantation. Finally, immunohistochemistry was used to validate the upregulated expression of key genes in human liver biopsy tissues. The significant upregulation of four key genes: GBP1, IL-18, CD53, FCER1G, in the TCMR group further supports their potential as predictive indicators and therapeutic targets for liver transplant TCMR. This finding also provides a theoretical basis for further exploration of their molecular mechanisms. However, we acknowledge that our study has limitations, primarily due to a scarcity of post-transplant liver biopsy samples, which has led to insufficient validation. Our data were derived from public databases, which may introduce an element of heterogeneity. Consequently, the specific functions and underlying mechanisms of key genes in liver transplant rejection require further investigation. The predictions for miRNA, lncRNA, and targeted drugs are also based on public databases and have not been experimentally validated. To address this, we plan to conduct additional experimental and clinical research in subsequent studies. An increase in sample size may improve the reliability and significance of the results. Validation of the current study using data from only three patients and three controls may limit the ability to generalize the results. We will improve this in subsequent studies. Conclusions To sum up, this work provides insights into the molecular mechanisms of TCMR through a series of bioinformatics methods such as differential expression analysis, WGCNA, enrichment analysis, immune infiltration analysis, GSEA enrichment analysis, and single-cell analysis. Additionally, drug analysis targeting key genes helps provide more references for the treatment of post-transplant rejection. Materials and methods Source of data The GSE145780 dataset was derived from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ). This dataset included RNA-seq data from liver biopsy samples of 129 individuals in the no rejection (NR) group and 37 individuals in the TCMR. The HRA002091 dataset (Illumina NovaSeq 6000) was downloaded from the National Genomics Data Center (NGDC) database ( https://ngdc.cncb.ac.cn/ ), with scRNA-seq data selected from liver biopsy samples of 2 individuals each from NR and TCMR groups. Acquisition of differentially expressed genes (DEGs) DEGs between the TCMR and NR groups were selected in the GSE145780 dataset by using the limma package (v 3.52.4), applying an adjusted p value 1 57 . To visually represent the results of the differential expression analysis, volcano map and heatmap plotted were created by the ggplot2 package (v 3.3.6) and the pheatmap package (v 1.0.12) respectively 58,59 . Filtering for key module genes by weighted gene co-expression network analysis (WGCNA) A co-expression network was constructed from the GSE145780 dataset using WGCNA package (v 1.70-3) 60 . Initially, hierarchical clustering was applied to the samples to detect and exclude outliers, thus improving the reliability of the analysis. Subsequently, an optimal soft threshold (β) was selected to acquire a network topology that closely aligns with a scale-free model.Following this, a dendrogram of modules was produced based on measures of adjacency and topological overlap, with dynamic tree-cutting algorithms employed to identify modules. The association between each module and TCMR was evaluated by pearson, with the module displaying the highest absolute correlation with TCMR designated as the central module for subsequent analysis. The genes contained within this pivotal module were earmarked for further investigative research. Identification and functional analysis of DEGs associated with liver transplant rejection reactions (LTR-DEGs) The VennDiagram package (v 1.7.3) was utilised to identify the intersection of DEGs and key module genes, thereby obtaining DEGs associated with liver transplant rejection reactions (LTR-DEGs) 61 . Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of LTR-DEGs was performed via clusterProfiler package (v 4.7.1.001) (adjusted p value < 0.05) 62 . Construction of Protein-Protein Interaction (PPI) network To explore potential mutualistic relationships between LTR-DEGs, a PPI network was constructed using the search tool for the retrieval of interacting genes (STRING) database ( http://string.embl.de/ ). The top 30 hub genes of LTR-DEGs were calculated using 4 algorithms [Degree, Maximal Clique Centrality (MCC), Maximum Neighborhood Component (MNC), and Density of Maximum Neighborhood Component (DMNC)] of cytoHubba plug-in in Cytoscape software (v 3.8.2), respectively, and the network diagrams of hub genes filtered by the 4 algorithms were shown 63 . Then, the hub genes of the 4 algorithms were taken intersection to obtain the candidate genes. Machine learning screening and performance evaluation of key genes Two machine learning models were constructed based on candidate genes to screen feature genes separately. The least absolute shrinkage and selection operator (LASSO) COX regression analysis was carried out using the glmnet package (v 4.1-6) to obtain LASSO-feature genes with 10-fold cross validation 64 . Next, support vector machine recursive feature elimination (SVM-RFE) analysis was performed through caret package (v 6.0–93), and the genes included in the portfolio with the lowest error rate were selected as SVM-RFE-feature genes 65 . The key genes were screened by overlapping LASSO-feature genes and SVM-RFE-feature genes. Finally, expression differences of key genes between TCMR and NR groups were compared using the Wilcoxon method in the GSE145780 dataset. Single-gene Gene Set Enrichment Analysis (GSEA) analysis The single-gene GSEA was performed to find the enriched regulatory pathways and biological functions of key genes via clusterProfiler (v 4.7.1.001) with adjusted p value < 0.05 66 . The top 5 results for KEGG significance were visualized separately. Immune infiltration analysis The proportion of 28 immune cell subtypes in each sample was computed using the single-sample GSEA (ssGSEA) algorithm via GSVA package (v 1.42.0) 67 . Subsequently, differential immune cells between the TCMR and NR groups were identified and visualized using box plots. Meanwhile, the correlation analysis was performed between differential immune cells and key genes using Spearman method. Construction of key genes-drug interaction network The drug with key genes interactions was predicted from the DGIdb database ( http://dgidb.genome.wustl.edu/ ). A key genes-drug network was constructed based on the predicted results. Then, the network was visualized using Cytoscape software (v 3.8.2) 68 . Construction of lncRNA-miRNA-mRNA network The mirTarbase database ( https://mirtarbase.cuhk.edu.cn/ ) was used to predict miRNAs targeting key genes. Next, the starBase database ( https://rnasysu.com/encori/ ) was utilized to predict lncRNAs targeting the miRNAs (clipExpNum > 5). Finally, a lncRNA-miRNA-mRNA network was constructed on the basis of these miRNAs and lncRNAs using the Cytoscape software (v 3.8.2). Single-cell RNA-seq analysis In the HRA002091 dataset, cells in scRNA-seq data were filtered (200 < nCount < 25000, 200 < nFeature < 6000) using the Seurat package (v 4.0.5) 69 . Next, cells with less than 20% of mitochondrial genes were retained. nCount, nFeature, and percent.mt values were compared between samples after quality control. Subsequently, the four samples were integrated for subsequent analyses. Then, the data were normalized using the VST method, and subsequently, highly variable genes were selected. Next, the data were subjected to principal component analysis (PCA). The top 20 principal component (PC) were identified for subsequent analysis. Immediately afterwards, the cells were subjected to unsupervised cluster analysis using the FindNeighbors and FindClusters functions of the Seurat package (v 4.0.5). The results of clustering were visualized using the t-distributed stochastic neighborhood embedding (t-SNE) method. Based on the cell clustering results, the cell subpopulations were annotated using the SingleR algorithm (v 1.4.1) and manual annotation (marker genes for each cell of liver tissue in the CellMarker database) 70 . Moreover, the number of each cell type in the TCMR and NR groups was also counted. Subsequently, the expression of key genes was observed in individual cell clusters and the results were visualized by violin charts. External validation with clinical samples In this study, liver biopsy specimens were collected from post-liver transplant patients at the Friendship Hospital affiliated with Capital Medical University. Ethical approval was secured from the appropriate ethics committee, ensuring compliance with international ethical standards and safeguarding participant rights. Prior to sample collection, all participants were fully informed about the study and provided written informed consent. The inclusion criteria were ( 1 ) post-liver transplantation, ( 2 ) availability of liver biopsy samples, and ( 3 ) identification of rejection or non-rejection based on pathological diagnosis. Exclusion criteria included patients with concurrent severe infections or other complications. Sample grouping was strictly based on the latest pathological diagnosis Banff scoring criteria, which include scores for portal inflammation, bile duct inflammation within the portal areas, and endothelitis, along with the Rejection Activity Index (RAI). The TCMR group (n = 3) consisted of samples with an RAI score > 4, while the control group (n = 3) comprised samples from patients with normal liver function serology one year post-surgery and an RAI < 3. The liver tissue samples mentioned above were embedded in paraffin, sectioned, and typically deparaffinized to rehydrate, followed by washing with PBS. After incubation in 3% H 2 O 2 for 10 minutes, antibodies included mouse anti-CD53 (sc-390185, Santa Cruz, 1:150), mouse anti-FCER1G (sc-390222, Santa Cruz, 1:150), mouse anti-ITGB2 (sc-8420, Santa Cruz, 1:150), rabbit anti-GBP1 (A0570, ABclonal, 1:100), and rabbit anti-IL-18 (ab243091, Abcam, 1:150) were added separately and incubated overnight at 4°C. The specimens were then incubated with a secondary antibody at 37°C for 1 hour, followed by diaminobenzidine staining. The average optical density (AOD) of IHC staining was calculated by automatically measuring the integrated optical density (IOD) of DAB-positive signals using ImageJ software, and the AOD (IOD/area) of the corresponding areas was computed to indicate the expression of the target protein. Statistical analysis All bioinformatics analyses were performed using the R programming language (v 4.2.2). The Wilcoxon test was employed to contrast the data from different groups. Declarations Author contributions WS, HD, YW, ZS, HZ, FM, QC, HD, KL, and JX made substantial contributions to conception and design, and revised the manuscript critically for important intellectual content. LZ, and JX revised the manuscript and gave final approval of the version to be published. All authors read and approved the final manuscript. Funding This study was supported by grants from the National Natural Science Foundation of China (Grant No. 82072737), Central guidance local special projects of Shanxi Province (Grant No. YDZJSX2021B012), Natural Science Foundation of Health Commission of Shanxi Provincial (Grnat No. 2020085 and 2022142). Data availability The datasets analysed during the study are available in the GEO [https://www.ncbi.nlm.nih.gov/geo/, GSE14578], and NGDC [https://ngdc.cncb.ac.cn/, HRA002091 (Illumina NovaSeq 6000)]. Acknowledgment Authors would like to appreciate the efforts of GEO and NGDC databases. Ethics approval and consent to participate This study was approved by the Medical Ethics Committee of the First Hospital of Shanxi Medical University (Approval Number NO.KYLL-2023-143) and the Ethics Committee of the Friendship Hospital affiliated with Capital Medical University (Approval Number 2023-P2-222-02). 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Supplementary Files SupplementaryTable1diffSigFC1padj0.05.xlsx SupplementaryTable2GOvennenrich.xlsx SupplementaryTable3KEGGvennenrich.xlsx SupplementaryTable9hubImmuneCellcor.xlsx SupplementaryTable10hubImmuneCellpvalue.xlsx SupplementaryTable11miTarbasemRNAmiRNA.xlsx SupplementaryTable12starbasemiRNAlncRNA.xlsx SupplementaryTable13Clinicaldata.xlsx SupplementaryTable401.ITGB2.res.xlsx SupplementaryTable502.FCER1G.res.xlsx SupplementaryTable603.IL18.res.xlsx SupplementaryTable704.GBP1.res.xlsx SupplementaryTable805.CD53.res.xlsx Supplementarymaterial.docx Supplementaryfigure1.tif Supplementaryfigure2.tif Supplementaryfigure3.tif Cite Share Download PDF Status: Published Journal Publication published 16 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Jul, 2024 Reviews received at journal 27 Jul, 2024 Reviews received at journal 10 Jul, 2024 Reviewers agreed at journal 24 Jun, 2024 Reviewers agreed at journal 22 Jun, 2024 Reviewers invited by journal 18 Jun, 2024 Editor assigned by journal 18 Jun, 2024 Editor invited by journal 18 Jun, 2024 Submission checks completed at journal 15 Jun, 2024 First submitted to journal 14 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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(A) The workflow of the study. (B-C) Volcano plot and heatmap of DEGs; Orange represents upregulated genes, gray represents genes with no significant difference, and green represents downregulated genes. (D) The sample clustering diagram shows an outlier sample; red represents T-cell-mediated rejection (TCMR) samples and white represents no rejection (NR) samples. (E) Re-cluster after removing outlier samples. (F) Analysis of network topology for various soft-threshold powers. (G) Clustering dendrogram of DEGs, genes are divided into different modules. (H) Heatmap of module-trait correlations. Each gene depicts the correlation coefficients and \u003cem\u003ep\u003c/em\u003e-values. Genes are colored according to correlation intensity: red for positive and blue for negative, as per the color legend.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4583452/v1/24643f75d3b172bcef567239.png"},{"id":60337955,"identity":"c46f5f1f-3605-4df5-8c49-6017acd5ddde","added_by":"auto","created_at":"2024-07-15 17:41:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1016098,"visible":true,"origin":"","legend":"\u003cp\u003eDefinition and functional analysis of DEGs associated with liver transplant rejection reactions (LTR-DEGs).\u003cstrong\u003e \u003c/strong\u003e(A) Venn diagram illustrates LTR-DEGs by overlapping DEGs and key module genes. (B) Lollipop diagram of LTR-DEGs’ Gene Ontology (GO)enrichment analysis. BP: biological process. CC: cellular components. MF: molecular functions. (C) In the chord diagram, the left half represents LTR-DEGs with colors indicating logFC values: red for up-regulated genes, with darker shades indicating higher fold differences; the right half shows GO terms, each in a unique color. (D) Lollipop diagram of LTR-DEGs' Kyoto Encyclopedia of Genes and Genomes (KEGG)pathway enrichment analysis, displaying the 20 most significantly different pathways. (E) Chord diagram of LTR-DEGs' KEGG pathway enrichment analysis, showing immune-related pathways and their enriched genes.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4583452/v1/594fb0c79b86bbb81bb5b88f.png"},{"id":60339591,"identity":"fb02f0b2-16bf-4a51-a7f9-38e09d42ea50","added_by":"auto","created_at":"2024-07-15 18:05:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2031308,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the Protein-Protein Interaction (PPI) network and key gene screening. (A) The nodes indicate proteins, and the letters represent gene symbols. (B) The PPI network consists of 4 algorithmic network graphs, darker node colors indicate higher scores for proteins [1. Maximal Clique Centrality (MCC), 2. Density of Maximum Neighborhood Component (DMNC), 3. Degree, 4. Maximum Neighborhood Component (MNC)]. (C) A Venn diagram illustrates candidate genes by overlapping the hub genes of the 4 algorithms. (D,E) The results of least absolute shrinkage and selection operator (LASSO) COX regression analysis. The dotted line on the left indicates the position with the smallest cross-validation error. At this position (Lambda.min), one identifies the corresponding log (Lambda) value on the horizontal axis. The upper horizontal axis displays the number of feature genes to find the optimal log (Lambda) value, identifying the relevant genes and their coefficients, and explaining the proportion of residuals in the model. (F) When the gene count is six, the error rate is at its lowest. (G) Venn diagram illustrates key genes by overlapping the results of two machine algorithms.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4583452/v1/89974e4e0812b1ae52513889.png"},{"id":60337952,"identity":"b8525f94-c1de-474b-9ee8-714faabf2102","added_by":"auto","created_at":"2024-07-15 17:41:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":509810,"visible":true,"origin":"","legend":"\u003cp\u003eImmune cell profiling in TCMR and NR groups. (A) Bar graph of immune scores for 28 immune cell types between TCMR and NR groups. (B) Comparative scoring of 28 immune cell types in two groups of samples. (C) Correlation between key genes and immune cells, the x-axis represents immune cells, and the y-axis represents biomarkers. *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.0001, ns: \u003cem\u003ep\u003c/em\u003e\u0026gt; 0.05.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4583452/v1/2ba8c43cdf52536a76ebe917.png"},{"id":60337943,"identity":"8f93abdb-c55b-42e2-98a4-27d449807f74","added_by":"auto","created_at":"2024-07-15 17:41:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":563740,"visible":true,"origin":"","legend":"\u003cp\u003eNetworks of key genes-drug interaction and lncRNA-miRNA-mRNA. (A) Drug-target network diagram for key genes, with green rectangles representing drugs and red shapes representing key genes. (B) lncRNA-miRNA-mRNA network diagram, where red triangles represent mRNAs, green circles represent miRNAs, and blue rectangles represent lncRNAs. Red lines in the diagram indicate interactions between miRNAs and mRNAs, while grey lines indicate interactions between lncRNAs and miRNAs.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4583452/v1/c3ce477eb9bdc8b15f175b0f.png"},{"id":60337944,"identity":"9b69f969-10d8-434f-9517-cf20646bf9b2","added_by":"auto","created_at":"2024-07-15 17:41:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":897405,"visible":true,"origin":"","legend":"\u003cp\u003eCell clustering analysis of single-cell RNA sequence (scRNA-seq) data. (A) Red dots represent high-variability genes, and black dots represent invariant genes; the greater the height on the y-axis, the larger the variance and difference of the genes. The names of the top 10 high-variability genes are also displayed. (B) t-distributed stochastic neighborhood embedding (t-SNE) plot colored by different cell clusters. (C) Bubble chart of classic marker genes for each cell group. (D) t-SNE plot of cell clustering annotation results. (E) Cell clustering annotation results (NR group). (F) Cell clustering annotation results (TCMR group). (G) Proportion of each cell group among all cells.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4583452/v1/2868109cbd8ef14185bbbbdd.png"},{"id":60337947,"identity":"55d1e34e-9076-4b85-89b4-0a9e5ffd6126","added_by":"auto","created_at":"2024-07-15 17:41:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":730399,"visible":true,"origin":"","legend":"\u003cp\u003eExpression differences of key genes across various cell groups. (A) Expression level differences of CD53 across different cell groups. (B) Expression level differences of FCER1G across different cell groups. (C) Expression level differences of GBP1 across different cell groups. (D) Expression level differences of IL-18 across different cell groups. (E) Expression level differences of ITGB2 across different cell groups.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4583452/v1/df1c227dd453d2dc92280136.png"},{"id":60338844,"identity":"0b92802f-8310-4c41-86d0-ec203165e4f5","added_by":"auto","created_at":"2024-07-15 17:49:19","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":4067310,"visible":true,"origin":"","legend":"\u003cp\u003eImmunohistochemical staining of key genes in liver biopsy tissue.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4583452/v1/08dd5ac01900ceea8410cfdb.png"},{"id":67148951,"identity":"ba482629-1543-4be3-9ea3-ac346f4b8ec2","added_by":"auto","created_at":"2024-10-21 16:10:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10610067,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4583452/v1/7550c4ba-6412-4256-9682-192a57762456.pdf"},{"id":60337935,"identity":"0423202c-3ef6-4eda-92c7-7e20bc7aba86","added_by":"auto","created_at":"2024-07-15 17:41:18","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27551,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1diffSigFC1padj0.05.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4583452/v1/6b2f2993190e40a53e7e8ab1.xlsx"},{"id":60337941,"identity":"f7a00fda-f599-4faf-a686-74c6dbbbd7c3","added_by":"auto","created_at":"2024-07-15 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17:41:20","extension":"docx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":14872,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4583452/v1/9f2fabb82496a61a5381d239.docx"},{"id":60338847,"identity":"f26e4644-be10-4b36-93eb-33ddfd3e323c","added_by":"auto","created_at":"2024-07-15 17:49:19","extension":"tif","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":446064,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4583452/v1/586e7dd0cd9e17962860cf9f.tif"},{"id":60337945,"identity":"1a258a9f-993a-47b0-b21d-4ca0df0f414d","added_by":"auto","created_at":"2024-07-15 17:41:19","extension":"tif","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":2493444,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-4583452/v1/feeb3c2dc9fd622c696929d5.tif"},{"id":60338848,"identity":"e10972ef-0829-42af-a72d-e28493468f6e","added_by":"auto","created_at":"2024-07-15 17:49:20","extension":"tif","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":3763428,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-4583452/v1/83c9594b5e27e89ba34ca338.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Key Genes and Exploration of Immune Activation Pathways in T-cell Mediated Rejection through Integrated Bulk and Single-Cell RNA-Seq Analysis with Machine Learning","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn 1963, Dr. Starzl pioneered human liver transplantation, marking a significant milestone in medical history \u003csup\u003e1\u003c/sup\u003e. While advancements in surgical techniques and perioperative care were made, early postoperative rejection resulted in one-year survival rates of only about 30% before the 1980s \u003csup\u003e2\u003c/sup\u003e. The introduction of immunosuppressive drugs, particularly cyclosporin A, revolutionized the field by significantly improving patient outcomes \u003csup\u003e3\u003c/sup\u003e. Subsequent drugs like tacrolimus and mycophenolate further enhanced survival rates. However, 15%-35% of liver transplant recipients still face T-cell-mediated rejection (TCMR) within two years post-transplant, highlighting the need for further molecular research to improve early diagnosis and treatment \u003csup\u003e4,5\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent advancements in next-generation sequencing, machine learning, and bioinformatics tools have dramatically enhanced our ability to analyze complex post-transplant data. These technologies allow for a detailed examination of immune cell diversity and gene expression changes associated with transplant rejection. Integrating omics research with clinical findings provides a deeper understanding of liver transplant rejection mechanisms and facilitates the development of personalized treatment strategies \u003csup\u003e6\u003c/sup\u003e. High-throughput sequencing technologies have enabled extensive studies on gene and protein expression alterations in both human and animal models, shedding light on the immune processes involved in rejection \u003csup\u003e7\u0026ndash;10\u003c/sup\u003e. Research has identified specific transcriptional markers in peripheral blood as predictive indicators of rejection. For instance, proteomic analysis has highlighted Heme Oxygenase-1 (HO-1) as a potential biomarker for predicting acute rejection after liver transplantation \u003csup\u003e11\u003c/sup\u003e. Additionally, RNA sequencing (RNA-seq) has revealed significant increases in CXCL8 in liver tissues of pediatric patients with subclinical rejection post-transplantation. Clinical studies have shown that serum CXCL8 levels can diagnose subclinical rejection with high accuracy \u003csup\u003e12\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn conclusion, identifying novel biomarkers for liver transplant rejection and elucidating their molecular mechanisms is crucial for early diagnosis and effective treatment. This study analyzes liver biopsy transcriptome data from post-liver transplant patients using publicly available databases. By integrating bulk and single-cell RNA sequencing (scRNA-seq) with machine learning algorithms, we identified five key genes associated with TCMR. We further explored the pathways and biological processes enriched by these genes, providing insights into the molecular mechanisms of TCMR and potential therapeutic targets in liver transplantation.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch Workflow\u003c/h2\u003e \u003cp\u003eWe identified LTR-DEGs by intersecting DEGs between the TCMR and NR groups with TCMR-related genes elucidated in the WGCNA. Following that, GO and KEGG analyses were conducted to explore the functions and pathways of these LTR-DEGs. A PPI network and machine learning algorithms were subsequently employed to pinpoint 5 key genes: ITGB2, FCER1G, IL-18, GBP1, and CD53. Single-gene GSEA analysis further revealed their biological function. Additionally, immune infiltration analysis was conducted to assess fluctuations in immune cells during TCMR and to examine the associations between these key genes and immune cells. Furthermore, We also constructed a lncRNA-miRNA-mRNA network pertinent to key genes, and predicted targeted drugs. The scRNA-seq data validated the differential expression of these key genes in distinct cell types. Lastly, we confirmed the expression levels of these genes in liver biopsy samples from post-transplantation patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of DEGs and Key Module Genes\u003c/h2\u003e \u003cp\u003eA total of 180 DEGs were obtained between the TCMR and NR groups, including 171 upregulated genes and 9 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-C, Supplementary Table\u0026nbsp;1). To further identify genes associated with TCMR, WGCNA was conducted. Sample clustering results showed the presence of an outlier sample (GSM4332835), which was removed and the samples were re-clustered (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD-E). When the soft threshold was set to meet a scale-free distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF), a total of 10 modules were obtained through the dynamic tree cutting algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). Among them, the MEblue module (cor\u0026thinsp;=\u0026thinsp;0.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5e-36) showed the highest correlation with TCMR. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). Therefore, this module was considered the key module, and the 5,498 genes within this module were defined as key module genes for subsequent analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAcquisition and functional enrichment of LTR-DEGs\u003c/h2\u003e \u003cp\u003eBased on the intersection of DEGs and key module genes, 119 LTR-DEGs were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Enrichment analysis revealed that these LTR-DEGs are involved in 525 GO terms and 38 KEGG pathways. The GO analysis showed that the biological processes (BP) predominantly relate to cytokine-mediated signaling pathways, granulocyte chemotaxis, myeloid cell migration, and antigen processing and presentation. The cellular components (CC) are mainly associated with the secretory granule membrane, MHC protein complex, and MHC class II protein complex. The molecular functions (MF) are chiefly related to chemokine activity, chemokine receptor binding, MHC protein complex binding, and G protein-coupled receptor binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-C, Supplementary Table\u0026nbsp;2). KEGG enrichment analysis included that pathways such as antigen processing and presentation, chemokine signaling pathways, and cytokine-cytokine receptor interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-E, Supplementary Table\u0026nbsp;3). This suggests that these LTR-DEGs are primarily involved in activating and regulating immune cells, as well as processing and presenting antigens, thereby initiating specific immune responses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eScreening and expression analysis of key genes\u003c/h2\u003e \u003cp\u003eA PPI network constructed from the 119 LTR-DEGs consisted of 114 nodes and 735 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Hub gene networks obtained through four algorithms are shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Ultimately, 10 candidate genes (CYBB, ITGB2, FCER1G, IL-18, CXCL11, GBP1, PLEK, CD53, LAPTM5, and C3AR1) were selected for further analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). LASSO regression analysis then selected 5 LASSO feature genes (ITGB2, FCER1G, IL-18, GBP1, and CD53) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-E). The SVM model had the lowest error rate with six genes (GBP1, FCER1G, ITGB2, CYBB, IL-18, and CD53) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Overlapping LASSO and SVM-RFE feature genes, five key genes were identified (ITGB2, FCER1G, IL-18, GBP1, and CD53) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Expression analysis showed significantly higher expression of these genes in the TCMR group than in the NR group (Supplementary Fig.\u0026nbsp;1). These genes, as novel biomarkers and therapeutic targets, provide deeper insight into TCMR mechanisms and may guide future diagnostic and therapeutic strategies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSingle-gene GSEA Analysis\u003c/h2\u003e \u003cp\u003eSingle-gene GSEA analysis identified that key genes are mainly enriched in antigen processing and presentation, cell cycle, T cell receptor pathway, chemokine signaling pathway, NK cell-mediated cytotoxicity, Toll-like receptor signaling pathway, etc. (Supplementary Fig.\u0026nbsp;2, Supplementary Tables\u0026nbsp;4\u0026ndash;8). These findings indicate complex functions of key genes in processes like antigen recognition, activation of immune cells, and regulation of immune responses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImmune-related analysis of key genes\u003c/h2\u003e \u003cp\u003eBar graphs displayed the proportions of 28 immune cells in each sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Significant differences in 27 immune cells were observed between the TCMR and NR groups, with a greater degree of differential immune cell infiltration in the TCMR group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Correlation analysis showed that CD53 had the strongest positive correlation with Th1 cells. There was a significant positive correlation between key genes and all differential immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, Supplementary Tables\u0026nbsp;9\u0026ndash;10).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDrug prediction for key genes and construction of lncRNA-miRNA-mRNA network\u003c/h3\u003e\n\u003cp\u003eWe have identified 26 therapeutic drugs targeting three central genes by the DGIdb database (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The network included 19 drugs targeting ITGB2 (ERLIZUMAB, LIFITEGRAST, cyclophosphamide, EFALIZUMAB, etc.), 7 drugs targeting IL-18 (IBOCTADEKIN, THYROXINE, etc.), and one drug targeting FCER1G (ASPIRIN). To identify the lncRNA-miRNA-mRNA network of key genes in TCMR, several online databases (mirTarbase and starBase) were used. The network comprised 4 mRNAs (ITGB2, FCER1G, IL-18, and GBP1), 8 miRNAs (hsa-miR-26b-5p, hsa-miR-1225-3p, hsa-miR-335-5p, etc.), and 43 lncRNAs (AC008040.1, HAGLR, AL158206.1, FGD5-AS1, AC069281.2, etc.) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, Supplementary Tables\u0026nbsp;11\u0026ndash;12).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eExpression analysis of key genes in different cell clusters\u003c/h2\u003e \u003cp\u003eAfter quality control, scRNA-seq data were available for subsequent analysis (Supplementary Fig.\u0026nbsp;3A). Cell screening and standard data processing yielded 2000 highly variable genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Subsequently, we further analyzed the top 20 PCs of the inflection point through principal component analysis (PCA) (Supplementary Fig.\u0026nbsp;3B). To assess the effectiveness of the clustering results, cell flow Sankey diagrams at different resolutions were drawn (Supplementary Fig.\u0026nbsp;3C). Unsupervised clustering divided these cells into 11 cell clusters, visualized using t-SNE (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Based on the expression patterns of marker genes in each cell cluster, these cell clusters were divided into 8 cell subtypes (T cells, B cells, myeloid cells, etc.) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-D). The clustering results for the NR and TCMR groups were presented separately (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE-F), with the highest proportion of T cells in both the TCMR and NR groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). The proportions of endothelial cells, hepatocytes, and neutrophils were significantly higher in the NR group than in the TCMR group. Expression validation results showed key genes were primarily expressed in T cells, myeloid cells, and NK cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-E). Significant differences in the expression of key genes between T cells and myeloid cells were observed between NR and TCMR groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eKey genes expression validation\u003c/h2\u003e \u003cp\u003eImmunohistochemical staining confirmed the expression levels of five key genes in TCMR patients and controls. Results showed FCER1G, IL-18, GBP1, and CD53 were upregulated in TCMR liver tissues compared to controls, while ITGB2 expression did not differ significantly (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). These findings indicate FCER1G, IL-18, GBP1, and CD53 have potential as predictive indicators of immune rejection post-liver transplantation and may play significant roles in immune activation during rejection.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eLiver transplantation is a proven treatment for end-stage liver disease. However, TCMR substantially risks graft failure and elevates patient mortality, highlighting the imperative for investigating its molecular determinants \u003csup\u003e13\u003c/sup\u003e. Such inquiries are fundamental to enhancing early detection and therapeutic interventions. While the original study utilized the dataset to develop a diagnostic system for rejection, this study delves deeper into the molecular mechanisms of TCMR and identifies potential drug targets, thus providing a valuable complement to the original research \u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the initial phase of our research, We identified DEGs between the TCMR and NR groups from the GSE145780 dataset and combined a WGCNA network to select LTR-DEGs. GO and KEGG enrichment analyses connected these LTR-DEGs with immune functions such as cytokine activation and antigen processing. Following this, we constructed a PPI network and used 4 algorithms to further identify the candidate genes. Machine learning further validated the reliability of our findings, pinpointing five key genes (ITGB2, FCER1G, IL-18, GBP1, and CD53) linked to TCMR. Further interrogation using scRNA-seq confirmed that the five key genes were upregulated in both T cells and myeloid cells within the TCMR group.\u003c/p\u003e \u003cp\u003eITGB2/CD18 encodes an integrin beta chain protein, which is specifically expressed by leukocytes and can form corresponding β2 integrin heterodimers with four different known alpha chains, participating in cell adhesion and cell surface-mediated signal transduction \u003csup\u003e15\u003c/sup\u003e. Mac-1 (CD11b/CD18) and LFA-1 (CD11a/CD18), as members of the β2 integrin family, play critical roles in mediating leukocyte adhesion to target structures and other immune cells \u003csup\u003e16\u003c/sup\u003e. Research found that their interaction with ICAM-1 facilitates adhesion, crucial for lymphocyte regulation and inflammation control during early liver transplant rejection \u003csup\u003e17\u003c/sup\u003e; this finding is consistent with our research results. Additionally, in mucosal biopsies from lung transplant recipients, ITGB2 has been identified as a key gene associated with lung transplant rejection\u003csup\u003e18\u003c/sup\u003e. And in a rabbit model of heterotopic heart transplantation, antibody therapy targeting ITGB2 has shown significant efficacy in preventing transplant rejection\u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLeukocyte surface antigen CD53, a member of the tetraspanin family, is a transmembrane signaling and pro-inflammatory protein widely expressed in various types of immune cells \u003csup\u003e20,21\u003c/sup\u003e. Literature reports that CD53 can induce homotypic cell adhesion by activating β2 integrin LFA-1 in NK, T, and B cells, promoting adhesion and migration of immune cells \u003csup\u003e22,23\u003c/sup\u003e. Additionally, studies have shown that CD53 stimulation enhances T cell proliferation. It drives the transition of naive T cells to effector/memory phenotypes. These effects have been observed in vitro in human T cells and in genetically modified mice. Further investigation revealed CD53's role in regulating migration rate and stability of CD45RO on T cell surfaces. This regulation impacts TCR signal transduction. The absence of CD53 results in a loss of CD45RO expression on T cells. Consequently, it alters the expression of CD45 isoforms and diminishes T cell activation \u003csup\u003e24\u003c/sup\u003e. In a transcriptomic analysis of post-kidney transplant recipients, CD53 was identified as a biomarker for acute rejection in kidney transplantation\u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFCER1G encodes the γ subunit of the high-affinity immunoglobulin E (IgE) Fc receptor, widely expressed in various types of immune cells. The FCER1G gene is involved in various biological processes, including neutrophil activation, T cell differentiation, immunoglobulin-mediated immune responses, and Fc receptor-mediated signaling pathways \u003csup\u003e26\u0026ndash;28\u003c/sup\u003e. Similar to this study, FCER1G has been proven to be an upregulated gene highly representative of acute rejection in heart, liver, and kidney transplants in a transcriptome data analysis of various solid organ transplants \u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGBP1 is a GTPase of the dynamin superfamily, involved in the regulation of membrane, cytoskeleton, and cell cycle progression dynamics \u003csup\u003e30\u003c/sup\u003e. Furthermore, GBP1 is key to host cell immunity and antibacterial protection, recognizing infection and inhibiting bacterial proliferation by activating inflammasomes and regulating pyroptosis \u003csup\u003e31\u003c/sup\u003e. GBP1 is highly expressed in macrophages, endothelial cells, and epithelial cells, with continued high expression levels in T cells after IFN-γ stimulation induced by interferon-γ (IFN-γ) \u003csup\u003e32\u003c/sup\u003e. In several studies of renal transplant rejection, researchers have confirmed through transcriptomic analysis and tissue biopsies that GBP1 may serve as a biological characteristic and predictive model for acute rejection\u003csup\u003e33\u0026ndash;35\u003c/sup\u003e. Recent research have found that GBP1 binds to lipopolysaccharide (LPS) during bacterial infection, mediating the recruitment and activation of inflammatory caspase-4 through cleavage of GSDMD to induce pyroptosis \u003csup\u003e36, 37\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIL-18 is a member of the IL-1 family of pro-inflammatory cytokines, originally identified in the cytoplasm of macrophages as an inactive precursor (pro-IL-18). This precursor is transformed into an active mature form by proteolytic cleavage \u003csup\u003e38\u003c/sup\u003e. Recent proteomic studies have revealed IL-18 plays a significant biological role in regulating innate and adaptive immunity \u003csup\u003e39\u003c/sup\u003e. It also effectively induces IFN-γ production, exerting various immunoregulatory functions in the presence of different cytokines. These functions include regulating the transformation of T cells to regulatory T cells, modulating Th1 and Th2 responses, and participating in Th17 responses \u003csup\u003e40\u0026ndash;42\u003c/sup\u003e. It has been documented in existing literature that IL-18 plays a significant role in the rejection reactions of various solid organs. In a rat orthotopic liver transplantation model, blocking the binding of IL-18 to its receptor significantly alleviated graft rejection\u003csup\u003e43\u003c/sup\u003e. Elevated levels of IL-18 have also been observed in acute rejection reactions following kidney and heart transplantation\u003csup\u003e44,45\u003c/sup\u003e.Historically, the maturation of IL-18 has been ascribed primarily to Caspase-1-mediated cleavage within the NLRP3 inflammasome complex \u003csup\u003e46,47\u003c/sup\u003e. However, recent studies indicate that in Caspase-4, as part of the non-canonical pyroptosis pathway, can recognize and cleave the same site on pro-IL-18, leading to its activation. This interaction establishes a Caspase-4\u0026ndash;IL-18 axis that links non-canonical pyroptosis to adaptive immunity \u003csup\u003e48\u003c/sup\u003e. Our findings lend support to the hypothesis that both GBP1 and IL-18 levels increase substantially in liver tissues following TCMR. This suggests that post-transplantation dysbiosis and the aberrant translocation of lipopolysaccharide (LPS) into the liver through the gut-liver axis might provoke non-canonical pyroptotic pathways in liver macrophages and other cells, mediated by GBP1. This cascade potentially triggers TCMR via cytokine release subsequent to cell death.\u003c/p\u003e \u003cp\u003eTCMR is characterized by a complex, multifactorial immune response, with extant experimental research underscoring the pivotal role of various immune cells in this process \u003csup\u003e49\u003c/sup\u003e. In our study, immune infiltration analysis of two groups of data revealed a significant increase in the majority of immune cells in TCMR tissues, with scRNA-seq clustering analysis further showing the most significant upregulation in T and myeloid cells. Additionally, correlation analysis of the five selected key genes with 28 types of immune cells showed strong correlations with T cells, B cells, myeloid cells, etc. scRNA-seq revealed that the expression of the five key genes in the TCMR group was significantly increased in T and myeloid cell clusters compared to the NR group. Integrating bulk RNA-seq and scRNA-seq analyses, these key genes appear to be pivotal in immune activation during TCMR, significantly influencing antigen presentation and lymphocyte activation between Antigen-Presenting Cell (APC) and T cells.\u003c/p\u003e \u003cp\u003eTo deepen the understanding of the specific mechanisms of TCMR, we explored differences in miRNA and lncRNA levels based on mRNA differences and constructed an entire ceRNA network, which may be involved in important biological pathways related to TCMR. Currently, some studies have found that these miRNAs affect the progression of certain immune-related diseases by regulating corresponding mRNAs. This holds important guiding significance for transplant rejection, which also involves aberrant activation and dysfunction of immune cells. Research has found that miR-346 expression is present in synovial cells activated by LPS induction of pattern recognition receptors (PRRs) in rheumatoid arthritis patients. This miRNA negatively regulates the IL-18 response in fibroblast-like synoviocytes by inhibiting the transcription of Bruton's tyrosine kinase. This mechanism has been validated in THP-1 cells, demonstrating miR-346's inhibitory effect on IL-18\u003csup\u003e50\u003c/sup\u003e. Researchers have found through dual-luciferase reporter analysis that miR-130a negatively regulates IL-18, thereby participating in the regulation of primary immune thrombocytopenia\u003csup\u003e51\u003c/sup\u003e. The miR-124-3p, which regulates GBP1, acts as a suppressor in various cancers and also plays a role in cell apoptosis and inflammatory lesions\u003csup\u003e52\u003c/sup\u003e. This approach potentially offers a new perspective for researching TCMR mechanisms, discovering novel therapeutic targets, and developing biomarkers. Moreover, drug prediction for the five key genes identified 26 therapeutic drugs corresponding to three key genes, with tacrolimus, mycophenolate mofetil, methylprednisolone, and cyclosporine already widely used in the treatment of liver transplant rejection \u003csup\u003e49,53,54\u003c/sup\u003e. Previous research has confirmed that colchicine inhibits T cell proliferation by blocking the expression of the IL-2R gene, damaging the antigen recognition process \u003csup\u003e55\u003c/sup\u003e. Lifitegrast, an integrin antagonist, has been proven in dry eye disease research to block the antigen transfer process from APC to T cells \u003csup\u003e56\u003c/sup\u003e. These targeted drugs may provide new insights for the prevention and treatment of TCMR after liver transplantation.\u003c/p\u003e \u003cp\u003eFinally, immunohistochemistry was used to validate the upregulated expression of key genes in human liver biopsy tissues. The significant upregulation of four key genes: GBP1, IL-18, CD53, FCER1G, in the TCMR group further supports their potential as predictive indicators and therapeutic targets for liver transplant TCMR. This finding also provides a theoretical basis for further exploration of their molecular mechanisms. However, we acknowledge that our study has limitations, primarily due to a scarcity of post-transplant liver biopsy samples, which has led to insufficient validation. Our data were derived from public databases, which may introduce an element of heterogeneity. Consequently, the specific functions and underlying mechanisms of key genes in liver transplant rejection require further investigation. The predictions for miRNA, lncRNA, and targeted drugs are also based on public databases and have not been experimentally validated. To address this, we plan to conduct additional experimental and clinical research in subsequent studies. An increase in sample size may improve the reliability and significance of the results. Validation of the current study using data from only three patients and three controls may limit the ability to generalize the results. We will improve this in subsequent studies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTo sum up, this work provides insights into the molecular mechanisms of TCMR through a series of bioinformatics methods such as differential expression analysis, WGCNA, enrichment analysis, immune infiltration analysis, GSEA enrichment analysis, and single-cell analysis. Additionally, drug analysis targeting key genes helps provide more references for the treatment of post-transplant rejection.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSource of data\u003c/h2\u003e \u003cp\u003eThe GSE145780 dataset was derived from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This dataset included RNA-seq data from liver biopsy samples of 129 individuals in the no rejection (NR) group and 37 individuals in the TCMR. The HRA002091 dataset (Illumina NovaSeq 6000) was downloaded from the National Genomics Data Center (NGDC) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngdc.cncb.ac.cn/\u003c/span\u003e\u003cspan address=\"https://ngdc.cncb.ac.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with scRNA-seq data selected from liver biopsy samples of 2 individuals each from NR and TCMR groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAcquisition of differentially expressed genes (DEGs)\u003c/h2\u003e \u003cp\u003eDEGs between the TCMR and NR groups were selected in the GSE145780 dataset by using the limma package (v 3.52.4), applying an adjusted \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt; 1 \u003csup\u003e57\u003c/sup\u003e. To visually represent the results of the differential expression analysis, volcano map and heatmap plotted were created by the ggplot2 package (v 3.3.6) and the pheatmap package (v 1.0.12) respectively \u003csup\u003e58,59\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFiltering for key module genes by weighted gene co-expression network analysis (WGCNA)\u003c/h2\u003e \u003cp\u003eA co-expression network was constructed from the GSE145780 dataset using WGCNA package (v 1.70-3) \u003csup\u003e60\u003c/sup\u003e. Initially, hierarchical clustering was applied to the samples to detect and exclude outliers, thus improving the reliability of the analysis. Subsequently, an optimal soft threshold (β) was selected to acquire a network topology that closely aligns with a scale-free model.Following this, a dendrogram of modules was produced based on measures of adjacency and topological overlap, with dynamic tree-cutting algorithms employed to identify modules. The association between each module and TCMR was evaluated by pearson, with the module displaying the highest absolute correlation with TCMR designated as the central module for subsequent analysis. The genes contained within this pivotal module were earmarked for further investigative research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and functional analysis of DEGs associated with liver transplant rejection reactions (LTR-DEGs)\u003c/h2\u003e \u003cp\u003eThe VennDiagram package (v 1.7.3) was utilised to identify the intersection of DEGs and key module genes, thereby obtaining DEGs associated with liver transplant rejection reactions (LTR-DEGs) \u003csup\u003e61\u003c/sup\u003e. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of LTR-DEGs was performed via clusterProfiler package (v 4.7.1.001) (adjusted \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003csup\u003e62\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of Protein-Protein Interaction (PPI) network\u003c/h2\u003e \u003cp\u003eTo explore potential mutualistic relationships between LTR-DEGs, a PPI network was constructed using the search tool for the retrieval of interacting genes (STRING) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string.embl.de/\u003c/span\u003e\u003cspan address=\"http://string.embl.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The top 30 hub genes of LTR-DEGs were calculated using 4 algorithms [Degree, Maximal Clique Centrality (MCC), Maximum Neighborhood Component (MNC), and Density of Maximum Neighborhood Component (DMNC)] of cytoHubba plug-in in Cytoscape software (v 3.8.2), respectively, and the network diagrams of hub genes filtered by the 4 algorithms were shown \u003csup\u003e63\u003c/sup\u003e. Then, the hub genes of the 4 algorithms were taken intersection to obtain the candidate genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eMachine learning screening and performance evaluation of key genes\u003c/h2\u003e \u003cp\u003eTwo machine learning models were constructed based on candidate genes to screen feature genes separately. The least absolute shrinkage and selection operator (LASSO) COX regression analysis was carried out using the glmnet package (v 4.1-6) to obtain LASSO-feature genes with 10-fold cross validation\u003csup\u003e64\u003c/sup\u003e. Next, support vector machine recursive feature elimination (SVM-RFE) analysis was performed through caret package (v 6.0\u0026ndash;93), and the genes included in the portfolio with the lowest error rate were selected as SVM-RFE-feature genes \u003csup\u003e65\u003c/sup\u003e. The key genes were screened by overlapping LASSO-feature genes and SVM-RFE-feature genes. Finally, expression differences of key genes between TCMR and NR groups were compared using the Wilcoxon method in the GSE145780 dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSingle-gene Gene Set Enrichment Analysis (GSEA) analysis\u003c/h2\u003e \u003cp\u003eThe single-gene GSEA was performed to find the enriched regulatory pathways and biological functions of key genes via clusterProfiler (v 4.7.1.001) with adjusted \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 \u003csup\u003e66\u003c/sup\u003e. The top 5 results for KEGG significance were visualized separately.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration analysis\u003c/h2\u003e \u003cp\u003eThe proportion of 28 immune cell subtypes in each sample was computed using the single-sample GSEA (ssGSEA) algorithm via GSVA package (v 1.42.0) \u003csup\u003e67\u003c/sup\u003e. Subsequently, differential immune cells between the TCMR and NR groups were identified and visualized using box plots. Meanwhile, the correlation analysis was performed between differential immune cells and key genes using Spearman method.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eConstruction of key genes-drug interaction network\u003c/h2\u003e \u003cp\u003eThe drug with key genes interactions was predicted from the DGIdb database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dgidb.genome.wustl.edu/\u003c/span\u003e\u003cspan address=\"http://dgidb.genome.wustl.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A key genes-drug network was constructed based on the predicted results. Then, the network was visualized using Cytoscape software (v 3.8.2) \u003csup\u003e68\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of lncRNA-miRNA-mRNA network\u003c/h2\u003e \u003cp\u003eThe mirTarbase database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mirtarbase.cuhk.edu.cn/\u003c/span\u003e\u003cspan address=\"https://mirtarbase.cuhk.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to predict miRNAs targeting key genes. Next, the starBase database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rnasysu.com/encori/\u003c/span\u003e\u003cspan address=\"https://rnasysu.com/encori/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to predict lncRNAs targeting the miRNAs (clipExpNum\u0026thinsp;\u0026gt;\u0026thinsp;5). Finally, a lncRNA-miRNA-mRNA network was constructed on the basis of these miRNAs and lncRNAs using the Cytoscape software (v 3.8.2).\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eSingle-cell RNA-seq analysis\u003c/h2\u003e \u003cp\u003eIn the HRA002091 dataset, cells in scRNA-seq data were filtered (200\u0026thinsp;\u0026lt;\u0026thinsp;nCount\u0026thinsp;\u0026lt;\u0026thinsp;25000, 200\u0026thinsp;\u0026lt;\u0026thinsp;nFeature\u0026thinsp;\u0026lt;\u0026thinsp;6000) using the Seurat package (v 4.0.5) \u003csup\u003e69\u003c/sup\u003e. Next, cells with less than 20% of mitochondrial genes were retained. nCount, nFeature, and percent.mt values were compared between samples after quality control. Subsequently, the four samples were integrated for subsequent analyses. Then, the data were normalized using the VST method, and subsequently, highly variable genes were selected. Next, the data were subjected to principal component analysis (PCA). The top 20 principal component (PC) were identified for subsequent analysis. Immediately afterwards, the cells were subjected to unsupervised cluster analysis using the FindNeighbors and FindClusters functions of the Seurat package (v 4.0.5). The results of clustering were visualized using the t-distributed stochastic neighborhood embedding (t-SNE) method. Based on the cell clustering results, the cell subpopulations were annotated using the SingleR algorithm (v 1.4.1) and manual annotation (marker genes for each cell of liver tissue in the CellMarker database) \u003csup\u003e70\u003c/sup\u003e. Moreover, the number of each cell type in the TCMR and NR groups was also counted. Subsequently, the expression of key genes was observed in individual cell clusters and the results were visualized by violin charts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eExternal validation with clinical samples\u003c/h2\u003e \u003cp\u003eIn this study, liver biopsy specimens were collected from post-liver transplant patients at the Friendship Hospital affiliated with Capital Medical University. Ethical approval was secured from the appropriate ethics committee, ensuring compliance with international ethical standards and safeguarding participant rights. Prior to sample collection, all participants were fully informed about the study and provided written informed consent. The inclusion criteria were (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) post-liver transplantation, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) availability of liver biopsy samples, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) identification of rejection or non-rejection based on pathological diagnosis. Exclusion criteria included patients with concurrent severe infections or other complications. Sample grouping was strictly based on the latest pathological diagnosis Banff scoring criteria, which include scores for portal inflammation, bile duct inflammation within the portal areas, and endothelitis, along with the Rejection Activity Index (RAI). The TCMR group (n\u0026thinsp;=\u0026thinsp;3) consisted of samples with an RAI score\u0026thinsp;\u0026gt;\u0026thinsp;4, while the control group (n\u0026thinsp;=\u0026thinsp;3) comprised samples from patients with normal liver function serology one year post-surgery and an RAI\u0026thinsp;\u0026lt;\u0026thinsp;3. The liver tissue samples mentioned above were embedded in paraffin, sectioned, and typically deparaffinized to rehydrate, followed by washing with PBS. After incubation in 3% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e for 10 minutes, antibodies included mouse anti-CD53 (sc-390185, Santa Cruz, 1:150), mouse anti-FCER1G (sc-390222, Santa Cruz, 1:150), mouse anti-ITGB2 (sc-8420, Santa Cruz, 1:150), rabbit anti-GBP1 (A0570, ABclonal, 1:100), and rabbit anti-IL-18 (ab243091, Abcam, 1:150) were added separately and incubated overnight at 4\u0026deg;C. The specimens were then incubated with a secondary antibody at 37\u0026deg;C for 1 hour, followed by diaminobenzidine staining. The average optical density (AOD) of IHC staining was calculated by automatically measuring the integrated optical density (IOD) of DAB-positive signals using ImageJ software, and the AOD (IOD/area) of the corresponding areas was computed to indicate the expression of the target protein.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll bioinformatics analyses were performed using the R programming language (v 4.2.2). The Wilcoxon test was employed to contrast the data from different groups.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWS, HD, YW, ZS, HZ, FM, QC, HD, KL, and JX made substantial contributions to conception and design, and revised the manuscript critically for important intellectual content. LZ, and JX revised the manuscript and gave final approval of the version to be published. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the National Natural Science Foundation of China (Grant No. 82072737), Central guidance local special projects of Shanxi Province (Grant No. YDZJSX2021B012), Natural Science Foundation of Health Commission of Shanxi Provincial (Grnat No. 2020085 and 2022142).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the study are available in the GEO [https://www.ncbi.nlm.nih.gov/geo/, GSE14578], and NGDC [https://ngdc.cncb.ac.cn/, HRA002091 (Illumina NovaSeq 6000)].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors would like to appreciate the efforts of GEO and NGDC databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Medical Ethics Committee of the First Hospital of Shanxi Medical University (Approval Number NO.KYLL-2023-143) and the Ethics Committee of the Friendship Hospital affiliated with Capital Medical University (Approval Number 2023-P2-222-02).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStarzl, T. 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Nature immunology 20, 163\u0026ndash;172, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41590-018-0276-y\u003c/span\u003e\u003cspan address=\"10.1038/s41590-018-0276-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"T-cell mediated rejection, Liver transplant rejection, Single-cell RNA sequencing, Enrichment analysis, Immune analysis","lastPublishedDoi":"10.21203/rs.3.rs-4583452/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4583452/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLiver transplantation is the definitive treatment for end-stage liver disease, yet T-cell mediated rejection (TCMR) remains a major challenge. This study aims to identify key genes associated with TCMR and their potential biological processes and mechanisms. The GSE145780 dataset was subjected to differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms to pinpoint key genes associated with TCMR. Gene Set Enrichment Analysis (GSEA), immune infiltration analysis were conducted, along with constructing regulatory networks were constructed to ascertain the biological relevance of these genes. Expression validation was performed using single-cell RNA-seq (scRNA-seq) data and liver biopsy tissues from patients. We identified 5 key genes ( ITGB2, FCER1G, IL-18, GBP1, and CD53) that are associated with immunological functions, such as chemotactic activity, antigen processing, and T cell differentiation. GSEA highlighted enrichment in chemokine signaling and antigen presentation pathways. A lncRNA-miRNA-mRNA network was delineated, and drug target prediction yielded 26 potential drugs. Evaluation of expression levels in non-rejection (NR) and TCMR groups exhibited significant disparities in T cells and myeloid cells. Tissue analyses from patients corroborated the upregulation of GBP1, IL-18, CD53, and FCER1G in TCMR cases. Through comprehensive analysis, this research has identified 4 genes intimately connected with TCMR following liver transplantation, shedding light on the underlying immune activation pathways and suggesting putative targets for therapeutic intervention.\u003c/p\u003e","manuscriptTitle":"Identification of Key Genes and Exploration of Immune Activation Pathways in T-cell Mediated Rejection through Integrated Bulk and Single-Cell RNA-Seq Analysis with Machine Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-15 17:41:13","doi":"10.21203/rs.3.rs-4583452/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-29T07:32:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-27T08:43:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-10T08:57:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291931217063023019347021010494399961749","date":"2024-06-24T16:21:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251475792763233611992841629615440035071","date":"2024-06-22T13:04:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-19T02:56:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-19T02:50:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-18T16:33:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-15T10:09:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-06-14T17:13:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"78f27585-5e90-4cfb-8db9-6798a9781c38","owner":[],"postedDate":"July 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":33669798,"name":"Biological sciences/Immunology/Transplant immunology"},{"id":33669799,"name":"Biological sciences/Immunology/Transplant immunology/Allograft"},{"id":33669800,"name":"Biological sciences/Biological techniques/Bioinformatics"}],"tags":[],"updatedAt":"2024-10-21T16:00:57+00:00","versionOfRecord":{"articleIdentity":"rs-4583452","link":"https://doi.org/10.1038/s41598-024-74874-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-10-16 15:57:21","publishedOnDateReadable":"October 16th, 2024"},"versionCreatedAt":"2024-07-15 17:41:13","video":"","vorDoi":"10.1038/s41598-024-74874-8","vorDoiUrl":"https://doi.org/10.1038/s41598-024-74874-8","workflowStages":[]},"version":"v1","identity":"rs-4583452","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4583452","identity":"rs-4583452","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}