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Abstract

Objective

This study aimed to explore the shared mechanisms and targets between immune checkpoint inhibitor-associated myocarditis (ICIM) and autoimmune myocarditis.

Methods

Relevant data were retrieved from public datasets and Gene Expression Omnibus (GEO) database. Gene set enrichment analysis (GSEA) of differentially expressed genes (DEGs) was used to identify significant shared signaling pathways between ICIM and non-ICI associated autoimmune myocarditis (NICIAM) represented by ICIM model and experimental autoimmune myocarditis (EAM) model, respectively. Cell type enrichment analysis and immune infiltration analysis by clusterProfiler and ImmuCellAI were performed to identify critical immune cell component involved in ICIM and NICIAM. Additionally, core shared genes across ICIM and NICIAM were identified and validated by various models and methods.

Results

Interferon-γ response, inflammatory response and allograft rejection signaling were identified as the shared signaling pathways associated with ICIM and NICIAM. Enrichment analysis of cell type supported an important role of increased infiltration of T cells and macrophages in both ICIM and NICIAM. However, the predominant increase of infiltrated T cells was CD4+ T cells in NICIAM, while that were CD8+ T cells in ICIM. Core shared genes Lck and Cd3d expression were found increased in both ICIM and NICIAM, and Lck inhibition was further identified and validated as potential therapeutic approach.

Conclusions

Our study initially established a comorbidity model to identify potential molecular mechanism including interferon-γ response, inflammatory response and allograft rejection signaling accounting for the concerns of myocarditis risk in patients with preexisting autoimmune disease (PAD) receiving ICI treatment, and supported the therapeutic potential of targeting Lck or Cd3d.

Keywords

immune checkpoint inhibitor associated myocarditis non-immune checkpoint inhibitor associated autoimmune myocarditis experimental autoimmune myocarditis differentially expressed genes autoimmunity Lck

Introduction

Cancer therapy was revolutionized by immune checkpoint inhibitors (ICIs) in recent years. ICIs are monoclonal antibodies that target intrinsic immune inhibitory molecules, such as cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), programmed cell death protein 1 (PD-1), or its ligand programmed cell death protein ligand 1 (PD-L1). The interaction of ICIs with immune inhibitory molecules on the surface of T cells release the brakes of T-cell cytotoxicity against tumor cells, which leads to immune cell activation in the tumor microenvironment but also elicits autoimmune toxicity.¹ Approximately 86%–96% of cancer patients treated with ICIs experienced autoimmune side-effects and particularly, 17%–59% of patients experienced severe to life-threatening side effects.² ICIs-associated myocarditis (ICIM) is an autoimmune disease which is rare (with a prevalence <1% of patients), but can be fulminant and potentially fatal, with the mortality rate of approximately 50%.³ Histologically, acute lymphocytic infiltrates in the myocardium is commonly found in patients with ICIM, implicating a T cell–mediated mechanism. Furthermore, previous studies found that CD8+ and CD4+ lymphocytic infiltration in the heart was a typical feature in ICIM patients as well as murine models.⁴ However, the mechanisms of ICIM development caused by CD8+ or CD4+ T cells in the heart remain to be elucidated, and there is lack of specific strategies to limit ICIM except for glucocorticoids and some nonselective immunosuppressants.^5–7

The predisposition to autoimmune myocarditis by systemic/local primary autoimmunity was demonstrated by previous studies, and the cardiac autoimmunity in the heart was commonly found.⁸ Given the increased potential of intensity and frequency of cardiac toxicity, patients with preexisting autoimmune disease (PAD) have historically been excluded from ICI clinical trials in consideration of the unleashing of the underlying autoimmunity in the heart or exacerbation of the PAD.⁹ However, several retrospective studies reported that cancer immunotherapy in patients with PAD did not appear to result in higher rates of sever immune-related adverse events (irAEs).^10–13 Experimental evidence demonstrated that autoimmune myocarditis at an active status appeared to be exacerbated by ICIs administration in mouse experimental autoimmune myocarditis model, while it was not significantly affected in a stable autoimmune condition.¹⁴ Moreover, the use of immunotherapy was not declined in patients with stable and manageable autoimmune disease by the current guidelines.^4,15 However, the mechanism accounting for the association of active PAD with potential risk of ICIM remains unclear, which may raise the concern of the use of ICIs in patients with potential preexisting cardiac autoimmunity and impede the development of treatment strategy against ICIM in this setting. Therefore, the exploration of the shared molecular mechanisms and targets between ICIM and non-ICI associated autoimmune myocarditis (NICIAM) may aid the resolution of the concerned issues.

In this study, we, to the best of our knowledge, are the first to investigate the shared mechanisms and targets between ICIM and NICIAM from several perspectives by bioinformatics methods. Our study aimed to identify shared pathophysiological mechanisms between ICIM and NICIAM,and provide new insights into the treatment of immune-related myocarditis.

Materials and methods

Data source and acquisition

In a previous study by Ji et al., transcriptome data from five treated cynomolgus monkeys (administrated with ipilimumab and nivolumab) and two controls (administrated with saline) were collected to investigate the mechanism of ICIM. The differentially expressed genes (DEGs) induced by ICIs in the heart of monkeys were obtained from the supplementary data of the article by Ji et al.,¹⁶ which was further filtered with the following criterions: DEGs having human homologous genes, |log2FoldChange| >.58 and adjusted p-value <.05. The experiment and analysis procedure of bulk RNA sequencing were detailed as previously described.¹⁶ As the mouse model of experimental autoimmune myocarditis (EAM) was widely used to investigate the pathological mechanism of NICIAM, the bulk RNA-seq data of mouse EAM and control at three time points (day10, day15 and day21) were therefore collected from Gene Expression Omnibus (GEO) database with accession number GSE155423, and the experiment procedure was described previously.¹⁷ GSE155423 consisted of four EAM samples and four control samples at each time point and the data were displayed as raw read counts. The EAM model was generated with myosin heavy chain-α peptides in GSE155423. Src/Lck inhibitor dasatinib was reported to switch off cytokine release and T cell cytotoxicity upon activation of T cells. The supplementary data derived from a published article by Khatri et al. involving mouse cardiac transplants at acute rejection phase were used to validate the effect of dasatinib on the mRNA expression of Lck and the infiltration of CD4+ and CD8+ T cells.¹⁸

Identification of DEGs, enrichment analysis, transcriptional factor-gene regulatory network and protein-protein interaction network construction based on bulk RNA-Seq

DEGs from GSE155423 RNA-seq dataset were identified using the DESeq2 package with the criterion of “adjusted p value <.05 and |log2FoldChange|> 1. For the data from Ji et al., gene set enrichment analysis (GSEA) with identified DEGs was performed using the fgsea package.¹⁹ For the GSE155423 data, enrichment analysis with intersected DEGs across three time points was performed using the clusterProfiler package.²⁰ Transcription factors (TFs) enrichment analysis was performed using the ChEA3 which was an online tool to predict TFs of given gene sets based on data from ChIP-seq, experimental validation and bioinformatics analysis. Top20 TFs with most target were selected to construct TF-gene regulatory network.²¹ Additionally, the reliability of TFs regulating target is recorded, which was indicated by ChEA3 according to the mean rank. Protein-protein interaction (PPI) analysis was performed with the Search Tool for the Retrieval of Interaction Gene/Proteins (STRING) database (https://string-db.org). The PPIs with a confidence more than 0.9 were selected for core genes identification which were conducted with the cytoHubba plug-in in Cytoscape software, and the top50 genes with high degree were identified as hub genes. The TF-gene and PPI network were finally analyzed and illustrated with Cytoscape software.

Time-course cluster analysis and immune infiltration analysis

For the GSE155423 RNA-seq data, the Mfuzz package was used to perform time-course cluster analysis with transcripts per million (TPM) transformed from raw gene count,²² and the number of clusters was set to eight. Immune infiltration analysis to predict immune cell abundance was performed using the ImmuCellAI- mouse tool based on log2 transformed TPM data, and the proportion of 36 types of immune cells were estimated according to the instruction.²³ Briefly, ImmuCellAI-mouse simulated the process of flow cytometry analysis by adopting a hierarchical strategy to estimate immune cell abundance of 36 types of immune cells including B cells(B1, follicular B, germinal center B, marginal zone B, memory B and plasma B cells), monocytes, dendritic cells (cDC1, cDC2, MoDC and pDC cells), natural killer (NK) cells, granulocytes (basophil, eosinophil, mast cell and neutrophils), macrophages (M1 and M2 macrophages) and T cells (CD4+ naïve, CD4+ memory, Treg, T helper, CD8+ naïve, CD8+ central memory, CD8+ effector memory, cytotoxic and exhausted cells, NKT and gamma-delta T cells).

Statistical analysis

All statistical analyses were performed and visualized using a web tool Hiplot which was based on R software.²⁴ The Student’s t test was used to compare values between the test and control groups and p-values <0.05 was considered significant.

Results

Exploring potential mechanisms associated with immune checkpoint inhibitor-associated myocarditis based on cynomolgus monkeys model

In the study by Ji et al., moderate or marked infiltration of CD8+ and CD4+ lymphocytes were found in ICIs treatment group but that were minimal in control group, displaying the characteristics of ICIM.¹⁶ According to the established criterion, 751 DEGs including 615 upregulated and 136 downregulated genes were identified after administration with a total of 4 doses of ipilimumab and nivolumab (Figure 1(a)). GSEA results revealed that allograft rejection, interferon gamma (IFN-γ) response and inflammatory response signaling were significantly upregulated, while fatty acid metabolism and oxidative phosphorylation signaling were significantly downregulated following ICIs treatment (Figure 1(b)). The PPI network of DEGs was constructed with STRING and 50 hub genes with high degree were displayed as Figure 1(c). As shown in Figure 1(c), Lck was the top hub gene in the network. TF enrichment analysis by ChEA3 revealed that 1632 potential TFs were enriched with the given 50 hub genes. The top 10 TFs with most targets were presented in Figure 1(d). Of the 20 TFs enriched, 14 TFs were also identified as DEGs associated with ICIM and the mean rank suggested by ChEA3 was shown in Table S1.

Figure 1.

Genes and signaling pathways associated with ICIM based on cynomolgus monkeys model. (a) Volcano plot of DEGs for ICIM. (b) Line graph for signaling pathways associated with ICIM. (c) PPI network of DEGs. The deeper color indicates higher degree in the network. (d) Interaction plot of predicted top10 TF by ChEA3 as well as DEGs with DEGs associated with ICIM. ICIM, immune checkpoint inhibitor-associated myocarditis; DEGs, differentially expressed genes; TF, transcription factor; PPI, protein-protein interaction.

Identification of genes and signal pathways associated with non-immune checkpoint inhibitor associated autoimmune myocarditis based on mouse experimental autoimmune myocarditis model

In GSE155423 dataset, bulk RNA sequencing of hearts mimicking NICIAM were performed to identify significant genes at different time points (day10, day15 and day21) based on mouse EAM model. The approach of differentially expresses analysis was applied to excavate DEGs associated with NICIAM at three time points according to the established criterion. Notably, the upregulated and downregulated genes varied substantially across the three time points (Figure S2), and most DEGs were identified at day15. To identify common DEGs, intersection analysis was performed and presented as Venn plot (Figure 2(a)). As a result, 366 common DEGs across the three time points were revealed, which were identified key genes associated with NICIAM. Additionally, signaling pathway enrichment analysis was performed using clusterProfiler to explore the involved pathways of the human homologous genes of the 366 genes based on Hallmark gene sets in MSigdb database (https://www.gsea-msigdb.org). As shown in Figure 2(b), the results by comparing the signaling pathway enrichment analysis in ICIM and NICIAM model showed that the genes were mainly enriched in IFN-γ response, allograft rejection, inflammatory response, TNFα signaling via NF-kB, complement, IL6-Jak-Stat3, IL2-Stat5, KRAS, apoptosis and IFN-α response signaling pathways. Moreover, time-course cluster analysis of the transcriptome sequencing data from mouse EAM model at three time points was performed with Mfuzz package to further verify the robustness of DEGs. As shown in Figure 3(a), two clusters (Cluster 2 and Cluster 4) involving 2264 genes obtained from the Mfuzz program were found to be consistently upregulated and downregulated, respectively. The intersection analysis between genes in the two clusters and the 366 common DEGs resulted in 8 key genes (Cd3d, Tet1, Ankrd2, Cxcr6, Dnase1l3, Cd5, Slc26a3 and Lck) associated NICIAM (Figure 3(b)).

Figure 2.

Genes and signaling pathways associated with NICIAM based on murine EAM model. (a) Venn diagram for the intersection analysis of DEGs across day 10, day 15 and day 21. (b) Bubble diagram for signaling pathways associated with NICIAM. NICIAM, non-immune checkpoint inhibitor associated autoimmune myocarditis; EAM, experimental autoimmune myocarditis.

Figure 3.

Identification of core genes associated with ICIM and NICIAM. (a) TCCA to identify genes associated with NICIAM in the EAM model at three time points with Mfuzz package. (b) Venn diagram for the intersection analysis of differentially expressed analysis and TCCA. (c) Venn diagram for the shared core genes associated with ICIM and NICIAM. TCCA, time-course cluster analysis; ICIM, immune checkpoint inhibitor-associated myocarditis; NICIAM, non-immune checkpoint inhibitor associated autoimmune myocarditis; EAM, experimental autoimmune myocarditis.

Exploration of shared key genes and signaling pathway for immune checkpoint inhibitor-associated myocarditis and non-immune checkpoint inhibitor associated autoimmune myocarditis

To explore the shared key genes, the intersection analysis between 8 key genes associated with NICIAM and 50 hub genes associated with ICIM was conducted. As shown in Figure 3(c), two key genes Lck and Cd3d were indicated as the shared genes. Additionally, the results showed that interferon-γ response, inflammatory response and allograft rejection signaling were the shared signaling pathways potentially associated with ICIM and NICIAM.

Immune infiltration analysis of immune checkpoint inhibitor-associated myocarditis and non-immune checkpoint inhibitor associated autoimmune myocarditis

To explore immune cell infiltration in the heart of mouse EAM model, the ImmuCellAI-mouse was used to investigate variation in the infiltration of 36 immune cells among samples from three time points. Abundant immune cell populations with various kinds in each sample were shown in Figure S3. As shown in Figure 4(a), several major immune cell types showed no significant difference between EAM and control at day10 but all of them were significantly different at day 15. Overall, major immune cell types showed similar trends at three time points when comparing EAM to control. Of which, the infiltration of T cell, CD4+ T cell, macrophage, T helper cell, NKT cell and cDC2 cells were increased and that of NK cell and Mast cell were decreased in EAM compared to control. Moreover, the variation of immune infiltration in ICIM was analyzed with enrichment analysis of cell type using the DEGs induced by ICI therapy. As shown in Figure 4(b), T cell, CD8+ T cell, macrophage, NKT cell and mast cell were enriched in ICIM.

Figure 4.

Immune infiltration analysis and enrichment analysis of immune cells. (a) Immune infiltration analysis in NICIAM model. (b) Enrichment analysis of immune cells in ICIM model. NICIAM, non-immune checkpoint inhibitor associated autoimmune myocarditis; ICIM, immune checkpoint inhibitor-associated myocarditis; NES, normalized enrichment score.

Therapeutic potential of Lck and Cd3d inhibition in autoimmune myocarditis

To explore the therapeutic potential of targeting Lck or Cd3d in alleviating autoreactivity in heart, we focused on Lck inhibition but not Cd3d inhibition because the lack of approved drug or its unacceptable toxicity regarding the later.^25–28 After a search of potential agents reported previously, Lck inhibitor, dasatinib was employed to test the effect of Lck inhibition on autoreactivity in heart by the supplementary data from Khatri et al. of whom the study attempted to investigate effect of dasatinib treatment on the common rejection module genes and immune reactivity in HLA-mismatched mouse cardiac transplantation model.¹⁸ As shown in Figure 5, the result of our secondary analysis revealed that dasatinib treatment reduced remarkably the mRNA expression of Lck at the inflammatory rejection phase, and also significantly reduced the infiltration of CD4+ and CD8+ T cells.

Figure 5.

Impact of dasatinib treatment on Lck expression and infiltration of CD4+ and CD8+ T cells in mouse cardiac transplant. (a) Lck mRNA expression. (b, c) CD4+ and CD8+ T cell infiltration.

Discussion

At present, the decision to challenge with ICI therapy under an autoimmune genetic background or after development of ICI associated cardiac irAEs is complex and needs to be individualized with multidisciplinary discussion, which might be facilitated by a comprehensive understanding of the link between ICIM and pre-existing autoimmunity in the heart. Virtually, the transcriptome alterations of heart-specific autoimmune responses in patient with ICIM were previously found relevant to experimental autoimmune myocarditis.²⁹ It is important to notice that the murine model of experimental autoimmune myocarditis (EAM) resembling the features of autoimmune myocarditis was commonly used to investigate the NICIAM,³⁰ which was therefore employed in the present study. As a result, Lck and Cd3d were identified as the shared genes contributing to the development of ICIM and NICIAM in which allograft rejection signal may be significantly implicated. Moreover, the results indicated that the development of ICIM and NICIAM might be dominated by different T cells.

Myocarditis could be classified based on its etiology, commonly including viral, physical noxa, pharmacologic, hematologic, and autoimmune subtypes.³⁰ Generally, autoimmune myocarditis can arise due to overactive inflammatory reactions mediated by T cells, causing myocardial cell lysis, necrosis, edema and inflammatory infiltration. Autoimmune myocarditis usually existing with mechanistic overlap between several categories is a consequence of lack of self-tolerance.³¹ As a typical type of autoimmune myocarditis, ICIM has signs and symptoms similar to NICIAM and the manifestation varied from asymptomatic courses to striking signs of heart failure or acute coronary syndrome with a high mortality rate.³² Moreover, the high incidence of severe myocarditis and the relatively early onset after initiating ICI therapy in patients with PAD supports a role of pre-existing autoimmune conditions that predispose to the development of myocarditis.³ However, the underlying mechanism for the predisposition remains unclear. Therefore, we investigated the common mechanism for ICIM and NICIAM, cooperating ICIM cynomolgus monkey model and EAM mouse model.

Drawing support from various analytical strategies, we firstly discussed the common mechanisms of ICIM and NICIAM. Functional enrichment analysis employing GSEA revealed that pathways involved in both ICIM and NICIAM predominantly included interferon-γ response, inflammatory response and allograft rejection. In common with NICIAM, the pathology of ICIM comes from the failure of inflammation and immune response to resolve itself, leading to an accumulative or fulminant cardiac damage prominently driven by effector T cells. Generally, inflammatory response is accompanied by interferon-γ response in T cell-mediated autoimmune cardiac diseases.³³ Severe inflammatory responses could be induced by over-activated immune response and looseness of immunosuppression environment in the heart, causing the release of a large amount of cytokines such as IL-6, TNF-α and 1L-1β initiated by T cells.^34,35 Indeed, the implication of interferon-γ response and inflammatory response in autoimmune myocarditis were widely investigated, demonstrating similar role in the development of unruly autoimmunity in ICIM and NICIAM [31-34]. Moreover, allograft rejection signal was identified as a common signal involved in ICIM and NICIAM in the present study. Previous studies showed that allograft rejection signal was initiated by CD4+, CD8+ T cell and B cell autoimmune response to cardiac myosin in allogeneic heart transplantation,^36,37 of which some pathological features and biologic properties were found nearly identical to NICIAM, such as increased inflammatory exudation and elevated brain natriuretic peptide.³⁸ Also, the phenotype of cardiac allograft rejection is often considered to be similar to ICIM.³⁹ Given the perceived similarity, medications in the setting of cardiac allograft rejection have been successfully employed in the treatment of NICIAM patients.⁴⁰ Collectively, the above evidence supported shared molecular mechanisms between ICIM and NICIAM.

As mentioned above, numerous studies have highlighted the importance of T cell in autoimmune myocarditis including ICIM and NICIAM. Nevertheless, the shared cell types involved in ICIM and NICIAM remained unclear. Histologically, previous studies demonstrated that cardiac autoimmunity associated with numerous autoimmune diseases and transplantation in most NICIAM cases leaded mainly to the phenotype similar to giant cell myocarditis, where a large number of CD68+ macrophages coexisting with T lymphocytes contributed to myocardial inflammation, fibrosis and apoptosis.³⁰ In ICIM, infiltrated cells in myocardium were composed of mostly CD8+ T cells, CD4+ T cells and CD68+ macrophages,³⁴ which was similar to the histological phenotype and immune landscape in NICIAM. In line with previous findings, our study employing enrichment analysis of cell type supported an important role of increased infiltrating of T cells and macrophages in both ICIM and NICIAM (Figure 4). However, the predominant increase of infiltrated T cells was CD4+ T cells in NICIAM, while that was CD8+ T cells in ICIM, suggesting the different contribution of T cell subsets involved in the development of ICIM and NICIAM. Indeed, previous in-vivo studies have reported CD8+>CD4+ lymphocytic infiltration in the myocardium in mice models of PD-1 blockade.³¹ Consequently, activation of CD68+ macrophages might be a common pathophysiologic feature of ICIM and NICIAM, which indicates that macrophages may play a key role and provide critical therapeutic target in the comorbidity model.

In the present study, Lck and Cd3d were identified as shared hub genes involved in the development of ICIM and NICIAM. Lck as a member of the Src family of protein tyrosine kinases (PTKs) is expressed only in T cells and natural killer (NK) cells.⁴¹ Lck plays a crucial role in T-cell development and activation due to its critical role in signal transduction of immune and inflammation through the T-cell CD4/CD8 coreceptors and the costimulatory molecule CD28, thus contributing to a variety of autoimmune disorders, inflammatory diseases and organ allograft rejection.⁴² Previous studies have showed that Lck expression was increased in viral myocarditis models, and Lck signal inhibition could suppress substantially the proliferation of activated T cells, even eliminate viral myocarditis via the inhibition of ERK or ZAP70 activation.^43–45 Moreover, Lck inhibition with Src inhibitor or CD28 blockade was found to significantly prolong murine cardiac allograft survival.^46,47 Efficacy of several orally active inhibitors of Lck was also demonstrated in mouse model of cardiac transplant rejection.^48,49 In line with the previous findings, our study demonstrated significant increase of Lck expression in ICIM and non-viral NICIAM animal models, and our secondary analysis with the data from Khatri et al. found that the Src inhibitor dasatanib markedly reduced the expression of Lck and infiltration of CD4+ and CD8+ T cells in cardiac allograft,¹⁸ suggesting a vital role of Lck in ICIM and NICIAM. In consideration of the pathophysiologic similarity of ICIM and NICIAM with cardiac allograft rejection response, we speculate that Lck inhibition may be a promising strategy to prevent ICIM and NICIAM. The protein encoded by Cd3d is a subunit of the T-cell receptor/CD3 complex (TCR/CD3 complex) presented on T-lymphocyte cell surface, and is involved in T-cell development and adaptive immune response.⁵⁰ Cd3d cooperating with TCR following Lck and FYN-mediated phosphorylation plays an essential role in thymocyte differentiation and T cell activation via interaction with coreceptors CD4 and CD8.^51,52 Previous study showed that Cd3d expression was increased in endomyocardial biopsies of dilated cardiomyopathy patients in accompany with increased markers of Th1 (IFNγ, T-bet, Eomesodermin), regulatory T-cells (Treg; FoxP3, TGFβ), and cytotoxic T-cells (CTLs: Perforin, Granulysin, Granzyme A).⁵³ Cd3d has also been found to be associated with prognosis in various tumors as well as several autoimmune diseases such as primary Sjögren's syndrome, Down syndrome and Chronic Fatigue syndrome.^50,54–56 However, few study has focused the role and mechanism of Cd3d in autoimmune myocarditis. Although an increased expression of Cd3d in ICIM and NICIAM was suggested by the present study, for which the mechanism is needed to be further investigated.

In spite of some novel findings in the present study, several limitations should be also acknowledged. First, the key genes Lck and Cd3d were identified based on two disease models in different species (ICIM in monkeys and NICIAM in mice) due to the lack of enough available data in the same species. Therefore, the results should be further validated in the future study. Second, previous work has indeed demonstrated that EAM and ICI myocarditis are associated with increases in the number of intracardiac T cells. However, the current study was underpowered to definitely determine whether the increased expression of Cd3d and Lck in both ICIM and NICIAM was due to either the increase in the number of T cells or the increase in gene expression on a per-cell basis in the heart, although the findings supported the therapeutic potential of targeting Lck or Cd3d. Future single-cell RNAseq studies are warranted. Third, the immune cell infiltration analysis in the present study was based on a statistical model of bulk RNAseq data but not direct measurements of immune cells, which may limit the accuracy of the results. Future histologic or flow cytometric analysis regarding the issue was needed to validate the findings.

Conclusion

In conclusion, the study revealed that the immune and inflammatory response (interferon-γ response, inflammatory response and allograft rejection) might account for the co-morbidity mechanisms of ICIM and NICIAM. Moreover, the results showed heterogeneity in cell types involved in the development of ICIM and NICIAM. Our work also identified two hub genes Lck and Cd3d as potential therapeutic targets for ICIM and NICIAM. Nevertheless, it should be noted that the conclusions were drew based on bioinformatics analysis and findings from the previous relevant study, more in-vivo and in-vitro studies are therefore required for validation in the future.

Supplemental Material

Supplemental Material - Identification of shared mechanisms and targets between immune checkpoint inhibitor-associated myocarditis and autoimmune myocarditis

Supplemental Material for Identification of shared mechanisms and targets between immune checkpoint inhibitor-associated myocarditis and autoimmune myocarditis by Kai Yang, Min Zhang, Dong Li, Yuandong Yu, Fengjun Cao and Guoxing Wan in European Journal of Inflammation.

Footnotes

Acknowledgments

The authors would like to acknowledge the researchers of public data for their sharing spirit in science.

Author contributions

Conceptualization, Guoxing Wan and Kai Yang; methodology, Min Zhang and Kai Yang; software, Yuandong Yu; validation, Min Zhang and Dong Li; formal analysis, Dong Li and Kai Yang; investigation, Fengjun Cao; resources, Fengjun Cao; data curation, Yuandong Yu; writing-original draft preparation, Kai Yang and Min Zhang; writing-review and editing, Dong Li and Guoxing Wan; visualization, Kai Yang; supervision, Guoxing Wan. All authors have read and agreed to the published version of the manuscript.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the National Natural Science Foundation of China (Grant No. 82204540) and Scientific Research Project of the Department of Education of Hubei Province (Grant No. Q20222111).

ORCID iD

Guoxing Wan

Supplemental Material

Supplemental material for this article is available online.

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