Abstract 4136727: The role of immune mechanism in Immune Checkpoint Inhibitor-Associated Myocarditis: seeking key genes

Background: Immune checkpoint inhibitors (ICIs) are extensively employed in cancer treatment; however, the development of ICI-associated myocarditis (ICI-MC) introduces a severe and potentially fatal complication with elusive pathophysiological mechanisms. This study aimed to pinpoint pivotal immune-related genes in ICI-MC and unveil potential therapeutic targets using bioinformatics. Methods: Utilizing the GSE180045 dataset (comprising three groups: Group A - ICI patients without immune adverse events, Group B - ICI patients with non-myocarditis immune adverse events, Group C - ICI patients with myocarditis), we scrutinized differentially expressed genes (DEGs) between ICI-MC samples (Group C) and non-myocarditis controls (Group A&B). These DEGs were cross-referenced with 1796 immune-related genes (IRGs) from the immPort database to derive IR-DEGs. We conducted functional enrichment analyses (GO, KEGG, GSEA), constructed a PPI network, and identified hub genes. Validation was performed using the GSE4172 dataset, and optimal feature genes (OFGs) were identified from the overlap of hub genes and validation DEGs. Predictions of target miRNAs were made, and a ceRNA network was constructed. Target drugs for hub genes were forecasted using the cMAP database. Results: We pinpointed 58 DEGs between ICI-MC and controls, leading to the identification of 32 IR-DEGs after intersecting with 1796 IRGs. Functional analyses revealed enrichment in cell lysis, CD8+ T cell receptor, natural killer cell-mediated cytotoxicity, and RAGE signaling. Up-regulated hub genes (IL7R, PRF1, GNLY, CD3G, NKG7, GZMH, GZMB, KLRB1, KLRK1, CD247) were highlighted. In the validation dataset, 407 DEGs were uncovered, resulting in the identification of 3 OFGs (KLRB1, NKG7, GZMH). Predicted target miRNAs, lincRNAs, and circRNAs formed a comprehensive ceRNA network. The top 10 drugs with elevated connectivity scores included acetohydroxamic-acid, suggesting caution in ICI treatment. Conclusion: NKG7, GZMH, and KLRB1 emerged as pivotal immune-related genes in ICI-MC. Biological enrichments encompassed cell lysis, CD8+ T cell receptor pathway, natural killer cell-mediated cytotoxicity, RAGE signaling, and proinflammatory pathways. The ceRNA network shed light on critical molecules, emphasizing the need to avoid drugs like acetohydroxamic-acid in ICI treatment.

Authors

Institutions

Publication Details

Journal
Circulation
Published
2024-11-12
DOI
https://doi.org/10.1161/circ.150.suppl_1.4136727
Primary Topic
Cancer Immunotherapy and Biomarkers
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 4136727: The role of immune mechanism in Immune Checkpoint Inhibitor-Associated Myocarditis: seeking key genes

Junyi Zhang, Shenglin Qu, Yafeng Zhou
Circulation
Cancer Immunotherapy and Biomarkers
article

Abstract 4136727: The role of immune mechanism in Immune Checkpoint Inhibitor-Associated Myocarditis: seeking key genes

Junyi Zhang, Shenglin Qu, Yafeng Zhou
article en

Abstract

Background: Immune checkpoint inhibitors (ICIs) are extensively employed in cancer treatment; however, the development of ICI-associated myocarditis (ICI-MC) introduces a severe and potentially fatal complication with elusive pathophysiological mechanisms. This study aimed to pinpoint pivotal immune-related genes in ICI-MC and unveil potential therapeutic targets using bioinformatics. Methods: Utilizing the GSE180045 dataset (comprising three groups: Group A - ICI patients without immune adverse events, Group B - ICI patients with non-myocarditis immune adverse events, Group C - ICI patients with myocarditis), we scrutinized differentially expressed genes (DEGs) between ICI-MC samples (Group C) and non-myocarditis controls (Group A&B). These DEGs were cross-referenced with 1796 immune-related genes (IRGs) from the immPort database to derive IR-DEGs. We conducted functional enrichment analyses (GO, KEGG, GSEA), constructed a PPI network, and identified hub genes. Validation was performed using the GSE4172 dataset, and optimal feature genes (OFGs) were identified from the overlap of hub genes and validation DEGs. Predictions of target miRNAs were made, and a ceRNA network was constructed. Target drugs for hub genes were forecasted using the cMAP database. Results: We pinpointed 58 DEGs between ICI-MC and controls, leading to the identification of 32 IR-DEGs after intersecting with 1796 IRGs. Functional analyses revealed enrichment in cell lysis, CD8+ T cell receptor, natural killer cell-mediated cytotoxicity, and RAGE signaling. Up-regulated hub genes (IL7R, PRF1, GNLY, CD3G, NKG7, GZMH, GZMB, KLRB1, KLRK1, CD247) were highlighted. In the validation dataset, 407 DEGs were uncovered, resulting in the identification of 3 OFGs (KLRB1, NKG7, GZMH). Predicted target miRNAs, lincRNAs, and circRNAs formed a comprehensive ceRNA network. The top 10 drugs with elevated connectivity scores included acetohydroxamic-acid, suggesting caution in ICI treatment. Conclusion: NKG7, GZMH, and KLRB1 emerged as pivotal immune-related genes in ICI-MC. Biological enrichments encompassed cell lysis, CD8+ T cell receptor pathway, natural killer cell-mediated cytotoxicity, RAGE signaling, and proinflammatory pathways. The ceRNA network shed light on critical molecules, emphasizing the need to avoid drugs like acetohydroxamic-acid in ICI treatment.

CirculationVol. 150(Suppl_1)
Soochow University (CN), Second Affiliated Hospital of Soochow University (CN), The Fourth Affiliated Hospital of Soochow University (Suzhou Dushu Lake Hospital)
Good health and well-being
Openalex Percentile: Top 64%
Cancer Immunotherapy and Biomarkers
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.