Novel immune cell infiltration-related biomarkers in atherosclerosis diagnosis
Background Immune cell infiltration (ICI) has a close relationship with the progression of atherosclerosis (AS). Therefore, the current study was aimed to explore the role of genes related to ICI and to investigate potential mechanisms in AS. Methods Single-sample gene set enrichment analysis (ssGSE...
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PeerJ Inc.
2023-05-01
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| author | Ruoyu Dong Jikuan Li Guangwei Jiang Ning Han Yaochao Zhang Xiaoming Shi |
| author_facet | Ruoyu Dong Jikuan Li Guangwei Jiang Ning Han Yaochao Zhang Xiaoming Shi |
| author_sort | Ruoyu Dong |
| collection | DOAJ |
| description | Background Immune cell infiltration (ICI) has a close relationship with the progression of atherosclerosis (AS). Therefore, the current study was aimed to explore the role of genes related to ICI and to investigate potential mechanisms in AS. Methods Single-sample gene set enrichment analysis (ssGSEA) was applied to explore immune infiltration in AS and controls. Genes related to immune infitration were mined by weighted gene co-expression network analysis (WGCNA). The function of those genes were analyzed by enrichment analyses of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The interactions among those genes were visualized in the protein-protein interaction (PPI) network, followed by identification of hub genes through Cytoscape software. A receiver operating characteristic (ROC) plot was generated to assess the performance of hub genes in AS diagnosis. The expressions of hub genes were measured by reverse transcription quantitative real-time PCR (RT-qPCR) in human leukemia monocyticcell line (THP-1) derived foam cells and macrophages, which mimic AS and control, respectively. Results We observed that the proportions of 27 immune cells were significantly elevated in AS. Subsequent integrative analyses of differential expression and WGCNA identified 99 immune cell-related differentially expressed genes (DEGs) between AS and control. Those DEGs were associated with tryptophan metabolism and extracellular matrix (ECM)-related functions. Moreover, by constructing the PPI network, we found 11 hub immune cell-related genes in AS. The expression pattern and receiver ROC analyses in two independent datasets showed that calsequestrin 2 (CASQ2), nexilin F-Actin binding protein (NEXN), matrix metallopeptidase 12 (MMP12), C-X-C motif chemokine ligand 10 (CXCL10), phospholamban (PLN), heme oxygenase 1 (HMOX1), ryanodine receptor 2 (RYR2), chitinase 3 like 1 (CHI3L1), matrix metallopeptidase 9 (MMP9), actin alpha cardiac muscle 1 (ACTC1) had good performance in distinguishing AS from control samples. Furthermore, those biomarkers were shown to be correlated with angiogenesis and immune checkpoints. In addition, we found 239 miRNAs and 47 transcription factor s (TFs), which may target those biomarkers and regulate their expressions. Finally, we found that RT-qPCR results were consistent with sequencing results. |
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| language | English |
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| spelling | doaj.art-b2e13b3559dc4b3a811bbcd34bb19c962023-12-03T13:36:39ZengPeerJ Inc.PeerJ2167-83592023-05-0111e1534110.7717/peerj.15341Novel immune cell infiltration-related biomarkers in atherosclerosis diagnosisRuoyu Dong0Jikuan Li1Guangwei Jiang2Ning Han3Yaochao Zhang4Xiaoming Shi5Department of Vascular Surgery, Hebei General Hospital, Shijiazhuang, Hebei, ChinaDepartment of Vascular Surgery, Hebei General Hospital, Shijiazhuang, Hebei, ChinaDepartment of Vascular Surgery, Hebei General Hospital, Shijiazhuang, Hebei, ChinaDepartment of Neurointervention, Hebei General Hospital, Shijiazhuang, Hebei, ChinaDepartment of Cardiothoracic Surgery, Cangzhou Central Hospital, Cangzhou, Hebei, ChinaDepartment of Vascular Surgery, Hebei General Hospital, Shijiazhuang, Hebei, ChinaBackground Immune cell infiltration (ICI) has a close relationship with the progression of atherosclerosis (AS). Therefore, the current study was aimed to explore the role of genes related to ICI and to investigate potential mechanisms in AS. Methods Single-sample gene set enrichment analysis (ssGSEA) was applied to explore immune infiltration in AS and controls. Genes related to immune infitration were mined by weighted gene co-expression network analysis (WGCNA). The function of those genes were analyzed by enrichment analyses of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The interactions among those genes were visualized in the protein-protein interaction (PPI) network, followed by identification of hub genes through Cytoscape software. A receiver operating characteristic (ROC) plot was generated to assess the performance of hub genes in AS diagnosis. The expressions of hub genes were measured by reverse transcription quantitative real-time PCR (RT-qPCR) in human leukemia monocyticcell line (THP-1) derived foam cells and macrophages, which mimic AS and control, respectively. Results We observed that the proportions of 27 immune cells were significantly elevated in AS. Subsequent integrative analyses of differential expression and WGCNA identified 99 immune cell-related differentially expressed genes (DEGs) between AS and control. Those DEGs were associated with tryptophan metabolism and extracellular matrix (ECM)-related functions. Moreover, by constructing the PPI network, we found 11 hub immune cell-related genes in AS. The expression pattern and receiver ROC analyses in two independent datasets showed that calsequestrin 2 (CASQ2), nexilin F-Actin binding protein (NEXN), matrix metallopeptidase 12 (MMP12), C-X-C motif chemokine ligand 10 (CXCL10), phospholamban (PLN), heme oxygenase 1 (HMOX1), ryanodine receptor 2 (RYR2), chitinase 3 like 1 (CHI3L1), matrix metallopeptidase 9 (MMP9), actin alpha cardiac muscle 1 (ACTC1) had good performance in distinguishing AS from control samples. Furthermore, those biomarkers were shown to be correlated with angiogenesis and immune checkpoints. In addition, we found 239 miRNAs and 47 transcription factor s (TFs), which may target those biomarkers and regulate their expressions. Finally, we found that RT-qPCR results were consistent with sequencing results.https://peerj.com/articles/15341.pdfImmune cell infiltrationAtherosclerosisWGCNADiagnosis |
| spellingShingle | Ruoyu Dong Jikuan Li Guangwei Jiang Ning Han Yaochao Zhang Xiaoming Shi Novel immune cell infiltration-related biomarkers in atherosclerosis diagnosis PeerJ Immune cell infiltration Atherosclerosis WGCNA Diagnosis |
| title | Novel immune cell infiltration-related biomarkers in atherosclerosis diagnosis |
| title_full | Novel immune cell infiltration-related biomarkers in atherosclerosis diagnosis |
| title_fullStr | Novel immune cell infiltration-related biomarkers in atherosclerosis diagnosis |
| title_full_unstemmed | Novel immune cell infiltration-related biomarkers in atherosclerosis diagnosis |
| title_short | Novel immune cell infiltration-related biomarkers in atherosclerosis diagnosis |
| title_sort | novel immune cell infiltration related biomarkers in atherosclerosis diagnosis |
| topic | Immune cell infiltration Atherosclerosis WGCNA Diagnosis |
| url | https://peerj.com/articles/15341.pdf |
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