Identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learning
ObjectiveThe decreased stability of atherosclerotic plaques increases the risk of ischemic stroke. However, the specific characteristics of dysregulated immune cells and effective diagnostic biomarkers associated with stability in atherosclerotic plaques are poorly characterized. This research aims...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-09-01
|
| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2022.956078/full |
| _version_ | 1811265959934033920 |
|---|---|
| author | Jing Wang Zijian Kang Zijian Kang Yandong Liu Zifu Li Yang Liu Yang Liu Jianmin Liu |
| author_facet | Jing Wang Zijian Kang Zijian Kang Yandong Liu Zifu Li Yang Liu Yang Liu Jianmin Liu |
| author_sort | Jing Wang |
| collection | DOAJ |
| description | ObjectiveThe decreased stability of atherosclerotic plaques increases the risk of ischemic stroke. However, the specific characteristics of dysregulated immune cells and effective diagnostic biomarkers associated with stability in atherosclerotic plaques are poorly characterized. This research aims to investigate the role of immune cells and explore diagnostic biomarkers in the formation of unstable plaques for the sake of gaining new insights into the underlying molecular mechanisms and providing new perspectives for disease detection and therapy.MethodUsing the CIBERSORT method, 22 types of immune cells between stable and unstable carotid atherosclerotic plaques from RNA-sequencing and microarray data in the public GEO database were quantitated. Differentially expressed genes (DEGs) were further calculated and were analyzed for enrichment of GO Biological Process and KEGG pathways. Important cell types and hub genes were screened using machine learning methods including least absolute shrinkage and selection operator (LASSO) regression and random forest. Single-cell RNA sequencing and clinical samples were further used to validate critical cell types and hub genes. Finally, the DGIdb database of gene–drug interaction data was utilized to find possible therapeutic medicines and show how pharmaceuticals, genes, and immune cells interacted.ResultsA significant difference in immune cell infiltration was observed between unstable and stable plaques. The proportions of M0, M1, and M2 macrophages were significantly higher and that of CD8+ T cells and NK cells were significantly lower in unstable plaques than that in stable plaques. With respect to DEGs, antigen presentation genes (CD74, B2M, and HLA-DRA), inflammation-related genes (MMP9, CTSL, and IFI30), and fatty acid-binding proteins (CD36 and APOE) were elevated in unstable plaques, while the expression of smooth muscle contraction genes (TAGLN, ACAT2, MYH10, and MYH11) was decreased in unstable plaques. M1 macrophages had the highest instability score and contributed to atherosclerotic plaque instability. CD68, PAM, and IGFBP6 genes were identified as the effective diagnostic markers of unstable plaques, which were validated by validation datasets and clinical samples. In addition, insulin, nivolumab, indomethacin, and α-mangostin were predicted to be potential therapeutic agents for unstable plaques.ConclusionM1 macrophages is an important cause of unstable plaque formation, and CD68, PAM, and IGFBP6 could be used as diagnostic markers to identify unstable plaques effectively. |
| first_indexed | 2024-04-12T20:33:22Z |
| format | Article |
| id | doaj.art-04f04cd010e841ee9e81047eba758e7a |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-12T20:33:22Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-04f04cd010e841ee9e81047eba758e7a2022-12-22T03:17:39ZengFrontiers Media S.A.Frontiers in Immunology1664-32242022-09-011310.3389/fimmu.2022.956078956078Identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learningJing Wang0Zijian Kang1Zijian Kang2Yandong Liu3Zifu Li4Yang Liu5Yang Liu6Jianmin Liu7Neurovascular Center, Changhai Hospital, Naval Medical University, Shanghai, ChinaDepartment of Rheumatology and Immunology, Second Affiliated Hospital of Naval Medical University, Shanghai, ChinaDepartment of Critical Care Medicine, Naval Medical Center of People's Liberation Army of China (PLA), Shanghai, ChinaDepartment of Geriatrics, Navy 905th Hospital, Shanghai, ChinaNeurovascular Center, Changhai Hospital, Naval Medical University, Shanghai, ChinaDepartment of Critical Care Medicine, Naval Medical Center of People's Liberation Army of China (PLA), Shanghai, ChinaDepartment of Cardiovascular Surgery, Institute of Cardiac Surgery, Changhai Hospital, Naval Medical University, Shanghai, ChinaNeurovascular Center, Changhai Hospital, Naval Medical University, Shanghai, ChinaObjectiveThe decreased stability of atherosclerotic plaques increases the risk of ischemic stroke. However, the specific characteristics of dysregulated immune cells and effective diagnostic biomarkers associated with stability in atherosclerotic plaques are poorly characterized. This research aims to investigate the role of immune cells and explore diagnostic biomarkers in the formation of unstable plaques for the sake of gaining new insights into the underlying molecular mechanisms and providing new perspectives for disease detection and therapy.MethodUsing the CIBERSORT method, 22 types of immune cells between stable and unstable carotid atherosclerotic plaques from RNA-sequencing and microarray data in the public GEO database were quantitated. Differentially expressed genes (DEGs) were further calculated and were analyzed for enrichment of GO Biological Process and KEGG pathways. Important cell types and hub genes were screened using machine learning methods including least absolute shrinkage and selection operator (LASSO) regression and random forest. Single-cell RNA sequencing and clinical samples were further used to validate critical cell types and hub genes. Finally, the DGIdb database of gene–drug interaction data was utilized to find possible therapeutic medicines and show how pharmaceuticals, genes, and immune cells interacted.ResultsA significant difference in immune cell infiltration was observed between unstable and stable plaques. The proportions of M0, M1, and M2 macrophages were significantly higher and that of CD8+ T cells and NK cells were significantly lower in unstable plaques than that in stable plaques. With respect to DEGs, antigen presentation genes (CD74, B2M, and HLA-DRA), inflammation-related genes (MMP9, CTSL, and IFI30), and fatty acid-binding proteins (CD36 and APOE) were elevated in unstable plaques, while the expression of smooth muscle contraction genes (TAGLN, ACAT2, MYH10, and MYH11) was decreased in unstable plaques. M1 macrophages had the highest instability score and contributed to atherosclerotic plaque instability. CD68, PAM, and IGFBP6 genes were identified as the effective diagnostic markers of unstable plaques, which were validated by validation datasets and clinical samples. In addition, insulin, nivolumab, indomethacin, and α-mangostin were predicted to be potential therapeutic agents for unstable plaques.ConclusionM1 macrophages is an important cause of unstable plaque formation, and CD68, PAM, and IGFBP6 could be used as diagnostic markers to identify unstable plaques effectively.https://www.frontiersin.org/articles/10.3389/fimmu.2022.956078/fullcarotid artery atherosclerosisunstable atherosclerosisimmune infiltrationmachine learningbiomarker |
| spellingShingle | Jing Wang Zijian Kang Zijian Kang Yandong Liu Zifu Li Yang Liu Yang Liu Jianmin Liu Identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learning Frontiers in Immunology carotid artery atherosclerosis unstable atherosclerosis immune infiltration machine learning biomarker |
| title | Identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learning |
| title_full | Identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learning |
| title_fullStr | Identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learning |
| title_full_unstemmed | Identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learning |
| title_short | Identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learning |
| title_sort | identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learning |
| topic | carotid artery atherosclerosis unstable atherosclerosis immune infiltration machine learning biomarker |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2022.956078/full |
| work_keys_str_mv | AT jingwang identificationofimmunecellinfiltrationanddiagnosticbiomarkersinunstableatheroscleroticplaquesbyintegratedbioinformaticsanalysisandmachinelearning AT zijiankang identificationofimmunecellinfiltrationanddiagnosticbiomarkersinunstableatheroscleroticplaquesbyintegratedbioinformaticsanalysisandmachinelearning AT zijiankang identificationofimmunecellinfiltrationanddiagnosticbiomarkersinunstableatheroscleroticplaquesbyintegratedbioinformaticsanalysisandmachinelearning AT yandongliu identificationofimmunecellinfiltrationanddiagnosticbiomarkersinunstableatheroscleroticplaquesbyintegratedbioinformaticsanalysisandmachinelearning AT zifuli identificationofimmunecellinfiltrationanddiagnosticbiomarkersinunstableatheroscleroticplaquesbyintegratedbioinformaticsanalysisandmachinelearning AT yangliu identificationofimmunecellinfiltrationanddiagnosticbiomarkersinunstableatheroscleroticplaquesbyintegratedbioinformaticsanalysisandmachinelearning AT yangliu identificationofimmunecellinfiltrationanddiagnosticbiomarkersinunstableatheroscleroticplaquesbyintegratedbioinformaticsanalysisandmachinelearning AT jianminliu identificationofimmunecellinfiltrationanddiagnosticbiomarkersinunstableatheroscleroticplaquesbyintegratedbioinformaticsanalysisandmachinelearning |