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Gu J, Yang W, Lin S, Ying D. Identification of co-expressed genes and immune infiltration features related to the progression of atherosclerosis. J Appl Genet 2024; 65:331-339. [PMID: 37996696 DOI: 10.1007/s13353-023-00801-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/25/2023] [Accepted: 10/26/2023] [Indexed: 11/25/2023]
Abstract
Atherosclerosis is a chronic inflammatory disease that affects arterial walls and is a leading cause of cardiovascular disease. Gene co-expression modules can provide insight into the molecular mechanisms underlying atherosclerosis progression. In this study, gene co-expression network analysis (WGCNA) was done to identify gene co-expression modules associated with atherosclerosis progression. Before conducting WGCNA, preprocessing and soft power selection were performed on the GSE28829, GSE100927, GSE43292, GSE10334, and GSE16134 datasets ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi ). Co-expression modules were identified using dynamic tree cuts, and their correlations and trait associations were visualized. Enrichment analysis was performed on the blue and magenta modules to identify biological processes (BP) and pathways related to atherosclerosis. The CIBERSORT algorithm was used to predict immune cell infiltration in early and advanced atherosclerotic plaques. We identified 12 co-expression modules, in which blue and magenta were most highly correlated with atherosclerosis progression. The blue module was enriched for inflammation- and immune-related BP and pathways, including phagosome, lysosome, osteoclast differentiation, chemokine signaling pathway, platelet activation, NF-kappa B signaling pathway, Fc gamma R-mediated phagocytosis, lipid and atherosclerosis, autophagy, and apoptosis. The magenta module was significantly enriched for vascular permeability regulation, positive and negative regulation of epithelial to mesenchymal transition, and lamellipodium. Additionally, the CIBERSORT algorithm predicted less abundance of T regulatory cells and monocytes in advanced compared to early atherosclerotic plaques. The enrichment analysis of BP, cellular components, molecular functions, and atherosclerosis-related pathways in the blue and magenta modules showed that inflammation and immune response played a key role in the progression of atherosclerosis. Our study provides insights into the molecular mechanisms underlying atherosclerosis progression and identifies potential therapeutic targets for the treatment of atherosclerosis. The identification of immune cell subtypes associated with atherosclerosis could lead to the development of immunomodulatory therapies to prevent or treat atherosclerosis.
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Affiliation(s)
- Junqing Gu
- Yuyao Municipal People's Hospital, Yuyao City, China
| | - Wenwei Yang
- Longshan Hospital, Cixi City, Yuyao City, China
| | - Shun Lin
- Linhai City First People's Hospital, Yuyao City, China
| | - Danqing Ying
- Yuyao City Lanjiang Street Community Health Service Center, Yuyao City, China.
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Mi N, Li Z, Zhang X, Gao Y, Wang Y, Liu S, Wang S. Identification of potential immunotherapeutic targets and prognostic biomarkers in Graves' disease using weighted gene co-expression network analysis. Heliyon 2024; 10:e27175. [PMID: 38468967 PMCID: PMC10926144 DOI: 10.1016/j.heliyon.2024.e27175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 12/11/2023] [Accepted: 02/26/2024] [Indexed: 03/13/2024] Open
Abstract
Graves' disease (GD) is an autoimmune disorder characterized by hyperthyroidism resulting from autoantibody-induced stimulation of the thyroid gland. Despite recent advancements in understanding GD's pathogenesis, the molecular processes driving disease progression and treatment response remain poorly understood. In this study, we aimed to identify crucial immunogenic factors associated with GD prognosis and immunotherapeutic response. To achieve this, we implemented a comprehensive screening strategy that combined computational immunogenicity-potential scoring with multi-parametric cluster analysis to assess the immunomodulatory genes in GD-related subtypes involving stromal and immune cells. Utilizing weighted gene co-expression network analysis (WGCNA), we identified co-expressed gene modules linked to cellular senescence and immune infiltration in CD4+ and CD8+ GD samples. Additionally, gene set enrichment analysis enabled the identification of hallmark pathways distinguishing high- and low-immune subtypes. Our WGCNA analysis revealed 21 gene co-expression modules comprising 1,541 genes associated with immune infiltration components in various stages of GD, including T cells, M1 and M2 macrophages, NK cells, and Tregs. These genes primarily participated in T cell proliferation through purinergic signaling pathways, particularly neuroactive ligand-receptor interactions, and DNA binding transcription factor activity. Three genes, namely PRSS1, HCRTR1, and P2RY4, exhibited robustness in GD patients across multiple stages and were involved in immune cell infiltration during the late stage of GD (p < 0.05). Importantly, HCRTR1 and P2RY4 emerged as potential prognostic signatures for predicting overall survival in high-immunocore GD patients (p < 0.05). Overall, our study provides novel insights into the molecular mechanisms driving GD progression and highlights potential key immunogens for further investigation. These findings underscore the significance of immune infiltration-related cellular senescence in GD therapy and present promising targets for the development of new immunotherapeutic strategies.
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Affiliation(s)
- Nianrong Mi
- Department of General Practice, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250013, China
| | - Zhe Li
- Department of Health Management Center, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250013, China
| | - Xueling Zhang
- Department of Integrated Chinese and Western Medicine, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250013, China
| | - Yingjing Gao
- Department of Endocrinology, Shandong First Medical University, Jinan, Shandong Province, 250013, China
| | - Yanan Wang
- Department of Endocrinology, Shandong First Medical University, Jinan, Shandong Province, 250013, China
| | - Siyan Liu
- Department of Endocrinology, Shandong First Medical University, Jinan, Shandong Province, 250013, China
| | - Shaolian Wang
- Department of Integrated Chinese and Western Medicine, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250013, China
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Li XT, Zhang JT, Yan HH, Su J, Cheng ML, Sun QH, Zhong WZ, Wu YL, Zhang DXC, Hou DJ. Gene co-expression modules integrated with immunoscore predicts survival of non-small cell lung cancer. Cancer Treat Res Commun 2020; 26:100297. [PMID: 33385734 DOI: 10.1016/j.ctarc.2020.100297] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 12/18/2020] [Accepted: 12/23/2020] [Indexed: 12/20/2022]
Abstract
BACKGROUND This study aimed to deconvolve the levels of infiltrating immune cells in non-small cell lung cancer (NSCLC) and to identify specific gene co-expression modules associated with prognosis of NSCLC. MATERIALS AND METHODS CIBERSORT algorithm was employed to infer the relative abundance of 22 immune cell subtypes in 1751 NSCLC subjects. The patterns of immune infiltration were identified for NSCLC with different clinical and genomic features and were used to construct an immunoscore by LASSO regression associated with NSCLC survival. Weighted gene co-expression network analysis (WGCNA) was employed to identify specific modules related to immunoscore and NSCLC survival. An integrated prognostic model was constructed with immunoscore combined with the available clinical variables and the selected gene modules to predict the prognosis of NSCLC. RESULTS We found distinct immune infiltration patterns for NSCLC with different genotype. EGFR-mutant NSCLC was characterized by enriched resting memory CD4+ T cell. An immunoscore was established based on the infiltration abundance of 17 selected immune cell subtypes. Patients with a low immunoscore had a prolonged survival and higher abundance of CD4+ T cell, resting dendritic cells and resting mast cells. The WGCNA analysis identified the gene modules significantly associated with immunoscore and the prognosis of NSCLC. The immunoscore was further incorporated with clinical parameters and selected gene modules to fit a predictive model which stratified patients into subgroups with significantly different survival. CONCLUSION The distinct immune profiles are associated with differential overall survival of NSCLC and the integrated model can robustly predict the prognosis of NSCLC.
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Affiliation(s)
- Xue-Tao Li
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, China; The Laboratory of Computational Medicine and Systems Biology, School of Medicine, South China University of Technology, Guangdong, Guangzhou, China
| | - Jia-Tao Zhang
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, China
| | - Hong-Hong Yan
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, China
| | - Jian Su
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, China
| | - Mei-Ling Cheng
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, China; The Laboratory of Computational Medicine and Systems Biology, School of Medicine, South China University of Technology, Guangdong, Guangzhou, China
| | - Qi-Hui Sun
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, China; The Laboratory of Computational Medicine and Systems Biology, School of Medicine, South China University of Technology, Guangdong, Guangzhou, China
| | - Wen-Zhao Zhong
- The Laboratory of Computational Medicine and Systems Biology, School of Medicine, South China University of Technology, Guangdong, Guangzhou, China
| | - Yi-Long Wu
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, China
| | - Dr Xu-Chao Zhang
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, China.
| | - Dr Jun Hou
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, China; The Laboratory of Computational Medicine and Systems Biology, School of Medicine, South China University of Technology, Guangdong, Guangzhou, China.
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