1
|
Jiang Y, Yu Z, Zheng H, Zhou X, Zhou M, Geng X, Zhu Y, Huang S, Gong Y, Guo L. An immune biomarker associated with EMT serves as a predictor for prognosis and drug response in bladder cancer. Aging (Albany NY) 2024; 16:205927. [PMID: 38980253 DOI: 10.18632/aging.205927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 04/22/2024] [Indexed: 07/10/2024]
Abstract
BACKGROUND Bladder cancer (BLCA), which develops from the upper endometrial of the bladder, is the sixth most prevalent cancer across the globe. WDHD1 (WD repeat and HMG-box DNA binding protein 1 gene) directly affects signaling, the cell cycle, and the development of the cell skeleton. Uncertainty surrounds WDHD1's function in BLCA immunity and prognosis, though. MATERIALS AND METHODS Using weighed gene co-expression network analysis (WGCNA), initially, we first identified 32 risk factors in genes with differential expression for this investigation. Then, using a variety of bioinformatic techniques and experimental validation, we examined the connections between WDHD1 and BLCA expression, clinical pathological traits, WDHD1-related proteins, upper-skin-intermediate conversion (EMT), immune cell immersion, convergence factors, immune markers, and drug sensitivity. RESULT The findings demonstrated that we constructed a 32-gene risk-predicting model where WDHD1 was elevated as a representative gene expression in BLCA and related to a range of clinical traits. Furthermore, high WDHD1 expression was a standalone predictor associated with a worse survival rate. The most commonly recruited cells and their evolutionary patterns were highlighted to better comprehend WDHD1's function in cancer. High WDHD1 expression was associated with many aspects of immunology. Finally, the study found that individuals with high expression of WDHD1 were drug-sensitive to four different broad-spectrum anti-cancer drugs. CONCLUSION These results describe dynamic changes in the tumor microenvironment in BLCA and provide evidence for the hypothesis that WDHD1 is a novel biomarker of tumor development. WDHD1 may therefore be a useful target for the detection and management of BLCA.
Collapse
Affiliation(s)
- Yike Jiang
- Department of Ultrasonography, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330000, China
- Second College of Clinical Medicine, Nanchang University, Nanchang, Jiangxi 330000, China
| | - Zichuan Yu
- Second College of Clinical Medicine, Nanchang University, Nanchang, Jiangxi 330000, China
| | - Hao Zheng
- Second College of Clinical Medicine, Nanchang University, Nanchang, Jiangxi 330000, China
| | - Xuanrui Zhou
- Second College of Clinical Medicine, Nanchang University, Nanchang, Jiangxi 330000, China
| | - Minqin Zhou
- Second College of Clinical Medicine, Nanchang University, Nanchang, Jiangxi 330000, China
| | - Xitong Geng
- Second College of Clinical Medicine, Nanchang University, Nanchang, Jiangxi 330000, China
| | - Yanting Zhu
- Second College of Clinical Medicine, Nanchang University, Nanchang, Jiangxi 330000, China
| | - Shuhan Huang
- Second College of Clinical Medicine, Nanchang University, Nanchang, Jiangxi 330000, China
| | - Yiyang Gong
- Second College of Clinical Medicine, Nanchang University, Nanchang, Jiangxi 330000, China
| | - Liangyun Guo
- Department of Ultrasonography, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330000, China
| |
Collapse
|
2
|
Cong L, He Y, Wu Y, Li Z, Ding S, Liang W, Xiao X, Zhang H, Wang L. Discovery and validation of molecular patterns and immune characteristics in the peripheral blood of ischemic stroke patients. PeerJ 2024; 12:e17208. [PMID: 38650649 PMCID: PMC11034498 DOI: 10.7717/peerj.17208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 03/18/2024] [Indexed: 04/25/2024] Open
Abstract
Background Stroke is a disease with high morbidity, disability, and mortality. Immune factors play a crucial role in the occurrence of ischemic stroke (IS), but their exact mechanism is not clear. This study aims to identify possible immunological mechanisms by recognizing immune-related biomarkers and evaluating the infiltration pattern of immune cells. Methods We downloaded datasets of IS patients from GEO, applied R language to discover differentially expressed genes, and elucidated their biological functions using GO, KEGG analysis, and GSEA analysis. The hub genes were then obtained using two machine learning algorithms (least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE)) and the immune cell infiltration pattern was revealed by CIBERSORT. Gene-drug target networks and mRNA-miRNA-lncRNA regulatory networks were constructed using Cytoscape. Finally, we used RT-qPCR to validate the hub genes and applied logistic regression methods to build diagnostic models validated with ROC curves. Results We screened 188 differentially expressed genes whose functional analysis was enriched to multiple immune-related pathways. Six hub genes (ANTXR2, BAZ2B, C5AR1, PDK4, PPIH, and STK3) were identified using LASSO and SVM-RFE. ANTXR2, BAZ2B, C5AR1, PDK4, and STK3 were positively correlated with neutrophils and gamma delta T cells, and negatively correlated with T follicular helper cells and CD8, while PPIH showed the exact opposite trend. Immune infiltration indicated increased activity of monocytes, macrophages M0, neutrophils, and mast cells, and decreased infiltration of T follicular helper cells and CD8 in the IS group. The ceRNA network consisted of 306 miRNA-mRNA interacting pairs and 285 miRNA-lncRNA interacting pairs. RT-qPCR results indicated that the expression levels of BAZ2B, C5AR1, PDK4, and STK3 were significantly increased in patients with IS. Finally, we developed a diagnostic model based on these four genes. The AUC value of the model was verified to be 0.999 in the training set and 0.940 in the validation set. Conclusion Our research explored the immune-related gene expression modules and provided a specific basis for further study of immunomodulatory therapy of IS.
Collapse
Affiliation(s)
- Lin Cong
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China
| | - Yijie He
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China
| | - Yun Wu
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China
| | - Ze Li
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China
| | - Siwen Ding
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China
| | - Weiwei Liang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China
| | - Xingjun Xiao
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China
| | - Huixue Zhang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China
| | - Lihua Wang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China
| |
Collapse
|
3
|
陈 浩, 李 振, 王 明, 卢 林, 唐 乾, 罗 良. [High expression of UBE2S promotes progression of hepatocellular carcinoma by increasing cancer cell stemness]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2024; 44:455-464. [PMID: 38597436 PMCID: PMC11006698 DOI: 10.12122/j.issn.1673-4254.2024.03.06] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Indexed: 04/11/2024]
Abstract
OBJECTIVE To investigate the expression of the ubiquitination enzyme UBE2S in different cell types in hepatocellular carcinoma (HCC) microenvironment and its impact on proliferation and stemness of HCC cells. METHODS TCGA and CPTAC database were used to analyze the transcriptional and promoter methylation levels and protein expressions of UBE2S in HCC. Specific expression patterns of UBE2S, intercellular communication and key transcription factors in different cell types were analyzed based on single-cell sequencing data from TISCH website. We further examined UBE2S expressions in clinical samples of HCC tissues, HCC cells and T cells using immunohistochemistry and immunofluorescence staining. We also tested the effects of UBE2S knockdown on stemness of HCC-LM3 and HepG2 cells using clone formation experiments and sphere formation assay. RESULTS Analysis based on TCGA database suggested significant overexpression of UBE2S in both paired and non-paired tumor tissues (P < 0.001), and its transcriptional level increased with tumor grades. The methylation level of UBE2S promoter was significantly decreased in HCC (P < 0.001), and its transcription level increased obviously in HCC with TP53 mutation (P < 0.001). Analysis of CPTAC database also demonstrated overexpression of UBE2S protein in HCC tissues (P < 0.001). Three prognostic models suggested that HCC patients with high UBE2S expression had poorer prognosis (P < 0.001). Single-cell sequencing data analysis revealed high expressions of UBE2S in T cells and high intensities of interaction between endothelial cells, epithelial cells and fibroblasts in HCC microenvironment. Immunohistochemistry and immunofluorescence staining demonstrated high UBE2S expressions in clinical samples of HCC tissues, HCC cells and T cells. In HCC-LM3 and HepG2 cells, UBE2S knockdown significantly inhibited cell clone formation and tumor sphere formation (P < 0.05). CONCLUSION UBE2S is highly expressed in T cells in HCC microenvironment in close correlation with a poor prognosis. High UBE2S expression promotes the stemness of HCC cells.
Collapse
Affiliation(s)
- 浩 陈
- 暨南大学临床医学博士后流动站,广东 广州 510632Postdoctoral Research Station of Clinical Medicine, Jinan University, Guangzhou 510632, China
- 右江民族医学院研究生学院,广西 百色 533000Graduate School, Youjiang Medical University for Nationalities, Baise 533000, China
- 皖南医学院病理解剖学教研室,安徽 芜湖 241002Department of Pathology, Wannan Medical College, Wuhu 241002, China
| | - 振汉 李
- 皖南医学院临床医学院,安徽 芜湖 241002School of Clinical Medicine, Wannan Medical College, Wuhu 241002, China
| | - 明婷 王
- 南京市第一医院产科,江苏 南京 210006Department of Obstetrics, Nanjing First Hospital, Nanjing 210006, China
| | - 林明 卢
- 皖南医学院病理解剖学教研室,安徽 芜湖 241002Department of Pathology, Wannan Medical College, Wuhu 241002, China
| | - 乾利 唐
- 右江民族医学院研究生学院,广西 百色 533000Graduate School, Youjiang Medical University for Nationalities, Baise 533000, China
| | - 良平 罗
- 暨南大学临床医学博士后流动站,广东 广州 510632Postdoctoral Research Station of Clinical Medicine, Jinan University, Guangzhou 510632, China
| |
Collapse
|
4
|
Tang H, Luo X, Shen X, Fan D, Rao J, Wan Y, Ma H, Guo X, Liu Z, Gao J. Lysosome-related biomarkers in preeclampsia and cancers: Machine learning and bioinformatics analysis. Comput Biol Med 2024; 171:108201. [PMID: 38428097 DOI: 10.1016/j.compbiomed.2024.108201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 01/21/2024] [Accepted: 02/18/2024] [Indexed: 03/03/2024]
Abstract
BACKGROUND Lysosomes serve as regulatory hubs, and play a pivotal role in human diseases. However, the precise functions and mechanisms of action of lysosome-related genes remain unclear in preeclampsia and cancers. This study aimed to identify lysosome-related biomarkers in preeclampsia, and further explore the biomarkers shared between preeclampsia and cancers. MATERIALS AND METHODS We obtained GSE60438 and GSE75010 datasets from the Gene Expression Omnibus database, pre-procesed them and merged them into a training cohort. The limma package in R was used to identify the differentially expressed mRNAs between the preeclampsia and normal control groups. Differentially expressed lysosome-related genes were identified by intersecting the differentially expressed mRNAs and lysosome-related genes obtained from Gene Ontology and GSEA databases. Gene Ontology annotations and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed using the DAVID database. The CIBERSORT method was used to analyze immune cell infiltration. Weighted gene co-expression analyses and three machine learning algorithm were used to identify lysosome-related diagnostic biomarkers. Lysosome-related diagnostic biomarkers were further validated in the testing cohort GSE25906. Nomogram diagnostic models for preeclampsia were constructed. In addition, pan-cancer analysis of lysosome-related diagnostic biomarkers were identified by was performed using the TIMER, Sangebox and TISIDB databases. Finally, the Drug-Gene Interaction, TheMarker and DSigDB Databases were used for drug-gene interactions analysis. RESULTS A total of 11 differentially expressed lysosome-related genes were identified between the preeclampsia and control groups. Three molecular clusters connected to lysosome were identified, and enrichment analysis demonstrated their strong relevance to the development and progression of preeclampsia. Immune infiltration analysis revealed significant immunity heterogeneity among different clusters. GBA, OCRL, TLR7 and HEXB were identified as lysosome-related diagnostic biomarkers with high AUC values, and validated in the testing cohort GSE25906. Nomogram, calibration curve, and decision curve analysis confirmed the accuracy of predicting the occurrence of preeclampsia based on OCRL and HEXB. Pan-cancer analysis showed that GBA, OCRL, TLR7 and HEXB were associated with the prognosis of patients with various tumors and tumor immune cell infiltration. Twelve drugs were identified as potential drugs for the treatment of preeclampsia and cancers. CONCLUSION This study identified GBA, OCRL, TLR7 and HEXB as potential lysosome-related diagnostic biomarkers shared between preeclampsia and cancers.
Collapse
Affiliation(s)
- Hai Tang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, 510080, China; Foshan Institute of Fetal Medicine, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, 528000, China; Department of Obstetrics, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, 528000, China
| | - Xin Luo
- Foshan Institute of Fetal Medicine, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, 528000, China; Department of Obstetrics, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, 528000, China
| | - Xiuyin Shen
- Foshan Institute of Fetal Medicine, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, 528000, China; Department of Obstetrics, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, 528000, China
| | - Dazhi Fan
- Foshan Institute of Fetal Medicine, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, 528000, China; Department of Obstetrics, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, 528000, China
| | - Jiamin Rao
- Foshan Institute of Fetal Medicine, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, 528000, China; Department of Obstetrics, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, 528000, China
| | - Yingchun Wan
- Foshan Institute of Fetal Medicine, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, 528000, China; Department of Obstetrics, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, 528000, China
| | - Huiting Ma
- Foshan Institute of Fetal Medicine, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, 528000, China; Department of Obstetrics, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, 528000, China
| | - Xiaoling Guo
- Foshan Institute of Fetal Medicine, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, 528000, China; Department of Obstetrics, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, 528000, China
| | - Zhengping Liu
- Foshan Institute of Fetal Medicine, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, 528000, China; Department of Obstetrics, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, 528000, China.
| | - Jie Gao
- Premarital Examination and Superior Examination Department, Jingzhou Gongan Maternal and Child Health Care Hospital, Jingzhou, Hubei, 434300, China.
| |
Collapse
|
5
|
Shang Y, Wang X, Su S, Ji F, Shao D, Duan C, Chen T, Liang C, Zhang D, Lu H. Identifying of immune-associated genes for assessing the obesity-associated risk to the offspring in maternal obesity: A bioinformatics and machine learning. CNS Neurosci Ther 2024; 30:e14700. [PMID: 38544384 PMCID: PMC10973700 DOI: 10.1111/cns.14700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 03/13/2024] [Accepted: 03/14/2024] [Indexed: 05/14/2024] Open
Abstract
BACKGROUND Perinatal exposure to maternal obesity predisposes offspring to develop obesity later in life. Immune dysregulation in the hypothalamus, the brain center governing energy homeostasis, is pivotal in obesity development. This study aimed to identify key candidate genes associated with the risk of offspring obesity in maternal obesity. METHODS We obtained obesity-related datasets from the Gene Expression Omnibus (GEO) database. GSE135830 comprises gene expression data from the hypothalamus of mouse offspring in a maternal obesity model induced by a high-fat diet model (maternal high-fat diet (mHFD) group and maternal chow (mChow) group), while GSE127056 consists of hypothalamus microarray data from young adult mice with obesity (high-fat diet (HFD) and Chow groups). We identified differentially expressed genes (DEGs) and module genes using Limma and weighted gene co-expression network analysis (WGCNA), conducted functional enrichment analysis, and employed a machine learning algorithm (least absolute shrinkage and selection operator (LASSO) regression) to pinpoint candidate hub genes for diagnosing obesity-associated risk in offspring of maternal obesity. We constructed a nomogram receiver operating characteristic (ROC) curve to evaluate the diagnostic value. Additionally, we analyzed immune cell infiltration to investigate immune cell dysregulation in maternal obesity. Furthermore, we verified the expression of the candidate hub genes both in vivo and in vitro. RESULTS The GSE135830 dataset revealed 2868 DEGs between the mHFD offspring and the mChow group and 2627 WGCNA module genes related to maternal obesity. The overlap of DEGs and module genes in the offspring with maternal obesity in GSE135830 primarily enriched in neurodevelopment and immune regulation. In the GSE127056 dataset, 133 DEGs were identified in the hypothalamus of HFD-induced adult obese individuals. A total of 13 genes intersected between the GSE127056 adult obesity DEGs and the GSE135830 maternal obesity module genes that were primarily enriched in neurodevelopment and the immune response. Following machine learning, two candidate hub genes were chosen for nomogram construction. Diagnostic value evaluation by ROC analysis determined Sytl4 and Kncn2 as hub genes for maternal obesity in the offspring. A gene regulatory network with transcription factor-miRNA interactions was established. Dysregulated immune cells were observed in the hypothalamus of offspring with maternal obesity. Expression of Sytl4 and Kncn2 was validated in a mouse model of hypothalamic inflammation and a palmitic acid-stimulated microglial inflammation model. CONCLUSION Two candidate hub genes (Sytl4 and Kcnc2) were identified and a nomogram was developed to predict obesity risk in offspring with maternal obesity. These findings offer potential diagnostic candidate genes for identifying obesity-associated risks in the offspring of obese mothers.
Collapse
Affiliation(s)
- Yanxing Shang
- Medical Research Center, Affiliated Hospital 2Nantong UniversityNantongChina
- Jiangsu Provincial Medical Key Discipline (Laboratory) Cultivation Unit, Medical Research CenterNantong First People's HospitalNantongChina
- Nantong Clinical Medical College of Kangda College of Nanjing Medical UniversityNantongChina
- Nantong Municipal Key Laboratory of Metabolic Immunology and Disease MicroenvironmentNantong First People's HospitalNantongChina
| | - Xueqin Wang
- Department of Endocrinology, Affiliated Hospital 2Nantong UniversityNantongChina
| | - Sixuan Su
- Medical Research Center, Affiliated Hospital 2Nantong UniversityNantongChina
- Jiangsu Provincial Medical Key Discipline (Laboratory) Cultivation Unit, Medical Research CenterNantong First People's HospitalNantongChina
- Nantong Clinical Medical College of Kangda College of Nanjing Medical UniversityNantongChina
- Nantong Municipal Key Laboratory of Metabolic Immunology and Disease MicroenvironmentNantong First People's HospitalNantongChina
- Department of Pathogen Biology, Medical CollegeNantong UniversityNantongChina
| | - Feng Ji
- Medical Research Center, Affiliated Hospital 2Nantong UniversityNantongChina
- Jiangsu Provincial Medical Key Discipline (Laboratory) Cultivation Unit, Medical Research CenterNantong First People's HospitalNantongChina
- Nantong Clinical Medical College of Kangda College of Nanjing Medical UniversityNantongChina
- Nantong Municipal Key Laboratory of Metabolic Immunology and Disease MicroenvironmentNantong First People's HospitalNantongChina
| | - Donghai Shao
- Medical Research Center, Affiliated Hospital 2Nantong UniversityNantongChina
- Jiangsu Provincial Medical Key Discipline (Laboratory) Cultivation Unit, Medical Research CenterNantong First People's HospitalNantongChina
- Nantong Clinical Medical College of Kangda College of Nanjing Medical UniversityNantongChina
- Nantong Municipal Key Laboratory of Metabolic Immunology and Disease MicroenvironmentNantong First People's HospitalNantongChina
| | - Chengwei Duan
- Medical Research Center, Affiliated Hospital 2Nantong UniversityNantongChina
- Jiangsu Provincial Medical Key Discipline (Laboratory) Cultivation Unit, Medical Research CenterNantong First People's HospitalNantongChina
- Nantong Clinical Medical College of Kangda College of Nanjing Medical UniversityNantongChina
- Nantong Municipal Key Laboratory of Metabolic Immunology and Disease MicroenvironmentNantong First People's HospitalNantongChina
| | - Tianpeng Chen
- Medical Research Center, Affiliated Hospital 2Nantong UniversityNantongChina
- Jiangsu Provincial Medical Key Discipline (Laboratory) Cultivation Unit, Medical Research CenterNantong First People's HospitalNantongChina
- Nantong Clinical Medical College of Kangda College of Nanjing Medical UniversityNantongChina
- Nantong Municipal Key Laboratory of Metabolic Immunology and Disease MicroenvironmentNantong First People's HospitalNantongChina
| | - Caixia Liang
- Medical Research Center, Affiliated Hospital 2Nantong UniversityNantongChina
- Jiangsu Provincial Medical Key Discipline (Laboratory) Cultivation Unit, Medical Research CenterNantong First People's HospitalNantongChina
- Nantong Clinical Medical College of Kangda College of Nanjing Medical UniversityNantongChina
- Nantong Municipal Key Laboratory of Metabolic Immunology and Disease MicroenvironmentNantong First People's HospitalNantongChina
| | - Dongmei Zhang
- Medical Research Center, Affiliated Hospital 2Nantong UniversityNantongChina
- Jiangsu Provincial Medical Key Discipline (Laboratory) Cultivation Unit, Medical Research CenterNantong First People's HospitalNantongChina
- Nantong Clinical Medical College of Kangda College of Nanjing Medical UniversityNantongChina
- Nantong Municipal Key Laboratory of Metabolic Immunology and Disease MicroenvironmentNantong First People's HospitalNantongChina
- Department of Pathogen Biology, Medical CollegeNantong UniversityNantongChina
| | - Hongjian Lu
- Medical Research Center, Affiliated Hospital 2Nantong UniversityNantongChina
- Jiangsu Provincial Medical Key Discipline (Laboratory) Cultivation Unit, Medical Research CenterNantong First People's HospitalNantongChina
- Department of Rehabilitation Medicine, Affiliated Hospital 2Nantong UniversityNantongChina
| |
Collapse
|
6
|
Geng Y, Liu Y, Wang M, Dong X, Sun X, Luo Y, Sun X. Identification and validation of platelet-related diagnostic markers and potential drug screening in ischemic stroke by integrating comprehensive bioinformatics analysis and machine learning. Front Immunol 2024; 14:1320475. [PMID: 38268925 PMCID: PMC10806171 DOI: 10.3389/fimmu.2023.1320475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 12/18/2023] [Indexed: 01/26/2024] Open
Abstract
Background Ischemic stroke (IS), caused by blood and oxygen deprivation due to cerebral thrombosis, has links to activated and aggregated platelets. Discovering platelet-related biomarkers, developing diagnostic models, and screening antiplatelet drugs are crucial for IS diagnosis and treatment. Methods and results Combining and normalizing GSE16561 and GSE22255 datasets identified 1,753 upregulated and 1,187 downregulated genes. Fifty-one genes in the platelet-related module were isolated using weighted gene co-expression network analysis (WGCNA) and other analyses, including 50 upregulated and one downregulated gene. Subsequent enrichment and network analyses resulted in 25 platelet-associated genes and six diagnostic markers for a risk assessment model. This model's area under the ROC curve outperformed single genes, and in the peripheral blood of the high-risk group, immune infiltration indicated a higher proportion of CD4, resting CD4 memory, and activated CD4 memory T cells, along with a lower proportion of CD8 T cells in comparison to the low-risk group. Utilizing the gene expression matrix and the CMap database, we identified two potential drugs for IS. Finally, a rat MACO/R model was used to validate the diagnostic markers' expression and the drugs' predicted anticoagulant effects. Conclusion We identified six IS platelet-related biomarkers (APP, THBS1, F13A1, SRC, PPBP, and VCL) for a robust diagnostic model. The drugs alpha-linolenic acid and ciprofibrate have potential antiplatelet effects in IS. This study advances early IS diagnosis and treatment.
Collapse
Affiliation(s)
- Yifei Geng
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
| | - Yuchen Liu
- Department of Internal Medicine, Peking Union Medical College Hospital, Beijing, China
- School of Clinical Science, Peking Union Medical College, Chinese Academy of Medical Science, Beijing, China
| | - Min Wang
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
| | - Xi Dong
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
| | - Xiao Sun
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
| | - Yun Luo
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
| | - Xiaobo Sun
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
| |
Collapse
|
7
|
Liu L, Cai Y, Deng C. Identification of ANXA3 as a biomarker associated with pyroptosis in ischemic stroke. Eur J Med Res 2023; 28:596. [PMID: 38102696 PMCID: PMC10725036 DOI: 10.1186/s40001-023-01564-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/03/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Pyroptosis plays an important role in the pathological process of ischemic stroke (IS). However, the exact mechanism of pyroptosis remains unclear. This paper aims to reveal the key molecular markers associated with pyroptosis in IS. METHODS We used random forest learning, gene set variation analysis, and Pearson correlation analysis to screen for biomarkers associated with pyroptosis in IS. Middle cerebral artery occlusion/reperfusion (MCAO/R) and oxygen and glucose deprivation/reoxygenation (OGD/R) models were constructed in vitro and in vivo. Cells were transfected with an Annexin A3 silencing (si-ANXA3) plasmid to observe the effects of ANXA3 on OGD/R + lipopolysaccharides (LPS)-induced pyroptosis. qRT‒PCR and western blotting were used to detect the expression of potential biomarkers and pyroptotic pathways. RESULTS Samples from a total of 170 IS patients and 109 healthy individuals were obtained from 5 gene expression omnibus databases. Thirty important genes were analyzed by random forest learning from the differentially expressed genes. Then, we investigated the relationship between the above genes and the pyroptosis score, obtaining three potential biomarkers (ANXA3, ANKRD22, ADM). ANXA3 and ADM were upregulated in the MCAO/R model, and the fold difference in ANXA3 expression was greater. Pyroptosis-related factors (NLRP3, NLRC4, AIM2, GSDMD-N, caspase-8, pro-caspase-1, cleaved caspase-1, IL-1β, and IL-18) were upregulated in the MCAO/R model. Silencing ANXA3 alleviated the expression of pyroptosis-related factors (NLRC4, AIM2, GSDMD-N, caspase-8, pro-caspase-1, cleaved caspase-1, and IL-18) induced by OGD/R + LPS or MCAO/R. CONCLUSION This study identified ANXA3 as a possible pyroptosis-related gene marker in IS through bioinformatics and experiments. ANXA3 could inhibit pyroptosis through the NLRC4/AIM2 axis.
Collapse
Affiliation(s)
- Linquan Liu
- Chronic Disease Management Department, The First Hospital of Hunan University of Chinese Medicine, Changsha, 410007, Hunan, China
- The Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of Cardio-Cerebral Diseases, College of Integrated Traditional Chinese and Western Medicine, Hunan University of Chinese Medicine, Changsha, 410208, Hunan, China
| | - Yahong Cai
- Chronic Disease Management Department, The First Hospital of Hunan University of Chinese Medicine, Changsha, 410007, Hunan, China
| | - Changqing Deng
- The Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of Cardio-Cerebral Diseases, College of Integrated Traditional Chinese and Western Medicine, Hunan University of Chinese Medicine, Changsha, 410208, Hunan, China.
| |
Collapse
|
8
|
Lu D, Cai H, Li Y, Chang W, Liu X, Dai Q, Yu W, Chen W, Qiao G, Xie H, Xiao X, Li Z. Investigating the ID3/SLC22A4 as immune-related signatures in ischemic stroke. Aging (Albany NY) 2023; 15:14803-14829. [PMID: 38112574 PMCID: PMC10781493 DOI: 10.18632/aging.205308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 11/03/2023] [Indexed: 12/21/2023]
Abstract
BACKGROUND Ischemic stroke (IS) is a fearful disease that can cause a variety of immune events. Nevertheless, precise immune-related mechanisms have yet to be systematically elucidated. This study aimed to identify immune-related signatures using machine learning and to validate them with animal experiments and single cell analysis. METHODS In this study, we screened 24 differentially expressed genes (DEGs) while identifying immune-related signatures that may play a key role in IS development through a comprehensive strategy between least absolute shrinkage and selection operation (LASSO) regression, support vector machine (SVM) and immune-related genes. In addition, we explored immune infiltration using the CIBERSORT algorithm. Finally, we performed validation in mouse brain tissue and single cell analysis. RESULTS We identified 24 DEGs for follow-up analysis. ID3 and SLC22A4 were finally identified as the better immune-related signatures through a comprehensive strategy among DEGs, LASSO, SVM and immune-related genes. RT-qPCR, western blot, and immunofluorescence revealed a significant decrease in ID3 and a significant increase in SLC22A4 in the middle cerebral artery occlusion group. Single cell analysis revealed that ID3 was mainly concentrated in endothelial_2 cells and SLC22A4 in astrocytes in the MCAO group. A CIBERSORT finds significantly altered levels of immune infiltration in IS patients. CONCLUSIONS This study focused on immune-related signatures after stroke and ID3 and SLC22A4 may be new therapeutic targets to promote functional recovery after stroke. Furthermore, the association of ID3 and SLC22A4 with immune cells may be a new direction for post-stroke immunotherapy.
Collapse
Affiliation(s)
- Dading Lu
- Department of Stroke Center, The First Hospital of China Medical University, Heping, Shenyang, Liaoning, China
- Department of Neurology, The First Hospital of China Medical University, Heping, Shenyang, Liaoning, China
- Department of Neurosurgery, The First Hospital of China Medical University, Heping, Shenyang, Liaoning, China
| | - Heng Cai
- Department of Neurosurgery, Shengjing Hospital, Shenyang, China medical University, Heping, Shenyang, China
| | - Yugang Li
- Department of Stroke Center, The First Hospital of China Medical University, Heping, Shenyang, Liaoning, China
| | - Wenyuan Chang
- Department of Neurology, The First Hospital of China Medical University, Heping, Shenyang, Liaoning, China
| | - Xiu Liu
- The First Clinical College, China Medical University, Shenbei, Shenyang, China
| | - Qiwei Dai
- Department of Neurosurgery, Shengjing Hospital, Shenyang, China medical University, Heping, Shenyang, China
| | - Wanning Yu
- Department of Neurosurgery, Shengjing Hospital, Shenyang, China medical University, Heping, Shenyang, China
| | - Wangli Chen
- Department of Neurosurgery, Shengjing Hospital, Shenyang, China medical University, Heping, Shenyang, China
| | - Guomin Qiao
- Department of Neurosurgery, Shengjing Hospital, Shenyang, China medical University, Heping, Shenyang, China
| | - Haojie Xie
- Department of Neurosurgery, Shengjing Hospital, Shenyang, China medical University, Heping, Shenyang, China
| | - Xiong Xiao
- Department of Neurosurgery, Shengjing Hospital, Shenyang, China medical University, Heping, Shenyang, China
| | - Zhiqing Li
- Department of Stroke Center, The First Hospital of China Medical University, Heping, Shenyang, Liaoning, China
- Department of Neurology, The First Hospital of China Medical University, Heping, Shenyang, Liaoning, China
- Department of Neurosurgery, The First Hospital of China Medical University, Heping, Shenyang, Liaoning, China
| |
Collapse
|
9
|
Song J, Zaidi SAA, He L, Zhang S, Zhou G. Integrative Analysis of Machine Learning and Molecule Docking Simulations for Ischemic Stroke Diagnosis and Therapy. Molecules 2023; 28:7704. [PMID: 38067435 PMCID: PMC10707570 DOI: 10.3390/molecules28237704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 11/13/2023] [Accepted: 11/17/2023] [Indexed: 12/18/2023] Open
Abstract
Due to the narrow therapeutic window and high mortality of ischemic stroke, it is of great significance to investigate its diagnosis and therapy. We employed weighted gene coexpression network analysis (WGCNA) to ascertain gene modules related to stroke and used the maSigPro R package to seek the time-dependent genes in the progression of stroke. Three machine learning algorithms were further employed to identify the feature genes of stroke. A nomogram model was built and applied to evaluate the stroke patients. We analyzed single-cell RNA sequencing (scRNA-seq) data to discern microglia subclusters in ischemic stroke. The RNA velocity, pseudo time, and gene set enrichment analysis (GSEA) were performed to investigate the relationship of microglia subclusters. Connectivity map (CMap) analysis and molecule docking were used to screen a therapeutic agent for stroke. A nomogram model based on the feature genes showed a clinical net benefit and enabled an accurate evaluation of stroke patients. The RNA velocity and pseudo time analysis showed that microglia subcluster 0 would develop toward subcluster 2 within 24 h from stroke onset. The GSEA showed that the function of microglia subcluster 0 was opposite to that of subcluster 2. AZ_628, which screened from CMap analysis, was found to have lower binding energy with Mmp12, Lgals3, Fam20c, Capg, Pkm2, Sdc4, and Itga5 in microglia subcluster 2 and maybe a therapeutic agent for the poor development of microglia subcluster 2 after stroke. Our study presents a nomogram model for stroke diagnosis and provides a potential molecule agent for stroke therapy.
Collapse
Affiliation(s)
- Jingwei Song
- Department of Medical Cell Biology and Genetics, Guangdong Key Laboratory of Genomic Stability and Disease Prevention, Shenzhen Key Laboratory of Anti-Aging and Regenerative Medicine, and Shenzhen Engineering Laboratory of Regenerative Technologies for Orthopaedic Diseases, Health Sciences Center, Shenzhen University, Shenzhen 518060, China; (J.S.); (S.A.A.Z.); (L.H.)
| | - Syed Aqib Ali Zaidi
- Department of Medical Cell Biology and Genetics, Guangdong Key Laboratory of Genomic Stability and Disease Prevention, Shenzhen Key Laboratory of Anti-Aging and Regenerative Medicine, and Shenzhen Engineering Laboratory of Regenerative Technologies for Orthopaedic Diseases, Health Sciences Center, Shenzhen University, Shenzhen 518060, China; (J.S.); (S.A.A.Z.); (L.H.)
| | - Liangge He
- Department of Medical Cell Biology and Genetics, Guangdong Key Laboratory of Genomic Stability and Disease Prevention, Shenzhen Key Laboratory of Anti-Aging and Regenerative Medicine, and Shenzhen Engineering Laboratory of Regenerative Technologies for Orthopaedic Diseases, Health Sciences Center, Shenzhen University, Shenzhen 518060, China; (J.S.); (S.A.A.Z.); (L.H.)
| | - Shuai Zhang
- Brain Research Centre, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Guangqian Zhou
- Department of Medical Cell Biology and Genetics, Guangdong Key Laboratory of Genomic Stability and Disease Prevention, Shenzhen Key Laboratory of Anti-Aging and Regenerative Medicine, and Shenzhen Engineering Laboratory of Regenerative Technologies for Orthopaedic Diseases, Health Sciences Center, Shenzhen University, Shenzhen 518060, China; (J.S.); (S.A.A.Z.); (L.H.)
- Lungene Biotech Ltd., Shenzhen 518060, China
- Senotherapeutics Ltd., Hangzhou 311100, China
| |
Collapse
|
10
|
Hu J, Yang X, Ren J, Zhong S, Fan Q, Li W. Identification and verification of characteristic differentially expressed ferroptosis-related genes in osteosarcoma using bioinformatics analysis. Toxicol Mech Methods 2023; 33:781-795. [PMID: 37488941 DOI: 10.1080/15376516.2023.2240879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/17/2023] [Accepted: 07/20/2023] [Indexed: 07/26/2023]
Abstract
BACKGROUND This study identified and verified the characteristic differentially expressed ferroptosis-related genes (CDEFRGs) in osteosarcoma (OS). METHODS We extracted ferroptosis-related genes (FRGs), identified differentially expressed FRGs (DEFRGs) in OS, and conducted correlation analysis between DEFRGs. Next, we conducted GO and KEGG analyses to explore the biological functions and pathways of DEFRGs. Furthermore, we used LASSO and SVM-RFE algorithms to screen CDEFRGs, and evaluated its accuracy in diagnosing OS through ROC curves. Then, we demonstrated the molecular function and pathway enrichment of CDEFRGs through GSEA analysis. In addition, we evaluated the differences in immune cell infiltration between OS and NC groups, as well as the correlation between CDEFRGs expressions and immune cell infiltrations. Finally, the expression of CDEFRGs was verified through qRT-PCR, western blotting, and immunohistochemistry experiments. RESULTS We identified 51 DEFRGs and the expression relationship between them. GO and KEGG analysis revealed their key functions and important pathways. Based on four CDEFRGs (PEX3, CPEB1, NOX1, and ALOX5), we built the OS diagnostic model, and verified its accuracy. GSEA analysis further revealed the important functions and pathways of CDEFRGs. In addition, there were differences in immune cell infiltration between OS group and NC group, and CDEFRGs showed significant correlation with certain infiltrating immune cells. Finally, we validated the differential expression levels of four CDEFRGs through external experiments. CONCLUSIONS This study has shed light on the molecular pathological mechanism of OS and has offered novel perspectives for the early diagnosis and immune-targeted therapy of OS patients.
Collapse
Affiliation(s)
- Jianhua Hu
- Department of Orthopedic Surgery, The First People's Hospital of Yunnan Province, Affiliated Hospital of Kunming University of Science and Technology, Kunming, P. R. China
- Faculty of Medical Science, Kunming University of Science and Technology, Kunming, P. R. China
| | - Xi Yang
- Department of Orthopedic Surgery, The First People's Hospital of Yunnan Province, Affiliated Hospital of Kunming University of Science and Technology, Kunming, P. R. China
- Yunnan Key Laboratory of Digital Orthopaedics, Kunming, P. R. China
| | - Jing Ren
- Department of Spinal Surgery, Qujing No. 1 Hospital, Affiliated Qujing Hospital of Kunming Medical University, Qujing, P. R. China
| | - Shixiao Zhong
- Faculty of Medical Science, Kunming University of Science and Technology, Kunming, P. R. China
- Yunnan Key Laboratory of Digital Orthopaedics, Kunming, P. R. China
| | - Qianbo Fan
- Faculty of Medical Science, Kunming University of Science and Technology, Kunming, P. R. China
- Yunnan Key Laboratory of Digital Orthopaedics, Kunming, P. R. China
| | - Weichao Li
- Department of Orthopedic Surgery, The First People's Hospital of Yunnan Province, Affiliated Hospital of Kunming University of Science and Technology, Kunming, P. R. China
- Faculty of Medical Science, Kunming University of Science and Technology, Kunming, P. R. China
- Yunnan Key Laboratory of Digital Orthopaedics, Kunming, P. R. China
| |
Collapse
|
11
|
Cao GZ, Hou JY, Zhou R, Tian LL, Wang ML, Zhang Y, Xu H, Yang HJ, Zhang JJ. Single-cell RNA sequencing reveals that VIM and IFITM3 are vital targets of Dengzhan Shengmai capsule to protect against cerebral ischemic injury. JOURNAL OF ETHNOPHARMACOLOGY 2023; 311:116439. [PMID: 37004745 DOI: 10.1016/j.jep.2023.116439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/23/2023] [Accepted: 03/25/2023] [Indexed: 06/19/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Ischemic stroke is one of the leading causes of mortality, but therapies are limited. Dengzhan Shengmai capsule (DZSM) was included by the Chinese Pharmacopoeia 2020 and has been broadly used for the treatment of ischemic stroke. However, the mechanism of DZSM against ischemic stroke is unclear. AIM OF THE STUDY This study used RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) to investigate the mechanism of action of DZSM against ischemic stroke. MATERIALS AND METHODS The rats were randomly divided into six groups: the Sham, I/R (water), I/R + DZSM-L (0.1134g/kg), I/R + DZSM-H (0.4536g/kg), I/R + NMDP (20mg/kg), and I/R + Ginaton (20mg/kg). The rats were administrated drugs for 5 days then followed by the ischemic brain injury caused by middle cerebral artery occlusion (MCAO). The neuroprotective effect was assessed by infraction rate, neurological deficit scores, regional cerebral blood flow (rCBF), hematoxylin and eosin (H&E) staining, and Nissl staining. Based on RNA-seq and scRNA-seq, the vital biological processes and core targets of DZSM against cerebral ischemia were revealed. Enzyme-linked immunosorbent assay (ELISA) and immunofluorescence (IF) staining were used to investigate the vital biological processes and core targets of DZSM against ischemic stroke. RESULTS Administration of DZSM significantly reduced the infarction rate and Zea Longa score, Garcia JH score, and ameliorated the reduction in rCBF. And alleviated the neuronal damage, such as increased neuronal density level and Nissl bodies density level. RNA-seq analysis revealed that DZSM played important roles in inflammation and apoptosis. ELISA and IF straining validation confirmed that DZSM significantly decreased the expression of IL-6, IL-1β, TNF-α, ICAM-1, IBA-1, MMP9, and Cleaved caspase-3 in MCAO rats. ScRNA-seq analysis identified 8 core targets in neurons including HSPB1, SPP1, MT2A, GFAP, IFITM3, VIM, CRIP1, and GPD1, and VIM and IFITM3 was verified to be decreased by DZSM in neurons. CONCLUSION Our study illustrates the neuroprotective effect of DZSM against ischemia stroke, and VIM and IFITM3 were identified as vital targets in neurons of DZSM in protecting against MCAO-induced I/R injury.
Collapse
Affiliation(s)
- Guang-Zhao Cao
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
| | - Jing-Yi Hou
- Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
| | - Rui Zhou
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
| | - Liang-Liang Tian
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
| | - Mao-Lin Wang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
| | - Yi Zhang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
| | - He Xu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
| | - Hong-Jun Yang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China; Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
| | - Jing-Jing Zhang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China; Chinese Institute for Brain Research, Beijing, 102206, China.
| |
Collapse
|
12
|
Zheng PF, Hong XQ, Liu ZY, Zheng ZF, Liu P, Chen LZ. m6A regulator-mediated RNA methylation modification patterns are involved in the regulation of the immune microenvironment in ischaemic cardiomyopathy. Sci Rep 2023; 13:5904. [PMID: 37041267 PMCID: PMC10090050 DOI: 10.1038/s41598-023-32919-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 04/04/2023] [Indexed: 04/13/2023] Open
Abstract
The role of RNA N6-methyladenosine (m6A) modification in the regulation of the immune microenvironment in ischaemic cardiomyopathy (ICM) remains largely unclear. This study first identified differential m6A regulators between ICM and healthy samples, and then systematically evaluated the effects of m6A modification on the characteristics of the immune microenvironment in ICM, including the infiltration of immune cells, the human leukocyte antigen (HLA) gene, and HALLMARKS pathways. A total of seven key m6A regulators, including WTAP, ZCH3H13, YTHDC1, FMR1, FTO, RBM15 and YTHDF3, were identified using a random forest classifier. A diagnostic nomogram based on these seven key m6A regulators could effectively distinguish patients with ICM from healthy subjects. We further identified two distinct m6A modification patterns (m6A cluster-A and m6A cluster-B) that are mediated by these seven regulators. Meanwhile, we also noted that one m6A regulator, WTAP, was gradually upregulated, while the others were gradually downregulated in the m6A cluster-A vs. m6A cluster-B vs. healthy subjects. In addition, we observed that the degree of infiltration of the activated dendritic cells, macrophages, natural killer (NK) T cells, and type-17 T helper (Th17) cells gradually increased in m6A cluster-A vs. m6A cluster-B vs. healthy subjects. Furthermore, m6A regulators, including FTO, YTHDC1, YTHDF3, FMR1, ZC3H13, and RBM15 were significantly negatively correlated with the above-mentioned immune cells. Additionally, several differential HLA genes and HALLMARKS signalling pathways between the m6A cluster-A and m6A cluster-B groups were also identified. These results suggest that m6A modification plays a key role in the complexity and diversity of the immune microenvironment in ICM, and seven key m6A regulators, including WTAP, ZCH3H13, YTHDC1, FMR1, FTO, RBM15, and YTHDF3, may be novel biomarkers for the accurate diagnosis of ICM. Immunotyping of patients with ICM will help to develop immunotherapy strategies with a higher level of accuracy for patients with a significant immune response.
Collapse
Affiliation(s)
- Peng-Fei Zheng
- Cardiology Department, Hunan Provincial People's Hospital, No. 61 West Jiefang Road, Furong District, ChangshaHunan, 410000, China
- Clinical Research Center for Heart Failure in Hunan Province, No. 61 West Jiefang Road, Furong District, Changsha, 410000, Hunan, China
- Epidemiology Department, Hunan Provincial People's Hospital, No. 61 West Jiefang Road, Furong District, Changsha, 410000, Hunan, China
| | - Xiu-Qin Hong
- Clinical Research Center for Heart Failure in Hunan Province, No. 61 West Jiefang Road, Furong District, Changsha, 410000, Hunan, China
- Epidemiology Department, Hunan Provincial People's Hospital, No. 61 West Jiefang Road, Furong District, Changsha, 410000, Hunan, China
| | - Zheng-Yu Liu
- Cardiology Department, Hunan Provincial People's Hospital, No. 61 West Jiefang Road, Furong District, ChangshaHunan, 410000, China
- Clinical Research Center for Heart Failure in Hunan Province, No. 61 West Jiefang Road, Furong District, Changsha, 410000, Hunan, China
- Epidemiology Department, Hunan Provincial People's Hospital, No. 61 West Jiefang Road, Furong District, Changsha, 410000, Hunan, China
| | - Zhao-Fen Zheng
- Cardiology Department, Hunan Provincial People's Hospital, No. 61 West Jiefang Road, Furong District, ChangshaHunan, 410000, China
- Clinical Research Center for Heart Failure in Hunan Province, No. 61 West Jiefang Road, Furong District, Changsha, 410000, Hunan, China
- Epidemiology Department, Hunan Provincial People's Hospital, No. 61 West Jiefang Road, Furong District, Changsha, 410000, Hunan, China
| | - Peng Liu
- Department of Cardiology, The Central Hospital of ShaoYang, No. 36 QianYuan Lane, Daxiang District, Shaoyang, 422000, Hunan, China.
| | - Lu-Zhu Chen
- Department of Cardiology, The Central Hospital of ShaoYang, No. 36 QianYuan Lane, Daxiang District, Shaoyang, 422000, Hunan, China.
| |
Collapse
|
13
|
Li Z, Qin Y, Liu X, Chen J, Tang A, Yan S, Zhang G. Identification of predictors for neurological outcome after cardiac arrest in peripheral blood mononuclear cells through integrated bioinformatics analysis and machine learning. Funct Integr Genomics 2023; 23:83. [PMID: 36930329 PMCID: PMC10023777 DOI: 10.1007/s10142-023-01016-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 03/05/2023] [Accepted: 03/06/2023] [Indexed: 03/18/2023]
Abstract
Neurological prognostication after cardiac arrest (CA) is important to avoid pursuing futile treatments for poor outcome and inappropriate withdrawal of life-sustaining treatment for good outcome. To predict neurological outcome after CA through biomarkers in peripheral blood mononuclear cells, four datasets were downloaded from the Gene Expression Omnibus database. GSE29546 and GSE74198 were used as training datasets, while GSE92696 and GSE34643 were used as verification datasets. The intersection of differentially expressed genes and hub genes from multiscale embedded gene co-expression network analysis (MEGENA) was utilized in the machine learning screening. Key genes were identified using support vector machine recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF). The results were validated using receiver operating characteristic curve analysis. An mRNA-miRNA network was constructed. The distribution of immune cells was evaluated using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). Five biomarkers were identified as predictors for neurological outcome after CA, with an area under the curve (AUC) greater than 0.7: CASP8 and FADD-like apoptosis regulator (CFLAR), human protein kinase X (PRKX), miR-483-5p, let-7a-5p, and let-7c-5p. Interestingly, the combination of CFLAR minus PRKX showed an even higher AUC of 0.814. The mRNA-miRNA network consisted of 30 nodes and 76 edges. Statistical differences were found in immune cell distribution, including neutrophils, NK cells active, NK cells resting, T cells CD4 memory activated, T cells CD4 memory resting, T cells CD8, B cells memory, and mast cells resting between individuals with good and poor neurological outcome after CA. In conclusion, our study identified novel predictors for neurological outcome after CA. Further clinical and laboratory studies are needed to validate our findings.
Collapse
Affiliation(s)
- Zhonghao Li
- Department of Emergency, China-Japan Friendship Hospital, 2 Ying Hua Dong Jie, Chaoyang District, Beijing, 10029, China
| | - Ying Qin
- Department of Emergency, China-Japan Friendship Hospital, 2 Ying Hua Dong Jie, Chaoyang District, Beijing, 10029, China
| | - Xiaoyu Liu
- Department of Emergency, China-Japan Friendship Hospital, 2 Ying Hua Dong Jie, Chaoyang District, Beijing, 10029, China
- Institute of Clinical Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical Collage, China-Japan Friendship Hospital, 2 Ying Hua Dong Jie, Chaoyang District, Beijing, 10029, China
| | - Jie Chen
- Department of Emergency, China-Japan Friendship Hospital, 2 Ying Hua Dong Jie, Chaoyang District, Beijing, 10029, China
- Institute of Clinical Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical Collage, China-Japan Friendship Hospital, 2 Ying Hua Dong Jie, Chaoyang District, Beijing, 10029, China
| | - Aling Tang
- Department of Emergency, China-Japan Friendship Hospital, 2 Ying Hua Dong Jie, Chaoyang District, Beijing, 10029, China
- Graduate School of Beijing University of Chinese Medicine, No. 11, Bei San Huan Dong Lu, Chaoyang District, Beijing, 10029, China
| | - Shengtao Yan
- Department of Emergency, China-Japan Friendship Hospital, 2 Ying Hua Dong Jie, Chaoyang District, Beijing, 10029, China.
| | - Guoqiang Zhang
- Department of Emergency, China-Japan Friendship Hospital, 2 Ying Hua Dong Jie, Chaoyang District, Beijing, 10029, China.
| |
Collapse
|
14
|
Zhang X, Wang X, Wang S, Zhang Y, Wang Z, Yang Q, Wang S, Cao R, Yu B, Zheng Y, Dang Y. Machine learning algorithms assisted identification of post-stroke depression associated biological features. Front Neurosci 2023; 17:1146620. [PMID: 36968495 PMCID: PMC10030717 DOI: 10.3389/fnins.2023.1146620] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 02/22/2023] [Indexed: 03/11/2023] Open
Abstract
ObjectivesPost-stroke depression (PSD) is a common and serious psychiatric complication which hinders functional recovery and social participation of stroke patients. Stroke is characterized by dynamic changes in metabolism and hemodynamics, however, there is still a lack of metabolism-associated effective and reliable diagnostic markers and therapeutic targets for PSD. Our study was dedicated to the discovery of metabolism related diagnostic and therapeutic biomarkers for PSD.MethodsExpression profiles of GSE140275, GSE122709, and GSE180470 were obtained from GEO database. Differentially expressed genes (DEGs) were detected in GSE140275 and GSE122709. Functional enrichment analysis was performed for DEGs in GSE140275. Weighted gene co-expression network analysis (WGCNA) was constructed in GSE122709 to identify key module genes. Moreover, correlation analysis was performed to obtain metabolism related genes. Interaction analysis of key module genes, metabolism related genes, and DEGs in GSE122709 was performed to obtain candidate hub genes. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and random forest, were used to identify signature genes. Expression of signature genes was validated in GSE140275, GSE122709, and GSE180470. Gene set enrichment analysis (GSEA) was applied on signature genes. Based on signature genes, a nomogram model was constructed in our PSD cohort (27 PSD patients vs. 54 controls). ROC curves were performed for the estimation of its diagnostic value. Finally, correlation analysis between expression of signature genes and several clinical traits was performed.ResultsFunctional enrichment analysis indicated that DEGs in GSE140275 enriched in metabolism pathway. A total of 8,188 metabolism associated genes were identified by correlation analysis. WGCNA analysis was constructed to obtain 3,471 key module genes. A total of 557 candidate hub genes were identified by interaction analysis. Furthermore, two signature genes (SDHD and FERMT3) were selected using LASSO and random forest analysis. GSEA analysis found that two signature genes had major roles in depression. Subsequently, PSD cohort was collected for constructing a PSD diagnosis. Nomogram model showed good reliability and validity. AUC values of receiver operating characteristic (ROC) curve of SDHD and FERMT3 were 0.896 and 0.964. ROC curves showed that two signature genes played a significant role in diagnosis of PSD. Correlation analysis found that SDHD (r = 0.653, P < 0.001) and FERM3 (r = 0.728, P < 0.001) were positively related to the Hamilton Depression Rating Scale 17-item (HAMD) score.ConclusionA total of 557 metabolism associated candidate hub genes were obtained by interaction with DEGs in GSE122709, key modules genes, and metabolism related genes. Based on machine learning algorithms, two signature genes (SDHD and FERMT3) were identified, they were proved to be valuable therapeutic and diagnostic biomarkers for PSD. Early diagnosis and prevention of PSD were made possible by our findings.
Collapse
Affiliation(s)
- Xintong Zhang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xiangyu Wang
- Department of Rehabilitation Medicine, The Affiliated Lianyungang Oriental Hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, China
| | - Shuwei Wang
- Department of Critical Care Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Yingjie Zhang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Zeyu Wang
- Department of Rehabilitation Medicine, Shanghai Ruijin Rehabilitation Hospital, Shanghai, China
| | - Qingyan Yang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Song Wang
- Department of Neurological Rehabilitation, Wuxi Yihe Rehabilitation Hospital, Wuxi, Jiangsu, China
| | - Risheng Cao
- Department of Science and Technology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Risheng Cao,
| | - Binbin Yu
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Binbin Yu,
| | - Yu Zheng
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Yu Zheng,
| | - Yini Dang
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- *Correspondence: Yini Dang,
| |
Collapse
|
15
|
Xie Y, Shi H, Han B. Bioinformatic analysis of underlying mechanisms of Kawasaki disease via Weighted Gene Correlation Network Analysis (WGCNA) and the Least Absolute Shrinkage and Selection Operator method (LASSO) regression model. BMC Pediatr 2023; 23:90. [PMID: 36829193 PMCID: PMC9951419 DOI: 10.1186/s12887-023-03896-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 02/07/2023] [Indexed: 02/26/2023] Open
Abstract
BACKGROUND Kawasaki disease (KD) is a febrile systemic vasculitis involvingchildren younger than five years old. However, the specific biomarkers and precise mechanisms of this disease are not fully understood, which can delay the best treatment time, hence, this study aimed to detect the potential biomarkers and pathophysiological process of KD through bioinformatic analysis. METHODS The Gene Expression Omnibus database (GEO) was the source of the RNA sequencing data from KD patients. Differential expressed genes (DEGs) were screened between KD patients and healthy controls (HCs) with the "limma" R package. Weighted gene correlation network analysis (WGCNA) was performed to discover the most corresponding module and hub genes of KD. The node genes were obtained by the combination of the least absolute shrinkage and selection operator (LASSO) regression model with the top 5 genes from five algorithms in CytoHubba, which were further validated with the receiver operating characteristic curve (ROC curve). CIBERSORTx was employed to discover the constitution of immune cells in KDs and HCs. Functional enrichment analysis was performed to understand the biological implications of the modular genes. Finally, competing endogenous RNAs (ceRNA) networks of node genes were predicted using online databases. RESULTS A total of 267 DEGs were analyzed between 153 KD patients and 92 HCs in the training set, spanning two modules according to WGCNA. The turquoise module was identified as the hub module, which was mainly enriched in cell activation involved in immune response, myeloid leukocyte activation, myeloid leukocyte mediated immunity, secretion and leukocyte mediated immunity biological processes; included type II diabetes mellitus, nicotinate and nicotinamide metabolism, O-glycan biosynthesis, glycerolipid and glutathione metabolism pathways. The node genes included ADM, ALPL, HK3, MMP9 and S100A12, and there was good performance in the validation studies. Immune cell infiltration analysis revealed that gamma delta T cells, monocytes, M0 macrophage, activated dendritic cells, activated mast cells and neutrophils were elevated in KD patients. Regarding the ceRNA networks, three intact networks were constructed: NEAT1/NORAD/XIST-hsa-miR-524-5p-ADM, NEAT1/NORAD/XIST-hsa-miR-204-5p-ALPL, NEAT1/NORAD/XIST-hsa-miR-524-5p/hsa-miR-204-5p-MMP9. CONCLUSION To conclude, the five-gene signature and three ceRNA networks constructed in our study are of great value in the early diagnosis of KD and might help to elucidate our understanding of KD at the RNA regulatory level.
Collapse
Affiliation(s)
- Yaxue Xie
- Department of Pediatrics, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China
| | - Hongshuo Shi
- College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250021, Shandong, China
| | - Bo Han
- Department of Pediatrics, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China. .,Department of Pediatrics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China.
| |
Collapse
|
16
|
Identification of Key Biomarkers Associated with Immunogenic Cell Death and Their Regulatory Mechanisms in Severe Acute Pancreatitis Based on WGCNA and Machine Learning. Int J Mol Sci 2023; 24:ijms24033033. [PMID: 36769358 PMCID: PMC9918120 DOI: 10.3390/ijms24033033] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 01/20/2023] [Accepted: 01/30/2023] [Indexed: 02/09/2023] Open
Abstract
Immunogenic cell death (ICD) is a form of programmed cell death with a strong sense of inflammatory detection, whose powerful situational awareness can cause the reactivation of aberrant immunity. However, the role of ICD in the pathogenesis of severe acute pancreatitis (SAP) has yet to be investigated. This study aims to explore the pivotal genes associated with ICD in SAP and how they relate to immune infiltration and short-chain fatty acids (SCFAs), in order to provide a theoretical foundation for further, in-depth mechanistic studies. We downloaded GSE194331 datasets from the Gene Expression Omnibus (GEO). The use of differentially expressed gene (DEG) analysis; weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression analysis allowed us to identify a total of three ICD-related hub genes (LY96, BCL2, IFNGR1) in SAP. Furthermore, single sample gene set enrichment analysis (ssGSEA) demonstrated that hub genes are closely associated with the infiltration of specific immune cells, the activation of immune pathways and the metabolism of SCFAs (especially butyrate). These findings were validated through the analysis of gene expression patterns in both clinical patients and rat animal models of SAP. In conclusion, the first concept of ICD in the pathogenesis of SAP was proposed in our study. This has important implications for future investigations into the pro-inflammatory immune mechanisms mediated by damage-associated molecular patterns (DAMPs) in the late stages of SAP.
Collapse
|
17
|
Qin X, Yi S, Rong J, Lu H, Ji B, Zhang W, Ding R, Wu L, Chen Z. Identification of anoikis-related genes classification patterns and immune infiltration characterization in ischemic stroke based on machine learning. Front Aging Neurosci 2023; 15:1142163. [PMID: 37032832 PMCID: PMC10076550 DOI: 10.3389/fnagi.2023.1142163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 02/27/2023] [Indexed: 04/11/2023] Open
Abstract
Introduction Ischemic stroke (IS) is a type of stroke that leads to high mortality and disability. Anoikis is a form of programmed cell death. When cells detach from the correct extracellular matrix, anoikis disrupts integrin junctions, thus preventing abnormal proliferating cells from growing or attaching to an inappropriate matrix. Although there is growing evidence that anoikis regulates the immune response, which makes a great contribution to the development of IS, the role of anoikis in the pathogenesis of IS is rarely explored. Methods First, we downloaded GSE58294 set and GSE16561 set from the NCBI GEO database. And 35 anoikis-related genes (ARGs) were obtained from GSEA website. The CIBERSORT algorithm was used to estimate the relative proportions of 22 infiltrating immune cell types. Next, consensus clustering method was used to classify ischemic stroke samples. In addition, we used least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms to screen the key ARGs in ischemic stroke. Next, we performed receiver operating characteristics (ROC) analysis to assess the accuracy of each diagnostic gene. At the same time, the nomogram was constructed to diagnose IS by integrating trait genes. Then, we analyzed the correlation between gene expression and immune cell infiltration of the diagnostic genes in the combined database. And gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analysis were performed on these genes to explore differential signaling pathways and potential functions, as well as the construction and visualization of regulatory networks using NetworkAnalyst and Cytoscape. Finally, we investigated the expression pattern of ARGs in IS patients across age or gender. Results Our study comprehensively analyzed the role of ARGs in IS for the first time. We revealed the expression profile of ARGs in IS and the correlation with infiltrating immune cells. And The results of consensus clustering analysis suggested that we can classify IS patients into two clusters. The machine learning analysis screened five signature genes, including AKT1, BRMS1, PTRH2, TFDP1 and TLE1. We also constructed nomogram models based on the five risk genes and evaluated the immune infiltration correlation, gene-miRNA, gene-TF and drug-gene interaction regulatory networks of these signature genes. The expression of ARGs did not differ by sex or age. Discussion This study may provide a beneficial reference for further elucidating the pathogenesis of IS, and render new ideas for drug screening, individualized therapy and immunotherapy of IS.
Collapse
Affiliation(s)
- Xiaohong Qin
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
- Central Laboratory, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Shangfeng Yi
- Department of Neurosurgery, Enshi Center Hospital, Enshi, Hubei, China
| | - Jingtong Rong
- Central Laboratory, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Haoran Lu
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
- Central Laboratory, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Baowei Ji
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Wenfei Zhang
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Rui Ding
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Liquan Wu
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
- *Correspondence: Liquan Wu,
| | - Zhibiao Chen
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
- Zhibiao Chen,
| |
Collapse
|
18
|
Liu C, Zhou Y, Zhou Y, Tang X, Tang L, Wang J. Identification of crucial genes for predicting the risk of atherosclerosis with system lupus erythematosus based on comprehensive bioinformatics analysis and machine learning. Comput Biol Med 2023; 152:106388. [PMID: 36470144 DOI: 10.1016/j.compbiomed.2022.106388] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 11/22/2022] [Accepted: 11/28/2022] [Indexed: 12/02/2022]
Abstract
BACKGROUND Systemic lupus erythematosus (SLE) has become a major public health problem over the years, and atherosclerosis (AS) is one of the main complications of SLE associated with serious cardiovascular consequences in this patient population. The present study aimed to identify potential biomarkers for SLE patients with AS. METHODS Five microarray datasets (GSE50772, GSE81622, GSE100927, GSE28829, GSE37356) were downloaded from the NCBI Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in AS. Weighted gene coexpression network analysis (WGCNA) was used to identify significant module genes associated with SLE. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (Lasso, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and random forest) were applied to identify hub genes. Subsequently, we generated a nomogram and receiver operating characteristic curve (ROC) for predicting the risk of AS in SLE patients. Finally, immune cell infiltrations were analyzed, and Consensus Cluster Analysis was conducted based on Single Sample Gene Set Enrichment Analysis (ssGSEA) scores. RESULTS Five hub genes (SPI1, MMP9, C1QA, CX3CR1, and MNDA) were identified and used to establish a nomogram that yielded a high predictive performance (area under the curve 0.900-0.981). Dysregulated immune cell infiltrations were found in AS, with positive correlations with the five hub genes. Consensus clustering showed that the optimal number of subtypes was 3. Compared to subtypes A and B, subtype C presented higher expression of the five hub genes, immune cell infiltration levels and immune checkpoint expression. CONCLUSION Our study systematically identified five candidate hub genes (SPI1, MMP9, C1QA, CX3CR1, MNDA) and established a nomogram that could predict the risk of AS with SLE using various bioinformatic analyses and machine learning algorithms. Our findings provide the foothold for future studies on potential crucial genes for AS in SLE patients. Additionally, the dysregulated immune cell proportions and immune checkpoint expressions in AS with SLE were identified.
Collapse
Affiliation(s)
- Chunjiang Liu
- Department of General Surgery, Division of Vascular Surgery, Shaoxing People's Hospital (Shaoxing Hospital of Zhejiang University), Shaoxing, 312000, China
| | - Yufei Zhou
- Department of Cardiology, Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Yue Zhou
- Department of General Surgery, Division of Vascular Surgery, Shaoxing People's Hospital (Shaoxing Hospital of Zhejiang University), Shaoxing, 312000, China
| | - Xiaoqi Tang
- Department of General Surgery, Division of Vascular Surgery, Shaoxing People's Hospital (Shaoxing Hospital of Zhejiang University), Shaoxing, 312000, China
| | - Liming Tang
- Department of General Surgery, Division of Vascular Surgery, Shaoxing People's Hospital (Shaoxing Hospital of Zhejiang University), Shaoxing, 312000, China.
| | - Jiajia Wang
- Department of Rheumatology, Shaoxing People's Hospital (Shaoxing Hospital of Zhejiang University), Shaoxing, 312000, China.
| |
Collapse
|
19
|
Ren P, Wang JY, Chen HL, Lin XW, Zhao YQ, Guo WZ, Zeng ZR, Li YF. Diagnostic model constructed by nine inflammation-related genes for diagnosing ischemic stroke and reflecting the condition of immune-related cells. Front Immunol 2022; 13:1046966. [PMID: 36582228 PMCID: PMC9792959 DOI: 10.3389/fimmu.2022.1046966] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 11/29/2022] [Indexed: 12/14/2022] Open
Abstract
Background Ischemic cerebral infarction is the most common type of stroke with high rates of mortality, disability, and recurrence. However, the known diagnostic biomarkers and therapeutic targets for ischemic stroke (IS) are limited. In the current study, we aimed to identify novel inflammation-related biomarkers for IS using machine learning analysis and to explore their relationship with the levels of immune-related cells in whole blood samples. Methods Gene expression profiles of healthy controls and patients with IS were download from the Gene Expression Omnibus. Analysis of differentially expressed genes (DEGs) was performed in healthy controls and patients with IS. Single-sample gene set enrichment analysis was performed to calculate inflammation scores, and weighted gene co-expression network analysis was used to analyze genes in significant modules associated with inflammation scores. Key DEGs in significant modules were then analyzed using LASSO regression analysis for constructing a diagnostic model. The effectiveness and specificity of the diagnostic model was verified in healthy controls and patients with IS and with cerebral hemorrhage (CH) using qRT-PCR. The relationship between diagnostic score and the levels of immune-related cells in whole blood were analyzed using Pearson correlations. Results A total of 831 DEGs were identified. Both chronic and acute inflammation scores were higher in patients with IS, while 54 DEGs were also clustered in the gene modules associated with chronic and acute inflammation scores. Among them, a total of 9 genes were selected to construct a diagnostic model. Interestingly, RT-qPCR showed that the diagnostic model had better diagnostic value for IS but not for CH. The levels of lymphocytes were lower in blood of patients with IS, while the levels of monocytes and neutrophils were increased. The diagnostic score of the model was negatively associated with the levels of lymphocytes and positively associated with levels of monocytes and neutrophils. Conclusions Taken together, the diagnostic model constructed using the inflammation-related genes TNFSF10, ID1, PAQR8, OSR2, PDK4, PEX11B, TNIP1, FFAR2, and JUN exhibited high and specific diagnostic value for IS and reflected the condition of lymphocytes, monocytes, and neutrophils in the blood. The diagnostic model may contribute to the diagnosis of IS.
Collapse
Affiliation(s)
- Peng Ren
- Beijing Institute of Basic Medical Sciences, Beijing, China,Department of Anesthesiology, Seventh Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jing-Ya Wang
- Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Hong-Lei Chen
- Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Xiao-Wan Lin
- Department of Anesthesiology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China
| | - Yong-Qi Zhao
- Beijing Institute of Basic Medical Sciences, Beijing, China,*Correspondence: Yun-Feng Li, ; Zhi-Rui Zeng, ; Wen-Zhi Guo, ; Yong-Qi Zhao,
| | - Wen-Zhi Guo
- Department of Anesthesiology, Seventh Medical Center of Chinese PLA General Hospital, Beijing, China,*Correspondence: Yun-Feng Li, ; Zhi-Rui Zeng, ; Wen-Zhi Guo, ; Yong-Qi Zhao,
| | - Zhi-Rui Zeng
- Guizhou Provincial Key Laboratory of Pathogenesis & Drug Research on Common Chronic Diseases, Department of Physiology, School of Basic Medical Sciences, Guizhou Medical University, Guizhou, China,*Correspondence: Yun-Feng Li, ; Zhi-Rui Zeng, ; Wen-Zhi Guo, ; Yong-Qi Zhao,
| | - Yun-Feng Li
- Beijing Institute of Basic Medical Sciences, Beijing, China,Beijing Institute of Pharmacology and Toxicology, State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Key Laboratory of Neuropsychopharmacology, Beijing, China,*Correspondence: Yun-Feng Li, ; Zhi-Rui Zeng, ; Wen-Zhi Guo, ; Yong-Qi Zhao,
| |
Collapse
|