1
|
Xiao X, Ding Z, Shi Y, Zhang Q. Causal Role of Immune Cells in Chronic Obstructive Pulmonary Disease: A Two-Sample Mendelian Randomization Study. COPD 2024; 21:2327352. [PMID: 38573027 DOI: 10.1080/15412555.2024.2327352] [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: 10/31/2023] [Accepted: 02/27/2024] [Indexed: 04/05/2024]
Abstract
Accumulating evidence has highlighted the importance of immune cells in the pathogenesis of chronic obstructive pulmonary disease (COPD). However, the understanding of the causal association between immunity and COPD remains incomplete due to the existence of confounding variables. In this study, we employed a two-sample Mendelian randomization (MR) analysis, utilizing the genome-wide association study database, to investigate the causal association between 731 immune-cell signatures and the susceptibility to COPD from a host genetics perspective. To validate the consistency of our findings, we utilized MR analysis results of lung function data to assess directional concordance. Furthermore, we employed MR-Egger intercept tests, Cochrane's Q test, MR-PRESSO global test, and "leave-one-out" sensitivity analyses to evaluate the presence of horizontal pleiotropy, heterogeneity, and stability, respectively. Inverse variance weighting results showed that seven immune phenotypes were associated with the risk of COPD. Analyses of heterogeneity and pleiotropy analysis confirmed the reliability of MR results. These results highlight the interactions between the immune system and the lungs. Further investigations into their mechanisms are necessary and will contribute to inform targeted prevention strategies for COPD.
Collapse
Affiliation(s)
- Xinru Xiao
- Department of Respiratory and Critical Care Medicine, The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Ziqi Ding
- Department of Respiratory and Critical Care Medicine, The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Yujia Shi
- Department of Respiratory and Critical Care Medicine, The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Qian Zhang
- Department of Respiratory and Critical Care Medicine, The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China
- Changzhou Medical Center, Nanjing Medical University, Changzhou, China
| |
Collapse
|
2
|
Shen HH, Zhang YY, Wang XY, Li MY, Liu ZX, Wang Y, Ye JF, Wu HH, Li MQ. Validation of mitochondrial biomarkers and immune dynamics in polycystic ovary syndrome. Am J Reprod Immunol 2024; 91:e13847. [PMID: 38661639 DOI: 10.1111/aji.13847] [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: 11/26/2023] [Revised: 03/26/2024] [Accepted: 04/09/2024] [Indexed: 04/26/2024] Open
Abstract
PROBLEM Polycystic ovary syndrome (PCOS), a prevalent endocrine-metabolic disorder, presents considerable therapeutic challenges due to its complex and elusive pathophysiology. METHOD OF STUDY We employed three machine learning algorithms to identify potential biomarkers within a training dataset, comprising GSE138518, GSE155489, and GSE193123. The diagnostic accuracy of these biomarkers was rigorously evaluated using a validation dataset using area under the curve (AUC) metrics. Further validation in clinical samples was conducted using PCR and immunofluorescence techniques. Additionally, we investigate the complex interplay among immune cells in PCOS using CIBERSORT to uncover the relationships between the identified biomarkers and various immune cell types. RESULTS Our analysis identified ACSS2, LPIN1, and NR4A1 as key mitochondria-related biomarkers associated with PCOS. A notable difference was observed in the immune microenvironment between PCOS patients and healthy controls. In particular, LPIN1 exhibited a positive correlation with resting mast cells, whereas NR4A1 demonstrated a negative correlation with monocytes in PCOS patients. CONCLUSION ACSS2, LPIN1, and NR4A1 emerge as PCOS-related diagnostic biomarkers and potential intervention targets, opening new avenues for the diagnosis and management of PCOS.
Collapse
Affiliation(s)
- Hui-Hui Shen
- Institute of Obstetrics and Gynecology, Hospital of Obstetrics and Gynecology, Fudan University, Shanghai, People's Republic of China
- Laboratory for Reproductive Immunology, Hospital of Obstetrics and Gynecology, Fudan University, Shanghai, People's Republic of China
| | - Yang-Yang Zhang
- Institute of Obstetrics and Gynecology, Hospital of Obstetrics and Gynecology, Fudan University, Shanghai, People's Republic of China
- Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Xuan-Yu Wang
- College of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
| | - Meng-Ying Li
- Institute of Obstetrics and Gynecology, Hospital of Obstetrics and Gynecology, Fudan University, Shanghai, People's Republic of China
| | - Zhen-Xing Liu
- Center of Reproduction and Genetics, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, People's Republic of China
| | - Ying Wang
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Qilu Hospital, Shandong University, Ji'nan, Shandong, People's Republic of China
| | - Jiang-Feng Ye
- Institute for Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore
| | - Hui-Hua Wu
- Center of Reproduction and Genetics, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, People's Republic of China
| | - Ming-Qing Li
- Institute of Obstetrics and Gynecology, Hospital of Obstetrics and Gynecology, Fudan University, Shanghai, People's Republic of China
- Laboratory for Reproductive Immunology, Hospital of Obstetrics and Gynecology, Fudan University, Shanghai, People's Republic of China
- Shanghai Key Laboratory of Female Reproductive Endocrine Related Diseases, Hospital of Obstetrics and Gynecology, Fudan University, Shanghai, People's Republic of China
| |
Collapse
|
3
|
Wang W, Chen G, Zhang W, Zhang X, Huang M, Li C, Wang L, Lu Z, Xia J. The crucial prognostic signaling pathways of pancreatic ductal adenocarcinoma were identified by single-cell and bulk RNA sequencing data. Hum Genet 2024:10.1007/s00439-024-02663-4. [PMID: 38526745 DOI: 10.1007/s00439-024-02663-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 02/24/2024] [Indexed: 03/27/2024]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with poor prognosis and high mortality. Although a large number of studies have explored its potential prognostic markers using traditional RNA sequencing (RNA-Seq) data, they have not achieved good prediction effect. In order to explore the possible prognostic signaling pathways leading to the difference in prognosis, we identified differentially expressed genes from one scRNA-seq cohort and four GEO cohorts, respectively. Then Cox and Lasso regression analysis showed that 12 genes were independent prognostic factors for PDAC. AUC and calibration curve analysis showed that the prognostic model had good discrimination and calibration. Compared with the low-risk group, the high-risk group had a higher proportion of gene mutations than the low-risk group. Immune infiltration analysis revealed differences in macrophages and monocytes between the two groups. Prognosis related genes were mainly distributed in fibroblasts, macrophages and type 2 ducts. The results of cell communication analysis showed that there was a strong communication between cancer-associated fibroblasts (CAF) and type 2 ductal cells, and collagen formation was the main interaction pathway.
Collapse
Affiliation(s)
- Wenwen Wang
- Department of Health Statistics, School of Military Preventive Medicine, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Fourth Military Medical University, Xi'an, 710032, Shaanxi Province, China.
| | - Guo Chen
- Shaanxi Provincial Key Laboratory of Infection and Immune Diseases, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi Province, China
| | - Wenli Zhang
- Shaanxi Provincial Key Laboratory of Infection and Immune Diseases, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi Province, China
| | - Xihua Zhang
- Department of Health Statistics, School of Military Preventive Medicine, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Fourth Military Medical University, Xi'an, 710032, Shaanxi Province, China
| | - Manli Huang
- Department of Health Statistics, School of Military Preventive Medicine, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Fourth Military Medical University, Xi'an, 710032, Shaanxi Province, China
| | - Chen Li
- Department of Health Statistics, School of Military Preventive Medicine, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Fourth Military Medical University, Xi'an, 710032, Shaanxi Province, China
| | - Ling Wang
- Department of Health Statistics, School of Military Preventive Medicine, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Fourth Military Medical University, Xi'an, 710032, Shaanxi Province, China
| | - Zifan Lu
- Shaanxi Provincial Key Laboratory of Infection and Immune Diseases, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi Province, China
| | - Jielai Xia
- Department of Health Statistics, School of Military Preventive Medicine, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Fourth Military Medical University, Xi'an, 710032, Shaanxi Province, China
| |
Collapse
|
4
|
Zhang Y, Li G. Predicting feature genes correlated with immune infiltration in patients with abdominal aortic aneurysm based on machine learning algorithms. Sci Rep 2024; 14:5157. [PMID: 38431726 PMCID: PMC10908806 DOI: 10.1038/s41598-024-55941-6] [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: 06/21/2023] [Accepted: 02/29/2024] [Indexed: 03/05/2024] Open
Abstract
Abdominal aortic aneurysm (AAA) is a condition characterized by a pathological and progressive dilatation of the infrarenal abdominal aorta. The exploration of AAA feature genes is crucial for enhancing the prognosis of AAA patients. Microarray datasets of AAA were downloaded from the Gene Expression Omnibus database. A total of 43 upregulated differentially expressed genes (DEGs) and 32 downregulated DEGs were obtained. Function, pathway, disease, and gene set enrichment analyses were performed, in which enrichments were related to inflammation and immune response. AHR, APLNR, ITGA10 and NR2F6 were defined as feature genes via machine learning algorithms and a validation cohort, which indicated high diagnostic abilities by the receiver operating characteristic curves. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method was used to quantify the proportions of immune infiltration in samples of AAA and normal tissues. We have predicted AHR, APLNR, ITGA10 and NR2F6 as feature genes of AAA. CD8 + T cells and M2 macrophages correlated with these genes may be involved in the development of AAA, which have the potential to be developed as risk predictors and immune interventions.
Collapse
Affiliation(s)
- Yufeng Zhang
- Department of Vascular Surgery, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, 271000, Shandong, China
- Postdoctoral Workstation, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250021, Shandong, China
- Department of Pulmonary and Critical Care Medicine, Jiangyin Hospital of Traditional Chinese Medicine, Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangyin, 214400, Jiangsu, China
| | - Gang Li
- Department of Vascular Surgery, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, 271000, Shandong, China.
| |
Collapse
|
5
|
Li S, Zhang J, Hou X, Wang Y, Li T, Xu Z, Chen F, Zhou Y, Wang W, Liu M. Prediction Model for Unfavorable Outcome in Spontaneous Intracerebral Hemorrhage Based on Machine Learning. J Korean Neurosurg Soc 2024; 67:94-102. [PMID: 37661087 PMCID: PMC10788551 DOI: 10.3340/jkns.2023.0118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 08/05/2023] [Accepted: 08/21/2023] [Indexed: 09/05/2023] Open
Abstract
OBJECTIVE The spontaneous intracerebral hemorrhage (ICH) remains a significant cause of mortality and morbidity throughout the world. The purpose of this retrospective study is to develop multiple models for predicting ICH outcomes using machine learning (ML). METHODS Between January 2014 and October 2021, we included ICH patients identified by computed tomography or magnetic resonance imaging and treated with surgery. At the 6-month check-up, outcomes were assessed using the modified Rankin Scale. In this study, four ML models, including Support Vector Machine (SVM), Decision Tree C5.0, Artificial Neural Network, Logistic Regression were used to build ICH prediction models. In order to evaluate the reliability and the ML models, we calculated the area under the receiver operating characteristic curve (AUC), specificity, sensitivity, accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR). RESULTS We identified 71 patients who had favorable outcomes and 156 who had unfavorable outcomes. The results showed that the SVM model achieved the best comprehensive prediction efficiency. For the SVM model, the AUC, accuracy, specificity, sensitivity, PLR, NLR, and DOR were 0.91, 0.92, 0.92, 0.93, 11.63, 0.076, and 153.03, respectively. For the SVM model, we found the importance value of time to operating room (TOR) was higher significantly than other variables. CONCLUSION The analysis of clinical reliability showed that the SVM model achieved the best comprehensive prediction efficiency and the importance value of TOR was higher significantly than other variables.
Collapse
Affiliation(s)
- Shengli Li
- Department of Neurosurgery, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Jianan Zhang
- Department of Anesthesia Operating Room, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Xiaoqun Hou
- Department of Neurosurgery, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Yongyi Wang
- Department of Neurosurgery, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Tong Li
- Department of Neurosurgery, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Zhiming Xu
- Department of Neurosurgery, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Feng Chen
- Department of Neurosurgery, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Yong Zhou
- Department of Neurosurgery, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Weimin Wang
- Department of Neurosurgery, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Mingxing Liu
- Department of Neurosurgery, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| |
Collapse
|
6
|
Zhang W, Zhao Y, Tian Y, Liang X, Piao C. Early Diagnosis of High-Risk Chronic Obstructive Pulmonary Disease Based on Quantitative High-Resolution Computed Tomography Measurements. Int J Chron Obstruct Pulmon Dis 2023; 18:3099-3114. [PMID: 38162987 PMCID: PMC10757779 DOI: 10.2147/copd.s436803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 12/15/2023] [Indexed: 01/03/2024] Open
Abstract
Purpose Quantitative computed tomography (QCT) techniques, focusing on airway anatomy and emphysema, may help to detect early structural changes of COPD disease. This retrospective study aims to identify high-risk COPD participants by using QCT measurements. Patients and Methods We enrolled 140 participants from the Second Affiliated Hospital of Shenyang Medical College who completed inspiratory high-resolution CT scans, pulmonary function tests (PFTs), and clinical characteristics recorded. They were diagnosed Non-COPD by PFT value of FEV1/FVC >70% and divided into two groups according percentage predicted FEV1 (FEV1%), low-risk COPD group: FEV1% ≥ 95%, high-risk group: 80% < FEV1% < 95%. The QCT measurements were analyzed by the Student's t-test (or Mann-Whitney U-test) method. Then, feature candidates were identified using the LASSO method. Meanwhile, the correlation between QCT measurements and PFTs was assessed by the Spearman rank correlation test. Furthermore, support vector machine (SVM) was performed to identify high-risk COPD participants. The performance of the models was evaluated in terms of accuracy (ACC), sensitivity (SEN), specificity (SPE), F1-score, and area under the ROC curve (AUC), with p <0.05 considered statistically significant. Results The SVM based on QCT measurements achieved good performance in identifying high-risk COPD patients with 85.71% of ACC, 88.34% of SEN, 84.00% of SPE, 83.33% of F1-score, and 0.93 of AUC. Further, QCT measurements integration of clinical data improved the performance with an ACC of 90.48%. The emphysema index (%LAA-950) of left lower lung was negatively correlated with PFTs (P < 0.001). The airway anatomy indexes of lumen diameter (LD) were correlated with PFTs. Conclusion QCT measurements combined with clinical information could provide an effective tool for an early diagnosis of high-risk COPD. The QCT indexes can be used to assess the pulmonary function status of high-risk COPD.
Collapse
Affiliation(s)
- Wenxiu Zhang
- Institute of Research and Clinical Innovations, Neusoft Medical Systems Co, Ltd, Shanghai, People’s Republic of China
| | - Yu Zhao
- Radiology Department, Second Affiliated Hospital of Shenyang Medical College, Shenyang, Liaoning, People’s Republic of China
| | - Yuchi Tian
- Institute of Research and Clinical Innovations, Neusoft Medical Systems Co, Ltd, Shanghai, People’s Republic of China
| | - Xiaoyun Liang
- Institute of Research and Clinical Innovations, Neusoft Medical Systems Co, Ltd, Shanghai, People’s Republic of China
| | - Chenghao Piao
- Radiology Department, Second Affiliated Hospital of Shenyang Medical College, Shenyang, Liaoning, People’s Republic of China
| |
Collapse
|
7
|
Zhang Z, Yu H, Wang Q, Ding Y, Wang Z, Zhao S, Bian T. A Macrophage-Related Gene Signature for Identifying COPD Based on Bioinformatics and ex vivo Experiments. J Inflamm Res 2023; 16:5647-5665. [PMID: 38050560 PMCID: PMC10693783 DOI: 10.2147/jir.s438308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 11/21/2023] [Indexed: 12/06/2023] Open
Abstract
Background This study aims to investigate the association between immune cells and the development of COPD, while providing a new method for the diagnosis of COPD according to the changes in immune microenvironment. Methods In this study, the "CIBERSORT" algorithm was used to estimate the tissue infiltration of 22 types of immune cells in GSE20257 and GSE10006. The "limma" package was used for differentially expressed analysis. The key modules associated with vital immune cells were identified using WGCNA. GO and KEGG enrichment analysis revealed the biological functions of the candidate genes. Ultimately, a novel diagnostic prediction model was constructed via machine learning methods and multivariate logistic regression analysis based on GSE20257. Furthermore, we examined the stability of the model on one internal test set (GSE10006), three external test sets (GSE8545, GSE57148 and GSE76925), one single-cell transcriptome dataset (GSE167295), macrophages (THP-M cells) and lung tissue from COPD patients. Results M0 macrophages (AUC > 0.7 in GSE20257 and GSE10006) were considered as the most important immune cells through exploring the immune microenvironment landscapes in COPD patients and healthy controls. The differentially expressed genes from GSE20257 and GSE10006 were divided into six and five modules via WGCNA, respectively. The green module in GSE20257 (cor = 0.41, P < 0.001) and the brown module in GSE10006 (cor = 0.67, P < 0.001) were highly correlated with M0 macrophages and were selected as key modules. Forty-one intersected genes obtained from two modules were primarily involved in regulation of cytokine production, regulation of innate immune response, specific granule, phagosome, lysosome, ferroptosis, and other biological processes. On the basis of the candidate genetic markers further characterized via the "Boruta" and "LASSO" algorithm for COPD, a diagnostic model comprising CLEC5A, FTL and SLC2A3 was constructed, which could accurately distinguish COPD patients from healthy controls in multiple datasets. GSE20257 as the training set has an AUC of 0.916. The AUCs of the internal test set and three external test sets were 0.873, 0.932, 0.675 and 0.688, respectively. Single-cell sequencing analysis suggested that CLEC5A, FTL and SLC2A3 were expressed in macrophages from COPD patients. The expressions of CLEC5A, FTL and SLC2A3 were up-regulated in THP-M cells and lung tissue from COPD patients. Conclusion According to the variations of immune microenvironment in COPD patients, we constructed and validated a novel macrophage M0-associated diagnostic model with satisfactory predictive value. CLEC5A, FTL and SLC2A3 are expected to be promising targets of immunotherapy in COPD.
Collapse
Affiliation(s)
- Zheming Zhang
- Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China
- Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, People’s Republic of China
| | - Haoda Yu
- Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China
- Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, People’s Republic of China
| | - Qi Wang
- Department of Gastroenterology, Affiliated Hospital of Jiangsu University, Zhenjiang, People’s Republic of China
| | - Yu Ding
- Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China
- Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, People’s Republic of China
| | - Ziteng Wang
- Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China
- Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, People’s Republic of China
| | - Songyun Zhao
- Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China
| | - Tao Bian
- Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China
- Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, People’s Republic of China
| |
Collapse
|
8
|
Zhou X, Tan F, Zhang S, Zhang T. Combining single-cell RNA sequencing data and transcriptomic data to unravel potential mechanisms and signature genes of the progression of idiopathic pulmonary fibrosis to lung adenocarcinoma and predict therapeutic agents. Funct Integr Genomics 2023; 23:346. [PMID: 37996625 DOI: 10.1007/s10142-023-01274-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 10/29/2023] [Accepted: 11/17/2023] [Indexed: 11/25/2023]
Abstract
Patients with idiopathic pulmonary fibrosis (IPF) have a significantly higher prevalence of lung adenocarcinoma (LUAD) than normal subjects, although the underlying association is unclear. The raw data involved were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis were used to screen for differentially expressed genes (DEGs) and modular signature genes (MSGs). Genes intersecting DEGs and MSGs were considered hub genes for IPF and LUAD. Machine learning algorithms were applied to capture epithelial cell-derived signature genes (EDSGs) shared. External cohort data were exploited to validate the robustness of EDSGs. Immunohistochemical staining and K-M plots were used to denote the prognostic value of EDSGs in LUAD. Based on EDSGs, we constructed a TF-gene-miRNA regulatory network. Molecular docking can validate the strength of action between candidate drugs and EDSGs. Epithelial cells, 650 DEGs, and 1773 MSGs were shared by IPF and LUAD. As for 379 hub genes, we performed pathway and functional enrichment analysis. By analyzing sc-RNA seq data, we identified 1234 marker genes of IPF epithelial cell-derived and 1481 of LUAD. And these genes shared 8 items with 379 hub genes. Through the machine learning algorithms, we further fished TRIM2, S100A14, CYP4B1, LMO7, and SFN. The ROC curves emphasized the significance of EDSGs in predicting the onset of LUAD and IPF. The TF-gene-miRNA network revealed regulatory relationships behind EDSGs. Finally, we predicted appropriate therapeutic agents. Our study preliminarily identified potential mechanisms between IPF and LUAD, which will inform subsequent studies.
Collapse
Affiliation(s)
- Xianqiang Zhou
- Department of Traditional Chinese Medicine, Jing'an District Central Hospital Affiliated to Fudan University, Shanghai, 200040, China
- Department of Pulmonary Diseases, Jing'an District Hospital of Traditional Chinese Medicine, Shanghai, 200072, China
| | - Fang Tan
- Department of Neurology, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, 230031, Anhui Province, China
| | - Suxian Zhang
- Department of Traditional Chinese Medicine, Jing'an District Central Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Tiansong Zhang
- Department of Traditional Chinese Medicine, Jing'an District Central Hospital Affiliated to Fudan University, Shanghai, 200040, China.
- Department of Pulmonary Diseases, Jing'an District Hospital of Traditional Chinese Medicine, Shanghai, 200072, China.
| |
Collapse
|
9
|
Nazari E, Khalili-Tanha G, Asadnia A, Pourali G, Maftooh M, Khazaei M, Nasiri M, Hassanian SM, Ghayour-Mobarhan M, Ferns GA, Kiani MA, Avan A. Bioinformatics analysis and machine learning approach applied to the identification of novel key genes involved in non-alcoholic fatty liver disease. Sci Rep 2023; 13:20489. [PMID: 37993474 PMCID: PMC10665370 DOI: 10.1038/s41598-023-46711-x] [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: 05/10/2023] [Accepted: 11/03/2023] [Indexed: 11/24/2023] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) comprises a range of chronic liver diseases that result from the accumulation of excess triglycerides in the liver, and which, in its early phases, is categorized NAFLD, or hepato-steatosis with pure fatty liver. The mortality rate of non-alcoholic steatohepatitis (NASH) is more than NAFLD; therefore, diagnosing the disease in its early stages may decrease liver damage and increase the survival rate. In the current study, we screened the gene expression data of NAFLD patients and control samples from the public dataset GEO to detect DEGs. Then, the correlation betweenbetween the top selected DEGs and clinical data was evaluated. In the present study, two GEO datasets (GSE48452, GSE126848) were downloaded. The dysregulated expressed genes (DEGs) were identified by machine learning methods (Penalize regression models). Then, the shared DEGs between the two training datasets were validated using validation datasets. ROC-curve analysis was used to identify diagnostic markers. R software analyzed the interactions between DEGs, clinical data, and fatty liver. Ten novel genes, including ABCF1, SART3, APC5, NONO, KAT7, ZPR1, RABGAP1, SLC7A8, SPAG9, and KAT6A were found to have a differential expression between NAFLD and healthy individuals. Based on validation results and ROC analysis, NR4A2 and IGFBP1b were identified as diagnostic markers. These key genes may be predictive markers for the development of fatty liver. It is recommended that these key genes are assessed further as possible predictive markers during the development of fatty liver.
Collapse
Affiliation(s)
- Elham Nazari
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ghazaleh Khalili-Tanha
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Alireza Asadnia
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ghazaleh Pourali
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mina Maftooh
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Khazaei
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohammadreza Nasiri
- Recombinant Proteins Research Group, The Research Institute of Biotechnology, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Seyed Mahdi Hassanian
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Ghayour-Mobarhan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Gordon A Ferns
- Division of Medical Education, Brighton & Sussex Medical School, Falmer, Brighton, BN1 9PH, Sussex, UK
| | - Mohammad Ali Kiani
- Department of Pediatrics, Ghaem Hospital, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Amir Avan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
- College of Medicine, University of Warith Al-Anbiyaa, Karbala, Iraq.
- Faculty of Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, 4000, Australia.
| |
Collapse
|
10
|
Liu S, Zhao X, Meng Q, Li B. Screening of potential biomarkers for polycystic ovary syndrome and identification of expression and immune characteristics. PLoS One 2023; 18:e0293447. [PMID: 37883387 PMCID: PMC10602247 DOI: 10.1371/journal.pone.0293447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/12/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Polycystic ovary syndrome (PCOS) seriously affects the fertility and health of women of childbearing age. We look forward to finding potential biomarkers for PCOS that can aid clinical diagnosis. METHODS We acquired PCOS and normal granulosa cell (GC) expression profiles from the Gene Expression Omnibus (GEO) database. After data preprocessing, differentially expressed genes (DEGs) were screened by limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Gene Set Enrichment Analysis (GSEA) were performed. Recursive feature elimination (RFE) algorithm and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to acquire feature genes as potential biomarkers. Time-dependent receiver operator characteristic curve (ROC curve) and Confusion matrix were used to verify the classification performance of biomarkers. Then, the expression characteristics of biomarkers in PCOS and normal cells were analyzed, and the insulin resistance (IR) score of samples was computed by ssGSEA. Immune characterization of biomarkers was evaluated using MCP counter and single sample gene set enrichment analysis (ssGSEA). Finally, the correlation between biomarkers and the scores of each pathway was assessed. RESULTS We acquired 93 DEGs, and the enrichment results indicated that most of DEGs in PCOS group were significantly enriched in immune-related biological pathways. Further screening results indicated that JDP2 and HMOX1 were potential biomarkers. The area under ROC curve (AUC) value and Confusion matrix of the two biomarkers were ideal when separated and combined. In the combination, the training set AUC = 0.929 and the test set AUC = 0.917 indicated good diagnostic performance of the two biomarkers. Both biomarkers were highly expressed in the PCOS group, and both biomarkers, which should be suppressed in the preovulation phase, were elevated in PCOS tissues. The IR score of PCOS group was higher, and the expression of JDP2 and HMOX1 showed a significant positive correlation with IR score. Most immune cell scores and immune infiltration results were significantly higher in PCOS. Comprehensive analysis indicated that the two biomarkers had strong correlation with immune-related pathways. CONCLUSION We acquired two potential biomarkers, JDP2 and HMOX1. We found that they were highly expressed in the PCOS and had a strong positive correlation with immune-related pathways.
Collapse
Affiliation(s)
- Shuang Liu
- The Reproductive Laboratory, Shenyang Jinghua Hospital, Shenyang, China
| | - Xuanpeng Zhao
- The Reproductive Laboratory, Shenyang Jinghua Hospital, Shenyang, China
| | - Qingyan Meng
- The Reproductive Laboratory, Shenyang Jinghua Hospital, Shenyang, China
| | - Baoshan Li
- The Reproductive Laboratory, Shenyang Jinghua Hospital, Shenyang, China
| |
Collapse
|
11
|
Wu J, Guo Y, Zuo ZF, Zhu ZW, Han L. MMP14 is a diagnostic gene of intrahepatic cholangiocarcinoma associated with immune cell infiltration. World J Gastroenterol 2023; 29:2961-2978. [PMID: 37274806 PMCID: PMC10237093 DOI: 10.3748/wjg.v29.i19.2961] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/24/2023] [Accepted: 04/23/2023] [Indexed: 05/16/2023] Open
Abstract
BACKGROUND Intrahepatic cholangiocarcinoma (ICC) is a malignant tumor of the hepatobiliary system with concealed onset, strong invasiveness and poor prognosis.
AIM To explore the disease characteristic genes that may be helpful in the diagnosis of ICC and affect immune cell infiltration.
METHODS We downloaded two ICC-related human gene expression profiles from GEO database as the training group (GSE26566 and GSE32958 datasets) for difference analysis, and performed enrichment analysis on differential genes. The least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF), three machine learning algorithms, were used to screen the characteristic genes. Double verification was carried out on GSE107943 and The Cancer Genome Atlas, two verification groups. Receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the diagnostic efficacy of genes for ICC. CIBERSORT and ssGSEA algorithms were used to evaluate the effect of characteristic genes on immune infiltration pattern. Human Protein Atlas (HPA) was used to analyze the protein expression level of the target gene.
RESULTS A total of 1091 differential genes were obtained in the training group. Enrichment analysis showed that the above genes were mainly enriched in small molecular catabolism, complement and coagulation cascade, bile secretion and other functions and pathways. Twenty-five characteristic genes were screened by LASSO regression, 19 by SVM-RFE algorithm, and 30 by RF algorithm. Three algorithms were used in combination to determine the characteristic gene of ICC: MMP14. The verification group confirmed that the genes had a high diagnostic accuracy (AUC values of the training group and the verification group were 0.960, 0.999, and 0.977, respectively). Comprehensive analysis of immune infiltration showed that MMP14 could affect the infiltration of monocytes, activated memory CD4 T cells, resting memory CD4 T cells, and other immune cells, and was closely related to the expression of CD200, cytotoxic T-lymphocyte-associated antigen 4, CD14, CD44, and other immune checkpoints. The results of immunohistochemistry in HPA database showed was indeed overexpressed in ICC.
CONCLUSION MMP14 can be used as a disease characteristic gene of ICC, and may regulate the distribution of immune-infiltrating cells in the ICC tumor microenvironment, which provides a new method for the determination of ICC diagnostic markers and screening of therapeutic targets.
Collapse
Affiliation(s)
- Jun Wu
- China Medical University, The General Hospital of Northern Theater Command Training Base for Graduate, Shenyang 110016, Liaoning Province, China
| | - Yang Guo
- Department of Hepatobiliary Surgery, The General Hospital of Northern Theater Command, Shenyang 110016, Liaoning Province, China
| | - Zhi-Fan Zuo
- Gynecological Radiotherapy Ward, Liaoning Provincial Cancer Hospital, Shenyang 110801, Liaoning province, China
| | - Zi-Wei Zhu
- China Medical University, The General Hospital of Northern Theater Command Training Base for Graduate, Shenyang 110016, Liaoning Province, China
| | - Lei Han
- Department of Hepatobiliary Surgery, The General Hospital of Northern Theater Command, Shenyang 110016, Liaoning Province, China
| |
Collapse
|
12
|
Wang W, Zhang TN, Yang N, Wen R, Wang YJ, Zhang BL, Yang YH, Liu CF. Transcriptome-wide identification of altered RNA m 6A profiles in cardiac tissue of rats with LPS-induced myocardial injury. Front Immunol 2023; 14:1122317. [PMID: 37275860 PMCID: PMC10237353 DOI: 10.3389/fimmu.2023.1122317] [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: 12/12/2022] [Accepted: 05/05/2023] [Indexed: 06/07/2023] Open
Abstract
Purpose Myocardial injury is a common complication in patients with endotoxaemia/sepsis, especially in children. Moreover, it develops through an unclear pathophysiological mechanism, and effective therapies are lacking. Recently, RNA modification, particularly N 6-methyladenosine (m6A) modification, has been found to be involved in various physiological processes and to play important roles in many diseases. However, the role of m6A modification in endotoxaemia/sepsis-induced myocardial injury is still in its infancy. Therefore, we attempted to construct the m6A modification map of myocardial injury in a rat model treated by lipopolysaccharide (LPS) and explore the role of m6A modification in LPS-induced myocardial injury. Method Myocardial injury adolescent rat model was constructed by intraperitoneal injection of LPS. m6A RNA Methylation Quantification Kit was used to detect overall level of m6A modification in rat cardiac tissue. m6A-specific methylated RNA immunoprecipitation followed by high-throughput sequencing (MeRIP-seq) and RNA sequencing (RNA-seq) were conducted to identify the altered m6A-modified genes and differentially expressed genes in cardiac tissue of rats treated by LPS and control rats (6 versus. 6). Bioinformatics was used to analyze the functions of differentially m6A modified genes, differentially expressed genes, and genes with both differential m6A modification and differential expression. qPCR was used to detect expression of m6A modification related enzymes. Result We found that the overall level of m6A modification in cardiac tissue of the LPS group was up-regulated compared with that of the control group. MeRIP-seq and RNA-seq results showed that genes with differential m6A modification, genes with differential expression and genes with both differential m6A modification and differential expression were closely associated with inflammatory responses and apoptosis. In addition, we found that m6A-related enzymes (Mettl16, Rbm15, Fto, Ythdc2 and Hnrnpg) were differentially expressed in the LPS group versus. the control group. Conclusion m6A modification is involved in the pathogenesis process of LPS-induced myocardial injury, possibly through the regulation of inflammatory response and apoptosis-related pathways. These results provide valuable information regarding the potential pathogenic mechanisms underlying LPS-induced myocardial injury.
Collapse
Affiliation(s)
- Wei Wang
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Tie-Ning Zhang
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Ni Yang
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Ri Wen
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Yu-Jing Wang
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Bing-Lun Zhang
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Yu-Hang Yang
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Chun-Feng Liu
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| |
Collapse
|
13
|
Zhang Y, Wang C, Xia Q, Jiang W, Zhang H, Amiri-Ardekani E, Hua H, Cheng Y. Machine learning-based prediction of candidate gene biomarkers correlated with immune infiltration in patients with idiopathic pulmonary fibrosis. Front Med (Lausanne) 2023; 10:1001813. [PMID: 36860337 PMCID: PMC9968813 DOI: 10.3389/fmed.2023.1001813] [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: 08/03/2022] [Accepted: 01/26/2023] [Indexed: 02/15/2023] Open
Abstract
Objective This study aimed to identify candidate gene biomarkers associated with immune infiltration in idiopathic pulmonary fibrosis (IPF) based on machine learning algorithms. Methods Microarray datasets of IPF were extracted from the Gene Expression Omnibus (GEO) database to screen for differentially expressed genes (DEGs). The DEGs were subjected to enrichment analysis, and two machine learning algorithms were used to identify candidate genes associated with IPF. These genes were verified in a validation cohort from the GEO database. Receiver operating characteristic (ROC) curves were plotted to assess the predictive value of the IPF-associated genes. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was used to evaluate the proportion of immune cells in IPF and normal tissues. Additionally, the correlation between the expression of IPF-associated genes and the infiltration levels of immune cells was examined. Results A total of 302 upregulated and 192 downregulated genes were identified. Functional annotation, pathway enrichment, Disease Ontology and gene set enrichment analyses revealed that the DEGs were related to the extracellular matrix and immune responses. COL3A1, CDH3, CEBPD, and GPIHBP1 were identified as candidate biomarkers using machine learning algorithms, and their predictive value was verified in a validation cohort. Additionally, ROC analysis revealed that the four genes had high predictive accuracy. The infiltration levels of plasma cells, M0 macrophages and resting dendritic cells were higher and those of resting natural killer (NK) cells, M1 macrophages and eosinophils were lower in the lung tissues of patients with IPF than in those of healthy individuals. The expression of the abovementioned genes was correlated with the infiltration levels of plasma cells, M0 macrophages and eosinophils. Conclusion COL3A1, CDH3, CEBPD, and GPIHBP1 are candidate biomarkers of IPF. Plasma cells, M0 macrophages and eosinophils may be involved in the development of IPF and may serve as immunotherapeutic targets in IPF.
Collapse
Affiliation(s)
- Yufeng Zhang
- Department of Pulmonary and Critical Care Medicine, Jiangyin Hospital of Traditional Chinese Medicine, Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangyin, Jiangsu, China
| | - Cong Wang
- Department of Pulmonary and Critical Care Medicine, Jiangyin Hospital of Traditional Chinese Medicine, Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangyin, Jiangsu, China
| | - Qingqing Xia
- Department of Pulmonary and Critical Care Medicine, Jiangyin Hospital of Traditional Chinese Medicine, Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangyin, Jiangsu, China
| | - Weilong Jiang
- Department of Pulmonary and Critical Care Medicine, Jiangyin Hospital of Traditional Chinese Medicine, Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangyin, Jiangsu, China
| | - Huizhe Zhang
- Department of Respiratory Medicine, Yancheng Hospital of Traditional Chinese Medicine, Yancheng Hospital Affiliated to Nanjing University of Chinese Medicine, Yancheng, Jiangsu, China
| | - Ehsan Amiri-Ardekani
- Department of Phytopharmaceuticals (Traditional Pharmacy), Faculty of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran,*Correspondence: Ehsan Amiri-Ardekani,
| | - Haibing Hua
- Department of Gastroenterology, Jiangyin Hospital of Traditional Chinese Medicine, Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangyin, Jiangsu, China,Haibing Hua,
| | - Yi Cheng
- Department of Respiratory Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,Yi Cheng,
| |
Collapse
|
14
|
Zhang Q, Guo Y, Zhang B, Liu H, Peng Y, Wang D, Zhang D. Identification of hub biomarkers of myocardial infarction by single-cell sequencing, bioinformatics, and machine learning. Front Cardiovasc Med 2022; 9:939972. [PMID: 35958412 PMCID: PMC9357907 DOI: 10.3389/fcvm.2022.939972] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 07/05/2022] [Indexed: 12/11/2022] Open
Abstract
Background Myocardial infarction (MI) is one of the first cardiovascular diseases endangering human health. Inflammatory response plays a significant role in the pathophysiological process of MI. Messenger RNA (mRNA) has been proven to play a key role in cardiovascular diseases. Single-cell sequencing (SCS) technology is a new technology for high-throughput sequencing analysis of genome, transcriptome, and epigenome at the single-cell level, and it also plays an important role in the diagnosis and treatment of cardiovascular diseases. Machine learning algorithms have a wide scope of utilization in biomedicine and have demonstrated superior efficiency in clinical trials. However, few studies integrate these three methods to investigate the role of mRNA in MI. The aim of this study was to screen the expression of mRNA, investigate the function of mRNA, and provide an underlying scientific basis for the diagnosis of MI. Methods In total, four RNA microarray datasets of MI, namely, GSE66360, GSE97320, GSE60993, and GSE48060, were downloaded from the Gene Expression Omnibus database. The function analysis was carried out by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) enrichment analysis. At the same time, inflammation-related genes (IRGs) were acquired from the GeneCards database. Then, 52 co-DEGs were acquired from differentially expressed genes (DEGs) in differential analysis, IRGs, and genes from SCS, and they were used to construct a protein-protein interaction (PPI) network. Two machine learning algorithms, namely, (1) least absolute shrinkage and selection operator and (2) support vector machine recursive feature elimination, were used to filter the co-DEGs. Gene set enrichment analysis (GSEA) was performed to screen the hub-modulating signaling pathways associated with the hub genes. The results were validated in GSE97320, GSE60993, and GSE48060 datasets. The CIBERSORT algorithm was used to analyze 22 infiltrating immune cells in the MI and healthy control (CON) groups and to analyze the correlation between these immune cells. The Pymol software was used for molecular docking of hub DEGs and for potential treatment of MI drugs acquired from the COREMINE. Results A total of 126 DEGs were in the MI and CON groups. After screening two machine learning algorithms and key co-DEGs from a PPI network, two hub DEGs (i.e., IL1B and TLR2) were obtained. The diagnostic efficiency of IL1B, TLR2, and IL1B + TLR2 showed good discrimination in the four cohorts. GSEA showed that KEGG enriched by DEGs were mainly related to inflammation-mediated signaling pathways, and GO biological processes enriched by DEGs were linked to biological effects of various inflammatory cells. Immune analysis indicated that IL1B and TLR2 were correlated with various immune cells. Dan shen, san qi, feng mi, yuan can e, can sha, san qi ye, san qi hua, and cha shu gen were identified as the potential traditional Chinese medicine (TCM) for the treatment of MI. 7-hydroxyflavone (HF) had stable combinations with IL1B and TLR2, respectively. Conclusion This study identified two hub DEGs (IL1B and TLR2) and illustrated its potential role in the diagnosis of MI to enhance our knowledge of the underlying molecular mechanism. Infiltrating immune cells played an important role in MI. TCM, especially HF, was a potential drug for the treatment of MI.
Collapse
Affiliation(s)
- Qunhui Zhang
- Research Center for High Altitude Medicine, Key Laboratory of High-Altitude Medicine (Ministry of Education), Key Laboratory of Application and Foundation for High Altitude Medicine Research in Qinghai Province (Qinghai-Utah Joint Research Key Lab for High Altitude Medicine), Qinghai University, Xining, China.,College of Eco-Environmental Engineering, Qinghai University, Xining, China
| | - Yang Guo
- Research Center for High Altitude Medicine, Key Laboratory of High-Altitude Medicine (Ministry of Education), Key Laboratory of Application and Foundation for High Altitude Medicine Research in Qinghai Province (Qinghai-Utah Joint Research Key Lab for High Altitude Medicine), Qinghai University, Xining, China.,College of Eco-Environmental Engineering, Qinghai University, Xining, China
| | - Benyin Zhang
- College of Eco-Environmental Engineering, Qinghai University, Xining, China
| | - Hairui Liu
- College of Eco-Environmental Engineering, Qinghai University, Xining, China
| | - Yanfeng Peng
- College of Eco-Environmental Engineering, Qinghai University, Xining, China
| | - Di Wang
- College of Eco-Environmental Engineering, Qinghai University, Xining, China
| | - Dejun Zhang
- Research Center for High Altitude Medicine, Key Laboratory of High-Altitude Medicine (Ministry of Education), Key Laboratory of Application and Foundation for High Altitude Medicine Research in Qinghai Province (Qinghai-Utah Joint Research Key Lab for High Altitude Medicine), Qinghai University, Xining, China.,College of Eco-Environmental Engineering, Qinghai University, Xining, China
| |
Collapse
|