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Du H, Sun J, Wang X, Zhao L, Liu X, Zhang C, Wang F, Wu J. FOSL2-mediated transcription of ISG20 induces M2 polarization of macrophages and enhances tumorigenic ability of glioblastoma cells. J Neurooncol 2024; 169:659-670. [PMID: 39073688 DOI: 10.1007/s11060-024-04771-7] [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: 03/13/2024] [Accepted: 07/05/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Interferon stimulated exonuclease gene 20 (ISG20) has been reported to be correlated with macrophage infiltration in glioblastoma (GBM) in previous bioinformatics-based studies. This study explores the exact effect of ISG20 on macrophage polarization in GBM. METHODS ISG20 expression in GBM tissues and cells was determined by RT-qPCR and/or immunohistochemistry. GBM cells were co-cultured with M0 macrophages (PMA-stimulated THP-1 cells) in vitro, followed by flow cytometry and ELISA to analyze the M2 polarization of macrophages. Fluorescence-contained GBM cells were intracranially injected into nude mice along with M0 macrophages to generate orthotopic xenograft tumor models. Upstream regulator of ISG20 was predicted using bioinformatics. Loss- or gain-of-function assays of Fos like 2 (FOSL2) and ISG20 were performed in GBM cells. DNA methylation level of FOSL2 was analyzed by bisulfite sequencing analysis. RESULTS ISG20 was found highly expressed in GBM tissues and cells. ISG20 silencing in GBM cells decreased CD206 and CD163 levels in the co-cultured macrophages and reduced secretion of IL-10 and TGF-β. It also enhanced survival of nude mice bearing xenograft tumors, blocked tumor growth, and suppressed M2 polarization of macrophages in vivo. FOSL2, highly expressed in GBM, bound to the ISG20 promoter to activate its transcription. FOSL2 silencing similarly blocked M2 polarization of macrophages, which was negated by ISG20 overexpression. The high FOSL2 expression in GBM was attributed to DNA hypomethylation. CONCLUSION This study demonstrates that FOSL2 is highly expressed in GBM due to DNA hypomethylation. It activates transcription of ISG20, thus promoting M2 polarization of macrophages and GBM development.
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Affiliation(s)
- Hailong Du
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, No. 215, Heping West Road, Shijiazhuang, 050000, Hebei, P.R. China
| | - Jianping Sun
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, No. 215, Heping West Road, Shijiazhuang, 050000, Hebei, P.R. China
| | - Xiaoliang Wang
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, No. 215, Heping West Road, Shijiazhuang, 050000, Hebei, P.R. China
| | - Lei Zhao
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, No. 215, Heping West Road, Shijiazhuang, 050000, Hebei, P.R. China
| | - Xiaosong Liu
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, No. 215, Heping West Road, Shijiazhuang, 050000, Hebei, P.R. China
| | - Chao Zhang
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, No. 215, Heping West Road, Shijiazhuang, 050000, Hebei, P.R. China
| | - Feng Wang
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, No. 215, Heping West Road, Shijiazhuang, 050000, Hebei, P.R. China
| | - Jianliang Wu
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, No. 215, Heping West Road, Shijiazhuang, 050000, Hebei, P.R. China.
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Mendapara K. Development and evaluation of a chronic kidney disease risk prediction model using random forest. Front Genet 2024; 15:1409755. [PMID: 38993480 PMCID: PMC11236722 DOI: 10.3389/fgene.2024.1409755] [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: 04/02/2024] [Accepted: 05/29/2024] [Indexed: 07/13/2024] Open
Abstract
This research aims to advance the detection of Chronic Kidney Disease (CKD) through a novel gene-based predictive model, leveraging recent breakthroughs in gene sequencing. We sourced and merged gene expression profiles of CKD-affected renal tissues from the Gene Expression Omnibus (GEO) database, classifying them into two sets for training and validation in a 7:3 ratio. The training set included 141 CKD and 33 non-CKD specimens, while the validation set had 60 and 14, respectively. The disease risk prediction model was constructed using the training dataset, while the validation dataset confirmed the model's identification capabilities. The development of our predictive model began with evaluating differentially expressed genes (DEGs) between the two groups. We isolated six genes using Lasso and random forest (RF) methods-DUSP1, GADD45B, IFI44L, IFI30, ATF3, and LYZ-which are critical in differentiating CKD from non-CKD tissues. We refined our random forest (RF) model through 10-fold cross-validation, repeated five times, to optimize the mtry parameter. The performance of our model was robust, with an average AUC of 0.979 across the folds, translating to a 91.18% accuracy. Validation tests further confirmed its efficacy, with a 94.59% accuracy and an AUC of 0.990. External validation using dataset GSE180394 yielded an AUC of 0.913, 89.83% accuracy, and a sensitivity rate of 0.889, underscoring the model's reliability. In summary, the study identified critical genetic biomarkers and successfully developed a novel disease risk prediction model for CKD. This model can serve as a valuable tool for CKD disease risk assessment and contribute significantly to CKD identification.
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Affiliation(s)
- Krish Mendapara
- Faculty of Health Sciences, Queen's University, Kingston, ON, Canada
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Zhang Y, Lyu Q, Han X, Wang X, Liu R, Hao J, Zhang L, Chen XM. Proteomic analysis of multiple organ dysfunction induced by rhabdomyolysis. J Proteomics 2024; 298:105138. [PMID: 38403185 DOI: 10.1016/j.jprot.2024.105138] [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: 10/26/2023] [Revised: 02/12/2024] [Accepted: 02/22/2024] [Indexed: 02/27/2024]
Abstract
Rhabdomyolysis (RM) leads to dysfunction in the core organs of kidney, lung and heart, which is an important reason for the high mortality and disability rate of this disease. However, there is a lack of systematic research on the characteristics of rhabdomyolysis-induced injury in various organs and the underlying pathogenetic mechanisms, and especially the interaction between organs. We established a rhabdomyolysis model, observed the structural and functional changes in kidney, heart, and lung. It is observed that rhabdomyolysis results in significant damage in kidney, lung and heart of rats, among which the pathological damage of kidney and lung was significant, and of heart was relatively light. Meanwhile, we analyzed the differentially expressed proteins (DEPs) in the kidney, heart and lung between the RM group and the sham group based on liquid chromatography-tandem mass spectrometry (LC-MS/MS). In our study, Serpina3n was significantly up-regulated in the kidney, heart and lung. Serpina3n is a secreted protein and specifically inhibits a variety of proteases and participates in multiple physiological processes such as complement activation, inflammatory responses, apoptosis pathways, and extracellular matrix metabolism. It is inferred that Serpina3n may play an important role in multiple organ damage caused by rhabdomyolysis and could be used as a potential biomarker. This study comprehensively describes the functional and structural changes of kidney, heart and lung in rats after rhabdomyolysis, analyzes the DEPs of kidney, heart and lung, and determines the key role of Serpina3n in multiple organ injury caused by rhabdomyolysis. SIGNIFICANCE: This study comprehensively describes the functional and structural changes of kidney, heart and lung in rats after rhabdomyolysis, analyzes the DEPs of kidney, heart and lung, and determines the key role of Serpina3n in multiple organ injury caused by rhabdomyolysis.
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Affiliation(s)
- Yan Zhang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing 100853, China; Graduate School of Chinese PLA General Hospital, Beijing 100853, China
| | - Qiang Lyu
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing 100853, China
| | - Xiao Han
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing 100853, China; Graduate School of Chinese PLA General Hospital, Beijing 100853, China
| | - Xu Wang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing 100853, China
| | - Ran Liu
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing 100853, China
| | - Jing Hao
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing 100853, China
| | - Li Zhang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing 100853, China.
| | - Xiang-Mei Chen
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing 100853, China.
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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.
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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.
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Lee M. Machine learning for small interfering RNAs: a concise review of recent developments. Front Genet 2023; 14:1226336. [PMID: 37519887 PMCID: PMC10372481 DOI: 10.3389/fgene.2023.1226336] [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: 05/24/2023] [Accepted: 07/04/2023] [Indexed: 08/01/2023] Open
Abstract
The advent of machine learning and its subsequent integration into small interfering RNA (siRNA) research heralds a new epoch in the field of RNA interference (RNAi). This review emphasizes the urgency and relevance of assimilating the plethora of contributions and advancements in this domain, particularly focusing on the period of 2019-2023. Given the rapid progression of deep learning technologies, our synthesis of recent research is paramount to staying apprised of the state-of-the-art methods being utilized. It not only offers a comprehensive insight into the confluence of machine learning and siRNA but also serves as a beacon, guiding future explorations in this intersectional research field. Our rigorous examination of studies promises a discerning perspective on the contemporary landscape of machine learning applications in siRNA design and function. This review is an effort to foster further discourse and propel academic inquiry in this multifaceted domain.
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Guo Y, Cen K, Hong K, Mai Y, Jiang M. Construction of a neural network diagnostic model for renal fibrosis and investigation of immune infiltration characteristics. Front Immunol 2023; 14:1183088. [PMID: 37359552 PMCID: PMC10288286 DOI: 10.3389/fimmu.2023.1183088] [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: 03/09/2023] [Accepted: 05/30/2023] [Indexed: 06/28/2023] Open
Abstract
Background Recently, the incidence rate of renal fibrosis has been increasing worldwide, greatly increasing the burden on society. However, the diagnostic and therapeutic tools available for the disease are insufficient, necessitating the screening of potential biomarkers to predict renal fibrosis. Methods Using the Gene Expression Omnibus (GEO) database, we obtained two gene array datasets (GSE76882 and GSE22459) from patients with renal fibrosis and healthy individuals. We identified differentially expressed genes (DEGs) between renal fibrosis and normal tissues and analyzed possible diagnostic biomarkers using machine learning. The diagnostic effect of the candidate markers was evaluated using receiver operating characteristic (ROC) curves and verified their expression using Reverse transcription quantitative polymerase chain reaction (RT-qPCR). The CIBERSORT algorithm was used to determine the proportions of 22 types of immune cells in patients with renal fibrosis, and the correlation between biomarker expression and the proportion of immune cells was studied. Finally, we developed an artificial neural network model of renal fibrosis. Results Four candidate genes namely DOCK2, SLC1A3, SOX9 and TARP were identified as biomarkers of renal fibrosis, with the area under the ROC curve (AUC) values higher than 0.75. Next, we verified the expression of these genes by RT-qPCR. Subsequently, we revealed the potential disorder of immune cells in the renal fibrosis group through CIBERSORT analysis and found that immune cells were highly correlated with the expression of candidate markers. Conclusion DOCK2, SLC1A3, SOX9, and TARP were identified as potential diagnostic genes for renal fibrosis, and the most relevant immune cells were identified. Our findings provide potential biomarkers for the diagnosis of renal fibrosis.
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Affiliation(s)
- Yangyang Guo
- Department of General Surgery, The First Affiliated Hospital of Ningbo University, Ningbo, China
- Department of Urology Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Kenan Cen
- Department of General Surgery, The First Affiliated Hospital of Ningbo University, Ningbo, China
| | - Kai Hong
- Department of General Surgery, The First Affiliated Hospital of Ningbo University, Ningbo, China
| | - Yifeng Mai
- Department of General Surgery, The First Affiliated Hospital of Ningbo University, Ningbo, China
| | - Minghui Jiang
- Department of Urology Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
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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.
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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,
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Yan P, Ke B, Song J, Fang X. Identification of immune-related molecular clusters and diagnostic markers in chronic kidney disease based on cluster analysis. Front Genet 2023; 14:1111976. [PMID: 36814902 PMCID: PMC9939663 DOI: 10.3389/fgene.2023.1111976] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 01/24/2023] [Indexed: 02/08/2023] Open
Abstract
Background: Chronic kidney disease (CKD) is a heterogeneous disease with multiple etiologies, risk factors, clinical manifestations, and prognosis. The aim of this study was to identify different immune-related molecular clusters in CKD, their functional immunological properties, and to screen for promising diagnostic markers. Methods: Datasets of 440 CKD patients were obtained from the comprehensive gene expression database. The core immune-related genes (IRGs) were identified by weighted gene co-expression network analysis. We used unsupervised clustering to divide CKD samples into two immune-related subclusters. Then, functional enrichment analysis was performed for differentially expressed genes (DEGs) between clusters. Three machine learning methods (LASSO, RF, and SVM-RFE) and Venn diagrams were applied to filter out 5 significant IRGs with distinguished subtypes. A nomogram diagnostic model was developed, and the prediction effect was verified using calibration curve, decision curve analysis. CIBERSORT was applied to assess the variation in immune cell infiltration among clusters. The expression levels, immune characteristics and immune cell correlation of core diagnostic markers were investigated. Finally, the Nephroseq V5 was used to assess the correlation among core diagnostic markers and renal function. Results: The 15 core IRGs screened were differentially expressed in normal and CKD samples. CKD was classified into two immune-related molecular clusters. Cluster 2 is significantly enriched in biological functions such as leukocyte adhesion and regulation as well as immune activation, and has a severe immune prognosis compared to cluster 1. A nomogram diagnostic model with reliable prediction of immune-related clusters was developed based on five signature genes. The core diagnostic markers LYZ, CTSS, and ISG20 were identified as playing an important role in the immune microenvironment and were shown to correlate meaningfully with immune cell infiltration and renal function. Conclusion: Our study identifies two subtypes of CKD with distinct immune gene expression patterns and provides promising predictive models. Along with the exploration of the role of three promising diagnostic markers in the immune microenvironment of CKD, it is anticipated to provide novel breakthroughs in potential targets for disease treatment.
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Feng C, Wang Z, Liu C, Liu S, Wang Y, Zeng Y, Wang Q, Peng T, Pu X, Liu J. Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis. Front Pharmacol 2022; 13:909784. [PMID: 36120336 PMCID: PMC9470879 DOI: 10.3389/fphar.2022.909784] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 07/29/2022] [Indexed: 11/13/2022] Open
Abstract
Renal biopsy is the gold standard for defining renal fibrosis which causes calcium deposits in the kidneys. Persistent calcium deposition leads to kidney inflammation, cell necrosis, and is related to serious kidney diseases. However, it is invasive and involves the risk of complications such as bleeding, especially in patients with end-stage renal diseases. Therefore, it is necessary to identify specific diagnostic biomarkers for renal fibrosis. This study aimed to develop a predictive drug target signature to diagnose renal fibrosis based on m6A subtypes. We then performed an unsupervised consensus clustering analysis to identify three different m6A subtypes of renal fibrosis based on the expressions of 21 m6A regulators. We evaluated the immune infiltration characteristics and expression of canonical immune checkpoints and immune-related genes with distinct m6A modification patterns. Subsequently, we performed the WGCNA analysis using the expression data of 1,611 drug targets to identify 474 genes associated with the m6A modification. 92 overlapping drug targets between WGCNA and DEGs (renal fibrosis vs. normal samples) were defined as key drug targets. A five target gene predictive model was developed through the combination of LASSO regression and stepwise logistic regression (LASSO-SLR) to diagnose renal fibrosis. We further performed drug sensitivity analysis and extracellular matrix analysis on model genes. The ROC curve showed that the risk score (AUC = 0.863) performed well in diagnosing renal fibrosis in the training dataset. In addition, the external validation dataset further confirmed the outstanding predictive performance of the risk score (AUC = 0.755). These results indicate that the risk model has an excellent predictive performance for diagnosing the disease. Furthermore, our results show that this 5-target gene model is significantly associated with many drugs and extracellular matrix activities. Finally, the expression levels of both predictive signature genes EGR1 and PLA2G4A were validated in renal fibrosis and adjacent normal tissues by using qRT-PCR and Western blot method.
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Affiliation(s)
- Chunxiang Feng
- Department of Urology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangdong Guangzhou, Wuhan, China
| | - Zhixian Wang
- Department of Urology, Wuhan Hospital of Traditional Chinese and Western Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Urology, Wuhan No. 1 Hospital, Wuhan, China
| | - Chang Liu
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shiliang Liu
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuxi Wang
- Department of Nephrology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuanyuan Zeng
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China
| | - Qianqian Wang
- Department of Urology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangdong Guangzhou, Wuhan, China
| | - Tianming Peng
- Department of Urology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangdong Guangzhou, Wuhan, China
| | - Xiaoyong Pu
- Department of Urology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangdong Guangzhou, Wuhan, China
- *Correspondence: Xiaoyong Pu, ; Jiumin Liu,
| | - Jiumin Liu
- Department of Urology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangdong Guangzhou, Wuhan, China
- *Correspondence: Xiaoyong Pu, ; Jiumin Liu,
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