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Unger Avila P, Padvitski T, Leote AC, Chen H, Saez-Rodriguez J, Kann M, Beyer A. Gene regulatory networks in disease and ageing. Nat Rev Nephrol 2024; 20:616-633. [PMID: 38867109 DOI: 10.1038/s41581-024-00849-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/15/2024] [Indexed: 06/14/2024]
Abstract
The precise control of gene expression is required for the maintenance of cellular homeostasis and proper cellular function, and the declining control of gene expression with age is considered a major contributor to age-associated changes in cellular physiology and disease. The coordination of gene expression can be represented through models of the molecular interactions that govern gene expression levels, so-called gene regulatory networks. Gene regulatory networks can represent interactions that occur through signal transduction, those that involve regulatory transcription factors, or statistical models of gene-gene relationships based on the premise that certain sets of genes tend to be coexpressed across a range of conditions and cell types. Advances in experimental and computational technologies have enabled the inference of these networks on an unprecedented scale and at unprecedented precision. Here, we delineate different types of gene regulatory networks and their cell-biological interpretation. We describe methods for inferring such networks from large-scale, multi-omics datasets and present applications that have aided our understanding of cellular ageing and disease mechanisms.
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Affiliation(s)
- Paula Unger Avila
- Cluster of Excellence on Cellular Stress Responses in Aging-associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Tsimafei Padvitski
- Cluster of Excellence on Cellular Stress Responses in Aging-associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Ana Carolina Leote
- Cluster of Excellence on Cellular Stress Responses in Aging-associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - He Chen
- Cluster of Excellence on Cellular Stress Responses in Aging-associated Diseases (CECAD), University of Cologne, Cologne, Germany
- Department II of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Julio Saez-Rodriguez
- Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg University, Heidelberg, Germany
| | - Martin Kann
- Department II of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Andreas Beyer
- Cluster of Excellence on Cellular Stress Responses in Aging-associated Diseases (CECAD), University of Cologne, Cologne, Germany.
- Center for Molecular Medicine Cologne, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
- Institute for Genetics, Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, Germany.
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Lu J, Rui J, Xu XY, Shen JK. Exploring the Role of Neutrophil-Related Genes in Osteosarcoma via an Integrative Analysis of Single-Cell and Bulk Transcriptome. Biomedicines 2024; 12:1513. [PMID: 39062086 PMCID: PMC11274533 DOI: 10.3390/biomedicines12071513] [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: 05/29/2024] [Revised: 06/29/2024] [Accepted: 07/05/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND The involvement of neutrophil-related genes (NRGs) in patients with osteosarcoma (OS) has not been adequately explored. In this study, we aimed to examine the association between NRGs and the prognosis as well as the tumor microenvironment of OS. METHODS The OS data were obtained from the TARGET-OS and GEO database. Initially, we extracted NRGs by intersecting 538 NRGs from single-cell RNA sequencing (scRNA-seq) data between aneuploid and diploid groups, as well as 161 up-regulated differentially expressed genes (DEGs) from the TARGET-OS datasets. Subsequently, we conducted Least Absolute Shrinkage and Selection Operator (Lasso) analyses to identify the hub genes for constructing the NRG-score and NRG-signature. To assess the prognostic value of the NRG signatures in OS, we performed Kaplan-Meier analysis and generated time-dependent receiver operating characteristic (ROC) curves. Gene enrichment analysis (GSEA) and gene set variation analysis (GSVA) were utilized to ascertain the presence of tumor immune microenvironments (TIMEs) and immunomodulators (IMs). Additionally, the KEGG neutrophil signaling pathway was evaluated using ssGSEA. Subsequently, PCR and IHC were conducted to validate the expression of hub genes and transcription factors (TFs) in K7M2-induced OS mice. RESULTS FCER1G and C3AR1 have been identified as prognostic biomarkers for overall survival. The findings indicate a significantly improved prognosis for OS patients. The effectiveness and precision of the NRG signature in prognosticating OS patients were validated through survival ROC curves and an external validation dataset. The results clearly demonstrate that patients with elevated NRG scores exhibit decreased levels of immunomodulators, stromal score, immune score, ESTIMATE score, and infiltrating immune cell populations. Furthermore, our findings substantiate the potential role of SPI1 as a transcription factor in the regulation of the two central genes involved in osteosarcoma development. Moreover, our analysis unveiled a significant correlation and activation of the KEGG neutrophil signaling pathway with FCER1G and C3AR1. Notably, PCR and IHC demonstrated a significantly higher expression of C3AR1, FCER1G, and SPI1 in Balb/c mice induced with K7M2. CONCLUSIONS Our research emphasizes the significant contribution of neutrophils within the TIME of osteosarcoma. The newly developed NRG signature could serve as a good instrument for evaluating the prognosis and therapeutic approach for OS.
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Affiliation(s)
- Jing Lu
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215025, China;
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200235, China; (J.R.); (X.-Y.X.)
| | - Jiang Rui
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200235, China; (J.R.); (X.-Y.X.)
| | - Xiao-Yu Xu
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200235, China; (J.R.); (X.-Y.X.)
| | - Jun-Kang Shen
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215025, China;
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Chen H, Zhang GX, Zhou XY. Identification of hub genes associated with Helicobacter pylori infection and type 2 diabetes mellitus: A pilot bioinformatics study. World J Diabetes 2024; 15:170-185. [PMID: 38464370 PMCID: PMC10921168 DOI: 10.4239/wjd.v15.i2.170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 11/21/2023] [Accepted: 12/27/2023] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Helicobacter pylori (H. pylori) infection is related to various extragastric diseases including type 2 diabetes mellitus (T2DM). However, the possible mechanisms connecting H. pylori infection and T2DM remain unknown. AIM To explore potential molecular connections between H. pylori infection and T2DM. METHODS We extracted gene expression arrays from three online datasets (GSE60427, GSE27411 and GSE115601). Differentially expressed genes (DEGs) commonly present in patients with H. pylori infection and T2DM were identified. Hub genes were validated using human gastric biopsy samples. Correlations between hub genes and immune cell infiltration, miRNAs, and transcription factors (TFs) were further analyzed. RESULTS A total of 67 DEGs were commonly presented in patients with H. pylori infection and T2DM. Five significantly upregulated hub genes, including TLR4, ITGAM, C5AR1, FCER1G, and FCGR2A, were finally identified, all of which are closely related to immune cell infiltration. The gene-miRNA analysis detected 13 miRNAs with at least two gene cross-links. TF-gene interaction networks showed that TLR4 was coregulated by 26 TFs, the largest number of TFs among the 5 hub genes. CONCLUSION We identified five hub genes that may have molecular connections between H. pylori infection and T2DM. This study provides new insights into the pathogenesis of H. pylori-induced onset of T2DM.
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Affiliation(s)
- Han Chen
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210003, Jiangsu Province, China
| | - Guo-Xin Zhang
- Department of Gastroenterology, Jiangsu Province Hospital, Nanjing 210029, Jiangsu Province, China
| | - Xiao-Ying Zhou
- Department of Gastroenterology, Jiangsu Province Hospital, Nanjing 210029, Jiangsu Province, China
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Shao X, Shi Y, Wang Y, Zhang L, Bai P, Wang J, Aniwan A, Lin Y, Zhou S, Yu P. Single-Cell Sequencing Reveals the Expression of Immune-Related Genes in Macrophages of Diabetic Kidney Disease. Inflammation 2024; 47:227-243. [PMID: 37777674 DOI: 10.1007/s10753-023-01906-2] [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: 06/05/2023] [Revised: 08/30/2023] [Accepted: 09/13/2023] [Indexed: 10/02/2023]
Abstract
Diabetic kidney disease (DKD) is characterized by macrophage infiltration, which requires further investigation. This study aims to identify immune-related genes (IRGs) in macrophage and explore their potential as therapeutic targets. This study analyzed isolated glomerular cells from three diabetic mice and three control mice. A total of 59 glomeruli from normal kidney samples and 66 from DKD samples were acquired from four kidney transcriptomic profiling datasets. Bioinformatics analysis was conducted using both single-cell RNA (scRNA) and bulk RNA sequencing data to investigate inflammatory responses in DKD. Additionally, the "AUCell" function was used to investigate statistically different gene sets. The significance of each interaction pair was determined by assigning a probability using "CellChat." The study also analyzed the biological diagnostic importance of immune hub genes for DKD and validated the expression of these immune genes in mice models. The top 2000 highly variable genes (HVGs) were identified after data normalization. Subsequently, a total of eight clusters were identified. It is worth mentioning that macrophages showed the highest percentage increase among all cell types in the DKD group. Furthermore, the present study observed significant differences in gene sets related to inflammatory responses and complement pathways. The study also identified several receptor-ligand pairs and co-stimulatory interactions between endothelial cells and macrophages. Notably, SYK, ITGB2, FCER1G, and VAV1 were identified as immunological markers of DKD with promising predictive ability. This study identified distinct cell clusters and four marker genes. SYK, ITGB2, FCER1G, and VAV1 may be important roles. Consequently, the present study extends our understanding regarding IRGs in DKD and provides a foundation for future investigations into the underlying mechanisms.
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Affiliation(s)
- Xian Shao
- NHC Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China
- Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, 300134, China
| | - Yueyue Shi
- Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300134, China
| | - Yao Wang
- Clinical Medical College & Affiliated Hospital of Chengdu University, Chengdu University, Chengdu, Sichuan, 610081, People's Republic of China
| | - Li Zhang
- NHC Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China
- Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, 300134, China
| | - Pufei Bai
- NHC Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China
- Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, 300134, China
| | - JunMei Wang
- NHC Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China
- Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, 300134, China
| | - Ashanjiang Aniwan
- NHC Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China
- Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, 300134, China
| | - Yao Lin
- NHC Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China
- Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, 300134, China
| | - Saijun Zhou
- NHC Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China
- Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, 300134, China
| | - Pei Yu
- NHC Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China.
- Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, 300134, China.
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Li S, Zhou H, Liang Y, Yang Q, Zhang J, Shen W, Lei L. Integrated analysis of transcriptome-wide m 6A methylation in a Cd-induced kidney injury rat model. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 256:114903. [PMID: 37054473 DOI: 10.1016/j.ecoenv.2023.114903] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 11/28/2022] [Accepted: 04/09/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND Accumulating evidence has demonstrated that N6-methyladenosine (m6A) plays important roles in a variety of diseases. However, the specific functions of m6A in CdCl2-induced kidney injury remain unclear. OBJECTIVE Here, we investigate a transcriptome-wide map of m6A modifications and explore the effects of m6A on Cd-induced kidney injury. MATERIALS AND METHODS The rat kidney injury model was constructed by subcutaneous injection of CdCl2 (0.5, 1.0, and 2.0 mg/kg). The m6A levels were measured by colorimetry. The level of expression of m6A-related enzymes were detected by reverse transcription quantitative real-time PCR analysis. Transcriptome-wide m6A methylome in CdCl2 (2.0 mg/kg) and the control group were profiled by methylated RNA immunoprecipitation sequencing (MeRIP-seq). Subsequently, the sequencing data were analyzed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), while gene set enrichment analysis (GSEA) confirmed the functional enrichment pathways of sequencing genes. In addition, a protein-protein interaction (PPI) network was applied to select hub genes. RESULTS The levels of m6A and m6A regulators (METTL3, METTL14, WTAP, YTHDF2) were significantly increased in CdCl2 groups. We identified a total of 2615 differentially expressed m6A peaks, 868 differentially expressed genes and 200 genes with significant changes in both m6A modification and gene expression levels. GO, KEGG, and GSEA analyses indicated that these genes were mainly enriched in inflammation and metabolism-related pathways such as in IL-17 signaling and fatty acid metabolism. According the result of the conjoint analysis, we identified the top ten hub genes (Fos, Hsp90aa1, Gata3, Fcer1g, Cftr, Cspg4, Atf3, Cdkn1a, Ptgs2, and Npy) which may be regulated by m6A and involve in CdCl2-induced kidney damage. CONCLUSION This study established a m6A transcriptional map in a CdCl2-induced kidney injury model and suggested that m6A may affect CdCl2 induced kidney injury via regulated the inflammation and metabolism related gene.
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Affiliation(s)
- Shuangjing Li
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - Han Zhou
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - Yufen Liang
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - Qian Yang
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - Jiachen Zhang
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - Weitong Shen
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - Lijian Lei
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China.
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Zhou H, Mu L, Yang Z, Shi Y. Identification of a novel immune landscape signature as effective diagnostic markers related to immune cell infiltration in diabetic nephropathy. Front Immunol 2023; 14:1113212. [PMID: 36969169 PMCID: PMC10030848 DOI: 10.3389/fimmu.2023.1113212] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 02/22/2023] [Indexed: 03/10/2023] Open
Abstract
BackgroundThe study aimed to identify core biomarkers related to diagnosis and immune microenvironment regulation and explore the immune molecular mechanism of diabetic nephropathy (DN) through bioinformatics analysis.MethodsGSE30529, GSE99325, and GSE104954 were merged with removing batch effects, and different expression genes (DEGs) were screened at a criterion |log2FC| >0.5 and adjusted P <0.05. KEGG, GO, and GSEA analyses were performed. Hub genes were screened by conducting PPI networks and calculating node genes using five algorithms with CytoHubba, followed by LASSO and ROC analysis to accurately identify diagnostic biomarkers. In addition, two different GEO datasets, GSE175759 and GSE47184, and an experiment cohort with 30 controls and 40 DN patients detected by IHC, were used to validate the biomarkers. Moreover, ssGSEA was performed to analyze the immune microenvironment in DN. Wilcoxon test and LASSO regression were used to determine the core immune signatures. The correlation between biomarkers and crucial immune signatures was calculated by Spearman analysis. Finally, cMap was used to explore potential drugs treating renal tubule injury in DN patients.ResultsA total of 509 DEGs, including 338 upregulated and 171 downregulated genes, were screened out. “chemokine signaling pathway” and “cell adhesion molecules” were enriched in both GSEA and KEGG analysis. CCR2, CX3CR1, and SELP, especially for the combination model of the three genes, were identified as core biomarkers with high diagnostic capabilities with striking AUC, sensitivity, and specificity in both merged and validated datasets and IHC validation. Immune infiltration analysis showed a notable infiltration advantage for APC co-stimulation, CD8+ T cells, checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation in the DN group. In addition, the correlation analysis showed that CCR2, CX3CR1, and SELP were strongly and positively correlated with checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation in the DN group. Finally, dilazep was screened out as an underlying compound for DN analyzed by CMap.ConclusionsCCR2, CX3CR1, and SELP are underlying diagnostic biomarkers for DN, especially in their combination. APC co-stimulation, CD8+ T cells, checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation may participate in the occurrence and development of DN. At last, dilazep may be a promising drug for treating DN.
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Affiliation(s)
- Huandi Zhou
- Department of Pathology, Hebei Medical University, Shijiazhuang, China
- Hebei Key Laboratory of Kidney Disease, Hebei Medical University, Shijiazhuang, Hebei, China
- Department of Radiotherapy, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Lin Mu
- Department of Pathology, Hebei Medical University, Shijiazhuang, China
- Hebei Key Laboratory of Kidney Disease, Hebei Medical University, Shijiazhuang, Hebei, China
- Department of Nephrology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Zhifen Yang
- Department of Pathology, Hebei Medical University, Shijiazhuang, China
- Hebei Key Laboratory of Kidney Disease, Hebei Medical University, Shijiazhuang, Hebei, China
- Gynecology and Obstetrics, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yonghong Shi
- Department of Pathology, Hebei Medical University, Shijiazhuang, China
- Hebei Key Laboratory of Kidney Disease, Hebei Medical University, Shijiazhuang, Hebei, China
- *Correspondence: Yonghong Shi,
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Zhong M, Zhu E, Li N, Gong L, Xu H, Zhong Y, Gong K, Jiang S, Wang X, Fei L, Tang C, Lei Y, Wang Z, Zheng Z. Identification of diagnostic markers related to oxidative stress and inflammatory response in diabetic kidney disease by machine learning algorithms: Evidence from human transcriptomic data and mouse experiments. Front Endocrinol (Lausanne) 2023; 14:1134325. [PMID: 36960398 PMCID: PMC10028207 DOI: 10.3389/fendo.2023.1134325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 02/14/2023] [Indexed: 03/09/2023] Open
Abstract
INTRODUCTION Diabetic kidney disease (DKD) is a long-term complication of diabetes and causes renal microvascular disease. It is also one of the main causes of end-stage renal disease (ESRD), which has a complex pathophysiological process. Timely prevention and treatment are of great significance for delaying DKD. This study aimed to use bioinformatics analysis to find key diagnostic markers that could be possible therapeutic targets for DKD. METHODS We downloaded DKD datasets from the Gene Expression Omnibus (GEO) database. Overexpression enrichment analysis (ORA) was used to explore the underlying biological processes in DKD. Algorithms such as WGCNA, LASSO, RF, and SVM_RFE were used to screen DKD diagnostic markers. The reliability and practicability of the the diagnostic model were evaluated by the calibration curve, ROC curve, and DCA curve. GSEA analysis and correlation analysis were used to explore the biological processes and significance of candidate markers. Finally, we constructed a mouse model of DKD and diabetes mellitus (DM), and we further verified the reliability of the markers through experiments such as PCR, immunohistochemistry, renal pathological staining, and ELISA. RESULTS Biological processes, such as immune activation, T-cell activation, and cell adhesion were found to be enriched in DKD. Based on differentially expressed oxidative stress and inflammatory response-related genes (DEOIGs), we divided DKD patients into C1 and C2 subtypes. Four potential diagnostic markers for DKD, including tenascin C, peroxidasin, tissue inhibitor metalloproteinases 1, and tropomyosin (TNC, PXDN, TIMP1, and TPM1, respectively) were identified using multiple bioinformatics analyses. Further enrichment analysis found that four diagnostic markers were closely related to various immune cells and played an important role in the immune microenvironment of DKD. In addition, the results of the mouse experiment were consistent with the bioinformatics analysis, further confirming the reliability of the four markers. CONCLUSION In conclusion, we identified four reliable and potential diagnostic markers through a comprehensive and systematic bioinformatics analysis and experimental validation, which could serve as potential therapeutic targets for DKD. We performed a preliminary examination of the biological processes involved in DKD pathogenesis and provide a novel idea for DKD diagnosis and treatment.
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Affiliation(s)
- Ming Zhong
- Department of Nephrology, Center of Kidney and Urology, the Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Enyi Zhu
- Department of Nephrology, Center of Kidney and Urology, the Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Na Li
- Department of Nephrology, Center of Kidney and Urology, the Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
- Edmond H. Fischer Translational Medical Research Laboratory, Scientific Research Center, The Seventh Affiliated Hospital, Sun Yat -Sen University, Shenzhen, China
| | - Lian Gong
- Department of Oncology, the Third Xiangya Hospital, Central South University, Changsha, China
| | - Hai Xu
- Division of Endocrinology and Rheumatology, Huangpi People’s Hospital, the Third Affiliated Hospital of Jianghan University, Wuhan, China
| | - Yong Zhong
- Department of Clinical Medicine, Hubei Enshi College, Enshi, China
| | - Kai Gong
- Department of Clinical Medicine, Xiangnan University, Chenzhou, China
| | - Shan Jiang
- Department of Nephrology, Center of Kidney and Urology, the Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Xiaohua Wang
- Department of Nephrology, Center of Kidney and Urology, the Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Lingyan Fei
- Department of Nephrology, Center of Kidney and Urology, the Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Chun Tang
- Department of Nephrology, Center of Kidney and Urology, the Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Yan Lei
- Department of Nephrology, Center of Kidney and Urology, the Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Zhongli Wang
- Department of Internal Medicine and Geriatrics, Zhongnan Hospital, Wuhan University School of Medicine, Wuhan, China
- *Correspondence: Zhongli Wang, ; Zhihua Zheng,
| | - Zhihua Zheng
- Department of Nephrology, Center of Kidney and Urology, the Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
- *Correspondence: Zhongli Wang, ; Zhihua Zheng,
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Yuan Y, Xiong X, Li L, Luo P. Novel targets in renal fibrosis based on bioinformatic analysis. Front Genet 2022; 13:1046854. [PMID: 36523757 PMCID: PMC9745177 DOI: 10.3389/fgene.2022.1046854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 10/25/2022] [Indexed: 08/14/2024] Open
Abstract
Background: Renal fibrosis is a widely used pathological indicator of progressive chronic kidney disease (CKD), and renal fibrosis mediates most progressive renal diseases as a final pathway. Nevertheless, the key genes related to the host response are still unclear. In this study, the potential gene network, signaling pathways, and key genes under unilateral ureteral obstruction (UUO) model in mouse kidneys were investigated by integrating two transcriptional data profiles. Methods: The mice were exposed to UUO surgery in two independent experiments. After 7 days, two datasets were sequenced from mice kidney tissues, respectively, and the transcriptome data were analyzed to identify the differentially expressed genes (DEGs). Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were executed. A Protein-Protein Interaction (PPI) network was constructed based on an online database STRING. Additionally, hub genes were identified and shown, and their expression levels were investigated in a public dataset and confirmed by quantitative real time-PCR (qRT-PCR) in vivo. Results: A total of 537 DEGs were shared by the two datasets. GO and the KEGG analysis showed that DEGs were typically enriched in seven pathways. Specifically, five hub genes (Bmp1, CD74, Fcer1g, Icam1, H2-Eb1) were identified by performing the 12 scoring methods in cytoHubba, and the receiver operating characteristic (ROC) curve indicated that the hub genes could be served as biomarkers. Conclusion: A gene network reflecting the transcriptome signature in CKD was established. The five hub genes identified in this study are potentially useful for the treatment and/or diagnosis CKD as biomarkers.
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Affiliation(s)
- Yuan Yuan
- Department of Urology, Wuhan Third Hospital and Tongren Hospital of Wuhan University, Wuhan, China
| | - Xi Xiong
- Department of Urology, Wuhan Third Hospital School of Medicine, Wuhan University of Science and Technology, Wuhan, China
| | - Lili Li
- Central Laboratory, Renmin Hospital of Wuhan University, Wuhan, China
| | - Pengcheng Luo
- Department of Urology, Wuhan Third Hospital and Tongren Hospital of Wuhan University, Wuhan, China
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Podgórska D, Cieśla M, Kolarz B. FCER1G Gene Hypomethylation in Patients with Rheumatoid Arthritis. J Clin Med 2022; 11:4664. [PMID: 36012903 PMCID: PMC9410058 DOI: 10.3390/jcm11164664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/29/2022] [Accepted: 08/05/2022] [Indexed: 11/21/2022] Open
Abstract
Rheumatoid arthritis (RA) is a chronic autoimmune disease that, when improperly treated, leads to disability in patients. Various factors that may cause the development and activity of RA are being considered. Epigenetic factors are also receiving increasing attention. In our study, we analyzed the association between FCER1G gene methylation and RA activity. We conducted our study in 50 RA patients and 24 controls. The patients were divided into two groups in terms of high disease activity and remission. Quantitative real-time methylation-specific PCR was used to analyze the methylation status of the investigated genes. We observed that RA patients have lower levels of methylation of the FCER1G gene compared to controls, but we did not find any difference in the methylation status of this gene between patients with high disease activity and remission. The results of this study suggest that FCER1G gene methylation may be a new potential epigenetic marker of RA that is independent of disease activity.
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Affiliation(s)
- Dominika Podgórska
- Department of Internal Diseases, Institute of Medical Sciences, College of Medical Sciences, University of Rzeszow, 35-959 Rzeszow, Poland
| | - Marek Cieśla
- College of Medical Sciences, University of Rzeszow, 35-959 Rzeszow, Poland
| | - Bogdan Kolarz
- Department of Internal Diseases, Institute of Medical Sciences, College of Medical Sciences, University of Rzeszow, 35-959 Rzeszow, Poland
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Xia M, Liu D, Liu H, Peng L, Yang D, Tang C, Chen G, Liu Y, Liu H. Identification of Hub Genes and Therapeutic Agents for IgA Nephropathy Through Bioinformatics Analysis and Experimental Validation. Front Med (Lausanne) 2022; 9:881322. [PMID: 35836957 PMCID: PMC9273898 DOI: 10.3389/fmed.2022.881322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 05/09/2022] [Indexed: 12/22/2022] Open
Abstract
Background IgA nephropathy (IgAN) is the most common primary glomerular disease and the leading cause of the end-stage renal disease in the world. The pathogenesis of IgAN has not been well elucidated, and yet treatment is limited. High-throughput microarray has been applied for elucidating molecular biomarkers and potential mechanisms involved in IgAN. This study aimed to identify the potential key genes and therapeutics associated with IgAN using integrative bioinformatics and transcriptome-based computational drug repurposing approach. Methods Three datasets of mRNA expression profile were obtained from the gene expression omnibus database and differentially expressed genes (DEGs) between IgAN glomeruli and normal tissue were identified by integrated analysis. Gene ontology and pathway enrichment analyses of the DEGs were performed by R software, and protein-protein interaction networks were constructed using the STRING online search tool. External dataset and immunohistochemical assessment of kidney biopsy specimens were used for hub gene validation. Potential compounds for IgAN therapy were obtained by Connectivity Map (CMap) analysis and preliminarily verified in vitro. Stimulated human mesangial cells were collected for cell proliferation and cell cycle analysis using cell counting kit 8 and flow cytometry, respectively. Results 134 DEGs genes were differentially expressed across kidney transcriptomic data from IgAN patients and healthy living donors. Enrichment analysis showed that the glomerular compartments underwent a wide range of interesting pathological changes during kidney injury, focused on anion transmembrane transporter activity and protein digestion and absorption mostly. Hub genes (ITGB2, FCER1G, CSF1R) were identified and verified to be significantly upregulated in IgAN patients, and associated with severity of renal lesions. Computational drug repurposing with the CMap identified tetrandrine as a candidate treatment to reverse IgAN hub gene expression. Tetrandrine administration significantly reversed mesangial cell proliferation and cell cycle transition. Conclusion The identification of DEGs and related therapeutic strategies of IgAN through this integrated bioinformatics analysis provides a valuable resource of therapeutic targets and agents of IgAN. Especially, our findings suggest that tetrandrine might be beneficial for IgAN, which deserves future research.
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Integrated Bioinformatics and Clinical Correlation Analysis of Key Genes, Pathways, and Potential Therapeutic Agents Related to Diabetic Nephropathy. DISEASE MARKERS 2022; 2022:9204201. [PMID: 35637650 PMCID: PMC9148260 DOI: 10.1155/2022/9204201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 05/03/2022] [Indexed: 11/25/2022]
Abstract
Background Diabetic nephropathy (DN) is a common microvascular complication of diabetes and a major cause of end-stage renal disease, resulting in a substantial socioeconomic burden around the world. Some unknown biomarkers, mechanisms, and potential novel agents regarding DN are yet to be identified. Methods GSE30528 and GSE1009 were downloaded as training datasets to identify differentially expressed genes (DEGs) of DN. Common DEGs were selected for further analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs were performed to explore molecular mechanisms and pathways. Protein-protein interaction (PPI) network of DEGs was used to identify the top 10 hub genes of DN. Expression profiles of the hub genes were validated in GSE96804 and GSE47183 datasets. The clinical correlation analyses were conducted to confirm the association between key genes and clinical characteristics in the Nephroseq v5 database. The Drug Gene Interaction Database was used to predict potential targeted drugs. Results 345 and 1228 DEGs were identified in GSE30528 and GSE1009, respectively; and 120 common DEGs were found. The biological process of DEGs was significantly enriched in kidney development. PI3K-Akt signaling pathway, focal adhesion, complement and coagulation cascades were significantly enriched KEGG pathways. The identified top10 hub genes were VEGFA, NPHS1, WT1, TJP1, CTGF, FYN, SYNPO, PODXL, TNNT2, and BMP2. VEGFA, NPHS1, WT1, CTGF, SYNPO, PODXL, and TNNT2 were significantly downregulated in DN. VEGFA, NPHS1, WT1, CTGF, SYNPO, and PODXL were positively correlated with glomerular filtration rate. The targeted drugs or molecular compounds were enalapril, sildenafil, and fenofibrate target for VEGFA; losartan target for NPHS1; halofuginone, deferoxamine, curcumin, and sirolimus target for WT1; and purpurogallin target for TNNT2. Conclusions VEGFA, NPHS1, WT1, CTGF, SYNPO, and PODXL are promising biomarkers for diagnosing and evaluating the progression of DN. The drug-gene interaction analyses provide a list of candidate drugs for the precise treatment of DN.
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Integrated Analysis of Multiple Microarray Studies to Identify Core Gene-Expression Signatures Involved in Tubulointerstitial Injury in Diabetic Nephropathy. BIOMED RESEARCH INTERNATIONAL 2022; 2022:9554658. [PMID: 35592524 PMCID: PMC9113875 DOI: 10.1155/2022/9554658] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 04/11/2022] [Accepted: 04/23/2022] [Indexed: 11/18/2022]
Abstract
Diabetic nephropathy is a leading cause of end-stage renal disease in both developed and developing countries. It is lack of specific diagnosis, and the pathogenesis remains unclarified in diabetic nephropathy, following the unsatisfactory effects of existing treatments. Therefore, it is very meaningful to find biomarkers with high specificity and potential targets. Two datasets, GSE30529 and GSE47184 from GEO based on diabetic nephropathy tubular samples, were downloaded and merged after batch effect removal. A total of 545 different expression genes screened with
were weighted gene coexpression correlation network analysis, and green module and blue module were identified. The results of KEGG analyses both in green module and GSEA analysis showed the same two enriched pathway, focal adhesion and viral myocarditis. Based on the intersection among WGCNA focal adhesion/Viral myocarditis, GSEA focal adhesion/viral myocarditis, and PPI network, 17 core genes, ACTN1, CAV1, PRKCB, PDGFRA, COL1A2, COL6A3, RHOA, VWF, FN1, HLA-F, HLA-DPB1, ITGB2, HLA-DRA, HLA-DMA, HLA-DPA1, HLA-B, and HLA-DMB, were identified as potential biomarkers in diabetic tubulointerstitial injury and were further validated externally for expression at GSE99325 and GSE104954 and clinical feature at nephroseq V5 online platform. CMap analysis suggested that two compounds, LY-294002 and bufexamac, may be new insights for therapeutics of diabetic tubulointerstitial injury. Conclusively, it was raised that a series of core genes may be as potential biomarkers for diagnosis and two prospective compounds.
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Xu H, Zhu Q, Tang L, Jiang J, Yuan H, Zhang A, Lou M. Prognostic and predictive value of FCER1G in glioma outcomes and response to immunotherapy. Cancer Cell Int 2021; 21:103. [PMID: 33579299 PMCID: PMC7881595 DOI: 10.1186/s12935-021-01804-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Accepted: 02/03/2021] [Indexed: 12/17/2022] Open
Abstract
PURPOSE Glioma is the most prevalent malignant form of brain tumors, with a dismal prognosis. Currently, cancer immunotherapy has emerged as a revolutionary treatment for patients with advanced highly aggressive therapy-resistant tumors. However, there is no effective biomarker to reflect the response to immunotherapy in glioma patient so far. So we aim to assess the clinical predictive value of FCER1G in patients with glioma. METHODS The expression level and correlation between clinical prognosis and FER1G levels were analyzed with the data from CGGA, TCGA, and GEO database. Univariate and multivariate cox regression model was built to predict the prognosis of glioma patients with multiple factors. Then the correlation between FCER1G with immune cell infiltration and activation was analyzed. At last, we predict the immunotherapeutic response in both high and low FCER1G expression subgroups. RESULTS FCER1G was significantly higher in glioma with greater malignancy and predicted poor prognosis. In multivariate analysis, the hazard ratio of FCER1G expression (Low versus High) was 0.66 and 95 % CI is 0.54 to 0.79 (P < 0.001), whereas age (HR = 1.26, 95 % CI 1.04-1.52), grade (HR = 2.75, 95 % CI 2.06-3.68), tumor recurrence (HR = 2.17, 95 % CI 1.81-2.62), IDH mutant (HR = 2.46, 95 % CI 1.97-3.01) and chemotherapeutic status (HR = 1.4, 95 % CI 1.20-1.80) are also included. Furthermore, we illustrated that gene FCER1G stratified glioma cases into high and low FCER1G expression subgroups that demonstrated with distinct clinical outcomes and T cell activation. At last, we demonstrated that high FCER1G levels presented great immunotherapeutic response in glioma patients. CONCLUSIONS This study demonstrated FCER1G as a novel predictor for clinical diagnosis, prognosis, and response to immunotherapy in glioma patient. Assess expression of FCER1G is a promising method to discover patients that may benefit from immunotherapy.
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Affiliation(s)
- Houshi Xu
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China.,Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang, 310029, China
| | - Qingwei Zhu
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China
| | - Lan Tang
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China
| | | | | | - Anke Zhang
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang, 310029, China.
| | - Meiqing Lou
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China.
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Xu B, Wang L, Zhan H, Zhao L, Wang Y, Shen M, Xu K, Li L, Luo X, Zhou S, Tang A, Liu G, Song L, Li Y. Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis. J Diabetes Res 2021; 2021:5546199. [PMID: 34124269 PMCID: PMC8169258 DOI: 10.1155/2021/5546199] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 05/02/2021] [Accepted: 05/05/2021] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVES Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients. METHODS GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN. RESULTS There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN. CONCLUSIONS We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.
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Affiliation(s)
- Bojun Xu
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072 Sichuan, China
| | - Lei Wang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education and Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Huakui Zhan
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072 Sichuan, China
| | - Liangbin Zhao
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072 Sichuan, China
| | - Yuehan Wang
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072 Sichuan, China
| | - Meng Shen
- Chengdu Seventh People's Hospital, Chengdu, 610213 Sichuan, China
| | - Keyang Xu
- Centre for Cancer and Inflammation Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong
| | - Li Li
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072 Sichuan, China
| | - Xu Luo
- The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning 530023, China
| | - Shasha Zhou
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072 Sichuan, China
| | - Anqi Tang
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072 Sichuan, China
| | - Gang Liu
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072 Sichuan, China
| | - Lu Song
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072 Sichuan, China
| | - Yan Li
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072 Sichuan, China
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