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Xia B, Zeng P, Xue Y, Li Q, Xie J, Xu J, Wu W, Yang X. Identification of potential shared gene signatures between gastric cancer and type 2 diabetes: a data-driven analysis. Front Med (Lausanne) 2024; 11:1382004. [PMID: 38903804 PMCID: PMC11187270 DOI: 10.3389/fmed.2024.1382004] [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: 02/04/2024] [Accepted: 05/22/2024] [Indexed: 06/22/2024] Open
Abstract
Background Gastric cancer (GC) and type 2 diabetes (T2D) contribute to each other, but the interaction mechanisms remain undiscovered. The goal of this research was to explore shared genes as well as crosstalk mechanisms between GC and T2D. Methods The Gene Expression Omnibus (GEO) database served as the source of the GC and T2D datasets. The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were utilized to identify representative genes. In addition, overlapping genes between the representative genes of the two diseases were used for functional enrichment analysis and protein-protein interaction (PPI) network. Next, hub genes were filtered through two machine learning algorithms. Finally, external validation was undertaken with data from the Cancer Genome Atlas (TCGA) database. Results A total of 292 and 541 DEGs were obtained from the GC (GSE29272) and T2D (GSE164416) datasets, respectively. In addition, 2,704 and 336 module genes were identified in GC and T2D. Following their intersection, 104 crosstalk genes were identified. Enrichment analysis indicated that "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were mutual pathways. Through the PPI network, 10 genes were identified as candidate hub genes. Machine learning further selected BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 as hub genes. Conclusion "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were revealed as possible crosstalk mechanisms. BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 were identified as shared genes and potential therapeutic targets for people suffering from GC and T2D.
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Affiliation(s)
- Bingqing Xia
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ping Zeng
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yuling Xue
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Qian Li
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou, China
| | - Jianhui Xie
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou, China
| | - Jiamin Xu
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou, China
| | - Wenzhen Wu
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou, China
| | - Xiaobo Yang
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou, China
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Li T, Yao J. Unveiling the hub genes in the SIGLECs family in colon adenocarcinoma with machine learning. Front Genet 2024; 15:1375100. [PMID: 38650859 PMCID: PMC11033367 DOI: 10.3389/fgene.2024.1375100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 03/25/2024] [Indexed: 04/25/2024] Open
Abstract
Background Despite the recognized roles of Sialic acid-binding Ig-like lectins (SIGLECs) in endocytosis and immune regulation across cancers, their molecular intricacies in colon adenocarcinoma (COAD) are underexplored. Meanwhile, the complicated interactions between different SIGLECs are also crucial but open questions. Methods We investigate the correlation between SIGLECs and various properties, including cancer status, prognosis, clinical features, functional enrichment, immune cell abundances, immune checkpoints, pathways, etc. To fully understand the behavior of multiple SIGLECs' co-evolution and subtract its leading effect, we additionally apply three unsupervised machine learning algorithms, namely, Principal Component Analysis (PCA), Self-Organizing Maps (SOM), K-means, and two supervised learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO) and neural network (NN). Results We find significantly lower expression levels in COAD samples, together with a systematic enhancement in the correlations between distinct SIGLECs. We demonstrate SIGLEC14 significantly affects the Overall Survival (OS) according to the Hazzard ratio, while using PCA further enhances the sensitivity to both OS and Disease Free Interval (DFI). We find any single SIGLEC is uncorrelated to the cancer stages, which can be significantly improved by using PCA. We further identify SIGLEC-1,15 and CD22 as hub genes in COAD through Differentially Expressed Genes (DEGs), which is consistent with our PCA-identified key components PC-1,2,5 considering both the correlation with cancer status and immune cell abundance. As an extension, we use SOM for the visualization of the SIGLECs and show the similarities and differences between COAD patients. SOM can also help us define subsamples according to the SIGLECs status, with corresponding changes in both immune cells and cancer T-stage, for instance. Conclusion We conclude SIGLEC-1,15 and CD22 as the most promising hub genes in the SIGLECs family in treating COAD. PCA offers significant enhancement in the prognosis and clinical analyses, while using SOM further unveils the transition phases or potential subtypes of COAD.
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Affiliation(s)
- Tiantian Li
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ji Yao
- Department of Astronomy, School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Astronomical Observatory, Shanghai, China
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Lian W, Ge S, Pang Q. Platycodin D ameliorates ammonia-induced pulmonary fibrosis by repressing TGF-β1-mediated extracellular matrix remodeling. Chem Biol Drug Des 2024; 103:e14446. [PMID: 38230787 DOI: 10.1111/cbdd.14446] [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: 09/11/2023] [Revised: 12/18/2023] [Accepted: 12/27/2023] [Indexed: 01/18/2024]
Abstract
Ammonia can induce pulmonary fibrosis in humans and animals. Platycodin D (PLD) possesses various bioactive activities including anti-fibrotic properties. In this study, we aimed to explore the activity and mechanism of PLD in pulmonary fibrosis induced by ammonia. The mouse model of ammonia-induced lung fibrosis was established, and the role of PLD was assessed by H&E and Masson's trichrome staining. The differentially expressed genes (DEGs) were identified by RNA-seq and subjected to GO and KEGG pathway analyses. BEAS-2B cells were treated with NH4 Cl alone or along with PLD. Results showed that PLD attenuated ammonia-induced pulmonary inflammation and fibrosis in vivo. The extracellular matrix (ECM)-receptor interaction pathway was predicted as a prominent pathway underlying the anti-fibrotic function of PLD. In ammonia-induced mouse models and NH4 Cl-treated BEAS-2B cells, PLD could repress the activation of the TGF-β1 pathway. By incubating lung fibroblast HFL1 cells with the conditioned medium of BEAS-2B cells treated with NH4Cl alone or along with PLD, PLD was confirmed to attenuate NH4 Cl-induced ECM deposition in HFL1 cells. Our findings demonstrate that PLD exerts a protective function in ammonia-induced pulmonary fibrosis by repressing TGF-β1-mediated ECM remodeling, suggesting the potential therapeutic value of PLD in this disease.
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Affiliation(s)
- Wenqi Lian
- College of Veterinary Medicine, Shanxi Agricultural University, Taigu, Shanxi, China
| | - Shihao Ge
- College of Veterinary Medicine, Shanxi Agricultural University, Taigu, Shanxi, China
| | - Quanhai Pang
- College of Veterinary Medicine, Shanxi Agricultural University, Taigu, Shanxi, China
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Xu D, Jiang C, Xiao Y, Ding H. Identification and validation of disulfidptosis-related gene signatures and their subtype in diabetic nephropathy. Front Genet 2023; 14:1287613. [PMID: 38028597 PMCID: PMC10658004 DOI: 10.3389/fgene.2023.1287613] [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: 09/02/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Background: Diabetic nephropathy (DN) is the most common complication of diabetes, and its pathogenesis is complex involving a variety of programmed cell death, inflammatory responses, and autophagy mechanisms. Disulfidptosis is a newly discovered mechanism of cell death. There are little studies about the role of disulfidptosis on DN. Methods: First, we obtained the data required for this study from the GeneCards database, the Nephroseq v5 database, and the GEO database. Through differential analysis, we obtained differential disulfidptosis-related genes. At the same time, through WGCNA analysis, we obtained key module genes in DN patients. The obtained intersecting genes were further screened by Lasso as well as SVM-RFE. By intersecting the results of the two, we ended up with a key gene for diabetic nephropathy. The diagnostic performance and expression of key genes were verified by the GSE30528, GSE30529, GSE96804, and Nephroseq v5 datasets. Using clinical information from the Nephroseq v5 database, we investigated the correlation between the expression of key genes and estimated glomerular filtration rate (eGFR) and serum creatinine content. Next, we constructed a nomogram and analyzed the immune microenvironment of patients with DN. The identification of subtypes facilitates individualized treatment of patients with DN. Results: We obtained 91 differential disulfidptosis-related genes. Through WGCNA analysis, we obtained 39 key module genes in DN patients. Taking the intersection of the two, we preliminarily screened 20 genes characteristic of DN. Through correlation analysis, we found that these 20 genes are positively correlated with each other. Further screening by Lasso and SVM-RFE algorithms and intersecting the results of the two, we identified CXCL6, CD48, C1QB, and COL6A3 as key genes in DN. Clinical correlation analysis found that the expression levels of key genes were closely related to eGFR. Immune cell infiltration is higher in samples from patients with DN than in normal samples. Conclusion: We identified and validated 4 DN key genes from disulfidptosis-related genes that CXCL6, CD48, C1QB, and COL6A3 may be key genes that promote the onset of DN and are closely related to the eGFR and immune cell infiltrated in the kidney tissue.
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Affiliation(s)
- Danping Xu
- School of Medicine, University of Electronic Science and Technology of China, Sichuan Provincial People’s Hospital, Chengdu, China
| | - Chonghao Jiang
- Affiliated Hospital of North China University of Science and Technology, Tangshan, China
| | - Yonggui Xiao
- North China University of Science and Technology, Tangshan, China
| | - Hanlu Ding
- Renal Division and Institute of Nephrology, Sichuan Academy of Medical Science and Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
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Yan M, Li W, Wei R, Li S, Liu Y, Huang Y, Zhang Y, Lu Z, Lu Q. Identification of pyroptosis-related genes and potential drugs in diabetic nephropathy. J Transl Med 2023; 21:490. [PMID: 37480090 PMCID: PMC10360355 DOI: 10.1186/s12967-023-04350-w] [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: 04/28/2023] [Accepted: 07/11/2023] [Indexed: 07/23/2023] Open
Abstract
BACKGROUND Diabetic nephropathy (DN) is one of the serious microvascular complications of diabetes mellitus (DM). A growing body of research has demonstrated that the inflammatory state plays a critical role in the incidence and development of DN. Pyroptosis is a new way of programmed cell death, which has the particularity of natural immune inflammation. The inhibition of inflammatory cytokine expression and regulation of pathways related to pyroptosis may be a novel strategy for DN treatment. The aim of this study is to identify pyroptosis-related genes and potential drugs for DN. METHODS DN differentially expressed pyroptosis-related genes were identified via bioinformatic analysis Gene Expression Omnibus (GEO) dataset GSE96804. Dataset GSE30528 and GSE142025 were downloaded to verify pyroptosis-related differentially expressed genes (DEGs). Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a pyroptosis-related gene predictive model. A consensus clustering analysis was performed to identify pyroptosis-related DN subtypes. Subsequently, Gene Set Variation Analysis (GSVA), Gene Ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to explore the differences between DN clusters. A protein-protein interaction (PPI) network was used to select hub genes and DGIdb database was utilized to screen potential therapeutic drugs/compounds targeting hub genes. RESULTS A total of 24 differentially expressed pyroptosis-related genes were identified in DN. A 16 gene predictive model was conducted via LASSO regression analysis. According to the expression level of these 16 genes, DN cases were divided into two subtypes, and the subtypes are mainly associated with inflammation, activation of immune response and cell metabolism. In addition, we identified 10 hub genes among these subtypes, and predicted 65 potential DN therapeutics that target key genes. CONCLUSION We identified two pyroptosis-related DN clusters and 65 potential therapeutical agents/compounds for DN, which might shed a light on the treatment of DN.
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Affiliation(s)
- Meng Yan
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, China.
- Department of Clinical Pharmacology, School of Pharmacy, Xuzhou Medical University, No. 209 Tongshan Road, Xuzhou, 221004, Jiangsu, China.
| | - Wenwen Li
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, China
| | - Rui Wei
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, China
| | - Shuwen Li
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, China
| | - Yan Liu
- Jiangsu Key Laboratory of Brain Disease and Bioinformation, Research Center for Biochemistry and Molecular Biology, Xuzhou Medical University, Xuzhou, China
- Key Laboratory of Genetic Foundation and Clinical Application, Department of Genetics, Xuzhou Engineering Research Center of Medical Genetics and Transformation, Xuzhou Medical University, Xuzhou, China
| | - Yuqian Huang
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, China
| | - Yunye Zhang
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, China
| | - Zihao Lu
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, China
| | - Qian Lu
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, China.
- Department of Clinical Pharmacology, School of Pharmacy, Xuzhou Medical University, No. 209 Tongshan Road, Xuzhou, 221004, Jiangsu, China.
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Zhao T, Cheng F, Zhan D, Li J, Zheng C, Lu Y, Qin W, Liu Z. The Glomerulus Multiomics Analysis Provides Deeper Insights into Diabetic Nephropathy. J Proteome Res 2023. [PMID: 37191251 DOI: 10.1021/acs.jproteome.2c00794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Although diabetic nephropathy (DN) is the leading cause of the end-stage renal disease, the exact regulation mechanisms remain unknown. In this study, we integrated the transcriptomics and proteomics profiles of glomeruli isolated from 50 biopsy-proven DN patients and 25 controls to investigate the latest findings about DN pathogenesis. First, 1152 genes exhibited differential expression at the mRNA or protein level, and 364 showed significant association. These strong correlated genes were divided into four different functional modules. Moreover, a regulatory network of the transcription factors (TFs)-target genes (TGs) was constructed, with 30 TFs upregulated at the protein levels and 265 downstream TGs differentially expressed at the mRNA levels. These TFs are the integration centers of several signal transduction pathways and have tremendous therapeutic potential for regulating the aberrant production of TGs and the pathological process of DN. Furthermore, 29 new DN-specific splice-junction peptides were discovered with high confidence; these peptides may play novel functions in the pathological course of DN. So, our in-depth integrative transcriptomics-proteomics analysis provided deeper insights into the pathogenesis of DN and opened the potential avenue for finding new therapeutic interventions. MS raw files were deposited into the proteomeXchange with the dataset identifier PXD040617.
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Affiliation(s)
- Tingting Zhao
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu 210002, China
| | - Fang Cheng
- Department of Bioinformatics, Beijing Pineal Diagnostics Co., Ltd., Beijing 102206, China
| | - Dongdong Zhan
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Jin'e Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Chunxia Zheng
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu 210002, China
| | - Yinghui Lu
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu 210002, China
| | - Weisong Qin
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu 210002, China
| | - Zhihong Liu
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu 210002, China
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Chen Y, Li G, Wei L, Weng J, Liu S, Gu M, Liu P, Zhu Y, Xiong A, Zeng H, Yu F. Tibial plateau fracture and RNA sequencing with osteogenesis imperfecta: a case report. Front Endocrinol (Lausanne) 2023; 14:1164386. [PMID: 37229455 PMCID: PMC10203611 DOI: 10.3389/fendo.2023.1164386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 04/10/2023] [Indexed: 05/27/2023] Open
Abstract
Osteogenesis imperfecta (OI) is a hereditary skeletal dysplasia with an incidence of approximately 1:15,000 to 20,000. OI is usually caused by the mutation of COL1A1 and COL1A2, which would encode the α-chain of type I collagen. OI is clinically characterized by decreased bone mass, increased risk of bone fragility, blue sclerae, and dentinogenesis. Case presentation A 29-year-old male patient was diagnosed with right tibial plateau fracture caused by slight violence. Physical examination revealed the following: height, 140 cm; weight, 70 kg; body mass index (BMI), 35.71 kg/m2; blue sclera and barrel chest were observed. X-ray examination showed left convex deformity of the thoracic vertebrae with reduced thoracic volume. Laboratory examinations revealed a decrease in both vitamin D and blood calcium levels. Bone mineral density (BMD) was lower than the normal range. After the preoperative preparation was completed, the open reduction and internal fixation of the right tibial plateau fracture were performed. Meanwhile, whole blood samples of this OI patient and the normal control were collected for RNA transcriptome sequencing. The RNA sequence analysis revealed that there were 513 differentially expressed genes (DEGs) between this OI patient and the normal control. KEGG-enriched signaling pathways were significantly enriched in extracellular matrix (ECM)-receptor interactions. Conclusion In this case, DEGs between this OI patient and the normal control were identified by RNA transcriptome sequencing. Moreover, the possible pathogenesis of OI was also explored, which may provide new evidence for the treatment of OI.
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Affiliation(s)
- Yixiao Chen
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, China
- National and Local Joint Engineering Research Center of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, China
| | - Guoqing Li
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, China
- National and Local Joint Engineering Research Center of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, China
| | - Liangchen Wei
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, China
- National and Local Joint Engineering Research Center of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, China
| | - Jian Weng
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, China
- National and Local Joint Engineering Research Center of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, China
| | - Su Liu
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, China
- National and Local Joint Engineering Research Center of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, China
| | - Mingxi Gu
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, China
| | - Pei Liu
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, China
- National and Local Joint Engineering Research Center of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, China
| | - Yuanchao Zhu
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, China
- National and Local Joint Engineering Research Center of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, China
| | - Ao Xiong
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, China
- National and Local Joint Engineering Research Center of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, China
| | - Hui Zeng
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, China
- National and Local Joint Engineering Research Center of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, China
| | - Fei Yu
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, China
- National and Local Joint Engineering Research Center of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, China
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Zhang X, Chao P, Zhang L, Xu L, Cui X, Wang S, Wusiman M, Jiang H, Lu C. Single-cell RNA and transcriptome sequencing profiles identify immune-associated key genes in the development of diabetic kidney disease. Front Immunol 2023; 14:1030198. [PMID: 37063851 PMCID: PMC10091903 DOI: 10.3389/fimmu.2023.1030198] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 02/16/2023] [Indexed: 03/31/2023] Open
Abstract
BackgroundThere is a growing public concern about diabetic kidney disease (DKD), which poses a severe threat to human health and life. It is important to discover noninvasive and sensitive immune-associated biomarkers that can be used to predict DKD development. ScRNA-seq and transcriptome sequencing were performed here to identify cell types and key genes associated with DKD.MethodsHere, this study conducted the analysis through five microarray datasets of DKD (GSE131882, GSE1009, GSE30528, GSE96804, and GSE104948) from gene expression omnibus (GEO). We performed single-cell RNA sequencing analysis (GSE131882) by using CellMarker and CellPhoneDB on public datasets to identify the specific cell types and cell-cell interaction networks related to DKD. DEGs were identified from four datasets (GSE1009, GSE30528, GSE96804, and GSE104948). The regulatory relationship between DKD-related characters and genes was evaluated by using WGCNA analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) datasets were applied to define the enrichment of each term. Subsequently, immune cell infiltration between DKD and the control group was identified by using the “pheatmap” package, and the connection Matrix between the core genes and immune cell or function was illuminated through the “corrplot” package. Furthermore, RcisTarget and GSEA were conducted on public datasets for the analysis of the regulation relationship of key genes and it revealed the correlation between 3 key genes and top the 20 genetic factors involved in DKD. Finally, the expression of key genes between patients with 35 DKD and 35 healthy controls were examined by ELISA, and the relationship between the development of DKD rate and hub gene plasma levels was assessed in a cohort of 35 DKD patients. In addition, we carried out immunohistochemistry and western blot to verify the expression of three key genes in the kidney tissue samples we obtained.ResultsThere were 8 cell types between DKD and the control group, and the number of connections between macrophages and other cells was higher than that of the other seven cell groups. We identified 356 different expression genes (DEGs) from the RNA-seq, which are enriched in urogenital system development, kidney development, platelet alpha granule, and glycosaminoglycan binding pathways. And WGCNA was conducted to construct 13 gene modules. The highest correlations module is related to the regulation of cell adhesion, positive regulation of locomotion, PI3K-Akt, gamma response, epithelial-mesenchymal transition, and E2F target signaling pathway. Then we overlapped the DEGs, WGCNA, and scRNA-seq, SLIT3, PDE1A and CFH were screened as the closely related genes to DKD. In addition, the findings of immunological infiltration revealed a remarkable positive link between T cells gamma delta, Macrophages M2, resting mast cells, and the three critical genes SLIT3, PDE1A, and CFH. Neutrophils were considerably negatively connected with the three key genes. Comparatively to healthy controls, DKD patients showed high levels of SLIT3, PDE1A, and CFH. Despite this, higher SLIT3, PDE1A, and CFH were associated with an end point rate based on a median follow-up of 2.6 years. And with the gradual deterioration of DKD, the expression of SLIT3, PDE1A, and CFH gradually increased.ConclusionsThe 3 immune-associated genes could be used as diagnostic markers and therapeutic targets of DKD. Additionally, we found new pathogenic mechanisms associated with immune cells in DKD, which might lead to therapeutic targets against these cells.
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Affiliation(s)
- Xueqin Zhang
- Department of Nephropathy, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang Uygur Autonomous Region, China
| | - Peng Chao
- Department of Cardiology, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang Uygur Autonomous Region, China
| | - Lei Zhang
- Department of Endocrine, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang Uygur Autonomous Region, China
| | - Lin Xu
- Department of Rheumatology Immunology, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang Uygur Autonomous Region, China
| | - Xinyue Cui
- Department of Nephropathy, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang Uygur Autonomous Region, China
| | - Shanshan Wang
- Department of Nephropathy, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang Uygur Autonomous Region, China
| | - Miiriban Wusiman
- Department of Nephropathy, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang Uygur Autonomous Region, China
| | - Hong Jiang
- Department of Nephropathy, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang Uygur Autonomous Region, China
- Nephrology Clinical Research Center, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang Uygur Autonomous Region, China
- *Correspondence: Chen Lu, ; Hong Jiang,
| | - Chen Lu
- Nephrology Clinical Research Center, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumuqi, Xinjiang Uygur Autonomous Region, China
- Department of Nephropathy, The First Affiliated Hospital of Xinjiang Medical University, Urumuqi, Xinjiang Uygur Autonomous Region, China
- *Correspondence: Chen Lu, ; Hong Jiang,
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Li B, Zhao X, Xie W, Hong Z, Zhang Y. Integrative analyses of biomarkers and pathways for diabetic nephropathy. Front Genet 2023; 14:1128136. [PMID: 37113991 PMCID: PMC10127684 DOI: 10.3389/fgene.2023.1128136] [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: 12/20/2022] [Accepted: 03/29/2023] [Indexed: 04/29/2023] Open
Abstract
Background: Diabetic nephropathy (DN) is a widespread diabetic complication and a major cause of terminal kidney disease. There is no doubt that DN is a chronic disease that imposes substantial health and economic burdens on the world's populations. By now, several important and exciting advances have been made in research on etiopathogenesis. Therefore, the genetic mechanisms underlying these effects remain unknown. Methods: The GSE30122, GSE30528, and GSE30529 microarray datasets were downloaded from the Gene Expression Omnibus database (GEO). Analyses of differentially expressed genes (DEGs), enrichment of gene ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed. Protein-protein interaction (PPI) network construction was completed by the STRING database. Hub genes were identified by Cytoscape software, and common hub genes were identified by taking intersection sets. The diagnostic value of common hub genes was then predicted in the GSE30529 and GSE30528 datasets. Further analysis was carried out on the modules to identify transcription factors and miRNA networks. As well, a comparative toxicogenomics database was used to assess interactions between potential key genes and diseases associated upstream of DN. Results: Samples from 19 DNs and 50 normal controls were identified in the GSE30122 dataset. 86 upregulated genes and 34 downregulated genes (a total of 120 DEGs). GO analysis showed significant enrichment in humoral immune response, protein activation cascade, complement activation, extracellular matrix, glycosaminoglycan binding, and antigen binding. KEGG analysis showed significant enrichment in complement and coagulation cascades, phagosomes, the Rap1 signaling pathway, the PI3K-Akt signaling pathway, and infection. GSEA was mainly enriched in the TYROBP causal network, the inflammatory response pathway, chemokine receptor binding, the interferon signaling pathway, ECM receptor interaction, and the integrin 1 pathway. Meanwhile, mRNA-miRNA and mRNA-TF networks were constructed for common hub genes. Nine pivotal genes were identified by taking the intersection. After validating the expression differences and diagnostic values of the GSE30528 and GSE30529 datasets, eight pivotal genes (TYROBP, ITGB2, CD53, IL10RA, LAPTM5, CD48, C1QA, and IRF8) were finally identified as having diagnostic values. Conclusion: Pathway enrichment analysis scores provide insight into the genetic phenotype and may propose molecular mechanisms of DN. The target genes TYROBP, ITGB2, CD53, IL10RA, LAPTM5, CD48, C1QA, and IRF8 are promising new targets for DN. SPI1, HIF1A, STAT1, KLF5, RUNX1, MBD1, SP1, and WT1 may be involved in the regulatory mechanisms of DN development. Our study may provide a potential biomarker or therapeutic locus for the study of DN.
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Affiliation(s)
- Bo Li
- Department of Endocrinology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China
| | - Xu Zhao
- Emergency and Critical Care Center, Renmin Hospital, Hubei University of Medicine, Shiyan, China
| | - Wanrun Xie
- Department of Endocrinology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China
| | - Zhenzhen Hong
- Department of Endocrinology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China
| | - Yi Zhang
- Department of Endocrinology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China
- *Correspondence: Yi Zhang,
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