scholarly journals Identification of Biomarkers Related To The Diagnosis And Prognosis of Thyroid Cancer Through Bioinformatics Analysis

2021 ◽  
Author(s):  
Xiao-Li Xie ◽  
Hua-Li Yin ◽  
Yu-Lin Pan ◽  
Guo-Xia Li ◽  
Chun-Yan Yuan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Bioinformatics Analysis ◽  
Prognostic Biomarkers ◽  
Cancer Tissue ◽  
Data Sets ◽  
Hub Genes ◽  
Normal Thyroid ◽  
Key Genes ◽  
Cancer Tissues

Abstract Background: Thyroid cancer is the most common malignant tumor of the head and neck. In recent years, the incidence of thyroid cancer (THCA) worldwide has rapidly increased and shows a trend in the younger generation. This study attempted to screen key genes and potential prognostic biomarkers for thyroid cancer using bioinformatics analysis.Methods: This study attempted to screen key genes and potential prognostic biomarkers for thyroid cancer using bioinformatics analysis. 101 cases of thyroid cancer and 78 cases of normal thyroid tissue were collected from three Gene Expression Omnibus (GEO) databases, then we identified the differentially expressed genes (DEGs) and conducted downstream analyses. Moreover, we screened hub genes by constructing a protein‐protein interaction (PPI) network. Finally, we assessed the expression level of hub genes in thyroid cancer tissue and its normal tissue using GEPIA and qRT-PCR respectively. Results: 159 upregulated and 251 downregulated genes were determined after gene integration of these three GEO data sets. Through PPI analysis, we consider the top 20 DEGs with high connectivity as the hub genes of THCA. After that, this study verified 20 central genes through the GEPIA database and found that only four hub genes (TOP2A, FN1, TIMP1, and MMP9) had significantly higher expression levels in thyroid cancer tissues than in normal thyroid tissues. We further analyzed the correlation between these four hub genes and the prognosis of patients with thyroid cancer, which suggests that FN1, MMP9, TIMP1 help assess the prognosis of patients with thyroid cancer. We performed GSEA analysis on these 4 hub genes simultaneously, found that the high expression of these 4 hub genes enriched the "cell cycle." Subsequently, we collected thyroid cancer tissue specimens, verified these four hub gene expression levels by RT-PCR, and found that only FN1 and TIMP1 genes in thyroid cancer tissues had significantly higher mRNA levels than normal tissues. Conclusions: Our research has identified 20 hub genes that may be related to the occurrence and development of thyroid cancer through multiple gene expression profile data sets and a series of comprehensive bioinformatics analyses. Further database and tissue validation analysis revealed that only 2 hub genes may be considered as potential prognostic biomarkers, including FN1 and TIMP1. In addition, these two hub genes are involved in the cell cycle, suggesting that they may play a role in the occurrence and development of thyroid cancer.

scholarly journals Integrated Bioinformatics Analysis of Gene Expression Profiles for Potential Biomarker Identification Towards Early Therapeutic Intervention in Pancreatitis and Pancreatic Ductal Adenocarcinoma

2021 ◽  
Author(s):  
Manoj M Wagle ◽  
Ananya Rao Kedige ◽  
Shama P Kabekkodu ◽  
Sandeep Mallya
Keyword(s):  
Gene Expression ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Bioinformatics Analysis ◽  
Ductal Adenocarcinoma ◽  
Rapid Progression ◽  
Hub Genes ◽  
Altered Expression ◽  
Biochemical Pathways ◽  
Potential Biomarker ◽  
Key Genes

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a malignancy associated with rapid progression and an abysmal prognosis. It has been reported that chronic pancreatitis can increase the risk of developing PDAC by 16-fold. Our study aims to identify the key genes and biochemical pathways mediating pancreatitis and PDAC. The gene expression datasets were retrieved from the EMBL-EBI ArrayExpress and NCBI GEO database. A total of 172 samples of normal pancreatic tissue, 68 samples of pancreatitis, and 306 samples of PDAC were used in this study. The differentially expressed genes (DEGs) identified were used to perform downstream analysis for ontology, interaction, and associated pathways. Furthermore, hub gene expression was validated using the GEPIA2 tool and survival analysis using the Kaplan-Meier (KM) plotter. The potential druggability of the hub genes identified was determined using the Drug-Gene Interaction Database (DGIdb). Our study identified a total of 45 genes found to have altered expression levels in both PDAC and pancreatitis. Over-representation analysis revealed that protein digestion and absorption pathway, ECM-receptor interaction pathway, PI3k-Akt signaling pathway, and proteoglycans in cancer pathways as significantly enriched. Module analysis revealed 15 hub genes with 92 edges, of which 14 were found to be in the druggable genome category. Through bioinformatics analysis, we identified key genes and biochemical pathways disrupted in pancreatitis and PDAC. The results can provide new insights into targeted therapy and intervening therapeutically at an earlier stage can be used as an effective strategy to decrease the incidence and severity of PDAC.

scholarly journals Identification of Hub Genes for Early Diagnosis and Predicting Prognosis in Colon Adenocarcinoma: A Bioinformatics Analysis

2021 ◽  
Author(s):  
Shuo Xu ◽  
Dingsheng Liu ◽  
Mingming Cui ◽  
Yao Zhang ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Early Diagnosis ◽  
Bioinformatics Analysis ◽  
Nuclear Division ◽  
Colon Adenocarcinoma ◽  
Functional Enrichment ◽  
Data Sets ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background: Colon adenocarcinoma (COAD) is among the most common digestive system malignancies worldwide. Although some molecular analyses of colon cancer were previously performed, the pathogenesis and gene signatures remain unclear. This study explored the genetic characteristics and molecular mechanisms underlying colon cancer development. Methods: Three gene expression data sets were obtained from the Gene Expression Omnibus (GEO) database. GEO2R was used to determine differentially expressed genes (DEGs) between COAD and normal tissues. Then, the intersection of the data sets was obtained. Metascape was used to perform the functional enrichment analyses. Next, STRING was used to build protein-protein interaction (PPI) networks. Hub genes were identified and analysed using Cytoscape. Next, survival analysis of the hub genes was performed. To explore the early diagnostic value of the hub genes, UALCAN was used to analyse expression characteristics. Finally, alterations in the hub genes were predicted and analysed by cBioPortal. Results: Altogether 436 DEGs were detected. The DEGs were mainly enriched in cell cycle phase transition, nuclear division, meiotic nuclear division, cytokinesis. Based on PPI networks, 20 hub genes were selected. Among them, 6 hub genes (CCNB1, CCNA2, AURKA, NCAPG, DLGAP5, and CENPE) showed significant prognostic value in colon cancer (p<0.05), while 5 hub genes (CDK1, CCNB1, CCNA2, MAD2L1, and DLGAP5) were associated with early colon cancer diagnosis. Conclusions: In conclusion, integrated bioinformatics analysis was used to identify hub genes that reveal the potential mechanism of carcinogenesis and progression of colon cancer. The hub genes might be novel biomarkers for early diagnosis, treatment, and prognosis of colon cancer.

scholarly journals Identifying MMP14 and COL12A1 as a potential combination of prognostic biomarkers in pancreatic ductal adenocarcinoma using integrated bioinformatics analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10419
Author(s):  
Jingyi Ding ◽  
Yanxi Liu ◽  
Yu Lai
Keyword(s):  
Gene Expression ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Prognostic Biomarkers ◽  
Ductal Adenocarcinoma ◽  
Receptor Interaction ◽  
Analysis Tool ◽  
Ppi Network ◽  
Hub Genes

Background Pancreatic ductal adenocarcinoma (PDAC) is a fatal malignant neoplasm. It is necessary to improve the understanding of the underlying molecular mechanisms and identify the key genes and signaling pathways involved in PDAC. Methods The microarray datasets GSE28735, GSE62165, and GSE91035 were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified by integrated bioinformatics analysis, including protein–protein interaction (PPI) network, Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The PPI network was established using the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. GO functional annotation and KEGG pathway analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. Hub genes were validated via the Gene Expression Profiling Interactive Analysis tool (GEPIA) and the Human Protein Atlas (HPA) website. Results A total of 263 DEGs (167 upregulated and 96 downregulated) were common to the three datasets. We used STRING and Cytoscape software to establish the PPI network and then identified key modules. From the PPI network, 225 nodes and 803 edges were selected. The most significant module, which comprised 11 DEGs, was identified using the Molecular Complex Detection plugin. The top 20 hub genes, which were filtered by the CytoHubba plugin, comprised FN1, COL1A1, COL3A1, BGN, POSTN, FBN1, COL5A2, COL12A1, THBS2, COL6A3, VCAN, CDH11, MMP14, LTBP1, IGFBP5, ALB, CXCL12, FAP, MATN3, and COL8A1. These genes were validated using The Cancer Genome Atlas (TCGA) and Genotype–Tissue Expression (GTEx) databases, and the encoded proteins were subsequently validated using the HPA website. The GO analysis results showed that the most significantly enriched biological process, cellular component, and molecular function terms among the 20 hub genes were cell adhesion, proteinaceous extracellular matrix, and calcium ion binding, respectively. The KEGG pathway analysis showed that the 20 hub genes were mainly enriched in ECM–receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and protein digestion and absorption. These findings indicated that FBN1 and COL8A1 appear to be involved in the progression of PDAC. Moreover, patient survival analysis performed via the GEPIA using TCGA and GTEx databases demonstrated that the expression levels of COL12A1 and MMP14 were correlated with a poor prognosis in PDAC patients (p < 0.05). Conclusions The results demonstrated that upregulation of MMP14 and COL12A1 is associated with poor overall survival, and these might be a combination of prognostic biomarkers in PDAC.

scholarly journals POS0851 IDENTIFICATION OF HUB GENES AND PATHWAYS IN DERMATOMYOSITIS BY BIOINFORMATICS ANALYSIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 680.1-680
Author(s):  
C. Zheng ◽  
S. X. Zhang ◽  
R. Zhao ◽  
L. Cheng ◽  
T. Kong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathway ◽  
Muscle Tissue ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Shanxi Province ◽  
Pathway Enrichment Analysis ◽  
Type I ◽  
Hub Genes ◽  
Key Genes

Background:Dermatomyositis (DM) is a chronic systemic autoimmune disease characterized by inflammatory infiltrates in the skin and muscle1. The genes and pathways in the inflamed myopathies in patients with DM are poorly understood2.Objectives:To identify the key genes and pathways associated with DM and further discover its pathogenesis.Methods:Muscle tissue gene expression profile (GSE143323) were acquired from the GEO database, which included 39 DM samples and 20 normal samples. The differentially expressed genes (DEGs) in DM muscle tissue were screened by adopting the R software. Gene ontology (GO) and Kyoto Encyclopedia of Genome (KEGG) pathway enrichment analysis was performed by Metascape online analysis tool. A protein-protein interaction (PPI) network was then constructed by STRING software using the genes in significantly different pathways. Network of DEGs was analyzed by Cytoscape software. And degree of nodes was used to screen key genes.Results:Totally, 126 DEGs were obtained, which contained 122 up-regulated and 4 down-regulated. GO analysis revealed that most of the DEGs were significantly enriched in type I interferon signaling pathway, response to interferon-gamma, collagen-containing extracellular matrix, response to interferon-alpha and bacterium, positive regulation of cell death, leukocyte chemotaxis. KEGG pathway analysis showed that upregulated DEGs enhanced pathways associated with the hepatitis C, complement and coagulation cascades, p53 signaling pathway, RIG-I-like receptor signaling, Osteoclast differentiation, and AGE-RAGE signaling pathway. Ten hub genes were identified in DM, they were ISG15, IRF7, STAT1, MX1, OASL, OAS2, OAS1, OAS3, GBP1, and IRF9 according to the Cytoscape software and cytoHubba plugin.Conclusion:The findings from this bioinformatics network analysis study identified the key hub genes that might provide new molecular markers for its diagnosis and treatment.References:[1]Olazagasti JM, Niewold TB, Reed AM. Immunological biomarkers in dermatomyositis. Curr Rheumatol Rep 2015;17(11):68. doi: 10.1007/s11926-015-0543-y [published Online First: 2015/09/26].[2]Chen LY, Cui ZL, Hua FC, et al. Bioinformatics analysis of gene expression profiles of dermatomyositis. Mol Med Rep 2016;14(4):3785-90. doi: 10.3892/mmr.2016.5703 [published Online First: 2016/09/08].[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

scholarly journals Identification of Key Genes and Pathways Associated With Irradiation in Breast Cancer Tissue and Breast Cancer Cell Lines

Dose-Response ◽  
2020 ◽  
Vol 18 (2) ◽  
pp. 155932582093125
Author(s):  
Changchun Zhu ◽  
Chang Ge ◽  
Junbo He ◽  
Xueying Zhang ◽  
Guoxing Feng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Signaling Pathway ◽  
Breast Cancer Cell Lines ◽  
Cancer Tissue ◽  
Interleukin 17 ◽  
Ppi Network ◽  
Hub Genes ◽  
Data Set ◽  
Key Genes

Radiotherapy is mainly a traditional treatment for breast cancer; however, the key genes and pathways in breast cancer associated with irradiation are not clear. In this study, we aimed to explore the messenger RNA expression changes between preradiation and postradiation breast cancer. The gene expression data set (GSE59733) was downloaded from Gene Expression Omnibus database. According to |log2FC (fold change) | ≥ 1 and with false discovery rate adjusted P value <.05, differentially expressed genes (DEGs) were screened and annotated by R programming software. The protein–protein interaction (PPI) network was conducted through STRING database, and subnetworks and hub genes were extracted by plug-in in Cytoscape. A total of 82 DEGs (74 upregulated and 8 downregulated genes) were identified. These DEGs mainly enriched in an intrinsic apoptotic signaling pathway and G-protein-coupled receptor binding. What’s more, tumor necrosis factor signaling pathway and interleukin 17 signaling pathway abnormally activated in postradiation tumor samples. Two characteristic subnetworks and 3 hub genes ( FOS, CCL2, and CXCL12) were strongly distinguished in PPI network. Moreover, the expression level of the hub genes was confirmed in irradiated MCF-7 cell and SUM-159 cell using quantitative real-time polymerase chain reaction assay. These findings imply that these hub genes may play momentous function in breast cancer to irradiation.

scholarly journals Identification of Prognostic Biomarkers of Ewing Sarcoma using Bioinformatics Analysis and Experiments

2021 ◽  
Author(s):  
Jun Zhang ◽  
Mingwei He ◽  
Wenyu Feng ◽  
Yun Liu ◽  
Xiaoting Luo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mrna Expression ◽  
Ewing Sarcoma ◽  
Bioinformatics Analysis ◽  
Prognostic Biomarkers ◽  
Expression Level ◽  
Significant Prognostic Factor ◽  
Rt Pcr ◽  
Hub Genes ◽  
Potential Biomarker

Abstract Background. Ewing sarcoma (EWS) is the second most familiar bone or soft-tissue sarcoma, making the identification of EWS biomarkers critically important. This study aimed to explore and validate potential prognostic biomarkers in EWS by bioinformatics analysis and experiments. Methods The microarray dataset GSE119546 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between EWS cells treated with and without Cyclin-dependent kinase 9 (CDK9) inhibitors were analyzed using R package limma. Next, we performed gene enrichment analysis and constructed a protein-protein interaction (PPI) network. The expression level of the top18 hub genes and the overall survival (OS) in patients with sarcoma were validated using the Oncomine and Gene Expression Profiling Interactive Analysis 2 (GEPIA2) databases. Furthermore, the mRNA expression of Bystin Like (BYSL), Nucleolar Protein 11 (NOL11), and P21-activated protein kinase-interacting protein 1 (PAK1IP1) was detected by RT-PCR in the EWS cell line (A-673), and the protein expression in EWS tissue was verified by immunohistochemistry (IHC).Results Our study identified 343 DEGs, which were associated with various cancer-related functions and pathways. A total of three DEGs, including BYSL, NOL11, and PAK1IP1, were identified as hub genes with prognostic values using GEPAI2 and Oncomine. Finally, RT-PCR results showed the mRNA expression level of BYSL, NOL11, and PAK1IP1 in A-673 cells was higher than that in human mesenchymal stem cells. IHC staining revealed a positive expression ratio of NOL11, BYSL, and PAK1IP1 was 63.6% (14/22), 31.9% (7/22), and 0% (0/22), respectively. Cox regression analysis confirmed NOL11 was a significant prognostic factor for OS. Conclusions. Our findings revealed that NOL11 may be a potential biomarker for EWS prognosis and treatment.

scholarly journals Identifications of Candidate Genes Significantly Associated With Rectal Cancer by Integrated Bioinformatics Analysis

2020 ◽  
Vol 19 ◽  
pp. 153303382097327
Author(s):  
Zhili Xu ◽  
Yan Li ◽  
Yiyi Cui ◽  
Yong Guo
Keyword(s):  
Gene Expression ◽  
Rectal Cancer ◽  
Candidate Genes ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Receptor Interaction ◽  
Cancer Tissue ◽  
Cancer Tissues ◽  
Core Genes

Introduction: Rectal cancer ranks as the eighth in cancer-related morbidity and the tenth in the cancer-related mortality. A few studies have explored several biomarkers for colorectal cancer. However, there is still a great need for us to excavate novel biomarkers with effective and efficient diagnostic and prognostic values to discover the etiology and pathogenesis of rectal cancer separately. Therefore, we aimed to identify more novel candidate genes that were significantly associated with rectal cancer through integrated bioinformatics analysis. Methods: We analyzed the gene expression profiles of GSE15781 and GSE20842 from Gene Expression Omnibus database to identify differentially expressed genes between normal rectal tissue and rectal cancer tissue. Results: We searched for core genes, carried out survival analysis and analyzed the expressions of core genes. We found that 142 genes were significantly upregulated, and 229 genes were significantly downregulated in all 3 independent studies. In KEGG analysis, the upregulated genes were significantly enriched in cytokine-cytokine receptor interaction, IL-17 signaling pathway, cell cycle, etc. The downregulated genes were primarily enriched in nitrogen metabolism, mineral absorption and pentose and glucuronate interconversions. Inhibin subunit beta B (INHBB) expressed markedly higher in rectal cancer tissues compared with normal tissues, and claudins (CLDN) 23 expressed significantly lower in rectal cancer tissues. Conclusion: In conclusion, we discovered that INHBB could provide a great significant diagnostic and prognostic values for rectal cancer.

scholarly journals Study on potential differentially expressed genes in stroke by bioinformatics analysis

2021 ◽  
Author(s):  
Xitong Yang ◽  
Pengyu Wang ◽  
Shanquan Yan ◽  
Guangming Wang
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Normal Control ◽  
Enrichment Analysis ◽  
Normal Control Group ◽  
Control Group ◽  
Ppi Network ◽  
Hub Genes ◽  
Pathway Enrichment ◽  
Key Genes

AbstractStroke is a sudden cerebrovascular circulatory disorder with high morbidity, disability, mortality, and recurrence rate, but its pathogenesis and key genes are still unclear. In this study, bioinformatics was used to deeply analyze the pathogenesis of stroke and related key genes, so as to study the potential pathogenesis of stroke and provide guidance for clinical treatment. Gene Expression profiles of GSE58294 and GSE16561 were obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IS and normal control group. The different expression genes (DEGs) between IS and normal control group were screened with the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), the function and pathway enrichment analysis of DEGS were performed. Then, a protein–protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. Cytoscape with CytoHubba were used to identify the hub genes. Finally, NetworkAnalyst was used to construct the targeted microRNAs (miRNAs) of the hub genes. A total of 85 DEGs were screened out in this study, including 65 upward genes and 20 downward genes. In addition, 3 KEGG pathways, cytokine − cytokine receptor interaction, hematopoietic cell lineage, B cell receptor signaling pathway, were significantly enriched using a database for labeling, visualization, and synthetic discovery. In combination with the results of the PPI network and CytoHubba, 10 hub genes including CEACAM8, CD19, MMP9, ARG1, CKAP4, CCR7, MGAM, CD79A, CD79B, and CLEC4D were selected. Combined with DEG-miRNAs visualization, 5 miRNAs, including hsa-mir-146a-5p, hsa-mir-7-5p, hsa-mir-335-5p, and hsa-mir-27a- 3p, were predicted as possibly the key miRNAs. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of ischemic stroke, and provide a new strategy for clinical therapy.

scholarly journals Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Bojun Xu ◽  
Lei Wang ◽  
Huakui Zhan ◽  
Liangbin Zhao ◽  
Yuehan Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diabetic Nephropathy ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Topological Analysis ◽  
Gene Expression Omnibus ◽  
Complement Cascade ◽  
Hub Genes ◽  
Novel Biomarkers

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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