scholarly journals Comprehensive analysis of DNA methylation and gene expression profiles in cholangiocarcinoma

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Cheng Zhang ◽  
Bingye Zhang ◽  
Di Meng ◽  
Chunlin Ge
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Ppi Networks ◽  
Differentially Methylated Genes ◽  
Tumorigenesis And Progression

Abstract Background The incidence of cholangiocarcinoma (CCA) has risen in recent years, and it has become a significant health burden worldwide. However, the mechanisms underlying tumorigenesis and progression of this disease remain largely unknown. An increasing number of studies have demonstrated crucial biological functions of epigenetic modifications, especially DNA methylation, in CCA. The present study aimed to identify and analyze methylation-regulated differentially expressed genes (MeDEGs) involved in CCA tumorigenesis and progression by bioinformatics analysis. Methods The gene expression profiling dataset (GSE119336) and gene methylation profiling dataset (GSE38860) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were identified using the limma packages of R and GEO2R, respectively. The MeDEGs were obtained by overlapping the DEGs and DMGs. Functional enrichment analyses of these genes were then carried out. Protein–protein interaction (PPI) networks were constructed using STRING and visualized in Cytoscape to determine hub genes. Finally, the results were verified based on The Cancer Genome Atlas (TCGA) database. Results We identified 98 hypermethylated, downregulated genes and 93 hypomethylated, upregulated genes after overlapping the DEGs and DMGs. These genes were mainly enriched in the biological processes of the cell cycle, nuclear division, xenobiotic metabolism, drug catabolism, and negative regulation of proteolysis. The top nine hub genes of the PPI network were F2, AHSG, RRM2, AURKB, CCNA2, TOP2A, BIRC5, PLK1, and ASPM. Moreover, the expression and methylation status of the hub genes were significantly altered in TCGA. Conclusions Our study identified novel methylation-regulated differentially expressed genes (MeDEGs) and explored their related pathways and functions in CCA, which may provide novel insights into a further understanding of methylation-mediated regulatory mechanisms in CCA.

scholarly journals Exploring the Key Genes and Pathways in the Formation of Corneal Scar Using Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Binfeng Liu ◽  
Ang Li ◽  
Hongbo Wang ◽  
Jialin Wang ◽  
Gongwei Zhai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Signal Pathways ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Corneal Scar

The Corneal wound healing results in the formation of opaque corneal scar. In fact, millions of people around the world suffer from corneal scars, leading to loss of vision. This study aimed to identify the key changes of gene expression in the formation of opaque corneal scar and provided potential biomarker candidates for clinical treatment and drug target discovery. We downloaded Gene expression dataset GSE6676 from NCBI-GEO, and analyzed the Differentially Expressed Genes (DEGs), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analyses, and protein-protein interaction (PPI) network. A total of 1377 differentially expressed genes were identified and the result of Functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) identification and protein-protein interaction (PPI) networks were performed. In total, 7 hub genes IL6 (interleukin-6), MMP9 (matrix metallopeptidase 9), CXCL10 (C-X-C motif chemokine ligand 10), MAPK8 (mitogen-activated protein kinase 8), TLR4 (toll-like receptor 4), HGF (hepatocyte growth factor), EDN1 (endothelin 1) were selected. In conclusion, the DEGS, Hub genes and signal pathways identified in this study can help us understand the molecular mechanism of corneal scar formation and provide candidate targets for the diagnosis and treatment of corneal scar.

scholarly journals Comprehensive analysis of DNA methylation and gene expression profiles in rectal cancer

2020 ◽  
Author(s):  
Cheng Zhang ◽  
Di Meng ◽  
Songjie Chao ◽  
Chunlin Ge
Keyword(s):  
Gene Expression ◽  
Rectal Cancer ◽  
Differentially Expressed Genes ◽  
Cox Regression ◽  
Expression Profiles ◽  
Methylation Status ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
Differentially Methylated Genes

Abstract BackgroundAbnormal hypomethylation of oncogenes and hypermethylation of tumor suppressor genes play important roles in human tumorigenesis and cancer progression, including those of rectal cancer (RC). However, conjoint analysis of RC involving both gene expression and methylation profiling datasets remains rare. This study aimed to identify methylation-regulated differentially expressed genes (MeDEGs) and to evaluate their prognostic value in RC through bioinformatics analysis.MethodsGene expression (GSE20842 and GSE68204) and gene methylation (GSE75546) profiling datasets were obtained from the Gene Expression Omnibus database. GEO2R was adopted to identify differentially expressed genes (DEGs) and differentially methylated genes (DMGs). MeDEGs were obtained by overlapping the DEGs and DMGs and then subjected to protein–protein interaction (PPI) network analysis using STRING. Modules and hub genes within the network were identified using MCODE and CytoHubba, respectively. Prognostic MeDEGs were selected by univariate Cox regression. Finally, our findings were validated based on The Cancer Genome Atlas (TCGA) database.ResultsIn total, 243 upregulated-hypomethylated and 51 downregulated-hypermethylated genes were identified as MeDEGs. A PPI network of MeDEGs was constructed with 290 nodes and 578 edges. Three modules and three hub genes—COL3A1, FPR1, and PLK1—within the network were identified. Three MeDEGs—NFE2, COMP, and LAMA1—were found to be survival-related. Furthermore, the expression and methylation status of two hub genes (excluding FPR1) and the three prognostic MeDEGs were also significantly altered in TCGA and were consistent with our findings.ConclusionsWe identified novel MeDEGs and explored their relationship with survival in RC. Our methodology may provide an effective bioinformatics basis for further understanding of the methylation-mediated regulatory mechanisms in RC.

scholarly journals Integrated bioinformatics analysis of aberrantly methylated-differentially expressed genes and pathways in age-related macular degeneration

2020 ◽  
Author(s):  
Yinchen Shen ◽  
Mo Li ◽  
Kun Liu ◽  
Xiaoyin Xu ◽  
Shaopin Zhu ◽  
...  
Keyword(s):  
Macular Degeneration ◽  
Differentially Expressed Genes ◽  
Age Related Macular Degeneration ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Ppi Networks ◽  
Age Related ◽  
Differentially Methylated Genes ◽  
Low Expression

Abstract Background: Age-related macular degeneration (AMD) represents the leading cause of visual impairment in the aging population. The goal of this study was to identify aberrantly methylated-differentially expressed genes (MDEGs) in AMD and explore the involved pathways by integrated bioinformatic analysis. Methods: Data of expression profiling GSE29801 and methylation profiling GSE102952 were obtained from the Gene Expression Omnibus database. We analyzed differentially methylated genes and differentially expressed genes in R software. Functional enrichment and protein–protein interaction (PPI) network analysis were performed using R package and Search Tool for the Retrieval of Interacting Genes online database. Hub genes were identified using Cytoscape. Results: 827 and 592 genes showed high and low expression, respectively, in GSE29801; 4117 hyper-methylated genes and 511 hypo-methylated genes were detected in GSE102952. After overlapping, we categorized 153 genes as hyper-methylated, low-expression genes (Hyper-LGs) and 24 genes as hypo-methylated, high-expression genes (Hypo-HGs). Four Hyper-LGs ( CKB , PPP3CA , TGFB2 , SOCS2 ) overlapped with AMD risk genes in Public Health Genomics and Precision Health Knowledge Base. KEGG pathway enrichment analysis indicated Hypo-HGs were enriched in the calcium signaling pathway, whereas Hyper-LGs were enriched in sphingolipid metabolism. In GO analysis, Hypo-HGs were enriched in fibroblast migration, membrane raft, coenzyme binding, etc. Hyper-LGs were enriched in mRNA transport, nuclear speck, DNA binding, etc. In PPI networks analysis, 23 nodes and 2 edges were established from Hypo-HGs, and 151 nodes and 73 edges were established from Hyper-LGs. Hub genes ( DHX9 , MAPT , PAX6 ) showed the greatest overlap. Conclusion: This study revealed potentially aberrantly MDEGs and pathways in AMD, which may improve the understanding of this disease.

scholarly journals Genomic Expression Profiling of Colorectal Cancer in Silico

2021 ◽  
Author(s):  
Feifei Liu ◽  
Yu Wang ◽  
Wenxue Li ◽  
Diancheng Li ◽  
Yuwei Xin ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Survival Analysis ◽  
Mrna Expression ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Analysis Tool ◽  
Hub Genes ◽  
Normal Tissues

Abstract Background: Colorectal cancer (CRC) is one of the most common malignancies of the digestive system; the progression and prognosis of which are affected by a complicated network of genes and pathways. The aim of this study was to identify potential hub genes associated with the progression and prognosis of colorectal cancer (CRC).Methods: We obtained gene expression profiles from GEO database to search differentially expressed genes (DEGs) between CRC tissues and normal tissue. Subsequently, we conducted a functional enrichment analysis, generated a protein–protein interaction (PPI) network to identify the hub genes, and analyzed the expression validation of the hub genes. Kaplan–Meier plotter survival analysis tool was performed to evaluate the prognostic value of hub genes expression in CRC patients.Results: A total of 370 samples, involving CRC and normal tissues were enrolled in this article. 283 differentially expressed genes (DEGs), including 62 upregulated genes and 221 downregulated genes between CRC and normal tissues were selected. We finally filtered out 6 hub genes, including INSL5, MTIM, GCG, SPP1, HSD11B2, and MAOB. In the database of TCGA-COAD, the mRNA expression of INSL5, MT1M, HSD11B2, MAOB in tumor is lower than that in normal; the mRNA expression of SPP1 in tumor is higher than that in normal. In the HPA database, the expression of INSL5, GCG, HSD11B2, MAOB in tumor is lower than that in normal tissues; the expression of SPP1 in the tumor is higher than that in normal tissues. Survival analysis revealed that INSL5, GCG, SPP1 and MT1M may serve as prognostic biomarkers in CRC. Conclusions: We screened out six hub genes to predict the occurrence and prognosis of patients with CRC using bioinformatics methods, which may provide new targets and ideas for diagnosis, prognosis and individualized treatment for CRC.

scholarly journals Bioinformatics-Based Study to Investigate Potential Differentially Expressed Genes and miRNAs in Hashimoto’s Thyroiditis

2021 ◽  
Author(s):  
Li Guoquan ◽  
Du Junwei ◽  
He Qi ◽  
Fu Xinghao ◽  
Ji Feihong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Inflammatory Responses ◽  
Expression Profiles ◽  
Treatment Options ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Chronic Lymphocytic Thyroiditis ◽  
Hub Genes

Abstract BackgroundHashimoto's thyroiditis (HT), also known as chronic lymphocytic thyroiditis, is a common autoimmune disease, which mainly occurs in women. The early manifestation was hyperthyroidism, however, hypothyroidism may occur if HT was not controlled for a long time. Numerous studies have shown that multiple factors, including genetic, environmental, and autoimmune factors, were involved in the pathogenesis of the disease, but the exact mechanisms were not yet clear. The aim of this study was to identify differentially expressed genes (DEGs) by comprehensive analysis and to provide specific insights into HT. MethodsTwo gene expression profiles (GSE6339, GSE138198) about HT were downloaded from the Gene Expression Omnibus (GEO) database. The DEGs were assessed between the HT and normal groups using the GEO2R. The DEGs were then sent to the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The hub genes were discovered using Cytoscape and CytoHubba. Finally, NetworkAnalyst was utilized to create the hub genes' targeted microRNAs (miRNAs). ResultsA total of 62 DEGs were discovered, including 60 up-regulated and 2 down-regulated DEGs. The signaling pathways were mainly engaged in cytokine interaction and cytotoxicity, and the DEGs were mostly enriched in immunological and inflammatory responses. IL2RA, CXCL9, IL10RA, CCL3, CCL4, CCL2, STAT1, CD4, CSF1R, and ITGAX were chosen as hub genes based on the results of the protein-protein interaction (PPI) network and CytoHubba. Five miRNAs, including mir-24-3p, mir-223-3p, mir-155-5p, mir-34a-5p, mir-26b-5p, and mir-6499-3p, were suggested as likely important miRNAs in HT. ConclusionsThese hub genes, pathways and miRNAs contribute to a better understanding of the pathophysiology of HT and offer potential treatment options for HT.

A Comprehensive Data Analysis of Differentially Regulated Genes in Melanoma

2020 ◽  
Author(s):  
Yanjie Han ◽  
Xinxin Li ◽  
Jiliang Yan ◽  
Chunyan Ma ◽  
Xin Wang ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Growth Factor Receptor ◽  
Principal Component ◽  
Distant Metastases ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background: Melanoma is the most deadly tumor in skin tumors and is prone to distant metastases. The incidence of melanoma has increased rapidly in the past few decades, and current trends indicate that this growth is continuing. This study was aimed to explore the molecular mechanisms of melanoma pathogenesis and discover underlying pathways and genes associated with melanoma.Methods: We used high-throughput expression data to study differential expression profiles of related genes in melanoma. The differentially expressed genes (DEGs) of melanoma in GSE15605, GSE46517, GSE7553 and the Cancer Genome Atlas (TCGA) datasets were analyzed. Differentially expressed genes (DEGs) were identified by paired t-test. Then the DEGs were performed cluster and principal component analyses and protein–protein interaction (PPI) network construction. After that, we analyzed the differential genes through bioinformatics and got hub genes. Finally, the expression of hub genes was confirmed in the TCGA databases and collected patient tissue samples.Results: Total 144 up-regulated DEGs and 16 down-regulated DEGs were identified. A total of 17 gene ontology analysis (GO) terms and 11 pathways were closely related to melanoma. Pathway of pathways in cancer was enriched in 8 DEGs, such as junction plakoglobin (JUP) and epidermal growth factor receptor (EGFR). In the PPI networks, 9 hub genes were obtained, such as loricrin (LOR), filaggrin (FLG), keratin 5 (KRT5), corneodesmosin (CDSN), desmoglein 1 (DSG1), desmoglein 3 (DSG3), keratin 1 (KRT1), involucrin (IVL) and EGFR. The pathway of pathways in cancer and its enriched DEGs may play important roles in the process of melanoma. The hub genes of DEGs may become promising melanoma candidate genes. Five key genes FLG, DSG1, DSG3, IVL and EGFR were identified in the TCGA database and melanoma tissues.Conclusions: The results suggested that FLG, DSG1, DSG3, IVL and EGFR might play important roles and potentially be valuable in the prognosis and treatment of melanoma.

scholarly journals Weighted gene co-expression network analysis to identify key modules and hub genes associated with paucigranulocytic asthma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Min Li ◽  
Wenye Zhu ◽  
Chu Wang ◽  
Yuanyuan Zheng ◽  
Shibo Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Differentially Expressed Genes ◽  
Immune Status ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes

Abstract Background Asthma is a heterogeneous disease that can be divided into four inflammatory phenotypes: eosinophilic asthma (EA), neutrophilic asthma (NA), mixed granulocytic asthma (MGA), and paucigranulocytic asthma (PGA). While research has mainly focused on EA and NA, the understanding of PGA is limited. In this study, we aimed to identify underlying mechanisms and hub genes of PGA. Methods Based on the dataset from Gene Expression Omnibus(GEO), weighted gene coexpression network analysis (WGCNA), differentially expressed genes (DEGs) analysis and protein–protein interaction (PPI) network analysis were conducted to construct a gene network and to identify key gene modules and hub genes. Functional enrichment analyses were performed to investigate the biological process, pathways and immune status of PGA. The hub genes were validated in a separate dataset. Results Compared to non-PGA, PGA had a different gene expression pattern, in which 449 genes were differentially expressed. One gene module significantly associated with PGA was identified. Intersection between the differentially expressed genes (DEGs) and the genes from the module that were most relevant to PGA were mainly enriched in inflammation and immune response regulation. The single sample Gene Set Enrichment Analysis (ssGSEA) suggested a decreased immune infiltration and function in PGA. Finally six hub genes of PGA were identified, including ADCY2, CXCL1, FPRL1, GPR109B, GPR109A and ADCY3, which were validated in a separate dataset of GSE137268. Conclusions Our study characterized distinct gene expression patterns, biological processes and immune status of PGA and identified hub genes, which may improve the understanding of underlying mechanism and provide potential therapeutic targets for PGA.

scholarly journals Identification of aberrantly methylated-differentially expressed genes and potential agents for Ewing’s sarcoma

2020 ◽  
Author(s):  
Shijie Gao ◽  
Guowang Li ◽  
Hao Yu ◽  
Shiyang Yuan ◽  
Wenxiang Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Differentially Expressed Genes ◽  
Ewing’S Sarcoma ◽  
Ewing's Sarcoma ◽  
Differentially Expressed ◽  
High Expression ◽  
Hub Genes ◽  
Low Expression

Abstract Background DNA methylation is a common epigenetic regulatory way, and it plays a critical role in various human diseases. However, the potential role of how DNA methylation impacts Ewing’s sarcoma (ES) is not clear. This study aimed to explore the regulatory role of DNA methylation in ES. Methods The microarray data of gene expression and methylation were downloaded from Gene Expression Omnibus (GEO) database, and analyzed via GEO2R. Venn analysis was then applied to identify aberrantly methylated differentially expressed genes (DEGs). Subsequently, Function and pathway enrichment analysis was conducted. Protein-protein interaction (PPI) network was constructed. Hub genes were determined. Besides, a connectivity map (CMap) analysis was performed to screen bioactive compounds for ES treatment. Results A total of 135 hypomethylated high expression genes and 523 hypermethylated low expression genes were identified. The hypomethylated high expression genes were enriched in signal transduction and the apoptosis process. Meanwhile, hypermethylated low expression genes were related to DNA replication and transcription regulation. We next determined 10 hub genes through PPI analysis, among them, C3, TF, and TCEB1 might serve as diagnostic and therapeutic targets. Furthermore, CMap analysis revealed 6 chemicals as potential options for ES treatment. Conclusions For the first time, we jointly analyzed gene profiling and methylation data about ES. The introduction of DNA methylation characteristics over DEGs is helpful to understand the pathogenesis of ES. The identified hub aberrantly methylated DEGs and chemicals might provide some novel insights on ES treatment.

scholarly journals Identification of Aberrantly Methylated-differentially Expressed Genes and Potential Agents for Ewing’s Sarcoma

2021 ◽  
Author(s):  
Shijie Gao ◽  
Guowang Li ◽  
Hao Yu ◽  
Shiyang Yuan ◽  
Wenxiang Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Differentially Expressed Genes ◽  
Ewing’S Sarcoma ◽  
Ewing's Sarcoma ◽  
Differentially Expressed ◽  
High Expression ◽  
Hub Genes ◽  
Low Expression

Abstract Background: DNA methylation is a common epigenetic regulatory way, and it plays a critical role in various human diseases. However, the potential role of how DNA methylation impacts Ewing’s sarcoma (ES) is not clear. This study aimed to explore the regulatory role of DNA methylation in ES.Methods: The microarray data of gene expression and methylation were downloaded from Gene Expression Omnibus (GEO) database, and analyzed via GEO2R. Venn analysis was then applied to identify aberrantly methylated differentially expressed genes (DEGs). Subsequently, Function and pathway enrichment analysis was conducted. Protein-protein interaction (PPI) network was constructed. Hub genes were determined. Besides, a connectivity map (CMap) analysis was performed to screen bioactive compounds for ES treatment.Results: A total of 135 hypomethylated high expression genes and 523 hypermethylated low expression genes were identified. The hypomethylated high expression genes were enriched in signal transduction and the apoptosis process. Meanwhile, hypermethylated low expression genes were related to DNA replication and transcription regulation. We next determined 10 hub genes through PPI analysis, among them, C3, TF, and TCEB1 might serve as diagnostic and therapeutic targets. Furthermore, CMap analysis revealed 6 chemicals as potential options for ES treatment. Conclusions: For the first time, we jointly analyzed gene profiling and methylation data about ES. The introduction of DNA methylation characteristics over DEGs is helpful to understand the pathogenesis of ES. The identified hub aberrantly methylated DEGs and chemicals might provide some novel insights on ES treatment.

scholarly journals Potential genes and pathways associated with heterotopic ossification derived from analyses of gene expression profiles

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Zhanyu Yang ◽  
Delong Liu ◽  
Rui Guan ◽  
Xin Li ◽  
Yiwei Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Kinase ◽  
Heterotopic Ossification ◽  
Signaling Pathway ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Heterotopic Bone ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background Heterotopic ossification (HO) represents pathological lesions that refer to the development of heterotopic bone in extraskeletal tissues around joints. This study investigates the genetic characteristics of bone marrow mesenchymal stem cells (BMSCs) from HO tissues and explores the potential pathways involved in this ailment. Methods Gene expression profiles (GSE94683) were obtained from the Gene Expression Omnibus (GEO), including 9 normal specimens and 7 HO specimens, and differentially expressed genes (DEGs) were identified. Then, protein–protein interaction (PPI) networks and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed for further analysis. Results In total, 275 DEGs were differentially expressed, of which 153 were upregulated and 122 were downregulated. In the biological process (BP) category, the majority of DEGs, including EFNB3, UNC5C, TMEFF2, PTH2, KIT, FGF13, and WISP3, were intensively enriched in aspects of cell signal transmission, including axon guidance, negative regulation of cell migration, peptidyl-tyrosine phosphorylation, and cell-cell signaling. Moreover, KEGG analysis indicated that the majority of DEGs, including EFNB3, UNC5C, FGF13, MAPK10, DDIT3, KIT, COL4A4, and DKK2, were primarily involved in the mitogen-activated protein kinase (MAPK) signaling pathway, Ras signaling pathway, phosphatidylinositol-3-kinase/protein kinase B (PI3K/Akt) signaling pathway, and Wnt signaling pathway. Ten hub genes were identified, including CX3CL1, CXCL1, ADAMTS3, ADAMTS16, ADAMTSL2, ADAMTSL3, ADAMTSL5, PENK, GPR18, and CALB2. Conclusions This study presented novel insight into the pathogenesis of HO. Ten hub genes and most of the DEGs intensively involved in enrichment analyses may be new candidate targets for the prevention and treatment of HO in the future.

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