PURPOSE: Duchenne muscular dystrophy (DMD) is currently the most commonly diagnosed form of muscular dystrophy due to mutations in the dystrophin gene. However, its pathological process remains unknown and there is a lack of specific molecular biomarkers. The aim of our study is to explore key regulatory connections underlying the progression of DMD. MATERIALS AND METHODS: The gene expression profile dataset GSE38417 of DMD was obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between DMD patients and healthy controls were screened using geo2R, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathway enrichment analyses. Then a protein-protein interaction (PPI) network and sub-network of modules were constructed. To investigate the regulatory network underlying DMD, a global triple network including miRNAs, mRNAs and transcription factors (TFs) was constructed. RESULTS: A total of 1811 DEGs were found between the DMD and control groups, among which HERC5, SKP2 and FBXW5 were defined as hub genes with a degree of connectivity >35 in the PPI network. Furthermore, the five TFs ZNF362, ATAT1, SPI1, TCF12 and ABCF2, as well as the eight miRNAs miR-124a, miR-200b/200c/429, miR-19a/b, miR-23a/b, miR-182, miR-144, miR-498 and miR-18a/b were identified as playing crucial roles in the molecular pathogenesis of DMD. CONCLUSIONS: This paper provides a comprehensive perspective on the miRNA-TF-mRNA co-regulatory network underlying DMD, although the bioinformatic findings need further validation in future studies.
MicroRNAs , Muscular Dystrophy, Duchenne , Computational Biology , Gene Ontology , Gene Regulatory Networks/genetics , Humans , MicroRNAs/genetics , MicroRNAs/metabolism , Muscular Dystrophy, Duchenne/genetics , RNA, Messenger/metabolism
T cell-mediated rejection (TCMR) and antibody-mediated rejection (ABMR) are severe post-transplantation complications for heart transplantation (HTx), whose molecular and immunological pathogenesis remains unclear. In the present study, the mRNA microarray data set GSE124897 containing 645 stable, 52 TCMR and 144 ABMR endomyocardial biopsies was obtained to screen for differentially expressed genes (DEGs) between rejected and stable HTx samples and to investigate immune cell infiltration. Functional enrichment analyses indicated roles of the DEGs primarily in immune-related mechanisms. Protein-protein interaction networks were then constructed, and ICAM1, CD44, HLA-A and HLA-B were identified as hub genes using the maximal clique centrality method. Immune cell infiltration analysis revealed differences in adaptive and innate immune cell populations between TCMR, ABMR and stable HTx samples. Additionally, hub gene expression levels significantly correlated with the degree and composition of immune cell infiltration in HTx rejection samples. Furthermore, drug-gene interactions were constructed, and 12 FDA-approved drugs were predicted to target hub genes. Finally, an external GSE2596 data set was used to validate the expression of the hub genes, and ROC curves indicated all four hub genes had promising diagnostic value for HTx rejection. This study provides a comprehensive perspective of molecular and immunological regulatory mechanisms underlying HTx rejection.
Biopsy/methods , ELAV-Like Protein 2/metabolism , Graft Rejection/immunology , Graft Rejection/metabolism , Heart Transplantation/adverse effects , Myocardium/metabolism , ELAV-Like Protein 2/genetics , Gene Expression Profiling , Gene Regulatory Networks , Humans , Postoperative Complications , Protein Interaction Maps
BACKGROUND AND AIMS: In the present study, we investigated the differentially expressed genes (DEGs), pathways and immune cell infiltration characteristics of pediatric and adult ulcerative colitis (UC). METHODS: We conducted DEG analysis using the microarray dataset GSE87473 containing 19 pediatric and 87 adult UC samples downloaded from the Gene Expression Omnibus. Gene ontology and pathway enrichment analyses were conducted using Metascape. We constructed the protein-protein interaction (PPI) network and the drug-target interaction network of DEGs and identified hub modules and genes using Cytoscape and analyzed immune cell infiltration in pediatric and adult UC using CIBERSORT. RESULTS: In total, 1700 DEGs were screened from the dataset. These genes were enriched mainly in inter-cellular items relating to cell junctions, cell adhesion, actin cytoskeleton and transmembrane receptor signaling pathways and intra-cellular items relating to the splicing, metabolism and localization of RNA. CDC42, POLR2A, RAC1, PIK3R1, MAPK1 and SRC were identified as hub DEGs. Immune cell infiltration analysis revealed higher proportions of naive B cells, resting memory T helper cells, regulatory T cells, monocytes, M0 macrophages and activated mast cells in pediatric UC, along with lower proportions of memory B cells, follicular helper T cells, γδ T cells, M2 macrophages, and activated dendritic cells. CONCLUSIONS: Our study suggested that hub genes CDC42, POLR2A, RAC1, PIK3R1, MAPK1 and SRC and immune cells including B cells, T cells, monocytes, macrophages and mast cells play vital roles in the pathological differences between pediatric and adult UC and may serve as potential biomarkers in the diagnosis and treatment of UC.
Colitis, Ulcerative , Computational Biology/methods , Intercellular Signaling Peptides and Proteins/genetics , Signal Transduction/genetics , Adult , Biomarkers , Child , Colitis, Ulcerative/blood , Colitis, Ulcerative/genetics , Colitis, Ulcerative/pathology , Gene Expression Profiling/methods , Humans , Immunity, Cellular/physiology , Paracrine Communication/physiology
This study aimed to explore key regulatory connections underlying lung transplant rejection. The differentially expressed genes (DEGs) between rejection and stable lung transplantation (LTx) samples were screened using R package limma, followed by functional enrichment analysis and protein-protein interaction network construction. Subsequently, a global triple network, including miRNAs, mRNAs, and transcription factors (TFs), was constructed. Furthermore, immune cell infiltration characteristics were analyzed to investigate the molecular immunology of lung transplant rejection. Finally, potential drug-target interactions were generated. In brief, 739 DEGs were found between rejection and stable LTx samples. PTPRC, IL-6, ITGAM, CD86, TLR8, TYROBP, CXCL10, ITGB2, and CCR5 were defined as hub genes. Eight TFs, including STAT1, SPIB, NFKB1, SPI1, STAT5A, RUNX1, VENTX, and BATF, and five miRNAs, including miR-335-5p, miR-26b-5p, miR-124-3p, miR-1-3p, and miR-155-5p, were involved in regulating hub genes. The immune cell infiltration analysis revealed higher proportions of activated memory CD4 T cells, follicular helper T cells, γδ T cells, monocytes, M1 and M2 macrophages, and eosinophils in rejection samples, besides lower proportions of resting memory CD4 T cells, regulatory T cells, activated NK cells, M0 macrophages, and resting mast cells. This study provided a comprehensive perspective of the molecular co-regulatory network underlying lung transplant rejection.
Graft Rejection/genetics , Graft Rejection/immunology , Lung Transplantation , Gene Regulatory Networks , Humans , Leukocytes/immunology , Macrophages/immunology , MicroRNAs , Protein Interaction Maps , RNA, Messenger , Respiratory Mucosa/cytology , Respiratory Mucosa/immunology , Transcription Factors
Aberrant activation of Notch signaling plays an oncogenic role in cancer development. Jagged1 (JAG1) is an important Notch ligand that triggers Notch signaling through cell-cell interactions. JAG1 overexpression has been reported in many different types of cancer and correlates with a poor clinical prognosis. JAG1/Notch signaling controls oncogenic processes in different cell types and cellular contexts. Furthermore, JAG1/Notch signaling cascades activate a number of oncogenic factors that regulate cellular functions such as proliferation, metastasis, drug-resistance, and angiogenesis. To suppress the severe toxicity of pan-Notch inhibitors, JAG1 is attracting increasing attention as a source of therapeutic targets for cancers. In this review, the oncogenic role of JAG1/Notch signaling in cancer is discussed, as well as implications of strategies to inhibit JAG1/Notch signaling activity.
Jagged-1 Protein/metabolism , Neoplasms/metabolism , Oncogene Proteins/metabolism , Receptors, Notch/metabolism , Signal Transduction , Adaptor Proteins, Signal Transducing/genetics , Adaptor Proteins, Signal Transducing/metabolism , Animals , Calcium-Binding Proteins/genetics , Calcium-Binding Proteins/metabolism , Female , Gene Expression Regulation, Neoplastic , Humans , Jagged-1 Protein/genetics , Male , Neoplasms/genetics , Neoplasms/pathology , Oncogene Proteins/genetics , Receptors, Notch/genetics
Delta-like ligands (DLLs) control Notch signaling. DLL1, DLL3 and DLL4 are frequently deregulated in cancer and influence tumor growth, the tumor vasculature and tumor immunity, which play different roles in cancer progression. DLLs have attracted intense research interest as anti-cancer therapeutics. In this review, we discuss the role of DLLs in cancer and summarize the emerging DLL-relevant targeting methods to aid future studies.
Deregulated Notch signaling is a key factor thought to facilitate the stem-like proliferation of cancer cells, thereby facilitating disease progression. Four subtypes of Notch receptor have been described to date, with each playing a distinct role in cancer development and progression, therefore warranting a careful and comprehensive examination of the targeting of each receptor subtype in the context of oncogenesis. Clinical efforts to translate the DAPT, which blocks Notch signaling, have been unsuccessful due to a combination of serious gastrointestinal side effects and a lack of complete blocking efficacy. There is therefore a clear need to identify better therapeutic strategies for targeting and manipulating Notch signaling. Notch2 is a Notch receptor that is commonly overexpressed in a range of cancers, and which is linked to a unique oncogenic mechanism. Successful efforts to block Notch2 signaling will depend upon doing so both efficiently and specifically in patients. As such, in the present review we will explore the role of Notch2 signaling in the development and progression of cancer, and we will assess agents and strategies with the potential to effectively disrupt Notch2 signaling and thereby yield novel cancer treatment regimens.
