scholarly journals Identification of Immune Infiltration, Differentially Expressed Genes, and Signaling Pathways in Fanconi Anemia Based on Bioinformatics Analysis

2022 ◽  
Author(s):  
Biyu Shen ◽  
Songsong Shi ◽  
Haoyang Chen ◽  
Yi Lu ◽  
Hengmei Cui ◽  
...  
Keyword(s):  
T Cells ◽  
Differentially Expressed Genes ◽  
Fanconi Anemia ◽  
Bioinformatics Analysis ◽  
Neutrophil Activation ◽  
Normal Group ◽  
Differentially Expressed ◽  
Expression Levels ◽  
Immune Infiltration ◽  
Kegg Analysis

Abstract Background and Objective: Fanconi anemia (FA) patients have a reduced ability to form blood cells, accompanied by multiple congenital malformations, mental retardation, solid tumors, and other symptoms. However, the molecular mechanism that causes FA is unclear, and few studies have addressed the regulatory mechanism of immune infiltration in FA. Here, we aimed to identify differentially expressed genes (DEGs), pathways, and immune infiltration involved in FA using integrated bioinformatics analysis and molecular mechanisms. Methods: The GEO gene chip database was searched for FA low density bone marrow tissue, and the content and proportion of 22 types of immune cells in the FA group and the normal group were analyzed using CIBERSORT. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of FA differentially expressed genes (DEGs) using R language and related package programs was also performed.Results: The expression levels of T cells regulatory (Tregs), M2 macrophages, T cells CD8, dendritic cells resting, and T cells CD4 naïve in FA were higher than in the normal group. Furthermore, the expression levels of naïve B cells, monocytes, and resting mast cells in FA were lower than in the normal group. GO analysis of FA differential genes showed that “neutrophil degranulation,” “neutrophil activation,” and “neutrophil activation involved in immune response,” were most frequently enriched among biological processes, with “specific granule,” “tertiary granule,” “tertiary granule lumen” among cellular components, and “carbohydrate binding” among molecular functions. For the KEGG analysis, “Asthma” was most often enriched.Conclusion: This study obtained useful data related to immune infiltration, DEGs, and gene pathways of FA, and provides new evidence for immunotherapy and clinical assessment of FA patients. These results are potentially a useful reference for subsequent related scientific research.

scholarly journals Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8390 ◽  
Author(s):  
Weisong Cai ◽  
Haohuan Li ◽  
Yubiao Zhang ◽  
Guangtao Han
Keyword(s):  
T Cells ◽  
Mast Cells ◽  
Signaling Pathway ◽  
Synovial Tissue ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Normal Controls

Background Osteoarthritis (OA) is the most common chronic degenerative joint disease and is mainly characterized by cartilage degeneration, subcartilage bone hyperplasia, osteophyte formation and joint space stenosis. Recent studies showed that synovitis might also be an important pathological change of OA. However, the molecular mechanisms of synovitis in OA are still not well understood. Objective This study was designed to identify key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis. Materials and Methods The gene expression profiles of GSE12021, GSE55235 and GSE55457 were downloaded from the GEO database. The differentially expressed genes (DEGs) were identified by the LIMMA package in Bioconductor, and functional enrichment analyses were performed. A protein-protein interaction network (PPI) was constructed, and module analysis was performed using STRING and Cytoscape. The CIBERSORT algorithm was used to analyze the immune infiltration of synovial tissue between OA and normal controls. Results A total of 106 differentially expressed genes, including 68 downregulated genes and 38 upregulated genes, were detected. The PPI network was assessed, and the most significant module containing 14 hub genes was identified. Gene Ontology analysis revealed that the hub genes were significantly enriched in immune cell chemotaxis and cytokine activity. KEGG pathway analysis showed that the hub genes were significantly enriched in the rheumatoid arthritis signaling pathway, IL-17 signaling pathway and cytokine-cytokine receptor interaction signaling pathway. The immune infiltration profiles varied significantly between osteoarthritis and normal controls. Compared with normal tissue, OA synovial tissue contained a higher proportion of memory B cells, naive CD4+ T cells, regulatory T cells, resting dendritic cells and resting mast cells, while naive CD4+ T cells, activated NK cells, activated mast cells and eosinophils contributed to a relatively lower portion (P > 0.05). Finally, the expression levels of 11 hub genes were confirmed by RT-PCR. Conclusion The hub genes and the difference in immune infiltration in synovial tissue between osteoarthritis and normal controls might provide new insight for understanding OA development.

scholarly journals SNAP25 Is a Potential Prognostic Biomarker for Prostate Cancer

2021 ◽  
Author(s):  
Longjiang Di ◽  
Maoli Gu ◽  
Yan Wu ◽  
Guoqiang Liu ◽  
Lishuo Zhang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
T Cells ◽  
Gene Ontology ◽  
Differentially Expressed Genes ◽  
Gleason Score ◽  
Immune Cells ◽  
Prognostic Biomarker ◽  
Differentially Expressed ◽  
Normal Prostate ◽  
Immune Infiltration

Abstract Background Prostate cancer is one of the most lethal cancers in male individuals. The Synaptosome associated protein 25 (SNAP25) gene is a key mediator of multiple biological functions in tumours. However, its significant impact on the prognosis in prostate cancer remains to be elucidated.Methods We performed a comprehensive analysis of the Cancer Genome Atlas dataset (TCGA) to identify the differentially expressed genes between prostate cancer and normal prostate tissue. We subjected the differentially expressed genes to gene ontology analysis and Kyoto Encyclopedia of Genes and Genomes functional analysis, and constructed a protein-protein interaction network. We then screened for pivotal genes to identify the hub genes of prognostic significance by performing Cox regression analysis. We identified SNAP25 as one such gene and analysed the relationship between its expression in prostate cancer to poor prognosis using Studio R. Results TCGA database demonstrated that SNAP25 was significantly downregulated in prostate cancer, and that its expression was significantly correlated with the Gleason score and pathological TNM stage of patients. The association between SNAP25 expression and tumour-infiltrating immune cells was evaluated using the Tumour Immune Estimation Resource site. Gene set enrichment and gene ontology analyses were used to analyse the function of SNAP25. We found that SNAP25 expression strongly correlated with overall survival in the Gleason score. In addition, SNAP25 was involved in the activation, differentiation, and migration of immune cells, and its expression was positively correlated with immune infiltration, including of B cells, CD8+ T cells, CD4+ T cells, neutrophils, dendritic cells, macrophages, and natural killer cells. SNAP25 expression was also positively correlated with chemokines/chemokine receptors, suggesting that SNAP25 might regulate the migration of immune cells. These molecular experiment results validate the low expression of SNAP25 seen in prostate cancer cells.Conclusion Our findings indicate a relationship between SNAP25 expression and prostate cancer, demonstrating that SNAP25 is a potential prognostic biomarker due to its vital role in immune infiltration.

scholarly journals Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis

2020 ◽  
Author(s):  
Yanzhi Ge ◽  
Li Zhou ◽  
Zuxiang Chen ◽  
Yingying Mao ◽  
Ting Li ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Immune Infiltration ◽  
Significant Difference

Abstract Background: The disability rate associated with rheumatoid arthritis (RA) ranks high among inflammatory joint diseases. However, the cause and potential molecular events are as yet not clear. Here, we aimed to identify differentially expressed genes (DEGs), pathways and immune infiltration involved in RA utilizing integrated bioinformatics analysis and investigating potential molecular mechanisms. Materials and methods: The expression profiles of GSE55235, GSE55457, GSE55584 and GSE77298 were downloaded from the Gene Expression Omnibus database, which contained 76 synovial membrane samples, including 49 RA samples and 27 normal controls. The microarray datasets were consolidated and DEGs were acquired and further analyzed by bioinformatics techniques. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using R (version 3.6.1) software, respectively. The protein-protein interaction (PPI) network of DEGs were developed utilizing the STRING database. Finally, the CIBERSORT was used to evaluate the infiltration of immune cells in RA. Results: A total of 828 DEGs were recognized, with 758 up-regulated and 70 down-regulated. GO and KEGG pathway analyses demonstrated that these DEGs focused primarily on cytokine receptor activity and relevant signaling pathways. The 30 most firmly related genes among DEGs were identified from the PPI network. The principal component analysis showed that there was a significant difference between the two tissues in infiltration immune. Conclusion: This study shows that screening for DEGs, pathways and immune infiltration utilizing integrated bioinformatics analyses could aid in the comprehension of the molecular mechanisms involved in RA development. Besides, our study provides valuable data related to DEGs, pathways and immune infiltration of RA and may provide new insight into the understanding of molecular mechanisms.

scholarly journals Identification of Differentially Expressed Genes Reveals BGN Predicting Overall Survival and Tumor Immune Infiltration of Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Weizhi Chen ◽  
Zhongheng Yang
Keyword(s):  
Gastric Cancer ◽  
T Cells ◽  
Differentially Expressed Genes ◽  
Treatment Strategies ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Potential Biomarker ◽  
Positive Correlations ◽  
First Time

Gastric cancer (GC) is one of the most widely occurring malignancies worldwide. Although the diagnosis and treatment strategies of GC have been greatly improved in the past few decades, the morbidity and lethality rates of GC are still rising due to lacking early diagnosis strategies and powerful treatments. In this study, a total of 37 differentially expressed genes were identified in GC by analyzing TCGA, GSE118897, GSE19826, and GSE54129. Using the PPI database, we identified 17 hub genes in GC. By analyzing the expression of hub genes and OS, MFAP2, BGN, and TREM1 were related to the prognosis of GC. In addition, our results showed that higher levels of BGN exhibited a significant correlation with shorter OS time in GC. Nomogram analysis showed that the dysregulation of BGN could predict the prognosis of GC. Moreover, we revealed that BGN had a markedly negative correlation with B cells but had positive correlations with CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells in GC samples. The pan-cancer analysis demonstrated that BGN was differentially expressed and related to tumor-infiltrating immune cells across human cancers. This study for the first time comprehensively revealed that BGN was a potential biomarker for the prediction of GC prognosis and tumor immune infiltration.

scholarly journals An Integrated Bioinformatics Analysis of the Potential Regulatory Effects of miR-21 on T-cell Related Target Genes in Multiple Sclerosis

2021 ◽  
Author(s):  
Mostafa Manian ◽  
Ehsan Sohrabi ◽  
Nahid Eskandari ◽  
Mohammad-Ali Assarehzadegan ◽  
Gordon A. Ferns ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
T Cells ◽  
T Cell ◽  
Differentially Expressed Genes ◽  
Target Genes ◽  
Bioinformatics Analysis ◽  
Mirna Gene ◽  
Differentially Expressed ◽  
Inflammatory Factors ◽  
Ppi Networks

Background: Overexpression of miR-21 is a characteristic feature of patients with Multiple Sclerosis (MS) and is involved in gene regulation and the expression enhancement of pro-inflammatory factors including IFNγ and TNF-α following stimulation of T-cells via the T Cell Receptor (TCR). In this study, a novel integrated bioinformatics analysis was used to obtain a better understanding of the involvement of miR-21 in the development of MS, its protein biomarker signatures, RNA levels, and drug interactions through existing microarray and RNA-seq datasets of MS.   Methods: In order to obtain data on the Differentially Expressed Genes (DEGs) in patients with MS and normal controls, the GEO2R web tool was used to analyze the Gene Expression Omnibus (GEO) datasets, and then Protein-Protein Interaction (PPI) networks of co-expressed DEGs were designed using STRING. A molecular network of miRNA-genes and drugs based on differentially expressed genes was created for T-cells of MS patients to identify the targets of miR-21, that may act as important regulators and potential biomarkers for early diagnosis, prognosis and, potential therapeutic targets for MS.   Results: It found that seven genes (NRIP1, ARNT, KDM7A, S100A10, AK2, TGFβR2, and IL-6R) are regulated by drugs used in MS and miR-21. Finally, three overlapping genes (S100A10, NRIP1, KDM7A) were identified between miRNA-gene-drug network and nineteen genes as hub genes which can reflect the pathophysiology of MS.    Conclusion: Our findings suggest that miR-21 and MS-related drugs can act synergistically to regulate several genes in the existing datasets, and miR-21 inhibitors have the potential to be used in MS treatment.

scholarly journals Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis

Hereditas ◽  
2021 ◽  
Vol 158 (1) ◽  
Author(s):  
Yanzhi Ge ◽  
Li Zhou ◽  
Zuxiang Chen ◽  
Yingying Mao ◽  
Ting Li ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Immune Infiltration ◽  
Significant Difference

Abstract Background The disability rate associated with rheumatoid arthritis (RA) ranks high among inflammatory joint diseases. However, the cause and potential molecular events are as yet not clear. Here, we aimed to identify differentially expressed genes (DEGs), pathways and immune infiltration involved in RA utilizing integrated bioinformatics analysis and investigating potential molecular mechanisms. Materials and methods The expression profiles of GSE55235, GSE55457, GSE55584 and GSE77298 were downloaded from the Gene Expression Omnibus database, which contained 76 synovial membrane samples, including 49 RA samples and 27 normal controls. The microarray datasets were consolidated and DEGs were acquired and further analyzed by bioinformatics techniques. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using R (version 3.6.1) software, respectively. The protein-protein interaction (PPI) network of DEGs were developed utilizing the STRING database. Finally, the CIBERSORT was used to evaluate the infiltration of immune cells in RA. Results A total of 828 DEGs were recognized, with 758 up-regulated and 70 down-regulated. GO and KEGG pathway analyses demonstrated that these DEGs focused primarily on cytokine receptor activity and relevant signaling pathways. The 30 most firmly related genes among DEGs were identified from the PPI network. The principal component analysis showed that there was a significant difference between the two tissues in infiltration immune. Conclusion This study shows that screening for DEGs, pathways and immune infiltration utilizing integrated bioinformatics analyses could aid in the comprehension of the molecular mechanisms involved in RA development. Besides, our study provides valuable data related to DEGs, pathways and immune infiltration of RA and may provide new insight into the understanding of molecular mechanisms.

scholarly journals HIV-1 Infection Transcriptomics: Meta-Analysis of CD4+ T Cells Gene Expression Profiles

Viruses ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 244 ◽  
Author(s):  
Antonio Victor Campos Coelho ◽  
Rossella Gratton ◽  
João Paulo Britto de Melo ◽  
José Leandro Andrade-Santos ◽  
Rafael Lima Guimarães ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Differentially Expressed Genes ◽  
Cd4 T Cells ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Infected Cells ◽  
Hiv 1

HIV-1 infection elicits a complex dynamic of the expression various host genes. High throughput sequencing added an expressive amount of information regarding HIV-1 infections and pathogenesis. RNA sequencing (RNA-Seq) is currently the tool of choice to investigate gene expression in a several range of experimental setting. This study aims at performing a meta-analysis of RNA-Seq expression profiles in samples of HIV-1 infected CD4+ T cells compared to uninfected cells to assess consistently differentially expressed genes in the context of HIV-1 infection. We selected two studies (22 samples: 15 experimentally infected and 7 mock-infected). We found 208 differentially expressed genes in infected cells when compared to uninfected/mock-infected cells. This result had moderate overlap when compared to previous studies of HIV-1 infection transcriptomics, but we identified 64 genes already known to interact with HIV-1 according to the HIV-1 Human Interaction Database. A gene ontology (GO) analysis revealed enrichment of several pathways involved in immune response, cell adhesion, cell migration, inflammation, apoptosis, Wnt, Notch and ERK/MAPK signaling.

scholarly journals A new family of markers to characterize the human γδ T-cell lineage

2019 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
T Cells ◽  
T Cell ◽  
Differentially Expressed Genes ◽  
Cell Lineage ◽  
Γδ T Cells ◽  
Differentially Expressed ◽  
Γδ T Cell ◽  
Differentiation Markers ◽  
Hematopoietic Stem ◽  
New Family

Prospective isolation of γδ T lymphocytes demands a comprehensive description of the molecules that distinguish T cells with γδ T-cell receptors (TCRs) (γδ T cells, or Tγδ) from those with αβTCRs (Tαβ). Here I describe some of the most differentially expressed genes in the γδ T cell when compared to the developmentally proximal but lineage-distinct Tαβ CD4+ and CD8+ lymphocytes. These genes encode cluster of differentiation markers, transcription factors, cell surface receptors and non-coding RNAs. As hematopoietic stem cells (HSCs) have been prospectively isolated based on the analysis of differentially expressed genes (1), any combination of these molecules may potentially be used to isolate Tγδ, perhaps even independent of the γδTCR. This description of the most striking identifying features of the Tγδ will be a resource for the isolation of a multi-potent common γδ T-cell progenitor.

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