Mutation spectra of the ITGB2 gene in Iranian families with leukocyte adhesion deficiency type 1

Yassaee, Vahid Reza; Hashemi‐Gorji, Feyzollah; Boosaliki, Sara; Parvaneh, Nima

doi:10.1016/j.humimm.2015.11.019

Cited by 22 publications

(17 citation statements)

References 24 publications

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“…ITGB2 produces a protein, known as CD18, which is a cell surface marker expressed on lymphocytes [62] and is involved in cell adhesion and cell-surface mediated signaling [63]. It has been demonstrated that mutation in the ITGB2 gene could lead to leukocyte adhesion deficiency [64]. And its expression in CLL cells predicts disease progression [65].…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Pathways in Myeloma side population cells by Bioinformatics Analysis

Qin

et al. 2020

Int. J. Med. Sci.

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Background: Multiple myeloma (MM) is the second most common hematological malignancy, which is still incurable and relapses inevitably, highlighting further understanding of the possible mechanisms. Side population (SP) cells are a group of enriched progenitor cells showing stem-like phenotypes with a distinct low-staining pattern with Hoechst 33342. Compared to main population (MP) cells, the underlying molecular characteristics of SP cells remain largely unclear. This bioinformatics analysis aimed to identify key genes and pathways in myeloma SP cells to provide novel biomarkers, predict MM prognosis and advance potential therapeutic targets. Methods: The gene expression profile GSE109651 was obtained from Gene Expression Omnibus database, and then differentially expressed genes (DEGs) with P-value <0.05 and |log2 fold-change (FC)| > 2 were selected by the comparison of myeloma light-chain (LC) restricted SP (LC/SP) cells and MP CD138 + cells. Subsequently, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis, protein-protein interaction (PPI) network analysis were performed to identify the functional enrichment analysis of the DEGs and screen hub genes. Cox proportional hazards regression was used to select the potential prognostic DEGs in training dataset (GSE2658). The prognostic value of the potential prognostic genes was evaluated by Kaplan-Meier curve and validated in another external dataset (MMRF-CoMMpass cohort from TCGA). Results: Altogether, 403 up-regulated and 393 down-regulated DEGs were identified. GO analysis showed that the up-regulated DEGs were significantly enriched in innate immune response, inflammatory response, plasma membrane and integral component of membrane, while the down-regulated DEGs were mainly involved in protoporphyrinogen IX and heme biosynthetic process, hemoglobin complex and erythrocyte differentiation. KEGG pathway analysis suggested that the DEGs were significantly enriched in osteoclast differentiation, porphyrin and chlorophyll metabolism and cytokine-cytokine receptor interaction. The top 10 hub genes, identified by the plug-in cytoHubba of the Cytoscape software using maximal clique centrality (MCC) algorithm, were ITGAM, MMP9, ITGB2, FPR2, C3AR1, CXCL1, CYBB, LILRB2, HP and FCER1G. Modules and corresponding GO enrichment analysis indicated that myeloma LC/SP cells were significantly associated with immune system, immune response and cell cycle. The predictive value of the prognostic model including TFF3, EPDR1, MACROD1, ARHGEF12, AMMECR1, NFATC2, HES6, PLEK2 and SNCA was identified, and validated in another external dataset (MMRF-CoMMpass cohort from TCGA). Conclusions: In conclusion, this study provides reliable molecular biomarkers for screening, prognosis, as well as novel therapeutic targets for myeloma LC/SP cells.

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Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Pathways in Myeloma side population cells by Bioinformatics Analysis

Qin

et al. 2020

Int. J. Med. Sci.

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“…ITGB2 gene is the most interactive hub gene in the network, and it is up-regulated, enriched in an inflammatory response and cell adhesion. ITGB2 gene is on chromosome 21 (21q22.3) and encodes integrin β2 protein (CD11b/CD18) (Yassaee et al, 2016). Upon inflammatory stimuli, CD11b/CD18 is rapidly activated via a conformational switch to mediate leukocyte migration from circulation to the inflamed tissue by binding to ICAM-1 (Hynes, 2002).…”

Section: Discussionmentioning

confidence: 99%

Identification of primary genes in glomeruli compartment of immunoglobulin A nephropathy by bioinformatic analysis

Miraji

Cheng

et al. 2019

PeerJ

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The current study is aimed to explore the specific genes which are responsible for the manifestation of Immunoglobulin A nephropathy (IgAN). Gene expression profiles GSE37460, GSE93798 and GSE104948 were analyzed using biological informatics methods to identify differentially expressed genes (DEGs) in IgAN glomeruli samples which were then compared to normal control samples. Subsequently, the DEGs were overlapped to explore genes with significant expression in at least two profiles. Finally, the enrichment analysis was conducted and the protein-protein interaction (PPI) network was constructed for the overlapping DEGs. A total of 28 genes were up-regulated and 10 genes were down-regulated. The up-regulated genes including CD44 and FN1 were chiefly involved in extracellular matrix receptors interaction pathway. In addition, CX3CR1 and CCL4 were associated with chemokine signaling pathway. ITGB2, PTPRC, FN1, and FCER1G were hub genes with a high degree of interaction in the PPI network. Therefore, this study identified many significant genes associated with extracellular matrix expansion and inflammatory mechanism which may be the novel biomarker and target candidates in IgAN.

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“…ITGB2 gene is the most interactive hub gene in the network, and it is up-regulated, enriched in an inflammatory response and cell adhesion. ITGB2 gene is on chromosome 21 (21q22.3) and encodes integrin β2 protein (CD11b/CD18) (Yassaee et al 2016). Upon inflammatory stimuli, CD11b/CD18 is rapidly activated via a conformational PeerJ reviewing PDF | (2018:10:31963:2:0:NEW 20 Apr 2019)…”

Section: Discussionmentioning

confidence: 99%

Peer Review #2 of "Identification of primary genes in glomeruli compartment of immunoglobulin A nephropathy by bioinformatic analysis (v0.2)"

2019

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The current study is aimed to explore the specific genes which are responsible for the manifestation of Immunoglobulin A nephropathy (IgAN). Gene expression profiles GSE37460, GSE93798 and GSE104948 were analyzed using biological informatics methods to identify differentially expressed genes (DEGs) in IgAN glomeruli samples which were then compared to normal control samples. Subsequently, the DEGs were overlapped to explore genes with significant expression in at least two profiles. Finally, the enrichment analysis was conducted and the protein-protein interaction (PPI) network was constructed for the overlapping DEGs. A total of 28 genes were up-regulated and whereas 10 genes were down-regulated. The up-regulated genes including CD44 and FN1 were chiefly involved in extracellular matrix receptors interaction pathway. In addition, CX3CR1 and CCL4 were associated with chemokine signaling pathway. ITGB2, PTPRC, FN1, and FCER1G were hub genes with a high degree of interaction in the PPI network. Therefore, this study identified many significant genes associated with extracellular matrix expansion and inflammatory mechanism which may be the novel biomarker and target candidates in IgAN.

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Mutation spectra of the ITGB2 gene in Iranian families with leukocyte adhesion deficiency type 1

Cited by 22 publications

References 24 publications

Identification of Key Genes and Pathways in Myeloma side population cells by Bioinformatics Analysis

Identification of Key Genes and Pathways in Myeloma side population cells by Bioinformatics Analysis

Identification of primary genes in glomeruli compartment of immunoglobulin A nephropathy by bioinformatic analysis

Peer Review #2 of "Identification of primary genes in glomeruli compartment of immunoglobulin A nephropathy by bioinformatic analysis (v0.2)"

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