scholarly journals MYBPC2 and MYL1 as Significant Gene Markers for Rhabdomyosarcoma

2021 ◽  
Vol 20 ◽  
pp. 153303382097966
Author(s):  
Zihang Chen ◽  
Xing-yu Li ◽  
Peng Guo ◽  
Dong-lai Wang
Keyword(s):  
Overall Survival ◽  
Gene Ontology ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Hub Genes ◽  
Tissue Samples ◽  
Protein Protein Interaction ◽  
Tumor Tissues ◽  
Protein Protein Interaction Network

Background: Rhabdomyosarcoma is the most common soft tissue tumor in children. Rhabdomyosarcoma commonly results in pain and bleeding caused by tumor compression and is prone to early metastasis and recurrence, which can seriously affect the therapeutic outcomes and long-term prognosis. Up to 37.7% of rhabdomyosarcomas may metastasize. Therefore, the molecular mechanisms underlying rhabdomyosarcoma must be explored to identify an effective target for its early diagnosis and specific treatment. Methods: A dataset of 18 rhabdomyosarcoma tissue samples and 6 healthy skeletal muscle samples was downloaded. Differentially expressed genes between rhabdomyosarcoma and healthy tissue samples were identified by GEO2R. Kyoto Encyclopedia of Genes and Genomes and gene ontology pathway enrichment analyses were performed. A protein–protein interaction network was constructed, and hub genes were identified. Expression and survival analyses of hub genes were performed. Additionally, 30 patients with rhabdomyosarcoma were recruited, and overall survival information and samples were collected. Reverse transcription quantitative real-time polymerase chain reaction assays were performed to verify the expression of MYBPC2 and MYL1 in rhabdomyosarcoma tumor tissues. The Kaplan–Meier method was used to explore overall survival based on our clinical data. Results: In total, 164 genes were up-regulated and 394 were down-regulated in rhabdomyosarcoma tumor tissues. Gene ontology analysis revealed that variations were predominantly enriched in the cell cycle, muscle contraction, muscle system processes, cytoskeleton, nucleotide binding, and cytoskeletal protein binding. The protein–protein interaction network revealed 3274 edges, and 441 nodes were constructed. Ten hub genes were identified; of these, MYBPC2 and MYL1 were significantly up-regulated in rhabdomyosarcoma. Compared with the healthy group, patients with rhabdomyosarcoma exhibiting high expression of MYBPC2 and MYL1 exhibited significantly worse overall survival. Conclusions: We found differentially expressed genes between rhabdomyosarcoma and healthy tissue samples. MYBPC2 and MYL1 may be involved in the pathogenesis of rhabdomyosarcoma and therefore deserve further exploration.

scholarly journals Integrated analysis and the identification of a circRNA-miRNA-mRNA network in the progression of abdominal aortic aneurysm

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12682
Author(s):  
Ke Si ◽  
Da Lu ◽  
Jianbo Tian
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Hub Genes ◽  
Tissue Samples ◽  
Protein Protein Interaction ◽  
Abdominal Aortic ◽  
Protein Protein Interaction Network

Background Abdominal aortic aneurysm (AAA) is a disease commonly seen in the elderly. The aneurysm diameter increases yearly, and the larger the AAA the higher the risk of rupture, increasing the risk of death. However, there are no current effective interventions in the early stages of AAA. Methods Four gene expression profiling datasets, including 23 normal artery (NOR) tissue samples and 97 AAA tissue samples, were integrated in order to explore potential molecular biological targets for early intervention. After preprocessing, differentially expressed genes (DEGs) between AAA and NOR were identified using LIMMA package. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were conducted using the DAVID database. The protein-protein interaction network was constructed and hub genes were identified using the STRING database and plugins in Cytoscape. A circular RNA (circRNA) profile of four NOR tissues versus four AAA tissues was then reanalyzed. A circRNA-miRNA-mRNA interaction network was constructed after predictions were made using the Targetscan and Circinteractome databases. Results A total of 440 DEGs (263 up-regulated and 177 down-regulated) were identified in the AAA group, compared with the NOR group. The majority were associated with the extracellular matrix, tumor necrosis factor-α, and transforming growth factor-β. Ten hub gene-encoded proteins (namely IL6, RPS27A, JUN, UBC, UBA52, FOS, IL1B, MMP9, SPP1 and CCL2) coupled with a higher degree of connectivity hub were identified after protein‐protein interaction network analysis. Our results, in combination with the results of previous studies revealed that miR-635, miR-527, miR-520h, miR-938 and miR-518a-5p may be affected by circ_0005073 and impact the expression of hub genes such as CCL2, SPP1 and UBA52. The miR-1206 may also be affected by circ_0090069 and impact RPS27A expression. Conclusions This circRNA-miRNA-mRNA network may perform critical roles in AAA and may be a novel target for early intervention.

scholarly journals Similarity of regulatory network between leukemia stem cells and normal hemopoietic stem cells

2018 ◽  
Vol 6 (4) ◽  
pp. 129-140
Author(s):  
Zhi-Jian Li ◽  
Xing-Ling Sui ◽  
Xue-Bo Yang ◽  
Wen Sun
Keyword(s):  
Stem Cells ◽  
Gene Ontology ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Impact Factors ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

AbstractTo reveal the biology of AML, we compared gene-expression profiles between normal hematopoietic cells from 38 healthy donors and leukemic blasts (LBs) from 26 AML patients. We defined the comparison of LB and unselected BM as experiment 1, LB and CD34+ isolated from BM as experiment 2, LB and unselected PB as experiment 3, and LB and CD34+ isolated from PB as experiment 4. Then, protein–protein interaction network of DEGs was constructed to identify critical genes. Regulatory impact factors were used to identify critical transcription factors from the differential co-expression network constructed via reanalyzing the microarray profile from the perspective of differential co-expression. Gene ontology enrichment was performed to extract biological meaning. The comparison among the number of DEGs obtained in four experiments showed that cells did not tend to differentiation and CD34+ was more similar to cancer stem cells. Based on the results of protein–protein interaction network,CREBBP,F2RL1,MCM2, andTP53were respectively the key genes in experiments 1, 2, 3, and 4. From gene ontology analysis, we found that immune response was the most common one in four stages. Our results might provide a platform for determining the pathology and therapy of AML.

scholarly journals Identification of Hub Genes Associated With Clinical Characteristics and Potential Therapy of Diabetic Nephropathy by Bioinformatics Analysis.

2020 ◽  
Author(s):  
Si Xu ◽  
Xiaoning Li ◽  
Sha Wu ◽  
Min Yang
Keyword(s):  
Diabetic Nephropathy ◽  
Network Analysis ◽  
Molecular Mechanism ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Theoretical Basis ◽  
Interaction Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Background: To provide theoretical basis for the molecular mechanism of the development of diabetic nephropathy and targeted molecular therapy by screening expressed genes based on bioinformatic analysis. Methods: We analyzed diabetic nephropathy microarray datasets derived from GEO database. Perl and R programming packages were used for data processing and analysis and for drawing. STRING online database and Cytoscape software were utilized for protein-protein interaction network analysis and screened for hub genes. Also, WebGestalt was used to analyze the relationship between genes and microRNAs. Nephroseq online tool was used to visualize the correlation between genes and clinical properties.Results: We found 91 differentially expressed genes between diabetic nephropathy tissues and normal control tissues. Protein-protein interaction network analysis screened out 5 key modules and a total of 14 hub genes were identified by integration, also11 microRNAs were associated with hub genes. Especially mir29 could regulate COL6A3 and COL15A1.Conclusions: The internal biological information in diabetic nephropathy can be revealed by integrative bioinformatical analysis, providing theoretical basis for further research on molecular mechanism and potential targets for diagnosis and therapeutics of diabetic nephropathy.

scholarly journals FCGR2A Could Function as a Prognostic Marker and Correlate with Immune Infiltration in Head and Neck Squamous Cell Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yongmei Dai ◽  
Wenhan Chen ◽  
Junpeng Huang ◽  
Tongjian Cui
Keyword(s):  
Gene Expression ◽  
Head And Neck ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Mrna Level ◽  
Interaction Network ◽  
Progression Free Survival ◽  
Tissue Samples ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Objective. We aim to investigate the correlation between FCGR2A mRNA level and prognosis of head and neck squamous cancer (HNSC) in public databases. In addition, we investigated the correlation between FCGR2A expression and clinicopathological characteristics and tumor-infiltrating immune cells in HNSC patients. Methods. FCGR2A mRNA expression in multiple cancers was analyzed based on Gene Expression Profiling Interactive Analysis. A protein-protein interaction network was obtained based on the STRING database. The 10 proteins most closely related to FCGR2A (i.e., CD3G, PLCG2, LAT, LYN, SYK, FCGR3A, PIK3R1, HCK, ITGAM, and ITGB2) were screened, followed by establishing the protein-protein interaction network. The correlation between FCGR2A expression and immunocytes was investigated, together with the effects of FCGR2A on the metastasis, recurrence, and survival of HNSC. Results. FCGR2A expression in several carcinoma tissues was significantly higher than that of adjacent tissues. Significant differences were noticed in the HNSC samples and the adjacent tissue samples except the seven samples of grade 4. There were statistical differences between the FCGR2A expression in tissues of grade 1, grade 2, and grade 3 ( P < 0.05 ). In the tissues of grade 4, the expression of FCGR2A was the lowest. The FCGR2A protein was a type of II-a receptor in γFc of the low-affinity immunoglobulin, which could bind with the Fc region of the immunoglobulin γ. There was a correlation between the FCGR2A gene and the distal HNSC metastasis. FCGR2A gene expression was correlated with the survival and prognosis. The GSE65858 dataset was selected for the validation. The FCGR2A expression was significantly correlated with total survival ( P = 0.0107 ) and progression-free survival ( P = 0.0362 ). Conclusions. Our findings shed light on the importance of FCGR2A in HNSC and illustrated a potential relationship between FCGR2A and tumor-immune interactions.

scholarly journals Exploring The Molecular Mechanisms of Classical Hodgkin Lymphoma Through Bioinformatics Analysis

2021 ◽  
Author(s):  
Zhu Lili ◽  
Zhu YuKun ◽  
Zhuangzhuang Tian ◽  
Yongsheng Li ◽  
Liyu Cao
Keyword(s):  
Hodgkin Lymphoma ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Molecular Mechanisms ◽  
Kegg Pathway ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Background Classic Hodgkin lymphoma (CHL) is the most common HL in the modern society. Although the treatment of cHL has made great progress, its molecular mechanisms have yet to be deciphered. Objectives The purpose of this study is to find out the crucial potential genes and pathways associated with cHL. Methods We downloaded the cHL microarray dataset (GSE12453) from Gene Expression Omnibus (GEO) database and to identify the differentially expressed genes (DEGs) between cHL samples and normal samples through the limma package in R. Then, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were carried out. Finally, we constructed the protein-protein interaction network to screen out the hub genes using Search Tool for the Retrieval of Interacting Genes (STRING) database. Results We screened out 788 DEGs in the cHL dataset, such as BATF3, IER3, RAB13 and FCRL2. GO functional enrichment analysis indicated that the DEGs were related with regulation of lymphocyte activation, secretory granule lumen and chemokine activity. KEGG pathway analysis showed that the genes enriched in Prion disease, Complement and coagulation cascades and Parkinson disease Coronavirus disease-COVID-19 pathway. Protein-protein interaction network construction identified 10 hub genes (IL6, ITGAM, CD86, FN1, MMP9, CXCL10, CCL5, CD19, IFNG, SELL, UBB) in the network. Conclusions In the present investigation, we identified several pathways and hub genes related to the occurrence and development of cHL, which may provide an important basis for further research and novel therapeutic targets and prognostic indicators for cHL.

scholarly journals Gliosarcoma Protein - Protein Interaction Network Analysis and Gene Ontology

2018 ◽  
Vol 11 (5) ◽  
Author(s):  
Mostafa Rezaei Tavirani ◽  
Vahid Mansouri ◽  
Sina Rezaei Tavirani ◽  
Saeed Hesami Tackallou ◽  
Mohammad Rostami - Nejad
Keyword(s):  
Gene Ontology ◽  
Network Analysis ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network
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