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 Identification of differentially expressed genes in hepatocellular carcinoma by integrated bioinformatic analysis

2019 ◽  
Author(s):  
Guangxin Yan ◽  
Zhaoyu Liu
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Dna Replication ◽  
Liver Cancer ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

AbstractHepatocellular carcinoma is one of the most common tumors in the world and has a high mortality rate. This study elucidates the mechanism of hepatocellular carcinoma- (HCC) related development. The HCC gene expression profile (GSE54238, GSE84004) was downloaded from Gene Expression Omnibus for comprehensive analysis. A total of 359 genes were identified, of which 195 were upregulated and 164 were downregulated. Analysis of the condensed results showed that “extracellular allotrope” is a substantially enriched term. “Cell cycle”, “metabolic pathway” and “DNA replication” are three significantly enriched Kyoto Encyclopedia of Genes and Genomespathways. Subsequently, a protein-protein interaction network was constructed. The most important module in the protein-protein interaction network was selected for path enrichment analysis. The results showed thatCCNA2, PLK1, CDC20, UBE2CandAURKAwere identified as central genes, and the expression of these five hub genes in liver cancer was significantly increased in The Cancer Genome Atlas. Univariate regression analysis was also performed to show that the overall survival and disease-free survival of patients in the high expression group were longer than in the expression group. In addition, genes in important modules are mainly involved in “cell cycle”, “DNA replication” and “oocyte meiosis” signaling pathways. Finally, through upstream miRNA analysis, mir-300 and mir-381-3p were found to coregulateCCNA2,AURKAandUBE2C. These results provide a set of targets that can help researchers to further elucidate the underlying mechanism of liver cancer.

scholarly journals Identification of Key Proteins Associated with Myocardial Infarction using Bioinformatics and Systems Biology

2018 ◽  
Author(s):  
Ishtiaque Ahammad
Keyword(s):  
Gene Expression ◽  
Myocardial Infarction ◽  
Systems Biology ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Gene Set Enrichment Analysis ◽  
New Drugs ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

AbstractMyocardial infarction, more commonly known as heart attack, is a huge health problem around the world. It is a result of inadequate blood supply to certain parts of the heart and death of heart muscle cells in that region. Although it has been around for a long time, newer and newer ways of probing myocardial infarction is being followed. Bioinformatics and Systems Biology are relatively recent fields to try to give new insights into myocardial infarction. Following the footsteps of others, this in silico study has tried to chime in on the investigation into myocardial infarction. The study began with the gene expression omnibus dataset uploaded to the NCBI from a whole-genome gene expression analysis carried out at Mayo Clinic in Rochester, Minnesota. From the dataset, differentially expressed genes following first-time myocardial infarction were identified and classified into up-regulated and down-regulated ones. Gene Set Enrichment Analysis (GSEA) was carried out on the up-regulated genes which were statistically significant and corresponded with the NCBI generated annotations. Protein-Protein Interaction Network for these genes was constructed. GSEA revealed 5 transcription factors, 5 microRNAs and 3 pathways significantly associated with them. From the Protein-Protein Interaction Network, 6 key proteins (hub nodes) have been identified. These 6 proteins may open a new window of opportunity for the discovery/design of new drugs for mitigating the damage caused by myocardial infarction.

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 Identification of Key Proteins Associated with Myocardial Infarction using Bioinformatics and Systems Biology

2018 ◽  
Author(s):  
Ishtiaque Ahammad
Keyword(s):  
Gene Expression ◽  
Myocardial Infarction ◽  
Systems Biology ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Gene Set Enrichment Analysis ◽  
New Drugs ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Myocardial infarction, more commonly known as heart attack, is a huge health problem around the world. It is a result of inadequate blood supply to certain parts of the heart and death of heart muscle cells in that region. Although it has been around for a long time, newer and newer ways of probing myocardial infarction is being followed. Bioinformatics and Systems Biology are relatively recent fields to try to give new insights into myocardial infarction. Following the footsteps of others, this in silico study has tried to chime in on the investigation into myocardial infarction. The study began with the gene expression omnibus dataset uploaded to the NCBI from a whole-genome gene expression analysis carried out at Mayo Clinic in Rochester, Minnesota. From the dataset, differentially expressed genes following first-time myocardial infarction were identified and classified into up-regulated and down-regulated ones. Gene Set Enrichment Analysis (GSEA) was carried out on the up-regulated genes which were statistically significant and corresponded with the NCBI generated annotations. Protein-Protein Interaction Network for these genes was constructed. GSEA revealed 5 transcription factors, 5 microRNAs and 3 pathways significantly associated with them. From the Protein-Protein Interaction Network, 6 key proteins (hub nodes) have been identified. These 6 proteins may open a new window of opportunity for the discovery/design of new drugs for mitigating the damage caused by myocardial infarction.

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.

Sign in / Sign up

Export Citation Format

Share Document