scholarly journals Integrating gene expression and protein-protein interaction network to prioritize cancer-associated genes

2012 ◽  
Vol 13 (1) ◽  
Author(s):  
Chao Wu ◽  
Jun Zhu ◽  
Xuegong Zhang
Keyword(s):  
Gene Expression ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

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 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 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 Transcriptional Regulation Based on Network of Autophagy Identifies Key Genes and Potential Mechanisms inHuman Osteoarthritis

Cartilage ◽  
2020 ◽  
pp. 194760352095163
Author(s):  
Jiamei Liu ◽  
Qin Fu ◽  
Shengye Liu
Keyword(s):  
Gene Expression ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Polymerase Chain Reaction Analysis ◽  
Analysis Tool ◽  
Protein Protein Interaction ◽  
Synovial Tissues ◽  
Protein Protein Interaction Network

Objective Osteoarthritis (OA) is a chronic arthropathy that frequently occurs in the middle-aged and elderly population. The aim of this study was to investigate the molecular mechanism of OA based on autophagy theory. Design We downloaded the gene expression profile from the Gene Expression Omnibus repository. Differentially expressed genes (DEGs) related to the keyword “autophagy” were identified using the scanGEO online analysis tool. DEGs representing the same expression trend were screened using the MATCH function. Clinical synovial specimens were collected for identification, pathological diagnosis, hematoxylin and eosin staining, and real-time polymerase chain reaction analysis. Differential expression of mRNAs in the synovial membrane tissues and chondrocyte monolayer samples from OA patients was used to identify potential OA biomarkers. Protein-protein interactions were established by the STRING website and visualized with Cytoscape. Functional and pathway enrichment analyses were performed using the Metascape database. Results GABARAPL1, GABARAPL2, and ATG13 were obtained as co-expressed autogenes in the 3 data sets. They were all downregulated among OA synovial tissues compared with non-OA synovial tissues ( P < 0.01). A protein-protein interaction network was constructed based on these 3 genes and included 63 genes. A functional analysis revealed that these genes were associated with autophagy-related functions. The top hub genes in the protein-protein interaction network were presented. Furthermore, 3 key modules were extracted to be core control modules. Conclusions These results offer an important molecular understanding of the key transcriptional regulatory genes and modules based on the network of potential autophagy mechanisms in human OA.

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