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Query: UNIPROT:P04637 (p53)
 77,613 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Hepatocellular carcinoma (HCC) is a widespread, common type of cancer in Asian countries, and the need for biomarker-matched molecularly targeted therapy for HCC has been increasingly recognized. However, the effective treatment for HCC is unclear. Therefore, identifying additional hub genes and pathways as novel prognostic biomarkers for HCC is necessary. In this study, the expression profiles of GSE121248, GSE45267 and GSE84402 were obtained from the Gene Expression Omnibus (GEO), including 132 HCC and 90 noncancerous liver tissues. Differentially expressed genes (DEGs) between HCC and noncancerous samples were identified by GEO2 R and Venn diagrams. In total, 109 DEGs were identified in these datasets, including 24 upregulated genes and 85 downregulated genes. Subsequently, Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) preliminary analyses of the DEGs were performed using DAVID. The protein-protein interaction (PPI) network of the DEGs was constructed with the Search Tool for the Retrieval of Interacting Genes (STRING) and visualized in Cytoscape. Module analysis of the PPI network was performed using MCODE to get hub genes. Moreover, the influence of the hub genes on overall survival was determined with Kaplan-Meier plotter. All hub genes were analyzed by Gene Expression Profiling Interactive Analysis (GEPIA) and KEGG. Overall, the hub genes DTL, CDK1, CCNB1, RACGAP1, ECT2, NEK2, BUB1B, PBK, TOP2A, ASPM, HMMR, RRM2, CDKN3, PRC1, and ANLN were upregulated in HCC, and the survival rate was lower for HCC with increased expression of these hub genes. CCNB1, CDK1, and RRM2 were enriched in the p53 signaling pathway, and CCNB1, CDK1, and BUB1B were enriched in the cell cycle. In brief, we screened 15 hub genes and pathways to identify potential prognostic markers for HCC treatment. However, the specific occurrence and development of HCC with expression of the hub genes should be verified in vivo and in vitro.
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PMID:Screening Hub Genes as Prognostic Biomarkers of Hepatocellular Carcinoma by Bioinformatics Analysis. 3182 16

Adenoid cystic carcinoma (ACC) is one of the most frequent malignancies of salivary glands. The objective of this study was to identify key genes and potential mechanisms during ACC samples.The gene expression profiles of GSE88804 data set were downloaded from Gene Expression Omnibus. The GSE88804 data set contained 22 samples, including 15 ACC samples and 7 normal salivary gland tissues. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were constructed, and protein-protein interaction network of differentially expressed genes (DEGs) was performed by Cytoscape. The top 10 hub genes were analyzed based on Gene Expression Profiling Interactive Analysis. Then, DEGs between ACC samples and normal salivary gland samples were analyzed by gene set enrichment analysis. Furthermore, miRTarBase and Cytoscape were used for visualization of miRNA-mRNA regulatory network. KEGG pathway analysis was undertaken using DIANA-miRPath v3.0.In total, 382 DEGs were identified, including 119 upregulated genes and 263 downregulated genes. GO analysis showed that DEGs were mainly enriched in extracellular matrix organization, extracellular matrix, and calcium ion binding. KEGG pathway analysis showed that DEGs were mainly enriched in p53 signaling pathway and salivary secretion. Expression analysis and survival analysis showed that ANLN, CCNB2, CDK1, CENPF, DTL, KIF11, and TOP2A are all highly expressed, which all may be related to poor overall survival. Predicted miRNAs of 7 hub DEGs mainly enriched in proteoglycans in cancer and pathways in cancer.This study indicated that identified DEGs and hub genes might promote our understanding of molecular mechanisms, which might be used as molecular targets or diagnostic biomarkers for ACC.
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PMID:Transcriptome analyses identify hub genes and potential mechanisms in adenoid cystic carcinoma. 3191 60

Molecular mechanisms underlying the pathogenesis and progression of malignant thyroid cancers, such as follicular thyroid carcinomas (FTCs), and how these differ from benign thyroid lesions, are poorly understood. In this study, we employed network-based integrative analyses of FTC and benign follicular thyroid adenoma (FTA) lesion transcriptomes to identify key genes and pathways that differ between them. We first analysed a microarray gene expression dataset (Gene Expression Omnibus GSE82208, n = 52) obtained from FTC and FTA tissues to identify differentially expressed genes (DEGs). Pathway analyses of these DEGs were then performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources to identify potentially important pathways, and protein-protein interactions (PPIs) were examined to identify pathway hub genes. Our data analysis identified 598 DEGs, 133 genes with higher and 465 genes with lower expression in FTCs. We identified four significant pathways (one carbon pool by folate, p53 signalling, progesterone-mediated oocyte maturation signalling, and cell cycle pathways) connected to DEGs with high FTC expression; eight pathways were connected to DEGs with lower relative FTC expression. Ten GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression DEGs. PPI analysis then identified 12 potential hub genes based on degree and betweenness centrality; namely, TOP2A, JUN, EGFR, CDK1, FOS, CDKN3, EZH2, TYMS, PBK, CDH1, UBE2C, and CCNB2. Moreover, transcription factors (TFs) were identified that may underlie gene expression differences observed between FTC and FTA, including FOXC1, GATA2, YY1, FOXL1, E2F1, NFIC, SRF, TFAP2A, HINFP, and CREB1. We also identified microRNA (miRNAs) that may also affect transcript levels of DEGs; these included hsa-mir-335-5p, -26b-5p, -124-3p, -16-5p, -192-5p, -1-3p, -17-5p, -92a-3p, -215-5p, and -20a-5p. Thus, our study identified DEGs, molecular pathways, TFs, and miRNAs that reflect molecular mechanisms that differ between FTC and benign FTA. Given the general similarities of these lesions and common tissue origin, some of these differences may reflect malignant progression potential, and include useful candidate biomarkers for FTC and identifying factors important for FTC pathogenesis.
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PMID:Network-Based Genetic Profiling Reveals Cellular Pathway Differences Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma. 3209 41

Atypical teratoid/rhabdoid tumor (ATRT) is a devastating intracranial tumor in children. Currently, its molecular mechanisms cannot be studied effectively because patient samples are limited, and many factors are involved in its pathogenesis. In this study, we analyzed three gene expression profile data sets obtained from the Gene Expression Omnibus (GEO) database to identify genes that participate in ATRT. The datasets were integrated and analyzed using the RobustRankAggreg method to screen for differentially expressed genes (DEGs). We identified 197 DEGs, including 94 downregulated and 103 upregulated genes which were then used for gene set enrichment analysis. The results showed that the downregulated genes were mainly enriched in synaptic vesicle cycle, nicotine addiction, and GABAergic synapse, whereas the upregulated genes were enriched in the cell cycle, p53 signaling pathway, and cellular senescence. Consistent with these results, gene set enrichment analysis showed that E2F targets, G2M checkpoints, and MYC targets were significantly enriched in datasets. Protein-protein interaction (PPI) network revealed that CDK1, CCNA2, BUB1B, CDC20, KIF11, KIF20A, KIF2C, NCAPG, NDC80, NUSAP1, PBK, RRM2, TPX2, TOP2A, and TTK were hub genes. NetworkAnalyst algorithm was used to predict the transcription factor (TF), and the results showed that MYC, SOX2, and KDM5B could regulate these hub genes. In conclusion, the present study brings a new perspective of ATRT pathogenesis and the strategy targeted to cell cycle related gene may be promising treatments for the disease.
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PMID:Identification of Hub Genes in Atypical Teratoid/Rhabdoid Tumor by Bioinformatics Analyses. 3244 Aug 21

Despite advances in the treatment of cervical cancer (CC), the prognosis of patients with CC remains to be improved. This study aimed to explore candidate gene targets for CC. CC datasets were downloaded from the Gene Expression Omnibus database. Genes with similar expression trends in varying steps of CC development were clustered using Short Time-series Expression Miner (STEM) software. Gene functions were then analyzed using the Gene Ontology (GO) database and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Protein interactions among genes of interest were predicted, followed by drug-target genes and prognosis-associated genes. The expressions of the predicted genes were determined using real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting. Red and green profiles with upward and downward gene expressions, respectively, were screened using STEM software. Genes with increased expression were significantly enriched in DNA replication, cell-cycle-related biological processes, and the p53 signaling pathway. Based on the predicted results of the Drug-Gene Interaction database, 17 drug-gene interaction pairs, including 3 red profile genes (TOP2A, RRM2, and POLA1) and 16 drugs, were obtained. The Cancer Genome Atlas data analysis showed that high POLA1 expression was significantly correlated with prolonged survival, indicating that POLA1 is protective against CC. RT-qPCR and Western blotting showed that the expressions of TOP2A, RRM2, and POLA1 gradually increased in the multistep process of CC. TOP2A, RRM2, and POLA1 may be targets for the treatment of CC. However, many studies are needed to validate our findings.
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PMID:Longitudinal Analysis of Gene Expression Changes During Cervical Carcinogenesis Reveals Potential Therapeutic Targets. 3248 45

Research on the amplification of oncogenes in thymic malignant tumor is limited. In this study, we aimed to determine the gene amplification status of receptor tyrosine kinases and other cell regulator genes in thymic malignant tumors, with a view toward the future introduction of molecular targeted therapy. In addition, we examined the usefulness of multiplex, ligation-dependent probe amplification (MLPA) in the semi-comprehensive detection of these gene amplifications. The participants of this study were nine patients with thymic carcinoma and one patient with atypical carcinoid who underwent resection at our department from 1999 to 2016. Twenty-four oncogenes (MDM4, MYCN, ALK, PDGFRA, KIT, KDR, DHFR, EGFR, MET, SMO, BRAF, FGFR1, MYC, ABL1, RET, CCND1, CCND2, CDK4, MDM2, AURKB, ERBB2, TOP2A, AURKA, AR) were analyzed for amplification by MLPA. In cases where amplification by MLPA was suspected, confirmation was performed by fluorescence in situ hybridization (FISH). Immunostaining for detected oncoproteins and p53 were performed in cases with confirmed oncogene amplification. MYC (2/10, 20%) and MDM2 (1/10, 10%) amplifications were detected using MLPA and FISH. Immunostaining in both cases was positive. The MDM2-amplified tumor relapsed and spread rapidly after operation despite the use of post-operative chemo-radiotherapy. MYC amplification may be involved in the carcinogenesis of thymic malignant tumors. In addition, MDM2 amplification may be a concern in the increased malignancy.
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PMID:Semi-comprehensive analysis of gene amplification in thymic malignant tumors using multiplex ligation-dependent probe amplification and fluorescence in situ hybridization. 3250 76

BACKGROUND We aimed to screen and identify central genetic and molecular targets involved in advancement of lung adenocarcinoma (LUAD) and to perform an integrated analysis and clinical validation. MATERIAL AND METHODS The GEO2R technique was utilized to assess differentially expressed genes (DEGs) among the gene sets GSE75037, GSE85716, and GSE118370. Subsequently, gene Ontology (GO) analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) analytical methods were executed to determine related biofunctions and signaling pathways, which were annotated with tools from the Database for Annotation, Visualization and Integrated Discovery (DAVID) resource. Then, a protein-protein interaction (PPI) network complex consisting of all detected DEGs was built with the STRING web interface. Cytohubba and MCODE plug-ins for Cytoscape software and Gene Expression Profiling Interactive Analysis (GEPIA) were employed to identify the hub genes. Finally, the mRNA expression of the identified hub genes was quantitatively validated by The Cancer Genome Atlas (TCGA) database analysis and real-time quantitative polymerase chain reaction (RT-qPCR). RESULTS We screened 146 upregulated DEGs and 431 downregulated DEGs with the criteria of |logFC| >1 and P<0.05, and the GO analysis indicated that DEGs were implicated in mitotic nuclear division (biological process, BP), the nucleus (cellular component, CC), and protein binding (molecular function, MF) and were associated with multiple KEGG pathways, such as the p53 signaling pathway in cancer. Then, the top 8 genes that predicted significantly different outcomes in LUAD patients were filtered from the DEGs and selected as hub genes. The TCGA database analysis and RT-qPCR results demonstrated that these genes were differentially expressed with the same trends in LUAD tissues compared with normal tissues. CONCLUSIONS Overall, we propose that 8 genes (PECAM1, CDK1, MKI67, SPP1, TOP2A, CHEK1, CCNB1, and RRM2) might be novel hub genes strongly associated with the progression and prognosis of LUAD.
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PMID:Comprehensive Analysis of Candidate Diagnostic and Prognostic Biomarkers Associated with Lung Adenocarcinoma. 3257 82

Retinoblastoma (RB) is the commonest malignant tumor of the infant retina. Besides genetic changes, epigenetic events are also considered to implicate the occurrence of RB. This study aimed to identify significantly altered protein-coding genes, DNA methylation, microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and their molecular functions and pathways associated with RB, and investigate the epigenetically regulatory mechanism of DNA methylation modification and non-coding RNAs on key genes of RB via bioinformatics method.We obtained multi-omics data on protein-coding genes, DNA methylation, miRNAs, and lncRNAs from the Gene Expression Omnibus database. We identified differentially expressed genes (DEGs) using the Limma package in R, discerned their biological functions and pathways using enrichment analysis, and conducted the modular analysis based on protein-protein interaction network to identify hub genes of RB. Survival analyses based on The Cancer Genome Atlas clinical database were performed to analyze prognostic values of key genes of RB. Subsequently, we identified the differentially methylated genes, differentially expressed miRNAs (DEMs) and lncRNAs (DELs), and intersected them with key genes to analyze possible targets of the underlying epigenetic regulatory mechanisms. Finally, the ceRNA network of lncRNAs-miRNAs-mRNAs was constructed using Cytoscape.A total of 193 DEGs, 74 differentially methylated-DEGs (DM-DEGs), 45 DEMs, 5 DELs were identified. The molecular pathways of DEGs were enriched in cell cycle, p53 signaling pathway, and DNA replication. A total of 10 key genes were identified and found significantly associated with poor survival outcome based on survival analyses, including CDK1, BUB1, CCNB2, TOP2A, CCNB1, RRM2, KIF11, KIF20A, NDC80, and TTK. We further found that hub genes MCM6 and KIF14 were differentially methylated, key gene RRM2 was targeted by DEMs, and key genes TTK, RRM2, and CDK1 were indirectly regulated by DELs. Additionally, the ceRNA network with 222 regulatory associations was constructed to visualize the correlations between lncRNAs-miRNAs-mRNAs.This study presents an integrated bioinformatics analysis of genetic and epigenetic changes that may be associated with the development of RB. Findings may yield many new insights into the molecular biomarker candidates and epigenetically regulatory targets of RB.
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PMID:Bioinformatics analysis of multi-omics data identifying molecular biomarker candidates and epigenetically regulatory targets associated with retinoblastoma. 3321 67


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