The effect of altering the promoter region of ubiquitous chromatin-opening element (UCOE) and matrix attachment region (MAR) on stable and efficient expression of genes was investigated. Four different promoters were tested, namely, oct4 containing an enhancer region, sox2 having a CpG island, nanog having no regulatory elements, and CMV containing a CpG island and an enhancer region. Eight reporter plasmids were constructed: pOCT4-UCOE, pOCT4-MAR, pSOX2-UCOE, pSOX2-MAR, pNANOG-UCOE, pNANOG-MAR, pCMV-UCOE, and pCMV-MAR. Stable and efficient expression was observed when UCOE combined with the oct4 promoter, whereas the sox2 was the best promoter suited for MAR. Comparison of the stable clones of oct4-UCOE and sox2-MAR showed that UCOE-regulated expression is more stable and efficient than MAR-regulated expression. When CpG island-containing promoter is linked with UCOE, stable and efficient expression could be observed. These data suggest that an enhancer region in the promoter leads to high, yet unstable expression when combined with UCOE, whereas CpG islands stabilize expression. In conclusion, UCOE and MAR interact with regulatory elements on the promoter by altering the chromatin open state and chromatin loop to regulate gene expression.
Chromatin/genetics* ; CpG Islands/genetics* ; Gene Expression ; Gene Expression Regulation ; Promoter Regions, Genetic/genetics*

OBJECTIVE: To screen differentially expressed gene (DEG) related to myelodysplastic syndrome (MDS) based on Gene Expression Omnibus (GEO) database, and explore the core genes and pathogenesis of MDS by analyzing the biological functions and related signaling pathways of DEG. METHODS: The expression profiles of GSE4619, GSE19429, GSE58831 including MDS patients and normal controls were downloaded from GEO database. The gene expression analysis tool (GEO2R) of GEO database was used to screen DEG according to | log FC (fold change) |≥1 and P<0.01. David online database was used to annotate gene ontology function (GO). Metascape online database was used to enrich and analyze differential genes in Kyoto Encyclopedia of Genes and Genomes (KEGG). The protein-protein interaction network (PPI) was constructed by using STRING database. CytoHubba and Mcode plug-ins of Cytoscape were used to analyze the key gene clusters and hub genes. R language was used to diagnose hub genes and draw the ROC curve. GSEA enrichment analysis was performed on GSE19429 according to the expression of LEF1. RESULTS: A total of 74 co-DEG were identified, including 14 up-regulated genes and 60 down regulated genes. GO enrichment analysis indicated that BP of down regulated genes was mainly enriched in the transcription and regulation of RNA polymerase II promoter, negative regulation of cell proliferation, and immune response. CC of down regulated genes was mainly enriched in the nucleus, transcription factor complexes, and adhesion spots. MF was mainly enriched in protein binding, DNA binding, and β-catenin binding. KEGG pathway was enriched in primary immunodeficiency, Hippo signaling pathway, cAMP signaling pathway, transcriptional mis-regulation in cancer and hematopoietic cell lineage. BP of up-regulated genes was mainly enriched in type I interferon signaling pathway and viral response. CC was mainly enriched in cytoplasm. MF was mainly enriched in RNA binding. Ten hub genes and three important gene clusters were screened by STRING database and Cytoscape software. The functions of the three key gene clusters were closely related to immune regulation. ROC analysis showed that the hub genes had a good diagnostic significance for MDS. GSEA analysis indicated that LEF1 may affect the normal function of hematopoietic stem cells by regulating inflammatory reaction, which further revealed the pathogenesis of MDS. CONCLUSION Bioinformatics can effectively screen the core genes and key signaling pathways of MDS, which provides a new strategy for the diagnosis and treatment of MDS.
Computational Biology ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Gene Ontology ; Humans ; Myelodysplastic Syndromes/genetics*

MicroRNA (miRNA) is a class of RNA which has been discovered in recent years and relates with genesis and development of tumors. MiRNA affects the genesis and development of tumors and carries out the function similar to oncogene and antioncogene through regulation of signaling pathway in which target genes involved, thereby the miRNA disregulation plays an important role in oncogenesis. More studies revealed that the miR-17-19 cluster closely correlates with tumorigenesis and has bifunctional effects of oncogene and antioncogene. In this review, the mechanism and function of the miR-17-19 cluster in tumorigenesis are summarized.
Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Genes, Tumor Suppressor ; Humans ; MicroRNAs ; Neoplasms ; genetics ; Oncogenes ; genetics

Hepatocellular carcinoma (HCC) is highly heterogeneous in nature and has been one of the most common cancer types worldwide. To ensure repeatability of identified gene expression patterns and comprehensively annotate the transcriptomes of HCC, we carefully curated 15 public HCC expression datasets that cover around 4000 clinical samples and developed the database HCCDB to serve as a one-stop online resource for exploring HCC gene expression with user-friendly interfaces. The global differential gene expression landscape of HCC was established by analyzing the consistently differentially expressed genes across multiple datasets. Moreover, a 4D metric was proposed to fully characterize the expression pattern of each gene by integrating data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). To facilitate a comprehensive understanding of gene expression patterns in HCC, HCCDB also provides links to third-party databases on drug, proteomics, and literatures, and graphically displays the results from computational analyses, including differential expression analysis, tissue-specific and tumor-specific expression analysis, survival analysis, and co-expression analysis. HCCDB is freely accessible at http://lifeome.net/database/hccdb.
Carcinoma, Hepatocellular ; genetics ; Databases, Genetic ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Humans ; Liver Neoplasms ; genetics

In order to enrich the transcriptome data of Fagopyrum dibotrys plants, analyze the genes encoding key enzyme involved in flavonoid biosynthesis pathway, and mine their functional genes, in this study, we performed RNA sequencing analysis for the rhizomes, roots, flowers, leaves and stems of F. dibotrys on the BGISEQ-500 sequencing platform. After de novo assembly of transcripts, a total of 205 619 unigenes were generated and 132 372 unigenes were obtained and annotated into seven public databases, of which, 81 327 unigenes were mapped to the GO database and most of the unigenes were annotated in cellular process, biological regulation, binding and catalytic activity. Besides, 86 922 unigenes were enriched in 136 pathways using KEGG database' and we identified 82 unigenes that encodes key enzymes involved in flavonoid biosynthesis. Comparing rhizome with root, flower, leaf or stem in F. dibotrys, 27 962 co-expressed differentially expressed genes(DEGs) were obtained. Among them, 23 515 DEGs of rhizome tissue-specific were enriched into 132 pathways and 13 unigenes were significantly enriched in biosynthesis of flavone and flavonol. In addition, we also identified 3 427 unigenes encoding 60 transcription factor(TFs) families as well as four unigenes encoding bHLH TFs were enriched in flavonoid biosynthesis. Our results greatly enriched the transcriptome database of plants, provided a reference for the analysis of key enzymes involved in flavonoid biosynthesis in plants, and will facilitate the study of the functions and regulatory mechanisms of key enzymes involved in flavonoid biosynthesis in F. dibotrys at the genetic level.
Biosynthetic Pathways/genetics* ; Fagopyrum ; Flavonoids ; Flowers ; Gene Expression Profiling ; Gene Expression Regulation, Plant ; Humans ; Transcriptome/genetics*

Antisense long chain noncoding RNA (lncRNA) is a new class of RNA molecules. In recent years, antisense lncRNA has been found to play an important role in many life activities including tumorigenesis and development. It has become a hot topic in biological research in recent years. Because of the special structure, many antisense lncRNAs have specific expression in tumor tissues and are closely related to the clinical data of the patients. Thus, antisense lncRNA is a potential tumor molecular marker. Further functional studies have shown that lncRNA can participate in gene regulation by means of miRNA sponge and RBP binding to play an important role in tumor cell cycle, apoptosis, angiogenesis, invasion and metastasis. More studies on the mechanisms of antisense lncRNA in cancer are of great theoretical significance in understanding the etiology and pathogenesis of malignant tumors and RNA language. At the same time, antisense lncRNA is a molecular marker or a potential target for the early diagnosis of malignant tumors. The antisense lncRNA may have a broad clinical application prospect in the evaluation of therapeutic effect, prognosis and even gene therapy.
Biomarkers, Tumor ; Gene Expression Regulation ; Gene Expression Regulation, Neoplastic ; Humans ; MicroRNAs ; genetics ; Neoplasms ; genetics ; RNA, Long Noncoding ; genetics

OBJECTIVETo study the differently expressed genes of oral squamous cell carcinoma (OSCC) tissue.

METHODSGene expression datasets related to oral squamous cell carcinoma in the gene expression omnibus (gene expression omnibus, GEO) repository were retrieved. Datasets were merged by normalization.Significantly expressed genes were obtained by statistical methods, and genes' functions, interactions, signaling pathways were analyzed accordingly.

RESULTSIn GEO, there were 1 125 records related to OSCC, and four of them were selected and merged to a super array data, within the super array data, 233 genes were significantly expressed (P < 0.05) , and the top 100 significantly expressed genes were selected as signature genes.Signature genes were more related to cell surface or cell-cell interactive activities. Clusters of interactive signature genes and the related signaling pathways were related with mitosis process.

CONCLUSIONSOSCC signature genes and the corresponding signaling pathways will provide not only an important clue for further research of the disease, but also reference for diagnosis and treatment.

microRNAs function as effective molecules in regulation of many biological functions of organisms; in most case they regulate gene expression moderately. Emerging evidence suggests that microRNAs play a key role in the regulation of immunological functions including innate and adaptive immune responses. The research on microRNAs would be helpful in elucidation of the mechanisms of human immune system and in development of potential therapies based on microRNAs.
Gene Expression Regulation ; Humans ; Immune System ; physiology ; MicroRNAs ; genetics ; physiology

MicroRNAs (miRNAs) are a class of highly conserved small noncoding RNAs which can regulate gene expression by post-transcriptional degradation or translational repression. miRNAs are involved in the regulation of cell apoptosis, proliferation, differentiation and other physiological processes, and are closely related with development of cancer. More recently, it has been proposed that the presence of genetic variations in microRNA genes, their biogenesis pathway and target binding sites can affect the miRNA processing machinery and targeting, therefore have a significant genetic effect. Since polymorphisms in a miRNA regulatory pathway can result in the loss or gain of a miRNA function and can affect the expression of hundreds of genes, more and more evidence suggested a strong association of miRNA polymorphisms with disease progression, diagnosis and prognosis. Whether in the pathogenesis research of complex diseases or finding biomarkers for diagnosis and prognosis, polymorphisms in the miRNA regulatory pathway have an extremely important value for research.
Animals ; Gene Expression Regulation ; Genetic Variation ; Humans ; MicroRNAs ; genetics ; physiology

Cancer gene expression data have the characteristics of high dimensionalities and small samples so it is necessary to perform dimensionality reduction of the data. Traditional linear dimensionality reduction approaches can not find the nonlinear relationship between the data points. In addition, they have bad dimensionality reduction results. Therefore a multiple weights locally linear embedding (LLE) algorithm with improved distance is introduced to perform dimensionality reduction in this study. We adopted an improved distance to calculate the neighbor of each data point in this algorithm, and then we introduced multiple sets of linearly independent local weight vectors for each neighbor, and obtained the embedding results in the low-dimensional space of the high-dimensional data by minimizing the reconstruction error. Experimental result showed that the multiple weights LLE algorithm with improved distance had good dimensionality reduction functions of the cancer gene expression data.
Algorithms ; Gene Expression Regulation, Neoplastic ; Genes, Neoplasm ; Humans ; Neoplasms ; genetics