Objective: To analyze the expression levels of the F9 gene and F9 protein in hepatocellular carcinoma by combining multiple gene chip data, real-time fluorescence quantitative PCR (RT qPCR), and immunohistochemistry. Additionally, explore their correlation with the occurrence and development of hepatocellular carcinoma, as well as with various clinical indicators and prognosis. Methods: The mRNA microarray dataset from the GEO database was analyzed to identify the F9 gene with significant expression differences associated with hepatocellular carcinoma. Liver cancer and adjacent tissues were collected from 18 cases of hepatocellular carcinoma. RT-qPCR method was used to detect the F9 gene expression level. Immunohistochemistry was used to detect the F9 protein level. Combined with the TCGA database information, the correlation between F9 gene expression level and prognostic and clinicopathological parameters was analyzed. The biological function of F9 co-expressed genes associated with hepatocellular carcinoma was analyzed by the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Statistical analysis was performed using Graphpad Prism software. Results: Meta-analysis results showed that the expression of the F9 gene was lower in HCC tissues than in non-cancerous tissues. Immunohistochemistry results were basically consistent with those of RT-qPCR. The data obtained from TCGA showed that the F9 gene had lower expression values in stages III-IV, T3-T4, and patients with vascular invasion. A total of 127 genes were selected for bioinformatics analysis as co-expressed genes of F9, which were highly enriched in redox processes and metabolic pathways. Conclusion: This study validates that the F9 gene and F9 protein are lower in HCC. The down-regulation of the F9 gene predicts adverse outcomes, which may provide a new therapeutic target for HCC.
Humans ; Carcinoma, Hepatocellular/pathology* ; Liver Neoplasms/pathology* ; Down-Regulation ; Prognosis ; Gene Expression ; Gene Expression Regulation, Neoplastic

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

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

OBJECTIVE: To analyze the molecular markers associated with occurrence, development and poor prognosis of acute myeloid leukemia (AML) by using the data of GEO and TCGA database, as well as multiomics analysis. METHODS: The transcriptome data meeting requirements were down-loaded from GEO database, the differentially expressed genes were screened by using the R language limma package, and the GO function enrichment analysis and KEGG pathway analysis were performed for differentially expressed genes, at the same time, the protein interaction network was contracted by using STRING database and cytoscape software to screen out the hub gene, then the prognosis analysis was carried out for hub gene by combination with the clinical information affected in TCGA database. RESULTS: 620 differentially expressed genes were screened out, among which 162 differentially expressed genes were up-regulated, and 458 differentially expressed genes were down-regulated. Based on the results of GO functional enrichment, the KEGG pathway enrichment and protein interaction network, CXCL4, CXCR4, CXCR1, CXCR2, CCL5 and JUN were selected as hub genes. The survival analysis showed that the high expression of CXCL4, CXCR1, and CCL5 was a risk factor for poor prognosis of patiants. CONCLUSION CXCL4, CXCR1 and CCL5 can be used as biomarkers for the occurrence and development of AML, which relateds with the unfavorable prognosis and can provide a basis for further study.
Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Humans ; Leukemia, Myeloid, Acute ; Prognosis ; Transcriptome

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.

Long non-coding RNAs(LncRNA)may play a key role in tumorigenesis by regulating gene expression and intervening transcription. Recent studies have demonstrated that a series of patterns including protein modification,chromosomal reconstruction,regulation of target gene expression,transcription intervention,epigenetic modification,and natural antisense transcript are involved in this process. This article reviews recent research advances in this aspect with an attempt to better understand the role of LncRNA in tumorigenesis.
Cell Transformation, Neoplastic ; Epigenesis, Genetic ; Gene Expression Regulation, Neoplastic ; Humans ; RNA, Long Noncoding

OBJECTIVETo gain insight into the mechanism by which sex-determining region of Y chromosome (SRY)-related high-mobility-group box 2 (SOX2) involved in carcinogenesis and cancer stem cells (CSCs).

DATA SOURCESThe data used in this review were mainly published in English from 2000 to present obtained from PubMed. The search terms were "SOX2," "cancer," "tumor" or "CSCs."

STUDY SELECTIONArticles studying the mitochondria-related pathologic mechanism and treatment of glaucoma were selected and reviewed.

RESULTSSOX2, a transcription factor that is the key in maintaining pluripotent properties of stem cells, is a member of SRY-related high-mobility group domain proteins. SOX2 participates in many biological processes, such as modulation of cell proliferation, regulation of cell death signaling, cell apoptosis, and most importantly, tumor formation and development. Although SOX2 has been implicated in the biology of various tumors and CSCs, the findings are highly controversial, and information regarding the underlying mechanism remains limited. Moreover, the mechanism by which SOX2 involved in carcinogenesis and tumor progression is rather unclear yet.

CONCLUSIONSHere, we review the important biological functions of SOX2 in different tumors and CSCs, and the function of SOX2 signaling in the pathobiology of neoplasia, such as Wnt/β-catenin signaling pathway, Hippo signaling pathway, Survivin signaling pathway, PI3K/Akt signaling pathway, and so on. Targeting towards SOX2 may be an effective therapeutic strategy for cancer therapy.

OBJECTIVETo elucidate the mechanism of curcumin in radiotherapy sensitization for colorectal cancer cells.

METHODSColorectal cancer HT-29 cells were cultured and treated with radiation and curcumin. MTT method was used to detect the cell growth inhibition. Then the high-throughput microarray was used to detect the differences in gene expression levels for each test group to identify differentially expressed genes, and each differential gene was validated by Western blotting.

RESULTSCell growth inhibition rates at 48-hour and 72-hour in curcumin combined with radiotherapy group were significantly higher than those in simple radiotherapy group (P<0.05). Expression of 95 genes associated with gene-injury repair was detected by microarray. Compared to simple radiotherapy group, LIG4 and PNKP expression was down-regulated, and XRCC5 and CCNH expression was up-regulated in the curcumin combined with radiotherapy group (all P<0.05). Western blotting revealed LIG4 and PNKP protein expression decreased, and XRCC5 and CCNH protein expression increased in the curcumin combined with radiotherapy group as compared to the simple radiotherapy group (all P<0.05).

CONCLUSIONRadiation sensitization effect of curcumin on colorectal cancer cells HT-29 may be associated with the regulation of genes of CCNH, LIG4, XRCC5, PNKP.