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Query: UMLS:C0019204 (hepatocellular carcinoma)
 71,386 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Hepatocellular carcinoma (HCC) is one of the most common types of malignant tumors with poor sensitivity to chemotherapy drugs and poor prognosis among patients. In the present study, we downloaded the original data from the Gene Expression Omnibus and compared gene expression profiles of liver cancer cells in patients with HCC with those of colon epithelial cells of healthy controls to identify differentially expressed genes (DEGs). After filtering target microRNAs (miRNA) from core DEGs, we cultured HepG2 cells in vitro, knocked down the miRNA and core mRNAs, and analyzed the effects. We found 228 differentially expressed genes between liver cancer tissue and healthy control tissue. We also integrated the protein-proteininteraction network and module analysis to screen 13 core genes, consisting of 12 up-regulated genes and 1 down-regulated gene. Five core genes were regulated hsa-miR-3613-3p, therefor we hypothesized that hsa-miR-3613-3p was a critical miRNA. After the transfection procedure, we found that changes in hsa-miR-3613-3p were the most obvious. Therefore, we speculated that hsa-miR-3613-3p was a main target miRNA. In addition, we transfected with si (BIRC5, CDK1, NUF2, ZWINT and SPC24), to target genes that can be targeted by miR-3613-3p. Our data shows that BIRC5, NUF2, and SPC24 may be promising liver cancer biomarkers that may not only predict disease occurrence but also potential personalized treatment options.
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PMID:MiR-3613-3p affects cell proliferation and cell cycle in hepatocellular carcinoma. 2919 Sep 74

Hepatocellular carcinoma (HCC) is a highly malignant tumor found in the bile duct epithelial cells, and the second most common tumor of the liver. However, the pivotal roles of most molecules of tumorigenesis in HCC are still unclear. Hence, it is essential to detect the tumorigenic mechanism and develop novel prognostic biomarkers for clinical application. The data of HCC mRNA-seq and clinical information from The Cancer Genome Atlas (TCGA) database were analyzed by weighted gene co-expression network analysis (WGCNA). Co-expression modules and clinical traits were constructed by the Pearson correlation analysis, interesting modules were selected and gene ontology and pathway enrichment analysis were performed. Intramodule analysis and protein-protein interaction construction of selected modules were conducted to screen hub genes. In addition, upstream transcription factors and microRNAs of hub genes were predicted by miRecords and NetworkAnalyst database. Afterward, a high connectivity degree of hub genes from two networks was picked out to perform the differential expression validation in the Gene Expression Profiling Interactive Analysis database and Human Protein Atlas database and survival analysis in Kaplan-Meier plotter online tool. By utilizing WGCNA, several hub genes that regulate the mechanism of tumorigenesis in HCC were identified, which was associated with clinical traits including the pathological stage, histological grade, and liver function. Surprisingly, ZWINT, CENPA, RACGAP1, PLK1, NCAPG, OIP5, CDCA8, PRC1, and CDK1 were identified statistically as hub genes in the blue module, which were closely implicated in pathological T stage and histologic grade of HCC. Moreover, these genes also were strongly associated with the HCC cell growth and division. Network and survival analyses found that nine hub genes may be considered theoretically as indicators to predict the prognosis of patients with HCC or clinical treatment target, it will be necessary for basic experiments and large-scale cohort studies to validate further.
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PMID:Identifying novel biomarkers in hepatocellular carcinoma by weighted gene co-expression network analysis. 3074 3

Hepatocellular carcinoma (HCC) is one of the most heterogeneous malignant cancers with no effective targets and treatments. However, the molecular pathogenesis of HCC remains largely uncertain. The aims of our study were to find crucial genes involved in HCC through multidimensional methods and revealed potential molecular mechanisms. Here, we reported the gene expression profile GSE121248 findings from 70 HCC and 37 adjacent normal tissues, all of which had chronic hepatitis B virus (HBV) infection, we were seeking to identify the dysregulated pathways, crucial genes and therapeutic targets implicated in HBV-associated HCC. We found 164 differentially expressed genes (DEGs) (92 downregulated genes and 72 upregulated genes). Gene ontology (GO) analysis of DEGs revealed significant functional enrichment of mitotic nuclear division, cell division, and the epoxygenase P450 pathway. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs were mainly enriched in metabolism, cell cycle regulation and the p53 signaling pathway. The Mcode plugin was calculated to construct a module complex of DEGs, and the module was mainly enriched in cell cycle checkpoints, RHO GTPase effectors and cytochrome P450. Considering a weak contribution of each gene, gene set enrichment analysis (GSEA) was performed, revealing results consistent with those described above. Six crucial proteins were selected based on the degree of centrality, including NDC80, ESR1, ZWINT, NCAPG, ENO3 and CENPF. Real-time quantitative PCR analysis validated the six crucial genes had the same expression trend as predicted. Furthermore, the methylation data of The Cancer Genome Atlas (TCGA) with HCC showed that mRNA expression of crucial genes was negatively correlated with methylation levels of their promoter region. The overall survival reflected that high expression of NDC80, CENPF, ZWINT, and NCAPG significantly predicted poor prognosis, whereas ESR1 high expression exhibited a favorable prognosis. The identification of the crucial genes and pathways would contribute to the development of novel molecular targets and biomarker-driven treatments for HCC.
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PMID:Identification of crucial genes based on expression profiles of hepatocellular carcinomas by bioinformatics analysis. 3141 Mar 10

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. Ample data have been reported to unravel the carcinogenesis over the past decades. Although pinpointing the cause of the HCC is challenging, this in and of itself may not be an insuperable problem. Indeed, the emergence of novel molecular targets has given rise to targeted therapy for HCC. Compared to traditional treatments, drugs with molecularly targeted agents are considered an optimal way to treat HCC. However, targeted approaches are currently limited among HCC patients. In our work, we explored more potential genes for targeted treatment of HCC. Initially, differentially expressed genes (DEGs) were identified in gene expression profiling interactive analysis (GEPIA) and NetworkAnalyst. Subsequently, 10 key genes were selected through enrichment analysis and PPI network construction. Based on the GEPIA and Oncomine databases, six upregulated genes were selected. High protein expression of these six genes were confirmed through the Human Protein Atlas database. In addition, these six genes were associated with unfavorable overall survival and progression-free survival based on Kaplan-Meier plotter bioinformatics. Moreover, gene expression was closely related to the tumor stages and pathological grades, as determined with UALCAN. More importantly, PTTG1, UBE2C, and ZWINT were identified as potential targets of anti-cancer drugs using cBioPortal. qPCR and western blot assays were used to show the high expression levels of the latter three genes in HCC cell lines. Collectively, these findings are expected to provide a theoretical basis for and give novel insights into clinical research of HCC.
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PMID:Promising diagnostic and prognostic value of six genes in human hepatocellular carcinoma. 3235 38