scholarly journals Centromere protein N may be a novel malignant prognostic biomarker for hepatocellular carcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11342
Author(s):  
Qingqing Wang ◽  
Xiaoyan Yu ◽  
Zhewen Zheng ◽  
Fengxia Chen ◽  
Ningning Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Western Blot ◽  
Colony Formation ◽  
Biological Significance ◽  
Gene Set Enrichment Analysis ◽  
Hub Genes ◽  
Centromere Protein ◽  
Huh7 Cells

Background Hepatocellular carcinoma (HCC) is one of the deadliest tumors. The majority of HCC is detected in the late stage, and the clinical results for HCC patients are poor. There is an urgent need to discover early diagnostic biomarkers and potential therapeutic targets for HCC. Methods The GSE87630 and GSE112790 datasets from the Gene Expression Omnibus (GEO) database were downloaded to analyze the differentially expressed genes (DEGs) between HCC and normal tissues. R packages were used for Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses of the DEGs. A Search Tool for Retrieval of Interacting Genes (STRING) database was used to develop a protein-protein interaction (PPI) network, and also cytoHubba, Molecular Complex Detection (MCODE), EMBL-EBI, CCLE, Gene Expression Profiling Interactive Analysis (GEPIA), and Oncomine analyses were performed to identify hub genes. Gene expression was verified with a third GEO dataset, GSE25097. The Cancer Genome Atlas (TCGA) database was used to explore the correlations between the hub genes and clinical indexes of HCC patients. The functions of the hub genes were enriched by gene set enrichment analysis (GSEA), and the biological significance of the hub genes was explored by real-time polymerase chain reaction (qRT-PCR), western blot, immunofluorescence, CCK-8, colony formation, Transwell and flow cytometry assays with loss-of-function experiments in vitro. Results Centromere protein N (CENPN) was screened as a hub gene affecting HCC tumorigenesis. Evaluation by Cox regression showed that a high level of CENPN expression was an independent danger variable for poor prognosis of HCC. GSEA showed that high CENPN expression was linked to the following pathways: liver cancer subclass proliferation, cell cycle, p53 signaling pathway, Rb1 pathway, positive regulation of cell cycle G1/S phase transition, and DNA damage response signal transduction by p53 class moderators. Further cell experiments showed that knocking down CENPN expression decreased the proliferation and colony-forming abilities of HepG2 and Huh7 cells as well as Ki67 expression in these cell lines. The cell cycle was arrested in G1 phase, which is consistent with previous experiments on CENPN downregulation., but neither migration nor invasion were significantly affected. Western blot results revealed that the expression of p53, p27, p21, CDK4, cyclin D1, CDK2, cyclin E, pRb, E2F1 and c-myc decreased after CENPN knockdown, but there was no significant change in total Rb levels. In addition, CENPN-knockdown cells subjected to irradiation showed significantly enhanced of γ-H2AX expression and reduced colony formation. Conclusion CENPN functions as an oncogene in HCC and may be a therapeutic target and promising prognostic marker for HCC.

scholarly journals Identification of a five-gene signature in association with overall survival for hepatocellular carcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11273
Author(s):  
Lei Yang ◽  
Weilong Yin ◽  
Xuechen Liu ◽  
Fangcun Li ◽  
Li Ma ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Overall Survival ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Enrichment Score ◽  
Hub Genes ◽  
Cox Regression Analysis

Background Hepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC. Methods The GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort. Results We screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.

Identification of Cellular Pathways Mediating Progression of Monoclonal Gammopathy (MGUS) to Multiple Myeloma (MM) Using Gene Expression Profiling (GEP).

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1673-1673
Author(s):  
Wee-Joo Chng ◽  
Gaofeng Huang ◽  
Peter Leif Bergsagel ◽  
Rafael Fonseca
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Differentially Expressed Gene ◽  
Biological Significance ◽  
Gene Set Enrichment Analysis ◽  
Enrichment Score ◽  
Validation Dataset ◽  
Similar Frequency ◽  
Metabolic Signature ◽  
Large Patient

Abstract Events mediating transformation from pre-malignant MGUS to MM is currently not well defined. Recurrent genetic abnormalities such as t(11;14), t(4;14), hyperdiploidy and chromosome 13 deletion are already present in MGUS at relatively similar frequency to MM. The unified deregulation of D-type cyclins is also already present in MGUS. Previous GEP studies have revealed little differences between MGUS and MM. A more recent study using a large patient cohort and new generation Affymetrix genechip identifies an MGUS signature but the functional and biological significance underlying this signature is unknown. In the current study, we analyze a cohort of 22 MGUS and 101 MM from the Mayo Clinic with GEP performed on Affymetrix U133A genechip using gene set enrichment analysis, a method that analyze differentially expressed gene between 2 phenotypes of interest in the context of published or curated genesets that represent specific biological, chemical, or molecular perturbation to cells. This method increases the sensitivity of identifying low but significant changes in gene expression. We made further modification to the original method that allows assessment in individual samples rather than the average across a phenotype further increasing the specificity of the output. In this analysis, 313 genesets were significant enriched for genes over-expressed in MM compared to MGUS, representing potential activated pathways that mediate transformation. When MM samples and genesets were clustered using the enrichment score for each genesets and samples, 4 cluster of genesets emerged, one including a number of MYC genesets, one including a number of cell cycle related genesets, one including genesets related to metabolic activity and another including a number of IFN related genesets. Further dissection of these correlated genesets to identify common enriched genes (leading edge genes) led to identification of a MYC core signature, tRNA core signature, Proteosome core signature and metabolic core signature which are highly correlated. From known literature and biology, it is likely that MYC activation leads to downstream activation of protein synthesis (tRNA signature), degradation (proteasome signature), and metabolic pathway (metabolic signature). This is verified by GEP results from in vitro modulation of MYC activation in cell lines. In addition, a cell cycle core signature and IFN core signature was identified. Activation of IFN and MYC core signatures accounts for almost 90% of MM patients. The remaining patients have a metabolic signature without MYC or IFN activation. The activation of the different core signatures is significantly correlated with certain TC classes when assessed by Chi-Square test. IFN activation is significantly correlated with D1 subtypes and negatively correlated with D2 subtypes. MYC activation is negatively correlated with t(11;14). Similar patterns were observed in a validation dataset of 351 MM patients from UAMS (GSE2677) with GEP performed on the U133plus2.0 chip. These results are validated at the protein level using IHC on TMA of the Mayo cohort. The activation of the IFN and MYC pathway may represent predominant mechanism in MGUS to MM progression.

scholarly journals The Identification of Core Gene Expression Signature in Hepatocellular Carcinoma

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Ning Li ◽  
Ling Li ◽  
Yongshun Chen
Keyword(s):  
Oxidative Stress ◽  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Financial Burden ◽  
Gene Expression Signature ◽  
Core Gene ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction

Hepatocellular carcinoma (HCC) is one of the most common malignancies, which causes serious financial burden worldwide. This study aims to investigate the potential mechanisms contributing to HCC and identify core biomarkers. The HCC gene expression profile GSE41804 was picked out to analyze the differentially expressed genes (DEGs). Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out using DAVID. We constructed a protein-protein interaction (PPI) network to visualize interactions of the DEGs. The survival analysis of these hub genes was conducted to evaluate their potential effects on HCC. In this analysis, 503 DEGs were captured (360 downregulated genes and 143 upregulated genes). Meanwhile, 15 hub genes were identified. GO analysis showed that the DEGs were mainly enriched in oxidative stress, cell cycle, and extracellular structure. KEGG analysis suggested the DEGs were enriched in the absorption, metabolism, and cell cycle pathway. PPI network disclosed that the top3 modules were mainly enriched in cell cycle, oxidative stress, and liver detoxification. In conclusion, our analysis uncovered that the alterations of oxidative stress and cell cycle are two major signatures of HCC. TOP2A, CCNB1, and KIF4A might promote the development of HCC, especially in proliferation and differentiation, which could be novel biomarkers and targets for diagnosis and treatment of HCC.

scholarly journals Iodide- and Glucose-Handling Gene Expression Regulated by Sorafenib or Cabozantinib in Papillary Thyroid Cancer

2015 ◽  
Vol 100 (5) ◽  
pp. 1771-1779 ◽  
Author(s):  
Maomei Ruan ◽  
Min Liu ◽  
Qianggang Dong ◽  
Libo Chen
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Signal Transduction ◽  
Cell Proliferation ◽  
Thyroid Cancer ◽  
Western Blot ◽  
Glucose Transporter ◽  
Signal Transduction Pathways ◽  
Papillary Thyroid ◽  
Cycle Arrest

Abstract Context: The aberrant silencing of iodide-handling genes accompanied by up-regulation of glucose metabolism presents a major challenge for radioiodine treatment of papillary thyroid cancer (PTC). Objective: This study aimed to evaluate the effect of tyrosine kinase inhibitors on iodide-handling and glucose-handling gene expression in BHP 2-7 cells harboring RET/PTC1 rearrangement. Main Outcome Measures: In this in vitro study, the effects of sorafenib or cabozantinib on cell growth, cycles, and apoptosis were investigated by cell proliferation assay, cell cycle analysis, and Annexin V-FITC apoptosis assay, respectively. The effect of both agents on signal transduction pathways was evaluated using the Western blot. Quantitative real-time PCR, Western blot, immunofluorescence, and radioisotope uptake assays were used to assess iodide-handling and glucose-handling gene expression. Results: Both compounds inhibited cell proliferation in a time-dependent and dose-dependent manner and caused cell cycle arrest in the G0/G1 phase. Sorafenib blocked RET, AKT, and ERK1/2 phosphorylation, whereas cabozantinib blocked RET and AKT phosphorylation. The restoration of iodide-handling gene expression and inhibition of glucose transporter 1 and 3 expression could be induced by either drug. The robust expression of sodium/iodide symporter induced by either agent was confirmed, and 125I uptake was correspondingly enhanced. 18F-fluorodeoxyglucose accumulation was significantly decreased after treatment by either sorafenib or cabozantinib. Conclusions: Sorafenib and cabozantinib had marked effects on cell proliferation, cell cycle arrest, and signal transduction pathways in PTC cells harboring RET/PTC1 rearrangement. Both agents could be potentially used to enhance the expression of iodide-handling genes and inhibit the expression of glucose transporter genes.

scholarly journals Integrated Transcriptomic Analysis Revealed Hub Genes and Pathways Involved in Sorafenib Resistance in Hepatocellular Carcinoma

2021 ◽  
Vol 27 ◽  
Author(s):  
Xili Jiang ◽  
Wei Zhang ◽  
Lifeng Li ◽  
Shucai Xie
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Expression Profiles ◽  
Quantitative Polymerase Chain Reaction ◽  
Pearson Correlation ◽  
Acquired Resistance ◽  
Rank Aggregation ◽  
Health Concern ◽  
Hub Genes ◽  
Multikinase Inhibitor

Hepatocellular carcinoma (HCC), a high mortality malignancy, has become a worldwide public health concern. Acquired resistance to the multikinase inhibitor sorafenib challenges its clinical efficacy and the survival benefits it provides to patients with advanced HCC. This study aimed to identify critical genes and pathways associated with sorafenib resistance in HCC using integrated bioinformatics analysis. Differentially expressed genes (DEGs) were identified using four HCC gene expression profiles (including 34 sorafenib-resistant and 29 sorafenib-sensitive samples) based on the robust rank aggregation method and R software. Gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool. A protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING), and small molecules reversing sorafenib resistance were searched for using the connectivity map (CMAP) database. Pearson correlation and survival analyses of hub genes were performed using cBioPortal and Gene Expression Profiling and Interactive Analysis (GEPIA). Finally, the expression levels of hub genes in sorafenib-resistant HCC cells were verified using quantitative polymerase chain reaction (q-PCR). A total of 165 integrated DEGs (66 upregulated and 99 downregulated in sorafenib resistant samples compared sorafenib sensitive ones) primarily enriched in negative regulation of endopeptidase activity, extracellular exosome, and protease binding were identified. Some pathways were commonly shared between the integrated DEGs. Seven promising therapeutic agents and 13 hub genes were identified. These findings provide a strategy and theoretical basis for overcoming sorafenib resistance in HCC patients.

scholarly journals Screening and Functional Prediction of Key Candidate Genes in Hepatitis B Virus-Associated Hepatocellular Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xia Chen ◽  
Ling Liao ◽  
Yuwei Li ◽  
Hengliu Huang ◽  
Qing Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Metabolic Process ◽  
Hub Genes ◽  
Kegg Pathway Analysis ◽  
B Virus

Background. The molecular mechanism by which hepatitis B virus (HBV) induces hepatocellular carcinoma (HCC) is still unknown. The genomic expression profile and bioinformatics methods were used to investigate the potential pathogenesis and therapeutic targets for HBV-associated HCC (HBV-HCC). Methods. The microarray dataset GSE55092 was downloaded from the Gene Expression Omnibus (GEO) database. The data was analyzed by the bioinformatics software to find differentially expressed genes (DEGs). Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, ingenuity pathway analysis (IPA), and protein-protein interaction (PPI) network analysis were then performed on DEGs. The hub genes were identified using Centiscape2.2 and Molecular Complex Detection (MCODE) in the Cytoscape software (Cytoscape_v3.7.2). The survival data of these hub genes was downloaded from the Gene Expression Profiling Interactive Analysis (GEPIA). Results. A total of 2264 mRNA transcripts were differentially expressed, including 764 upregulated and 1500 downregulated in tumor tissues. GO analysis revealed that these DEGs were related to the small-molecule metabolic process, xenobiotic metabolic process, and cellular nitrogen compound metabolic process. KEGG pathway analysis revealed that metabolic pathways, complement and coagulation cascades, and chemical carcinogenesis were involved. Diseases and biofunctions showed that DEGs were mainly associated with the following diseases or biological function abnormalities: cancer, organismal injury and abnormalities, gastrointestinal disease, and hepatic system disease. The top 10 upstream regulators were predicted to be activated or inhibited by Z-score and identified 25 networks. The 10 genes with the highest degree of connectivity were defined as the hub genes. Cox regression revealed that all the 10 genes (CDC20, BUB1B, KIF11, TTK, EZH2, ZWINT, NDC80, TPX2, MELK, and KIF20A) were related to the overall survival. Conclusion. Our study provided a registry of genes that play important roles in regulating the development of HBV-HCC, assisting us in understanding the molecular mechanisms that underlie the carcinogenesis and progression of HCC.

JQ1, a Potential Therapeutic Molecule for Myeloid Leukemia with PTEN Deficiency

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 5899-5899 ◽  
Author(s):  
Nicholas J Baltz ◽  
Natalia C Colorado ◽  
Yan Yan ◽  
Shelly Lensing ◽  
Delli Robinson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Western Blot ◽  
Colony Formation ◽  
Myeloid Leukemia ◽  
Leukemia Cell ◽  
K562 Cells ◽  
Leukemia Cells ◽  
Jak Inhibitors ◽  
Facs Analysis ◽  
Leukemia Cell Lines

Abstract Acute myeloid leukemia (AML) is a hematologic malignancy that continues to have high relapse and treatment-related mortality rates, despite recent advances in clinical management and therapy. Janus kinase (JAK) inhibitors inhibit the activity of the JAK/STAT pathway and have demonstrated some clinical responses in AML patients. However, survival analysis suggests that more than half of AML patients do not benefit from treatment with JAK inhibitors. Furthermore, PTEN deficiency is frequently found in patients in the late stages of cancer, which causes hyperactivated AKT and MAPK pathways. However, emerging data suggests that leukemia cells with PTEN deficiency are resistant to MAPK inhibitors. Over the past decade, it has been demonstrated that dysregulated epigenetics play an important role in myeloid leukemogenesis. The bromodomain and extraterminal domain (BET) family includes adaptor proteins Brd2, Brd3, Brd4, and Brdt that regulate gene expression via binding to acetylated chromatin and subsequently activating RNA Polymerase II driven transcriptional elongation, resulting in the promotion of gene expression. BRD4 is a BET protein required for disease maintenance in AML. JQ1 is a small molecule that interferes with transcriptional regulators, such as BRD4, by preventing them from interacting with acetylated regions of the genome and thus inhibiting the transcriptional activation of BRD4 target genes. Prior research in lymphocytic leukemia cell lines suggests that JQ1 also decreases STAT5-dependent gene transcriptional activities. We hypothesize that the inhibition of BET proteins may correct the over-activated transcriptional activities in myeloid leukemia cells and induce disease regression. We tested our hypothesis in PTEN deficient myeloid leukemia cell lines, TF-1a and K562, and used human cord blood mononuclear cells (CB) for normal cell comparison. Methods: 1) To test whether JQ1 can inhibit colony formation, we seeded cells on 0.3% agar and McCoys' 5A medium supplemented with nutrients and 15% fetal bovine serum, without cytokines, and added JQ1 diluents to the cultures at concentrations of 32.5-1000nM overnight after the cultures were established. 2) To test whether JQ1 can inhibit leukemia cell proliferation, we cultured cells in liquid medium with JQ1 for 48-72 hours, and quantified the viable cells using alamarBlue® assay. 3) To investigate whether JAK/STAT5 activity is altered by JQ1 in leukemia cells, we quantified phosphorylated STAT5 (pSTAT5) in cells via flow cytometry and western blot. We treated the cells with JQ1 at various concentrations for 2 hours and then stimulated the cells for 15 minutes in medium with 0.5% BSA and 10ng/mL GM-CSF prior to staining the cells with anti-pStat5 (pY694) antibody conjugated with Alexa Fluor® 647 for FACS analysis or lysing the cells for western blot analysis. Results: In the colony formation assay, we found that TF-1a cells were more sensitive to JQ1 than the CB cells and K562, with an IC50 of 62.5-125 nM for TF-1a cells (p<0.0001), and 250-500nM for both CB and K562 cells, respectively. Proliferation assay results also supported that TF1a cells are sensitive to JQ1 with an IC50 of 125-250nM, whereas neither CB nor K562 reached the IC50 in the tested concentration range. This suggests that the IC50 of JQ1 for TF1a cells is achievable at concentrations that are mostly nontoxic to normal CB cells, but K562 cells are not sensitive to JQ1. FACS analysis revealed that pSTAT5 is constitutively activated in K562 cells but not in TF-1a cells. Interestingly, the levels of pSTAT5 in both TF-1a and K562 cells were not altered by JQ1 treatment at tested concentrations, which was confirmed by western blot. Conclusions: Our data suggest: 1) JQ1 and other bromodomain inhibitors could be potential therapeutic molecules for selected myeloid leukemias; 2) JQ1 inhibition on colony formation and proliferation in TF-1a cells is not pSTAT5 related. Further studies are underway to test whether JQ1 is effective in primary mouse leukemia cells with Pten deficiency. Disclosures No relevant conflicts of interest to declare.

Translational significance of a newly identified hepatocellular carcinoma tumor suppressor gene on chromosome 8p.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e15097-e15097
Author(s):  
Han Chong Toh ◽  
Francis Enane ◽  
Marissa Teo ◽  
Hideki Makishima ◽  
JoAnna Ng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Tumor Suppressor ◽  
Tumor Suppressor Gene ◽  
Snp Array ◽  
Uniparental Disomy ◽  
Biological Significance ◽  
Suppressor Gene ◽  
Cell Cycle Exit ◽  
Malignant Liver

e15097 Background: After deletion of 17p that removes the tumor suppressor gene (TSG) TP53, deletion of 8p is the next most common chromosome abnormality in hepatocellular carcinoma (HCC). However, 8p TSG are insufficiently defined. Methods: Integrated genomic analysis of HCC and non-malignant liver obtained at therapeutic segmentectomy from the same patients. Results: A minimally deleted region on 8p was identified by SNP array. This incorporated GATA4. Therefore, GATA4 was Sanger sequenced in paired HCC/non-malignant liver: recurrent somatic non-synonymous missense mutations were identified in exon 4 (V267M n=5) or exon 6 (S357T n=6, R362N n=2, T366R n=2). Biallelic abnormalities were deletion and mutation (n=6) or mutation and uniparental disomy (n=4), with mutation or deletion of at least one GATA4 allele in 29/47 (62%) of HCC cases. The other GATA4 exons were mutation free. Although missense mutation is not intrinsically expected to decrease GATA4 expression, GATA4 mRNA was significantly decreased in cases with mutation as well as deletion (p<0.01) compared to non-malignant liver or wild-type GATA4 HCC. GATA4 drives liver differentiation, and the biological significance of GATA4 deficiency was demonstrated by significant enrichment (49%) for liver differentiation genes (p<1.2exp-124, Benjamini corrected) amongst genes with decreased expression in HCC compared to non-malignant liver. From an oncogenesis perspective, the most important of these hepatocyte genes (e.g., HNF4A, CEBPD) antagonize MYC to terminate proliferation: GATA4 introduction (expression vector) into HCC cells containing mutated or deleted GATA4 (HepG2 and PLC respectively) restored HNF4A and CEBPD expression, suppressed MYC protein, upregulated p27/CDKN1B that mediates cell cycle exit by maturation and significantly decreased HCC proliferation without apoptosis. In objectively quantified immunohistochemical analyses (ImageIQ), HCC cases with GATA4 mutation/deletion had significantly increased MYC protein (p<0.05). Conclusions: 8p deletion/GATA4 mutation in HCC suppresses cell cycle exit by maturation, thus complementing 17p deletion that suppresses cell cycle exit by apoptosis.

RNA profiling of neuroendocrine tumors (NETs) to define new targets and mechanisms of progression.

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 302-302
Author(s):  
Namrata Vijayvergia ◽  
Suraj Peri ◽  
Karthik Devarajan ◽  
Jianming Pei ◽  
Yulan Gong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Drug Targets ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Enrichment Score ◽  
Rna Profiling ◽  
Histone Lysine Methyltransferase ◽  
Canonical Pathways ◽  
Lysine Methyltransferase

302 Background: NETs lack mutations in the “classical” signaling pathways but share mutations in regulators of gene expression (Jiao; 2011). We compared gene expression in PD & WD NETs to identify novel targets and biomarkers of differentiation. Methods: High quality RNA, extracted from paraffin blocks of deidentified NETs under an IRB-approved protocol, was profiled using a 770 gene panel (nCounter PanCancer pathway, Nanostring Technologies). The resulting data was used to identify the differentially expressed genes between PD and WD NETs using limma software (Ritchie; 2015). Gene Set Enrichment Analysis (Subramanian; 2005) identified differential pathway enrichment by calculating a Normalized Enrichment Score (NES). Results: Analysis of 16 PD and 23 WD NET samples identified 154 genes as extreme outliers ( > 2 fold up/downregulation between the subtypes). Compared to WD NETS, drug targets of interest overexpressed in PD NETs were histone lysine methyltransferase EZH2, and a cell cycle regulator CHEK1 (6.5x and 8.1x, respectively, p < 0.001). In contrast, serine/threonine protein kinase PAK 3 was upregulated in WD (10.6x, p < 0.001). These and other biomarkers will be further validated by immunolabeling of tissue sections. We also found differential enrichment of canonical pathways in PD versus WD NETs (table). Conclusions: Extreme outlier transcripts identified in PD & WD NETs support investigation of inhibitors of EZH2 (e.g. EPZ6438) and CHEK1 (e.g. LY2606368) in PD and PAK3(e.g. FRAX597) in WD NETs. Genes involved in cell cycle regulation and DNA repair in PD NETs and calcium / G protein coupled receptor signaling in WD NET account for biological differences between the 2 molecular subtypes and warrant future investigation as classifiers for NETs. Our findings provide mechanistic insights into the biology of NET and targets for therapy with direct clinical implications.[Table: see text]

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