Neoangiogenesis-related genes are hallmarks of fast-growing hepatocellular carcinomas and worst survival. Results from a prospective study

Villa, Erica; Critelli, Rosina Maria; Lei, Barbara; Marzocchi, G; Cammà, Calogero; Giannelli, Gianluigi; Pontisso, Patrizia; Cabibbo, Giuseppe; Enea, Marco; Colopi, Stefano; Caporali, Cristian; Pollicino, Teresa; Milosa, Fabiola; Karampatou, Aimilia; Todesca, Paola; Bertolini, Elena; Macciò, Livia; Martínez‐Chantar, María Luz; Turola, Elena; Buono, Mariagrazia Del; Maria, Nicola De; Ballestri, Stefano; Schepis, Filippo; Loria, Paola; Gerunda, G.E.; Losi, Luisa; Cillo, Umberto

doi:10.1136/gutjnl-2014-308483

Cited by 208 publications

(249 citation statements)

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“…We found that the TMEM173 mRNA expression was decreased in tumor tissues in both GSE54236 [14] (P = 0.045) and GSE26411 [15] (P<0.001) datasets (Fig 1A and 1B). We next investigated the protein expression of TMEM173 in HCC samples and adjacent non-tumor tissues.…”

Section: Resultsmentioning

confidence: 97%

Decreased Expression of TMEM173 Predicts Poor Prognosis in Patients with Hepatocellular Carcinoma

et al. 2016

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Hepatocellular carcinoma (HCC) is one of the most lethal cancer types, and chronic infection with Hepatitis B Virus (HBV) is identified as the strongest risk factor for HCC. Transmembrane Protein 173 (TMEM173) is a pattern recognition receptor which functions as a major regulator of the innate immune response to viral and bacterial infections. However, the prognostic value of TMEM173 in HCC remains elusive. Thus, we aimed to evaluate the potential prognostic significance of TMEM173 expression in HCC patients following curative resection. Immunohistochemistry was used to detect TMEM173 expression in 96 HCC patients. We found that TMEM173 protein expression was remarkably decreased in tumor tissues compared to non-tumor tissues, and that TMEM173 staining intensity was inversely correlated with tumor size, tumor invasion TNM stage and overall survival (OS) in HCC patients. Multivariate analysis supported TMEM173 as an independent prognostic factor, and identified that combining TMEM173 expression with TNM stage showed better prognostic efficiency for OS in HCC patients. In summary, TMEM173 was discovered having an independent prognostic value and may serve as a potential immunotherapeutic target for HCC.

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Section: Resultsmentioning

confidence: 97%

Decreased Expression of TMEM173 Predicts Poor Prognosis in Patients with Hepatocellular Carcinoma

et al. 2016

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“…METTL7A expression was analysed using RNA-Seq datasets from the Cancer Genome Atlas (TCGA) project (Weinstein et al 2013) and a microarray gene expression dataset from the NCBI Gene Expression Omnibus (GEO) database (GEO542361) (Villa et al 2015).…”

Section: Mettl7a As a Novel Tumor Suppressor In Hccmentioning

confidence: 99%

“…Two publically available datasets for survival data analysis of patients with HCC with respect to METTL7A expression: RNA-Seq datasets (including 370 HCC tumors and 50 adjacent NT liver samples) from TCGA (LIHC) (Weinstein et al 2013) (https://tcga-data.nci.nih.gov/tcga/) and microarray gene expression datasets (including 81 HCC tumors and 80 adjacent NT liver samples) from GEO54236 (Villa et al 2015) (http://www.ncbi.nlm.nih.gov/gds). Prior to the survival analysis, raw RNA-Seq counts were normalized using the total numbers of mappable reads across all samples, while the microarray data normalization was performed using the Cross-Correlation method (Chua et al 2006).…”

Section: Publically Available Databasesmentioning

confidence: 99%

“…In coding regions, A-to-I RNA editing can lead to a codon change and the consequent alterations of proteincoding sequences since inosine is interpreted by the ribosome as guanosine (Nishikura 2010). The differential editing frequencies of these recoding sites are found to impact on human diseases such as neurological diseases and cancer (Martinez et al 2008;Chen et al 2013;Galeano et al 2013;Han et al 2014;Paz-Yaacov et al 2015;Villa et al 2015). In non-coding regions, the vast majority of A-to-I RNA editing sites are in repetitive Alu elements embedded in 3' untranslated regions (3'UTRs) (Farajollahi and Maas 2010) and have unknown functional relevance.…”

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confidence: 99%

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An RNA editing/binding-independent gene regulatory mechanism of ADARs and its clinical implication in cancer

Liu

Song

Chan

et al. 2016

Preprint

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Adenosine-to-inosine (A-to-I) editing, catalysed by Adenosine DeAminases acting on double-stranded RNA (dsRNA) (ADAR), occurs predominantly in the 3' untranslated regions (3'UTRs). Here we uncover an unanticipated link between ADARs (ADAR1 and ADAR2) and the expression of target genes undergoing extensive 3'UTR editing. Using METTL7A (Methyltransferase Like 7A), a novel tumor suppressor as an exemplary target gene, we demonstrate that its expression could be repressed by ADARs beyond their RNA editing and dsRNA binding functions. ADARs interact with Dicer to augment the processing of pre-miR-27a to mature miR-27a. Consequently, mature miR-27a targets the METTL7A 3'UTR to repress its expression level. In sum, our study unveils that the extensive 3'UTR editing is merely a footprint of ADAR binding, and is dispensable for the regulation of at least a subset of target genes. Instead, ADARs contribute to cancer progression by regulating cancer-related gene expression through their non-canonical functions independent of RNA editing and dsRNA binding.The functional significance of ADARs is much more diverse than previously appreciated and this gene regulatory function of ADARs is most likely to be of higher importance than the best-studied editing function. This novel non-editing side of ADARs opens another door to target cancer. This study is timely and represents a major break-through in the field of ADAR gene regulation and cancer biology. (215 words)peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/062778 doi: bioRxiv preprint first posted online Qi, L., et al., Page 3 Adenosine DeAminases acting on dsRNA (ADAR) are highly conserved family of enzymes catalysing adenosine to inosine deamination (A-to-I editing) (Bass and Weintraub 1988;Wagner et al. 1989).There are 3 ADAR proteins (ADAR1, ADAR2 and ADAR3) in humans which all share a common modular structure characterized by 2 to 3 N-terminal dsRNA binding domains (dsRBDs) and a conserved C-terminal catalytic deaminase domain (Nishikura 2010;Qi et al. 2014). Being the beststudied function associated with ADAR1 and ADAR2 (ADARs), A-to-I RNA editing contributes to multi-level gene regulation depending on where it occurs. ADAR3, which has no documented deaminase activity, is only expressed in central nervous system (Melcher et al. 1996). In coding regions, A-to-I RNA editing can lead to a codon change and the consequent alterations of proteincoding sequences since inosine is interpreted by the ribosome as guanosine (Nishikura 2010). The differential editing frequencies of these recoding sites are found to impact on human diseases such as neurological diseases and cancer (Martinez et al. 2008;Chen et al. 2013;Galeano et al. 2013;Han et al. 2014;Paz-Yaacov et al. 2015;Villa et al. 2015). In non-coding regions, the vast majority of A-to-I RNA editing sites are in repetitive Alu elements embedded in 3' untranslated regions (3'UTRs) (Fa...

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“…Villa E et al detected whole genome microarray expression profiling of 161 HCC samples, and revealed that five-gene signature ( ANGPT2 , NETO2 , NR4A1 , DLL4 , ESM1 ) was able to predict fast growth and worst survival of HCC patients [6]. The exploration of prognostic markers may facilitate individualized therapies.…”

Section: Introductionmentioning

confidence: 99%

A transcriptome profile in hepatocellular carcinomas based on integrated analysis of microarray studies

Wang

et al. 2017

Diagn Pathol

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BackgroundDespite new treatment options for hepatocellular carcinomas (HCC) recently, 5-year survival remains poor, ranging from 50 to 70%, which may attribute to the lack of early diagnostic biomarkers. Thus, developing new biomarkers for early diagnosis of HCC, is extremely urgent, aiming to decrease HCC-related deaths.MethodsIn the study, we conducted a comprehensive characterization of gene expression data of HCC based on a bioinformatics method. The results were confirmed by real time polymerase chain reaction (RT-PCR) and TCGA database to prove the credibility of this integrated analysis.ResultsAfter integrating analysis of seven HCC gene expression datasets, 1167 differential expressed genes (DEGs) were identified. These genes mainly participated in the process of cell cycle, oocyte meiosis, and oocyte maturation mediated by progesterone. The results of experiments and TCGA database validation in 10 genes was in full accordance with findings in integrated analysis, indicating the high credibility of our integrated analysis of different gene expression datasets. ASPM, CCT3, and NEK2 was showed to be significantly associated with overall survival of HCC patients in TCGA database.ConclusionThis method of integrated analysis may be a useful tool to minish the heterogeneity of individual microarray, hopefully outputs more accurate HCC transcriptome profiles based on large sample size, and explores some potential biomarkers and therapy targets for HCC.Electronic supplementary materialThe online version of this article (doi:10.1186/s13000-016-0596-x) contains supplementary material, which is available to authorized users.

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Neoangiogenesis-related genes are hallmarks of fast-growing hepatocellular carcinomas and worst survival. Results from a prospective study

Abstract: ClinicalTrials.gov Identifier: NCT01657695.

Cited by 208 publications

References 37 publications

Decreased Expression of TMEM173 Predicts Poor Prognosis in Patients with Hepatocellular Carcinoma

Decreased Expression of TMEM173 Predicts Poor Prognosis in Patients with Hepatocellular Carcinoma

An RNA editing/binding-independent gene regulatory mechanism of ADARs and its clinical implication in cancer

A transcriptome profile in hepatocellular carcinomas based on integrated analysis of microarray studies

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