Browse
Submit Data
Databases
API
Help

Dataset Information

26 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Identification of targets of JS-K against HBV-positive human hepatocellular carcinoma HepG2.2.15 cells with iTRAQ proteomics.

ABSTRACT: JS-K, a nitric oxide-releasing diazeniumdiolates, is effective against various tumors. We have discovered that JS-K was effective against Hepatitis B virus (HBV)-positive HepG2.2.15 cells. This study used iTRAQ to identify differentially expressed proteins following JS-K treatment of HepG2.2.15 cells. Silenced Transgelin (shTAGLN-2.15) cells were constructed, and the cell viability was analyzed by the CCK8 assay after treatment with JS-K. There were 182 differentially expressed proteins in JS-K treated-HepG2.2.15 cells; 73 proteins were up-regulated and 109 proteins were down-regulated. These proteins were categorized according to GO classification. KEGG enrichment analysis showed that Endocytosis, Phagosome and Proteoglycans were the most significant pathways. RT-PCR confirmed that the expression levels of TAGLN, IGFBP1, SMTN, SERPINE1, ANXA3, TMSB10, LGALS1 and KRT19 were significantly up-regulated, and the expression levels of C5, RBP4, CHKA, SIRT5 and TRIM14 were significantly down-regulated in JS-K treated-HepG2.2.15 cells. Western blotting confirmed the increased levels of USP13 and TAGLN proteins in JS-K treated-HepG2.2.15 cells. Molecular docking revealed the binding of JS-K to TAGLN and shTAGLN-2.15 cells were resistant to JS-K cytotoxicity, suggesting that TAGLN could be an important target in JS-K anti-HBV-positive liver cancer cells. These proteomic findings could shed new insights into mechanisms underlying the effect of JS-K against HBV-related HCC.

SUBMITTER: Liu Z

PROVIDER: S-EPMC8129129 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Recurrent HBV Integration Targets as Potential Drivers in Hepatocellular Carcinoma.

Project description:Chronic hepatitis B virus (HBV) infection is the major etiology of hepatocellular carcinoma (HCC), frequently with HBV integrating into the host genome. HBV integration, found in 85% of HBV-associated HCC (HBV-HCC) tissue samples, has been suggested to be oncogenic. Here, we investigated the potential of HBV-HCC driver identification via the characterization of recurrently targeted genes (RTGs). A total of 18,596 HBV integration sites from our in-house study and others were analyzed. RTGs were identified by applying three criteria: at least two HCC subjects, reported by at least two studies, and the number of reporting studies. A total of 396 RTGs were identified. Among the 28 most frequent RTGs, defined as affected in at least 10 HCC patients, 23 (82%) were associated with carcinogenesis and 5 (18%) had no known function. Available breakpoint positions from the three most frequent RTGs, TERT, MLL4/KMT2B, and PLEKHG4B, were analyzed. Mutual exclusivity of TERT promoter mutation and HBV integration into TERT was observed. We present an RTG consensus through comprehensive analysis to enable the potential identification and discovery of HCC drivers for drug development and disease management.

| S-EPMC8224658 | biostudies-literature

Gene expression profile of HBV-positive hepatocellular carcinoma.

Project description:Investigate the intrinsic molecular mechanisms of HBV-related HCCs by RNA-seq.

2023-11-30 | GSE198946 | GEO

HBV Integration-mediated Cell Apoptosis in HepG2.2.15.

Project description:Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the second leading cause of cancer deaths in the word. Hepatitis B virus (HBV) infection plays an important role in the development of HCC. However, the mechanisms by which HBV integration affects host cells remain poorly understood. HepG2.2.15 cell line is derived from HCC cell line HepG2 with stable transfection HBV expression. In this study, HepG2.2.15 cells showed decreased proliferation, G1 cell cycle arrest and increased apoptosis, when compared to HepG2 cells. HBV capture sequencing was conducted in both genome and transcriptome level, followed by RNA expression sequencing in HepG2.2.15. Here, CAMSAP2/CCDC12/DPP7/OR4F3 were found to be targets for HBV integration in both genome and transcriptome level, accompanied by alteration in their expression when compared to HepG2. Among these genes, DPP7 was the only one gene with HBV integration into its exon, meanwhile DPP7 expression level was also downregulated in HepG2.2.15 as compared to HepG2. Furthermore, DPP7 knockdown resulted in increased apoptosis through upregulation of the Bax/Bcl2 ratio in HepG2 cells. Our results suggest that HBV integration of DPP7 was involved in cell apoptosis.

| S-EPMC6692610 | biostudies-literature

Serum microRNA profiles as prognostic biomarkers for HBV-positive hepatocellular carcinoma.

Project description:To establish serum microRNA profiles as prognostic biomarkers in hepatocellular carcinoma patients (HCCs), we used deep sequencing to screen serum microRNAs in a discovery set .Twelve up-regulated serum miRNAs were selected for qPCR analysis in a training set. MiR-192-5p and miR-29a-3p were identified and associated with HCC prognosis. HCCs with high concentrations of miR-192-5p and miR-29a-3p had poorer overall survival (OS) and progression-free survival (PFS) than those with low concentrations. We calculated a prognostic index (PI) score and classified patients into low-, medium- and high-risk groups. OS and PFS among the 3 groups from the training set were significantly different (all P < 0.05). PI (PIOS, PIPFS) score was the only independent prognostic predictor for OS and PFS of HCCs in the training set. These results were further confirmed in a validation set. In conclusion, differentially expressed serum miRNAs can be helpful for predicting survival in HCCs.

| S-EPMC5216748 | biostudies-literature

Gene expression profile of HBV-positive hepatocellular carcinoma.

Project description:Gene expression profile of HBV-positive hepatocellular carcinoma.

| PRJNA817553 | ENA

Metabolomic Profiles for HBV Related Hepatocellular Carcinoma Including Alpha-Fetoproteins Positive and Negative Subtypes.

Project description:Background: Hepatocellular carcinoma (HCC) is very common globally prevalent cancer. Due to its poor clinical prognosis, increasing the diagnostic rate of HCC is urgently needed. Herein, we validate discovered metabolomic biomarkers to distinguish Hepatitis B virus (HBV)-related HCC, including alpha-fetoprotein (AFP) negative (AFP-) and positive (AFP+) individuals. Methods: We recruited 130 HCC subjects (independent case-control, randomized clinical cohorts) to our study. We separated the subjects randomly into two panels: (1) 58 individuals for the discovery panel; and (2) 72 individuals for the validation panel. For each panel, gender and age-matched hepatitis B group (HBG) and healthy group were included as controls. Plasma samples were collected for metabolic profiling by liquid chromatography-mass spectrometry-based metabolomics assays. We applied both non-targeted metabolomics analyses and targeted metabolomics analyses. Significantly changed metabolites (SCMs) were identified. The power of SCMs to discriminate HCC and HBG or healthy group was determined by receiver operating characteristic curve (ROC) analysis. Results: Ten SCMs were selected form the discovery panel, and further verified in the validation panel. ROC analyses indicated that 1 SCMs (LysoPC (24:0)) could discriminate HCC from HBG (AUC = 0.765). Further, 8 SCMs including (LysoPC (17:0), LysoPC (20:4(8Z,11Z,14Z,17Z)), LysoPC (22:0), LysoPC (24:0), PE (P-16:0/22:4(7Z,10Z,13Z,16Z)), SM (d18:1/22:1(13Z)), Creatinine, and L-Isoleucine) displayed a heightened ability to discriminate between HCC and healthy controls (AUC were more than 0.800). Most of these SCMs were important in lipid metabolism. Conclusions: LysoPC (24:0) could distinguished HCC from HBG, and 8 SCMs distinguished HCC from healthy controls. LysoPC and other metabolites have the potential to serve as non-invasive biomarkers for HBV related AFP- and AFP+ HCC.

| S-EPMC6803550 | biostudies-literature

MicroRNA profile differences in Hepatitis B virus (HBV) positive and negative hepatocellular carcinoma

Project description:MicroRNAs (miRNAs) exhibit essential regulatory functions related to cell growth, apoptosis, development and differentiation. Dysregulated expression of miRNAs is associated with a wide variety of human diseases. As such miRNA signatures are valuable as biomarkers for disease and for making treatment decisions. Hepatitis B virus (HBV) is a major risk factor for hepatocellular carcinoma (HCC). Here we screened for miRNAs in chronic HBV associated HCC. To evaluate the effect of HBV infection on the change in expression of miRNAs, 12 pairs of samples from HCC and non-tumor tissues (including 6 HBV-positive HCC and 6 HBV-negative HCC and their non-tumor tissues) were collected. The extracted RNAs were evaluated to detect the expression of miRNAs. Using ANOVA to screen the differential expression of miRNAs at P-value â¤ 0.01, fold change â¥ 2 or â¤ 0.5, 225 miRNAs were detected.

2016-06-30 | E-MTAB-4809 | biostudies-arrayexpress

Integrated Analysis of the Altered lncRNA, microRNA, and mRNA Expression in HBV-Positive Hepatocellular Carcinoma.

Project description:Hepatitis B virus (HBV) infection is the most prominent risk factor for developing hepatocellular carcinoma (HCC), which can increase the incidence of HCC by more than 100 times. Accumulated evidence has revealed that non-coding RNAs (ncRNAs) play a regulatory role in various tumors through the long non-coding RNA (lncRNA)-microRNA (miRNA)-mRNA regulation axis. However, the involvement of the ncRNA regulatory network in the progression of HBV infection-induced HCC remains elusive. In the current work, five tumor samples from patients with hepatitis B surface antigen (HBsAg)-positive HCC and three tumor samples from patients with HBsAg-negative HCC were collected for whole-transcriptome sequencing. Between the two groups, 841 lncRNAs, 54 miRNAs, and 1118 mRNAs were identified to be differentially expressed (DE). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that DE genes were mainly involved in cancer-related pathways, including Wnt and MAPK signaling pathways. The Gene Expression Omnibus (GEO) analysis further validated the selected DE mRNAs. The DE lncRNA-miRNA-mRNA network was built to explore the effect of HBV infection on the regulation of ncRNAs in HCC. These findings provide novel insights into the role of HBV infection in the progression of HCC.

| S-EPMC9146868 | biostudies-literature

CRISPR/Cas9-mediated knockout of HBsAg inhibits proliferation and tumorigenicity of HBV-positive hepatocellular carcinoma cells.

Project description:Chronic hepatitis B virus (HBV) infection remains the most common risk factor for hepatocellular carcinoma (HCC). High HBV surface antigen (HBsAg) levels are highly correlated with hepatocarcinogenesis and HBV-associated HCC development. However, the role and detailed mechanisms associated with HBsAg in HCC development remain elusive. In this study, we designed specific single guide RNAs (sgRNAs) targeting the open reading frames, preS1/preS2/S, of the HBV genome and established HBsAg knockout HCC cell lines using the CRISPR/Cas9 system. We showed that knockout of HBsAg in HCC cell lines decreased HBsAg expression and significantly attenuated HCC proliferation in vitro, as well as tumorigenicity in vivo. We also found that overexpression of HBsAg, including the large (LHBs), middle (MHBs), and small (SHBs) surface proteins promoted proliferation and tumor formation in HCC cells. Moreover, we demonstrated that knockout of HBsAg in HCC cells decreased interleukin (IL)-6 production and inhibited signal transducer and activator of transcription 3 (STAT3) signaling, while overexpression of HBsAg induced a substantial accumulation of pY-STAT3. Collectively, these results highlighted the tumorigenic role of HBsAg and implied that the IL-6-STAT3 pathway may be implicated in the HBsAg-mediated malignant potential of HBV-associated HCC.

| S-EPMC6221038 | biostudies-literature

Serum LncRNAs Profiles Serve as Novel Potential Biomarkers for the Diagnosis of HBV-Positive Hepatocellular Carcinoma.

Project description:BackgroundHepatocellular carcinoma (HCC) is a common malignancy that has a poor prognosis because there is lack of methods for early diagnosis. We aimed to utilize two serum long non-coding RNAs (lncRNAs), uc001ncr and AX800134, to diagnose hepatitis B virus (HBV)-positive HCC.MethodslncRNA microarrays were utilized to measure the differential expression of lncRNAs between tumor tissues and corresponding non-tumor tissues in HBV-positive hapatocellular carcinoma. uc001ncr and AX800134 were selected as candidate lncRNAs and detected in three independent cohorts containing a total of 684 participants (healthy individuals and chronic HBV patients and HBV-positive HCC patients) who were recruited between March 2011 and December 2012. A logistic regression model was constructed using a training cohort (n = 353) and validated using an independent cohort (n = 181). The area under the receiver operating characteristic curve (AUC) was utilized to evaluate the diagnostic accuracy.ResultsWe determined that a panel based on the expression of uc001ncr and AX800134 accurately diagnosed HBV-positive HCC (AUC values of 0.9494 and 0.9491 for the training and validation cohorts, respectively). The diagnostic performance of the panel remained high in patients with AFP≤400 ng/ml (AUC values of 0.9371 and 0.9527 for the training and validation cohorts, respectively). The panel also diagnosed early HCC (AUC values of 0.9450 and 0.9564 for the training and validation cohorts, respectively).ConclusionOur results indicated that the serum expression of uc001ncr and AX800134 has potential as novel potential biomarker for the diagnosis of HCC, especially in patients with AFP≤400 ng/ml or early-stage disease (BCLC 0+A).

| S-EPMC4684503 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data