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Osteopontin and latent-TGF ? binding-protein 2 as potential diagnostic markers for HBV-related hepatocellular carcinoma.

ABSTRACT: Chronic Hepatitis B (HB) is the main risk factor for chronic liver disease (CLD) and hepatocellular carcinoma (HCC) in many low-resource countries, where diagnosis is constrained by lack of clinical, histopathological and biomarker resources. We have used proteomics to detect plasma biomarkers that outperform ?-Fetoprotein (AFP), the most widely used biomarker for HCC diagnosis in low-resource contexts. Deep-plasma proteome analysis was performed in HCC patients, patients with CLD and in HB-carrier controls from Thailand (South-East Asia) and The Gambia (West-Africa). Mass spectrometry profiling identified latent-transforming growth factor ? binding-protein 2 (LTBP2) and Osteopontin (OPN) as being significantly elevated in HCC versus CLD and controls. These two proteins were further analyzed by ELISA in a total of 684 plasma samples, including 183 HCC, 274 CLD and 227 asymptomatic controls. When combined, LTBP2 and OPN showed an area under the receiver operating curve of 0.85 in distinguishing HCC from CLD in subjects with AFP <20 ng/mL. In a prospective cohort of 115 CLD patients from Korea, increased plasma levels of LTBP2 and/or OPN were detected in plasma collected over 2 years prior to diagnosis in 21 subjects who developed HCC. Thus, the combination of LTBP2 and OPN outperformed AFP for diagnosis and prediction of HCC and may therefore improve biomarker-based detection of HBV-related HCC.

SUBMITTER: da Costa AN

PROVIDER: S-EPMC4617682 | biostudies-other | 2015 Jan

REPOSITORIES: biostudies-other

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Osteopontin and latent-TGF β binding-protein 2 as potential diagnostic markers for HBV-related hepatocellular carcinoma.

da Costa Andre Nogueira AN Plymoth Amelie A Santos-Silva Daniela D Ortiz-Cuaran Sandra S Camey Suzy S Guilloreau Paule P Sangrajrang Suleeporn S Khuhaprema Thiravud T Mendy Maimuna M Lesi Olufunmilayo A OA Chang Hee-Kyung HK Oh Jin-Kyoung JK Lee Duk-Hee DH Shin Hai-Rim HR Kirk Gregory D GD Merle Philippe P Beretta Laura L Hainaut Pierre P

International journal of cancer 20140515 1

Chronic Hepatitis B (HB) is the main risk factor for chronic liver disease (CLD) and hepatocellular carcinoma (HCC) in many low-resource countries, where diagnosis is constrained by lack of clinical, histopathological and biomarker resources. We have used proteomics to detect plasma biomarkers that outperform α-Fetoprotein (AFP), the most widely used biomarker for HCC diagnosis in low-resource contexts. Deep-plasma proteome analysis was performed in HCC patients, patients with CLD and in HB-carr ...[more]

PMID: 24803312

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Project description:Background: Hepatocellular carcinoma (HCC) is a main cause of malignancy-related death all over the world with a poor prognosis. The current research is focused on developing novel prognostic and diagnostic models of Hepatocellular carcinoma from the perspective of hepatitis B virus (HBV)-related genes, and predicting its prognostic characteristics and potential reliable biomarkers for Hepatocellular carcinoma diagnosis. Methods: As per the information related to Hepatocellular carcinoma expression profile and the clinical data in multiple public databases, we utilized limma for assessing the differentially expressed genes (DEGs) in HBV vs non- hepatitis B virus groups, and the gene set was enriched, analyzed and annotated by WebGestaltR package. Then, STRING was employed to investigate the protein interactions. A risk model for evaluating Hepatocellular carcinoma prognosis was built with Lasso Cox regression analysis. The effect patients receiving immunotherapy was predicted using Tumor Immune Dysfunction and Exclusion (TIDE). Additionally, pRRophetic was used to investigate the drug sensitivity. Lastly, the Support Vector Machine (SVM) approach was utilized for building the diagnostic model. Results: The Hepatocellular Carcinoma Molecular Atlas 18 (HCCDB18) data set was utilized for the identification of 1344 HBV-related differentially expressed genes, mainly associated with cell division activities. Five functional modules were established and then we built a prognostic model in accordance with the protein-protein interaction (PPI) network. Five HBV-related genes affecting prognosis were identified for constructing a prognostic model. Then, the samples were assigned into RS-high and -low groups as per their relevant prognostic risk score (RS). High-risk group showed worse prognosis, higher mutation rate of TP53, lower sensitivity to immunotherapy but higher response to chemotherapeutic drugs than low-risk group. Finally, the hepatitis B virus diagnostic model of Hepatocellular carcinoma was established. Conclusion: In conclusion, the prognostic and diagnostic models of hepatitis B virus gene-related Hepatocellular carcinoma were constructed. ABCB6, IPO7, TIMM9, FZD7, and ACAT1, the five HBV-related genes that affect the prognosis, can work as reliable biomarkers for the diagnosis of Hepatocellular carcinoma, giving a new insight for improving the prognosis, diagnosis, and treatment outcomes of HBV-type Hepatocellular carcinoma.

Project description:BackgroundIncreasing evidence demonstrated that long noncoding RNA (lncRNA) could affect inflammatory tumor immune microenvironment by modulating gene expression and could be used as a biomarker for HBC-related hepatocellular carcinoma (HCC) but still needs further research. The aim of the present study was to determine an lncRNA signature for the diagnosis of HBV-related HCC.MethodsHBV-related HCC expression profiles (GSE55092, GSE19665, and GSE84402) were abstracted from the GEO (Gene Expression Omnibus) data resource, and R package limma and RobustRankAggreg were employed to identify common differentially expressed genes (DEGs). Using machine learning, optimal diagnostic lncRNA molecular markers for HBV-related HCC were identified. The expression of candidate lncRNAs was cross-validated in GSE121248, and an ROC (receiver operating characteristic) curve of lncRNA biomarkers was carried out. Additionally, a coexpression network and functional annotation was built, after which a PPI (protein-protein interaction) network along with module analysis were conducted with the Cytoscape open source software.ResultA total of 38 DElncRNAs and 543 DEmRNAs were identified with a fold change larger than 2.0 and a P value < 0.05. By machine learning, AL356056.2, AL445524.1, TRIM52-AS1, AC093642.1, EHMT2-AS1, AC003991.1, AC008040.1, LINC00844, and LINC01018 were screened out as optional diagnostic lncRNA biosignatures for HBV-related HCC. The AUC (areas under the curve) of the SVM (support vector machine) model and random forest model were 0.957 and 0.904, respectively, and the specificity and sensitivity were 95.7 and 100% and 94.3 and 86.5%, respectively. The results of functional enrichment analysis showed that the integrated coexpressed DEmRNAs shared common cascades in the p53 signaling pathway, retinol metabolism, PI3K-Akt signaling cascade, and chemical carcinogenesis. The integrated DEmRNA PPI network complex was found to be comprised of 87 nodes, and two vital modules with a high degree were selected with the MCODE app.ConclusionThe present study identified nine potential diagnostic biomarkers for HBV-related HCC, all of which could potentially modulated gene expression related to inflammatory conditions in the tumor immune microenvironment. The functional annotation of the target DEmRNAs yielded novel evidence in evaluating the precise functions of lncRNA in HBV-related HCC.

Project description:BackgroundAlpha-fetoprotein (AFP), routinely used for diagnosis of hepatocellular carcinoma (HCC), is limited with relatively low sensitivity and high false positivity in HBV-related HCC (HBV-HCC). Thus, an alternative approach was explored to improve specificity/sensitivity for diagnosis of HBV-HCC, using the combination of AFP, inflammatory score, and liver function.MethodsChronic hepatitis B (CHB) (n = 510) and HBV-HCC (n = 473) patients were identified retrospectively for this study. The diagnostic value of single vs combined biomarkers for HBV-HCC was analyzed, using ROC curve.ResultsIt was observed that elderliness, male sex, cirrhosis, HBeAg+ or no-antiviral therapy, and elevation of ALT, AST, neutrophil-lymphocyte ratio (NLR), and AFP were associated with developing HBV-HCC. However, the cut-off ALT defined by Chinese standard, but not by AASLD, was a risk factor. Interestingly, AFP of HBeAg- HBV-HCC patients without cirrhosis was significantly higher than that of the HBeAg+ patients. AUC values for AFP, ALT, AST, or NLR were 0.84 (95% CI: 0.815-0.862), 0.533 (95% CI: 0.501-0.565), 0.696 (95% CI: 0.666-0.725), or 0.684 (95% CI: 0.654-0.713) with optimal cut-off at 7.21 ng/mL, 43 IU/mL, 38 IU/mL, or 2.61, respectively. Combination of AFP with ALT, AST, and NLR improved the diagnostic performance for HBV-HCC, compared to any of the single biomarkers or any other combinations among these patients (except no-cirrhosis).ConclusionsElderliness, male sex, elevated ALT, AST, NLR, AFP, cirrhosis, HBeAg+ , and no-antiviral treatment were independent risk factors for HBV-HCC. AASLD standard of ALT cut-off value may not be suitable for the Chinese population. Regular monitoring of HCC among HBeAg- patients with abnormal AFP may improve the management of HBV-HCC. The diagnostic performance of AFP combined with ALT, AST, and NLR for HBV-HCC was superior to single biomarker or any other combinations among these patients, and its diagnostic equation can be used as useful tool for differentiation of HBV-HCC from CHB.

Dataset Information

Osteopontin and latent-TGF ? binding-protein 2 as potential diagnostic markers for HBV-related hepatocellular carcinoma.

Publications

Osteopontin and latent-TGF β binding-protein 2 as potential diagnostic markers for HBV-related hepatocellular carcinoma.

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