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Prognostic MicroRNA Panel for HCV-Associated HCC: Integrating Computational Biology and Clinical Validation.

ABSTRACT: Early detection of hepatocellular carcinoma (HCC) will reduce morbidity and mortality rates of this widely spread disease. Dysregulation in microRNA (miRNA) expression is associated with HCC progression. The objective is to identify a panel of differentially expressed miRNAs (DE-miRNAs) to enhance HCC early prediction in hepatitis C virus (HCV) infected patients. Candidate miRNAs were selected using a bioinformatic analysis of microarray and RNA-sequencing datasets, resulting in nine DE-miRNAs (miR-142, miR-150, miR-183, miR-199a, miR-215, miR-217, miR-224, miR-424, and miR-3607). Their expressions were validated in the serum of 44 healthy individuals, 62 non-cirrhotic HCV patients, 67 cirrhotic-HCV, and 72 HCV-associated-HCC patients using real-time PCR (qPCR). There was a significant increase in serum concentrations of the nine-candidate miRNAs in HCC and HCV patients relative to healthy individuals. MiR-424, miR-199a, miR-142, and miR-224 expressions were significantly altered in HCC compared to non-cirrhotic patients. A panel of five miRNAs improved sensitivity and specificity of HCC detection to 100% and 95.12% relative to healthy controls. Distinguishing HCC from HCV-treated patients was achieved by 70.8% sensitivity and 61.9% specificity using the combined panel, compared to alpha-fetoprotein (51.4% sensitivity and 60.67% specificity). These preliminary data show that the novel miRNAs panel (miR-150, miR-199a, miR-224, miR-424, and miR-3607) could serve as a potential non-invasive biomarker for HCC early prediction in chronic HCV patients. Further prospective studies on a larger cohort of patients should be conducted to assess the potential prognostic ability of the miRNAs panel.

SUBMITTER: Dabbish AM

PROVIDER: S-EPMC9265118 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background & aimsThe Liver Cancer Risk test algorithm (LCR1-LCR2) is a multianalyte blood test combining proteins involved in liver cell repair (apolipoprotein-A1 and haptoglobin), known hepatocellular carcinoma (HCC) risk factors (sex, age, and gamma-glutamyl transferase), a marker of fibrosis (alpha2-macroglobulin) and alpha-fetoprotein (AFP), a specific marker of HCC. The aim was to externally validate the LCR1-LCR2 in patients with chronic HCV (CHC) treated or not with antivirals.MethodsPre-included patients were from the Hepather cohort, a multicentre prospective study in adult patients with CHC in France. LCR1-LCR2 was assessed retrospectively in patients with the test components and AFP, available at baseline. The co-primary study outcome was the negative predictive value (NPV) of LCR1-LCR2 for the occurrence of HCC at 5 years and for survival without HCC according to the predetermined LCR1-LCR2 cut-offs. The cut-offs were adjusted for risk covariables and for the response to HCV treatment, and were quantified using time-dependent proportional hazards models.ResultsIn total, 4,903 patients, 1,026 (21.9%) with baseline cirrhosis, were included in the study. Patients were followed for a median of 5.7 (IQR 4.2-11.3) years. A total of 3,788/4,903 (77.3%) patients had a sustained virological response. There were 137 cases of HCC at 5 years and 214 at the end of follow-up. HCC occurred at 5 years in 24/3,755 patients with low-risk LCR1-LCR2 compared with 113/1,148 patients with high-risk LCR1-LCR2. The NPV was 99.4% (95% CI 99.1-99.6). Similar findings (hazard ratio, 10.8; 95% CI, 8.1-14.3; p <0.001) were obtained after adjustment for exposure to antivirals, age, sex, geographical origin, HCV genotype 3, alcohol consumption, and type 2 diabetes mellitus.ConclusionsThe results showed that LCR1-LCR2 can be used to successfully identify patients with HCV at very low risk of HCC at 5 years.Lay summaryHepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related death worldwide and the fastest growing cause of cancer death in many countries. We constructed and internally validated a new multianalyte blood test to assess this Liver Cancer Risk (LCR1-LCR2). This study confirmed the performance of LCR1-LCR2 in patients with chronic HCV in the national French cohort Hepather, and its ability to identify patients at a very low risk of HCC at 5 years.Clinical trials registrationThe study is registered at ClinicalTrials.gov (NCT01953458).

Project description:Targeted next-generation sequencing (NGS) enables the identification of genomic variants in cancer patients with high sensitivity at relatively low costs, and has thus opened the era to personalized human oncology. Veterinary medicine tends to adopt new technologies at a slower pace compared to human medicine due to lower funding, nonetheless it embraces technological advancements over time. Hence, it is reasonable to assume that targeted NGS will be incorporated into routine veterinary practice in the foreseeable future. Many animal diseases have well-researched human counterparts and hence, insights gained from the latter might, in principle, be harnessed to elucidate the former. Here, we present the TiHoCL targeted NGS panel as a proof of concept, exemplifying how functional genomics and network approaches can be effectively used to leverage the wealth of information available for human diseases in the development of targeted sequencing panels for veterinary medicine. Specifically, the TiHoCL targeted NGS panel is a molecular tool for characterizing and stratifying canine lymphoma (CL) patients designed based on human non-Hodgkin lymphoma (NHL) research outputs. While various single nucleotide polymorphisms (SNPs) have been associated with high risk of developing NHL, poor prognosis and resistance to treatment in NHL patients, little is known about the genetics of CL. Thus, the ~100 SNPs featured in the TiHoCL targeted NGS panel were selected using functional genomics and network approaches following a literature and database search that shielded ~500 SNPs associated with, in nearly all cases, human hematologic malignancies. The TiHoCL targeted NGS panel underwent technical validation and preliminary functional assessment by sequencing DNA samples isolated from blood of 29 lymphoma dogs using an Ion Torrent™ PGM System achieving good sequencing run metrics. Our design framework holds new possibilities for the design of similar molecular tools applied to other diseases for which limited knowledge is available and will improve drug target discovery and patient care.

Project description:summary Ovarian cancer is the leading cause of death among gynecologic malignancies. This is partly due to a non-durable response to chemotherapy. Prediction of resistance to chemotherapy could be a key role in more personalized treatment. In the current study we aimed to examine if microRNA based predictors could predict resistance to chemotherapy in ovarian cancer, and to investigate if the predictors could be prognostic factors for progression free and overall survival. Methods Predictors of chemotherapy-resistance were developed based on correlation between miRNA expression and differences in measured growth inhibition in a variety of human cancer cell lines in the presence of Carboplatin, Paclitaxel and Docetaxel (deposited under accession number E-MTAB-327 in the ArrayExpress database of the European Bioinformatics Institute, 2012). These predictors were then, retrospectively, blindly validated in a cohort of 170 epithelial ovarian cancer patients treated with Carboplatin and Paclitaxel or Docetaxel as first line treatment. Results In a multivariate cox proportional analysis the predictors of chemotherapy-resistance were not able to predict time to progression after end of chemotherapy (hazard ratio: 0.64, 95% CI: 0.36 – 1.12, P=0.117). However, in a multivariate logistic analysis, where time to progression was considered as either more or less than 6 months, the predictors match clinical observed chemotherapy-resistance (odds ratio: 0.19, 95% CI: 0.05-0.73, P=0.015). Neither univariate nor multivariate, time-dependent, cox analysis for progression free survival (PFS) or overall survival (OS) in all 170 patients showed to match predicted resistance to chemotherapy (PFS: hazard ratio: 0.69, 95% CI: 0.40-1.19, P=0.183, OS: hazard ratio: 0.76, 95% CI: 0.42-1.40, P=0.386). Conclusion In the current study, microRNA based predictors of chemotherapy-resistance did not demonstrate any convincing correlation to clinical observed chemotherapy-resistance, progression free survival, or overall survival, in patients with epithelial ovarian cancer. However the predictors did reflect relapse more or less than 6 months.

Project description:ObjectiveOvarian cancer is the leading cause of death among gynecologic malignancies. This is partly due to a non-durable response to chemotherapy. Prediction of resistance to chemotherapy could be a key role in more personalized treatment. In the current study we aimed to examine if microRNA based predictors could predict resistance to chemotherapy in ovarian cancer, and to investigate if the predictors could be prognostic factors for progression free and overall survival.MethodsPredictors of chemotherapy-resistance were developed based on correlation between miRNA expression and differences in measured growth inhibition in a variety of human cancer cell lines in the presence of Carboplatin, Paclitaxel and Docetaxel. These predictors were then, retrospectively, blindly validated in a cohort of 170 epithelial ovarian cancer patients treated with Carboplatin and Paclitaxel or Docetaxel as first line treatment.ResultsIn a multivariate cox proportional analysis the predictors of chemotherapy-resistance were not able to predict time to progression after end of chemotherapy (hazard ratio: 0.64, 95% CI: 0.36-1.12, P = 0.117). However, in a multivariate logistic analysis, where time to progression was considered as either more or less than 6 months, the predictors match clinical observed chemotherapy-resistance (odds ratio: 0.19, 95% CI: 0.05-0.73, P = 0.015). Neither univariate nor multivariate, time-dependent, cox analysis for progression free survival (PFS) or overall survival (OS) in all 170 patients showed to match predicted resistance to chemotherapy (PFS: hazard ratio: 0.69, 95% CI: 0.40-1.19, P = 0.183, OS: hazard ratio: 0.76, 95% CI: 0.42-1.40, P = 0.386).ConclusionIn the current study, microRNA based predictors of chemotherapy-resistance did not demonstrate any convincing correlation to clinical observed chemotherapy-resistance, progression free survival, or overall survival, in patients with epithelial ovarian cancer. However the predictors did reflect relapse more or less than 6 months.

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Prognostic MicroRNA Panel for HCV-Associated HCC: Integrating Computational Biology and Clinical Validation.

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