Prognostic MicroRNA Panel for HCV-Associated HCC: Integrating Computational Biology and Clinical Validation.
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ABSTRACT:
SUBMITTER: Dabbish AM
PROVIDER: S-EPMC9265118 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:While the use of computer tools to simulate complex processes such as computer circuits is normal practice in fields like engineering, the majority of life sciences/biological sciences courses continue to rely on the traditional textbook and memorization approach. To address this issue, we explored the use of the Cell Collective platform as a novel, interactive, and evolving pedagogical tool to foster student engagement, creativity, and higher-level thinking. Cell Collective is a Web-based platform used to create and simulate dynamical models of various biological processes. Students can create models of cells, diseases, or pathways themselves or explore existing models. This technology was implemented in both undergraduate and graduate courses as a pilot study to determine the feasibility of such software at the university level. First, a new (In Silico Biology) class was developed to enable students to learn biology by "building and breaking it" via computer models and their simulations. This class and technology also provide a non-intimidating way to incorporate mathematical and computational concepts into a class with students who have a limited mathematical background. Second, we used the technology to mediate the use of simulations and modeling modules as a learning tool for traditional biological concepts, such as T cell differentiation or cell cycle regulation, in existing biology courses. Results of this pilot application suggest that there is promise in the use of computational modeling and software tools such as Cell Collective to provide new teaching methods in biology and contribute to the implementation of the "Vision and Change" call to action in undergraduate biology education by providing a hands-on approach to biology.
| S-EPMC4366374 | biostudies-literature
Project description:Genetic screens are powerful methods for the discovery of gene-phenotype associations. However, a systems biology approach to genetics must leverage the massive amount of "omics" data to enhance the power and speed of functional gene discovery in vivo. Thus far, few computational methods for gene function prediction have been rigorously tested for their performance on a genome-wide scale in vivo. In this work, we demonstrate that integrating genome-wide computational gene prioritization with large-scale genetic screening is a powerful tool for functional gene discovery. To discover genes involved in neural development in Drosophila, we extend our strategy for the prioritization of human candidate disease genes to functional prioritization in Drosophila. We then integrate this prioritization strategy with a large-scale genetic screen for interactors of the proneural transcription factor Atonal using genomic deficiencies and mutant and RNAi collections. Using the prioritized genes validated in our genetic screen, we describe a novel genetic interaction network for Atonal. Lastly, we prioritize the whole Drosophila genome and identify candidate gene associations for ten receptor-signaling pathways. This novel database of prioritized pathway candidates, as well as a web application for functional prioritization in Drosophila, called Endeavour-HighFly, and the Atonal network, are publicly available resources. A systems genetics approach that combines the power of computational predictions with in vivo genetic screens strongly enhances the process of gene function and gene-gene association discovery.
| S-EPMC2628282 | biostudies-literature
Project description:BackgroundHepatocellular carcinoma (HCC) is an aggressive, malignant cancer with a complex pathogenesis. However, effective therapeutic targets and prognostic biomarkers are limited. Sorafenib provides delaying cancer progression and survival improvement in advanced HCC. But despite 10 years of research on the clinical application of sorafenib, predictive markers for its therapeutic effect are lacking.MethodsThe clinical significance and molecular functions of SIGLEC family members were assessed by a comprehensive bioinformatic analysis. The datasets included in this study (ICGC-LIRI-JP, GSE22058 and GSE14520) are mainly based on patients with HBV infections or HBV-related liver cirrhosis. The TCGA, GEO, and HCCDB databases were used to explore the expression of SIGLEC family genes in HCC. The Kaplan-Meier Plotter database was used to evaluate relationships between the expression levels of SIGLEC family genes and prognosis. Associations between differentially expressed genes in the SIGLEC family and tumour-associated immune cells were evaluated using TIMER.ResultsThe mRNA levels of most SIGLEC family genes were significantly lower in HCC than in normal tissues. Low protein and mRNA expression levels of SIGLECs were strongly correlated with tumour grade and clinical cancer stage in patients with HCC. Tumour-related SIGLEC family genes were associated with tumour immune infiltrating cells. High SIGLEC expression was significantly related to a better prognosis in patients with advanced HCC treated with sorafenib.ConclusionsSIGLEC family genes have potential prognostic value in HCC and may contribute to the regulation of cancer progression and immune cell infiltration. More importantly, our results revealed that SIGLEC family gene expression may be used as a prognostic marker for HCC patients treated with sorafenib.
| S-EPMC9938566 | biostudies-literature
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).
| S-EPMC8187244 | biostudies-literature
Project description: Not available
| S-DIXA-D-1047 | biostudies-other
Project description:BackgroundThis study reports the first collection of validated microRNA genes in the sea squirt, Ciona intestinalis. MicroRNAs are processed from hairpin precursors to ~22 nucleotide RNAs that base pair to target mRNAs and inhibit expression. As a member of the subphylum Urochordata (Tunicata) whose larval form has a notochord, the sea squirt is situated at the emergence of vertebrates, and therefore may provide information about the evolution of molecular regulators of early development.ResultsIn this study, computational methods were used to predict 14 microRNA gene families in Ciona intestinalis. The microRNA prediction algorithm utilizes configurable microRNA sequence conservation and stem-loop specificity parameters, grouping by miRNA family, and phylogenetic conservation to the related species, Ciona savignyi. The expression for 8, out of 9 attempted, of the putative microRNAs in the adult tissue of Ciona intestinalis was validated by Northern blot analyses. Additionally, a target prediction algorithm was implemented, which identified a high confidence list of 240 potential target genes. Over half of the predicted targets can be grouped into the gene ontology categories of metabolism, transport, regulation of transcription, and cell signaling.ConclusionThe computational techniques implemented in this study can be applied to other organisms and serve to increase the understanding of the origins of non-coding RNAs, embryological and cellular developmental pathways, and the mechanisms for microRNA-controlled gene regulatory networks.
| S-EPMC2243180 | biostudies-literature
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.
2017-02-01 | GSE94320 | GEO
Project description:PurposeFew efforts have been made to integrate a next generation sequencing (NGS) panel into standard clinical treatment of ovarian cancer. The aim of this study was to investigate the clinical utility of NGS and to identify clinically impactful information beyond targetable alterations.Materials and methodsWe conducted a retrospective review of 84 patients with ovarian cancer who underwent NGS between March 1, 2017, and July 31, 2018, at the Yonsei Cancer Hospital. We extracted DNA from formalin-fixed, paraffin-embedded tissue samples of ovarian cancer. The TruSight Tumor 170 gene panel was used to prepare libraries, and the MiSeq instrument was used for NGS.ResultsOf the 84 patients, 55 (65.1%) had high-grade serous carcinomas. Seventy-three (86.7%) patients underwent NGS at the time of diagnosis, and 11 (13.3%) underwent NGS upon relapse. The most common genetic alterations were in TP53 (64%), PIK3CA (15%), and BRCA1/2 (13%), arising as single nucleotide variants and indels. MYC amplification (27%) was the most common copy number variation and fusion. Fifty-seven (67.9%) patients had more than one actionable alteration other than TP53. Seven (8.3%) cases received matched-target therapy based on the following sequencing results: BRCA1 or 2 mutation, poly ADP ribose polymerase inhibitor (n=5); PIK3CA mutation, AKT inhibitor (n=1); and MLH1 mutation, PD-1 inhibitor (n=1). Fifty-three (63.0%) patients had a possibility of treatment change, and 8 (9.5%) patients received genetic counseling.ConclusionImplementation of NGS may help in identifying patients who might benefit from targeted treatment therapies and genetic counseling.
| S-EPMC6753345 | biostudies-literature
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.
| S-EPMC5363866 | biostudies-literature
Project description:ObjectivesThe exhausted CD8+T (Tex) cells are a unique cell population of activated T cells that emerges in response to persistent viral infection or tumor antigens. Tex cells showed the characteristics of aging cells, including weakened self-renewal ability, effector function inhibition, sustained high expression of inhibitory receptors including PD-1, TIGIT, TIM-3, and LAG-3, and always accompanied by metabolic and epigenetic reprogramming. Tex cells are getting more and more attention in researching immune-related diseases and tumor immunotherapy. However, studies on Tex-related models for tumor prognosis are still lacking. We hope to establish a risk model based on Tex-related genes for HCC prognosis.MethodsTex-related GEO datasets from different pathologic factors (chronic HBV, chronic HCV, and telomere shortening) were analyzed respectively to acquire differentially expressed genes (DEGs) by the 'limma' package of R. Genes with at least one intersection were incorporated into Tex-related gene set. GO, KEGG, and GSEA enrichment analyses were produced. Hub genes and the PPI network were established and visualized by the STRING website and Cytoscape software. Transcription factors and targeting small molecules were predicted by the TRUST and CLUE websites. The Tex-related HCC prognostic model was built by Cox regression and verified based on different datasets. Tumor immune dysfunction and exclusion (TIDE) and SubMap algorithms tested immunotherapy sensitivity. Finally, qRT-PCR and Flow Cytometry was used to confirm the bioinformatic results.ResultsHub genes such as AKT1, CDC6, TNF and their upstream transcription factor ILF3, Regulatory factor X-associated protein, STAT3, JUN, and RELA/NFKB1 were identified as potential motivators for Tex. Tex-related genes SLC16A11, CACYBP, HSF2, and ATG10 built the HCC prognostic model and helped with Immunotherapy sensitivity prediction.ConclusionOur study demonstrated that Tex-related genes might provide accurate prediction for HCC patients in clinical decision-making, prognostic assessment, and immunotherapy. In addition, targeting the hub genes or transcription factors may help to reverse T cell function and enhance the effect of tumor immunotherapy.
| S-EPMC10106715 | biostudies-literature