Polymorphic Variants in NR1I3 and UGT2B7 Predict Taxane Neurotoxicity and Have Prognostic Relevance in Patients With Breast Cancer: A Case-Control Study

Prediction models of persistent taxane-induced peripheral neuropathy among breast cancer survivors using whole-exome sequencing

Persistent taxane-induced peripheral neuropathy (TIPN) is highly prevalent among early-stage breast cancer survivors (ESBCS) and has detrimental effect on quality of life. We leveraged logistic regression models to develop and validate polygenic prediction models to estimate the risk of persistent PN symptoms in a training cohort and validation cohort taking clinical risk factors into account. Based on 337 whole-exome sequenced ESBCS two of five prediction models for individual PN symptoms obtained AUC results above 60% when validated. Using the model for numbness in feet (35 SNVs) in the test cohort, 73% survivors were correctly predicted. For tingling in feet (55 SNVs) 70% were correctly predicted. Both models included SNVs from the ADAMTS20, APT6V0A2, CCDC88C, CYP2C8, EPHA5, NR1H3, PSKH2/APTV0D2, and SCN10A genes. For cramps in feet, difficulty climbing stairs and difficulty opening a jar the validation was unsuccessful. Polygenic prediction models including clinical risk factors can estimate the risk of persistent taxane-induced numbness in feet and tingling in feet in ESBCS.

Genetic Biomarkers of Sorafenib Response in Patients with Hepatocellular Carcinoma

The identification of biomarkers for predicting inter-individual sorafenib response variability could allow hepatocellular carcinoma (HCC) patient stratification. SNPs in angiogenesis- and drug absorption, distribution, metabolism, and excretion (ADME)-related genes were evaluated to identify new potential predictive biomarkers of sorafenib response in HCC patients. Five known SNPs in angiogenesis-related genes, including VEGF-A, VEGF-C, HIF-1a, ANGPT2, and NOS3, were investigated in 34 HCC patients (9 sorafenib responders and 25 non-responders). A subgroup of 23 patients was genotyped for SNPs in ADME genes. A machine learning classifier method was used to discover classification rules for our dataset. We found that only the VEGF-A (rs2010963) C allele and CC genotype were significantly associated with sorafenib response. ADME-related gene analysis identified 10 polymorphic variants in ADH1A (rs6811453), ADH6 (rs10008281), SULT1A2/CCDC101 (rs11401), CYP26A1 (rs7905939), DPYD (rs2297595 and rs1801265), FMO2 (rs2020863), and SLC22A14 (rs149738, rs171248, and rs183574) significantly associated with sorafenib response. We have identified a genetic signature of predictive response that could permit non-responder/responder patient stratification. Angiogenesis- and ADME-related genes correlation was confirmed by cumulative genetic risk score and network and pathway enrichment analysis. Our findings provide a proof of concept that needs further validation in follow-up studies for HCC patient stratification for sorafenib prescription.

Exploiting DNA Ligase III addiction of multiple myeloma by flavonoid Rhamnetin

Background

DNA ligases are crucial for DNA repair and cell replication since they catalyze the final steps in which DNA breaks are joined. DNA Ligase III (LIG3) exerts a pivotal role in Alternative-Non-Homologous End Joining Repair (Alt-NHEJ), an error-prone DNA repair pathway often up-regulated in genomically unstable cancer, such as Multiple Myeloma (MM). Based on the three-dimensional (3D) LIG3 structure, we performed a computational screening to identify LIG3-targeting natural compounds as potential candidates to counteract Alt-NHEJ activity in MM.

Methods

Virtual screening was conducted by interrogating the Phenol Explorer database. Validation of binding to LIG3 recombinant protein was performed by Saturation Transfer Difference (STD)—nuclear magnetic resonance (NMR) experiments. Cell viability was analyzed by Cell Titer-Glo assay; apoptosis was evaluated by flow cytometric analysis following Annexin V-7AAD staining. Alt-NHEJ repair modulation was evaluated using plasmid re-joining assay and Cytoscan HD. DNA Damage Response protein levels were analyzed by Western blot of whole and fractionated protein extracts and immunofluorescence analysis. The mitochondrial DNA (mtDNA) copy number was determined by qPCR. In vivo activity was evaluated in NOD-SCID mice subcutaneously engrafted with MM cells.

Results

Here, we provide evidence that a natural flavonoid Rhamnetin (RHM), selected by a computational approach, counteracts LIG3 activity and killed Alt-NHEJ-dependent MM cells. Indeed, Nuclear Magnetic Resonance (NMR) showed binding of RHM to LIG3 protein and functional experiments revealed that RHM interferes with LIG3-driven nuclear and mitochondrial DNA repair, leading to significant anti-MM activity in vitro and in vivo.

Conclusion

Taken together, our findings provide proof of concept that RHM targets LIG3 addiction in MM and may represent therefore a novel promising anti-tumor natural agent to be investigated in an early clinical setting.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03705-z.

A Python Clustering Analysis Protocol of Genes Expression Data Sets

Gene expression and SNPs data hold great potential for a new understanding of disease prognosis, drug sensitivity, and toxicity evaluations. Cluster analysis is used to analyze data that do not contain any specific subgroups. The goal is to use the data itself to recognize meaningful and informative subgroups. In addition, cluster investigation helps data reduction purposes, exposes hidden patterns, and generates hypotheses regarding the relationship between genes and phenotypes. Cluster analysis could also be used to identify bio-markers and yield computational predictive models. The methods used to analyze microarrays data can profoundly influence the interpretation of the results. Therefore, a basic understanding of these computational tools is necessary for optimal experimental design and meaningful data analysis. This manuscript provides an analysis protocol to effectively analyze gene expression data sets through the K-means and DBSCAN algorithms. The general protocol enables analyzing omics data to identify subsets of features with low redundancy and high robustness, speeding up the identification of new bio-markers through pathway enrichment analysis. In addition, to demonstrate the effectiveness of our clustering analysis protocol, we analyze a real data set from the GEO database. Finally, the manuscript provides some best practice and tips to overcome some issues in the analysis of omics data sets through unsupervised learning.

Pharmacogenetics of taxane-induced neurotoxicity in breast cancer: Systematic review and meta-analysis

Taxane-based chemotherapy regimens are used as first-line treatment for breast cancer. Neurotoxicity, mainly taxane-induced peripheral neuropathy (TIPN), remains the most important dose-limiting adverse event. Multiple genes may be associated with TIPN; however, the strength and direction of the association remain unclear. For this reason, we systematically reviewed observational studies of TIPN pharmacogenetic markers in breast cancer treatment. We conducted a systematic search of terms alluding to breast cancer, genetic markers, taxanes, and neurotoxicity in Ovid, ProQuest, PubMed, Scopus, Virtual Health, and Web of Science. We assessed the quality of evidence and bias profile. We extracted relevant variables and effect measures. Whenever possible, we performed random-effects gene meta-analyses and examined interstudy heterogeneity with meta-regression models and subgroup analyses. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and STrengthening the REporting of Genetic Association Studies (STREGA) reporting guidance. A total of 42 studies with 19,431 participants were included. These evaluated 262 single-nucleotide polymorphisms (SNPs) across 121 genes. We conducted meta-analyses on 23 genes with 60 SNPs (19 studies and 6246 participants). Thirteen individual SNPs (ABCB1-rs2032582, ABCB1-rs3213619, BCL6/-rs1903216, /CAND1-rs17781082, CYP1B1-rs1056836, CYP2C8-rs10509681, CYP2C8-rs11572080, EPHA5-rs7349683, EPHA6-rs301927, FZD3-rs7001034, GSTP1-rs1138272, TUBB2A-rs9501929, and XKR4-rs4737264) and the overall SNPs' effect in four genes (CYP3A4, EphA5, GSTP1, and SLCO1B1) were statistically significantly associated with TIPN through meta-analysis. In conclusion, through systematic review and meta-analysis, we found that polymorphisms, and particularly 13 SNPs, are associated with TIPN, suggesting that genetics does play a role in interindividual predisposition. Further studies could potentially use these findings to develop individual risk profiles and guide decision making.

A Prognostic and Carboplatin Response Predictive Model in Ovarian Cancer: A Mono-Institutional Retrospective Study Based on Clinics and Pharmacogenomics

Carboplatin is the cornerstone of ovarian cancer (OC) treatment, while platinum-response, dependent on interindividual variability, is the major prognostic factor for long-term outcomes. This retrospective study was focused on explorative search of genetic polymorphisms in the Absorption, Distribution, Metabolism, Excretion (ADME) genes for the identification of biomarkers prognostic/predictive of platinum-response in OC patients. Ninety-two advanced OC patients treated with carboplatin-based therapy were enrolled at our institution. Of these, we showed that 72% of patients were platinum-sensitive, with a significant benefit in terms of OS (p = 0.001). We identified an inflammatory-score with a longer OS in patients with lower scores as compared to patients with the maximum score (p = 0.001). Thirty-two patients were genotyped for 1931 single nucleotide polymorphisms (SNPs) and five copy number variations (CNVs) by the DMET Plus array platform. Among prognostic polymorphisms, we found a potential role of UGT2A1 both as a predictor of platinum-response (p = 0.01) and as prognostic of survival (p = 0.05). Finally, we identified 24 SNPs related to OS. UGT2A1 correlates to an "inflammatory-score" and retains a potential prognostic role in advanced OC. These data provide a proof of concept that warrants further validation in follow-up studies for the definition of novel biomarkers in this aggressive disease.

Biomarkers of Chemotherapy-Induced Peripheral Neuropathy: Current Status and Future Directions

Chemotherapy induced peripheral neuropathy (CIPN) is an often severe and debilitating complication of multiple chemotherapeutic agents that can affect patients of all ages, across cancer diagnoses. CIPN can persist post-therapy, and significantly impact the health and quality of life of cancer survivors. Identifying patients at risk for CIPN is challenging due to the lack of standardized objective measures to assess for CIPN. Furthermore, there are no approved preventative treatments for CIPN, and therapeutic options for CIPN remain limited once it develops. Biomarkers of CIPN have been studied but are not widely used in clinical practice. They can serve as an important clinical tool to identify individuals at risk for CIPN and to better understand the pathogenesis and avenues for treatment of CIPN. Here we review promising biomarkers of CIPN in humans and their clinical implications.

Microarray Data Analysis Protocol

Microarrays are broadly used in the omic investigation and have several areas of applications in biology and medicine, providing a significant amount of data for a single experiment. Different kinds of microarrays are available, identifiable by characteristics such as the type of probes, the surface used as support, and the method used for the target detection. To better deal with microarray datasets, the development of microarray data analysis protocols simple to use as well as able to produce accurate reports, and comprehensible results arise. The object of this paper is to provide a general protocol showing how to choose the best software tool to analyze microarray data, allowing to efficiently figure out genomic/pharmacogenomic biomarkers.

Risk Alleles for Multiple Myeloma Susceptibility in ADME Genes

The cause of multiple myeloma (MM) remains largely unknown. Several pieces of evidence support the involvement of genetic and multiple environmental factors (i.e., chemical agents) in MM onset. The inter-individual variability in the bioactivation, detoxification, and clearance of chemical carcinogens such as asbestos, benzene, and pesticides might increase the MM risk. This inter-individual variability can be explained by the presence of polymorphic variants in absorption, distribution, metabolism, and excretion (ADME) genes. Despite the high relevance of this issue, few studies have focused on the inter-individual variability in ADME genes in MM risk. To identify new MM susceptibility loci, we performed an extended candidate gene approach by comparing high-throughput genotyping data of 1936 markers in 231 ADME genes on 64 MM patients and 59 controls from the CEU population. Differences in genotype and allele frequencies were validated using an internal control group of 35 non-cancer samples from the same geographic area as the patient group. We detected an association between MM risk and ADH1B rs1229984 (OR = 3.78; 95% CI, 1.18–12.13; p = 0.0282), PPARD rs6937483 (OR = 3.27; 95% CI, 1.01–10.56; p = 0.0479), SLC28A1 rs8187737 (OR = 11.33; 95% CI, 1.43–89.59; p = 0.005), SLC28A2 rs1060896 (OR = 6.58; 95% CI, 1.42–30.43; p = 0.0072), SLC29A1 rs8187630 (OR = 3.27; 95% CI, 1.01–10.56; p = 0.0479), and ALDH3A2 rs72547554 (OR = 2.46; 95% CI, 0.64–9.40; p = 0.0293). The prognostic value of these genes in MM was investigated in two public datasets showing that shorter overall survival was associated with low expression of ADH1B and SLC28A1. In conclusion, our proof-of-concept findings provide novel insights into the genetic bases of MM susceptibility.

Computer-Aided Drug Design (CADD) to De-Orphanize Marine Molecules: Finding Potential Therapeutic Agents for Neurodegenerative and Cardiovascular Diseases

Computer-aided drug design (CADD) techniques allow the identification of compounds capable of modulating protein functions in pathogenesis-related pathways, which is a promising line on drug discovery. Marine natural products (MNPs) are considered a rich source of bioactive compounds, as the oceans are home to much of the planet's biodiversity. Biodiversity is directly related to chemodiversity, which can inspire new drug discoveries. Therefore, natural products (NPs) in general, and MNPs in particular, have been used for decades as a source of inspiration for the design of new drugs. However, NPs present both opportunities and challenges. These difficulties can be technical, such as the need to dive or trawl to collect the organisms possessing the compounds, or biological, due to their particular marine habitats and the fact that they can be uncultivable in the laboratory. For all these difficulties, the contributions of CADD can play a very relevant role in simplifying their study, since, for example, no biological sample is needed to carry out an in-silico analysis. Therefore, the amount of natural product that needs to be used in the entire preclinical and clinical study is significantly reduced. Here, we exemplify how this combination between CADD and MNPs can help unlock their therapeutic potential. In this study, using a set of marine invertebrate molecules, we elucidate their possible molecular targets and associated therapeutic potential, establishing a pipeline that can be replicated in future studies.

Tools in Pharmacogenomics Biomarker Identification for Cancer Patients

The understanding of the biological differences which underlie the inter-individual variability in drug response improved the efficacy of cancer therapy in the era of precision medicine. In fact molecularly targeted drugs and immunotherapy represent a revolution in cancer treatment. The identification of genetic predictive and/or prognostic biomarkers linked to drug pharmacokinetics (PK) and pharmacodynamics (PD) is allowed by the development of high-throughput omics tools for detecting and understanding biological differences among individuals, in order to improve drug efficacy and minimize risk of toxicity. Personalized medicine in cancer treatment reduces costs of the healthcare system. Unfortunately, pharmacogenomics biomarkers discovery is influenced by complexity, need of high-quality evidence, and a validation process for regulatory purposes. This chapter is focused on the critic analysis of presently available pharmacogenomics tools for discovering or testing genetic polymorphic variants in drug metabolizing enzyme to be introduced in clinical practice for the prospective stratification of cancer patients.

Management of Side Effects in the Personalized Medicine Era: Chemotherapy-Induced Peripheral Neurotoxicity

Pharmacogenomics is a powerful tool to predict individual response to treatment, in order to personalize therapy, and it has been explored extensively in oncology practice. Not only efficacy on the malignant disease has been investigated but also the possibility to predict adverse effects due to drug administration. Chemotherapy-induced peripheral neurotoxicity (CIPN) is one of those. This potentially severe and long-lasting/permanent side effect of commonly administered anticancer drugs can severely impair quality of life (QoL) in a large cohort of long survival patients. So far, a pharmacogenomics-based approach in CIPN regard has been quite delusive, making a methodological improvement warranted in this field of interest: even the most refined genetic analysis cannot be effective if not applied correctly. Here we try to devise why it is so, suggesting how THE "bench-side" (pharmacogenomics) might benefit from and should cooperate with THE "bed-side" (clinimetrics), in order to make genetic profiling effective if applied to CIPN.

Using MMRFBiolinks R-Package for Discovering Prognostic Markers in Multiple Myeloma

Multiple myeloma (MM) is the second most frequent hematological malignancy in the world although the related pathogenesis remains unclear. Gene profiling studies, commonly carried out through next-generation sequencing (NGS) and Microarrays technologies, represent powerful tools for discovering prognostic markers in MM. NGS technologies have made great leaps forward both economically and technically gaining in popularity. As NGS techniques becomes simpler and cheaper, researchers choose NGS over microarrays for more of their genomic applications. However, Microarrays still provide significant benefits with respect to NGS. For instance, RNA-Seq requires more complex bioinformatic analysis with respect to Microarray as well as it lacks of standardized protocols for analysis. Therefore, a synergy between the two technologies may be well expected in the future. In order to take up this challenge, a valid tool for integrative analysis of MM data retrieved through NGS techniques is MMRFBiolinks, a new R package for integrating and analyzing datasets from the Multiple Myeloma Research Foundation (MMRF) CoMMpass (Clinical Outcomes in MM to Personal Assessment of Genetic Profile) study, available at MMRF Researcher Gateway (MMRF-RG), and at the National Cancer Institute Genomic Data Commons (NCI-GDC) Data Portal. Instead of developing a completely new package from scratch, we decided to leverage TC-GABiolinks, an R/Bioconductor package, because it provides some useful methods to access and analyze MMRF-CoMMpass data. An integrative analysis workflow based on the usage of MMRFBiolinks is illustrated.In particular, it leads towards a comparative analysis of RNA-Seq data stored at GDC Data Portal that allows to carry out a Kaplan Meier (KM ) Survival Analysis and an enrichment analysis for a Differential Gene Expression (DGE) gene set.Furthermore, it deals with MMRF-RG data for analyzing the correlation between canonical variants and treatment outcome as well as treatment class. In order to show the potential of the workflow, we present two case studies. The former deals with data of MM Bone Marrow sample types available at GDC Data Portal. The latter deals with MMRF-RG data for analyzing the correlation between canonical variants in a gene set obtained from the case study 1 and the treatment outcome as well as the treatment class.

Clustering Methods for Microarray Data Sets

Microarrays are experimental methods that can provide information about gene expression and SNP data that hold great potential for new understanding, driving advances in functional genomics and clinical and molecular biology. Cluster analysis is used to analyze data that are not a priori to contain any specific subgroup. The goal is to use the data itself to recognize meaningful and informative subgroups. Also, cluster analysis helps data reduction purposes, exposes hidden patterns, and generates hypotheses regarding the relationship between genes and phenotypes. This chapter outlines a collection of cluster methods suitable for the analysis of microarray data sets.

Identification and Roles of miR-29b-1-3p and miR29a-3p-Regulated and Non-Regulated lncRNAs in Endocrine-Sensitive and Resistant Breast Cancer Cells

Despite improvements in the treatment of endocrine-resistant metastatic disease using combination therapies in patients with estrogen receptor α (ERα) primary tumors, the mechanisms underlying endocrine resistance remain to be elucidated. Non-coding RNAs (ncRNAs), including microRNAs (miRNA) and long non-coding RNAs (lncRNA), are targets and regulators of cell signaling pathways and their exosomal transport may contribute to metastasis. Previous studies have shown that a low expression of miR-29a-3p and miR-29b-3p is associated with lower overall breast cancer survival before 150 mos. Transient, modest overexpression of miR-29b1-3p or miR-29a-3p inhibited MCF-7 tamoxifen-sensitive and LCC9 tamoxifen-resistant cell proliferation. Here, we identify miR-29b-1/a-regulated and non-regulated differentially expressed lncRNAs in MCF-7 and LCC9 cells using next-generation RNA seq. More lncRNAs were miR-29b-1/a-regulated in LCC9 cells than in MCF-7 cells, including DANCR, GAS5, DSCAM-AS1, SNHG5, and CRND. We examined the roles of miR-29-regulated and differentially expressed lncRNAs in endocrine-resistant breast cancer, including putative and proven targets and expression patterns in survival analysis using the KM Plotter and TCGA databases. This study provides new insights into lncRNAs in endocrine-resistant breast cancer.

Neuromuscular complications of cancer therapy

PURPOSE OF THE REVIEW

The neuromuscular complications of cancer therapy include chemotherapy-induced peripheral neurotoxicity (CIPN), immune-related neuromuscular complications to immune checkpoint inhibitors and radiation-induced neuropathy/plexopathy. With a wider focus on CIPN, we will discuss new pathogenetic insights, recent predictive biomarkers and emerging therapies for neuromuscular complications of cancer therapy.

RECENT FINDINGS

Findings from recent preclinical studies have improved our knowledge on new CIPN pathogenetic pathways, including the activation of senescence-like processes in neurons, axonal degeneration and neuroinflammation. Metabolomics and serum neurofilament light chain levels appear the most promising biomarkers to predict CIPN development and severity. There is some recent evidence of promising pharmacological compounds to prevent or treat CIPN, and new drugs are in early development and testing.

SUMMARY

A multimodal assessment, with neurophysiological, imaging and patient-reported outcome measures, coupled with the use of reliable blood or genetic biomarkers, may offer pathogenetic grounds for future preventive and symptomatic strategies for the multidisciplinary treatment of neuromuscular complications of cancer therapy.

Pharmacogenomics Biomarker Discovery and Validation for Translation in Clinical Practice

Interindividual variability in drug efficacy and toxicity is a major challenge in clinical practice. Variations in drug pharmacokinetics (PKs) and pharmacodynamics (PDs) can be, in part, explained by polymorphic variants in genes encoding drug metabolizing enzymes and transporters (absorption, distribution, metabolism, and excretion) or in genes encoding drug receptors. Pharmacogenomics (PGx) has allowed the identification of predictive biomarkers of drug PKs and PDs and the current knowledge of genome-disease and genome-drug interactions offers the opportunity to optimize tailored drug therapy. High-throughput PGx genotyping, from targeted to more comprehensive strategies, allows the identification of PK/PD genotypes to be developed as clinical predictive biomarkers. However, a biomarker needs a robust process of validation followed by clinical-grade assay development and must comply to stringent regulatory guidelines. We here discuss the methodological challenges and the emerging technological tools in PGx biomarker discovery and validation, at the crossroad among molecular genetics, bioinformatics, and clinical medicine.

Construction and Analysis of a Long Non-Coding RNA-Associated Competing Endogenous RNA Network Identified Potential Prognostic Biomarkers in Luminal Breast Cancer

Purpose

To construct a competing endogenous RNA (ceRNA) topology network of RNA-seq data and micro RNA-seq (miRNA-seq) data to identify key prognostic long non-coding RNA (lncRNAs) in luminal breast cancer, and validate the results by human luminal breast cancer samples.

Materials and Methods

The RNA-seq data and miRNA-seq data of luminal A breast cancer in the The Cancer Genome Atlas (TCGA) database were downloaded and compared with those in the miRcode database to obtain lncRNA–miRNA relationship pairs. Final target genes were predicted by all three databases (miRDB, miRTarBase, and TargetScan), thereby obtaining the miRNA-messenger RNA (miRNA-mRNA) relationship pairs and a ceRNA topology network was constructed, then mRNA enrichment analysis, ceRNA topological and stability analysis, univariate and multivariate Cox regression analysis were performed. Overall survival (OS) was evaluated and the key prognostic RNAs were identified. The expression difference between normal and tumor, as well as the correlation of high expression in tumor with pathological parameters (Ki-67, Grade, tumor diameter) were validated by human breast cancer specimens.

Results

A ceRNA topology network was constructed and six lncRNAs were finally identified (The higher expression of PART1, IGF2.AS, WT1.AS, OIP5.AS1, and SLC25A5.AS1 was associated with poor prognosis while AL035706.1 was adverse) and the poor prognostic ones were higher expressed in tumor tissue and correlated with a higher Ki-67 (>10%), tumor grades (II, III) and tumor diameters (>1.5 cm). Using six lncRNAs, we constructed a prognostic model, which performed well for the classification of prognosis in the module.

Conclusion

We identified and verified six biomarkers (OS-predicting) in luminal breast cancer, which significantly enriched the prediction and potential targets of this subtype.

DMETTM Genotyping: Tools for Biomarkers Discovery in the Era of Precision Medicine

The knowledge of genetic variants in genes involved in drug metabolism may be translated into reduction of adverse drug reactions, increase of efficacy, healthcare outcomes improvement and economic benefits. Many high-throughput tools are available for the genotyping of Single Nucleotide Polymorphisms (SNPs) known to be related to drugs and xenobiotics metabolism. DMET^TM platform represents an example of SNPs panel to discover biomarkers correlated to efficacy or toxicity in common and rare diseases. The difficulty in analyzing the mole of information generated by DMET^TM platform led to the development and implementation of algorithms and tools for statistical and data mining analysis. These softwares allow efficient handling of the omics data to validate the explorative SNPs identified by DMET assay and to correlate them with drug efficacy, toxicity and/or cancer susceptibility. In this review we present a suite of bioinformatic frameworks for the preprocessing and analysis of DMET-SNPs data. In particular, we introduce a workflow that uses the GenoMetric Query Language, a high-level query language specifically designed for genomics, able to query public datasets (such as ENCODE, TCGA, GENCODE annotation dataset, etc.) as well as to combine them with private datasets (e.g., output from Affymetrix® DMET^TM Platform).

Long non-coding RNA F11-AS1 inhibits HBV-related hepatocellular carcinoma progression by regulating NR1I3 via binding to microRNA-211-5p

Long non-coding RNAs (lncRNAs) could regulate growth and metastasis of hepatocellular carcinoma (HCC). In this study, we aimed to investigate the mechanism of lncRNA F11-AS1 in hepatitis B virus (HBV)-related HCC. The relation of lncRNA F11-AS1 expression in HBV-related HCC tissues to prognosis was analysed in silico. Stably HBV-expressing HepG2.2.15 cells were established to explore the regulation of lncRNA F11-AS1 by HBx protein, as well as to study the effects of overexpressed lncRNA F11-AS1 on proliferation, migration, invasion and apoptosis in vitro. Subsequently, the underlying interactions and roles of lncRNA F11-AS1/miR-211-5p/NR1I3 axis in HBV-related HCC were investigated. Additionally, the influence of lncRNA F11-AS1 and miR-211-5p on tumour growth and metastasis capacity of HepG2.2.15 cells were studied on tumour-bearing nude mice. Poor expression of lncRNA F11-AS1 was correlated with poor prognosis in patients with HBV-related HCC, and its down-regulation was caused by the HBx protein. lncRNA F11-AS1 was proved to up-regulate the NR1I3 expression by binding to miR-211-5p. Overexpression of lncRNA F11-AS1 reduced the proliferation, migration and invasion, yet induced apoptosis of HepG2.2.15 cells in vitro, which could be abolished by overexpression of miR-211-5p. Additionally, either lncRNA F11-AS1 overexpression or miR-211-5p inhibition attenuated the tumour growth and metastasis capacity of HepG2.2.15 cells in vivo. Collectively, lncRNA F11-AS1 acted as a modulator of miR-211-5p to positively regulate the expression of NR1I3, and the lncRNA F11-AS1/miR-211-5p/NR1I3 axis participated in HBV-related HCC progression via interference with the cellular physiology of HCC.