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BackgroundReliable prognostic biomarkers to distinguish indolent from aggressive prostate cancer (PCa) are lacking. Many studies investigated microRNAs (miRs) as PCa prognostic biomarkers, often reporting inconsistent findings. We present a systematic review of these; also systematic reanalysis of public miR-profile datasets to identify tissue-derived miRs prognostic of biochemical recurrence (BCR) in patients undergoing radical prostatectomy.MethodsIndependent PubMed searches were performed for relevant articles from January 2007 to December 2019. For the review, 128 studies were included. Pooled-hazard-ratios (HRs) for miRs in multiple studies were calculated using a random-effects model (REM). For the reanalysis, five studies were included and Cox proportional-hazard models, testing miR association with BCR, performed for miRs profiled in all.ResultsSystematic review identified 120 miRs as prognostic. Five (let-7b-5p, miR-145-5p, miR152-3p, miR-195-5p, miR-224-5p) were consistently associated with progression in multiple cohorts/studies. In the reanalysis, ten (let-7a-5p, miR-148a-3p, miR-203a-3p, miR-26b-5p, miR30a-3p, miR-30c-5p, miR-30e-3p, miR-374a-5p, miR-425-3p, miR-582-5p) were significantly prognostic of BCR. Of these, miR-148a-3p (HR = 0.80/95% CI = 0.68-0.94) and miR-582-5p (HR = 0.73/95% CI = 0.61-0.87) were also reported in prior publication(s) in the review.ConclusionsFifteen miRs were consistently associated with disease progression in multiple publications or datasets. Further research into their biological roles is warranted to support investigations into their performance as prognostic PCa biomarkers.

BackgroundWe evaluated the therapeutic potential of combining the monocarboxylate transporter 1 (MCT1) inhibitor AZD3965 with the mitochondrial respiratory Complex I inhibitor IACS-010759, for the treatment of diffuse large B-cell lymphoma (DLBCL), a potential clinically actionable strategy to target tumour metabolism.MethodsAZD3965 and IACS-010759 sensitivity were determined in DLBCL cell lines and tumour xenograft models. Lactate concentrations, oxygen consumption rate and metabolomics were examined as mechanistic endpoints. In vivo plasma concentrations of IACS-010759 in mice were determined by LC-MS to select a dose that reflected clinically attainable concentrations.ResultsIn vitro, the combination of AZD3965 and IACS-010759 is synergistic and induces DLBCL cell death, whereas monotherapy treatments induce a cytostatic response. Significant anti-tumour activity was evident in Toledo and Farage models when the two inhibitors were administered concurrently despite limited or no effect on the growth of DLBCL xenografts as monotherapies.ConclusionsThis is the first study to examine a combination of two distinct approaches to targeting tumour metabolism in DLBCL xenografts. Whilst nanomolar concentrations of either AZD3965 or IACS-010759 monotherapy demonstrate anti-proliferative activity against DLBCL cell lines in vitro, appreciable clinical activity in DLBCL patients may only be realised through their combined use.

Neuroblastoma is the most common paediatric solid tumour and prognosis remains poor for high-risk cases despite the use of multimodal treatment. Analysis of public drug sensitivity data showed neuroblastoma lines to be sensitive to indisulam, a molecular glue that selectively targets RNA splicing factor RBM39 for proteosomal degradation via DCAF15-E3-ubiquitin ligase. In neuroblastoma models, indisulam induces rapid loss of RBM39, accumulation of splicing errors and growth inhibition in a DCAF15-dependent manner. Integrative analysis of RNAseq and proteomics data highlight a distinct disruption to cell cycle and metabolism. Metabolic profiling demonstrates metabolome perturbations and mitochondrial dysfunction resulting from indisulam. Complete tumour regression without relapse was observed in both xenograft and the Th- MYCN transgenic model of neuroblastoma after indisulam treatment, with RBM39 loss, RNA splicing and metabolic changes confirmed in vivo. Our data show that dual-targeting of metabolism and RNA splicing with anticancer indisulam is a promising therapeutic approach for high-risk neuroblastoma. The prognosis of high-risk neuroblastoma is poor despite the availability of multimodal treatment. Here the authors show that high-risk neuroblastoma is sensitive to indisulam, a selective degrader of the splicing factor RBM39 through the dual targeting of RNA splicing and metabolism.

Metabolic profiling, metabolomic and metabonomic studies mainly involve the multicomponent analysis of biological fluids, tissue and cell extracts using NMR spectroscopy and/or mass spectrometry (MS). We summarize the main NMR spectroscopic applications in modern metabolic research, and provide detailed protocols for biofluid (urine, serum/plasma) and tissue sample collection and preparation, including the extraction of polar and lipophilic metabolites from tissues. 1 H NMR spectroscopic techniques such as standard 1D spectroscopy, relaxation-edited, diffusion-edited and 2D J-resolved pulse sequences are widely used at the analysis stage to monitor different groups of metabolites and are described here. They are often followed by more detailed statistical analysis or additional 2D NMR analysis for biomarker discovery. The standard acquisition time per sample is 4–5 min for a simple 1D spectrum, and both preparation and analysis can be automated to allow application to high-throughput screening for clinical diagnostic and toxicological studies, as well as molecular phenotyping and functional genomics.

Developmental periods in early life may be particularly vulnerable to impacts of environmental exposures. Human research on this topic has generally focused on single exposure-health effect relationships. The \"exposome\" concept encompasses the totality of exposures from conception onward, complementing the genome. The Human Early-Life Exposome (HELIX) project is a new collaborative research project that aims to implement novel exposure assessment and biomarker methods to characterize early-life exposure to multiple environmental factors and associate these with omics biomarkers and child health outcomes, thus characterizing the \"early-life exposome.\" Here we describe the general design of the project. In six existing birth cohort studies in Europe, HELIX will estimate prenatal and postnatal exposure to a broad range of chemical and physical exposures. Exposure models will be developed for the full cohorts totaling 32,000 mother-child pairs, and biomarkers will be measured in a subset of 1,200 mother-child pairs. Nested repeat-sampling panel studies (n = 150) will collect data on biomarker variability, use smartphones to assess mobility and physical activity, and perform personal exposure monitoring. Omics techniques will determine molecular profiles (metabolome, proteome, transcriptome, epigenome) associated with exposures. Statistical methods for multiple exposures will provide exposure-response estimates for fetal and child growth, obesity, neurodevelopment, and respiratory outcomes. A health impact assessment exercise will evaluate risks and benefits of combined exposures. HELIX is one of the first attempts to describe the early-life exposome of European populations and unravel its relation to omics markers and health in childhood. As proof of concept, it will form an important first step toward the life-course exposome.

Metabolic profiling, metabolomic and metabonomic studies require robust study protocols for any large-scale comparisons and evaluations. Detailed methods for solution-state NMR spectroscopy have been summarized in an earlier protocol. This protocol details the analysis of intact tissue samples by means of high-resolution magic-angle-spinning (HR-MAS) NMR spectroscopy and we provide a detailed description of sample collection, preparation and analysis. Described here are 1 H NMR spectroscopic techniques such as the standard one-dimensional, relaxation-edited, diffusion-edited and two-dimensional J-resolved pulse experiments, as well as one-dimensional 31 P NMR spectroscopy. These are used to monitor different groups of metabolites, e.g., sugars, amino acids and osmolytes as well as larger molecules such as lipids, non-invasively. Through the use of NMR-based diffusion coefficient and relaxation times measurements, information on molecular compartmentation and mobility can be gleaned. The NMR methods are often combined with statistical analysis for further metabonomics analysis and biomarker identification. The standard acquisition time per sample is 8–10 min for a simple one-dimensional 1 H NMR spectrum, giving access to metabolite information while retaining tissue integrity and hence allowing direct comparison with histopathology and MRI/MRS findings or the evaluation together with biofluid metabolic-profiling data.

Background Environment and diet in early life can affect development and health throughout the life course. Metabolic phenotyping of urine and serum represents a complementary systems-wide approach to elucidate environment–health interactions. However, large-scale metabolome studies in children combining analyses of these biological fluids are lacking. Here, we sought to characterise the major determinants of the child metabolome and to define metabolite associations with age, sex, BMI and dietary habits in European children, by exploiting a unique biobank established as part of the Human Early-Life Exposome project ( http://www.projecthelix.eu ). Methods Metabolic phenotypes of matched urine and serum samples from 1192 children (aged 6–11) recruited from birth cohorts in six European countries were measured using high-throughput 1 H nuclear magnetic resonance (NMR) spectroscopy and a targeted LC-MS/MS metabolomic assay (Biocrates Absolute IDQ p180 kit). Results We identified both urinary and serum creatinine to be positively associated with age. Metabolic associations to BMI z-score included a novel association with urinary 4-deoxyerythronic acid in addition to valine, serum carnitine, short-chain acylcarnitines (C3, C5), glutamate, BCAAs, lysophosphatidylcholines (lysoPC a C14:0, lysoPC a C16:1, lysoPC a C18:1, lysoPC a C18:2) and sphingolipids (SM C16:0, SM C16:1, SM C18:1). Dietary-metabolite associations included urinary creatine and serum phosphatidylcholines (4) with meat intake, serum phosphatidylcholines (12) with fish, urinary hippurate with vegetables, and urinary proline betaine and hippurate with fruit intake. Population-specific variance (age, sex, BMI, ethnicity, dietary and country of origin) was better captured in the serum than in the urine profile; these factors explained a median of 9.0% variance amongst serum metabolites versus a median of 5.1% amongst urinary metabolites. Metabolic pathway correlations were identified, and concentrations of corresponding metabolites were significantly correlated ( r  > 0.18) between urine and serum. Conclusions We have established a pan-European reference metabolome for urine and serum of healthy children and gathered critical resources not previously available for future investigations into the influence of the metabolome on child health. The six European cohort populations studied share common metabolic associations with age, sex, BMI z-score and main dietary habits. Furthermore, we have identified a novel metabolic association between threonine catabolism and BMI of children.

The ability to adapt to low-nutrient microenvironments is essential for tumor cell survival and progression in solid cancers, such as colorectal carcinoma (CRC). Signaling by the NF-κB transcription factor pathway associates with advanced disease stages and shorter survival in patients with CRC. NF-κB has been shown to drive tumor-promoting inflammation, cancer cell survival, and intestinal epithelial cell (IEC) dedifferentiation in mouse models of CRC. However, whether NF-κB affects the metabolic adaptations that fuel aggressive disease in patients with CRC is unknown. Here, we identified carboxylesterase 1 (CES1) as an essential NF-κB-regulated lipase linking obesity-associated inflammation with fat metabolism and adaptation to energy stress in aggressive CRC. CES1 promoted CRC cell survival via cell-autonomous mechanisms that fuel fatty acid oxidation (FAO) and prevent the toxic build-up of triacylglycerols. We found that elevated CES1 expression correlated with worse outcomes in overweight patients with CRC. Accordingly, NF-κB drove CES1 expression in CRC consensus molecular subtype 4 (CMS4), which is associated with obesity, stemness, and inflammation. CES1 was also upregulated by gene amplifications of its transcriptional regulator HNF4A in CMS2 tumors, reinforcing its clinical relevance as a driver of CRC. This subtype-based distribution and unfavorable prognostic correlation distinguished CES1 from other intracellular triacylglycerol lipases and suggest CES1 could provide a route to treat aggressive CRC.

Childhood obesity poses a significant public health challenge, yet the molecular intricacies underlying its pathobiology remain elusive. Leveraging extensive multi-omics profiling (methylome, miRNome, transcriptome, proteins and metabolites) and a rich phenotypic characterization across two parts of Europe within the population-based Human Early Life Exposome project, we unravel the molecular landscape of childhood obesity and associated metabolic dysfunction. Our integrative analysis uncovers three clusters of children defined by specific multi-omics profiles, one of which characterized not only by higher adiposity but also by a high degree of metabolic complications. This high-risk cluster exhibits a complex interplay across many biological pathways, predominantly underscored by inflammation-related cascades. Further, by incorporating comprehensive information from the environmental risk-scape of the critical pregnancy period, we identify pre-pregnancy body mass index and environmental pollutants like perfluorooctanoate and mercury as important determinants of the high-risk cluster. Overall, our work helps to identify potential risk factors for prevention and intervention strategies early in the life course aimed at mitigating obesity and its long-term health consequences. Obesity encompasses numerous interconnected pathological mechanisms. Here, the authors show that integrating multi-omics data uncovers distinct molecular profiles and prenatal factors linked to childhood obesity and metabolic dysfunction, providing insights for early prevention and intervention strategies.

Background Transcriptomic data is often used to build statistical models which are predictive of a given phenotype, such as disease status. Genes work together in pathways and it is widely thought that pathway representations will be more robust to noise in the gene expression levels. We aimed to test this hypothesis by constructing models based on either genes alone, or based on sample specific scores for each pathway, thus transforming the data to a ‘pathway space’. We progressively degraded the raw data by addition of noise and examined the ability of the models to maintain predictivity. Results Models in the pathway space indeed had higher predictive robustness than models in the gene space. This result was independent of the workflow, parameters, classifier and data set used. Surprisingly, randomised pathway mappings produced models of similar accuracy and robustness to true mappings, suggesting that the success of pathway space models is not conferred by the specific definitions of the pathway. Instead, predictive models built on the true pathway mappings led to prediction rules with fewer influential pathways than those built on randomised pathways. The extent of this effect was used to differentiate pathway collections coming from a variety of widely used pathway databases. Conclusions Prediction models based on pathway scores are more robust to degradation of gene expression information than the equivalent models based on ungrouped genes. While models based on true pathway scores are not more robust or accurate than those based on randomised pathways, true pathways produced simpler prediction rules, emphasizing a smaller number of pathways.