Nanoparticulate strategies for the delivery of miRNA mimics and inhibitors in anticancer therapy and its potential utility in oral submucous fibrosis

MicroRNAs (miRNAs) are naturally occurring noncoding RNAs with multiple functionalities. They are dysregulated in several conditions and can serve as disease biomarkers, therapeutic targets and therapeutic agents. Translation of miRNA therapeutics to the clinic poses several challenges related to the safe and effective delivery of these agents to the site of action. Nanoparticulate carriers hold promise in this area by enhancing targeting efficiency and reducing off-target effects. This paper reviews recent advances in the delivery strategies of miRNAs in anticancer therapy, with a focus on lipid-based, polymeric, inorganic platforms, cell membrane-derived vesicles and bacterial minicells. Additionally, this review explores the potentiality of miRNAs in the treatment of oral submucous fibrosis, a potentially premalignant condition of the oral cavity with no definitive treatment to date.

Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer

Gut microbiome is gaining interest because of its links with several diseases, including colorectal cancer (CRC), as well as the possibility of being used to obtain non-intrusive predictive disease biomarkers. Here we performed a meta-analysis of 1042 fecal metagenomic samples from seven publicly available studies. We used an interpretable machine learning approach based on functional profiles, instead of the conventional taxonomic profiles, to produce a highly accurate predictor of CRC with better precision than those of previous proposals. Moreover, this approach is also able to discriminate samples with adenoma, which makes this approach very promising for CRC prevention by detecting early stages in which intervention is easier and more effective. In addition, interpretable machine learning methods allow extracting features relevant for the classification, which reveals basic molecular mechanisms accounting for the changes undergone by the microbiome functional landscape in the transition from healthy gut to adenoma and CRC conditions. Functional profiles have demonstrated superior accuracy in predicting CRC and adenoma conditions than taxonomic profiles and additionally, in a context of explainable machine learning, provide useful hints on the molecular mechanisms operating in the microbiota behind these conditions.

Human disease biomarkers: challenges, advances, and trends in their validation

Biomarkers are important tools in the medical field, once they allow better prediction, characterization, and treatment of diseases. In this scenario, it is essential that biomarkers are highly accurate. Thus, biomarker validation is an essential part of ensuring the effectiveness of a biomarker. Validation of biomarkers is the process by which biomarkers are evaluated for accuracy and consistency, as well as their ability to inform the condition of health or disease. Although, there is no unique measure that can be used to determine the validity for all biomarkers, there are general criteria that all biomarkers must meet to be useful. In this work, we review the definition of biomarkers and discuss the validity components. We then critically discuss the main methods used to validate biomarkers and consider some examples of biomarkers of the diseases which most killer in the world (cardiovascular diseases, cancer, and viral infections), highlighting the potential biochemical pathways of these biomarkers in the biological system. In addition, we also comment on the omic strategies used in the biomarker discovery process and conclude with information about perspectives in biomarker validation through imaging techniques.

Reference Standards for Newborn Screening of Metabolic Disorders by Tandem Mass Spectrometry: A Nationwide Study on Millions of Chinese Neonatal Populations

Introduction: The major clinical problem presently confronting the Chinese newborn screening (NBS) programs by tandem mass spectrometry (MS/MS) is the lack of comprehensive reference intervals (RIs) for disease biomarkers. To close this gap, the Chinese National Center for Clinical Laboratories (NCCL) launched a nationwide study to investigate the dynamic pattern of 35 MS/MS NBS biomarkers and establish accurate and robust RIs.Methods: Blood spot samples from 4,714,089 Chinese neonates were tested in participating centers/laboratories and used for study analysis. MS/MS NBS biomarker trends were visually assessed by their concentrations over age. Specific partitions were determined arbitrarily by each day and sex or by the statistical method of Harris and Boyd. RIs, corresponding to the 2.5th and 97.5th percentiles, as well as the 1th, 25th, 75th and 99th percentiles were calculated for each reference partition using a non-parametric rank approach.Results: Most MS/MS NBS biomarkers fluctuated during the first week of life, followed by a relatively stable concentration. Age and sex-specific RIs were established and presented an improved specificity over the RIs used in participating centers/laboratories. Females demonstrated higher 2.5th and 97.5th percentiles in all amino acids except arginine and ornithine than males, whereas males showed higher 2.5th and 97.5th percentiles in most acylcarnitines.Conclusion: The present study determined the dynamic trends of 35 MS/MS biomarkers and established age and sex-specific RIs, valuably contributing to the current literature and timely evaluation of neonatal health and disease.

Osteosarcoma: novel prognostic biomarkers using circulating and cell-free tumour DNA

Osteosarcoma (OS) is the most common primary bone tumour in children and adolescents. Despite treatment with curative-intent, many patients die of this disease. Biomarkers for assessment of disease burden and prognoses for osteosarcoma are not available. Circulating-free (cfDNA) and -tumour DNA (ctDNA) are promising biomarkers for disease surveillance in several major cancer types, however only two such studies are reported for OS. In this combined discovery and validation study, we identified four novel methylation-based biomarkers in 171 OS tumours (test set) and comprehensively validated our findings in silico in two independent osteosarcoma sample datasets (n= 162, n=107) and experimentally using digital droplet PCR (ddPCR, n=20 OS tumours). Custom ddPCR assays for these biomarkers were able to detect ctDNA in 40% of pre-operative plasma samples (n=72). ctDNA was detected in 5/17 (29%) post-operative plasma samples from patients who experienced a subsequent relapse post-operatively. Both cfDNA levels and ctDNA detection independently correlated with overall survival, p=0.0015, p=0.0096, respectively. Combining both assays increased the prognostic value of the data. Our findings illustrate the utility of mutation-independent methylation-based markers, broadly applicable ctDNA assays for tumour surveillance and prognostication. This study lays the foundation for multi-institutional collaborative studies to explore the utility of plasma-derived biomarkers for predicting clinical outcome of OS.