A New Approach to Melanoma Treatment: microRNAs

Although immunotherapy and targeted therapy have radically changed melanoma treatment, the development of resistance and reduction of patient responses are still significant problems. Small molecule inhibitors are needed to overcome this situation, and biomarkers that can estimate whether patients will reply to existing treatments need to be developed. miRNAs are involved in diverse processes such as tumor development, tumor progression, metastasis, and invasion. While some miRNAs act as tumor suppressors, others may be oncogenic. miRNAs also contribute to the processes involved in drug resistance. There is increasing evidence demonstrating the possible effect of miRNAs on the diagnosis and treatment markers of melanoma. The manuscript focuses on the current challenges in melanoma treatment, highlighting issues such as the development of resistance and reduced patient responses despite the revolutionary advancements in targeted therapy and immunotherapy. It underscores the need for small molecule inhibitors and the creation of biomarkers for predicting patient responses to current treatments. The role of miRNAs in processes such as tumor development, metastasis, and invasion has been highlighted. While certain miRNAs function as tumor suppressors, others may exhibit oncogenic properties. Furthermore, increasing evidence is presented demonstrating the potential significance of miRNAs as markers for the symptom and identification of melanoma. These findings indicate a promising avenue for future research and clinical applications. In summary, the article effectively communicates key insights, making it a valuable resource for those interested in melanoma research and treatment.

Parkinson's Disease Diagnosis Using miRNA Biomarkers and Deep Learning

BACKGROUND

The current standard for Parkinson's disease (PD) diagnosis is often imprecise and expensive. However, the dysregulation patterns of microRNA (miRNA) hold potential as a reliable and effective non-invasive diagnosis of PD.

METHODS

We use data mining to elucidate new miRNA biomarkers and then develop a machine-learning (ML) model to diagnose PD based on these biomarkers.

RESULTS

The best-performing ML model, trained on filtered miRNA dysregulated in PD, was able to identify miRNA biomarkers with 95.65% accuracy. Through analysis of miRNA implicated in PD, thousands of descriptors reliant on gene targets were created that can be used to identify novel biomarkers and strengthen PD diagnosis.

CONCLUSIONS

The developed ML model based on miRNAs and their genomic pathway descriptors achieved high accuracies for the prediction of PD.

CESCProg: a compact prognostic model and nomogram for cervical cancer based on miRNA biomarkers

Cervical squamous cell carcinoma, more commonly cervical cancer, is the fourth common cancer among women worldwide with substantial burden of disease, and less-invasive, reliable and effective methods for its prognosis are necessary today. Micro-RNAs are increasingly recognized as viable alternative biomarkers for direct diagnosis and prognosis of disease conditions, including various cancers. In this work, we addressed the problem of systematically developing an miRNA-based nomogram for the reliable prognosis of cervical cancer. Towards this, we preprocessed public-domain miRNA -omics data from cervical cancer patients, and applied a cascade of filters in the following sequence: (i) differential expression criteria with respect to controls; (ii) significance with univariate survival analysis; (iii) passage through dimensionality reduction algorithms; and (iv) stepwise backward selection with multivariate Cox modeling. This workflow yielded a compact prognostic DEmiR signature of three miRNAs, namely hsa-miR-625-5p, hs-miR-95-3p, and hsa-miR-330-3p, which were used to construct a risk-score model for the classification of cervical cancer patients into high-risk and low-risk groups. The risk-score model was subjected to evaluation on an unseen test dataset, yielding a one-year AUROC of 0.84 and five-year AUROC of 0.71. The model was validated on an out-of-domain, external dataset yielding significantly worse prognosis for high-risk patients. The risk-score was combined with significant features of the clinical profile to establish a predictive prognostic nomogram. Both the miRNA-based risk score model and the integrated nomogram are freely available for academic and not-for-profit use at CESCProg, a web-app (https://apalania.shinyapps.io/cescprog).

Evaluation of circulating microRNA profiles in blood as potential candidate biomarkers in a subacute ruminal acidosis cow model - a pilot study

BACKGROUND

Subacute ruminal acidosis (SARA) is a metabolic disorder often observed in high-yielding dairy cows, that are fed diets high in concentrates. We hypothesized that circulating miRNAs in blood of cows could serve as potential candidate biomarkers to detect animals with metabolic dysbalances such as SARA. MicroRNAs (miRNAs) are a class of small non-coding RNAs, serving as regulators of a plethora of molecular processes. To test our hypothesis, we performed a pilot study with non-lactating Holstein-Friesian cows fed a forage diet (FD; 0% concentrate, n = 4) or a high-grain diet (HG; 65% concentrate, n = 4) to induce SARA. Comprehensive profiling of miRNA expression in plasma and leucocytes were performed by next generation sequencing (NGS). The success of our model to induce SARA was evaluated based on ruminal pH and was evidenced by increased time spent with a pH threshold of 5.8 for an average period of 320 min/d.

RESULTS

A total of 520 and 730 miRNAs were found in plasma and leucocytes, respectively. From these, 498 miRNAs were shared by both plasma and leucocytes, with 22 miRNAs expressed exclusively in plasma and 232 miRNAs expressed exclusively in leucocytes. Differential expression analysis revealed 10 miRNAs that were up-regulated and 2 that were down-regulated in plasma of cows when fed the HG diet. A total of 63 circulating miRNAs were detected exclusively in the plasma of cows with SARA, indicating that these animals exhibited a higher number and diversity of circulating miRNAs. Considering the total read counts of miRNAs expressed when fed the HG diet, differentially expressed miRNAs ( log₂ fold change) and known function, we have identified bta-miR-11982, bta-miR-1388-5p, bta-miR-12034, bta-miR-2285u, and bta-miR-30b-3p as potential candidates for SARA-biomarker in cows by NGS. These were further subjected to validation using small RNA RT-qPCR, confirming the promising role of bta-miR-30b-3p and bta-miR-2285.

CONCLUSION

Our data demonstrate that dietary change impacts the release and expression of miRNAs in systemic circulation, which may modulate post-transcriptional gene expression in cows undergoing SARA. Particularly, bta-miR-30b-3p and bta-miR-2285 might serve as promising candidate biomarker predictive for SARA and should be further validated in larger cohorts.

Multiplexed smFRET Nucleic Acid Sensing Using DNA Nanotweezers

The multiplexed detection of disease biomarkers is part of an ongoing effort toward improving the quality of diagnostic testing, reducing the cost of analysis, and accelerating the treatment processes. Although significant efforts have been made to develop more sensitive and rapid multiplexed screening methods, such as microarrays and electrochemical sensors, their limitations include their intricate sensing designs and semi-quantitative detection capabilities. Alternatively, fluorescence resonance energy transfer (FRET)-based single-molecule counting offers great potential for both the sensitive and quantitative detection of various biomarkers. However, current FRET-based multiplexed sensing typically requires the use of multiple excitation sources and/or FRET pairs, which complicates labeling schemes and the post-analysis of data. We present a nanotweezer (NT)-based sensing strategy that employs a single FRET pair and is capable of detecting multiple targets. Using DNA mimics of miRNA biomarkers specific to triple-negative breast cancer (TNBC), we demonstrated that the developed sensors are sensitive down to the low picomolar range (≤10 pM) and can discriminate between targets with a single-base mismatch. These simple hybridization-based sensors hold great promise for the sensitive detection of a wider spectrum of nucleic acid biomarkers.

DNA computing for gastric cancer analysis and functional classification

Early identification of key biomarkers of malignant cancer is vital for patients' prognosis and therapies. There is research demonstrating that microRNAs are important biomarkers for cancer analysis. In this article, we used the DNA strand displacement mechanism (DSD) to construct the DNA computing system for cancer analysis. First, gene chips were obtained through bioinformatical training. These microRNA data and clinical traits were obtained from the Cancer Genome Atlas (TCGA) dataset. Second, we analyzed the expression data by using a weighted gene co-expression network (WGCNA) and found four biomarkers for two clinic features, respectively. Last, we constructed a DSD-based DNA computing system for cancer analysis. The inputs of the system are these identified biomarkers; the outputs are the fluorescent signals that represent their corresponding traits. The experiment and simulation results demonstrated the reliability of the DNA computing system. This DSD simulation system is lab-free but clinically meaningful. We expect this innovative method to be useful for rapid and accurate cancer diagnosis.

Reliable miRNA biomarker quantification in clinical practice - are we there yet?

Blood-borne miRNAs serve as disease diagnostic biomarkers and await clinical validation. Here, we evaluated Cel-miR-39-3p and miRNA16-5p as calibrator for the quantification of 15 miRNAs linked to hepatic impairment. We added defined copy numbers of Cel-miR-39-3p to plasma of healthy controls (N = 5) and patient samples undergoing liver resection (N = 51). The miRNAs were isolated according to SOPs and quantified by RT-qPCR using the 2^-(ΔΔ-CT)-method. Although miRNA16-5p and the spike-in control behaved similar in qPCR assays (R² = 0.8591) the spike-in control suffered from high inter-patient variability (median 7.6-fold) and low recoveries (median 5.6%, 95% CI 1.5-11.8%). Adding Cel-miR-39-3p to blood samples prior to RNA-isolation improved the recoveries (median 105.7%; 95% CI 29.9-219.9%), yet the inter-patient variability remained high (median 7.2-fold). Alike, we observed significant variability in CT-values for miRNA16-5p (range 14.7-fold) thus rendering this internal, blood-borne reference gene unacceptable as comparator. Specifically, 10 out of 15 diagnostic miRNAs failed the criteria R² ≥ 0.8 even though we added a defined copy number of Cel-miR-39-3p. This suggests interference of the spike-in control with individual miRNAs in the assay. Our study highlights current limitations in the quantification of blood-borne miRNAs that is of particularly importance when used for disease diagnostic and therapeutic interventions.

Multi-omics network characterization reveals novel microRNA biomarkers and mechanisms for diagnosis and subtyping of kidney transplant rejection

BACKGROUND

Kidney transplantation is an optimal method for treatment of end-stage kidney failure. However, kidney transplant rejection (KTR) is commonly observed to have negative effects on allograft function. MicroRNAs (miRNAs) are small non-coding RNAs with regulatory role in KTR genesis, the identification of miRNA biomarkers for accurate diagnosis and subtyping of KTR is therefore of clinical significance for active intervention and personalized therapy.

METHODS

In this study, an integrative bioinformatics model was developed based on multi-omics network characterization for miRNA biomarker discovery in KTR. Compared with existed methods, the topological importance of miRNA targets was prioritized based on cross-level miRNA-mRNA and protein-protein interaction network analyses. The biomarker potential of identified miRNAs was computationally validated and explored by receiver-operating characteristic (ROC) evaluation and integrated "miRNA-gene-pathway" pathogenic survey.

RESULTS

Three miRNAs, i.e., miR-145-5p, miR-155-5p, and miR-23b-3p, were screened as putative biomarkers for KTR monitoring. Among them, miR-155-5p was a previously reported signature in KTR, whereas the remaining two were novel candidates both for KTR diagnosis and subtyping. The ROC analysis convinced the power of identified miRNAs as single and combined biomarkers for KTR prediction in kidney tissue and blood samples. Functional analyses, including the latent crosstalk among HLA-related genes, immune signaling pathways and identified miRNAs, provided new insights of these miRNAs in KTR pathogenesis.

CONCLUSIONS

A network-based bioinformatics approach was proposed and applied to identify candidate miRNA biomarkers for KTR study. Biological and clinical validations are further needed for translational applications of the findings.

Lipopolysaccharide (LPS) induced inflammatory changes to differentially expressed miRNAs of the host inflammatory response

In veterinary medicine, inflammation in swine is evaluated principally by clinical signs. This method is often unreliable when assessing large animal populations because of inconsistent interpretations of clinical observations. This study examined whether changes in miRNA expression can predict the severity of the inflammatory response in swine after administration of Escherichia coli lipopolysaccharide (LPS). Whole blood from swine challenged with LPS at 0.125 μg/kg to 2.0 μg/kg body weight was collected at 0, 1, 3, and 8 h post LPS-challenge. Mature miRNAs were extracted from plasma and quantitative real-time-PCR (qRT-PCR) was used to evaluate the 84 most abundant swine miRNAs found in plasma. The miRNA changes in expression were assessed using the comparative CT Method (ΔΔCT method) for normalization with an exogenous control. The results revealed that expression of ssc-let-7e-5p, ssc-mir-22-3p, and ssc-miR-146a-5p were the most significantly changed miRNA over the time course. At 1 h post-LPS, ssc-let-7e-5p decreased as the LPS dosage levels increased from 0.125 to 1.0 μg/kg. Similarly, as the LPS doses increased from 0.125 to 0.5 μg/kg, ssc-miR-22-3p levels significantly decreased at 1 h post-LPS. In the 2.0 μg/kg LPS, ssc-miR-146a-5p levels increased between 0 and 3 h post-LPS; however, expression was downregulated with a 145 % decrease from 3 to 8 h. The three miRNA biomarkers suggest potentially useful surrogate endpoints for the evaluation of inflammatory and endotoxemia responses in swine.

Unraveling Molecular Pathways Altered in MeCP2-Related Syndromes, in the Search for New Potential Avenues for Therapy

Methyl-CpG-binding protein 2 (MeCP2) is an X-linked epigenetic modulator whose dosage is critical for neural development and function. Loss-of-function mutations in MECP2 cause Rett Syndrome (RTT, OMIM #312750) while duplications in the Xq28 locus containing MECP2 and Interleukin-1 receptor-associated kinase 1 (IRAK1) cause MECP2 duplication syndrome (MDS, OMIM #300260). Both are rare neurodevelopmental disorders that share clinical symptoms, including intellectual disability, loss of speech, hand stereotypies, vasomotor deficits and seizures. The main objective of this exploratory study is to identify novel signaling pathways and potential quantitative biomarkers that could aid early diagnosis and/or the monitoring of disease progression in clinical trials. We analyzed by RT-PCR gene expression in whole blood and microRNA (miRNA) expression in plasma, in a cohort of 20 females with Rett syndrome, 2 males with MECP2 duplication syndrome and 28 healthy controls, and correlated RNA expression with disease and clinical parameters. We have identified a set of potential biomarker panels for RTT diagnostic and disease stratification of patients with microcephaly and vasomotor deficits. Our study sets the basis for larger studies leading to the identification of specific miRNA signatures for early RTT detection, stratification, disease progression and segregation from other neurodevelopmental disorders. Nevertheless, these data will require verification and validation in further studies with larger sample size including a whole range of ages.

Extracellular vesicles - propagators of neuropathology and sources of potential biomarkers and therapeutics for neurodegenerative diseases

Neurodegenerative diseases are characterised by the irreversible degeneration of neurons in the central or peripheral nervous systems. These include amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's disease (PD) and prion diseases. Small extracellular vesicles (sEVs), a type of EV involved in cellular communication, have been well documented as propagating neurodegenerative diseases. These sEVs carry cargo, such as proteins and RNA, to recipient cells but are also capable of promoting protein misfolding, thus actively contributing to the progression of these diseases. sEV secretion is also a compensatory process for lysosomal dysfunction in the affected cells, despite inadvertently propagating disease to recipient cells. Despite this, sEV miRNAs have biomarker potential for the early diagnosis of these diseases, while stem cell-derived sEVs and those generated through exogenous assistance demonstrate the greatest therapeutic potential. This Review will highlight novel advancements in the involvement of sEVs as propagators of neuropathology, biomarkers and potential therapeutics in neurodegenerative diseases.

Evaluation of miRNA detection methods for the analytical characteristic necessary for clinical utilization

miRNAs are promising biomarkers but methods for their measurement are not clear. We therefore examined three miRNA detection technologies and considered the analytical characteristics essential for clinical utilization. TaqMan assays, SplintR-qPCR and miREIA were compared for their absolute quantification bias, conformity and robustness. Absolute concentrations of miR-142-5p, miR-23a-3p and miR-93-5p were measured with all three methods using 30 samples. Robustness was evaluated by measurement of miR-21-5p in five uniform experiments. Correlations were miRNA-specific, but we observed a different absolute concentration range in RT-qPCR (fmol/μl) and methods evading the RT process (amol/μl). Consistently, RT-less methods reported better robustness (CV 8-19%) than RT-qPCR (CV 39-50%). The calibration curve in TaqMan Advanced assay was influenced by dilution media. Methods avoiding RT seem to be a promising future alternative for miRNA measurement.

Environmental neurotoxicant manganese regulates exosome-mediated extracellular miRNAs in cell culture model of Parkinson's disease: Relevance to α-synuclein misfolding in metal neurotoxicity

Many chronic neurodegenerative disorders share a common pathogenic mechanism involving the aggregation and deposition of misfolded proteins. Recently, it was shown that these aggregated proteins could be transferred from one cell to another via extracellular nanovesicles called exosomes. Initially thought to be a means of cellular waste removal, exosomes have since been discovered to actively participate in cell-to-cell communication. Importantly, various inflammatory and signaling molecules, as well as small RNAs are selectively packaged in these vesicles. Considering the important role of environmental manganese (Mn) in Parkinson's disease (PD)-like neurological disorders, we characterized the effect of Mn on exosome content and release using an MN9D dopaminergic cell model of PD, which was generated to stably express wild-type human α-synuclein (αSyn). Mn exposure (300μM MnCl₂) for 24h induced the release of exosomes into the extracellular media prior to cytotoxicity, as determined by NanoSight particle analysis and electron microscopy. Strikingly, Western blot analysis revealed that Mn treatment in αSyn-expressing cells increases the protein Rab27a, which regulates the release of exosomes from cells. Moreover, next-generation sequencing showed more small RNAs in exosomes isolated from Mn-exposed cells than from control exosomes. Our miRNA profiling analysis led to the discovery of increased expression of certain miRNAs previously shown to regulate key biological pathways, including protein aggregation, autophagy, inflammation and hypoxia. Collectively, our results provide a glimpse of Mn's role in modulating extracellular miRNA content through exosomal release from dopaminergic neuronal cells and thus potentially contributing to progressive neurodegeneration. Further characterization of extracellular miRNAs and their targets will have major impacts on biomarker discovery and translational strategies for environmentally linked neurodegenerative diseases including PD.

Strategies to use microRNAs as therapeutic targets

MicroRNAs (miRNAs) are non-coding RNAs generated from endogenous hairpin-shaped transcripts that powerfully regulate gene expression at post-transcriptional level. Each miRNA is capable to regulate the expression levels of hundreds of transcripts and each mRNA may have more than one miRNA recognition sequence. There is emerging evidence that deregulation of miRNA expression leads to the alteration of pivotal physiological functions contributing to the development of diseases and neoplasms, including pituitary adenoma. This review is aimed at providing the up-to-date knowledge concerning deregulated miRNAs of pituitary tumors and their functions. In order to take stock, pituitary tumors have been sub-divided in different classes on the basis of tumor features (histotype, dimension, aggressiveness). The overview takes full consideration of the recent advances in miRNAs role as potential therapeutics and biomarkers.

Integrated systems for exosome investigation

Extracellular vesicles, including exosomes, are currently being investigated to better understand their biogenesis and biological functions. There is also a rapidly growing interest in utilizing exosomes present in patient biofluids for molecular diagnostics in the clinic. Exosomes are natural shuttles of RNA and protein cargo, making them attractive as potential therapeutic delivery vehicles. Here, we describe the methods for using the latest tools and technologies to study exosomes to better understand their roles in cell-to-cell communication, for discovery of clinical biomarkers and to engineer exosomes for therapeutic applications.