Potential Disease Biomarkers Research Articles

Incremental role of Cardio-Ankle Vascular Index in predicting ischemic threshold in patients with suspected coronary artery disease

Abstract Background The Cardio-Ankle Vascular Index (CAVI) quantifies arterial stiffness and has emerged as a potential biomarker for cardiovascular disease and its associated risk factors. This study explores the prognostic utility of CAVI in predicting myocardial ischemia in patients with suspected coronary artery disease (CAD). Additionally, it examines the relationship between global longitudinal strain (GLS) and the wall motion score index (WMSI) during both rest and stress echocardiography. Methods We conducted a retrospective analysis of patients with suspected CAD who underwent CAVI measurement and stress echocardiography within a three-month window. Coronary angiography was utilized to evaluate anatomical stenosis (>50%). We used multivariate analysis to identify predictors of the ischemic threshold. Results In our cohort of 127 patients (average age 61.4 ± 9.1, 78% male), the prevalence of hypertension, dyslipidemia, and diabetes was 76%, 68% and 47% of patients, respectively. Prior coronary intervention and a history of myocardial infarction were showed significant prevalence at 33% and 9%, respectively. CAD distribution included absence in 15%, left main (LM) disease in 8%, proximal left anterior descending (LAD) artery disease in 38%, mid-to-distal LAD disease in 50%, left circumflex artery disease in 46%, and right coronary artery disease in 57%. A robust correlation between GLS and WMSI was observed both at rest and during peak stress (r=0.68 and r=0.57, p<0.001, respectively). High CAVI (≥8.5), resting GLS in the LAD(<-15.5%), and peak GLS during stress (<-15.3%) emerged as significant ischemic threshold predictors in multivariate analysis (AUC = 0.82, p<0.0001). Conclusion The correlation between WMSI and GLS provides valuable insights into cardiac mechanics. These findings underscore the clinical relevance of incorporating CAVI, GLS, and WMSI into routine assessments for patients with suspected CAD to enhance prognostic stratification.ROC for final predictive model

Exploring the diagnostic potential of miRNA signatures in the Fabry disease serum: A comparative study of automated and manual sample isolations.

Fabry disease, an X-linked lysosomal storage disorder caused by galactosidase α (GLA) gene mutations, exhibits diverse clinical manifestations, and poses significant diagnostic challenges. Early diagnosis and treatment are crucial for improved patient outcomes, pressing the need for reliable biomarkers. In this study, we aimed to identify miRNA candidates as potential biomarkers for Fabry disease using the KingFisher™ automated isolation method and NanoString nCounter® miRNA detection assay. Clinical serum samples were collected from both healthy subjects and Fabry disease patients. RNA extraction from the samples was performed using the KingFisher™ automated isolation method with the MagMAX mirVanaTM kit or manually using the Qiagen miRNeasy kit. The subsequent NanoString nCounter® miRNA detection assay showed consistent performance and no correlation between RNA input concentration and raw count, ensuring reliable and reproducible results. Interestingly, the detection range and highly differential miRNA between the control and disease groups were found to be distinct depending on the isolation method employed. Nevertheless, enrichment analysis of miRNA-targeting genes consistently revealed significant associations with angiogenesis pathways in both isolation methods. Additionally, our investigation into the impact of enzyme replacement therapy on miRNA expression indicated that some differential miRNAs may be sensitive to treatment. Our study provides valuable insights to identify miRNA biomarkers for Fabry disease. While different isolation methods yielded various detection ranges and highly differential miRNAs, the consistent association with angiogenesis pathways suggests their significance in disease progression. These findings lay the groundwork for further investigations and validation studies, ultimately leading to the development of non-invasive and reliable biomarkers to aid in early diagnosis and treatment monitoring for Fabry disease.

The relation between dietary polysaccharide intake and urinary excretion of tetraglucoside.

The urinary metabolite tetraglucoside (Glc4) is a potential biomarker for hepatic glycogen storage diseases (GSDs). Glc4 is believed to reflect body glycogen content and/or turnover. However, dietary polysaccharide intake may influence Glc4 excretion, potentially limiting the utility of Glc4 as a monitoring biomarker in hepatic GSDs. We aimed to investigate the association of dietary polysaccharide intake with Glc4 excretion. Urinary Glc4 excretion (mmol/mmol creatinine and mmol/24 h) was analyzed using a validated LC-MS/MS method. Data was analyzed from 65 kidney transplant recipients and 58 healthy kidney donors in the TransplantLines cohort study. Spearman's correlation and multivariable linear regression analyses were performed. In the multivariable analysis, dry lean body mass (DLBM), dietary polysaccharide intake, transplantation status, age, sex, and glycated hemoglobin (HbA1c) served as independent variables. Daily variation was examined in 21 healthy individuals of urinary Glc4 excretion in 2-h collections over a 24-h period. Mixed generalized additive models were built to study the association of prior polysaccharide intake with Glc4 excretion. No (univariate) associations were found between polysaccharide intake and Glc4 excretion. However, a significant interaction between DLBM and polysaccharide on 24 h Glc4 excretion was observed in the multivariate analysis. Glc4 excretion throughout the day exhibited no relationship to prior polysaccharide intake. Our findings suggest an indirect effect of polysaccharide intake on Glc4 excretion, potentially due to changes in muscle glycogen content and/or turnover. We have found no evidence that dietary polysaccharides under normal intakes increase urinary Glc4 directly.

Importance of biological markers in the assessment of endothelial dysfunction

Endothelial dysfunction (ED) is a pathogenetic link in many cardio­vas­cular diseases. One of the promising approaches to non-invasi­ve diagnostics and assessment of the ED severity may be the deter­mi­nation of specific blood biomarkers. This review is devoted to the patho­genetic role of some biochemical and molecular factors associated with ED, as potential biomarkers of noncommunicable diseases.

EDITORIAL: RETURNING THE CENTRAL LYMPHATIC SYSTEM TO THE CENTER OF LYMPHOLOGY

After the introduction of conventional oil contrast lymphography and the founding of the discipline of lymphology, great impetus was given to investigating central lymphatic system and its disorders along with lymphatic involvement and specifically lymphostasis in common diseases of major organs such as the liver (cirrhosis) and heart (heart failure). Gradually interest shifted to more peripheral disorders such as limb lymphedema and its treatment by physical and surgical measures. At the same time, basic lymphology turned to the study of isolated ex vivo and in vitro, including lymphatic endothelial models and more recently, molecular lymphology focusing on lymphatic growth and modulating factors, genes and proteins underlying primary lymphedema, and more potential biomarkers of lymphatic disease have gained prominence. However, it has been advances in lymphatic imaging, namely lymphoscintigraphy with SPECT-CT high resolution 3-D pictures and magnetic resonance imaging (contrast and non-contrast) of the peripheral and particularly central lymphatic system by more invasive means combined with endovascular interventional techniques to treat complex and life-threatening lymphatic disorders that has returned the central lymphatic system to the center of lymphology, where the journey began.

Exploring serum bile acids as potential noninvasive biomarkers for nonalcoholic fatty liver disease

BackgroundBile acids are vital regulators of liver metabolism, and their dysregulation is closely linked with the progression of nonalcoholic fatty liver disease (NAFLD). Profiling these bile acids may provide valuable diagnostic and prognostic markers for these conditions. This study aimed to evaluate bile acid profiles in NAFLD patients and assess their potential as biomarkers for diagnosing and predicting disease progression. Serum levels of 14 bile acids were measured in 25 normal healthy controls (NHC), 35patients with metabolic dysfunction–associated steatotic liver disease (MASLD), and 40 patients with NASH, categorized by the NAFLD Activity Score (NAS). Quantification was performed using high-performance liquid chromatography-tandem mass spectrometry (HPLC–MS/MS).ResultsPrimary unconjugated bile acids, CA and CDCA, along with conjugated acids GCA, GCDCA, TCA, and TCDCA, were significantly elevated in both MASLD and NASH compared to NHC (all p < 0.05). While levels increased progressively from NHC to MASLD to NASH, no significant differences were observed between MASLD and NASH except for GCA and TCA (P < 0.05). Similarly, secondary bile acids LCA, TLCA, GUDCA, and TUDCA were higher in MASLD and NASH compared to NHC (all p < 0.05).Logistic regression identified CA (odds ratio = 2.05, p = 0.02), CDCA (odds ratio = 1.58, p = 0.04), GCA (odds ratio = 1.92, p = 0.03) and DCA (odds ratio = 2.06, p = 0.04) as significant predictors of fibrosis. For active inflammation, GCA (odds ratio = 2.04, p = 0.04), and TCA (odds ratio = 1.94, p = 0.04) were significant predictors. In steatosis, CA, CDCA, GCA, DCA, TDCA, TLCA, and UDCA were notable predictors, with high odds ratios.ConclusionThe study highlights significant alterations in bile acid profiles associated with NAFLD progression. Specific bile acids, such as CA, GCA, TCA, and TCDCA are strong predictors of disease severity, indicating their potential as biomarkers for NAFLD treatment and prognosis.

Associations between Kidney Disease Progression and Metabolomic Profiling in Stable Kidney Transplant Recipients-A 3 Year Follow-Up Prospective Study.

Background: kidney transplant recipients are exposed to multiple pathogenic pathways that may alter short and long-term allograft survival. Metabolomic profiling is useful for detecting potential biomarkers of kidney disease with a predictive capacity. This field is still under development in kidney transplantation and metabolome analysis is faced with analytical challenges. We performed a cross-sectional study including stable kidney transplant patients and aimed to search for relevant associations between baseline plasmatic and urinary metabolites and relevant outcomes over a follow-up period of 3 years. Methods: we performed a cross-sectional study including 72 stable kidney transplant patients with stored plasmatic and urinary samples at the baseline evaluation which were there analyzed by nuclear magnetic resonance in order to quantify and describe metabolites. We performed a 3-year follow-up and searched for relevant associations between renal failure outcomes and baseline metabolites. Between-group comparisons were made after classification by observed estimated glomerular filtration rate slope during the follow-up: positive slope and negative slope. Results: The mean estimated GFR (glomerular filtration rate) was higher at baseline in the patients who exhibited a negative slope during the follow-up (63.4 mL/min/1.73 m2 vs. 55.8 mL/min/1.73 m2, p = 0,019). After log transformation and division by urinary creatinine, urinary dimethylamine (3.63 vs. 3.16, p = 0.027), hippuric acid (7.33 vs. 6.29, p = 0.041), and acetone (1.88 vs. 1, p = 0.023) exhibited higher concentrations in patients with a negative GFR slope when compared to patients with a positive GFR slope. By computing a linear regression, a significant low-strength regression equation between the log 2 transformed plasmatic level of glycine and the estimated glomerular filtration rate was found (F (1,70) = 5.15, p = 0.026), with an R2 of 0.069. Several metabolites were correlated positively with hand grip strength (plasmatic tyrosine with r = 0.336 and p = 0.005 and plasmatic leucine with r = 0.371 and p = 0.002). Other urinary metabolites were found to be correlated negatively with hand grip strength (dimethylamine with r = -0.250 and p = 0.04, citric acid with r = -0.296 and p = 0.014, formic acid with r = -0.349 and p = 0.004, and glycine with r = -0.306 and p = 0.01). Conclusions: some metabolites had different concentrations compared to kidney transplant patients with negative and positive slopes, and significant correlations were found between hand grip strength and urinary and plasmatic metabolites.

GCNPMDA: Human microbe-disease association prediction by hierarchical graph convolutional network with layer attention

Microorganisms play a crucial role in various physiological processes, including metabolism, immune defense, nutrition absorption, defense against cancer, and protection against pathogen colonization. Changes in microbial communities serve as potential biomarkers for diseases, offering significant insights into disease treatment and diagnosis. However, the association between microorganisms and diseases is still unclear, and more computational methods are needed to predict potential associations. In this paper, we introduce a novel computational model, the Graph Convolutional Network to Predict Microbe-Disease Associations (GCNPMDA), which employs layer attention mechanisms (see Figure 1). GCNPMDA integrates known microbe-disease associations, microbe–microbe similarities, and disease–disease similarities into a heterogeneous network. The model utilizes a Graph Convolutional Network (GCN) to learn embeddings for diseases and microbes. To enhance attribute information, microbe–microbe similarities are computed using Cosine similarity, Jaccard similarity, Gaussian kernel, and functional information, while disease–disease similarities are computed using Cosine similarity, Jaccard similarity, Gaussian kernel, and symptom information. Additionally, attention mechanisms are applied to combine embeddings from multiple graph convolution layers. The model’s predictive effectiveness is evaluated on Human Microbe-Disease Association Database (HMDAD). Leave-one-out cross-validation (LOOCV) was conducted. The Area Under ROC Curve (AUC) of LOOCV is 0.98. The 5-fold cross-validation (5-fold CV) on HMDAD yields average AUC of 0.98 ± 0.009. Furthermore, we carried out a case study of type 2 diabetes (T2D), inflammatory bowel disease (IBD), and rheumatoid arthritis. Based on existing literature evidence, it was confirmed that 6, 7, and 7 of the top-10 inferred microbes have established associations with T2D, IBD, and rheumatoid arthritis, respectively. GCNPMDA demonstrates potential efficacy in identifying disease-related microbes, offering a promising tool to uncover the intricate relationship between microorganisms and their human hosts.

Uncovering neural substrates across Alzheimer's disease stages using contrastive variational autoencoder.

Alzheimer's disease is the most common major neurocognitive disorder. Although currently, no cure exists, understanding the neurobiological substrate underlying Alzheimer's disease progression will facilitate early diagnosis and treatment, slow disease progression, and improve prognosis. In this study, we aimed to understand the morphological changes underlying Alzheimer's disease progression using structural magnetic resonance imaging data from cognitively normal individuals, individuals with mild cognitive impairment, and Alzheimer's disease via a contrastive variational autoencoder model. We used contrastive variational autoencoder to generate synthetic data to boost the downstream classification performance. Due to the ability to parse out the nonclinical factors such as age and gender, contrastive variational autoencoder facilitated a purer comparison between different Alzheimer's disease stages to identify the pathological changes specific to Alzheimer's disease progression. We showed that brain morphological changes across Alzheimer's disease stages were significantly associated with individuals' neurofilament light chain concentration, a potential biomarker for Alzheimer's disease, highlighting the biological plausibility of our results.

Enzymatic TET-1 inhibition highlights different epigenetic behaviours of IL-1β and TNFα in tumour progression of OS cell lines

Osteosarcoma (OS) is the most frequent primary malignant bone tumour, whose heterogeneity represents a major challenge for common antitumour therapies. Inflammatory cytokines are known to be necessary for OS progression. Therefore, to optimise therapy, it is important to discover reliable biomarkers by identifying the mechanism generating OS and investigating the inflammatory pathways that support the undifferentiated state. In this work, we highlight the differences of epigenetic activities of IL-1β and TNFα, and the susceptibility of TET-1 enzymatic inhibition, in tumour progression of three different OS cell lines. Investigating DNA methylation of IL-6 promoter and determining its expression, we found that TET enzymatic inhibition influences proliferation induced by inflammatory cytokines in OS cell lines. Moreover, Bobcat 339 treatment blocks IL-1β epigenetic action on IL-6 promoter, while only partially those of TNFα as well as inhibits IL-1β-dependent epithelial–mesenchymal transition (EMT) process, but only partially those of TNFα. In conclusion, this work highlights that IL-1β and TNFα have different effects on DNA demethylation in OS cell lines, making DNA methylation a potential biomarker of disease. Specifically, in IL-1β treatment, TET-1 inhibition completely blocks tumour progression, while in TNFα actions, it is only partially effective. Given that these two inflammatory pathways can be therapeutic targets for treating these tumours, knowledge of their distinct epigenetic behaviours can be useful for developing precise and specific therapeutic strategies for this disease.

Circulating MicroRNA as a Potential Biomarker for Skeletal Disease in Primary Hyperparathyroidism: A Case-control Study.

The goal of this study was to characterize the microRNA (miRNA) expression signatures in patients with Primary hyperparathyroidism (PHPT) and identify miRNA biomarkers of bone homeostasis. PHPT is associated with increased bone turnover and decreased bone mass. miRNA are markers of bone remodeling. We performed a prospective case-control study of postmenopausal females with PHPT and control subjects matched for race, age, and bone mineral density (BMD). We collected clinical and biochemical data, assessed BMD by dual-energy x-ray absorptiometry, and measured 27 serum miRNAs related to bone remodeling. We used linear regression to assess the correlation between miRNA levels, conventional biochemical markers, and BMD. A total of 135 subjects were evaluated, including 49 with PHPT (discovery group), 47 control patients without PHPT, and an independent validation cohort of 39 PHPT patients. Of 27 miRNAs evaluated, 9 (miR-335-5p, miR-130b-3p, miR-125b-5p, miR-23a-3p, miR-152-3p, miR-582-5p, miR-144-5p, miR-320a, and miR-19b-3p) were differentially expressed in PHPT compared with matched control subjects. All 9 differentially expressed miRNAs significantly correlated with levels of serum parathyroid hormone (PTH), and 8 of the 9 correlated with calcium levels. No differentially expressed miRNAs were consistently correlated with markers of BMD. Subjects with PHPT segregate from controls based on the signature of these 9 miRNAs on principle component analysis. These data suggest that PHPT is characterized by a unique miRNA signature that is distinct from postmenopausal and idiopathic osteoporosis. Levels of specific miRNAs significantly correlate with PTH, suggesting that bone remodeling in PHPT may be mediated in part by PTH-induced changes in miRNA.

Is cardiovascular risk profiling from UK Biobank retinal images using explicit deep learning estimates of traditional risk factors equivalent to actual risk measurements? A prospective cohort study design

ObjectiveDespite extensive exploration of potential biomarkers of cardiovascular diseases (CVDs) derived from retinal images, it remains unclear how retinal images contribute to CVD risk profiling and how the results can...

Reference-Based OCT Angiogram Super-Resolution With Learnable Texture Generation.

Optical coherence tomography angiography (OCTA) can visualize retinal microvasculature and is important to qualitatively and quantitatively identify potential biomarkers for different retinal diseases. However, the resolution of optical coherence tomography (OCT) angiograms inevitably decreases when increasing the field-of-view (FOV) given a fixed acquisition time. To address this issue, we propose a novel reference-based super-resolution (RefSR) framework to preserve the resolution of the OCT angiograms while increasing the scanning area. Specifically, textures from the normal RefSR pipeline are used to train a learnable texture generator (LTG), which is designed to generate textures according to the input. The key difference between the proposed method and traditional RefSR models is that the textures used during inference are generated by the LTG instead of being searched from a single reference (Ref) image. Since the LTG is optimized throughout the whole training process, the available texture space is significantly enlarged and no longer limited to a single Ref image, but extends to all textures contained in the training samples. Moreover, our proposed LTGNet does not require an Ref image at the inference phase, thereby becoming invulnerable to the selection of the Ref image. Both experimental and visual results show that LTGNet has competitive performance and robustness over state-of-the-art methods, indicating good reliability and promise in real-life deployment. The source code is available at https://github.com/RYY0722/LTGNet.

Cortisol and cortisone determination by disposable pipette extraction (DPX) and ultra-efficient liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) in urine and saliva samples from patients with Parkinson's disease.

Higher serum cortisol levels appear to be associated with stress that can overlap or manifest anxiety, fatigue, depression, and sleep dysfunction. These are common and intrusive non-motor symptoms of Parkinson's disease (PD). Thus, stress has been proposed to mediate Parkinson's disease development, and cortisol has been suggested as a biomarker for the generation of stress-related symptoms in Parkinson's disease. This study describes sensitive and robust disposable pipette extraction (DPX) and ultra-efficient liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) method to determine cortisol and cortisone (as potential endocrine biomarkers for Parkinson's disease) in 24-h urine and saliva samples obtained from Parkinson's disease patients. Important parameters on DPX extraction were optimized to achieve the best extraction recovery and cleanup efficiency. The proposed method was linear from 0.5 (lower limit of quantification) to 500ngmL-1 for cortisol and from 3.0 (lower limit of quantification) to 500ngmL-1 for cortisone. To determine whether urinary cortisol and urinary cortisone are adequate as biomarkers to evaluate the level of anxiety in patients suffering from Parkinson's disease, twenty-nine Parkinson's disease patients (18 with anxiety and 11 without anxiety) were selected for urine analysis. Based on the obtained results, 24-h urine samples obtained from Parkinson's disease patients with anxiety had higher cortisone levels than samples obtained from healthy controls. Receiving operating curves (ROC) analysis, which presented the area under the ROC curve (AUC = 0.733), showed that urinary cortisone levels (µg/24-h urine) were sensitive (56.3%) and specific (93.3%) for distinguishing Parkinson's disease patients with anxiety from healthy controls. In terms of salivary results, PD patients' samples taken 30min after waking up had higher cortisol and cortisone levels than healthy controls, while their samples taken at night had lower cortisol and cortisone levels.

Prognostic role of systemic immune-inflammation index versus other cardiac markers in acute myocarditis in young adults.

Aim: Myocarditis, an inflammatory disease of the myocardium, can range from asymptomatic cases to severe forms such as fulminant myocarditis. The systemic immune-inflammation index (SII) has emerged as a potential biomarker for various inflammatory diseases. This study aimed to determine the effect of SII on the prognosis of young adults with acute myocarditis and compare it with other cardiac markers.Methods: We retrospectively analyzed patients aged 18-40years who were admitted to the emergency department with a diagnosis of acute myocarditis between January 2014 and January 2024. Patients were divided into non-fulminant and fulminant myocarditis groups based on diagnostic criteria.Results: SII, troponin I and N-terminal pro-brain natriuretic peptide (NT-proBNP) levels were significantly higher in the fulminant myocarditis group (p<0.001 for all). Logistic regression analysis identified SII and NT-proBNP as independent predictors of fulminant myocarditis but not for troponin I (p=0.064). The optimal cutoff value for SII in diagnosing fulminant myocarditis was 1020, with a sensitivity of 91% and specificity of 83%, outperforming troponin I. Patients with SII ≥1020 had a significantly higher risk of adverse outcomes.Conclusion: The SII enables early detection of adverse outcomes and is an independent predictor of prognosis in young adults with myocarditis.

Back to the Origin: Mechanisms of circRNA-Directed Regulation of Host Genes in Human Disease.

Circular RNAs (circRNAs) have been shown to be pivotal regulators in various human diseases by participating in gene splicing, acting as microRNA (miRNA) sponges, interacting with RNA-binding proteins (RBPs), and translating into short peptides. As the back-splicing products of pre-mRNAs, many circRNAs can modulate the expression of their host genes through transcriptional, post-transcriptional, translational, and post-translational control via interaction with other molecules. This review provides a detailed summary of these regulatory mechanisms based on the class of molecules that they interact with, which encompass DNA, mRNA, miRNA, and RBPs. The co-expression of circRNAs with their parental gene productions (including linear counterparts and proteins) provides potential diagnostic biomarkers for multiple diseases. Meanwhile, the different regulatory mechanisms by which circRNAs act on their host genes via interaction with other molecules constitute complex regulatory networks, which also provide noticeable clues for therapeutic strategies against diseases. Future research should explore whether these proven mechanisms can play a similar role in other types of disease and clarify further details about the cross-talk between circRNAs and host genes. In addition, the regulatory relationship between circRNAs and their host genes in circRNA circularization, degradation, and cellular localization should receive further attention.

Quantitative proteomics reveals differential extracellular vesicle cargo from M1 and M2 monocyte-derived human macrophages.

Extracellular vesicles (EVs) mediate intercellular communication by carrying molecular cargo that facilitate diverse physiological processes. Macrophages, playing central roles in immune responses, release EVs that modulate various cellular functions. Given the distinct roles of M1 and M2 macrophage states, understanding the proteomic profiles of their EVs is important for elucidation of EV-mediated signalling and identifying potential biomarkers for diseases involving macrophage polarisation. We employed quantitative proteomics combined with bioinformatics to characterise the proteomic profile of EVs released by M1 and M2 monocyte-derived macrophages. We identified 1,731 proteins in M1/M2 EVs, 132 of which were significantly differentially between M1 and M2. Proteomic data, together with pathway analysis, found that M1/M2 macrophage EV cargo relate to cellular source, and may play roles in shaping immune responses, with M1 EV cargo associated with promotion of pro-inflammatory and antiviral functions, while M2 EV cargo associated with immune regulation and tissue repair. M1 EV cargo was associated with cytokine/chemokine signalling pathways, DNA damage, methylation, and oxidative stress. M2 EV cargo were associated with macrophage alternative-activation signalling pathways, antigen presentation, and lipid metabolism. We also report that macrophage EVs carry metallothioneins, and other related proteins involved in response to metals and oxidative stress.

MiR-16-5p may modulate migration and proliferation through TP53 and LncRNA-NEAT1 in triple-negative breast cancer

IntroductionMicroRNAs (miRNAs) are potential candidate clinical biomarkers for many diseases. This study explored the role and potential mechanisms of miR-16-5p in triple-negative breast cancer (TNBC). ResultsThe expression of miR-16-5p was lower in wax block sections obtained from 47 TNBC patient samples. Overexpression of miR-16-5p decreased the migratory abilities and the cell proliferation in MDA-MB-231 cells. We found miR-16-5p modulated TP53 and LncRNA-NEAT1 in bioinformatics analysis. In transfection experiments, the higher expression of lncRNA-NEAT1 and TP53 were observed in miR-16-5p inhibitor group. ConclusionOur data demonstrated that miR-16-5p was lowly expressed and acted as a tumor suppressor in TNBC. Moreover, miR-16-5p regulated TNBC cell proliferation and migratory capacity by modulating TP53 and lncRNA-NEAT1 in vitro.

The plasma miRNAome in ADNI: Signatures to aid the detection of at-risk individuals.

MicroRNAs are short non-coding RNAs that control proteostasis at the systems level and are emerging as potential prognostic and diagnostic biomarkers for Alzheimer's disease (AD). We performed small RNA sequencing on plasma samples from 847 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. We identified microRNA signatures that correlate with AD diagnoses and help predict the conversion from mild cognitive impairment (MCI) to AD. Our data demonstrate that plasma microRNA signatures can be used to not only diagnose MCI, but also, critically, predict the conversion from MCI to AD. Moreover, combined with neuropsychological testing, plasma microRNAome evaluation helps predict MCI to AD conversion. These findings are of considerable public interest because they provide a path toward reducing indiscriminate utilization of costly and invasive testing by defining the at-risk segment of the aging population. We provide the first analysis of the plasma microRNAome for the ADNI study. The levels of several microRNAs can be used as biomarkers for the prediction of conversion from MCI to AD. Adding the evaluation of plasma microRNA levels to neuropsychological testing in a clinical setting increases the accuracy of MCI to AD conversion prediction.

Angiopoietin-like 4 is a potential biomarker for diabetic kidney disease in type 2 diabetes patients.

The association between serum angiopoietin-like 4 (ANGPTL4) levels and the severity of diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus remains unclear. A total of 1,115 type 2 diabetes mellitus patients were analyzed in this cross-sectional study. DKD index included DKD stages defined by estimated glomerular filtration rate, the albuminuria grades and DKD risk management grades. Serum levels of ANGPTL4 and other biomarkers were detected. Multivariable-adjusted linear and logistic analyses were used to study the association between ANGPTL4 and DKD. The protein levels of ANGPTL4 were assessed in the kidney. Renal tubular cells were stimulated with glucose to study ANGPTL4 expression. Compared with the participants in the third or fourth quantile of ANGPTL4, those in the first or second quantile of ANGPTL4 were younger, with lower glycated hemoglobin, triglycerides and urinary albumin creatinine ratio (all P < 0.05). There was a negative nonlinear relationship between ANGPTL4 and estimated glomerular filtration rate in type 2 diabetes mellitus patients. One standard deviation increased serum ANGPTL4 levels, the odds ratio of having DKD was 1.40 (95% confidence interval 1.08-1.80). The mediation analysis showed that triglycerides did not mediate the association between ANGPTL4 and DKD. Furthermore, ANGPTL4 could be the strongest among multiple panels of biomarkers in its association of DKD. Compared with mice at 8 weeks-of-age, db/db mice at 18 weeks-of-age had increased ANGPTL4 expression in glomeruli and tubular segments. In vitro, glucose could stimulate ANGPTL4 expression in tubular cells in a dose-dependent manner. ANGPTL4 could be a potential marker and therapeutic target for DKD treatment.