Serum microRNAs Research Articles

Circulating Serum Micro-RNA as Non-Invasive Diagnostic Biomarkers of Endometriosis.

Endometriosis (END) is a painful gynecological condition. Clinical examination, imaging, and laparoscopy can provide a definitive diagnosis of END. Nonetheless, non-invasive biomarkers could help enhance and streamline the diagnostic process. Micro-RNAs (miRNAs), a family of small non-coding RNAs, could serve as useful non-invasive biomarkers for END. The aim of this study was to perform serum miRNA profiling in a retrospective cohort of women to identify miRNAs that are differentially expressed in END compared to control patients. RNA was isolated from serum samples of 67 END patients and 60 control women. The expression profile of a 754-miRNA panel was studied with RT-qPCR performed on a QuantStudio 12K Flex with the TaqMan OpenArray miRNA panel. A Censored Regression Model was used for miRNA differential expression analysis. Several gene-enrichment algorithms were employed to identify pathways related to the target genes of differentially expressed miRNAs. One hundred and thirty miRNAs were detected in at least 75% of samples from either the END or the control group. Sixteen miRNAs were significantly modulated between the END and control groups. Enrichment analysis identified targets significantly overrepresented in numerous pathways involved in biological processes related to END, including inflammation, angiogenesis, cellular invasion, cell-cycle/cell proliferation, and estrogen and progesterone hormonal signaling. Our study indicates that differentially expressed miRNAs between END patients and controls can be identified through liquid biopsy. Our findings also suggest a potential role for serum miRNAs in the pathophysiology of END, warranting further investigations for their use as non-invasive biomarkers.

Integrated Analysis of Serum and Tissue microRNA Transcriptome for Biomarker Discovery in Gastric Cancer.

Gastric cancer (GC) poses a significant global health challenge, demanding a detailed exploration of its molecular landscape. Studies suggest that exposure to environmental pollutants can lead to changes in microRNA (miRNA) expression patterns, which may contribute to the development and progression of GC. MiRNAs have emerged as crucial regulators implicated in GC pathogenesis. The largest GC serum miRNA dataset to date, comprising 1417 non-cancer controls and 1417 GC samples was used. We conducted a comprehensive analysis of miRNA expression profiles. Differential expression analysis, co-expression network construction, and machine learning models were employed to identify key serum miRNAs and their association with clinical parameters. Weighted Gene Co-expression Network Analysis (WGCNA) and immune infiltration analysis were used to validate the importance of the key miRNA. A total of 1766 differentially expressed miRNAs were identified, with miR-1290, miR-1246, and miR-451a among the top up-regulated, and miR-6875-5p, miR-6784-5p, miR-1228-5p, and miR-6765-5p among the top down-regulated. WGCNA revealed that modules M1 and M5 were significantly associated with GC subtypes and disease status. MiRNA-target gene network analysis identified prognostically significant genes TP53, EMCN, CBX8, and ALDH1A3. Machine learning models LASSO, SVM, randomforest, and XGBOOST demonstrated the diagnostic potential of miRNA profiles. Tissue and serum miR-187 emerged as an independent prognostic factor, influencing patient survival across clinical parameters. Gene expression and immune cell infiltration were different in tissues stratified by miR-187 expression. In summary, the integration of differential gene expression, co-expression analysis, and immune cell profiling provided insights into the molecular intricacies of GC progression.

Different expression patterns of inflammation-related genes and serum microRNAs in young-onset ischemic stroke

Brain ischemia results in the activation of a cascade of inflammatory responses, contributing to the pathogenesis of stroke. This study aimed to assess the patterns of possible changes in the expression of specific inflammation-associated protein-encoding genes and miRNAs in the peripheral blood between the acute and chronic phase of young-onset cryptogenic (Cryp) and large-artery atherosclerotic (LAA) stroke. Blood and serum were collected from patients with cryptogenic and large-artery atherosclerotic stroke at the stroke onset and 1-year follow-up. The relative expression of inflammation-related genes was analysed at the mRNA and miRNA levels using real-time quantitative PCR. Moreover, the relationship between the relative gene expression levels and clinical data was assessed using several different statistical approaches. Seventy-three patients were included in this study, with a median age of 47 (IQR, 9) years. Approximately 72% were men. In patients with cryptogenic stroke, at the mRNA level, ICAM1, CXCL8, TNF, NFKBIA, PYCARD, IL1B, and IL18 were observed to be upregulated at the stroke onset compared to the 1-year follow-up. In patients with LAA stroke, only the expression of NFKBIA was significantly higher during acute stroke. Further, the miRNA serum levels of miR-21, miR-122, and miR-155 were higher at the onset of stroke in patients with cryptogenic stroke but not in those with LAA stroke. The differences between the relative gene expression levels during acute stroke and at the 1-year follow-up were more pronounced in patients with cryptogenic stroke with no cardiovascular risk factors. The expression changes of inflammatory genes in whole blood and miRNAs in the serum differ in patients with cryptogenic and LAA stroke.

Dual mature microRNA-responsive logic biosensing platform based on CRISPR/Cas12a and DNA nanocage

Mature microRNAs play crucial roles in tumorigenesis and progression. However, their potential as cancer biomarkers is limited by the sequence interference of precursor microRNAs and the occurrence of false positive signals mediated by single microRNAs. Herein, we reported a dual mature microRNA-responsive second-order (YES-AND) logic biosensing platform for accurate cancer diagnosis. Specifically, DNA nanocages were conceived as the first stage of “YES” gates, capable of signal transduction through strand displacement reactions, and realizing size-selective discrimination of mature microRNAs and pre-microRNAs. Subsequently, CRISPR/Cas12a system served as the second stage of “AND” gate, wherein dual activators cooperatively triggered trans-cleavage. As a proof-of-concept, this second-order logic biosensing platform was successfully applied to detect non-small cell lung cancer-related mature microRNA in clinical serum, and showed remarkable sensitivity (Lod = 100 pM) and trueness (recovery ≥ 90%). Our study represents a significant step forward in the development of intelligent biosensors capable of performing complex computations within pathological networks, and opens up broader possibilities for applications in biological science study and clinic disease diagnosis.

Noninvasive multi-cancer detection using blood-based cell-free microRNAs.

Patients diagnosed with early-stage cancers have a substantially higher chance of survival than those with late-stage diseases. However, the option for early cancer screening is limited, with most cancer types lacking an effective screening tool. Here we report a miRNA-based blood test for multi-cancer early detection based on examination of serum microRNA microarray data from cancer patients and controls. First, a large multi-cancer training set that included 1,408 patients across 7 cancer types and 1,408 age- and gender-matched non-cancer controls was used to develop a 4-microRNA diagnostic model using 10-fold cross-validation. In three independent validation sets comprising a total of 4,875 cancer patients across 13 cancer types and 3,722 non-cancer participants, the 4-microRNA model achieved greater than 90% sensitivity for 9 cancer types (lung, biliary tract, bladder, colorectal, esophageal, gastric, glioma, pancreatic, and prostate cancers) and 75-84% sensitivity for 3 cancer types (sarcoma, liver, and ovarian cancer), while maintaining greater than 99% specificity. The sensitivity remained to be > 99% for patients with stage 1 lung cancer. Our study provided novel evidence to support the development of an inexpensive and accurate miRNA-based blood test for multi-cancer early detection.

A combined model of six serum microRNAs as diagnostic markers for hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) is associated with high morbidity and mortality, and its poor prognosis is mainly due to the lack of an effective means of early diagnosis. This study aimed to identify a group of serum microRNAs (miRNAs) as potential biomarkers for the diagnosis of HCC. MethodsWe collected 190 HCC cases, 109 benign lesions of the liver, 40 cases of non-HCC tumors, and 130 healthy controls. The 469 participants were divided into training and validation sets. A literature search revealed 12 miRNAs closely associated with HCC. In the training set, significantly differentially expressed miRNAs (DEmiRNAs) were screened using real-time quantitative PCR, and a diagnostic model of HCC was constructed using logistic regression analysis. An independent validation was performed using a validation set. The identified DE miRNAs were subjected to target gene prediction and functional analyses. ResultsCompared to the controls, the levels of miR-21, miR-221, miR-801, and miR-1246 significantly decreased in HCC (P < 0.05), while the levels of miR-26a and miR-122 significantly increased (P < 0.05). A diagnostic model based on the six DE miRNAs was successfully constructed, with AUC values of 0.953 for the training set and 0.952 for the verification set. Finally, 100 target genes of the DE miRNAs were predicted and were significantly enriched in the B cell receptor, neurotrophin, ferroptosis, and EGFR tyrosine kinase inhibitor resistance signaling pathways. ConclusionsThe constructed diagnostic model based on six DE miRNA combinations has important clinical value for the early diagnosis of HCC

Identification and evaluation of a serum microRNA panel to diagnose colorectal cancer patients.

Screening plays a crucial role in the early detection of colorectal cancer, greatly reducing mortality rates. The objective of this study was to identify a non-invasive diagnostic method utilizing serum microRNA expression for the diagnosis of colorectal cancer patients. The study consisted of three stages. In the first stage, 129 patients with colorectal cancer and 129 normal subjects were recruited as the training set for the development of a blood miRNA panel. The second stage involved recruiting 200 patients from each group as the validation cohort. Finally, a blinded study was conducted in the third stage, with 260 patients recruited to determine the predictive value of our miRNA panel. Serum samples were prospectively collected from colorectal cancer patients and normal subjects between 2017 and 2021 at Queen Mary Hospital in Hong Kong. Quantitative PCR was utilized to detect the serum levels of candidate microRNAs, and a multiple linear regression model was employed to formulate a serum microRNA panel for diagnosing colorectal cancer patients. The performance of the panel was evaluated using ROC analysis. Our study showed that the values of three pairs of serum microRNAs, namely miR-106b-5p/miR-1246, miR-106b-5p/miR-16 and miR-106b-5p/miR-21-5p, exhibited statistically significant differences between colorectal cancer patients and normal subjects. A serum microRNA panel formulated from these three pairs of microRNAs demonstrated high accuracy in diagnosing colorectal cancer patients from normal subjects, with an AUC of approximately 0.9. The serum miRNA test proved to be a feasible and promising non-invasive biomarker for the diagnosis of colorectal cancer patients in comparison to normal subjects.

Kawasaki Disease: An update on Genetics and Pathophysiology.

Kawasaki disease (KD), a systemic vasculitic condition predominantly affecting children, remains a significant challenge in pediatric health care. First identified in 1967, KD is now recognized as the primary cause of pediatric ischemic heart disease in developed countries. This review provides a comprehensive update of KD, focusing on biomarkers, pathophysiology, and genetic associations. KD's clinical manifestation, including symptoms such as persistent fever and mucocutaneous changes, often overlaps with other pediatric conditions, complicating its diagnosis. This ambiguity, especially in cases of incomplete KD, highlights the critical need for specific biomarkers and more precise diagnostic methods. Recent studies have made promising advancements in identifying serum biomarkers and microRNAs, contributing to the development of rapid diagnostic tools. However, these are yet to be fully integrated into clinical practice. The article focuses on the pathophysiological aspects of KD, highlighting the potential for targeted therapies and personalized medicine approaches based on genetic predispositions. Collaborative efforts in global research and raising public awareness about KD are emphasized as key strategies for improving its management. This review presents the current understanding of KD while pointing out the gaps and future directions in research and clinical care. The ultimate goal is to enhance diagnostic accuracy, optimize treatment strategies, and improve patient outcomes, thereby addressing the complexities of this enigmatic and potentially life-threatening condition in pediatric medicine.

Serum miRNA improves the accuracy of a multivariate index assay for triage of an adnexal mass

ObjectiveTo determine whether a multimodal assay combining serum microRNA with protein biomarkers and metadata improves triage assessment of an adnexal mass. MethodsSerum samples from 468 training subjects (191 cancer cases and 277 benign adnexal mass controls or healthy controls) were analyzed for seven protein biomarkers and 180 miRNA. Circulating analyte data were combined with age and menopausal status (metadata) into a neural network model to classify samples as cases or controls. Forward regression with ten-fold cross-validation minimized the dimensionality of the model while maximizing linear separation between cases and controls. Model validation proceeded using both internal (44 cases and 56 controls) and external validation sets (51 cases and 59 controls). ResultsThe total study population comprised 678 subjects, including 286 cases and 392 controls. Overall, 290 (43%) of the subjects were premenopausal. A panel of 10 miRNA delivered optimal performance when combined with protein and metadata features. The combined model improved the Receiver Operator Characteristic Area Under the Curve (ROC AUC) on the internal (AUC = 0.9; 95% CI 0.81–0.95) and external validation sets (AUC = 0.95; 95% CI 0.90–0.98) compared to miRNA alone or proteins plus metadata (without miRNA). On external validation, the combined model offered 92% sensitivity at 80% specificity overall, with 80% and 100% sensitivity for early and late-stage cancers, respectively, including 78% sensitivity for early-stage, serous ovarian cancers and 82% sensitivity for early-stage, non-serous cancers. ConclusionsA multimodal assay combining miRNA with protein biomarkers, age, and menopausal status improves surgical triage of an adnexal mass.

Machine learning-based noninvasive diagnostic classifiers for the prediction of cancer tissue of origin using serum microRNAs.

101 Background: Noninvasive multi-cancer early detection (MCED) with or without tissue of origin (TOO) has the potential to reduce cancer-related mortality by analyzing circulating cell-free nucleic acids and/or proteins in blood. Accurate prediction of TOO following a positive MCED test would guide selection of confirmatory tests, thereby expediting the definitive diagnosis and prompt initiation of the most appropriate treatment, tailored to the specific cancer type. Here, we report the development of machine learning-based diagnostic classifiers that predict TOO for 13 cancer types with high accuracy using serum microRNAs. Methods: Eight serum miRNA microarray datasets from GEO totaling 6,283 patients across 13 cancer types were used in this study. The patients were split, with an approximate 3:2 ratio, into a training (n=3,844) and a validation set (n=2,439). An ensemble of classifiers was constructed in the training set via the “one vs. rest” approach, thus one classifier for each cancer type. Random forest models with recursive feature elimination (RFE) selected the optimal set of miRNAs that was fed into support vector machine models to generate a prediction probability for each cancer type. The type with the highest probability was considered the predicted cancer type. The performance of these classifiers was evaluated in the validation set in two steps with the 1st using all cancer types and the 2nd using the top 2 or 3 cancer types from the 1st step to achieve a refined prediction. Results: RFE selected 426 miRNAs for building the 13 classification models. In the validation set comprising 2,439 patients across 12 cancer types, the classifiers correctly predicted cancer types for 1,922 (79%) samples based on the highest prediction probability. The accuracy increased to 92% and 95% based on top 2 and 3 predictions. In particular, based on top 3 predictions, the accuracy was &gt;95% for bladder, breast, prostate, gastric, glioma and lung cancers, &gt;85% for ovarian, liver and esophageal cancers, 78% for pancreatic cancer and sarcoma, and 67% for colorectal cancer. Conclusions: With 95% accuracy in narrowing TOO down to 3 organ sites, the miRNA-based TOO classifiers could be used clinically as a reflex test for the simple and highly accurate MCED screening models previously developed ( Cancers 2022,14:1450; ESMO 2024). Together, they support the development of an inexpensive, accurate and noninvasive blood test for MCED with TOO.

Diagnostic and Prognostic Role of Circulating microRNAs in Patients with Coronary Artery Disease-Impact on Left Ventricle and Arterial Function.

Recent studies reported that circulating microRNAs (miRNAs) can target different metalloproteases (MMPs) involved in matrix remodeling and plaque vulnerability. Consequently, they might have a role in the diagnosis and prognosis of coronary artery disease. To quantify circulating miRNAs (miRNA126, miRNA146, and miRNA21) suggested to have possible cardiovascular implications, as well as levels of MMP-1 and MMP-9, and to determine their association with left ventricular (LV) function and with arterial function, in patients with either ST-segment elevation acute myocardial infarction (STEMI) or stable ischemic heart disease (SIHD). A total of 90 patients with coronary artery disease (61% men, 58 ± 12 years), including 60 patients with STEMI and 30 patients with SIHD, were assessed within 24 h of admission, by measuring serum microRNAs, and serum MMP-1 and MMP-9. LV function was assessed by measuring ejection fraction (EF) by 2D and 3D echocardiography, and global longitudinal strain (GLS) by speckle tracking. Arterial function was assessed by echo tracking, CAVI, and peripheral Doppler. Circulating levels of miRNA146, miRNA21, and MMP1 were significantly increased in patients with STEMI vs. SIHD (p = 0.0001, p = 0.0001, p = 0.04, respectively). MiRNA126 negatively correlated with LVEF (r = -0.33, p = 0.01) and LV deformation parameters (r = -0.31, p = 0.03) in patients with STEMI and negatively correlated with ABI parameters (r = -0.39, p = 0.03, r = -0.40, p = 0.03, respectively) in patients with SIHD. MiRNA146 did not have any significant correlations, while higher values of miRNA21 were associated with lower values of GLS in STEMI patients and with higher values of GLS in SIHD patients. Both MMP1 and MMP9 correlated negatively with LVEF (r = -0.27, p = 0.04, r = -0.40, p = 0.001, respectively) and GLS in patients with STEMI, and positively with arterial stiffness in patients with SIHD (r = 0.40 and r = 0.32, respectively; both p < 0.05). MiRNA126, miRNA21, and both MMP1 and MMP9 are associated with LV and arterial function parameters in patients with acute coronary syndrome. Meanwhile, they inversely correlate with arterial function in patients with chronic atherosclerotic disease. However, further studies are needed to establish whether these novel biomarkers have diagnosis and prognosis significance.

AB062. Investigating miR-21 as a non-invasive prognostic biomarker in glioma patients.

Glioma prognosis remains a challenge due to its high recurrence and resistance to treatment. Diagnosis and follow-up in resource-constrained regions often lead to significant patient attrition. Serum microRNAs (miRNAs) are seen to be aberrantly expressed in malignancies can be found in tumor tissues and peripheral samples. This offers a pathway for non-invasive liquid biopsies. miR-21 is an established biomarker for glioma prognosis, which needs to be validated in our population. We collected 89 intraoperative tumor tissue samples, and 42 pre- and post-operative serum samples from glioma patients, ten control tissues, eight healthy serum samples and analysed for miR-21 expression through quantitative polymerase chain reaction (qPCR) analysis. Correlational analysis with molecular markers isocitrate dehydrogenase (IDH), Ki-67, ATRX, p53, and survival curves through the Kaplan-Meier method were calculated in high and low miR-21 expression groups. The hazard ratio was quantitatively determined using Cox regression analysis, considering both univariate associations and multivariate correlations with clinical parameters. miR-21 expression in tissue was significantly upregulated with increase of glioma grades (P<0.001) and in patients above 50 years (P=0.003) age group. Whereas no gender bias was seen in its expression pattern. Its expression did not show any correlation with tumor volume (r=0.22, P=0.08). A similar expression pattern of miR-21 was observed in serum samples of glioma. IDH-wildtype (P=2.06e-03) and high Ki-67 (P=2.50e-03) patient group showed significant upregulation of miR-21 expression compared to IDH-mutant and low Ki-67 group. Patients with low miR-21 expression had significantly longer overall survival (OS) than patients with high miR-21 expression (P =0.006). Similarly, quantitative hazard analysis indicates that patients in the high expression group have 2.77 times higher risk of mortality [95% confidence interval (CI): 0.19-0.92], in comparison to patients in the low expression group (P=0.008). Our findings validate the utility of miR-21 as a prognostic serum biomarker to help diagnose and assess treatment response in advancing glioma grades, within our population.

A dual-mode electrochemical biosensor based on GO-Fe3O4 doped PEDOT nanocomposite for the ultrasensitive assay of microRNA

MicroRNA, as a distinctive biomarker, plays a crucial role in the early prognosis and diagnosis of numerous severe diseases. However, due to its inherent properties such as low abundance, small size, and high sequence similarity, the sensitive and accurate detection of microRNA remains a major challenge. Herein, a dual-mode electrochemical biosensing platform was developed for microRNA detection, based on poly(3,4-ethylenedioxythiophene) (PEDOT) doped with graphene oxide-Fe3O4 (GO-Fe3O4) nanocomposite. The GO-Fe3O4/PEDOT composite demonstrated a porous microstructure, outstanding conductivity, and robust catalytic activity towards nitrite. It was electrodeposited onto the electrode surface in a one-step process using the cyclic voltammetry method (CV). The microRNA biosensor was obtained by anchoring DNA with amino groups to the GO-Fe3O4/PEDOT layer through the formation of amide bonds. The designed dual-mode microRNA biosensor demonstrated a broad linear range spanning from 10−15 M to 10−6 M, with low detection limits of 5.18 × 10−15 M and 7.36 × 10−15 M when using chronocoulometry (CC) and amperometric i-t curve (i-t) modes, respectively. Furthermore, a dual-mode electrochemical biosensor has been successfully developed and utilized for the detection of microRNA in human serum, demonstrating its potential for precise and sensitive microRNA detection and its practical application value in clinical medicine.

Promising microRNAs in pre-diagnostic serum associated with lung cancer up to eight years before diagnosis: a HUNT study

IntroductionBlood biomarkers for early detection of lung cancer (LC) are in demand. There are few studies of the full microRNome in serum of asymptomatic subjects that later develop LC. Here we searched for novel microRNA biomarkers in blood from non-cancer, ever-smokers populations up to eight years before diagnosis.MethodsSerum samples from 98,737 subjects from two prospective population studies, HUNT2 and HUNT3, were considered initially. Inclusion criteria for cases were: ever-smokers; no known cancer at study entrance; 0–8 years from blood sampling to LC diagnosis. Each future LC case had one control matched to sex, age at study entrance, pack-years, smoking cessation time, and similar HUNT Lung Cancer Model risk score. A total of 240 and 72 serum samples were included in the discovery (HUNT2) and validation (HUNT3) datasets, respectively, and analysed by next-generation sequencing. The validated serum microRNAs were also tested in two pre-diagnostic plasma datasets from the prospective population studies NOWAC (n = 266) and NSHDS (n = 258). A new model adding clinical variables was also developed and validated.ResultsFifteen unique microRNAs were discovered and validated in the pre-diagnostic serum datasets when all cases were contrasted against all controls, all with AUC > 0.60. In combination as a 15-microRNAs signature, the AUC reached 0.708 (discovery) and 0.703 (validation). A non-small cell lung cancer signature of six microRNAs showed AUC 0.777 (discovery) and 0.806 (validation). Combined with clinical variables of the HUNT Lung Cancer Model (age, gender, pack-years, daily cough parts of the year, hours of indoor smoke exposure, quit time in years, number of cigarettes daily, body mass index (BMI)) the AUC reached 0.790 (discovery) and 0.833 (validation). These results could not be validated in the plasma samples.ConclusionThere were a few significantly differential expressed microRNAs in serum up to eight years before diagnosis. These promising microRNAs alone, in concert, or combined with clinical variables have the potential to serve as early diagnostic LC biomarkers. Plasma is not suitable for this analysis. Further validation in larger prospective serum datasets is needed.

Serum microRNAs as new biomarkers for detecting subclinical hemolysis in the nonacute phase of G6PD deficiency

Glucose-6-phosphate dehydrogenase (G6PD) deficiency is one of the most common enzymopathies worldwide. Patients with G6PD deficiency are usually asymptomatic throughout their life but can develop acute hemolysis after exposure to free radicals or certain medications. Several studies have shown that serum miRNAs can be used as prognostic biomarkers in various types of hemolytic anemias. However, the impact of G6PD deficiency on circulating miRNA profiles is largely unknown. The present study aimed to assess the use of serum miRNAs as biomarkers for detecting hemolysis in the nonacute phase of G6PD deficiency. Patients with severe or moderate G6PD Viangchan (871G > A) deficiency and normal G6PD patients were enrolled in the present study. The biochemical hemolysis indices were normal in the three groups, while the levels of serum miR-451a, miR-16, and miR-155 were significantly increased in patients with severe G6PD deficiency. In addition, 3D analysis of a set of three miRNAs (miR-451a, miR-16, and miR-155) was able to differentiate G6PD-deficient individuals from healthy individuals, suggesting that these three miRNAs may serve as potential biomarkers for patients in the nonhemolytic phase of G6PD deficiency. In conclusion, miRNAs can be utilized as additional biomarkers to detect hemolysis in the nonacute phase of G6PD deficiency.

Evaluation of serum MicroRNA 21, MicroRNA 192 and serum TGFβ1 in type 2 diabetes mellitus patients and their relation to diabetic nephropathy

BackgroundDiabetic nephropathy (DN) is a frequent and long-lasting microvascular consequence that has an established connection with diabetes. It serves as the primary etiological agent of end-stage renal disease, a critical renal disorder that develops on a worldwide level. The molecular pathophysiology of DN is multifactorial, such as transforming growth factor-beta [TGF-β] which affects the expression of miRNAs such as miRNA-21 and miRNA-192 during renal fibrosis. However, to date, the clinical application is inadequate due to discrepancies observed in the published data. This cross-sectional investigation aimed to assess the correlation between serum TGF-β1, miRNA-21 and 192, and glycemic control, metabolic abnormalities, and renal function in patients with type II diabetes.MethodsBased on the albumin/creatinine ratio (ACR), fifty subjects with type II diabetes were divided into three categories: Group I consisted of individuals with normoalbuminuria (n = 16), Group II of microalbuminuria (n = 16), and Group III of overt proteinuria (n = 18). All participants were subjected to the estimation of mature miRNA-21 and miRNA-192 by TaqMan two-step stem loop qRT-PCR and serum TGFβ1 level by ELISA.ResultsThere was an upregulation in miRNA-21 expression in the 3 different groups of patients (p value = 0.043). The serum fold change (FC) of miRNA-21 showed significantly greater median values in patients with overt proteinuria compared to those with normoalbuminuria (5.57 FC versus 1.11 FC, p = 0.017). A positive correlation (r = 0.343) (p = 0.013) was observed between the ACR and the median levels of miRNA-21, which was statistically significant. No statistically significant distinctions were detected in the concentrations of serum TGF-β1 or miRNA-192 among the three patient groups (p values of 0.234 and 0.225, respectively).ConclusionThe findings of the present research implied that miRNA-21 might function as an early indicator of renal pathology associated with diabetes mellitus (DM).

Constructing diagnostic signature of serum microRNAs using machine learning for early pan-cancer detection

BackgroundCancer is a major public health concern and the second leading cause of death worldwide. Various studies have reported the use of serum microRNAs (miRNAs) as non-invasive biomarkers for cancer detection. However, large-scale pan-cancer studies based on serum miRNAs have been relatively scarce.MethodsAn optimized machine learning workflow, combining least absolute shrinkage and selection operator (LASSO) analyses, recursive feature elimination (RFE), and fourteen kinds of machine learning algorithms, was use to screen out candidate miRNAs from 2540 serum miRNAs and constructed a potent diagnostic signature (Cancer-related Serum miRNA Signatures) for pan-cancer detection, based on a serum miRNA expression dataset of 38,223 samples.ResultCancer-related Serum miRNA Signatures performed well in pan-cancer detection with an area under curve (AUC) of 0.999, 94.51% sensitivity, and 99.49% specificity in the external validation cohort, and represented an acceptable diagnostic performance for identifying early-stage tumors. Furthermore, the ability of multi-classification of tumors by serum miRNAs in pancreatic, colorectal, and biliary tract cancers was lower than that in other cancers, which showed accuracies of 59%, 58.5%, and 28.9%, respectively, indicating that the difference in serum miRNA expression profiles among a small number of tumor subtypes was not as significant as that between cancer samples and non-cancer controls.ConclusionWe have developed a serum miRNA signature using machine learning that may be a cost-effective risk tool for pan-cancer detection. Our findings will benefit not only the predictive diagnosis of cancer but also a preventive and more personalized screening plan.

Potential biomarkers for multiple sclerosis stage from targeted proteomics and microRNA sequencing.

Multiple sclerosis is a chronic demyelinating disease of the central nervous system. There is a need for new circulating biomarkers for multiple sclerosis, in particular, markers that differentiate multiple sclerosis subtypes (relapsing-remitting, secondary progressive and primary progressive multiple sclerosis), as this can help in making treatment decisions. In this study, we explore two classes of potential multiple sclerosis biomarkers-proteins and microRNAs-circulating in the cerebrospinal fluid and serum. Targeted medium-throughput proteomics (92 proteins) and microRNA sequencing were performed on serum samples collected in a cross-sectional case-control cohort (cohort I, controls n = 30, multiple sclerosis n = 75) and a prospective multiple sclerosis cohort (cohort II, n = 93). For cohort I, we also made these measurements in paired cerebrospinal fluid samples. In the cohort I cerebrospinal fluid, we observed differences between multiple sclerosis and controls for 13 proteins, including some previously described to be markers for multiple sclerosis [e.g. CD27, C-X-C motif chemokine 13 (CXCL13) and interleukin-7 (IL7)]. No microRNAs were significantly differentially expressed between multiple sclerosis and controls in the cerebrospinal fluid. In serum, 10 proteins, including angiopoietin-1 receptor (TIE2), and 16 microRNAs were significantly different between relapsing-remitting multiple sclerosis and secondary progressive multiple sclerosis after performing a meta-analysis combining both cohorts. In the prospective part of the study, participants with relapsing-remitting multiple sclerosis were followed for around 3 years, during which time 12 participants converted to secondary progressive multiple sclerosis. In these longitudinally collected serum samples, we observed a peak in granzyme B, A and H proteins around the time of conversion. Single-sample enrichment analysis of serum microRNA profiles revealed that the peak in granzyme B levels around conversion coincides with enrichment for microRNAs that are enriched in CD4+, CD8+ and natural killer cells (e.g. miRNA-150). We identified several proteins and microRNAs in serum that represent potential biomarkers for relapsing-remitting and secondary progressive multiple sclerosis. Conversion to secondary progressive disease is marked by a peak in granzyme B levels and enrichment for immune-related microRNAs. This indicates that specific immune cell-driven processes may contribute to the conversion of relapsing-remitting multiple sclerosis to secondary progressive multiple sclerosis.

Differentially expressed microRNAs in prediagnostic serum linked to lung cancer up to eight years before diagnosis in prospective, population-based cohorts: A HUNT study.

8041 Background: With lung cancer incidence and mortality on the rise, blood biomarkers are increasingly in demand for early detection. The aim of this study is to discover and validate novel biomarkers based on next-generation sequencing performed on blood samples from non-cancer, ever-smokers up to eight years prior to diagnosis. Methods: Initial consideration was given to serum samples from the population-based prospective HUNT2 and HUNT3 cohorts, age &gt; 18 (n = 98,737). Inclusion criteria for cases were ever-smokers, no prior cancer at study entry, and 0–8 years between blood sampling and lung cancer diagnosis in the discovery and 0-4 years in the validation cohort. Among future cases, equal numbers of adenocarcinoma, squamous cell carcinoma and small-cell lung carcinoma were included. Every subsequent case of lung cancer had a matched control, ratio 1:1. The controls were matched for sex, age at study entry, pack-years, smoking cessation duration, comparable HUNT Lung Cancer Model risk score and no cancer diagnosis until the end of the study. The total number of serum samples included in the discovery and validation dataset were 240 (from HUNT2) and 72 (from HUNT3), respectively. The samples were analyzed by genome-wide small RNA seq (Illumina). Results: When we contrasted all cases against all controls, nine differentially expressed microRNAs with AUC &gt; 0.60 (FDR &lt; 0.25) and total raw count of &gt; 0.5 appeared in common in the discovery and validation datasets. Among these, three microRNAs were associated with non-small cell metastatic with mean AUC of 0.65 and 0.76 in the discovery and validation datasets, respectively. Furthermore, the signature of these three microRNAs in combination reached an AUC of 0.75 (discovery) and 0.90 (validation). Conclusions: Up to eight years before diagnosis, a small number of substantially differentially expressed microRNAs were found in serum. These intriguing microRNAs can potentially function as early diagnostic indicators for lung cancer, either alone or in combination. Further investigation and validation of these results in a larger prospective serum dataset are required.

Tumor-Derived Exosomal miR-143-3p Induces Macrophage M2 Polarization to Cause Radiation Resistance in Locally Advanced Esophageal Squamous Cell Carcinoma.

We aimed to determine whether monitoring tumor-derived exosomal microRNAs (miRNAs) could be used to assess radiotherapeutic sensitivity in patients with locally advanced esophageal squamous cell carcinoma (ESCC). RNA sequencing was employed to conduct a comparative analysis of miRNA expression levels during radiotherapy, focusing on identifying miRNAs associated with progression. Electron microscopy confirmed the existence of exosomes, and co-cultivation assays and immunofluorescence validated their capacity to infiltrate macrophages. To determine the mechanism by which exosomal miR-143-3p regulates the interplay between ESCC cells and M2 macrophages, ESCC cell-derived exosomes were co-cultured with macrophages. Serum miR-143-3p and miR-223-3p were elevated during radiotherapy, suggesting resistance to radiation and an unfavorable prognosis for ESCC. Increased levels of both miRNAs independently predicted shorter progression-free survival (p = 0.015). We developed a diagnostic model for ESCC using serum microRNAs, resulting in an area under the curve of 0.751. Radiotherapy enhanced the release of miR-143-3p from ESCC cell-derived exosomes. Immune cell infiltration analysis at the Cancer Genome Atlas (TCGA) database revealed that ESCC cell-derived miR-143-3p triggered M2 macrophage polarization. Mechanistically, miR-143-3p upregulation affected chemokine activity and cytokine signaling pathways. Furthermore, ESCC cell exosomal miR-143-3p could be transferred to macrophages, thereby promoting their polarization. Serum miR-143-3p and miR-223-3p could represent diagnostic and prognostic markers for patients with ESCC undergoing radiotherapy. Unfavorable prognosis could be linked to the increased levels of ESCC cell-derived exosomal miR-143-3p, which might promote tumor progression by interacting with macrophages.