OBJECTIVE: The study focuses on enhancing breast cancer (BC) prognosis through early detection, aiming to establish a non-invasive, clinically viable BC screening method using specific serum miRNA levels.
METHODS: Involving 11,349 participants across BC, 11 other cancer types, and control groups, the study identified serum biomarkers through feature selection and developed two BC screening models using six machine learning algorithms. These models underwent evaluation across test, internal, and external validation sets, assessing performance metrics like accuracy, sensitivity, specificity, and the area under the curve (AUC). Subgroup analysis was conducted to test model stability.
RESULTS: Based on the three serum miRNA biomarkers (miR-1307-3p, miR-5100, and miR-4745-5p), a BC screening model, SM4BC3miR model, was developed. This model achieved AUC performances of 0.986, 0.986, and 0.939 on the test, internal, and external sets, respectively. Furthermore, the SSM4BC model, utilizing ratio scores of miR-1307-3p/miR-5100 and miR-4745-5p/miR-5100, showed AUCs of 0.973, 0.980, and 0.953, respectively. Subgroup analyses underscored both models\' robustness and stability.
CONCLUSIONS: This research introduced the SM4BC3miR and SSM4BC models, leveraging three specific serum miRNA biomarkers for breast cancer screening. Demonstrating high accuracy and stability, these models present a promising approach for early detection of breast cancer. However, their practical application and effectiveness in clinical settings remain to be further validated.

Background Pre-eclampsia remains a leading cause of maternal and foetal mortality with a poorly understood pathophysiology. It can lead to a range of clinical presentations, but proteinuria and hypertension are key components of the diagnosis. These signs arise due to disordered placental implantation due to poor trophoblastic invasion, resulting in placental oxidative stress due to hypoxia. Oxidative stress triggers the release of syncytiotrophoblast microvesicles (STMBs), of which placenta-derived exosomes may be a key component. The high specificity of exosomes for their cell of origin makes them ideal candidates as diagnostic biomarkers. We are particularly interested in the miRNAs (microRNAs) contained within these exosomes, as they may give us an insight into the genomic regulation within the pre-eclamptic placenta that leads to the disease state. The development of workflows for miRNA quantitation may enable us to identify novel biomarkers. Methods We extracted exosomes and purified total RNA from 23 serum samples using the Norgen Plasma/Serum Exosome Purification and RNA Isolation Midi Kit. We then used the bioanalyser to determine the concentration and quality of the RNA obtained. It uses rapid electrophoresis, requires minimal sample sizes, and can assess the quality of genetic material as small as 25 bases. Results We have successfully isolated RNA from these samples; however, the concentration of the total RNA was too low for downstream molecular analysis. We did gain insight into how to optimise and develop the workflow so that, with each attempt, the yield increased. Our greatest concentrations were obtained by combining serum samples from multiple patients, demonstrating that we needed a higher volume to optimise the yield. Future studies should aim to obtain samples specifically for use in this research so that we can process a larger volume of serum. Conclusions We have also noted that there is a positive correlation between the overall concentration of total RNA and a high sFlt-1/PlGF ratio. Preliminary analysis from Illumina identified with a high degree of confidence the presence of three miRNAs, namely, mir-498(46), mir-122(1), and mir-134(41). Further work is necessary to validate these findings and should focus on the possible future role of these miRNAs as biomarkers for the early diagnosis of pre-eclampsia.

UNASSIGNED: Pancreatic cancer (PC) is a lethal malignancy that ranks seventh in terms of global cancer-related mortality. Despite advancements in treatment, the five-year survival rate remains low, emphasizing the urgent need for reliable early detection methods. MicroRNAs (miRNAs), a group of non-coding RNAs involved in critical gene regulatory mechanisms, have garnered significant attention as potential diagnostic and prognostic biomarkers for pancreatic cancer (PC). Their suitability stems from their accessibility and stability in blood, making them particularly appealing for clinical applications.
UNASSIGNED: In this study, we analyzed serum miRNA expression profiles from three independent PC datasets obtained from the Gene Expression Omnibus (GEO) database. To identify serum miRNAs associated with PC incidence, we employed three machine learning algorithms: Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. We developed an artificial neural network model to assess the accuracy of the identified PC-related serum miRNAs (PCRSMs) and create a nomogram. These findings were further validated through qPCR experiments. Additionally, patient samples with PC were classified using the consensus clustering method.
UNASSIGNED: Our analysis revealed three PCRSMs, namely hsa-miR-4648, hsa-miR-125b-1-3p, and hsa-miR-3201, using the three machine learning algorithms. The artificial neural network model demonstrated high accuracy in distinguishing between normal and pancreatic cancer samples, with verification and training groups exhibiting AUC values of 0.935 and 0.926, respectively. We also utilized the consensus clustering method to classify PC samples into two optimal subtypes. Furthermore, our investigation into the expression of PCRSMs unveiled a significant negative correlation between the expression of hsa-miR-125b-1-3p and age.
UNASSIGNED: Our study introduces a novel artificial neural network model for early diagnosis of pancreatic cancer, carrying significant clinical implications. Furthermore, our findings provide valuable insights into the pathogenesis of pancreatic cancer and offer potential avenues for drug screening, personalized treatment, and immunotherapy against this lethal disease.

In our previous study, osteosarcoma advanced locally, and metastasis was promoted through the secretion of large number of small extracellular vesicles, followed by suppressing osteoclastogenesis via the upregulation of microRNA (miR)-146a-5p. An additional 12 miRNAs in small extracellular vesicles were also detected ≥6× as frequently in high-grade malignancy with the capacity to metastasize as in those with a low metastatic potential. However, the utility of these 13 miRNAs for determining the prognosis or diagnosis of osteosarcoma has not been validated in the clinical setting. In the present study, the utility of these miRNAs as prognostic and diagnostic markers was therefore assessed. In total, 30 patients with osteosarcoma were retrospectively reviewed, and the survival rate was compared according to the serum miRNA levels in 27 patients treated with chemotherapy and surgery. In addition, to confirm diagnostic competency for osteosarcoma, the serum miRNA levels were compared with those in patients with other bone tumors (n=112) and healthy controls (n=275). The patients with osteosarcoma with high serum levels of several miRNAs (miR-146a-5p, miR-1260a, miR-487b-3p, miR-1260b and miR-4758-3p) exhibited an improved survival rate compared with those with low levels. In particular, patients with high serum levels of miR-1260a exhibited a significantly improved overall survival rate, metastasis-free survival rate and disease-free survival rate compared with those with low levels. Thus, serum miR-1260a may potentially be a prognostic marker for patients with osteosarcoma. Moreover, patients with osteosarcoma had higher serum miR-1261 levels than those with benign or intermediate-grade bone tumors and thus may be a potential therapeutic target, in addition to being useful for differentiating whether or not a bone tumor is high-grade. A larger investigation is required to clarify the actual utility of these miRNAs in the clinical setting.

Currently, no clinically relevant non-invasive biomarkers are available for screening of multiple cancer types. In this study, we developed a serum diagnostic signature based on 5-methylcytosine (m5C)-related miRNAs (m5C-miRNAs) for multiple-cancer detection. Serum miRNA expression data and the corresponding clinical information of patients were collected from the Gene Expression Omnibus database. Serum samples were then randomly assigned to the training or validation cohort at a 1:1 ratio. Using the identified m5C-miRNAs, an m5C-miRNA signature for cancer detection was established using a support vector machine algorithm. The constructed m5C-miRNA signature displayed excellent accuracy, and its areas under the curve were 0.977, 0.934, and 0.965 in the training cohort, validation cohort, and combined training and validation cohort, respectively. Moreover, the diagnostic capability of the m5C-miRNA signature was unaffected by patient age or sex or the presence of noncancerous disease. The m5C-miRNA signature also displayed satisfactory performance for distinguishing tumor types. Importantly, in the detection of early-stage cancers, the diagnostic performance of the m5C-miRNA signature was obviously superior to that of conventional tumor biomarkers. In summary, this work revealed the value of serum m5C-miRNAs in cancer detection and provided a new strategy for developing non-invasive and cost effective tools for large-scale cancer screening.

BACKGROUND: We aimed to evaluate the role of circulating miRNAs as a biomarker of the persistence of papillary thyroid cancer (PTC) in patients with an \"uninformative\" thyroglobulin (Tg) measurement.
METHODS: We prospectively enrolled 49 consecutive PTC patients with Tg-positive antibodies (TgAb) who had undergone a (near)-total thyroidectomy and 131I therapy (RIT). The serum thyroid stimulating hormone (TSH), Tg, and TgAb levels were measured before and at 6 and 12 months after RIT, respectively. The serum miRNA (221, 222, 375, 155, and 146b) levels were measured simultaneously.
RESULTS: The response to the initial therapy was assessed according to the 2015 ATA criteria. A decrease in 50% or more of serum miRNA over time was observed in 41/49 PTC patients, who showed an excellent response (ER), but six and two patients were classified to have an indeterminate/incomplete biochemical or incomplete structural response to initial therapy.
CONCLUSIONS: Serum miRNA kinetics emerge as a promising biomarker for the early detection of a persistent disease in PTC patients with uninformative Tg results.

UNASSIGNED: The accuracy of CA125 or clinical examination in ovarian cancer (OVC) screening is still facing challenges. Serum miRNAs have been considered as promising biomarkers for clinical applications. Here, we propose a single sample classifier (SSC) method based on within-sample relative expression orderings (REOs) of serum miRNAs for OVC diagnosis.
UNASSIGNED: Based on the stable REOs within 4,965 non-cancer serum samples, we developed the SSC for OVC in the training cohort (GSE106817: OVC = 200, non-cancer = 2,000) by focusing on highly reversed REOs within OVC. The best diagnosis is achieved using a combination of reversed miRNA pairs, considering the largest evaluation index and the lowest number of miRNA pairs possessed according to the voting rule. The SSC was then validated in internal data (GSE106817: OVC = 120, non-cancer = 759) and external data (GSE113486: OVC = 40, non-cancer = 100).
UNASSIGNED: The obtained 13-miRPairs classifier showed high diagnostic accuracy on distinguishing OVC from non-cancer controls in the training set (sensitivity = 98.00%, specificity = 99.60%), which was reproducible in internal data (sensitivity = 98.33%, specificity = 99.21%) and external data (sensitivity = 97.50%, specificity = 100%). Compared with the published models, it stood out in terms of correct positive predictive value (PPV) and negative predictive value (NPV) (PPV = 96.08% and NPV=95.16% in training set, and both above 99% in validation set). In addition, 13-miRPairs demonstrated a classification accuracy of over 97.5% for stage I OVC samples. By integrating other non-OVC serum samples as a control, the obtained 17-miRPairs classifier could distinguish OVC from other cancers (AUC>92% in training and validation set).
UNASSIGNED: The REO-based SSCs performed well in predicting OVC (including early samples) and distinguishing OVC from other cancer types, proving that REOs of serum miRNAs represent a robust and non-invasive biomarker.

Bone tumor is a kind of rare cancer, the location of which is mainly in bone tissue as well as cartilage tissue. Bone tumor is mainly classified into benign and malignant types. The survival rate of patients with bone tumors can be considerably improved by early detection, and the danger of amputation caused by bone tumors can be greatly reduced. In this study, we first screened the top 25% serum miRNAs with the greatest variance in patients with malignant and benign bone tumor and healthy individuals. The expression of serum miRNAs in patients with bone tumor was then examined using unsupervised clustering and PCA, and the results revealed that the overall expression of serum miRNAs was ineffective in distinguishing patients with benign/malignant bone tumors. Subsequently, we screened 19 miRNA biomarkers that could be used to determine the benign/malignant bone tumor of patients by LASSO logistic regression. These genes were validated using ROC curves. Results showed that there were 11 miRNAs that could accurately distinguish benign/malignant bone tumor alone. These 11 miRNAs were, namely, hsa-miR-192-5p, hsa-miR-137, hsa-miR-142-3p, hsa-miR-155-3p, hsa-miR-1205, hsa-miR-1273a, hsa-miR-3187-3p, hsa-miR-1255b-2-3p, hsa-miR-1288-5p, hsa-miR-6836-5p, and hsa-miR-6862-5p. Next, we established a diagnostic model using logistic regression and validated the diagnostic model using ROC curves; the result of which showed that the model had good diagnostic efficacy. Then, we also verified that the diagnostic model established by these 11 miRNAs could distinguish patients with benign/malignant bone tumor using unsupervised clustering as well as PCA. Finally, by using qPCR, we validated the expression of 11 miRNAs in the serum of patients with malignant and benign bone tumors, as well as healthy volunteers. The results were consistent with the trend of miRNAs expression in public databases. In summary, we examined the differential expression of serum miRNAs in individuals with benign and malignant bone tumors and discovered 11 miRNA biomarkers that could be utilized to discriminate between the two.

The low specificity of prostate-specific antigen contributes to overdiagnosis and ov ertreatment of prostate cancer (PCa) patients. Hence, there is an urgent need for inclusive diagnostic platforms that could improve the diagnostic accuracy of PCa. Dysregulated miRNAs are closely associated with the progression and recurrence and have emerged as promising diagnostic and prognostic biomarkers for PCa. Nevertheless, simple, rapid, and ultrasensitive quantification of serum miRNAs is highly challenging. This study designed, synthesized, and demonstrated the practicability of DNA-linked gold nanoprobes (DNA-AuNPs) for the single-step quantification of miR-21/miR-141/miR-375. In preclinical study, the assay differented PCa Pten conditional knockout (PtencKO) mice compared to their age-matched Pten wild-type (PtenWT) control mice. In human sera, receiver operating characteristic (ROC) curve-based correlation analyses revealed clear discrimination between PCa patients from normal healthy controls using training and validation sets. Overall, we established integrated nano-biosensing technology for the PCR-free, non-invasive liquid biopsies of multiple miRNAs for PCa diagnosis.

So far the intimate link between serum microRNA (miRNA) and uterine inflammation in mares is unknown. We aimed (I) to investigate expression profile of eca-miR-155, eca-miR-223, eca-miR-17, eca-miR-200a, and eca-miR-205 (II) and to measure concentrations of interleukin 6 (IL-6), and prostaglandins (PGF2α and PGE2) in serum of mares with healthy and abnormal uterine status (endometritis). This study was conducted on 80 Arabian mares: young (4-7 years), and old (8-14 years). Mares were divided into 48 sub-fertile (endometritis) and 32 fertile (control) at stud farms. Serum was collected for measuring IL-6, PGF2α, and PGE2, as well as miRNA isolation and qRT-PCR. Concentrations of IL-6, PGE2, and PGF2α were higher in mares with endometritis compared to control. Age of mares had a remarkable effect on IL-6, PGE2, and PGF2α concentrations. Relative abundance of eca-miR-155, eca-miR-223, eca-miR-17, eca-miR-200a, and eca-miR-205 was higher in both young and old mares with endometritis. We noticed that eca-miR-155, eca-miR-223, eca-miR-200a, and eca-miR-205 revealed higher expression level in old than young mares with endometritis. This is the first study that has revealed the changes in cell free miRNA and serum inflammatory mediators during endometritis, and these findings could be used for a better understanding the pathophysiology mechanisms of endometritis in equine.