Serum miRNA-based Prediction of Axillary Lymph Node Metastasis in Breast Cancer

Construction and validation of a novel nomogram for prediction of lymph node metastasis in HER2-positive breast cancer: based on the optimal number of examined lymph nodes for accurate nodal staging

PURPOSE

This study aimed to construct and validate a novel nomogram for prediction of lymph node metastasis in HER2-positive breast cancer based on the optimal number of examined lymph nodes (ELNs) for accurate nodal staging.

METHODS

We included 4,040 patients diagnosed with HER2-positive breast cancer from the SEER database, randomly allocating them into training and validation cohorts in a 7:3 ratio. The optimal number of ELNs was identified via piecewise linear regression. The association of ELNs count with nodal migration was evaluated through Logistic Regression (LR) analysis and Random Forest (RF). The nomogram was constructed, and its' performance was evaluated by the receiver operating characteristic curves, calibration curve and Decision curve analysis curves.

RESULTS

The optimal number of ELNs was 13. LR and RF identified the optimal number of ELNs, radiotherapy status, chemotherapy status, T stage, and grade as independent predictive variables for node metastasis, which were used in the nomogram's construction. And the area under the curve values for the nomogram were 0.829 (95% confidence interval (CI): 0.813-0.845) and 0.833 (95% CI:0.808-0.858) in the training and test split respectively, surpassing those of the optimal number of ELNs (0.649, 95% CI: 0.631-0.667 and 0.676, 95% CI:0.648-0.704). Calibration plots exhibited low Brier scores (0.150 for training split, 0.145 for test split).

CONCLUSION

This study developed a novel nomogram that integrates the optimal number of ELNs with other independent risk factors, facilitating individualized prediction of lymph node metastasis in patients with HER2-positive breast cancer.

Evaluating MicroRNAs as diagnostic tools for lymph node metastasis in breast cancer: Findings from a systematic review and meta-analysis

Lymph node metastasis (LNM) significantly affects the prognosis and clinical management of breast cancer (BC) patients. This systematic review and meta-analysis aim to identify microRNAs (miRNAs) associated with LNM in BC and evaluate their potential diagnostic and prognostic value. Following PRISMA guidelines, a comprehensive literature search was conducted in PubMed, Web of Science, and SCOPUS databases, to assess the role of miRNAs in LNM BC. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used to evaluate the quality of included studies. A total of 84 miRNAs were identified as differentially expressed in BC patients with LNM. Of these, a meta-analysis was performed in two microRNAs that were present in at least 3 different articles with a coherent expression direction: miR-155 and miR-34a. The meta-analysis returned a pooled a Log2 fold change of 1.50 for miR-155 (upregulated) and -0.53 for miR-34a (downregulated) with no evidence of publication bias, and a low risk of bias and applicability concerns. To conclude, this study names miR-155 and miR-34a as potential diagnostic biomarkers for LNM in BC, although further experimental validation is necessary to confirm these findings and develop non-invasive diagnostic tools for clinical use.

Axillary lymph node metastasis in breast cancer: from historical axillary surgery to updated advances in the preoperative diagnosis and axillary management

Axillary lymph node status, which was routinely assessed by axillary lymph node dissection (ALND) until the 1990s, is a crucial factor in determining the stage, prognosis, and therapeutic strategy used for breast cancer patients. Axillary surgery for breast cancer patients has evolved from ALND to minimally invasive approaches. Over the decades, the application of noninvasive imaging techniques, machine learning approaches and emerging clinical prediction models for the detection of axillary lymph node metastasis greatly improves clinical diagnostic efficacy and provides optimal surgical selection. In this work, we summarize the historical axillary surgery and updated perspectives of axillary management for breast cancer patients.

Sentinel lymph node-related lncRNA typing affects breast cancer prognosis and treatment response through the immune cell microenvironment

The sentinel lymph node (SLN) plays a crucial role in the early treatment of breast cancer. The present study aims to investigate the impact of SLN-associated long noncoding RNAs (lncRNAs) on breast cancer and the influence of molecular subtyping based on related genes on prognosis. To identify SLN-associated lncRNAs, we conducted differential expression analysis using 2 high-throughput sequencing techniques. In addition, ConsensusClusterPlus was employed to establish lncRNA molecular subtypes. Subsequently, comprehensive analysis using LASSO regression was performed to construct an optimal model for predicting breast cancer prognosis. Finally, various functional annotation databases were utilized to elucidate the potential functions of the predictive model. Through differential expression analysis, we identified 14 SLN-associated lncRNAs. These genes primarily influence TNF signaling pathways. Furthermore, we found that lncRNA H19 is a prominent regulatory factor among these 14 gene expressions. By utilizing ConsensusClusterPlus, we successfully stratified the IR samples into 2 distinct subtypes. Through LASSO regression, we established a prognosis model predominantly impacting various immune cells and drug resistance. After verifying 10 pairs of organizations through PCR, we found differences in 6 lncRNAs between the 2 groups of SNLs. At the same time, in the subsequent analysis of immune infiltration and drug targets, it was found that TRPC2 plays a very critical role in breast cancer. Our study highlights the significance of SLN-associated lncRNAs, unveiling the intricate mechanisms underlying the progression of breast cancer. These findings provide novel insights and potential targets for future therapeutic interventions.

MicroRNAs as behind-the-scenes molecules in breast cancer metastasis and their therapeutic role through novel microRNA-based delivery strategies

Breast cancer is the primary cause of cancer-related death and the most frequent malignancy among women in Western countries. Although there have been advancements in combination treatments and targeted therapies for the metastatic diseases management, metastatic breast cancer is still the second most common cause of cancer-related deaths among U.S. women. The routes of metastasis encompass invasion, intravasation, circulation, extravasation, infiltration into a remote location to establish a metastatic niche, and the formation of micro-metastases in a new environment. Each of these processes is regulated by changes in gene expression. MicroRNAs (miRNAs) are widely expressed by a variety of organisms and have a key role in cell activities including suppressing or promoting cancer through regulating various pathways. Target gene expression is post-transcriptionally regulated by miRNAs, which contribute to the development, spread, and metastasis of breast cancer. In this study, we comprehensively discussed the role of miRNAs as predictors of breast cancer metastasis, their correlation with the spread of the disease to certain organs, and their potential application as targets for breast cancer treatment. We also provided molecular mechanisms of miRNAs in the progression of breast cancer, as well as current challenges in miRNA-based therapeutic approaches. Furthermore, as one of the primary issues with the treatment of solid malignancies is the efficient delivery of miRNAs, we examined a number of cutting-edge carriers for miRNA-based therapies and CRISPR/Cas9 as a targeted therapy for breast cancer.

Prospects for liquid biopsy using microRNA and extracellular vesicles in breast cancer

Among the analytes circulating in body fluids, microRNAs, a type of non-coding RNA and known to exist 2655 in primates, have attracted attention as a novel biomarker for cancer screening. MicroRNAs are signaling molecules with important gene expression regulatory functions that can simultaneously control many gene functions and multiple different pathways in living organisms. These microRNAs are transported in extracellular vesicles (EVs), which are lipid bilayers with 50-150 nm in diameter, and are used as communication tools between cells. Furthermore, the EVs that carry these microRNAs circulate in the bloodstream and have other important implications for understanding the pathogenesis and diagnosis of breast cancer. The greatest benefit from cancer screening is the reduction in breast cancer mortality rate through early detection. Other benefits include reduced incidence of breast cancer, improved quality of life, prognosis prediction, contribution to personalized medicine, and relative healthcare cost containment. This paper outlines the latest developments in liquid biopsy for breast cancer, especially focusing on microRNA and EV diagnostics.

Multiphases DCE-MRI Radiomics Nomogram for Preoperative Prediction of Lymphovascular Invasion in Invasive Breast Cancer

RATIONALE AND OBJECTIVES

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics had been used to evaluate lymphovascular invasion (LVI) in patients with breast cancer. However, no studies had explored the associations between features from delayed phase as well as multiphases DCE-MRI and the LVI status. Thus, we aimed to develop an efficient nomogram based on multiphases DCE-MRI to predict the LVI status in invasive (IBC) breast cancer patients.

MATERIALS AND METHODS

A retrospective analysis was conducted on preoperative clinical, pathological, and DCE-MRI data of 173 breast cancer patients. All patients were randomly assigned into training set (n=121) and validation set (n=52) in 7:3 ratio. The clinical, pathologic, and conventional MRI characteristics were then subjected to univariate and multivariate logistic regression analysis, and the clinical risk factors with P < 0.05 in the multivariate logistic regression were used to build clinical models. Different single-phase models (early phase, peak phase, and terminal phase), as well as a multiphases model integrating radiomics features from multiple phases, were established. Furthermore, a preoperative radiomics nomogram model was constructed by combining the rad-score of the multiphases model with clinicopathologic independent risk factors. Finally, the performance of the multiphases model, clinical model, and rad-score was compared using receiver operating characteristic (ROC) curves, area under the curve (AUC) values, and decision curve analysis (DCA). The clinical utility of the rad-score was evaluated using calibration curves, and Delong test was used to compare the differences in AUC values among the different models.

RESULTS

The axillary lymph nodes (ALN) status and Ki-67 had been identified as clinicopathologic independent predictors and a clinical model had been constructed. Image features that were extracted from the terminal phase of the DCE-MRI exhibited notably superior predictive performances compared to features from the other single phases. Particularly, in the multiphases model, terminal phase features were identified as potentially providing more predictive information. Among the nine features that were found to be associated with LVI in the multiphase model, one was derived from the early phase, two from the peak phase, and six from the terminal phase, indicating that terminal phase features contributed significantly more information towards predicting LVI. Evaluation of the nomogram performance revealed promising results in both the training set (AUCs: clinical model vs. multiphase model vs. nomogram=0.734 vs. 0.840 vs. 0.876) and the validation set (AUCs: clinical model vs. multiphase model vs. nomogram=0.765 vs. 0.753 vs. 0.832).

CONCLUSION

The DCE-MRI-based radiomics model demonstrated utility in predicting LVI status, features of the terminal phase offered more valuable information particularly. The preoperative radiomics nomogram enhanced the diagnostic capability of identifying LVI status in IBC patients, and might aid clinicians in making personalized treatment decisions.

The role of circRNAs and miRNAs in drug resistance and targeted therapy responses in breast cancer

MicroRNAs (miRNAs) are small non-coding RNAs comprising 19-24 nucleotides that indirectly control gene expression. In contrast to other non-coding RNAs (ncRNAs), circular RNAs (circRNAs) are defined by their covalently closed loops, forming covalent bonds between the 3' and 5' ends. circRNAs regulate gene expression by interacting with miRNAs at transcriptional or post-transcriptional levels. Accordingly, circRNAs and miRNAs control many biological events related to cancer, including cell proliferation, metabolism, cell cycle, and apoptosis. Both circRNAs and miRNAs are involved in the pathogenesis of diseases, such as breast cancer. This review focuses on the latest discoveries on dysregulated circRNAs and miRNAs related to breast cancer, highlighting their potential as biomarkers for clinical diagnosis, prognosis, and chemotherapy response.

Potential utility of pretreatment serum miRNAs for optimal treatment selection in advanced high-grade serous ovarian cancer

OBJECTIVE

The primary treatment of patients with advanced ovarian cancer is selected from whether primary debulking surgery or neoadjuvant chemotherapy. We investigated whether pretreatment serum microRNA profiles are useful for selecting patients with advanced high-grade serous ovarian cancer who obtain better outcomes from undergoing primary debulking surgery or neoadjuvant chemotherapy.

METHODS

Consecutive patients with clinical stage IIIB-IVB and serum microRNA data were selected. Patients who underwent primary debulking surgery or neoadjuvant chemotherapy were subjected to 1:1 propensity score matching before comparing their progression-free survival using Cox modelling. Progression-free probabilities for the selected microRNA profiles were calculated, and the estimated progression-free survival with the recommended primary treatment was determined and compared with the actual progression-free survival of the patients.

RESULTS

Of the 108 patients with stage IIIB-IVB disease, the data of 24 who underwent primary debulking surgery or neoadjuvant chemotherapy were compared. Eleven and three microRNAs were independent predictors of progression-free survival in patients who underwent primary debulking surgery and neoadjuvant chemotherapy, respectively. Two microRNAs correlated significantly with complete resection of the tumours in primary debulking surgery. No differences were found between the actual and estimated progression-free survival in the primary debulking surgery and neoadjuvant chemotherapy groups (P > 0.05). The recommended and actual primary treatments were identical in 27 (56.3%) of the 48 patients. The median improved survival times between recommended and actual treatment were 11.7 and 32.6 months for patients with actual primary debulking surgery and neoadjuvant chemotherapy, respectively.

CONCLUSIONS

Pretreatment microRNA profiles could be used to select subgroups of patients who benefited more from primary debulking surgery or neoadjuvant chemotherapy and might contribute to selecting the optimal primary treatment modality in advanced high-grade serous ovarian cancer patients.

Cell-free and extracellular vesicle microRNAs with clinical utility for solid tumors

As cutting-edge technologies applied for the study of body fluid molecular biomarkers are continuously evolving, clinical applications of these biomarkers improve. Diverse forms of circulating molecular biomarkers have been described, including cell-free DNA (cfDNA), circulating tumor cells (CTCs), and cell-free microRNAs (cfmiRs), although unresolved issues remain in their applicability, specificity, sensitivity, and reproducibility. Translational studies demonstrating the clinical utility and importance of cfmiRs in multiple cancers have significantly increased. This review aims to summarize the last 5 years of translational cancer research in the field of cfmiRs and their potential clinical applications to diagnosis, prognosis, and monitoring disease recurrence or treatment responses with a focus on solid tumors. PubMed was utilized for the literature search, following rigorous exclusion criteria for studies based on tumor types, patient sample size, and clinical applications. A total of 136 studies on cfmiRs in different solid tumors were identified and divided based on tumor types, organ sites, number of cfmiRs found, methodology, and types of biofluids analyzed. This comprehensive review emphasizes clinical applications of cfmiRs and summarizes underserved areas where more research and validations are needed.

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

BACKGROUND

Cancer 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.

METHODS

An 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.

RESULT

Cancer-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.

CONCLUSION

We 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.

Identifying Key Genes Involved in Axillary Lymph Node Metastasis in Breast Cancer Using Advanced RNA-Seq Analysis: A Methodological Approach with GLMQL and MAS

Our study aims to address the methodological challenges frequently encountered in RNA-Seq data analysis within cancer studies. Specifically, it enhances the identification of key genes involved in axillary lymph node metastasis (ALNM) in breast cancer. We employ Generalized Linear Models with Quasi-Likelihood (GLMQLs) to manage the inherently discrete and overdispersed nature of RNA-Seq data, marking a significant improvement over conventional methods such as the t-test, which assumes a normal distribution and equal variances across samples. We utilize the Trimmed Mean of M-values (TMMs) method for normalization to address library-specific compositional differences effectively. Our study focuses on a distinct cohort of 104 untreated patients from the TCGA Breast Invasive Carcinoma (BRCA) dataset to maintain an untainted genetic profile, thereby providing more accurate insights into the genetic underpinnings of lymph node metastasis. This strategic selection paves the way for developing early intervention strategies and targeted therapies. Our analysis is exclusively dedicated to protein-coding genes, enriched by the Magnitude Altitude Scoring (MAS) system, which rigorously identifies key genes that could serve as predictors in developing an ALNM predictive model. Our novel approach has pinpointed several genes significantly linked to ALNM in breast cancer, offering vital insights into the molecular dynamics of cancer development and metastasis. These genes, including ERBB2, CCNA1, FOXC2, LEFTY2, VTN, ACKR3, and PTGS2, are involved in key processes like apoptosis, epithelial-mesenchymal transition, angiogenesis, response to hypoxia, and KRAS signaling pathways, which are crucial for tumor virulence and the spread of metastases. Moreover, the approach has also emphasized the importance of the small proline-rich protein family (SPRR), including SPRR2B, SPRR2E, and SPRR2D, recognized for their significant involvement in cancer-related pathways and their potential as therapeutic targets. Important transcripts such as H3C10, H1-2, PADI4, and others have been highlighted as critical in modulating the chromatin structure and gene expression, fundamental for the progression and spread of cancer.

Machine Learning-Based Breast Tumor Ultrasound Radiomics for Pre-operative Prediction of Axillary Sentinel Lymph Node Metastasis Burden in Early-Stage Invasive Breast Cancer

OBJECTIVE

The aim of the work described here was to assess the application of ultrasound (US) radiomics with machine learning (ML) classifiers to the prediction of axillary sentinel lymph node metastasis (SLNM) burden in early-stage invasive breast cancer (IBC).

METHODS

In this study, 278 early-stage IBC patients with at least one SLNM (195 in the training set and 83 in the test set) were studied at our institution. Pathologic SLNM burden was used as the reference standard. The US radiomics features of breast tumors were extracted by using 3D-Slicer and PyRadiomics software. Four ML classifiers-linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF) and decision tree (DT)-were used to construct radiomics models for the prediction of SLNM burden. The combined clinicopathologic-radiomics models were also assessed with respect to sensitivity, specificity, accuracy and areas under the curve (AUCs).

RESULTS

Among the US radiomics models, the SVM classifier achieved better predictive performance with an AUC of 0.920 compared with RF (AUC = 0.874), LDA (AUC = 0.835) and DT (AUC = 0.800) in the test set. The clinicopathologic model had low efficacy, with AUCs of 0.678 and 0.710 in the training and test sets, respectively. The combined clinicopathologic (C) factors and SVM classifier (C + SVM) model improved the predictive ability with an AUC of 0.934, sensitivity of 86.7%, specificity of 89.9% and accuracy of 91.0% in the test set.

CONCLUSION

ML-based US radiomics analysis, as a novel and promising predictive tool, is conducive to a precise clinical treatment strategy.

Recent advances in novel miRNA mediated approaches for targeting breast cancer

Breast cancer (BC) is considered one of the most frequent cancers among woman worldwide. While conventional therapy has been successful in treating many cases of breast cancer, drug resistance, heterogenicity, tumour features and recurrence, invasion, metastasis and the presence of breast cancer stem cells can hinder the effect of treatments, and can reduce the quality of life of patients. MicroRNAs (miRNAs) are short non-coding RNA molecules that play a crucial role in the development and progression of breast cancer. Several studies have reported that aberrant expression of specific miRNAs is associated with the pathogenesis of breast cancer. However, miRNAs are emerging as potential biomarkers and therapeutic targets for breast cancer. Understanding their role in breast cancer biology could help develop more effective treatments for this disease. The present study discusses the biogenesis and function of miRNAs, as well as miRNA therapy approaches for targeting and treating breast cancer cells.

Study on the Relationship Between Differentially Expressed Proteins in Breast Cancer and Lymph Node Metastasis

INTRODUCTION

Lymph node metastasis is a cause of poor prognosis in breast cancer. Mass spectrometry-based proteomics aims to map the protein landscapes of biological samples and profile tumors more comprehensively. Here, proteomics was employed to identify differentially expressed proteins (DEPs) that were associated with lymph node metastasis.

METHODS

Tandem mass tag (TMT) quantitative proteomic approaches were applied for extensive profiling of conditioned medium of MDA-MB-231 and MCF7 cell lines and serums of patients who did or did not have lymph node metastasis, and DEPs were analyzed by bioinformatics. Furthermore, potential secreted or membrane proteins MUC5AC, ITGB4, CTGF, EphA2, S100A4, PRDX2, and PRDX6 were selected for verification in 114 tissue microarray samples of breast cancer using the immunohistochemical method. The relevant data was analyzed and processed by independent sample t test, chi-square test, or Fisher's exact test using SPSS 22.0 software.

RESULTS

In the conditioned medium of MDA-MB-231 cell lines, 154 proteins were upregulated, while 136 were downregulated compared to those of MCF7. In the serum of patients with breast cancer and lymph node metastasis, 17 proteins were upregulated, and 5 proteins were downregulated compared to those without lymph node metastasis. Furthermore, according to tissue verification, CTGF, EphA2, S100A4, and PRDX2 were associated with breast cancer lymph node metastasis.

CONCLUSION

Our study provides a new perspective for the understanding of the role of DEPs (especially CTGF, EphA2, S100A4, and PRDX2) in the development and metastasis of breast cancer. They could become potential diagnostic and prognostic biomarkers and therapeutic targets.

Liquid Biopsy for Oral Cancer Diagnosis: Recent Advances and Challenges

"Liquid biopsy" is an efficient diagnostic tool used to analyse biomaterials in human body fluids, such as blood, saliva, breast milk, and urine. Various biomaterials derived from a tumour and its microenvironment are released into such body fluids and contain important information for cancer diagnosis. Biomaterial detection can provide "real-time" information about individual tumours, is non-invasive, and is more repeatable than conventional histological analysis. Therefore, over the past two decades, liquid biopsy has been considered an attractive diagnostic tool for malignant tumours. Although biomarkers for oral cancer have not yet been adopted in clinical practice, many molecular candidates have been investigated for liquid biopsies in oral cancer diagnosis, such as the proteome, metabolome, microRNAome, extracellular vesicles, cell-free DNAs, and circulating tumour cells. This review will present recent advances and challenges in liquid biopsy for oral cancer diagnosis.

Downregulated miR-367-3p, miR-548aq-5p, and miR-4710 in Human Whole Blood: Potential Biomarkers for Breast Cancer With Axillary Lymph Node Metastasis

BACKGROUND

Increasing studies have shown that microRNAs (miRNAs) have great diagnostic value in cancer. Axillary lymph node metastasis (ALNM) is closely related to the prognosis of breast cancer. However, it remains unknown whether miRNAs in whole blood could be promising biomarkers in breast cancer ALNM.

METHODS

An miRNA microarray was used to screen potential differentially expressed miRNA candidates in whole blood of three breast cancer patients with ALNM and three without ALNM. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect candidate differentially expressed miRNAs in the whole blood of 109 breast cancer patients. Furthermore, bioinformatics analysis was carried to predict the potential targets and enriched pathway of miRNAs.

RESULTS

QRT-PCR validated the fact that miR-367-3p, miR-548aq-5p and miR-4710 are downregulated in breast cancer with ALNM compared to it without ALNM. Receiver operating characteristic (ROC) curve analysis revealed that miR-367-3p, miR-548aq-5p and miR-4710 have good diagnostic values. Notably, the three-miRNA signature showed better predictive value, with an area under ROC curve (AUC) of 0.7414. Bioinformatics analysis revealed that the miRNAs could participate in a complex network and thus be involved in cancer-related pathways.

CONCLUSIONS

Our findings support the potential of miR-367-3p, miR-548aq-5p and miR-4710 and the three-miRNA signature as biomarkers for breast cancer with ALNM.

Identification of Three Circulating MicroRNAs in Plasma as Clinical Biomarkers for Breast Cancer Detection

The diagnostic value of microRNAs (miRNAs) for breast cancer (BC) is largely unknown. Here, our research aim was to explore new circulating miRNAs for BC diagnosis. First, we identified 14 common differentially expressed miRNAs in tissues by TCGA_BRCA and GSE97811 datasets and preliminarily validated them in serum by the GSE73002 dataset. Furthermore, we examined three plasma miRNAs in BC patients (n = 108) and healthy subjects (n = 103) by RT−PCR, namely, hsa-miR-100-5p, hsa-miR-191-5p and hsa-miR-342-3p. The levels of these three miRNAs in BC patients were higher than those in healthy controls (p < 0.05). The ROC curve analysis revealed that these three miRNAs had high diagnostic efficacy for BC and early-stage BC. The combination of hsa-miR-100-5p and hsa-miR-191-5p was the optimal combination for the diagnosis of BC and early-stage BC. Additionally, hsa-miR-100-5p was correlated with stage I−II, T1 stage, N0 stage and Luminal A subtype (p < 0.05). Hsa-miR-191-5p and hsa-miR-342-3p were irrelevant to TNM stage, T stage, N stage and molecular subtypes. Meanwhile, the biological function analysis indicated that these three miRNAs are mainly involved in the calcium signaling pathway, MAPK signaling pathway and microRNAs in cancer. In conclusion, these three miRNAs demonstrate a positive effect on detection and discovery in BC.

Overview of non-coding RNAs in breast cancers

Breast cancer in women is the second most common cancer and the fifth leading cause of cancer death worldwide. Although earlier diagnosis and detection of breast cancer has resulted in lower mortality rates, further advances in prevention, detection, and treatment are needed to improve outcomes and survival for women with breast cancer as well as to offer a personalized therapeutic approach. It is now well-established that non-coding RNAs (ncRNAs) represent 98% of the transcriptome but in-depth knowledge about their involvement in the regulation of gene expression is lacking. A growing body of research indicates that ncRNAs are essential for tumorigenesis by regulating the expression of tumour-related genes. In this review, we focus on their implication in breast cancer genesis but also report the latest knowledge of their theragnostic and therapeutic role. We highlight the need for accurate quantification of circulating ncRNAs which is determinant to develop reliable biomarkers. Further studies are mandatory to finally enter the era of personalized medicine for women with breast cancer.

Inflammation and Oxidative Stress Role of S100A12 as a Potential Diagnostic and Therapeutic Biomarker in Acute Myocardial Infarction

Acute myocardial infarction (AMI) is one of the most serious cardiovascular diseases with high morbidity and mortality. Numerous studies have indicated that S100A12 may has an essential role in the occurrence and development of AMI, and in-depth studies are currently lacking. The purpose of this study is to investigate the effect of S100A12 on inflammation and oxidative stress and to determine its clinical applicability in AMI. Here, AMI datasets used to explore the expression pattern of S100A12 in AMI were derived from the Gene Expression Omnibus (GEO) database. The pooled standard average deviation (SMD) was calculated to further determine S100A12 expression. The overlapping differentially expressed genes (DEGs) contained in all included datasets were recognized by the GEO2R tool. Then, functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, were carried out to determine the molecular function of overlapping DEGs. Gene set enrichment analysis (GSEA) was conducted to determine unrevealed mechanisms of S100A12. Summary receiver operating characteristic (SROC) curve analysis and receiver operating characteristic (ROC) curve analysis were carried out to identify the diagnostic capabilities of S100A12. Moreover, we screened miRNAs targeting S100A12 using three online databases (miRWalk, TargetScan, and miRDB). In addition, by comprehensively using enzyme-linked immunosorbent assay (ELISA), real-time quantitative PCR (RT-qPCR), Western blotting (WB) methods, etc., we used the AC16 cells to validate the expression and underlying mechanism of S100A12. In our study, five datasets related to AMI, GSE24519, GSE60993, GSE66360, GSE97320, and GSE48060 were included; 412 overlapping DEGs were identified. Protein-protein interaction (PPI) network and functional analyses showed that S100A12 was a pivotal gene related to inflammation and oxidative stress. Then, S100A12 overexpression was identified based on the included datasets. The pooled standard average deviation (SMD) also showed that S100A12 was upregulated in AMI (SMD = 1.36, 95% CI: 0.70-2.03, p = 0.024). The SROC curve analysis result suggested that S100A12 had remarkable diagnostic ability in AMI (AUC = 0.90, 95% CI: 0.87-0.92). And nine miRNAs targeting S100A12 were also identified. Additionally, the overexpression of S100A12 was further confirmed that it maybe promote inflammation and oxidative stress in AMI through comprehensive in vitro experiments. In summary, our study suggests that overexpressed S100A12 may be a latent diagnostic biomarker and therapeutic target of AMI that induces excessive inflammation and oxidative stress. Nine miRNAs targeting S100A12 may play a crucial role in AMI, but further studies are still needed. Our work provides a positive inspiration for the in-depth study of S100A12 in AMI.

Potential utility of miRNAs for liquid biopsy in breast cancer

Breast cancer (BC) remains the most prevalent malignancy due to its incidence rate, recurrence, and metastasis in women. Conventional strategies of cancer detection– mammography and tissue biopsy lack the capacity to detect the complete cancer genomic landscape. Besides, they often give false- positive or negative results. The presence of this and other disadvantages such as invasiveness, high-cost, and side effects necessitates developing new strategies to overcome the BC burden. Liquid biopsy (LB) has been brought to the fore owing to its early detection, screening, prognosis, simplicity of the technique, and efficient monitoring. Remarkably, microRNAs (miRNAs)– gene expression regulators seem to play a major role as biomarkers detected in the samples of LB. Particularly, miR-21 and miR-155 among other possible candidates seem to serve as favorable biomarkers in the diagnosis and prognosis of BC. Hence, this review will assess the potential utility of miRNAs as biomarkers and will highlight certain promising candidates for the LB approach in the diagnosis and management of BC that may optimize the patient outcome.

Sensitive and specific detection of breast cancer lymph node metastasis through dual-modality magnetic particle imaging and fluorescence molecular imaging: a preclinical evaluation

PURPOSE

A sensitive and specific imaging method to detect metastatic cancer cells in lymph nodes to detect the early-stage breast cancer is still a challenge. The purpose of this study was to investigate a novel breast cancer-targeting and tumour microenvironment ATP-responsive superparamagnetic iron oxide nanoparticles (SPIOs) imaging probe (abbreviated as SPIOs@A-T) that was developed to detect lymph node metastasis through fluorescence molecular imaging (FMI) and magnetic particle imaging (MPI).

METHODS

The conjugation of the targeted peptide CREKA and SPIOs was via linker sulfo-SMCC, while the dsDNA-Cy5.5 was modified on SPIOs through the conjugation between maleimide group in sulfo-SMCC and sulfydryl group in dsDNA-Cy5.5. SPIOs@A-T was characterised for its imaging properties, targeting ability and toxicity in vitro. Mice with metastatic lymph node (MLN) of breast cancer were established to evaluate the FMI and MPI imaging strategy in vivo. Healthy mice with normal lymph node (NLN) were used as control group. Histological examination and biosafety evaluation were performed for further assessment.

RESULTS

After injection with SPIOs@A-T, the obvious high fluorescent intensity and MPI signal were observed in MLN group than those in NLN group. FMI can specifically light up MLN using an ATP-responsive fluorescence design. On the other hand, MPI could complement the limitation of imaging depth from FMI and could detect MLN more sensitively. Besides, the biosafety evaluation results showed SPIOs@A-T had no detectable biological toxicity.

CONCLUSION

SPIOs@A-T imaging probe in combination with FMI and MPI can provide a promising novel method for the precise detection of MLN in vivo.

Identification of circulating microRNAs as potential biomarkers for hepatic necroinflammation in patients with autoimmune hepatitis

Objective

MicroRNAs (miRNAs) are implicated in the pathogenesis of autoimmune diseases and could be biomarkers of disease activity. This study aimed to identify highly expressed circulating miRNAs in patients with autoimmune hepatitis (AIH) and to evaluate their association with clinical characteristics.

Methods

Microarray analyses were performed, and miRNA expression profiling for AIH, primary biliary cholangitis (PBC) and overlap syndrome (OS) using the serum of patients and healthy individuals was done. Samples were divided into discovery and test sets to identify candidate miRNAs that could discriminate AIH from PBC; the former included 21 AIH and 23 PBC samples, while the latter included five AIH and eight PBC samples.

Results

Among 11 candidate miRNAs extracted in the discovery set, 4 (miR-3196, miR-6125, miR-4725–3 p and miR-4634) were specifically and highly expressed in patients with AIH in the test set. These four miRNAs discriminated AIH from PBC with high sensitivity (0.80–1.00) and specificity (0.88–1.00). In situ hybridisation analysis revealed that these miRNAs were expressed in the cytoplasm of hepatocytes in patients with AIH. Their expression levels were highest in untreated patients with AIH, followed by those in untreated patients with OS. They drastically or moderately decreased after prednisolone treatment. Histological analysis demonstrated that the expression levels of miR-3196, miR-6125 and miR-4634 in patients with AIH and OS were correlated with severe hepatic necroinflammatory activity.

Conclusion

These circulating miRNAs are suggested to reflect hepatic necroinflammatory activity and serve as AIH-related and treatment-responsive biomarkers. These miRNAs could be beneficial in developing new therapeutic strategies for AIH.

Dynamic contrast-enhanced MRI radiomics nomogram for predicting axillary lymph node metastasis in breast cancer

Purpose

The goal of this study is to develop and validate a radiomics nomogram integrating the radiomics features from DCE-MRI and clinical factors for the preoperative diagnosis of axillary lymph node (ALN) metastasis in breast cancer patients.

Procedures

A total of 432 patients with breast cancer were enrolled in this retrospective study and divided into a training cohort (n = 296) and a validation cohort (n = 136). Radiomics features were extracted from the second phase of dynamic contrast enhanced (DCE) MRI images. The least absolute shrinkage and selection operator (LASSO) regression method was used to screen optimal features and construct a radiomics signature in the training cohort. Multivariable logistic regression analysis was used to establish a radiomics nomogram model based on the radiomics signature and clinical factors. The predictive performance of the nomogram was quantified with respect to discrimination and calibration, which was further evaluated in the independent validation cohort.

Results

Fourteen ALN metastasis-related features were selected to construct the radiomics signature, with an area under the curve (AUC) of 0.847 and 0.805 in the training and validation cohorts, respectively. The nomogram was established by incorporating the histological grade, multifocality, MRI report lymph node status and radiomics signature and showed good calibration and excellent performance for ALN detection (AUC of 0.907 and 0.874 in the training and validation cohorts, respectively). The decision curve, which demonstrated the radiomics nomogram, displayed promising clinical utility.

Conclusions

The radiomics nomogram can be used as a noninvasive and reliable tool to assist clinicians in accurately predicting ALN metastasis in breast cancer preoperatively.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-022-00450-w.

Combining Intravoxel Incoherent Motion Diffusion Weighted Imaging and Texture Analysis for a Nomogram to Predict Early Treatment Response to Concurrent Chemoradiotherapy in Cervical Cancer Patients

This study aimed to predict early treatment response to concurrent chemoradiotherapy (CCRT) by combining intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) with texture analysis (TA) for cervical cancer patients and to develop a nomogram for estimating the risk of residual tumor. Ninty-three cervical cancer patients underwent conventional MRI and IVIM-DWI before CCRT. We conducted TA using T2WI. The patients were allocated to partial response (PR) and complete response (CR) groups on the basis of posttreatment MRI. Multivariate logistic regression analysis on IVIM-DWI parameters and texture features was employed to filter the independent predictors and construct the predictive nomogram. Its discrimination and calibration performances were estimated. Multivariate analysis on the IVIM-DWI parameters showed that D and f were independent predictors (OR = 4.029 and 0.889, resp.; p < 0.05). However, the multivariate analysis on the texture features indicated that GLCM-correlation, GLRLM-LRE, and GLSZM-ZE were independent predictors (OR = 43.789, 9.774, and 23.738, resp.;p < 0.05). The combination of IVIM-DWI parameters and texture features exhibited the highest predictive performance (AUC = 0.975). The nomogram to identify the patients with high-risk residual tumors exhibited an acceptable predictive performance and stability with a C-index of 0.953. Decision curve analysis demonstrated the clinical use of the nomogram. The results demonstrate that D, f, GLCM-correlation, GLRLM-LRE, and GLSZM-ZE were independent predictors for cervical cancer. The nomogram combining IVIM-DWI parameters and texture features makes it possible to identify cervical cancer patients at a high risk of residual tumor after CCRT.

A nine-hub-gene signature of metabolic syndrome identified using machine learning algorithms and integrated bioinformatics

Early risk assessments and interventions for metabolic syndrome (MetS) are limited because of a lack of effective biomarkers. In the present study, several candidate genes were selected as a blood-based transcriptomic signature for MetS. We collected so far the largest MetS-associated peripheral blood high-throughput transcriptomics data and put forward a novel feature selection strategy by combining weighted gene co-expression network analysis, protein-protein interaction network analysis, LASSO regression and random forest approaches. Two gene modules and 51 hub genes as well as a 9-hub-gene signature associated with metabolic syndrome were identified. Then, based on this 9-hub-gene signature, we performed logistic analysis and subsequently established a web nomogram calculator for metabolic syndrome risk (https://xjtulgz.shinyapps.io/DynNomapp/). This 9-hub-gene signature showed excellent classification and calibration performance (AUC = 0.968 in training set, AUC = 0.883 in internal validation set, AUC = 0.861 in external validation set) as well as ideal potential clinical benefit.

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

Objectives

To develop and validate a deep learning (DL)-based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast cancer (EBC) patients with clinically negative ALN.

Methods

A total of 1,058 EBC patients with pathologically confirmed ALN status were enrolled from May 2010 to August 2020. A DL core-needle biopsy (DL-CNB) model was built on the attention-based multiple instance-learning (AMIL) framework to predict ALN status utilizing the DL features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) of breast CNB specimens annotated by two pathologists. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curve (AUCs) were analyzed to evaluate our model.

Results

The best-performing DL-CNB model with VGG16_BN as the feature extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865) in predicting positive ALN metastasis in the independent test cohort. Furthermore, our model incorporating the clinical data, which was called DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The interpretation of DL-CNB model showed that the top signatures most predictive of ALN metastasis were characterized by the nucleus features including density (p = 0.015), circumference (p = 0.009), circularity (p = 0.010), and orientation (p = 0.012).

Conclusion

Our study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC.

microRNA-based diagnostic and therapeutic applications in cancer medicine

It has been almost two decades since the first link between microRNAs and cancer was established. In the ensuing years, this abundant class of short noncoding regulatory RNAs has been studied in virtually all cancer types. This tremendously large body of research has generated innovative technological advances for detection of microRNAs in tissue and bodily fluids, identified the diagnostic, prognostic, and/or predictive value of individual microRNAs or microRNA signatures as potential biomarkers for patient management, shed light on regulatory mechanisms of RNA-RNA interactions that modulate gene expression, uncovered cell-autonomous and cell-to-cell communication roles of specific microRNAs, and developed a battery of viral and nonviral delivery approaches for therapeutic intervention. Despite these intense and prolific research efforts in preclinical and clinical settings, there are a limited number of microRNA-based applications that have been incorporated into clinical practice. We review recent literature and ongoing clinical trials that highlight most promising approaches and standing challenges to translate these findings into viable microRNA-based clinical tools for cancer medicine. This article is categorized under: RNA in Disease and Development > RNA in Disease.

Body Composition Changes in Hepatocellular Carcinoma: Prediction of Survival to Transcatheter Arterial Chemoembolization in Combination With Clinical Prognostic Factors

Treatment-related toxicities and decreased levels of patient performance during cancer therapy might contribute to body composition changes (BCC) and thereby impact outcomes. This study investigated the association between BCC during transcatheter arterial chemoembolization (TACE) and outcome in patients with hepatocellular carcinoma (HCC), and developed a nomogram for predicting survival in combination with clinical prognostic factors (CPF). Pretreatment and posttreatment computed tomography (CT) images of 75 patients with HCC who were treated between 2015 and 2018 were analyzed. The bone mineral density (BMD), cross-sectional area of paraspinal muscles (CSA_muscle), subcutaneous fat area (SFA), and visceral fat area (VFA) were measured from two sets of CT images. Count the changes in body composition during treatment and sort out the CPF of patients. Using cox regression models, CSA_muscle change, SFA change, VFA change, child-push class, and portal vein thrombosis were independent prognostic factors for overall survival (OS) (HR=5.932, 2.384, 3.140, 1.744, 1.794, respectively. P < 0.05). Receiver operating characteristic curves (ROCs) showed the prediction model combination of BCC and CPF exhibited the highest predictive performance (AUC=0.937). Independent prognostic factors were all contained into the prognostic nomogram, the concordance index (C-index) of prognostic nomogram was 0.787 (95% CI, 0.675−0.887). Decision curve analysis (DCA) demonstrated that the prognostic nomogram was clinically useful. Nomogram-based risk classification systems were also constructed to facilitate risk stratification in HCC for optimization of clinical management. In conclusion, we identified CSA_muscle change, SFA change, VFA change, Child-Pugh class, and portal vein thrombosis were independent prognostic factors for HCC. The prognostic nomogram with a combination of BCC and CPF that can be applied in the individualized prediction of survival in patients with HCC after TACE.

Cancer Epigenetic Biomarkers in Liquid Biopsy for High Incidence Malignancies

Early alterations in cancer include the deregulation of epigenetic events such as changes in DNA methylation and abnormal levels of non-coding (nc)RNAs. Although these changes can be identified in tumors, alternative sources of samples may offer advantages over tissue biopsies. Because tumors shed DNA, RNA, and proteins, biological fluids containing these molecules can accurately reflect alterations found in cancer cells, not only coming from the primary tumor, but also from metastasis and from the tumor microenvironment (TME). Depending on the type of cancer, biological fluids encompass blood, urine, cerebrospinal fluid, and saliva, among others. Such samples are named with the general term "liquid biopsy" (LB). With the advent of ultrasensitive technologies during the last decade, the identification of actionable genetic alterations (i.e., mutations) in LB is a common practice to decide whether or not targeted therapy should be applied. Likewise, the analysis of global or specific epigenetic alterations may also be important as biomarkers for diagnosis, prognosis, and even for cancer drug response. Several commercial kits that assess the DNA promoter methylation of single genes or gene sets are available, with some of them being tested as biomarkers for diagnosis in clinical trials. From the tumors with highest incidence, we can stress the relevance of DNA methylation changes in the following genes found in LB: SHOX2 (for lung cancer); RASSF1A, RARB2, and GSTP1 (for lung, breast, genitourinary and colon cancers); and SEPT9 (for colon cancer). Moreover, multi-cancer high-throughput methylation-based tests are now commercially available. Increased levels of the microRNA miR21 and several miRNA- and long ncRNA-signatures can also be indicative biomarkers in LB. Therefore, epigenetic biomarkers are attractive and may have a clinical value in cancer. Nonetheless, validation, standardization, and demonstration of an added value over the common clinical practice are issues needed to be addressed in the transfer of this knowledge from "bench to bedside".

Screening of miRNAs associated with lymph node metastasis in Her-2-positive breast cancer and their relationship with prognosis

The aim of this study was to identify some biomarkers for predicting lymph node metastasis and prognosis of human epidermal growth factor receptor 2 (Her-2)-positive breast cancer (BC). We analyzed correlations between microRNAs (miRNAs) and the prognosis of patients with BC based on data collected from The Cancer Genome Atlas (TCGA) database. The expression levels of miR-455, miR-143, and miR-99a were measured in clinical samples of Her-2-positive BC patients with different degrees of lymph node metastasis. We investigated the impacts of overexpressed miR-455 on the proliferation and invasiveness of MDA-MB-453 cells and measured its effects on the expression of long non-coding RNA (lncRNA) metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) by quantitative real-time polymerase chain reaction (qRT-PCR). The expression of miR-455 was significantly and positively correlated to the prognosis and overall survival (OS) of the BC (P=0.028), according to TCGA information. The expression level of miR-455 was positively correlated with OS and relapse-free survival (RFS) of patients with Her-2-positive BC, and was negatively correlated with the number of metastatic lymph nodes (P<0.05). Transwell assay suggested that MDA-MB-453 cells became much less invasive (P<0.01) after being transfected with miR-455 mimics. During the qRT-PCR, the expression level of MALAT1 declined significantly after transfection (P<0.01). Overexpressed miR-455 significantly inhibited the proliferation and migration of MDA-MB-453 cells and the expression of MALAT1. We conclude that miR-455 may be a useful potential biomarker for forecasting lymph node metastasis and the prognosis of Her-2-positive BC patients. miR-455 may play an important role in lymph node metastasis of BC by interacting with MALAT1.

Radiomics Nomogram of DCE-MRI for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer

Purpose

This study aimed to establish and validate a radiomics nomogram based on dynamic contrast-enhanced (DCE)-MRI for predicting axillary lymph node (ALN) metastasis in breast cancer.

Method

This retrospective study included 296 patients with breast cancer who underwent DCE-MRI examinations between July 2017 and June 2018. A total of 396 radiomics features were extracted from primary tumor. In addition, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select the features. Radiomics signature and independent risk factors were incorporated to build a radiomics nomogram model. Calibration and receiver operator characteristic (ROC) curves were used to confirm the performance of the nomogram in the training and validation sets. The clinical usefulness of the nomogram was evaluated by decision curve analysis (DCA).

Results

The radiomics signature consisted of three ALN-status-related features, and the nomogram model included the radiomics signature and the MR-reported lymph node (LN) status. The model showed good calibration and discrimination with areas under the ROC curve (AUC) of 0.92 [95% confidence interval (CI), 0.87-0.97] in the training set and 0.90 (95% CI, 0.85-0.95) in the validation set. In the MR-reported LN-negative (cN0) subgroup, the nomogram model also exhibited favorable discriminatory ability (AUC, 0.79; 95% CI, 0.70-0.87). DCA findings indicated that the nomogram model was clinically useful.

Conclusions

The MRI-based radiomics nomogram model could be used to preoperatively predict the ALN metastasis of breast cancer.

Extracellular MicroRNAs as Intercellular Mediators and Noninvasive Biomarkers of Cancer

Simple Summary

There are an extensive number of publications regarding the role of endogenous miRNAs as regulators of gene expression in cancer. However, extracellular miRNAs have emerged as a novel mechanism of cell-to-cell communication in normal conditions and disease and have drawn a large amount of interest as regulators of gene expression and as potential non-invasive biomarkers in cancer. Despite this high interest and the abundance of research on the biology and role of extracellular miRNAs in cancer, they are not yet completely understood. The aim of this review is to highlight the relevant biological characteristics of extracellular miRNAs that enable them to function as intercellular mediators of gene expression regulation and provide the recently published evidence of the specific role of extracellular miRNAs in tumor development and progression.

Abstract

MicroRNAs (miRNAs) are released by different types of cells through highly regulated mechanisms under normal and pathological conditions. These extracellular miRNAs can be delivered into recipient cells for functional purposes, acting as cell-to-cell signaling mediators. It has been discovered that cancer cells release miRNAs into their surroundings, targeting normal cells or other cancer cells, presumably to promote tumor development and progression. These extracellular miRNAs are associated with oncogenic mechanisms and, because they can be quantified in blood and other bodily fluids, may be suitable noninvasive biomarkers for cancer detection. This review summarizes recent evidence of the role of extracellular miRNAs as intercellular mediators, with an emphasis on their role in the mechanisms of tumor development and progression and their potential value as biomarkers in solid tumors. It also highlights the biological characteristics of extracellular miRNAs that enable them to function as regulators of gene expression, such as biogenesis, gene silencing mechanisms, subcellular compartmentalization, and the functions and mechanisms of release.

Extracellular microRNA profiling in human follicular fluid: new biomarkers in female reproductive potential

MicroRNAs (miRNAs) are small, about 22 nucleotides, non-coding RNAs which regulate a wide range of gene expression during post-transcriptional stage. They are released into intra- and extracellular microenvironments and play vital roles in different physiological and pathological pathways. Due to easy accessibility, detection of extracellular miRNAs in body fluids, e.g. serum, plasma, cerebrospinal fluid, and follicular fluid, has been explored in recent years. Since miRNAs are stable at unsuitable conditions, scientists have been investigating to use them as biomarkers in different fields of medicines. It goes without saying that experienced biomarkers would be required in reproductive medicine as well. Biomarkers can help clinicians and embryologists to diagnose disorders and assess the embryo quality via molecular pattern which is more reliable than nowadays routine methods. Follicular fluid as a noninvasive fluid in assisted reproductive techniques (ART) has attracted researchers as a rich pool for biomarkers, and miRNAs are not exception. Although miRNA biomarkers in reproduction field are located on their initial stage and there is a long path to move forward, several meticulous studies have been performed and discovered their associations with various conditions. In this regard, we summarize the reported miRNAs in follicular fluid and their correlations with female infertility and ART success rate, while subsequent investigations are required.

Novel hepatotoxicity biomarkers of extracellular vesicle (EV)-associated miRNAs induced by CCl4

Recent findings have revealed that extracellular vesicles (EVs) are secreted from cells and circulate in the blood. EVs are classified as exosomes (40-100 nm), microvesicles (50-1,000 nm) or apoptotic bodies (500-2,000 nm). EVs contain mRNAs, microRNAs, and DNAs and have the ability to transfer them from cell to cell. Recently, especially in humans, the diagnostic accuracy of tumor cell type-specific EV-associated miRNAs as biomarkers has been found to be more than 90 %. In addition, microRNAs contained in EVs in blood are being identified as specific biomarkers of chemical-induced inflammation and organ damage. Therefore, microRNAs contained in the EVs released into the blood from tissues and organs in response to adverse events such as exposure to chemical substances and drugs are expected to be useful as novel biomarkers for toxicity assessment. In this study, C57BL/6 J male mice orally dosed with carbon tetrachloride (CCl4) were used as a hepatotoxicity animal model. Here, we report that not only the known hepatotoxicity biomarkers miR-122 and miR-192 but also 42 novel EV-associated biomarkers were upregulated in mice dosed with CCl4. Some of these novel biomarkers may be expected to be able to use for better understanding the mechanism of toxicity. These results suggest that our newly developed protocol using EV-associated miRNAs as a biomarker would accelerate the rapid evaluation of toxicity caused by chemical substances and/or drugs.

Circulating microRNAs: Next-generation Cancer Detection

Early detection of cancer is crucial for its ultimate control and the prevention of malignant progression. In Japan, a nationwide project was conducted between 2014 and 2019 to develop novel cancer detection tools using serum microRNAs (miRNAs). Using the National Cancer Center Biobank, we collected more than 10,000 serum samples from patients with malignant diseases, including rare cancers such as ovarian cancer, gliomas, and sarcomas. Subsequently, comprehensive miRNA microarray analyses were performed for all samples. This serum miRNA database provides insights regarding miRNA biomarker candidates for each cancer type. Here, we summarize the major achievements of this national project. Notably, although circulating miRNAs packaged in extracellular vesicles are thought to be a cell-to-cell communication tool, the functional characteristics of the miRNAs listed in the project are still unknown. We hope that our findings will help elucidate the biological functions of circulating miRNAs.

DNA methylation-mediated repression of exosomal miR-652-5p expression promotes oesophageal squamous cell carcinoma aggressiveness by targeting PARG and VEGF pathways

Exosomal microRNAs (miRNAs) have been recently shown to play vital regulatory and communication roles in cancers. In this study, we showed that the expression levels of miR-652-5p in tumour tissues and serum samples of oesophageal squamous cell carcinoma (OSCC) patients were lower compared to non-tumorous tissues and serum samples from healthy subjects, respectively. Decreased expression of miR-652-5p was correlated with TNM stages, lymph node metastasis, and short overall survival (OS). More frequent CpG sites hypermethylation in the upstream of miR-652-5p was found in OSCC tissues compared to adjacent normal tissues. Subsequently, miR-652-5p downregulation promoted the proliferation and metastasis of OSCC, and regulated cell cycle both in cells and in vivo. The dual-luciferase reporter assay confirmed that poly (ADP-ribose) glycohydrolase (PARG) and vascular endothelial growth factor A (VEGFA) were the direct targets of miR-652-5p. Moreover, the delivery of miR-652-5p agomir suppressed tumour growth and metastasis, and inhibited the protein expressions of PARG and VEGFA in nude mice. Taken together, our findings provide novel insight into the molecular mechanism underlying OSCC pathogenesis.

A miRNA-based diagnostic model predicts resectable lung cancer in humans with high accuracy

Lung cancer, the leading cause of cancer death worldwide, is most frequently detected through imaging tests. In this study, we investigated serum microRNAs (miRNAs) as a possible early screening tool for resectable lung cancer. First, we used serum samples from participants with and without lung cancer to comprehensively create 2588 miRNAs profiles; next, we established a diagnostic model based on the combined expression levels of two miRNAs (miR-1268b and miR-6075) in the discovery set (208 lung cancer patients and 208 non-cancer participants). The model displayed a sensitivity of 99% and specificity of 99% in the validation set (1358 patients and 1970 non-cancer participants) and exhibited high sensitivity regardless of histological type and pathological TNM stage of the cancer. Moreover, the diagnostic index markedly decreased after lung cancer resection. Thus, the model we developed has the potential to markedly improve screening for resectable lung cancer.

Asakura, Kadota et al. demonstrate the diagnostic potential of serum microRNAs for resectable lung cancer. Their diagnostic model based on the combined expression levels of two miRNAs predicts resectable lung cancer with 99% sensitivity, regardless of histological types and pathological stages of cancer, suggesting its promising, diagnostic utility.

miR-492 Promotes Cancer Progression by Targeting GJB4 and Is a Novel Biomarker for Bladder Cancer

Background

Bladder cancer is the most common urinary system malignancy in the United States and is characterized by its diverse prognosis and high recurrence rate. However, the molecular mechanisms underlying its progression remain unknown. Accumulating evidence suggests a critical role for miRNAs in bladder cancer progression.

Methods and results

In this study, we found that miR-492 expression levels were significantly higher in bladder cancer tissue and the serum of bladder cancer patients by bioinformatics analysis and a panel of clinical samples. The results of receiver operating characteristic curve analysis suggested the potential diagnostic value of serum miR-492 for bladder cancer. In vitro and in vivo functional assays showed that knockdown of miR-492 suppressed proliferation and metastasis of bladder cancer cells. Gap junction beta-4 protein was predicted to be a direct target of miR-492, which was validated using a luciferase reporter assay. Further cellular functional assays showed that suppression of miR-492 abrogated bladder cancer cell proliferation and metastasis by targeting gap junction beta-4 protein.

Conclusion

miR-492 promotes cancer progression by targeting GJB4 and is a novel biomarker for bladder cancer.

Competitive Endogenous RNA Network Construction and Comparison of Lung Squamous Cell Carcinoma in Smokers and Nonsmokers

Background

Lung squamous cell carcinoma (LUSC) is a subtype of highly malignant lung cancer with poor prognosis, for which smoking is the main risk factor. However, the underlying genetic and molecular mechanisms of smoking-related LUSC remain largely unknown.

Methods

We mined existing LUSC-related mRNA, miRNA, and lncRNA transcriptome data and corresponding clinical data from The Cancer Genome Atlas (TCGA) database and divided them into smoking and nonsmoking groups, followed by differential expression analysis. Functional enrichment analysis of the unique differentially expressed mRNAs of the two groups was performed using the DAVID database. Subsequently, the lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network of LUSC in smoking and nonsmoking groups was constructed. Finally, survival analyses were performed to determine the effects of differentially expressed lncRNAs/mRNAs/miRNAs that were involved in the ceRNA network on overall survival and to discover the hub genes.

Results

A total of 1696 lncRNAs, 125 miRNAs, and 3246 mRNAs and 1784 lncRNAs, 96 miRNAs, and 3229 mRNAs with differentially expressed profiles were identified in the smoking and nonsmoking groups, respectively. The ceRNA network and survival analysis revealed four lncRNAs (LINC00466, DLX6-AS1, LINC00261, and AGBL1), one miRNA (hsa-mir-210), and two mRNAs (CITED2 and ENPP4), with the potential as biomarkers for smoking-related LUSC diagnosis and prognosis.

Conclusion

Taken together, our research has identified the differences in the ceRNA regulatory networks between smoking and nonsmoking LUSC, which could lay the foundation for future clinical research.

Serum microRNA profile enables preoperative diagnosis of uterine leiomyosarcoma

Uterine leiomyosarcoma (ULMS) is the major subtype of uterine sarcoma (US) and contributes to uterine cancer deaths. Although preoperative diagnosis of US remains challenging, frequent application of laparoscopic surgery for benign uterine leiomyomas (ULM) requires precise exclusion of US. MicroRNAs are stably present in the bloodstream, and the application of circulating miRNAs as disease biomarkers has been recognized. In the present study, we aimed to identify diagnostic biomarkers for distinguishing US from ULM by focusing on circulating miRNAs. All serum samples were collected preoperatively between 2009 and 2017, and all cases were histopathologically diagnosed. Whole miRNA profiles were obtained using a miRNA microarray. By analyzing expression levels of the miRNAs, candidate miRNAs were selected based on diagnostic performance in discriminating US from ULM, and a diagnostic model was then constructed. A total of 90 serum samples were analyzed, and clustering analyses revealed that the profiles of ULMS were distinct from those of controls. Based on leave-one-out cross-validation, seven miRNAs were selected as biomarker candidates. Based on model construction, the optimal model consisted of two miRNAs (miR-1246 and miR-191-5p), with an area under the receiver operating characteristic curve (AUC) for identifying ULMS of 0.97 (95% confidence interval [CI], 0.91-1.00). In contrast, serum lactate dehydrogenase had an AUC of only 0.64 (95% CI, 0.34-0.94). Seven serum miRNAs with high diagnostic performance for preoperative US screening were detected, and a promising diagnostic model for ULMS was generated.

Ultrasound-based radiomics nomogram: A potential biomarker to predict axillary lymph node metastasis in early-stage invasive breast cancer

PURPOSE

To establish a radiomics nomogram integrating clinical factors and radiomics features from ultrasound for the preoperative diagnosis axillary lymph node (ALN) status in patients with early-stage invasive breast cancer (EIBC).

MATERIALS AND METHODS

Between September 2016 and December 2018, four hundred twenty-six ultrasound manually segmented images of patients with EIBC were enrolled in our retrospective study, which were divided into a primary cohort (n = 300) and a validation cohort (n = 126). A radiomics signature was built with the least absolute shrinkage and selection operator (LASSO) algorithm in the primary cohort. Multivariable logistic regression analysis was used to establish a radiomics nomogram model based on radiomics signature and clinical variables. The performance of nomogram was quantified with respect to discrimination and calibration. The radiomics model was further evaluated in the internal validation cohort.

RESULTS

The radiomics signature, consisted of fourteen selected ALN-status-related features, achieved moderate prediction efficacy with an area under the curve (AUC) of 0.78 and 0.71 in the primary and validation cohorts respectively. The radiomics nomogram, comprising tumor size, US-reported LN status and radiomics signature, showed good calibration and favorite performance for ALN detection (AUC 0.84 and 0.81 in the primary and validation cohort). The decision curve which was demonstrated the radiomics nomogram displayed good clinical utility.

CONCLUSION

The radiomics nomogram could hold promise as a non-invasive and reliable tool in predicting ALN metastasis and may facilitate to develop more effective preoperative decision-making.