Novel combination of serum microRNA for detecting breast cancer in the early stage

Temporally controlled multistep division of DNA droplets for dynamic artificial cells

Synthetic droplets mimicking bio-soft matter droplets formed via liquid-liquid phase separation (LLPS) in living cells have recently been employed in nanobiotechnology for artificial cells, molecular robotics, molecular computing, etc. Temporally controlling the dynamics of synthetic droplets is essential for developing such bio-inspired systems because living systems maintain their functions based on the temporally controlled dynamics of biomolecular reactions and assemblies. This paper reports the temporal control of DNA-based LLPS droplets (DNA droplets). We demonstrate the timing-controlled division of DNA droplets via time-delayed division triggers regulated by chemical reactions. Controlling the release order of multiple division triggers results in order control of the multistep droplet division, i.e., pathway-controlled division in a reaction landscape. Finally, we apply the timing-controlled division into a molecular computing element to compare microRNA concentrations. We believe that temporal control of DNA droplets will promote the design of dynamic artificial cells/molecular robots and sophisticated biomedical applications.

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.

Ductal Carcinoma In Situ

In breast cancer (BC) pathogenesis models, normal cells acquire somatic mutations and there is a stepwise progression from high-risk lesions and ductal carcinoma in situ to invasive cancer. The precancer biology of mammary tissue warrants better characterization to understand how different BC subtypes emerge. Primary methods for BC prevention or risk reduction include lifestyle changes, surgery, and chemoprevention. Surgical intervention for BC prevention involves risk-reducing prophylactic mastectomy, typically performed either synchronously with the treatment of a primary tumor or as a bilateral procedure in high-risk women. Chemoprevention with endocrine therapy carries adherence-limiting toxicity.

Identification and validation of screening models for breast cancer with 3 serum miRNAs in an 11,349 samples mixed cohort

PURPOSE

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.

CONCLUSION

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.

Expression profile and gap-junctional transfer of microRNAs in the bovine cumulus-oocyte complex

MicroRNAs (miRNA) are important regulators of oocyte maturation, playing a key role in modulating gene expression both in a temporal- and spatial-specific manner. These small non-coding RNAs are involved in important processes during oocyte maturation, acting as messengers between the oocyte and its surrounding cumulus cells. Despite its significance, the bidirectional communication mechanism is still unknown. To test miRNA communication between oocyte and surrounding cumulus cells through the gap junctions the gap junctions were either blocked with carbenoxolone or not. MiRNA sequencing of oocytes at 1, 6, and 22 h of in vitro maturation was then performed. Among the differentially expressed miRNAs, bta-miR-21-5p, a regulator of cumulus cell viability and oocyte maturation, was the only previously known miRNA. Furthermore, by labeling a bta-miR-21-5p mimic with FAM, crossing of this miRNA through the gap junctions within the cumulus-oocyte complex could be visualized and internalization in the oocyte was confirmed by RT-qPCR. In conclusion, this study provides, for the first time, evidence that miRNA communication within the bovine cumulus-oocyte complex is enabled through the gap junctional network.

Programmable Computational RNA Droplets Assembled via Kissing-Loop Interaction

DNA droplets, artificial liquid-like condensates of well-engineered DNA sequences, allow the critical aspects of phase-separated biological condensates to be harnessed programmably, such as molecular sensing and phase-state regulation. In contrast, their RNA-based counterparts remain less explored despite more diverse molecular structures and functions ranging from DNA-like to protein-like features. Here, we design and demonstrate computational RNA droplets capable of two-input AND logic operations. We use a multibranched RNA nanostructure as a building block comprising multiple single-stranded RNAs. Its branches engaged in RNA-specific kissing-loop (KL) interaction enables the self-assembly into a network-like microstructure. Upon two inputs of target miRNAs, the nanostructure is programmed to break up into lower-valency structures that are interconnected in a chain-like manner. We optimize KL sequences adapted from viral sequences by numerically and experimentally studying the base-wise adjustability of the interaction strength. Only upon receiving cognate microRNAs, RNA droplets selectively show a drastic phase-state change from liquid to dispersed states due to dismantling of the network-like microstructure. This demonstration strongly suggests that the multistranded motif design offers a flexible means to bottom-up programming of condensate phase behavior. Unlike submicroscopic RNA-based logic operators, the macroscopic phase change provides a naked-eye-distinguishable readout of molecular sensing. Our computational RNA droplets can be applied to in situ programmable assembly of computational biomolecular devices and artificial cells from transcriptionally derived RNA within biological/artificial cells.

A unique circulating microRNA pairs signature serves as a superior tool for early diagnosis of pan-cancer

Cancer remains a major burden globally and the critical role of early diagnosis is self-evident. Although various miRNA-based signatures have been developed in past decades, clinical utilization is limited due to a lack of precise cutoff value. Here, we innovatively developed a signature based on pairwise expression of miRNAs (miRPs) for pan-cancer diagnosis using machine learning approach. We analyzed miRNA spectrum of 15832 patients, who were divided into training, validation, test, and external test sets, with 13 different cancers from 10 cohorts. Five different machine-learning (ML) algorithms (XGBoost, SVM, RandomForest, LASSO, and Logistic) were adopted for signature construction. The best ML algorithm and the optimal number of miRPs included were identified using area under the curve (AUC) and youden index in validation set. The AUC of the best model was compared to previously published 25 signatures. Overall, Random Forest approach including 31 miRPs (31-miRP) was developed, proving highly efficient in cancer diagnosis across different datasets and cancer types (AUC range: 0.980-1.000). Regarding diagnosis of cancers at early stage, 31-miRP also exhibited high capacities, with AUC ranging from 0.961 to 0.998. Moreover, 31-miRP exhibited advantages in differentiating cancers from normal tissues (AUC range: 0.976-0.998) as well as differentiating cancers from corresponding benign lesions. Encouragingly, comparing to previously published 25 different signatures, 31-miRP also demonstrated clear advantages. In conclusion, 31-miRP acts as a powerful model for cancer diagnosis, characterized by high specificity and sensitivity as well as a clear cutoff value, thereby holding potential as a reliable tool for cancer diagnosis at early stage.

Integrated analysis of diverse cancer types reveals a breast cancer-specific serum miRNA biomarker through relative expression orderings analysis

PURPOSE

Serum microRNA (miRNA) holds great potential as a non-invasive biomarker for diagnosing breast cancer (BrC). However, most diagnostic models rely on the absolute expression levels of miRNAs, which are susceptible to batch effects and challenging for clinical transformation. Furthermore, current studies on liquid biopsy diagnostic biomarkers for BrC mainly focus on distinguishing BrC patients from healthy controls, needing more specificity assessment.

METHODS

We collected a large number of miRNA expression data involving 8465 samples from GEO, including 13 different cancer types and non-cancer controls. Based on the relative expression orderings (REOs) of miRNAs within each sample, we applied the greedy, LASSO multiple linear regression, and random forest algorithms to identify a qualitative biomarker specific to BrC by comparing BrC samples to samples of other cancers as controls.

RESULTS

We developed a BrC-specific biomarker called 7-miRPairs, consisting of seven miRNA pairs. It demonstrated comparable classification performance in our analyzed machine learning algorithms while requiring fewer miRNA pairs, accurately distinguishing BrC from 12 other cancer types. The diagnostic performance of 7-miRPairs was favorable in the training set (accuracy = 98.47%, specificity = 98.14%, sensitivity = 99.25%), and similar results were obtained in the test set (accuracy = 97.22%, specificity = 96.87%, sensitivity = 98.02%). KEGG pathway enrichment analysis of the 11 miRNAs within the 7-miRPairs revealed significant enrichment of target mRNAs in pathways associated with BrC.

CONCLUSION

Our study provides evidence that utilizing serum miRNA pairs can offer significant advantages for BrC-specific diagnosis in clinical practice by directly comparing serum samples with BrC to other cancer types.

wMKL: multi-omics data integration enables novel cancer subtype identification via weight-boosted multi-kernel learning

BACKGROUND

Cancer is a heterogeneous disease driven by complex molecular alterations. Cancer subtypes determined from multi-omics data can provide novel insight into personalised precision treatment. It is recognised that incorporating prior weight knowledge into multi-omics data integration can improve disease subtyping.

METHODS

We develop a weighted method, termed weight-boosted Multi-Kernel Learning (wMKL) which incorporates heterogeneous data types as well as flexible weight functions, to boost subtype identification. Given a series of weight functions, we propose an omnibus combination strategy to integrate different weight-related P-values to improve subtyping precision.

RESULTS

wMKL models each data type with multiple kernel choices, thus alleviating the sensitivity and robustness issue due to selecting kernel parameters. Furthermore, wMKL integrates different data types by learning weights of different kernels derived from each data type, recognising the heterogeneous contribution of different data types to the final subtyping performance. The proposed wMKL outperforms existing weighted and non-weighted methods. The utility and advantage of wMKL are illustrated through extensive simulations and applications to two TCGA datasets. Novel subtypes are identified followed by extensive downstream bioinformatics analysis to understand the molecular mechanisms differentiating different subtypes.

CONCLUSIONS

The proposed wMKL method provides a novel strategy for disease subtyping. The wMKL is freely available at https://github.com/biostatcao/wMKL .

Cellular and Exosomal MicroRNAs: Emerging Clinical Relevance as Targets for Breast Cancer Diagnosis and Prognosis

Breast cancer accounts for the highest cancer cases globally, with 12% of occurrences progressing to metastatic breast cancer with a low survival rate and limited effective early intervention strategies augmented by late diagnosis. Moreover, a low concentration of prognostic and predictive markers hinders disease monitoring. Circulating and exosomal microRNAs (miRNAs) have recently shown a considerable interplay in breast cancer, standing out as effective diagnostic and prognostic markers. The primary functions are as gene regulatory agents at the genetic and epigenetic levels. An array of dysregulated miRNAs stimulates cancer-promoting mechanisms, activating oncogenes and controlling tumor-suppressing genes and mechanisms. Exosomes are vastly studied extracellular vesicles, carrying, and transporting cargo, including noncoding RNAs with premier roles in oncogenesis. Translocation of miRNAs from the circulation to exosomes, with RNA-binding proteins in stress-induced conditions, has shown significant cooperation in function to promote breast cancer. This review examines cellular and exosomal miRNA biogenesis and loading, the clinical implications of their dysregulation, their function in diagnosis, prognosis, and prediction of breast cancer, and in regulating cancer signaling pathways. The influence of cellular and exosomal miRNAs presents clinical significance on breast cancer diagnosis, subtyping, staging, prediction, and disease monitoring during treatment, hence a potent marker 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.

Liquid biopsy using non-coding RNAs and extracellular vesicles for breast cancer management

This article examines liquid biopsy using non-coding RNAs and extracellular vesicles in detail. Liquid biopsy is emerging as a prominent non-invasive diagnostic tool in the treatment of breast cancer. We will elucidate the roles of these molecules in early detection, monitoring treatment effectiveness, and prognostic assessment of breast cancer. Additionally, the clinical significance of these molecules will be discussed. We aim to delve into the distinct characteristics of these molecules and their possible roles in breast cancer management, with an anticipation of their contribution to future diagnostic and therapeutic advancements.

Plasma extracellular vesicle microRNAs reflecting the therapeutic effect of the CBP/β-catenin inhibitor PRI-724 in patients with liver cirrhosis

There is an unmet need for antifibrotic therapies to prevent the progression of liver cirrhosis. Previously, we conducted an exploratory trial to assess the safety and antifibrotic efficacy of PRI-724, a selective CBP/β-catenin inhibitor, in patients with liver cirrhosis. PRI-724 was well tolerated and exerted a potential antifibrotic effect. Here, we investigated whether the profiles of circulating microRNAs packaged in extracellular vesicles (EV-miRNAs) are associated with responses to liver fibrosis treatments. Eighteen patients who received PRI-724 for 12 weeks in a phase 1/2a study were classified as responders (n = 10) or non-responders (n = 8) based on changes in liver stiffness. Plasma samples were obtained before and after PRI-724 administration and the levels of EV-miRNAs were analyzed. Three miRNAs (miR-6510-5p, miR-6772-5p, and miR-4261) were identified as predictors of response or non-response to PRI-724, and the levels of three other miRNAs (miR-939-3p, miR-887-3p, and miR-7112-5p) correlated with the efficacy of treatment. Expression of miR-887-3p was detected in hepatocytes and was decreased significantly in liver tissue following PRI-724 treatment. In addition, transfection of a miR-887-3p mimic activated hepatic stellate cells. Thus, decreases in the miR-887-3p level in blood may reflect recovery from liver fibroses in patients with liver cirrhosis treated with PRI-724, although further validation studies are warranted to confirm this.

Dynamics of a diffusive model for cancer stem cells with time delay in microRNA-differentiated cancer cell interactions and radiotherapy effects

Understand the dynamics of cancer stem cells (CSCs), prevent the non-recurrence of cancers and develop therapeutic strategies to destroy both cancer cells and CSCs remain a challenge topic. In this paper, we study both analytically and numerically the dynamics of CSCs under radiotherapy effects. The dynamical model takes into account the diffusion of cells, the de-differentiation (or plasticity) mechanism of differentiated cancer cells (DCs) and the time delay on the interaction between microRNAs molecules (microRNAs) with DCs. The stability of the model system is studied by using a Hopf bifurcation analysis. We mainly investigate on the critical time delay τ c , that represents the time for DCs to transform into CSCs after the interaction of microRNAs with DCs. Using the system parameters, we calculate the value of τ c for prostate, lung and breast cancers. To confirm the analytical predictions, the numerical simulations are performed and show the formation of spatiotemporal circular patterns. Such patterns have been found as promising diagnostic and therapeutic value in management of cancer and various diseases. The radiotherapy is applied in the particular case of prostate model. We calculate the optimum dose of radiation and determine the probability of avoiding local cancer recurrence after radiotherapy treatment. We find numerically a complete eradication of patterns when the radiotherapy is applied before a time t < τ c . This scenario induces microRNAs to act as suppressors as experimentally observed in prostate cancer. The results obtained in this paper will provide a better concept for the clinicians and oncologists to understand the complex dynamics of CSCs and to design more efficacious therapeutic strategies to prevent the non-recurrence of cancers.

Differences in serum and plasma levels of microRNAs and their time-course changes after blood collection

Background

Serum and plasma are used for measurements of microRNAs (miRNAs) as biomarkers of various diseases. However, no consistent findings have been obtained regarding differences in serum and plasma levels of miRNAs. The purpose of this study was to clarify differences in serum and plasma levels of total miRNAs and their time-course changes after blood collection.

Methods

Venous blood was collected from healthy men, and samples were prepared at the time points of 0, 15, 30, 60 and 180 min after blood collection for plasma and after clot formation for serum. Levels of total miRNAs were analyzed by the hybridization method using the 3D-Gene miRNA Oligo chip.

Results

About one third of 2632 miRNAs tested showed levels high enough for comparison of serum and plasma levels and for investigation of their time-course changes. Levels of 299 miRNAs at time 0 were significantly different in serum and plasma. Levels of representative platelet-derived miRNAs including miR-185-5p, -22-3p and -320b were significantly higher in plasma than in serum, while levels of representative erythrocyte-derived miRNAs including miR-451a, -486-5p and -92a-3p were not significantly different in serum and plasma. Plasma levels of 173 miRNAs and 6 miRNAs showed significant decreasing and increasing tendencies, respectively, while there were no miRNAs in serum that showed significant time-course changes.

Conclusion

The results suggest that careful attention should be paid when comparing serum and plasma levels of miRNAs and that plasma samples should be prepared early after blood collection.

An evolutionary learning-based method for identifying a circulating miRNA signature for breast cancer diagnosis prediction

Breast cancer (BC) is one of the most commonly diagnosed cancers worldwide. As key regulatory molecules in several biological processes, microRNAs (miRNAs) are potential biomarkers for cancer. Understanding the miRNA markers that can detect BC may improve survival rates and develop new targeted therapeutic strategies. To identify a circulating miRNA signature for diagnostic prediction in patients with BC, we developed an evolutionary learning-based method called BSig. BSig established a compact set of miRNAs as potential markers from 1280 patients with BC and 2686 healthy controls retrieved from the serum miRNA expression profiles for the diagnostic prediction. BSig demonstrated outstanding prediction performance, with an independent test accuracy and area under the receiver operating characteristic curve were 99.90% and 0.99, respectively. We identified 12 miRNAs, including hsa-miR-3185, hsa-miR-3648, hsa-miR-4530, hsa-miR-4763-5p, hsa-miR-5100, hsa-miR-5698, hsa-miR-6124, hsa-miR-6768-5p, hsa-miR-6800-5p, hsa-miR-6807-5p, hsa-miR-642a-3p, and hsa-miR-6836-3p, which significantly contributed towards diagnostic prediction in BC. Moreover, through bioinformatics analysis, this study identified 65 miRNA-target genes specific to BC cell lines. A comprehensive gene-set enrichment analysis was also performed to understand the underlying mechanisms of these target genes. BSig, a tool capable of BC detection and facilitating therapeutic selection, is publicly available at https://github.com/mingjutsai/BSig.

Deeply integrating latent consistent representations in high-noise multi-omics data for cancer subtyping

Cancer is a complex and high-mortality disease regulated by multiple factors. Accurate cancer subtyping is crucial for formulating personalized treatment plans and improving patient survival rates. The underlying mechanisms that drive cancer progression can be comprehensively understood by analyzing multi-omics data. However, the high noise levels in omics data often pose challenges in capturing consistent representations and adequately integrating their information. This paper proposed a novel variational autoencoder-based deep learning model, named Deeply Integrating Latent Consistent Representations (DILCR). Firstly, multiple independent variational autoencoders and contrastive loss functions were designed to separate noise from omics data and capture latent consistent representations. Subsequently, an Attention Deep Integration Network was proposed to integrate consistent representations across different omics levels effectively. Additionally, we introduced the Improved Deep Embedded Clustering algorithm to make integrated variable clustering friendly. The effectiveness of DILCR was evaluated using 10 typical cancer datasets from The Cancer Genome Atlas and compared with 14 state-of-the-art integration methods. The results demonstrated that DILCR effectively captures the consistent representations in omics data and outperforms other integration methods in cancer subtyping. In the Kidney Renal Clear Cell Carcinoma case study, cancer subtypes were identified by DILCR with significant biological significance and interpretability.

DiCleave: a deep learning model for predicting human Dicer cleavage sites

BACKGROUND

MicroRNAs (miRNAs) are a class of non-coding RNAs that play a pivotal role as gene expression regulators. These miRNAs are typically approximately 20 to 25 nucleotides long. The maturation of miRNAs requires Dicer cleavage at specific sites within the precursor miRNAs (pre-miRNAs). Recent advances in machine learning-based approaches for cleavage site prediction, such as PHDcleav and LBSizeCleav, have been reported. ReCGBM, a gradient boosting-based model, demonstrates superior performance compared with existing methods. Nonetheless, ReCGBM operates solely as a binary classifier despite the presence of two cleavage sites in a typical pre-miRNA. Previous approaches have focused on utilizing only a fraction of the structural information in pre-miRNAs, often overlooking comprehensive secondary structure information. There is a compelling need for the development of a novel model to address these limitations.

RESULTS

In this study, we developed a deep learning model for predicting the presence of a Dicer cleavage site within a pre-miRNA segment. This model was enhanced by an autoencoder that learned the secondary structure embeddings of pre-miRNA. Benchmarking experiments demonstrated that the performance of our model was comparable to that of ReCGBM in the binary classification tasks. In addition, our model excelled in multi-class classification tasks, making it a more versatile and practical solution than ReCGBM.

CONCLUSIONS

Our proposed model exhibited superior performance compared with the current state-of-the-art model, underscoring the effectiveness of a deep learning approach in predicting Dicer cleavage sites. Furthermore, our model could be trained using only sequence and secondary structure information. Its capacity to accommodate multi-class classification tasks has enhanced the practical utility of our model.

Effective exosomes in breast cancer: focusing on diagnosis and treatment of cancer progression

Breast cancer (BC) is the most prevalent aggressive malignant tumor in women worldwide and develops from breast tissue. Although cutting-edge treatment methods have been used and current mortality rates have decreased, BC control is still not satisfactory. Clarifying the underlying molecular mechanisms will help clinical options. Extracellular vesicles known as exosomes mediate cellular communication by delivering a variety of biomolecules, including proteins, oncogenes, oncomiRs, and even pharmacological substances. These transferable bioactive molecules can alter the transcriptome of target cells and affect signaling pathways that are related to tumors. Numerous studies have linked exosomes to BC biology, including therapeutic resistance and the local microenvironment. Exosomes' roles in tumor treatment resistance, invasion, and BC metastasis are the main topics of discussion in this review.

MicroRNA Expression Profile in Early-Stage Breast Cancers

BACKGROUND

Breast cancer is one of the leading causes of cancer deaths in women. Early diagnosis offers the best hope for a cure. Ductal carcinoma in situ is considered a precursor of invasive ductal carcinoma of the breast. In this study, we carried out microRNA sequencing from 7 ductal carcinoma in situ (DCIS), 6 infiltrating ductal carcinomas (IDC Stage IIA) with paired normal, and 5 unpaired normal breast tissue samples.

METHODS

We have deployed miRge for microRNA analysis, DESeq for differential expression analysis, and Cytoscape for competing endogenous RNA network investigation.

RESULTS

Here, we identified 76 miRNAs that were differentially expressed in DCIS and IDC. Additionally, we provide preliminary evidence of miR-365b-3p and miR-7-1-3p being overexpressed, and miR-6507-5p, miR-487b-3p, and miR-654-3p being downregulated in DCIS relative to normal breast tissue. We also identified a miRNA miR-766-3p that was overexpressed in earlystage IDCs. The overexpression of miR-301a-3p in DCIS and IDC was confirmed in 32 independent breast cancer tissue samples.

CONCLUSION

Higher expression of miR-301a-3p is associated with poor overall survival in The Cancer Genome Atlas Breast Cancer (TCGA-BRCA) dataset, indicating that it may be associated with DCIS at high risk of progressing to IDC and warrants deeper investigation.

Circulating small extracellular vesicles microRNAs plus CA-125 for treatment stratification in advanced ovarian cancer

BACKGROUND

No residual disease (R0 resection) after debulking surgery is the most critical independent prognostic factor for advanced ovarian cancer (AOC). There is an unmet clinical need for selecting primary or interval debulking surgery in AOC patients using existing prediction models.

METHODS

RNA sequencing of circulating small extracellular vesicles (sEVs) was used to discover the differential expression microRNAs (DEMs) profile between any residual disease (R0, n = 17) and no residual disease (non-R0, n = 20) in AOC patients. We further analyzed plasma samples of AOC patients collected before surgery or neoadjuvant chemotherapy via TaqMan qRT-PCR. The combined risk model of residual disease was developed by logistic regression analysis based on the discovery-validation sets.

RESULTS

Using a comprehensive plasma small extracellular vesicles (sEVs) microRNAs (miRNAs) profile in AOC, we identified and optimized a risk prediction model consisting of plasma sEVs-derived 4-miRNA and CA-125 with better performance in predicting R0 resection. Based on 360 clinical human samples, this model was constructed using least absolute shrinkage and selection operator (LASSO) and logistic regression analysis, and it has favorable calibration and discrimination ability (AUC:0.903; sensitivity:0.897; specificity:0.910; PPV:0.926; NPV:0.871). The quantitative evaluation of Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) suggested that the additional predictive power of the combined model was significantly improved contrasted with CA-125 or 4-miRNA alone (NRI = 0.471, IDI = 0.538, p < 0.001; NRI = 0.122, IDI = 0.185, p < 0.01).

CONCLUSION

Overall, we established a reliable, non-invasive, and objective detection method composed of circulating tumor-derived sEVs 4-miRNA plus CA-125 to preoperatively anticipate the high-risk AOC patients of residual disease to optimize clinical therapy.

An innovative charge-based extracellular vesicle isolation method for highly efficient extraction of EV-miRNAs from liquid samples: miRQuick

Extracellular vesicle-derived microRNAs (EV-miRNAs) are promising biomarkers for early cancer diagnosis. However, existing EV-miRNA extraction technologies have a complex two-step process that results in low extraction efficiency and inconsistent results. This study aimed to develop and evaluate a new single-step extraction method, called miRQuick, for efficient and high-recovery extraction of EV-miRNAs from samples. The miRQuick method involves adding positively charged substances to the sample, causing negatively charged EVs to quickly aggregate and precipitate. A membrane lysate is then added to extract only miRNA. The entire process can be completed within an hour using standard laboratory equipment. We validated the miRQuick method using various analytical techniques and compared its performance to other methods for plasma, urine and saliva samples. The miRQuick method demonstrated significantly higher performance than other methods, not only for blood plasma but also for urine and saliva samples. Furthermore, we successfully extracted and detected nine biomarker candidate miRNAs in the plasma of breast cancer patients using miRQuick. Our results demonstrate that miRQuick is a rapid and efficient method for EV-miRNA extraction with excellent repeatability, making it suitable for various applications including cancer diagnosis.

Human Metapneumovirus-Induced Host microRNA Expression Impairs the Interferon Response in Macrophages and Epithelial Cells

Human metapneumovirus (HMPV) is a nonsegmented, single-stranded negative RNA virus and a member of the Pneumoviridae family. During HMPV infection, macrophages play a critical role in defending the respiratory epithelium by secreting large amounts of type I interferon (IFN). MicroRNAs (miRNAs) are small, noncoding, single-stranded RNAs that play an essential role in regulating gene expression during normal cellular homeostasis and disease by binding to specific mRNAs, thereby regulating at the transcriptional and post-transcriptional levels with a direct impact on the immune response and other cellular processes. However, the role of miRNAs in macrophages and respiratory viral infections remains largely unknown. Here, we characterized the susceptibility of THP-1-derived macrophages to HMPV infection and the effect of hsa-miR-4634 on these cells. Transfection of an miRNA mimic and inhibitor demonstrated that hsa-miR-4634 regulates the IFN response in HMPV-infected macrophages, suggesting that HMPV induces the expression of the miRNA as a subversion mechanism of the antiviral response. This effect was not limited to macrophages, as a similar effect was also observed in epithelial cells. Overall, our results demonstrate that hsa-miR-4634 is an important factor in regulating the IFN response in macrophages and epithelial cells during HMPV infection.

The Diversity of Liquid Biopsies and Their Potential in Breast Cancer Management

Analyzing blood as a so-called liquid biopsy in breast cancer (BC) patients has the potential to adapt therapy management. Circulating tumor cells (CTCs), extracellular vesicles (EVs), cell-free DNA (cfDNA) and other blood components mirror the tumoral heterogeneity and could support a range of clinical decisions. Multi-cancer early detection tests utilizing blood are advancing but are not part of any clinical routine yet. Liquid biopsy analysis in the course of neoadjuvant therapy has potential for therapy (de)escalation.Minimal residual disease detection via serial cfDNA analysis is currently on its way. The prognostic value of blood analytes in early and metastatic BC is undisputable, but the value of these prognostic biomarkers for clinical management is controversial. An interventional trial confirmed a significant outcome benefit when therapy was changed in case of newly emerging cfDNA mutations under treatment and thus showed the clinical utility of cfDNA analysis for therapy monitoring. The analysis of PIK3CA or ESR1 variants in plasma of metastatic BC patients to prescribe targeted therapy with alpesilib or elacestrant has already arrived in clinical practice with FDA-approved tests available and is recommended by ASCO. The translation of more liquid biopsy applications into clinical practice is still pending due to a lack of knowledge of the analytes' biology, lack of standards and difficulties in proving clinical utility.

Circulating miRNAs signature on breast cancer: the MCC-Spain project

PURPOSE

To build models combining circulating microRNAs (miRNAs) able to identify women with breast cancer as well as different types of breast cancer, when comparing with controls without breast cancer.

METHOD

miRNAs analysis was performed in two phases: screening phase, with a total n = 40 (10 controls and 30 BC cases) analyzed by Next Generation Sequencing, and validation phase, which included 131 controls and 269 cases. For this second phase, the miRNAs were selected combining the screening phase results and a revision of the literature. They were quantified using RT-PCR. Models were built using logistic regression with LASSO penalization.

RESULTS

The model for all cases included seven miRNAs (miR-423-3p, miR-139-5p, miR-324-5p, miR-1299, miR-101-3p, miR-186-5p and miR-29a-3p); which had an area under the ROC curve of 0.73. The model for cases diagnosed via screening only took in one miRNA (miR-101-3p); the area under the ROC curve was 0.63. The model for disease-free cases in the follow-up had five miRNAs (miR-101-3p, miR-186-5p, miR-423-3p, miR-142-3p and miR-1299) and the area under the ROC curve was 0.73. Finally, the model for cases with active disease in the follow-up contained six miRNAs (miR-101-3p, miR-423-3p, miR-139-5p, miR-1307-3p, miR-331-3p and miR-21-3p) and its area under the ROC curve was 0.82.

CONCLUSION

We present four models involving eleven miRNAs to differentiate healthy controls from different types of BC cases. Our models scarcely overlap with those previously reported.

Efficient plasma metabolic fingerprinting as a novel tool for diagnosis and prognosis of gastric cancer: a large-scale, multicentre study

OBJECTIVE

Metabolic biomarkers are expected to decode the phenotype of gastric cancer (GC) and lead to high-performance blood tests towards GC diagnosis and prognosis. We attempted to develop diagnostic and prognostic models for GC based on plasma metabolic information.

DESIGN

We conducted a large-scale, multicentre study comprising 1944 participants from 7 centres in retrospective cohort and 264 participants in prospective cohort. Discovery and verification phases of diagnostic and prognostic models were conducted in retrospective cohort through machine learning and Cox regression of plasma metabolic fingerprints (PMFs) obtained by nanoparticle-enhanced laser desorption/ionisation-mass spectrometry (NPELDI-MS). Furthermore, the developed diagnostic model was validated in prospective cohort by both NPELDI-MS and ultra-performance liquid chromatography-MS (UPLC-MS).

RESULTS

We demonstrated the high throughput, desirable reproducibility and limited centre-specific effects of PMFs obtained through NPELDI-MS. In retrospective cohort, we achieved diagnostic performance with areas under curves (AUCs) of 0.862-0.988 in the discovery (n=1157 from 5 centres) and independent external verification dataset (n=787 from another 2 centres), through 5 different machine learning of PMFs, including neural network, ridge regression, lasso regression, support vector machine and random forest. Further, a metabolic panel consisting of 21 metabolites was constructed and identified for GC diagnosis with AUCs of 0.921-0.971 and 0.907-0.940 in the discovery and verification dataset, respectively. In the prospective study (n=264 from lead centre), both NPELDI-MS and UPLC-MS were applied to detect and validate the metabolic panel, and the diagnostic AUCs were 0.855-0.918 and 0.856-0.916, respectively. Moreover, we constructed a prognosis scoring system for GC in retrospective cohort, which can effectively predict the survival of GC patients.

CONCLUSION

We developed and validated diagnostic and prognostic models for GC, which also contribute to advanced metabolic analysis towards diseases, including but not limited to GC.

Application value of miRNA-182 as a biomarker for cancer diagnosis: a systematic review with meta-analysis

Aim: This study aims to establish the potential reliability and validity of miRNA-182 as a diagnostic tool in oncology, and hence to contribute to the decision-making process in clinical settings. Materials & methods: To further evaluate the role of miRNA-182 as a cancer biomarker, we conducted a search of the PubMed, Cochrane Library, Wanfang and China National Knowledge Infrastructure databases of existing literature. Conclusion: These results suggest that miRNA-182 could function as a potential molecular marker for cancer detection and diagnosis. The effect of miRNA-182 on tumor development should be further studied to confirm these results and add to the current understanding of its role in cancer.

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.

Predictive miRNAs Patterns in Blood of Breast Cancer Patients Demonstrating Resistance Towards Neoadjuvant Chemotherapy

Objective

The effect of chemotherapy in patients with breast cancer (BC) is uncertain. This study attempted to analyze serum microRNAs (miRNAs) in NAC resistant and sensitive BC patients and develop a miRNA-based nomogram model. To further help clinicians make treatment decisions for hormone receptor-positive patients.

Methods

A total of 110 BC patients with NAC were recruited and assigned in sensitive and resistant group, and 4 sensitive patients and 3 resistant patients were subjected to high-throughput sequencing. The functions of their target genes were analyzed by GO and KEGG. Five BC-related reported miRNAs were selected for expression pattern measurement by RT-qPCR and multivariate logistic analysis. The nomogram model was developed using R 4.0.1, and its predictive efficacy, consistency and clinical application value in development and validation groups were evaluated using ROC, calibration and decision curves.

Results

There were 44 differentially-expressed miRNAs in resistant BC patients. miR-3646, miR-4741, miR-6730-3p, miR-6831-5p and miR-8485 were candidate for resistance diagnosis in BC. Logistic multiple regression analysis showed that miR-4741 (or = 0.30, 95% CI = 0.08-0.63, P = 0.02) and miR-6831-5p (or = 0.48, 95% CI = 0.24-0.78, P = 0.01) were protective factors of BC resistance. The ROC curves showed a sensitivity of 0.884 and 0.750 for miR-4741 and miR-6831-5P as markers of resistance, suggesting that they can be used as independent risk factors for BC resistance. The other 3 miRNAs can be used as calibration factors to establish the risk prediction model of resistance in BC. In risk model, the prediction accuracy of resistance of BC is about 78%. 5-miRNA signature diagnostic models can help clinicians provide personalized treatment for NAC resistance BC patients to improve patient survival.

Conclusion

MiR-4741 and miR-6831-5p are independent risk factors for breast cancer resistance. This study constructed a nomogram model of NAC resistance in BC based on 5 differentially-expressed serum miRNAs.

Artificial intelligence-driven pan-cancer analysis reveals miRNA signatures for cancer stage prediction

The ability to detect cancer at an early stage in patients who would benefit from effective therapy is a key factor in increasing survivability. This work proposes an evolutionary supervised learning method called CancerSig to identify cancer stage-specific microRNA (miRNA) signatures for early cancer predictions. CancerSig established a compact panel of miRNA signatures as potential markers from 4,667 patients with 15 different types of cancers for the cancer stage prediction, and achieved a mean performance: 10-fold cross-validation accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of 84.27% ± 6.31%, 0.81 ± 0.12, 0.80 ± 0.10, and 0.80 ± 0.06, respectively. The pan-cancer analysis of miRNA signatures suggested that three miRNAs, hsa-let-7i-3p, hsa-miR-362-3p, and hsa-miR-3651, contributed significantly toward stage prediction across 8 cancers, and each of the 67 miRNAs of the panel was a biomarker of stage prediction in more than one cancer. CancerSig may serve as the basis for cancer screening and therapeutic selection..

Review deciphering potent therapeutic approaches targeting Notch signaling pathway in breast cancer

In the current period of drug development, natural products have provided an unrivaled supply of anticancer medications. By modifying the cancer microenvironment and various signaling pathways, natural products and their derivatives and analogs play a significant role in cancer treatment. These substances are effective against several signaling pathways, particularly the cell death pathways (apoptosis and autophagy) and embryonic developmental pathways (Notch, Wnt, and Hedgehog pathways). Natural products have a long history, but more research is needed to understand their current function in the research and development of cancer treatments and the potential for natural products to serve as a significant source of therapeutic agents in the future. Several target-specific anticancer medications failed to treat cancer, necessitating research into natural compounds with multiple target properties. To help develop a better treatment plan for managing breast cancer, this review has outlined the anticancerous potential of several therapeutic approaches targeting the notch signaling system in breast tumors.

MicroRNA Dysregulation in Early Breast Cancer Diagnosis: A Systematic Review and Meta-Analysis

Breast cancer continues to be the leading cause of death in women worldwide. Mammography, which is the current gold standard technique used to diagnose it, presents strong limitations in early ages where breast cancer is much more aggressive and fatal. MiRNAs present in numerous body fluids might represent a new line of research in breast cancer biomarkers, especially oncomiRNAs, known to play an important role in the suppression and development of neoplasms. The aim of this systematic review and meta-analysis was to evaluate dysregulated miRNA biomarkers and their diagnostic accuracy in breast cancer. Two independent researchers reviewed the included studies according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. A protocol for this review was registered in PROSPERO with the registration number "CRD42021256338". Observational case-control-based studies analyzing concentrations of microRNAs which have been published within the last 10 years were selected, and the concentrations of miRNAs in women with breast cancer and healthy controls were analyzed. Random-effects meta-analyses of miR-155 were performed on the studies which provided enough data to calculate diagnostic odds ratios. We determined that 34 microRNAs were substantially dysregulated and could be considered biomarkers of breast cancer. Individually, miR-155 provided better diagnostic results than mammography on average. However, when several miRNAs are used to screen, forming a panel, sensitivity and specificity rates improve, and they can be associated with classic biomarkers such us CA-125 or CEA. Based on the results of our meta-analysis, miR-155 might be a promising diagnostic biomarker for this patient population.

Adaptive Sparse Multi-Block PLS Discriminant Analysis: An Integrative Method for Identifying Key Biomarkers from Multi-Omics Data

With the growing use of high-throughput technologies, multi-omics data containing various types of high-dimensional omics data is increasingly being generated to explore the association between the molecular mechanism of the host and diseases. In this study, we present an adaptive sparse multi-block partial least square discriminant analysis (asmbPLS-DA), an extension of our previous work, asmbPLS. This integrative approach identifies the most relevant features across different types of omics data while discriminating multiple disease outcome groups. We used simulation data with various scenarios and a real dataset from the TCGA project to demonstrate that asmbPLS-DA can identify key biomarkers from each type of omics data with better biological relevance than existing competitive methods. Moreover, asmbPLS-DA showed comparable performance in the classification of subjects in terms of disease status or phenotypes using integrated multi-omics molecular profiles, especially when combined with other classification algorithms, such as linear discriminant analysis and random forest. We have made the R package called asmbPLS that implements this method publicly available on GitHub. Overall, asmbPLS-DA achieved competitive performance in terms of feature selection and classification. We believe that asmbPLS-DA can be a valuable tool for multi-omics research.

Extracellular Vesicles in Breast Cancer: From Biology and Function to Clinical Diagnosis and Therapeutic Management

Breast cancer (BC) is the first worldwide most frequent cancer in both sexes and the most commonly diagnosed in females. Although BC mortality has been thoroughly declining over the past decades, there are still considerable differences between women diagnosed with early BC and when metastatic BC is diagnosed. BC treatment choice is widely dependent on precise histological and molecular characterization. However, recurrence or distant metastasis still occurs even with the most recent efficient therapies. Thus, a better understanding of the different factors underlying tumor escape is mainly mandatory. Among the leading candidates is the continuous interplay between tumor cells and their microenvironment, where extracellular vesicles play a significant role. Among extracellular vesicles, smaller ones, also called exosomes, can carry biomolecules, such as lipids, proteins, and nucleic acids, and generate signal transmission through an intercellular transfer of their content. This mechanism allows tumor cells to recruit and modify the adjacent and systemic microenvironment to support further invasion and dissemination. By reciprocity, stromal cells can also use exosomes to profoundly modify tumor cell behavior. This review intends to cover the most recent literature on the role of extracellular vesicle production in normal and cancerous breast tissues. Specific attention is paid to the use of extracellular vesicles for early BC diagnosis, follow-up, and prognosis because exosomes are actually under the spotlight of researchers as a high-potential source of liquid biopsies. Extracellular vesicles in BC treatment as new targets for therapy or efficient nanovectors to drive drug delivery are also summarized.

Mucins as Potential Biomarkers for Early Detection of Cancer

Early detection significantly correlates with improved survival in cancer patients. So far, a limited number of biomarkers have been validated to diagnose cancers at an early stage. Considering the leading cancer types that contribute to more than 50% of deaths in the USA, we discuss the ongoing endeavors toward early detection of lung, breast, ovarian, colon, prostate, liver, and pancreatic cancers to highlight the significance of mucin glycoproteins in cancer diagnosis. As mucin deregulation is one of the earliest events in most epithelial malignancies following oncogenic transformation, these high-molecular-weight glycoproteins are considered potential candidates for biomarker development. The diagnostic potential of mucins is mainly attributed to their deregulated expression, altered glycosylation, splicing, and ability to induce autoantibodies. Secretory and shed mucins are commonly detected in patients' sera, body fluids, and tumor biopsies. For instance, CA125, also called MUC16, is one of the biomarkers implemented for the diagnosis of ovarian cancer and is currently being investigated for other malignancies. Similarly, MUC5AC, a secretory mucin, is a potential biomarker for pancreatic cancer. Moreover, anti-mucin autoantibodies and mucin-packaged exosomes have opened new avenues of biomarker development for early cancer diagnosis. In this review, we discuss the diagnostic potential of mucins in epithelial cancers and provide evidence and a rationale for developing a mucin-based biomarker panel for early cancer detection.

DNA Droplets: Intelligent, Dynamic Fluid

Breathtaking advances in DNA nanotechnology have established DNA as a promising biomaterial for the fabrication of programmable higher-order nano/microstructures. In the context of developing artificial cells and tissues, DNA droplets have emerged as a powerful platform for creating intelligent, dynamic cell-like machinery. DNA droplets are a microscale membrane-free coacervate of DNA formed through phase separation. This new type of DNA system couples dynamic fluid-like property with long-established DNA programmability. This hybrid nature offers an advantageous route to facile and robust control over the structures, functions, and behaviors of DNA droplets. This review begins by describing programmable DNA condensation, commenting on the physical properties and fabrication strategies of DNA hydrogels and droplets. By presenting an overview of the development pathways leading to DNA droplets, it is shown that DNA technology has evolved from static, rigid systems to soft, dynamic systems. Next, the basic characteristics of DNA droplets are described as intelligent, dynamic fluid by showcasing the latest examples highlighting their distinctive features related to sequence-specific interactions and programmable mechanical properties. Finally, this review discusses the potential and challenges of numerical modeling able to connect a robust link between individual sequences and macroscopic mechanical properties of DNA droplets.

An Epidemiological Systematic Review with Meta-Analysis on Biomarker Role of Circulating MicroRNAs in Breast Cancer Incidence

Breast cancer (BC) is a multifactorial disease caused by an interaction between genetic predisposition and environmental exposures. MicroRNAs are a group of small non-coding RNA molecules, which seem to have a role either as tumor suppressor genes or oncogenes and seem to be related to cancer risk factors. We conducted a systematic review and meta-analysis to identify circulating microRNAs related to BC diagnosis, paying special attention to methodological problems in this research field. A meta-analysis was performed for microRNAs analyzed in at least three independent studies where sufficient data to make analysis were presented. Seventy-five studies were included in the systematic review. A meta-analysis was performed for microRNAs analyzed in at least three independent studies where sufficient data to make analysis were presented. Seven studies were included in the MIR21 and MIR155 meta-analysis, while four studies were included in the MIR10b metanalysis. The pooled sensitivity and specificity of MIR21 for BC diagnosis were 0.86 (95%CI 0.76-0.93) and 0.84 (95%CI 0.71-0.92), 0.83 (95%CI 0.72-0.91) and 0.90 (95%CI 0.69-0.97) for MIR155, and 0.56 (95%CI 0.32-0.71) and 0.95 (95%CI 0.88-0.98) for MIR10b, respectively. Several other microRNAs were found to be dysregulated, distinguishing BC patients from healthy controls. However, there was little consistency between included studies, making it difficult to identify specific microRNAs useful for diagnosis.

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.

A review of prognostic and predictive biomarkers in breast cancer

Breast cancer (BC) is a common cancer all over the world that affects women. BC is one of the leading causes of cancer mortality in women, which today has decreased with the advancement of technology and new diagnostic and therapeutic methods. BCs are histologically divided into in situ and invasive carcinoma, and both of them can be divided into ductal and lobular. The main function after the diagnosis of invasive breast cancer is which patient should use chemotherapy, which patient should receive adjuvant therapy, and which should not. If the decision is for adjuvant therapy, the next challenge is to identify the most appropriate treatment or combination of treatments for a particular patient. Addressing the first challenge can be helped by prognostic biomarkers, while addressing the second challenge can be done by predictive biomarkers. Among the molecular markers related to BC, ER, PR, HER2, and the Mib1/Ki-67 proliferation index are the most significant ones and are tightly confirmed in the standard care of all primary, recurrent, and metastatic BC patients. CEA and CA-15-3 antigens are the most valuable markers of serum tumors in BC patients. Determining the series of these markers helps monitor response to the treatment and early detection of recurrence or metastasis. miRNAs have been demonstrated to be intricate in mammary gland growth, proliferation, and formation of BC known to be incriminated in BC biology. By combining established prognostic factors with valid prognostic/predicted biomarkers, we can start the journey to personalized treatment for every recently diagnosed BC patient.

Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning

Differentiating cancer subtypes is crucial to guide personalized treatment and improve the prognosis for patients. Integrating multi-omics data can offer a comprehensive landscape of cancer biological process and provide promising ways for cancer diagnosis and treatment. Taking the heterogeneity of different omics data types into account, we propose a hierarchical multi-kernel learning (hMKL) approach, a novel cancer molecular subtyping method to identify cancer subtypes by adopting a two-stage kernel learning strategy. In stage 1, we obtain a composite kernel borrowing the cancer integration via multi-kernel learning (CIMLR) idea by optimizing the kernel parameters for individual omics data type. In stage 2, we obtain a final fused kernel through a weighted linear combination of individual kernels learned from stage 1 using an unsupervised multiple kernel learning method. Based on the final fusion kernel, k-means clustering is applied to identify cancer subtypes. Simulation studies show that hMKL outperforms the one-stage CIMLR method when there is data heterogeneity. hMKL can estimate the number of clusters correctly, which is the key challenge in subtyping. Application to two real data sets shows that hMKL identified meaningful subtypes and key cancer-associated biomarkers. The proposed method provides a novel toolkit for heterogeneous multi-omics data integration and cancer subtypes identification.

Breast cancer tumor microenvironment affects Treg/IL-17-producing Treg/Th17 cell axis: Molecular and therapeutic perspectives

The tumor microenvironment (TME) comprises a variety of immune cells, among which T cells exert a prominent axial role in tumor development or anti-tumor responses in patients with breast cancer (BC). High or low levels of anti-inflammatory cytokines, such as transforming growth factor β, in the absence or presence of proinflammatory cytokines, such as interleukin-6 (IL-6), delineate the fate of T cells toward either regulatory T (Treg) or T helper 17 (Th17) cells, respectively. The transitional state of RORγt⁺Foxp3⁺ Treg (IL-17-producing Treg) resides in the middle of this reciprocal polarization, which is known as Treg/IL-17-producing Treg/Th17 cell axis. TME secretome, including microRNAs, cytokines, and extracellular vesicles, can significantly affect this axis. Furthermore, immune checkpoint inhibitors may be used to reconstruct immune cells; however, some of these novel therapies may favor tumor development. Therefore, understanding secretory and cell-associated factors involved in their differentiation or polarization and functions may be targeted for BC management. This review discusses microRNAs, cytokines, and extracellular vesicles (as secretome), as well as transcription factors and immune checkpoints (as cell-associated factors), which influence the Treg/IL-17-producing Treg/Th17 cell axis in BC. Furthermore, approved or ongoing clinical trials related to the modulation of this axis in the TME of BC are described to broaden new horizons of promising therapeutic approaches.

Machine learning in the development of targeting microRNAs in human disease

A microRNA is a small, single-stranded, non-coding ribonucleic acid that plays a crucial role in RNA silencing and can regulate gene expression. With the in-depth study of miRNA in development and disease, miRNA has become an attractive target for novel therapeutic strategies. Exploring miRNA targeting therapy only through experiments is expensive and laborious, so it is essential to develop novel and efficient computational methods to narrow down the search. Recent advances in machine learning applied in biomedical informatics provide opportunities to explore miRNA-targeting drugs, thus promoting miRNA therapeutics. This review provides an overview of recent advancements in miRNA targeting therapeutic using machine learning. First, we mainly describe the basics of predicting miRNA targeting drugs, including pharmacogenomic data resources and data preprocessing. Then we present primary machine learning algorithms and elaborate their application in discovering relationships among miRNAs, drugs, and diseases. Along with the progress of miRNA targeting therapeutics, we finally analyze and discuss the current challenges and opportunities that machine learning confronts.

Tumor Suppressive Role of MicroRNAs in Triple Negative Breast Cancer

Triple-negative breast cancers are highly aggressive, a heterogeneous form of breast cancer with a high re-occurrence rate that further lacks an efficient treatment strategy and prognostic marker. The tumor microenvironment of the disease comprises cancer-associated fibroblasts, cancer stem cells, immunological molecules, epithelial-mesenchymal transition, and a metastatic microenvironment that contributes to disease progression and metastasis to distant sites. Emerging evidence indicated that miRNA clusters would be of clinical utility as they exert an oncogenic or tumor suppressor role in TNBC. The present review article aims to highlight the therapeutic significance of miRNA in targeting the above-mentioned signaling cascades and modulating the intracellular crosstalk in the tumor microenvironment of TNBC. Prognostic implications of miRNAs to depict disease-free survival, distant metastasis-free survival, relapse-free survival, and overall survival outcome were also unveiled.

The Diagnostic Power of Circulating miR-1246 in Screening Cancer: An Updated Meta-analysis

Background

MicroRNA-1246 (miR-1246), an oncomiR that regulates the expression of multiple cancer-related genes, has been attracted and studied as a promising indicator of various tumors. However, diverse conclusions on diagnostic accuracy have been shown due to the small sample size and limited studies included. This meta-analysis is aimed at systematically assessing the performance of extracellular circulating miR-1246 in screening common cancers.

Methods

We searched the PubMed/MEDLINE, Web of Science, Cochrane Library, and Google Scholar databases for relevant studies until November 28, 2022. Then, the summary receiver operating characteristic (SROC) curves were drawn and calculated area under the curve (AUC), diagnostic odds ratio (DOR), sensitivity, and specificity values of circulating miR-1246 in the cancer surveillance.

Results

After selection and quality assessment, 29 eligible studies with 5914 samples (3232 cases and 2682 controls) enrolled in the final analysis. The pooled AUC, DOR, sensitivity, and specificity of circulating miR-1246 in screening cancers were 0.885 (95% confidence interval (CI): 0.827-0.892), 27.7 (95% CI: 17.1-45.0), 84.2% (95% CI: 79.4-88.1), and 85.3% (95% CI: 80.5-89.2), respectively. Among cancer types, superior performance was noted for breast cancer (AUC = 0.950, DOR = 98.5) compared to colorectal cancer (AUC = 0.905, DOR = 47.6), esophageal squamous cell carcinoma (AUC = 0.757, DOR = 8.0), hepatocellular carcinoma (AUC = 0.872, DOR = 18.6), pancreatic cancer (AUC = 0.767, DOR = 12.3), and others (AUC = 0.887, DOR = 27.5, P = 0.007). No significant publication bias in DOR was observed in the meta-analysis (funnel plot asymmetry test with P = 0.652; skewness value = 0.672, P = 0.071).

Conclusion

Extracellular circulating miR-1246 may serve as a reliable biomarker with good sensitivity and specificity in screening cancers, especially breast cancer.

Identification of serum microRNAs as potential diagnostic biomarkers for detecting precancerous lesions of gastric cancer

Aim

Gastric mucosal changes associated with chronic gastritis are known to be precancerous lesions of gastric cancer. We aimed to identify individuals with a high risk of gastric cancer by detection of microRNAs (miRNA) in the blood as biomarkers.

Methods

Of 1206 individuals screened, 144 who were positive for Helicobacter pylori (H. pylori) by the serum antibody test and who underwent endoscopy were the subjects of this study. For the gross assessment of mucosal inflammation, we applied the Kimura-Takemoto classification, in which normal mucosa was defined as grade 0, and atrophy was categorized as grade 1 (C-1 and C-2), grade 2 (C-3 and O-1), and grade 3 (O-2 and O-3). Serum samples were divided into two phases and used for miRNA microarray profiling. We compared the expression of miRNAs in grade 3 mucosa and other grades. Expression in gastric cancer was confirmed with TCGA data.

Results

miR-196b-3p was significantly upregulated, and miR-92a-2-5p was downregulated (P < .05 and q < 0.2). TCGA data showed a high expression of miR-196b-3p in gastric cancer cases (P < .001). Comparing grade 3 and the others, the area under the receiver operating characteristic curve using the detected miRNAs was as high as about 0.7. Furthermore, the combination of miRNAs resulted in higher accuracy. In terms of the significance of the combinatory mRNAs, the combination of three miRNAs (miR-196b-3p, miR-92a-2-5p, and miR-6791-3p) revealed high sensitivity and specificity, with the area under the curve exceeding 0.8.

Conclusion

The identified combinatory miRNAs may represent promising biomarkers of precancerous lesions in gastric cancer.

Identification of the Minimum Combination of Serum microRNAs to Predict the Recurrence of Colorectal Cancer Cases

BACKGROUND

Serum microRNAs (miRNAs) have been recognized as potential stable biomarkers for various types of cancer. Considering the clinical applications, there are certain critical requirements, such as minimizing the number of miRNAs, reproducibility in a longitudinal clinical course, and superiority to conventional tumor markers, such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9. This study aimed to identify serum miRNAs that indicate the recurrence of colorectal cancer (CRC), surpassing inter-tumor heterogeneity.

METHODS

We conducted an analysis of 434 serum samples from 91 patients with CRC and 71 healthy subjects. miRNAs were obtained from Toray Co., Ltd, and miRNA profiles were analyzed using a three-step approach. miRNAs that were highly expressed in patients with CRC than in the healthy controls in the screening phase, and those that were highly expressed in the preoperative samples than in the 1-month postoperative samples in the discovery phase, were extracted. In the validation phase, the extracted miRNAs were evaluated in 323 perioperative samples, in chronological order.

RESULTS

A total of 12 miRNAs (miR-25-3p, miR-451a, miR-1246, miR-1268b, miR-2392, miR-4480, miR-4648, miR-4732-5p, miR-4736, miR-6131, miR-6776-5p, and miR-6851-5p) were significantly concordant with the clinical findings of tumor recurrence, however their ability to function as biomarkers was comparable with CEA. In contrast, the combination of miR-1246, miR-1268b, and miR-4648 demonstrated a higher area under the curve (AUC) than CEA. These three miRNAs were upregulated in primary CRC tissues.

CONCLUSION

We identified ideal combinatorial miRNAs to predict CRC recurrence.

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.

Establishment of preanalytical conditions for microRNA profile analysis of clinical plasma samples

The relationship between the expression of microRNAs (miRNAs) in blood and a variety of diseases has been investigated. MiRNA-based liquid biopsy has attracted much attention, and cancer-specific miRNAs have been reported. However, the results of analyses of the expression of these miRNAs vary among studies. The reproduction of results regarding miRNA expression levels could be difficult if there are differences in the data acquisition process. Previous studies have shown that the anticoagulant type used during plasma preparation and sample storage conditions could contribute to differences in measured miRNA levels. Thus, the impact of these preanalytical conditions on comprehensive miRNA expression profiles was examined. First, the miRNA expression profiles of samples obtained from healthy volunteers were analyzed using next-generation sequencing. Based on an analysis of the library concentration, human genome identification rate, ratio of unique sequences and expression profiles, the optimal preanalytical conditions for obtaining highly reproducible miRNA expression profiles were established. The optimal preanalytical conditions were as follows: ethylenediaminetetraacetic acid (EDTA) as the anticoagulant, whole-blood storage at room temperature within 6 hours, and plasma storage at 4°C or -20°C within 30 days. Next, plasma samples were collected from 60 cancer patients (3 facilities × 20 patients/facility), and miRNA expression profiles were analyzed. There were no significant differences in measurements except in the expression of erythrocyte-derived hsa-miR-451a. However, the variation in hsa-miR-451a levels was smaller among facilities than among individuals. This finding suggests that samples obtained from the same facility could show significantly different degrees of hemolysis across individuals. We found that the standardization of anticoagulant use and storage conditions contributed to reducing the variation in sample quality across facilities. The findings from this study could be useful in developing protocols for collecting samples from multiple facilities for cancer screening tests.

Tumor-Derived Small Extracellular Vesicles Involved in Breast Cancer Progression and Drug Resistance

Breast cancer is one of the most serious and terrifying threats to the health of women. Recent studies have demonstrated that interaction among cancer cells themselves and those with other cells, including immune cells, in a tumor microenvironment potentially and intrinsically regulate and determine cancer progression and metastasis. Small extracellular vesicles (sEVs), a type of lipid-bilayer particles derived from cells, with a size of less than 200 nm, are recognized as one form of important mediators in cell-to-cell communication. sEVs can transport a variety of bioactive substances, including proteins, RNAs, and lipids. Accumulating evidence has revealed that sEVs play a crucial role in cancer development and progression, with a significant impact on proliferation, invasion, and metastasis. In addition, sEVs systematically coordinate physiological and pathological processes, such as coagulation, vascular leakage, and stromal cell reprogramming, to bring about premetastatic niche formation and to determine metastatic organ tropism. There are a variety of oncogenic factors in tumor-derived sEVs that mediate cellular communication between local stromal cells and distal microenvironment, both of which are important in cancer progression and metastasis. Tumor-derived sEVs contain substances that are similar to parental tumor cells, and as such, sEVs could be biomarkers in cancer progression and potential therapeutic targets, particularly for predicting and preventing future metastatic development. Here, we review the mechanisms underlying the regulation by tumor-derived sEVs on cancer development and progression, including proliferation, metastasis, drug resistance, and immunosuppression, which coordinately shape the pro-metastatic microenvironment. In addition, we describe the application of sEVs to the development of cancer biomarkers and potential therapeutic modalities and discuss how they can be engineered and translated into clinical practice.

Cancer Biomarkers in Liquid Biopsy for Early Detection of Breast Cancer: A Systematic Review

Background:

Breast cancer (BC) is the most common neoplasm in women worldwide. Liquid biopsy (LB) is a non-invasive diagnostic technique that allows the analysis of biomarkers in different body fluids, particularly in peripheral blood and also in urine, saliva, nipple discharge, volatile respiratory fluids, nasal secretions, breast milk, and tears. The objective was to analyze the available evidence related to the use of biomarkers obtained by LB for the early diagnosis of BC.

Methods:

Articles related to the use of biomarkers for the early diagnosis of BC due to LB, published between 2010 and 2022, from the databases (WoS, EMBASE, PubMed, and SCOPUS) were included. The MInCir diagnostic scale was applied in the articles to determine their methodological quality (MQ). Descriptive statistics were used, as well as determination of weighted averages of each variable, to analyze the extracted data. Sensitivity, specificity, and area under the curve values for specific biomarkers (individual or in panels) are described.

Results:

In this systematic review (SR), 136 articles met the selection criteria, representing 17 709 patients with BC. However, 95.6% were case-control studies. In 96.3% of cases, LB was performed in peripheral blood samples. Most of the articles were based on microRNA (miRNA) analysis. The mean MQ score was 25/45 points. Sensitivity, specificity, and area under the curve values for specific biomarkers (individual or in panels) have been found.

Conclusions:

The determination of biomarkers through LB is a useful mechanism for the diagnosis of BC. The analysis of miRNA in peripheral blood is the most studied methodology. Our results indicate that LB has a high sensitivity and specificity for the diagnosis of BC, especially in early stages.