Integrated extracellular microRNA profiling for ovarian cancer screening

Two-Dimensional Field-Effect Transistor Sensors: The Road toward Commercialization

The evolutionary success in information technology has been sustained by the rapid growth of sensor technology. Recently, advances in sensor technology have promoted the ambitious requirement to build intelligent systems that can be controlled by external stimuli along with independent operation, adaptivity, and low energy expenditure. Among various sensing techniques, field-effect transistors (FETs) with channels made of two-dimensional (2D) materials attract increasing attention for advantages such as label-free detection, fast response, easy operation, and capability of integration. With atomic thickness, 2D materials restrict the carrier flow within the material surface and expose it directly to the external environment, leading to efficient signal acquisition and conversion. This review summarizes the latest advances of 2D-materials-based FET (2D FET) sensors in a comprehensive manner that contains the material, operating principles, fabrication technologies, proof-of-concept applications, and prototypes. First, a brief description of the background and fundamentals is provided. The subsequent contents summarize physical, chemical, and biological 2D FET sensors and their applications. Then, we highlight the challenges of their commercialization and discuss corresponding solution techniques. The following section presents a systematic survey of recent progress in developing commercial prototypes. Lastly, we summarize the long-standing efforts and prospective future development of 2D FET-based sensing systems toward commercialization.

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.

Comprehensive analysis of competitive endogenous RNAs network: Identification and validation of prediction model composed of mRNA signature and miRNA signature in gastric cancer

Gastric cancer (GC), one of the most lethal malignant tumors, is highly aggressive with a poor prognosis, while the molecular mechanisms underlying it remain largely unknown. Although advanced imaging techniques and comprehensive treatment facilitate the diagnosis and survival of some GC patients, the precise diagnosis and prognosis are still a challenge. The present study used publicly available gene expression profiles from The Cancer Genome Atlas and Gene Expression Omnibus datasets including mRNA, micro (mi)RNA and circular (circ)RNA of GC to establish a competing endogenous RNA network (ceRNA). Further, the present study performed least absolute shrinkage and selector operator regression analysis on the hub RNAs to establish a prediction model with mRNA and miRNA. The ceRNA network contained 109 edges and 56 nodes and the visible network contains 13 miRNAs, 9 circRNAs and 34 mRNAs. The five mRNA-based signature were CTF1, FKBP5, RNF128, GSTM2 and ADAMTS1. The area under curve (AUC) value of the diagnosis training cohort was 0.9975. The prognosis of the high-risk group (RiskScore >4.664) was worse compared with that of the low-risk group (RiskScore ≤4.664; P<0.05) in the training cohort. The five miRNA-based signature were miR-145-5p, miR-615-3p, miR-6507-5p, miR-937-3p and miR-99a-3p. The AUC value of the diagnosis training cohort was 0.9975. The prognosis of the high-risk group (RiskScore >1.621) was worse compared with that of the low-risk group (RiskScore ≤1.621; P<0.05) in the training cohort. The validation cohorts indicated that both five mRNA and five miRNA-based signatures had strong predictive power in diagnosis and prognosis for GC. In conclusion, a ceRNA network was established for GC and a five mRNA-based signature and a five miRNA-based signature was identified that enabled diagnosis and prognosis of GC by assigning patient to a high-risk group or low-risk group.

A Novel Blood-Based microRNA Diagnostic Model with High Accuracy for Multi-Cancer Early Detection

Early detection is critical to reduce cancer deaths as treating early stage cancers is more likely to be successful. However, patients with early stage diseases are often asymptomatic and thus less likely to be diagnosed. Here, we utilized four microarray datasets with a standardized platform to investigate comprehensive microRNA expression profiles from 7536 serum samples. A 4-miRNA diagnostic model was developed from the lung cancer training set (n = 416, 208 lung cancer patients and 208 non-cancer participants). The model showed 99% sensitivity and specificity in the lung cancer validation set (n = 3328, 1358 cancer patients and 1970 non-cancer participants); and the sensitivity remained to be >99% for patients with stage 1 disease. When applied to the additional combined dataset of 3792 participants including 2038 cancer patients across 12 different cancer types and 1754 independent non-cancer controls, the model demonstrated high sensitivities ranging from 83.2 to 100% for biliary tract, bladder, colorectal, esophageal, gastric, glioma, liver, pancreatic, and prostate cancers, and showed reasonable sensitivities of 68.2 and 72.0% for ovarian cancer and sarcoma, respectively, while maintaining 99.3% specificity. Our study provided a proof-of-concept data in demonstrating that the 4-miRNA model has the potential to be developed into a simple, inexpensive and noninvasive blood test for early detection of multiple cancers with high accuracy.

Establishment and Comprehensive Analysis of Underlying microRNA-mRNA Interactive Networks in Ovarian Cancer

Background

The rate of ovarian cancer (OC) is one of the highest in women's reproductive systems. An improperly expressed microRNA (miRNA) has been discovered to have a vital role in the pathophysiology of OC. However, more research into OC's miRNA-message RNA (mRNA) gene interaction network is required.

Methods

Firstly, the microarray data sets GSE25405 and GSE119055 from the GEO (Gene Expression Omnibus) database were downloaded and then analyzed with the GEO2R tool aiming at identifying DEMs (differential expressed miRNAs) between ovarian malignant tissue and ovarian normal tissue. The whole consistently changed miRNAs were then screened out to be candidate DEMs. For estimating underlying upstream transcription factors, FunRich was employed. miRNet was utilized to determine putative DEMs' downstream target genes. The R program was then used to do the GO annotation as well as the analysis of KEGG pathway enrichment for target genes. The PPI (protein-protein interaction), as well as the DEM-hub gene networks, were created by the Cytoscape software and STRING database. Finally, we chose the GSE74448 dataset to test the precision of hub gene expressions.

Results

We have screened out six (five upregulated and one downregulated) DEMs. The majority of upregulated and downregulated DEMs are likely regulated by SP1 (specificity protein 1). SP4 (s protein 4), POU2F1 (POU class 2 homeobox 1), MEF2A (myocyte-specific enhancer factor 2A), ARID3A (AT-rich interaction domain 3A), and EGR1 (early growth response 1) can regulate upregulated and downregulated DEMs. We have found 807 target genes (656 upregulated and 151 downregulated DEM), being generally enriched in focal adhesion and proteoglycans in cancer, gastric cancer, hepatocellular carcinoma, as well as breast cancer. The majority of hub genes are projected to be controlled by hsa-miR-429, hsa-miR-140-5p, hsa-miR-199a-5p, and hsa-miR-199a-3p after the DEM-hub gene network was built. VEGFA (vascular endothelial growth factor A), EZH2 (enhancer of zeste 2 polycomb repressive complex 2 subunit), and HIF1A (hypoxia inducible factor 1 subunit alpha) expressions are consistent with the GSE74448 dataset in the first 18 hub genes.

Conclusion

We have built an underlying miRNA-mRNA interacting network in OC, giving us unparalleled insight into the disease's diagnosis and treatment.

Non-invasive human skin transcriptome analysis using mRNA in skin surface lipids

Non-invasive acquisition of mRNA data from the skin can be extremely useful for understanding skin physiology and diseases. Inspired by the holocrine process, in which the sebaceous glands secrete cell contents into the sebum, we focused on the possible presence of mRNAs in skin surface lipids (SSLs). We found that measurable levels of human mRNAs exist in SSLs, where the sebum protects them from degradation by RNases. The AmpliSeq transcriptome analysis was modified to measure SSL-RNA levels, and our results revealed that the SSL-RNAs predominantly comprised mRNAs derived from sebaceous glands, the epidermis, and hair follicles. Analysis of SSL-RNAs non-invasively collected from patients with atopic dermatitis revealed increased expression of inflammation-related genes and decreased expression of terminal differentiation-related genes, consistent with the results of previous reports. Further, we found that lipid synthesis-related genes were downregulated in the sebaceous glands of patients with atopic dermatitis. These results indicate that the analysis of SSL-RNAs is a promising strategy to understand the pathophysiology of skin diseases.

Inoue et al develop a non-invasive method of analyzing human skin mRNA using RNA in skin surface lipids collected with oil-blotting films. The authors outline the validation of this methodology and describe an application to determine transcriptome in skin surface lipids in patients with atopic dermatitis versus healthy skin.

Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery

BACKGROUND

Artificial intelligence (AI), including machine learning (ML) and deep learning, has the potential to revolutionize biomedical research. Defined as the ability to "mimic" human intelligence by machines executing trained algorithms, AI methods are deployed for biomarker discovery.

OBJECTIVE

We detail the advancements and challenges in the use of AI for biomarker discovery in ovarian and pancreatic cancer. We also provide an overview of associated regulatory and ethical considerations.

METHODS

We conducted a literature review using PubMed and Google Scholar to survey the published findings on the use of AI in ovarian cancer, pancreatic cancer, and cancer biomarkers.

RESULTS

Most AI models associated with ovarian and pancreatic cancer have yet to be applied in clinical settings, and imaging data in many studies are not publicly available. Low disease prevalence and asymptomatic disease limits data availability required for AI models. The FDA has yet to qualify imaging biomarkers as effective diagnostic tools for these cancers.

CONCLUSIONS

Challenges associated with data availability, quality, bias, as well as AI transparency and explainability, will likely persist. Explainable and trustworthy AI efforts will need to continue so that the research community can better understand and construct effective models for biomarker discovery in rare cancers.

MicroRNA Signatures in the Upper Urinary Tract Urothelial Carcinoma Scenario: Ready for the Game Changer?

Upper urinary tract urothelial carcinoma (UTUC) represents a minor subgroup of malignancies arising in the urothelium of the renal pelvis or ureter. The estimated annual incidence is around 2 cases per 100,000 people, with a mean age at diagnosis of 73 years. UTUC is more frequently diagnosed in an invasive or metastatic stage. However, even though the incidence of UTUC is not high, UTUC tends to be aggressive and rapidly progressing with a poor prognosis in some patients. A significant challenge in UTUC is ensuring accurate and timely diagnosis, which is complicated by the non-specific nature of symptoms seen at the onset of disease. Moreover, there is a lack of biomarkers capable of identifying the early presence of the malignancy and guide-tailored medical treatment. However, the growing understanding of the molecular biology underlying UTUC has led to the discovery of promising new biomarkers. Among these biomarkers, there is a class of small non-coding RNA biomarkers known as microRNAs (miRNAs) that are particularly promising. In this review, we will analyze the main characteristics of UTUC and focus on microRNAs as possible novel tools that could enter clinical practice in order to optimize the current diagnostic and prognostic algorithm.

Investigation of miRNA expression profiles using cohort samples reveals potential early detectability of colorectal cancers by serum miR-26a-5p before clinical diagnosis

Previous studies have investigated the usefulness of microRNA (miRNA/miR) expression data for the early detection of colorectal cancer (CRC). However, limited data are available regarding miRNAs that detect CRC before clinical diagnoses. Accordingly, the present study investigated the early detectability of CRC by miRNAs using the preserved serum samples of the cohort participants affected with CRC within 2 years of study enrollment. First, the significant miRNAs were revealed using clinical CRC samples for a (seven early CRCs and seven controls) microarray analysis based on significance analysis of microarrays. Next, replicability was verified by reverse transcription-quantitative (RT-q)PCR (eight early CRCs and eight controls, together with 12 CRCs and 12 controls). Finally, early detectability was tested using the cohort samples of Japan Multi-Institutional Collaborative Cohort Study (17 CRCs and 17 controls) to reveal how a certain number of patients developed CRC within 2 years after participation. In the discovery phase, miRNA expression measurements were conducted using a 3D-Gene Human miRNA Oligo Chip for 2,555 miRNAs, and RT-qPCR analyses were performed to validate the replicability. In the first validation set with eight CRCs with early clinical stage and eight age- and gender-matched controls, miR-26a-5p and miR-223-3p demonstrated the highest diagnostic accuracy of area under the curve (AUC)=1.000 (sensitivity and specificity 100%). In an examination of the predictability of CRC incidence using pre-clinical cohort samples, miR-26a-5p demonstrated good predictability of advanced CRC incidence with an AUC of 0.840. Overall, the present study revealed serum miR-26a-5p as a potential early detection marker for CRC.

A method establishment and comparison of in vivo lung cancer model development platforms for evaluation of tumour metabolism and pharmaceutical efficacy

BACKGROUND

Currently, the identification of accurate biomarkers for the diagnosis of patients with early-stage lung cancer remains difficult. Fortunately, metabolomics technology can be used to improve the detection of plasma metabolic biomarkers for lung cancer. In a previous study, we successfully utilised machine learning methods to identify significant metabolic markers for early-stage lung cancer diagnosis. However, a related research platform for the investigation of tumour metabolism and drug efficacy is still lacking.

HYPOTHESIS/PURPOSE

A novel methodology for the comprehensive evaluation of the internal tumour-metabolic profile and drug evaluation needs to be established.

METHODS

The optimal location for tumour cell inoculation was identified in mouse chest for the non-traumatic orthotopic lung cancer mouse model. Microcomputed tomography (micro-CT) was applied to monitor lung tumour growth. Proscillaridin A (P.A) and cisplatin (CDDP) were utilised to verify the anti-lung cancer efficacy of the platform. The top five clinically valid biomarkers, including proline, L-kynurenine, spermidine, taurine and palmitoyl-L-carnitine, were selected as the evaluation indices to obtain a suitable lung cancer mouse model for clinical metabolomics research by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS).

RESULTS

The platform was successfully established, achieving 100% tumour development rate and 0% surgery mortality. P.A and CDDP had significant anti-lung cancer efficacy in the platform. Compared with the control group, four biomarkers in the orthotopic model and two biomarkers in the metastatic model had significantly higher abundance. Principal component analysis (PCA) showed a significant separation between the orthotopic/metastatic model and the control/subcutaneous/KRAS transgenic model. The platform was mainly involved in arginine and proline metabolism, tryptophan metabolism, and taurine and hypotaurine metabolism.

CONCLUSION

This study is the first to simulate clinical metabolomics by comparing the metabolic phenotype of plasma in different lung cancer mouse models. We found that the orthotopic model was the most suitable for tumour metabolism. Furthermore, the anti-tumour drug efficacy was verified in the platform. The platform can very well match the clinical reality, providing better lung cancer diagnosis and securing more precise evidence for drug evaluation in the future.

Nanomaterial-based biosensor developing as a route toward in vitro diagnosis of early ovarian cancer

The grand challenges of ovarian cancer early diagnosis have led to an alarmingly high mortality rate from ovarian cancer (OC) in the past half century. In vitro diagnosis (IVD) has great potential in the early diagnosis of OC through non-invasive and dynamic analysis of biomarkers. However, common IVDs often fail to provide reliable test results due to lack of sensitivity, specificity, and convenience. In recent years, the discovery of new biomarkers and the progress of nanomaterials can solve the shortcomings of traditional IVD for early OC. These emerging biosensors based on nanomaterials offer great improvements in convenience, speed, selectivity, and sensitivity of IVD. In this review, we firstly systematically summarized the limits of commercial IVD biosensors of OC and the latest discovery of new biomarkers for OC. The representative optimization strategies for six potential ovarian cancer biomarkers are systematically discussed with emphasis on nanomaterial selection and the design of detection principles. Then, various strategies adopted by emerging biosensors based on nanomaterials are also introduced in detail, including optical, electrochemical, microfluidic, and surface plasmon sensors. Finally, current challenges of early OC IVD are proposed, and future research directions on this promising field are also discussed.

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Failure to diagnose OC early will lead to high mortality.

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The detection of OC-related biomarkers by IVD method will achieve early diagnosis of OC.

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The development of nanomaterials-based biosensors is expected to enhance efficiency of detection.

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Strategies and progress for nanomaterials-based biosensors are systematically reviewed.

A prognostic model based on immune-related long noncoding RNAs for patients with epithelial ovarian cancer

BACKGROUND

Long noncoding RNAs (lncRNAs) are important regulators of gene expression and can affect a variety of physiological processes. Recent studies have shown that immune-related lncRNAs play an important role in the tumour immune microenvironment and may have potential application value in the treatment and prognosis prediction of tumour patients. Epithelial ovarian cancer (EOC) is characterized by a high incidence and poor prognosis. However, there are few studies on immune-related lncRNAs in EOC. In this study, we focused on immune-related lncRNAs associated with survival in EOC.

METHODS

We downloaded mRNA data for EOC patients from The Cancer Genome Atlas (TCGA) database and mRNA data for normal ovarian tissue from the Genotype-Tissue Expression (GTEx) database and identified differentially expressed genes through differential expression analysis. Immune-related lncRNAs were obtained through intersection and coexpression analysis of differential genes and immune-related genes from the Immunology Database and Analysis Portal (ImmPort). Samples in the TCGA EOC cohort were randomly divided into a training set, validation set and combination set. In the training set, Cox regression analysis and LASSO regression were performed to construct an immune-related lncRNA signature. Kaplan-Meier survival analysis, time-dependent ROC curve analysis, Cox regression analysis and principal component analysis were performed for verification in the training set, validation set and combination set. Further studies of pathways and immune cell infiltration were conducted through Gene Set Enrichment Analysis (GSEA) and the Timer data portal.

RESULTS

An immune-related lncRNA signature was identified in EOC, which was composed of six immune-related lncRNAs (KRT7-AS, USP30-AS1, AC011445.1, AP005205.2, DNM3OS and AC027348.1). The signature was used to divide patients into high-risk and low-risk groups. The overall survival of the high-risk group was lower than that of the low-risk group and was verified to be robust in both the validation set and the combination set. The signature was confirmed to be an independent prognostic biomarker. Principal component analysis showed the different distribution patterns of high-risk and low-risk groups. This signature may be related to immune cell infiltration (mainly macrophages) and differential expression of immune checkpoint-related molecules (PD-1, PDL1, etc.).

CONCLUSIONS

We identified and established a prognostic signature of immune-related lncRNAs in EOC, which will be of great value in predicting the prognosis of clinical patients and may provide a new perspective for immunological research and individualized treatment in EOC.

Identifying Potential miRNA Biomarkers for Gastric Cancer Diagnosis Using Machine Learning Variable Selection Approach

Aim: This study aimed to accurately identification of potential miRNAs for gastric cancer (GC) diagnosis at the early stages of the disease. Methods: We used GSE106817 data with 2,566 miRNAs to train the machine learning models. We used the Boruta machine learning variable selection approach to identify the strong miRNAs associated with GC in the training sample. We then validated the prediction models in the independent sample GSE113486 data. Finally, an ontological analysis was done on identified miRNAs to eliciting the relevant relationships. Results: Of those 2,874 patients in the training the model, there were 115 (4%) patients with GC. Boruta identified 30 miRNAs as potential biomarkers for GC diagnosis and hsa-miR-1343-3p was at the highest ranking. All of the machine learning algorithms showed that using hsa-miR-1343-3p as a biomarker, GC can be predicted with very high precision (AUC; 100%, sensitivity; 100%, specificity; 100% ROC; 100%, Kappa; 100) using with the cut-off point of 8.2 for hsa-miR-1343-3p. Also, ontological analysis of 30 identified miRNAs approved their strong relationship with cancer associated genes and molecular events. Conclusion: The hsa-miR-1343-3p could be introduced as a valuable target for studies on the GC diagnosis using reliable biomarkers.

The Role of miRNA in Ovarian Cancer: an Overview

Ovarian cancer (OC) is a highly malignant disease that seriously threatens women’s health and poses challenges for clinicians. MicroRNAs (miRNAs) have recently been intensively studied in the field of oncology due to their regulatory roles in gene expressions through RNA degradation and/or translation inhibition. This review summarizes the current studies on miRNAs in OC and introduces the latest updates of miRNAs in the early screening, treatment, and prognostic prediction of OC, thereby demonstrating the clinical significance of miRNAs in OC. Further exploration on potential targets of miRNAs in OC may provide new insights on optimizing the diagnosis and treatment of OC. MiRNAs are important driving factors for the progression of OC and the dysregulation of miRNAs can serve as biomarkers in the diagnosis, treatment and prognosis of OC. Therefore, miRNAs are potential biological targets for early screening, targeted therapy, drug resistance monitoring, and prognosis improvement in malignancies such as OC.

Bisphenol A induces miR-708-5p through an ER stress-mediated mechanism altering neuronatin and neuropeptide Y expression in hypothalamic neuronal models

Bisphenol A (BPA) is an endocrine disrupting chemical that promotes obesity. It acts on the hypothalamus by increasing expression of the orexigenic neuropeptides, Npy and Agrp. Exactly how BPA dysregulates energy homeostasis is not completely clear. Since microRNAs (miRNA) have emerged as crucial weight regulators, the question of whether BPA could alter hypothalamic miRNA profiles was examined. Treatment of the mHypoA-59 cell line with 100 μM BPA altered a specific subset of miRNAs, and the most upregulated was miR-708-5p. BPA was found to increase the levels of miR-708-5p, and its parent gene Odz4, through the ER stress-related protein Chop. Overexpression of an miR-708-5p mimic resulted in a reduction of neuronatin, a proteolipid whose loss of expression is associated with obesity, and an increase in orexigenic Npy expression, thus potentially increasing feeding through converging regulatory pathways. Therefore, hypothalamic exposure to BPA can increase miR-708-5p that controls neuropeptides directly linked to obesity.

The Role of MiRNA in Cancer: Pathogenesis, Diagnosis, and Treatment

Cancer is also determined by the alterations of oncogenes and tumor suppressor genes. These gene expressions can be regulated by microRNAs (miRNA). At this point, researchers focus on addressing two main questions: "How are oncogenes and/or tumor suppressor genes regulated by miRNAs?" and "Which other mechanisms in cancer cells are regulated by miRNAs?" In this work we focus on gathering the publications answering these questions. The expression of miRNAs is affected by amplification, deletion or mutation. These processes are controlled by oncogenes and tumor suppressor genes, which regulate different mechanisms of cancer initiation and progression including cell proliferation, cell growth, apoptosis, DNA repair, invasion, angiogenesis, metastasis, drug resistance, metabolic regulation, and immune response regulation in cancer cells. In addition, profiling of miRNA is an important step in developing a new therapeutic approach for cancer.

Current Research Progress of the Role of LncRNA LEF1-AS1 in a Variety of Tumors

Long non-coding RNAs (lncRNA), as key regulators of cell proliferation and death, are involved in the regulation of various processes in the nucleus and cytoplasm, involving biological developmental processes in the fields of immunology, neurobiology, cancer, and stress. There is great scientific interest in exploring the relationship between lncRNA and tumors. Many researches revealed that lymph enhancer-binding factor 1-antisense RNA 1 (LEF1-AS1), a recently discovered lncRNA, is downregulated in myeloid malignancy, acting mainly as a tumor suppressor, while it is highly expressed and carcinogenic in glioblastoma (GBM), lung cancer, hepatocellular carcinoma (HCC), osteosarcoma, colorectal cancer (CRC), oral squamous cell carcinoma (OSCC), prostatic carcinoma, retinoblastoma, and other malignant tumors. Furthermore, abnormal LEF1-AS1 expression was associated with tumorigenesis, development, survival, and prognosis via the regulation of target genes and signaling pathways. This review summarizes the existing data on the expression, functions, underlying mechanism, relevant signaling pathways, and clinical significance of LEF1-AS1 in cancer. It is concluded that LEF1-AS1 can serve as a novel biomarker for the diagnosis and prognosis of various tumors, thus deserves further attention in the future.

Brain Tumors and Circulating microRNAs: A Systematic Review and Diagnostic Meta-Analysis

PURPOSE

Brain tumors (BT) are among the most prevalent cancers in recent years. Various studies have examined the diagnostic role of microRNAs in different diseases; however, their diagnostic role in BT has not been comprehensively investigated. Therefore, this meta-analysis was performed to assess microRNAs in the blood of patients with BTs accurately.

METHODS

Twenty-six eligible studies were included for analysis. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), area under curve (AUC), Q*index, summary receiver-operating characteristic (SROC) were assessed using the Meta-Disc V.1.4 and Comprehensive Meta-Analysis V.3.3 software. The Egger's test was used to evaluate publication bias in this study.

RESULTS

The diagnostic accuracy of microRNA was high in identifying BT based on the pooled sensitivity 0.82 (95% CI: 0.816 - 0.84), specificity 0.82 (95% CI: 0.817 - 0.84), PLR 5.101 (95% CI: 3.99 - 6.51), NLR 0.187 (95% CI: 0.149 - 0.236), DOR 34.07 (95% CI: 22.56 - 51.43) as well as AUC (0.92), and Q*-index (0.86). Subgroup analyses was also performed for sample types (serum/plasma), reference genes (RNU6, miR-39, and miR-24), and region to determine the diagnostic power of microRNAs in the diagnosis of BT using pooled sensitivity, specificity, PLR, NLR, AUC, and DOR.

CONCLUSION

This meta-analysis proved that circulating microRNAs were the potential markers for BT and could potentially be used as non-invasive early detection biomarkers.

Circular RNA circ_0001287 inhibits the proliferation, metastasis, and radiosensitivity of non-small cell lung cancer cells by sponging microRNA miR-21 and up-regulating phosphatase and tensin homolog expression

As a type of non-coding RNA, circular RNA (circRNA) figures prominently in human cancer progression. Nonetheless, the expression, function, and regulatory mechanism of circ_0001287 in non-small cell lung cancer (NSCLC) remain obscure. In this work, quantitative real-time polymerase chain reaction (qRT-PCR) was implemented to quantify circ_0001287 and miR-21 expressions in NSCLC tissues and cells. The relationship between circ_0001287 expression and the clinicopathological parameters of NSCLC patients was examined. Cell counting kit-8 (CCK-8), 5-bromo-2©-deoxyuridine (BrdU), and Transwell experiments were conducted to detect the multiplication, migration, and invasion of NSCLC cells after circ_0001287 was overexpressed or knocked down. The survival of NSCLC cells was studied using colony formation experiment under different doses of radiation. RNA immunoprecipitation (RIP) experiment and luciferase reporter gene experiment verified the binding relationship between circ_0001287 and miR-21. Western blot was employed to examine the regulatory effects of circ_0001287 and miR-21 on phosphatase and tensin homolog (PTEN) expression. We reported that circ_0001287 expression was down-modulated in NSCLC tissues and cell lines. Besides, circ_0001287 low expression was associated with low differentiation and positive lymph node invasion of NSCLC. Circ_0001287 overexpression suppressed the multiplication, migration, invasion, and radioresistance of NSCLC cells, whereas circ_0001287 knockdown promoted the above phenotypes. Circ_0001287 could adsorb miR-21 and repress its expression, and indirectly up-modulate PTEN expression in NSCLC cells. Taken together, circ_0001287/miR-21/PTEN axis is probably involved in regulating NSCLC cell multiplication, metastasis, and radioresistance.

Rhophilin rho GTPase binding protein 1-antisense RNA 1 (RHPN1-AS1) promotes ovarian carcinogenesis by sponging microRNA-485-5p and releasing DNA topoisomerase II alpha (TOP2A)

Ovarian cancer (OC) is the most common and lethal gynecological cancer worldwide. Long non-coding RNAs (lncRNAs) and sponging microRNAs (miRNAs) serve as key regulators in the biological processes of OC. We sought to evaluate the effect of the RHPN1-AS1-miR-485-5p-DNA topoisomerase II alpha (TOP2A) axis in regulating OC progression. RHPN1-AS1, miR-485-5p, and TOP2A levels in OC tissues and cells were determined by RT-qPCR. The interaction of RHPN1-AS1/miR-485-5p/TOP2A was assessed using luciferase, RNA immunoprecipitation, and RNA pull-down assays. RHPN1-AS1 silencing allowed us to explore its biological function by measuring cell viability, proliferation, migration, invasion, and apoptosis in OC cells. In vivo experiments were performed to verify the in vitro findings. We found that the RHPN1-AS1 and TOP2A levels were significantly enhanced, whereas the miR-485-5p levels were reduced in OC tissues and cells. RHPN1-AS1 silencing attenuated cell growth, facilitated apoptosis in OC cells, and inhibited tumor growth in vivo. Notably, RHPN1-AS1 negatively regulating miR-485-5p promoted the TOP2A expression in OC cells. In conclusion, RHPN1-AS1 sponging miR-485-5p accelerated the progression of OC by elevating TOP2A expression, which makes it a promising target for the treatment of OC patients.

Integrated miRNA-mRNA Expression Profiles Revealing Key Molecules in Ovarian Cancer Based on Bioinformatics Analysis

Ovarian cancer is one of the leading causes of gynecological malignancy-related deaths. The underlying molecular development mechanism has however not been elucidated. In this study, we used bioinformatics to reveal critical molecular and biological processes associated with ovarian cancer. The microarray datasets of miRNA and mRNA expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Besides, we performed target prediction of the identified differentially expressed miRNAs. The overlapped differentially expressed genes (DEGs) were obtained combined with miRNA targets predicted and the DEGs identified from the mRNA dataset. The Cytoscape software was used to design a regulatory network of miRNA-gene. Moreover, the overlapped DEGs in the network were subjected to enrichment analysis to explore the associated biological processes. The molecular protein-protein interaction (PPI) network was used to identify the key genes among the DEGs of prognostic value for ovarian cancer, and the genes were evaluated via Kaplan-Meier curve analysis. A total of 186 overlapped DEGs were identified. Through miRNA-gene network analysis, we found that miR-195-5p, miR-424-5p, and miR-497-5p highly exhibited targeted association with overlapped DEGs. The three miRNAs are critical in the regulatory network and act as tumor suppressors. The overlapped DEGs were mainly associated with protein metabolism, histogenesis, and development of the reproductive system and ocular tissues. The PPI network identified 10 vital genes that promote tumor progression. Survival analysis found that CEP55 and CCNE1 may be associated with the prognosis of ovarian cancer. These findings provide insights to understand the pathogenesis of ovarian cancer and suggest new candidate biomarkers for early screening of ovarian cancer.

Dementia subtype prediction models constructed by penalized regression methods for multiclass classification using serum microRNA expression data

There are many subtypes of dementia, and identification of diagnostic biomarkers that are minimally-invasive, low-cost, and efficient is desired. Circulating microRNAs (miRNAs) have recently gained attention as easily accessible and non-invasive biomarkers. We conducted a comprehensive miRNA expression analysis of serum samples from 1348 Japanese dementia patients, composed of four subtypes-Alzheimer's disease (AD), vascular dementia, dementia with Lewy bodies (DLB), and normal pressure hydrocephalus-and 246 control subjects. We used this data to construct dementia subtype prediction models based on penalized regression models with the multiclass classification. We constructed a final prediction model using 46 miRNAs, which classified dementia patients from an independent validation set into four subtypes of dementia. Network analysis of miRNA target genes revealed important hub genes, SRC and CHD3, associated with the AD pathogenesis. Moreover, MCU and CASP3, which are known to be associated with DLB pathogenesis, were identified from our DLB-specific target genes. Our study demonstrates the potential of blood-based biomarkers for use in dementia-subtype prediction models. We believe that further investigation using larger sample sizes will contribute to the accurate classification of subtypes of dementia.

Extracellular microRNA profiling for prognostic prediction in patients with high-grade serous ovarian carcinoma

High-grade serous ovarian carcinoma is a leading cause of death in female patients worldwide. MicroRNAs (miRNAs) are stable noncoding RNAs in the peripheral blood that reflect a patient's condition, and therefore, they have received substantial attention as noninvasive biomarkers in various diseases. We previously reported the usefulness of serum miRNAs as diagnostic biomarkers. Here, we investigated the prognostic impact of the serum miRNA profile. We used the GSE106817 dataset, which included preoperative miRNA profiles of patients with ovarian malignancies. Excluding patients with other malignancy or insufficient prognostic information, we included 175 patients with high-grade serous ovarian carcinoma. All patients except four underwent surgery and received chemotherapy as initial treatment. The median follow-up period was 54.6 months (range, 3.5-144.1 months). Univariate Cox regression analysis revealed that higher levels of miR-187-5p and miR-6870-5p were associated with both poorer progression-free survival (PFS) and overall survival (OS), and miR-1908-5p, miR-6727-5p, and miR-6850-5p were poor prognostic indicators of PFS. The OS and PFS prognostic indices were then calculated using the expression values of three prognostic miRNAs. Multivariate Cox regression analysis showed that both indices were significantly independent poor prognostic factors (hazard ratio for OS and PFS, 2.343 [P = .015] and 2.357 [P = .005], respectively). In conclusion, circulating miRNA profiles can potentially provide information to predict the prognosis of patients with high-grade serous ovarian carcinoma. Therefore, there is a strong demand for early clinical application of circulating miRNAs as noninvasive biomarkers.

Versatile Luminol/Dissolved Oxygen/Fe@Fe2O3 Nanowire Ternary Electrochemiluminescence System Combined with Highly Efficient Strand Displacement Amplification for Ultrasensitive microRNA Detection

Herein, a versatile ECL biosensor was fabricated for ultrasensitive detection of microRNA-21 (miRNA-21) from cancer cells based on a novel H₂O₂-free electrochemiluminescence (ECL) system (luminol/dissolved oxygen/Fe@Fe₂O₃ nanowires). Compared with the previously reported coreaction accelerator that needed a negative potential to produce reactive oxygen species (ROS), these newly discovered Fe@Fe₂O₃ nanowires could generate ROS in the detection solution immediately without the application of voltage, which narrowed down the detection potential range to avoid side reactions, favoring their practical application in biological systems. Especially, the Fe@Fe₂O₃ nanowires could produce H^• for activating dissolved oxygen into ROS to improve the ECL intensity dramatically, which initiates a novel pathway to promote the generation of ROS for the ECL system. In addition, an original strand displacement amplification coupled with strand displacement reaction (SDA-SDR) was developed to improve the conversion efficiency of the target for sensitive detection of miRNA-21. By virtue of the SDR, a quadruple quenching effect was achieved through each output DNA strand of SDA; hence, the nucleic acid signal amplification efficiency was effectively enhanced. As expected, on account of the superb activation performance of Fe@Fe₂O₃ nanowires and the outstanding amplification efficiency of the SDR-SDA strategy, the fabricated ECL biosensor realized ultrasensitive detection of miRNA-21 with a detection limit down to 52.5 aM. The established ECL sensing platform ushered a new route for H₂O₂-free detection and a promising biomarker assay method for clinical diagnosis.

Small Extracellular Vesicles: Functions and Potential Clinical Applications as Cancer Biomarkers

Cancer, as the second leading cause of death worldwide, is a major public health concern that imposes a heavy social and economic burden. Effective approaches for either diagnosis or therapy of most cancers are still lacking. Dynamic monitoring and personalized therapy are the main directions for cancer research. Cancer-derived extracellular vesicles (EVs) are potential disease biomarkers. Cancer EVs, including small EVs (sEVs), contain unique biomolecules (protein, nucleic acid, and lipids) at various stages of carcinogenesis. In this review, we discuss the biogenesis of sEVs, and their functions in cancer, revealing the potential applications of sEVs as cancer biomarkers.

Contribution of platelet-derived microRNAs to serum microRNAs in healthy men

Platelets are a major source of microRNAs (miRNAs) in blood. Relationships between circulating platelet-derived miRNAs were investigated to elucidate their significance as biomarkers. Total miRNAs in serum were analyzed using the 3D-Gene miRNA Oligo chip. Among 22 miRNAs that are included in platelets and play functional roles, sufficient miRNA levels for comparison were detected for 11 miRNAs (let-7b-5p, miR-16-5p, miR-17-5p, miR-24-3p, miR-107, miR-126-3p, miR-150-3p, miR-191-5p, miR-197-3p, miR-223-3p, and miR-326). Among 55 pairs prepared by these miRNAs, relatively strong correlations (Spearman's correlation coefficient >0.8) were shown between miRNAs of 7 pairs including let-7b-5p and miR-16-5p, let-7b-5p and miR-17-5p, let-7b-5p and miR-107, miR-16-5p and miR-17-5p, miR-16-5p and miR-107, miR-17-5p and miR-107, and miR-107 and miR-126-3p. In principal component analysis, the first principal component consisted of let-7b-5p, miR-16-5p, miR-17-5p, miR-107, miR-126-3p, and miR-191-5p. These six miRNAs may be useful biomarkers that reflect platelet condition and function.

MicroRNA-33b replacement effect on growth and migration inhibition in ovarian cancer cells

PURPOSE

Ovarian cancer is a devastating gynecological disease which is considered the major cause of cancer fatality around the world. The down-regulation of microRNA-33b (miR-33b) was reported in some malignancies. Hence, we transfected the miR-33b mimic into SKOV3 cells and evaluated the impacts of this interference on the growth and migration repression of these tumor cells as well as on targeted genes expression.

METHODS

In our study, transfecting the miR-33b mimic and inhibitor, negative control (NC), and NC inhibitor were established using Lipofectamine 2000. The cytotoxic effects of miR-33b were evaluated by MTT. To assess the miR-33b effects on cell migration, a scratching test was applied. The expression levels of miR-33b, ADAMTS, C-Myc, MMP9, K-Ras, and CXCR4 were evaluated using qRT-PCR.

RESULTS

These findings indicate that transfection of miR-143 mimic had no marked effects on the SKOV3 cell line. As expected, miR-33b relative expression levels were as follows: miR-33b mimic >NC and NC inhibitor >miR-33b inhibitor (p < 0.01). Moreover, transfected miR-33b mimic could suppress SKOV3 cells' proliferation, whereas transfected miR-33b inhibitor could promote cell proliferation (p < 0.01). MiR-33b overexpression significantly down-regulated the MMP9, CXCR-4, c-Myc, ADAMTS, and K-Ras mRNA levels (p < 0.05).

CONCLUSION

As expected, these results confirm the tumor-suppressive effect of miR-33b in the SKOV3 ovarian cancer cell line by reducing cell survival, proliferation, and migration.

Preliminary evaluation of miR-1307-3p in human serum for detection of 13 types of solid cancer using microRNA chip

Early detection and treatment are crucial for increasing the five-year survival rates of various cancers. Low-cost and convenient cancer screening tests are also critically important. Circulating microRNAs are reported as potential biomarkers for various cancers. Recently, miR-1307-3p was found to be a cancer-related microRNA. We evaluated the expression levels of miR-1307-3p in sera obtained from 254 patients with thirteen types of cancer (colon cancer, lung cancer, gastric cancer, liver cancer, bladder cancer esophageal cancer, breast cancer, ovarian cancer, prostate cancer, pancreatic cancer, biliary tract cancer, brain cancer, sarcoma) and 27 non-cancer samples using isothermal amplification and microRNA chip. The expression levels of miR-1307-3p in sera obtained from cancer patients were clearly different from those obtained from non-cancer samples and differentiated the validation cohort into cancer patients and non-cancer control with high accuracy (AUC: 0.98; sensitivity: 0.98; specificity: 0.85). These results showed the potential relevance of miR-1307-3p in serum for the development of new diagnostic examination tools for cancer patients.

CancerMIRNome: an interactive analysis and visualization database for miRNome profiles of human cancer

MicroRNAs (miRNAs), which play critical roles in gene regulatory networks, have emerged as promising diagnostic and prognostic biomarkers for human cancer. In particular, circulating miRNAs that are secreted into circulation exist in remarkably stable forms, and have enormous potential to be leveraged as non-invasive biomarkers for early cancer detection. Novel and user-friendly tools are desperately needed to facilitate data mining of the vast amount of miRNA expression data from The Cancer Genome Atlas (TCGA) and large-scale circulating miRNA profiling studies. To fill this void, we developed CancerMIRNome, a comprehensive database for the interactive analysis and visualization of miRNA expression profiles based on 10 554 samples from 33 TCGA projects and 28 633 samples from 40 public circulating miRNome datasets. A series of cutting-edge bioinformatics tools and machine learning algorithms have been packaged in CancerMIRNome, allowing for the pan-cancer analysis of a miRNA of interest across multiple cancer types and the comprehensive analysis of miRNome profiles to identify dysregulated miRNAs and develop diagnostic or prognostic signatures. The data analysis and visualization modules will greatly facilitate the exploit of the valuable resources and promote translational application of miRNA biomarkers in cancer. The CancerMIRNome database is publicly available at http://bioinfo.jialab-ucr.org/CancerMIRNome.

Ascites in ovarian cancer: MicroRNA deregulations and their potential roles in ovarian carcinogenesis

Ovarian cancer comprises the most lethal gynecologic malignancy and is accompanied by the high potential for the incidence of metastasis, recurrence and chemotherapy resistance, often associated with a formation of ascitic fluid. The differentially expressed ascites-derived microRNAs may be linked to ovarian carcinogenesis. The article focuses on a number of miRNAs that share a common expression pattern as determined by independent studies using ascites samples and with regard to their functions and outcomes in experimental and clinical investigations.Let-7b and miR-143 have featured as tumor suppressors in ovarian cancer, which is in line with data on other types of cancer. Although two miRNAs, i.e. miR-26a-5p and miR-145-5p, act principally as tumor suppressor miRNAs, they occasionally exhibit oncogenic roles. The performance of miR-95-3p, upregulated in ascites, is open to debate given the current lack of supportive data on ovarian cancer; however, data on other cancers indicates its probable oncogenic role. Different findings have been reported for miR-182-5p and miR-200c-3p; in addition to their presumed oncogenic roles, contrasting findings have indicated their ambivalent functions. Further research is required for the identification and evaluation of the potential of specific miRNAs in the diagnosis, prediction, treatment and outcomes of ovarian cancer patients.

MicroRNAs in blood act as biomarkers of colorectal cancer and indicate potential therapeutic targets

Association studies have linked alterations of blood-derived microRNAs (miRNAs) with colorectal cancer (CRC). Here, we performed a microarray-based comparison of the profiles of 2549 miRNAs in 80 blood samples from healthy donors and patients with colorectal adenomas, colorectal diverticulitis and CRC at different stages. Confirmation by quantitative real-time PCR (RT-PCR) was complemented by validation of identified molecules in another 36 blood samples. No variations in miRNA levels were observed in samples from patients with colorectal adenomas and diverticulitis or from healthy donors. However, there were 179 CRC-associated miRNAs of differential abundance compared to healthy controls. Only three - miR-1225-5p, miR-1207-5p and miR-4459 - exhibited increased levels at all CRC stages. Most deregulated miRNAs (128/179, 71%) specifically predicted metastatic CRC. Pathway analysis found several cancer-related pathways to which the miRNAs contribute in various ways. In conclusion, miRNA levels in blood vary throughout CRC progression and affect cellular functions relevant to haematogenous CRC progression and dissemination. The identified biomarker and therapeutic candidates require further confirmation of their clinical relevance.

Graphene oxide-based qRT-PCR assay enables the sensitive and specific detection of miRNAs for the screening of ovarian cancer

Circulating microRNAs (miRNAs) have the potential to become reliable and noninvasive biomarkers for ovarian cancer (OC) diagnosis; however, the conventional miRNAs detection techniques exhibit enduring limitations of low sensitivity and specificity. Graphene oxide (GO), a novel nanomaterial, is at the forefront of material design for extensive biomedical applications. Owing to the excellent water affinity and single-stranded DNA (ssDNA) adsorption characteristics of GO, we designed and developed a GO-based qRT-PCR assay for the detection of miRNAs associated with OC. In the GO-based qRT-PCR system, GO could significantly improve the sensitivity and specificity of the qRT-PCR assay by noncovalently interacting with primers and ssDNA and reducing the occurrence of non-specific amplification. Moreover, the detection of miRNAs associated with OC confirmed that GO-based qRT-PCR assay could differentiate benign ovarian tumors from OC (sensitivity, 0.91; specificity, 1.00). Collectively, these findings provide robust evidence that GO-based qRT-PCR assay can be effectively used as a promising method to detect miRNAs for the screening of OC patients.

Multi-analytical test based on serum miRNAs and proteins quantification for ovarian cancer early detection

Advanced ovarian cancer is one of the most lethal gynecological tumor, mainly due to late diagnoses and acquired drug resistance. MicroRNAs (miRNAs) are small-non coding RNA acting as tumor suppressor/oncogenes differentially expressed in normal and epithelial ovarian cancer and has been recognized as a new class of tumor early detection biomarkers as they are released in blood fluids since tumor initiation process. Here, we evaluated by droplet digital PCR (ddPCR) circulating miRNAs in serum samples from healthy (N = 105) and untreated ovarian cancer patients (stages I to IV) (N = 72), grouped into a discovery/training and clinical validation set with the goal to identify the best classifier allowing the discrimination between earlier ovarian tumors from health controls women. The selection of 45 candidate miRNAs to be evaluated in the discovery set was based on miRNAs represented in ovarian cancer explorative commercial panels. We found six miRNAs showing increased levels in the blood of early or late-stage ovarian cancer groups compared to healthy controls. The serum levels of miR-320b and miR-141-3p were considered independent markers of malignancy in a multivariate logistic regression analysis. These markers were used to train diagnostic classifiers comprising miRNAs (miR-320b and miR-141-3p) and miRNAs combined with well-established ovarian cancer protein markers (miR-320b, miR-141-3p, CA-125 and HE4). The miRNA-based classifier was able to accurately discriminate early-stage ovarian cancer patients from health-controls in an independent sample set (Sensitivity = 80.0%, Specificity = 70.3%, AUC = 0.789). In addition, the integration of the serum proteins in the model markedly improved the performance (Sensitivity = 88.9%, Specificity = 100%, AUC = 1.000). A cross-study validation was carried out using four data series obtained from Gene Expression Omnibus (GEO), corroborating the performance of the miRNA-based classifier (AUCs ranging from 0.637 to 0.979). The clinical utility of the miRNA model should be validated in a prospective cohort in order to investigate their feasibility as an ovarian cancer early detection tool.

OCaMIR-A Noninvasive, Diagnostic Signature for Early-Stage Ovarian Cancer: A Multi-cohort Retrospective and Prospective Study

PURPOSE

Due to the lack of effective screening approaches and early detection biomarkers, ovarian cancer has the highest mortality rates among gynecologic cancers. Herein, we undertook a systematic biomarker discovery and validation approach to identify microRNA (miRNA) biomarkers for the early detection of ovarian cancer.

EXPERIMENTAL DESIGN

During the discovery phase, we performed small RNA sequencing in stage I high-grade serous ovarian cancer (n = 31), which was subsequently validated in multiple, independent data sets (TCGA, n = 543; GSE65819, n = 87). Subsequently, we performed multivariate logistic regression-based training in a serum data set (GSE106817, n = 640), followed by its independent validation in three retrospective data sets (GSE31568, n = 85; GSE113486, n = 140; Czech Republic cohort, n = 192) and one prospective serum cohort (n = 95). In addition, we evaluated the specificity of OCaMIR, by comparing its performance in several other cancers (GSE31568 cohort, n = 369).

RESULTS

The OCaMIR demonstrated a robust diagnostic accuracy in the stage I high-grade serous ovarian cancer patients in the discovery cohort (AUC = 0.99), which was consistently reproducible in both stage I (AUC = 0.96) and all stage patients (AUC = 0.89) in the TCGA cohort. Logistic regression-based training and validation of OCaMIR achieved AUC values of 0.89 (GSE106817), 0.85 (GSE31568), 0.86 (GSE113486), and 0.82 (Czech Republic cohort) in the retrospective serum validation cohorts, as well as prospective validation cohort (AUC = 0.92). More importantly, OCaMIR demonstrated a significantly superior diagnostic performance compared with CA125 levels, even in stage I patients, and was more cost-effective, highlighting its potential role for screening and early detection of ovarian cancer.

CONCLUSIONS

Small RNA sequencing identified a robust noninvasive miRNA signature for early-stage serous ovarian cancer detection.

Future Screening Prospects for Ovarian Cancer

Simple Summary

Ovarian cancer (OC) has the highest mortality rate of all gynecological cancers. It is usually diagnosed in late stages (FIGO III-IV), and therefore, overall survival is very poor. If diagnosed at the early stages, ovarian cancer has a 90% five-year survival rate. Liquid biopsy has a good potential to improve early ovarian cancer detection and is discussed in this review.

Abstract

Current diagnostic tools used in clinical practice such as transvaginal ultrasound, CA 125, and HE4 are not sensitive and specific enough to diagnose OC in the early stages. A lack of early symptoms and an effective asymptomatic population screening strategy leads to a poor prognosis in OC. New diagnostic and screening methods are urgently needed for early OC diagnosis. Liquid biopsies have been considered as a new noninvasive and promising method, using plasma/serum, uterine lavage, and urine samples for early cancer detection. We analyzed recent studies on molecular biomarkers with specific emphasis on liquid biopsy methods and diagnostic efficacy for OC through the detection of circulating tumor cells, circulating cell-free DNA, small noncoding RNAs, and tumor-educated platelets.

Identification of serum prognostic marker miRNAs and construction of microRNA-mRNA networks of esophageal cancer

Esophageal cancer is a common tumor of the digestive system with poor prognosis. This study was to gain a better understanding of the mechanisms involved in esophageal cancer and to identify new prognostic markers. We downloaded the esophageal cancer miRNA expression profile microarray data (GSE113740, GSE112264, GSE122497, GSE113486, and GSE106817) from the GEO database, extracted the esophageal cancer miRNA sequencing data from The Cancer Genome Atlas (TCGA) database, and then used a bioinformatics approach to select common differentially expressed miRNAs (DEMs). Differentially expressed genes (DEGs) were selected by predicting DEM target genes using the miRWalk database and intersecting with differential genes obtained from TCGA database for esophageal cancer. The STRING database was used to obtain protein-protein interaction (PPI) relationships to construct the DEM-DEG network. Furthermore, we selected core genes and core miRNAs associated with esophageal cancer prognosis by performing survival and univariate/multivariate COX analysis on DEMs and DEGs in the network and performed GSEA analysis on core genes alone, and finally the expression of the markers was verified by qPCR in esophageal cancer cell lines Eca109, SKGT-4 and normal esophageal epithelial cells HEEC. Nine DEMs were obtained, of which three were upregulated and six were downregulated, and 326 DEGs were obtained, of which 105 were upregulated and 221 were downregulated. Survival univariate/multivariate COX analysis revealed that five genes, ZBTB16, AQP4, ADCYAP1R1, PDGFD, and VIPR2, and two microRNAs, miR-99a-5p, and miR-508-5p, were related to esophageal cancer prognosis. GSEA analysis showed that the following genes may be involved in esophageal cancer prognosis: ZBTB16 may through the MTOR signaling pathway, AQP4 through the GNRH signaling pathway, ADCYAP1R1 through the PPAR signaling pathway, VIPR2 through the P53 signaling pathway and PDGFD through the PENTOSE-PHOSPHATE signaling pathway.

Integrated microRNA and mRNA signatures associated with overall survival in epithelial ovarian cancer

Ovarian cancer (OC), the eighth-leading cause of cancer-related death among females worldwide, is mainly represented by epithelial OC (EOC) that can be further subdivided into four subtypes: serous (75%), endometrioid (10%), clear cell (10%), and mucinous (3%). Major reasons for high mortality are the poor biological understanding of the OC mechanisms and a lack of reliable markers defining each EOC subtype. MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate gene expression primarily by targeting messenger RNA (mRNA) transcripts. Their aberrant expression patterns have been associated with cancer development, including OC. However, the role of miRNAs in tumorigenesis is still to be determined, mainly due to the lack of consensus regarding optimal methodologies for identification and validation of miRNAs and their targets. Several tools for computational target prediction exist, but false interpretations remain a problem. The experimental validation of every potential miRNA-mRNA pair is not feasible, as it is laborious and expensive. In this study, we analyzed the correlation between global miRNA and mRNA expression patterns derived from microarray profiling of 197 EOC patients to identify the signatures of miRNA-mRNA interactions associated with overall survival (OS). The aim was to investigate whether these miRNA-mRNA signatures might have a prognostic value for OS in different subtypes of EOC. The content of our cohort (162 serous carcinomas, 15 endometrioid carcinomas, 11 mucinous carcinomas, and 9 clear cell carcinomas) reflects a real-world scenario of EOC. Several interaction pairs between 6 miRNAs (hsa-miR-126-3p, hsa-miR-223-3p, hsa-miR-23a-5p, hsa-miR-27a-5p, hsa-miR-486-5p, and hsa-miR-506-3p) and 8 mRNAs (ATF3, CH25H, EMP1, HBB, HBEGF, NAMPT, POSTN, and PROCR) were identified and the findings appear to be well supported by the literature. This indicates that our study has a potential to reveal miRNA-mRNA signatures relevant for EOC. Thus, the evaluation on independent cohorts will further evaluate the performance of such findings.

Blood Circulating miRNA Pairs as a Robust Signature for Early Detection of Esophageal Cancer

Esophageal cancer (EC) is a common malignant tumor in the digestive system which is often diagnosed at the middle and late stages. Noninvasive diagnosis using circulating miRNA as biomarkers enables accurate detection of early-stage EC to reduce mortality. We built a diagnostic signature consisting of four miRNA pairs for the early detection of EC using individualized Pairwise Analysis of Gene Expression (iPAGE). Profiling of miRNA expression identified 496 miRNA pairs with significant relative expression change. Four miRNA pairs consistently selected from LASSO were used to construct the final diagnostic model. The performance of the signature was validated using two independent datasets, yielding both AUCs and PRCs over 0.99. Furthermore, precision, recall, and F-score were also evaluated for clinical application, when a fixed threshold is given, resulting in all the scores are larger than 0.92 in the training set, test set, and two validation sets. Our results suggested that the 4-miRNA signature is a new biomarker for the early diagnosis of patients with EC. The clinical use of this signature would have improved the detection of EC for earlier therapy and more favorite prognosis.

miRNome and Functional Network Analysis of PGRMC1 Regulated miRNA Target Genes Identify Pathways and Biological Functions Associated With Triple Negative Breast Cancer

Background

Increased expression of the progesterone receptor membrane component 1, a heme and progesterone binding protein, is frequently found in triple negative breast cancer tissue. The basis for the expression of PGRMC1 and its regulation on cellular signaling mechanisms remain largely unknown. Therefore, we aim to study microRNAs that target selective genes and mechanisms that are regulated by PGRMC1 in TNBCs.

Methods

To identify altered miRNAs, whole human miRNome profiling was performed following AG-205 treatment and PGRMC1 silencing. Network analysis identified miRNA target genes while KEGG, REACTOME and Gene ontology were used to explore altered signaling pathways, biological processes, and molecular functions.

Results

KEGG term pathway analysis revealed that upregulated miRNAs target specific genes that are involved in signaling pathways that play a major role in carcinogenesis. While multiple downregulated miRNAs are known oncogenes and have been previously demonstrated to be overexpressed in a variety of cancers. Overlapping miRNA target genes associated with KEGG term pathways were identified and overexpression/amplification of these genes was observed in invasive breast carcinoma tissue from TCGA. Further, the top two genes (CCND1 and YWHAZ) which are highly genetically altered are also associated with poorer overall survival.

Conclusions

Thus, our data demonstrates that therapeutic targeting of PGRMC1 in aggressive breast cancers leads to the activation of miRNAs that target overexpressed genes and deactivation of miRNAs that have oncogenic potential.

Comprehensive serum and tissue microRNA profiling in dedifferentiated liposarcoma

Sarcoma is a rare cancer with several subtypes; therefore, our understanding of the pathogenesis of sarcoma is limited, and designing effective treatments is difficult. Circulating microRNAs (miRNAs), including exosomal miRNAs, have attracted attention as biomarkers in cancer. However, the roles of miRNAs and exosomes in sarcoma remain unclear. The present analysis of tissue and serum miRNA expression in osteosarcoma, Ewing's sarcoma and dedifferentiated liposarcoma (DDLPS) identified miR-1246, −4532, −4454, −619-5p and −6126 as biomarkers for DDLPS. These miRNAs were highly expressed in human DDLPS cell lines and exosomes, suggesting that they are secreted from DDLPS tissues. The present results suggested that specific miRNAs may be used as biomarkers for early diagnosis or treatment targets in DDLPS.

Construction of a Nine-MicroRNA-Based Signature to Predict the Overall Survival of Esophageal Cancer Patients

BACKGROUND

Esophageal cancer (EC) is a common malignant tumor. MicroRNAs (miRNAs) play a key role in the occurrence and metastasis and are closely related to the prognosis of EC. Therefore, it will provide a powerful tool to predict the overall survival (OS) of EC patients based on miRNAs expression in EC tissues and blood samples.

METHODS

Five independent databases, TCGA, GSE106817, GSE113486, GSE122497, and GSE112264, were used to construct nine-miRna signature and nomograms for prognosis. The bioinformatics analysis was used to predict the enrichment pathways of targets.

RESULTS

A total of 132 overexpressed miRNAs and 23 suppressed miRNAs showed significant differential expression in both EC serum and tissue samples compared with normal samples. We also showed that nine miRNAs were related to the prognosis of EC. Higher levels of miR-15a-5p, miR-92a-3p, miR-92a-1-5p, miR-590-5p, miR-324-5p, miR-25-3p, miR-181b-5p, miR-421, and miR-93-5p were correlated to the shorter survival time in patients with EC. In addition, we constructed a risk prediction model based on the levels of nine differentially expressed miRNAs (DEMs) and found that the OS time of EC patients with high-risk score was shorter than that of EC patients with low-risk score. Furthermore, our results showed that the risk prediction scores of EC samples were higher than those of normal samples. Finally, the area under the curve (AUC) was used to analyze the risk characteristics of EC and normal controls. By calculating the AUC and the calibration curve, the RNA signature showed a good performance. Bioinformatics analysis showed that nine DEMs were associated with several crucial signaling, including p53, FoxO, PI3K-Akt, HIF-1, and TORC1 signaling. Finally, 14 messenger RNAs (mRNAs) were identified as hub targets of nine miRNAs, including BTRC, SIAH1, RNF138, CDC27, NEDD4L, MKRN1, RLIM, FBXO11, RNF34, MYLIP, FBXW7, RNF4, UBE3C, and RNF111. TCGA dataset validation showed that these hub targets were significantly differently expressed in EC tissues compared with normal samples.

CONCLUSION

We have constructed maps and nomograms of nine-miRna risk signals associated with EC prognosis. Bioinformatics analysis revealed that nine DEMs were associated with several crucial signaling, including p53, FoxO, PI3K-Akt, HIF-1, and TORC1 signaling, in EC. We think that this study will provide clinicians with an effective decision-making tool.

Investigating function of long noncoding RNA of HOTAIRM1 in progression of SKOV3 ovarian cancer cells

Ovarian cancer is one of the most heterogeneous malignancies in the field of gynecologic oncology. Deregulation of long noncoding RNAs (lncRNAs) is implicated in carcinogenesis. Therefore, the present study was conducted to investigate the possible role of lncRNA of HOXA transcript antisense intergenic RNA myeloid-specific 1(HOTAIRM1) in progression of SKOV3 cells in ovarian cancer and also its underlying molecular mechanisms. HOTAIRM1 expression level will be measured by real-time polymerase chain reaction (PCR) in SKOV3 cells. For determining the effect of HOTAIRM1 silencing on progression of SKOV3 cells, siHOTAIRM1 will be designed and transfected into cells using a liposomal approach. MTT and trypan blue assays will be used to determine the effect of HOTAIRM1 silencing on cell proliferation. Apoptosis of the cells will be detected by flow cytometry. Furthermore, expressions of apoptosis-related genes and Wnt pathway-related proteins and genes will be analyzed by Western blot and real-time PCR. HOTAIRM1 was overexpressed in SKOV3 cells. Silencing of HOTAIRM1 alleviated cell proliferation, and increased cell apoptosis of SKOV3 cells. Moreover, siHOTAIRM1 significantly increased expression of pro-apoptotic agents, such as Bad and Bax, while it decreased expressions of Bid and Bcl-2 (anti-apoptotic agents). Also, silencing of HOTAIRM1 resulted in a suppressed expression of Wnt pathway-related proteins and also expression of its downstream target gene, matrix metalloproteinase 9(MMP9). Our findings provided new insights into function of lncRNA of HOTAIRM1 in progression of ovarian cancer by modulating Wnt pathway and its downstream target gene, MMP9.

Extracellular vesicles: Their emerging roles in the pathogenesis of respiratory diseases

Alveoli are the basic structure of the lungs, consisting of various types of parenchymal and bone marrow-derived cells including alveolar macrophages. These various types of cells have several important functions; thus, communication between these cells plays an important role in homeostasis as well as in the pathophysiology of diseases in the lungs. For a better understanding of the pathophysiology of lung diseases, researchers have isolated each type of lung cell to investigate the changes in their gene expressions, including their humoral factor or adhesion molecules, to reveal the intercellular communication among these cells. In particular, investigations during the past decade have focused on extracellular vesicles, which are lipid bilayer delimited vesicles released from a cell that can move among various cells and transfer substances, including microRNAs, mRNAs and proteins, thus, functioning as intercellular messengers. Extracellular vesicles can be classified into three general groups: apoptotic bodies, exosomes, and microparticles. Extracellular vesicles, especially exosomes and microparticles, are attracting increasing attention from pulmonologists as tools for understanding pathogenesis and disease diagnosis. Here, we review studies, including our own, on exosomes and microparticles and their roles in both lung homeostasis and the pathogenesis of lung diseases such as idiopathic pulmonary fibrosis, chronic obstructive lung diseases, and acute respiratory distress syndrome. This review also addresses the roles of extracellular vesicles in COVID-19, the current global public health crisis.

In Silico screening of circulating tumor DNA, circulating microRNAs, and long non-coding RNAs as diagnostic molecular biomarkers in ovarian cancer: A comprehensive meta-analysis

BACKGROUND

Ovarian cancer (OC) is a leading cause of death in gynecological malignancies worldwide. Multitudinous studies have suggested the potential of circulating tumor DNA (ctDNA), circulating microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) as novel diagnostic molecular biomarkers for OC. Here, we include three updated meta-analysis methods using different molecular biomarkers to evaluate their discriminative value in OC diagnosis.

METHODS

We conducted three meta-analyses after searching different databases, and 23 eligible articles, including 8 concerning ctDNA, 11 concerning miRNAs, and 4 concerning lncRNAs, were found. Further, we pooled data concerning the sensitivity, specificity, and other indicators of accuracy for ctDNA/miRNAs/lncRNAs in the diagnosis of OC. The heterogeneity was further explored by meta-regressions and subgroup analyses, and Deeks' funnel plots were used to measure the publication bias of these three meta-analyses.

RESULTS

In all, this meta-analysis included 1732 OC patients and 3958 controls. The sensitivity of ctDNA for OC diagnosis was superior to that of lncRNA and miRNA (84% vs. 81% vs. 78%). Moreover, the specificity and area under the receiver-operating characteristic (ROC) curve (AUC) of ctDNA were 91% and 94%, which were significantly higher than those of miRNA and lncRNAs (78% and 85%; 78% and 86%, respectively). No significant difference was observed among the two meta-analyses of ctDNA and lncRNA (P > 0.05) with regard to publication bias, while the meta-analysis of miRNA observed a significantly small publication bias (P < 0.05).

CONCLUSION

ctDNA/miRNAs/lncRNAs may be promising molecular biomarkers for OC diagnosis. Further large-scale studies are needed to verify the potential applicability of ctDNA/miRNAs/lncRNAs molecular signatures alone or in combination as diagnostic molecular biomarkers for OC.

Finding associations in a heterogeneous setting: statistical test for aberration enrichment

Most two-group statistical tests find broad patterns such as overall shifts in mean, median, or variance. These tests may not have enough power to detect effects in a small subset of samples, e.g., a drug that works well only on a few patients. We developed a novel statistical test targeting such effects relevant for clinical trials, biomarker discovery, feature selection, etc. We focused on finding meaningful associations in complex genetic diseases in gene expression, miRNA expression, and DNA methylation. Our test outperforms traditional statistical tests in simulated and experimental data and detects potentially disease-relevant genes with heterogeneous effects.

Predict Colon Cancer by Pairing Plasma miRNAs: Establishment of a Normalizer-Free, Cross-Platform Model

BACKGROUND

Plasma miRNAs are emerging biomarkers for colon cancer (CC) diagnosis. However, the lack of robust internal references largely limits their clinical application. Here we propose a ratio-based, normalizer-free algorithm to quantitate plasma miRNA for CC diagnosis.

METHODS

A miRNA-pair matrix was established by pairing differentially expressed miRNAs in the training group from GSE106817. LASSO regression was performed to select variables. To maximize the performance, four algorithms (LASSO regression, random forest, logistic regression, and SVM) were tested for each biomarker combination. Data from GSE106817 and GSE112264 were used for internal and external verification. RT-qPCR data acquired from another cohort were also used for external validation.

RESULTS

After validation through four algorithms, we obtained a 4-miRNA pair model (miR-1246 miR-451a; miR-1246 miR-4514; miR-654-5p miR-575; miR-4299 miR-575) that showed good performance in differentiating CC from normal controls with a maximum AUC of 1.00 in internal verification and 0.93 in external verification. Tissue validation showed a maximum AUC of 0.81. Further external validation using RT-qPCR data exhibited good classifier ability with an AUC of 0.88.

CONCLUSION

We established a cross-platform prediction model robust against sample-specific disturbance, which is not only well-performed in predicting CC but also promising in the diagnosis of other diseases.

Urinary MicroRNA-Based Diagnostic Model for Central Nervous System Tumors Using Nanowire Scaffolds

There are no accurate mass screening methods for early detection of central nervous system (CNS) tumors. Recently, liquid biopsy has received a lot of attention for less-invasive cancer screening. Unlike other cancers, CNS tumors require efforts to find biomarkers due to the blood-brain barrier, which restricts molecular exchange between the parenchyma and blood. Additionally, because a satisfactory way to collect urinary biomarkers is lacking, urine-based liquid biopsy has not been fully investigated despite the fact that it has some advantages compared to blood or cerebrospinal fluid-based biopsy. Here, we have developed a mass-producible and sterilizable nanowire-based device that can extract urinary microRNAs efficiently. Urinary microRNAs from patients with CNS tumors (n = 119) and noncancer individuals (n = 100) were analyzed using a microarray to yield comprehensive microRNA expression profiles. To clarify the origin of urinary microRNAs of patients with CNS tumors, glioblastoma organoids were generated. Glioblastoma organoid-derived differentially expressed microRNAs (DEMs) included 73.4% of the DEMs in urine of patients with parental tumors but included only 3.9% of those in urine of noncancer individuals, which suggested that many CNS tumor-derived microRNAs could be identified in urine directly. We constructed the diagnostic model based on the expression of the selected microRNAs and found that it was able to differentiate patients and noncancer individuals at a sensitivity and specificity of 100 and 97%, respectively, in an independent dataset. Our findings demonstrate that urinary microRNAs extracted with the nanowire device offer a well-fitted strategy for mass screening of CNS tumors.