Integrated extracellular microRNA profiling for ovarian cancer screening

IGF2BP3 recruits NUDT21 to regulate SPTBN1 alternative polyadenylation and drive ovarian cancer progression

Ovarian cancer (OC) is one of the deadliest gynecological malignancies. As the prevalent post-transcriptional regulation, alternative polyadenylation (APA) plays a crucial role in various tumors. Here we identify that the APA regulator NUDT21 is upregulated in OC and promotes malignant progression. We further demonstrate that IGF2BP3 interacts with NUDT21, which suggests m6A modification could regulate APA processing. Mechanistically, IGF2BP3, recognizing the m6A-modified site in intron 32 of SPTBN1, recruits NUDT21 to promote the usage of the SPTBN1 proximal polyadenylation site (PAS), thus increasing the generation of short transcripts in OC cells. Intriguingly, the SPTBN1 long variant demonstrates tumor-suppressive properties, whereas the short variant enhances oncogenic activity in OC. Subsequently, we illustrate that the long isoform inhibits tumor growth and metastasis by binding to CDK1 and blocking the G2/M phase of the cell cycle. In conclusion, this study uncovers a previously unrecognized regulatory mechanism in OC, which could provide potential therapeutic strategies for OC.

Advancing Ischemic Stroke Prognosis: Key Role of MiR-155 Non-Coding RNA

Ischemic stroke (IS) is the leading cause of long-term disability and the second leading cause of death worldwide. It remains a significant clinical problem because only supportive therapies exist, such as thrombolytic agents and surgical thrombectomy, which do not restore function. Understanding the molecular pathogenesis of IS, including dysfunction in oxidative homeostasis, apoptosis, neuroinflammation and neuroprotection, is crucial to developing therapies. Non-coding RNAs (ncRNAs) are master regulators, and one ncRNA that stands out is miR-155, a pro-inflammatory micro-RNA elevated in stroke. This review addresses the biological mechanisms reported in the literature that support using miR-155 as a biomarker and therapeutic agent to treat IS in patients.

Distinctive features of blood- and ascitic fluid-derived extracellular vesicles in ovarian cancer patients

BACKGROUND

Ovarian cancer (OC) is a highly aggressive malignancy characterized by early dissemination of cancer cells from the surface of the ovary to the peritoneum. To gain a deeper understanding of the mechanisms associated with this intraperitoneal spread, we aimed to characterize the role of extracellular vesicles (EVs) in metastatic colonization in OC.

METHODS

To this purpose, a total of 150 samples of ascitic fluids, blood serum, tumor and normal tissues from 60 OC patients, were extensively analyzed to characterize the EVs released in blood and ascitic fluids of OC patients, in terms of size, expression of superficial epitopes and abundance of miRNAs biocargo.

RESULTS

A statistically significant difference in the size of EVs derived from ascitic fluid and serum was identified. Analysis of surface protein expression highlighted twenty epitopes with a significant difference between the two biological matrices, of which 18 were over- and two were under-expressed in ascitic fluid. With regard to miRNA levels, Principal Component Analysis (PCA) assessed four distinct clusters representing tumor tissue, normal tissue, ascitic fluid, and serum. A prominent difference in circulating miRNAs was observed in serum and ascitic fluid highlighting 98 miRNAs significantly deregulated (P-adj < 0.05) between the two bodily fluids. Deregulated miRNAs and epitopes underline an enrichment in ascites in components contributing to the metastatic spread.

CONCLUSION

The results highlight a clear difference between the two biological fluids, suggesting that tumor selectively releases specific EVs populations in serum or ascites. In this context, it seems that ascites-derived EVs play a major role in modulating EMT and metastatic cascade, which is a key feature of OC.

The Role of microRNA in the Prognosis and Diagnosis of Ovarian Cancer

Ovarian cancer (OC) is one of the most common cancers in women. Biomarkers for OC are still being sought. The aim of this review was to evaluate microRNAs in the prognosis and diagnosis of OC. We conducted a literature review searching for articles published from January 2014 to September 2024. We included articles presenting the association of microRNAs with ovarian cancer prognosis, where patient survival was shown by the Kaplan-Meier curve, and articles presenting the association of microRNAs with ovarian cancer diagnosis, where the results were presented as an ROC curve. MicroRNAs are promising clinical markers in ovarian cancer patients. As is shown here, expression (high or low) of various miRNAs was differentially associated with survival in OC patients, with some miRNAs being associated with a longer survival and some with a shorter survival. In the absence of diagnostic markers for OC, the raised role of miRNAs in diagnosis seems all the more important. The diagnostic value of miRNAs has been shown, mostly as blood biomarkers, although they have also been evaluated as tissue or urine markers. MiRNAs have an important role as clinical biomarkers for ovarian cancer, not only as single molecules, but also as biomarker pairs or panels of miRNAs. It should be noted that most of the miRNAs reviewed here have been studied once, so despite the promising results, it seems necessary to conduct studies to confirm or negate the results obtained.

Biobanking and gynecologic oncology - Special considerations, challenges and opportunities

Biobanks of gynecological tissues occupy a critical niche in oncologic research. They are essential components of contemporary research strategies on gynecologic malignancy by integrating clinical, molecular and longitudinal biospecimen data. They also implement protocols for quality control, regulate sample and data sharing, provide ethical and regulatory oversight, and establish governance mechanisms to regulate their function. Gynecologic tissue biobanks also face some unique challenges. The broad heterogeneity of disease entities encompassed under this domain include common and rare malignancies, each with unique molecular subtypes that must be integrated into biobank information systems. Specimen acquisition extends beyond conventional tissues to include cervicovaginal microbiomes and ascitic fluid. Thus, gynecologic tissue biobanks should develop tailored collection strategies and the establishment of dedicated gynecologic tissue repositories that enable the aggregation of rare specimens through collaborative networks. This article emphasizes the need for high-quality annotation of biospecimens, the incorporation of multi-omics approaches to enhance the translational approaches, challenges associated with integration of high dimensional datasets, the role of biobank networks, and various ethical and cultural considerations concerning gynecologic biobanks. Emerging technologies that integrate multi omics, spatial biology and liquid biopsies now offer enhanced opportunities that augment classical specimen collection and should be integrated into standardized protocols.

Leveraging miRNA-mediated expression profiles to predict prognosis and identify distinct molecular subtypes in ovarian cancer: a multi-cohort study

Ovarian cancer (OV) remains the deadliest gynecological malignancy, with non-coding RNA-mediated transcriptomic deregulation significantly influencing its prognosis and heterogeneous progression. In this study, we prioritized miRNA-mediated gene expression profiles by identifying key negative correlations between miRNA-mRNA pairs. We developed a machine learning-based non-coding index (NCI), incorporating a four-gene signature (GAS1, GFPT2, ZFHX4, and KCNA1) to predict patient prognosis and therapeutic response. Validation across multiple datasets revealed that OV patients with higher NCI scores had significantly poorer survival outcomes and resistance to immunotherapy. Additionally, we established a four-class subtyping taxonomy through unsupervised clustering, validated in four independent datasets. The S1 and S3 subtypes were characterized by high NCI scores, abundant stromal and immune infiltration, with the S3 subtype exhibiting the worst survival. Conversely, the S2 subtype showed downregulation of immune response genes, while the S4 subtype displayed epithelial differentiation and favourable prognosis. Integrative analyses of bulk and single-cell transcriptomic data revealed that the S3 subtype had a significantly higher fibroblast proportion compared to other subtypes, whereas the S1 subtype was marked by high T cell content. Through ridge regression-based drug sensitivity analyses, we prioritized candidate therapeutics for each subtype. Notably, the S3 subtype demonstrated sensitivity to dasatinib but resistance to methotrexate. Finally, we developed a user-friendly Shiny-based website to facilitate the application of our prognostic and subtype classification models (https://jli-bioinfo.shinyapps.io/NCI_online/). This study establishes a critical prognostic marker and proposes a novel molecular classification framework grounded in miRNA-regulated gene expression profiles, advancing our understanding of the non-coding mechanisms driving OV heterogeneity.

Effects of Clinical Covariates on Serum miRNA Expression among Women without Ovarian Cancer

BACKGROUND

Serum miRNAs are potential biomarkers for ovarian cancer; however, many factors may influence miRNA expression. To understand potential confounders in miRNA analysis, we examined how sociodemographic factors and comorbidities, including known ovarian cancer risk factors, influence serum miRNA levels in women without ovarian cancer.

METHODS

Data from 1,576 women from the Mass General Brigham Biobank collected between 2012 and 2019, excluding subjects previously or subsequently diagnosed with ovarian cancer, were examined. Using a focused panel of 179 miRNA probes optimized for serum profiling, miRNA expression was measured by flow cytometry using the Abcam FirePlex assay and correlated with subjects' electronic medical records.

RESULTS

The study population broadly reflected the New England population. The median age of subjects was 49 years, 34% were current or prior smokers, 33% were obese (body mass index > 30 kg/m2), 49% were postmenopausal, and 11% had undergone prior bilateral oophorectomy. Significant differences in miRNA expression were observed among ovarian risk factors such as age, obesity, menopause, BRCA1 or BRCA2 germline mutations, or existence of breast cancer in family history. Additionally, miRNA expression was significantly altered by prior bilateral oophorectomy, hypertension, and hypercholesterolemia. Other variables, such as smoking; parity; age at menarche; hormonal replacement therapy; oral contraception; breast, endometrial, or colon cancer; and diabetes, were not associated with significant changes in the panel when corrected for multiple testing.

CONCLUSIONS

Serum miRNA expression patterns are significantly affected by patient demographics, exposure history, and medical comorbidities.

IMPACT

Understanding confounders in serum miRNA expression is important for refining clinical assays for cancer screening.

Analysis of endometrial liquid‑based cytology samples to detect somatic mutations and classify ovarian cancer

Ovarian cancer has a poor prognosis, and screening methods have not been established. Biomarkers based on molecular genetic characteristics must be identified to develop diagnostic and therapeutic strategies for all cancer types, particularly ovarian cancer. The present study aimed to evaluate the usefulness of genetic analysis of cervical and endometrial liquid-based cytology (LBC) specimens for detecting somatic mutations in patients with ovarian cancer. The data of 19 patients with ovarian cancer treated between August 2019 and July 2022 were analyzed. LBC specimens from the cervix and endometrium of patients with preoperatively suspected ovarian cancer were collected, and genomic DNA was extracted from these LBC specimens and surgically removed cancer tissue sections for genetic analysis. Next-generation sequencing (NGS) analysis of cervical and endometrial LBC revealed genetic mutations similar to those in formalin-fixed, paraffin-embedded (FFPE) tissues in 42% of ovarian cancer cases, including negative cervical and endometrial cytology cases and early-stage cases. The pathogenic variants detected were PIK3CA (n=1), RB1 (n=1) and TP53 (n=6). In high-grade serous carcinoma (HGSC) cases, the diagnosis rate was 54.5%, which was higher than that of other histological types. In univariate analysis of patients with HGSC, the presence of serous tubal intraepithelial carcinoma tended to be associated with the detection of somatic mutations in LBC samples. NGS analysis of cervical and endometrial LBC samples revealed genetic variants similar to those in FFPE tissues from ovarian cancer cases and may be useful as a noninvasive screening method for detecting somatic mutations and classifying ovarian cancer.

Early Diagnosis of Ovarian Cancer: A Comprehensive Review of the Advances, Challenges, and Future Directions

Ovarian cancer (OC), the seventh most common cancer in women and the most lethal gynecological malignancy, is a significant global health challenge, with >324,000 new cases and >200,000 deaths being reported annually. OC is characterized by late-stage diagnosis, a poor prognosis, and 5-year survival rates ranging from 93% (early stage) to 20% (advanced stage). Despite advances in genomics and proteomics, effective early-stage diagnostic tools and population-wide screening strategies remain elusive, contributing to high mortality rates. The complex pathogenesis of OC involves diverse histological subtypes and genetic predispositions, including BRCA1/2 mutations; notably, a considerable proportion of OC cases have a hereditary component. Current diagnostic modalities, including imaging techniques (transvaginal ultrasound, computed/positron emission tomography, and magnetic resonance imaging) and biomarkers (CA-125 and human epididymis protein 4), with varying degrees of sensitivity and specificity, have limited efficacy in detecting early-stage OC. Emerging technologies, such as liquid biopsy, multiomics, and artificial intelligence (AI)-assisted diagnostics, may enhance early detection. Liquid biopsies using circulating tumor DNA and microRNAs are popular minimally invasive diagnostic tools. Integrated multiomics has advanced biomarker discovery. AI algorithms have improved imaging interpretation and risk prediction. Novel screening methods including organoids and multiplex panels are being explored to overcome current diagnostic limitations. This review highlights the critical need for continued research and innovation to enhance early diagnosis, reduce mortality, and improve patient outcomes in OC and posits personalized medicine, integrated emerging technologies, and targeted global initiatives and collaborative efforts, which address care access disparities and promote cost-effective, scalable screening strategies, as potential tools to combat OC.

Molecular Biomarkers in Borderline Ovarian Tumors: Towards Personalized Treatment and Prognostic Assessment

Borderline Ovarian Tumours (BOTs) are a heterogenous group of ovarian neoplasms which have increased mitotic activity but lack stromal invasion. We performed a narrative review of the literature, aiming to identify prognostic molecular biomarkers that can potentially be used for treatment personalisation. We identified and discussed BRAF/KRAS, Cancer Antigen 125 (Ca 125), Calprotectin, p16ink4a, and Microsatellite instability (MSI) as the most studied biomarkers related to BOTs. Overall, BRAF and KRAS mutations are associated with earlier-stage and favourable prognosis; KRASmt may indicate extraovarian disease in serous BOT (sBOT). Ca125, the only currently clinically used biomarker, can be assessed pre-operatively and has an established role in post-operative surveillance, especially when it is raised pre-operatively or a high potential for malignant transformation is suspected post-operatively. p16ink4a expression trends could also indicate the malignant transformation of the tumour. Calprotectin has an inferior specificity to Ca125 and is not yet established as a biomarker, whilst there is very limited evidence available for MSI. As new evidence is coming along with artificial intelligence platforms, these biomarkers can be integrated and used towards the development of a precision model for treatment stratification and counselling in women diagnosed with BOTs.

Color-Coded Traffic Signal Method Combined with Nanodiamond Quantum Sensing for Accurate miRNA Detection

Background noise interferes with the accurate detection of early tumor biomarkers. This study introduces a method that effectively reduces background noise to enhance detection accuracy by combining a color-coded signaling approach with the unique fluorescent properties and room-temperature tunable quantum spin characteristics of fluorescent diamonds (FNDs) with nitrogen-vacancy centers. In this approach, a red signal indicates the presence of the target analyte within the spectral region, a green signal indicates its absence, and a yellow signal indicates the need for further analysis using FNDs' quantum spin properties for optical detection magnetic resonance (ODMR) to distinguish the FND signal from background noise. Preliminary results demonstrate that this method enables the detection of breast cancer-related miRNA-21 and miRNA-96 concentrations as low as 1 fM within a 100 × 100 μm2 area, achieving single-molecule detection capability. This method is suitable for accurate biomarker identification and detection under high-background fluorescence conditions.

Harnessing DNA computing and nanopore decoding for practical applications: from informatics to microRNA-targeting diagnostics

DNA computing represents a subfield of molecular computing with the potential to become a significant area of next-generation computation due to the high programmability inherent in the sequence-dependent molecular behaviour of DNA. Recent studies in DNA computing have extended from mathematical informatics to biomedical applications, with a particular focus on diagnostics that exploit the biocompatibility of DNA molecules. The output of DNA computing devices is encoded in nucleic acid molecules, which must then be decoded into human-recognizable signals for practical applications. Nanopore technology, which utilizes an electrical and label-free decoding approach, provides a unique platform to bridge DNA and electronic computing for practical use. In this tutorial review, we summarise the fundamental knowledge, technologies, and methodologies of DNA computing (logic gates, circuits, neural networks, and non-DNA input circuity). We then focus on nanopore-based decoding, and highlight recent advances in medical diagnostics targeting microRNAs as biomarkers. Finally, we conclude with the potential and challenges for the practical implementation of these techniques. We hope that this tutorial will provide a comprehensive insight and enable the general reader to grasp the fundamental principles and diverse applications of DNA computing and nanopore decoding, and will inspire a wide range of scientists to explore and push the boundaries of these technologies.

Evaluation of Plasma microRNA-222 as a Biomarker for Gastric Cancer

Background: The dysregulation of microRNAs (miRNAs) has been detected in patients with gastric cancer (GC), which inspired the use of miRNAs as a novel biomarker for GC. In this study, we investigated the previously reported miRNA dysfunction in cancer tissues as a potential plasma biomarker for GC using quantitative reverse transcriptase polymerase chain reaction (RT-PCR). Methods: The published miRNA abnormalities were searched in the microRNA Cancer Association Database. Plasma samples were collected from patients with GC (n = 26) and controls (n = 17). The sensitivity and specificity of polyadenylation RT-PCR (PA-RT) and stem-loop RT-PCR (SL-RT) were compared. Statistical comparisons between patients with GC and controls were performed to identify miRNA biomarkers, and correlation analyses between the threshold cycle (Ct) values of miRNAs and various blood biochemical parameters were performed to elucidate the confounding factors. Results: mir-17, mir-21, mir-31, mir-99b, mir-222, and U6 were selected. PA-RT showed greater sensitivity and lower specificity than SL-RT (PA-RT vs. SL-RT, mean Ct: 19.6 vs. 29.2; coefficient of variation: 0.42 vs. 0.10). Adopting SL-RT owing to its higher specificity, only mir-222 was significantly upregulated in patients with GC (GC vs. control, miRNA expression: 15.4 vs. 5.27, p = 0.0098). Regarding the correlation between blood biochemical parameters and cells with miRNA expression, mir-31 and mir-99b were correlated with blood urea nitrogen, mir-17, mir-21, and mir-99b were negatively correlated with platelets, and mir-21 was correlated with neutrophils. No obvious correlations were noted between mir-222 expression and blood parameters. Receiver operating characteristic (ROC) curve analysis indicated that mir-222 identified GC patients with a maximum area under the curve (0.73, 95% confidence interval 0.57-0.89). Conclusions: Plasma mir-222 was confirmed to be dysregulated in patients with GC, irrespective of blood biochemical parameters.

Circulating microRNAs as Diagnostic Biomarkers to Detect Specific Stages of Ovarian Cancer: A Comprehensive Meta-Analysis

Ovarian cancer (OC) is one of the most common gynecological cancers [...].

A Chemiluminescence Signal Amplification Method for MicroRNA Detection: The Combination of Molecular Aptamer Beacons with Enzyme-Free Hybridization Chain Reaction

The association between microRNA (miRNA) and various diseases has been established; miRNAs have the potential to be biomarkers for these diseases. Nevertheless, the challenge of correctly quantifying an miRNA arises from its low abundance and a high degree of family homology. Therefore, in the present study, we devised a chemiluminescence (CL) detection method for miRNAs, known as the hybridization chain reaction (HCR)-CL, utilizing the enzyme-free signal amplification technology of HCR. The proposed methodology obviates the need for temperature conversion and offers a straightforward procedure owing to the absence of enzymatic participation, and the lumino-H2O2-mediated CL reaction occurs at a high rate. The technique successfully detected 2.5 amol of the target analyte and 50 amol of miR-146b in a 1% concentration of human serum. In summary, the method developed in this study is characterized by its ease of operation, cost-effectiveness, remarkable analytical prowess, and ability to detect miRNA without the need for total RNA extraction from serum samples. This method is expected to be widely used for biological sample testing in clinical settings.

Simultaneous detection of multiple microRNAs based on fluorescence resonance energy transfer under a single excitation wavelength

MicroRNAs (miRNAs) play a key role in physiological processes, and their dysregulation is closely related to various human diseases. Simultaneous detection of multiple miRNAs is pivotal to cancer diagnosis at an early stage. However, most multicomponent analyses generally involve multiple excitation wavelengths, which are complicated and often challenging to simultaneously acquire multiple detection signals. In this study, a convenient and sensitive sensor was developed to simultaneously detection of multiple miRNAs under a single excitation wavelength through the fluorescence resonance energy transfer between the carbon dots (CDs)/quantum dots (QDs) and graphene oxide (GO). A hybridization chain reaction (HCR) was triggered by miRNA-141 and miRNA-21, resulting in the high sensitivity with a limit of detection (LOD) of 50 pM (3σ/k) for miRNA-141 and 60 pM (3σ/k) for miRNA-21. This simultaneous assay also showed excellent specificity discrimination against the mismatch. Furthermore, our proposed method successfully detected miRNA-21 and miRNA-141 in human serum samples at a same time, indicating its diagnostic potential in a clinical setting.

Integration of RCA-Based DNA Nanoscaffold with Target Triggered RNA-Cleaving DNAzyme for Sensitive Detection of miRNA21

Cascaded amplification showed promising potential for detection of trace target miRNAs in molecular diagnosis and prevention of many diseases. In this study, miRNA21 was chosen as the target, and rolling circle amplification (RCA)-based DNA nanoscaffold was integrated with target triggered RNA-cleaving DNAzyme for sensitive detection of miRNA21. That is, the H1 probe was bound with the long-chain product of RCA to self-assemble into DNA nanoscaffold. Target miRNA21 triggered the hybridization chain reaction (HCR) located on the nanoscaffold, and led to rapid proximity of DNAzyme fragments modified at both ends of the H2 probe, which realized the cyclic cleavage of self-quenching substrate probe efficiently, and the fluorescence signal was restored. The results demonstrated that the proposed assay was sensitive, 0.76 pM of miRNA21 can be detected. The proposed assay was specific; only one-base mismatched miRNA21 can be effectively recognized, other nucleic acid sequence and the serum matrix did not cause any interference. The proposed assay was accurate; recoveries from 82.1 to 115.0% can be obtained in the spiked fetal bovine serum (FBS). The flexible and programmable characteristics of DNA nanoscaffold and DNAzyme provide a confident and robust strategy for more sensitive nucleic acid detection, and can be developed to be a universal sensing platform for detecting other miRNAs just needing modification on the corresponding sequence of H1 probe in HCR.

Comprehensive analysis of electrochemical biosensors for early ovarian cancer detection

Ovarian cancer is one of the leading causes of mortality among women worldwide. However, early detection can significantly reduce mortality rates and mitigate subsequent complications related to both economic burden and mental well-being. Despite the development in the field of medical diagnosis, the death rates due to ovarian cancer have sharply increased. Among the recent technologies suggested as suitable diagnostic techniques for the early detection of ovarian cancer, biosensor technology has emerged as a cutting-edge technology, with electrochemical biosensors providing one of the most efficient types of biosensors. Therefore, this review discusses the application of electrochemical biosensors as a viable alternative to conventional diagnostic techniques for the timely identification of ovarian cancer, its advantages over other types of biosensors and conventional diagnostic techniques, and the types of electrochemical biosensors.

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

OBJECTIVE

To determine whether a multimodal assay combining serum microRNA with protein biomarkers and metadata improves triage assessment of an adnexal mass.

METHODS

Serum samples from 468 training subjects (191 cancer cases and 277 benign adnexal mass controls or healthy controls) were analyzed for seven protein biomarkers and 180 miRNA. Circulating analyte data were combined with age and menopausal status (metadata) into a neural network model to classify samples as cases or controls. Forward regression with ten-fold cross-validation minimized the dimensionality of the model while maximizing linear separation between cases and controls. Model validation proceeded using both internal (44 cases and 56 controls) and external validation sets (51 cases and 59 controls).

RESULTS

The total study population comprised 678 subjects, including 286 cases and 392 controls. Overall, 290 (43%) of the subjects were premenopausal. A panel of 10 miRNA delivered optimal performance when combined with protein and metadata features. The combined model improved the Receiver Operator Characteristic Area Under the Curve (ROC AUC) on the internal (AUC = 0.9; 95% CI 0.81-0.95) and external validation sets (AUC = 0.95; 95% CI 0.90-0.98) compared to miRNA alone or proteins plus metadata (without miRNA). On external validation, the combined model offered 92% sensitivity at 80% specificity overall, with 80% and 100% sensitivity for early and late-stage cancers, respectively, including 78% sensitivity for early-stage, serous ovarian cancers and 82% sensitivity for early-stage, non-serous cancers.

CONCLUSIONS

A multimodal assay combining miRNA with protein biomarkers, age, and menopausal status improves surgical triage of an adnexal mass.

A deep learning method to integrate extracelluar miRNA with mRNA for cancer studies

MOTIVATION

Extracellular miRNAs (exmiRs) and intracellular mRNAs both can serve as promising biomarkers and therapeutic targets for various diseases. However, exmiR expression data is often noisy, and obtaining intracellular mRNA expression data usually involves intrusive procedures. To gain valuable insights into disease mechanisms, it is thus essential to improve the quality of exmiR expression data and develop noninvasive methods for assessing intracellular mRNA expression.

RESULTS

We developed CrossPred, a deep-learning multi-encoder model for the cross-prediction of exmiRs and mRNAs. Utilizing contrastive learning, we created a shared embedding space to integrate exmiRs and mRNAs. This shared embedding was then used to predict intracellular mRNA expression from noisy exmiR data and to predict exmiR expression from intracellular mRNA data. We evaluated CrossPred on three types of cancers and assessed its effectiveness in predicting the expression levels of exmiRs and mRNAs. CrossPred outperformed the baseline encoder-decoder model, exmiR or mRNA-based models, and variational autoencoder models. Moreover, the integration of exmiR and mRNA data uncovered important exmiRs and mRNAs associated with cancer. Our study offers new insights into the bidirectional relationship between mRNAs and exmiRs.

AVAILABILITY AND IMPLEMENTATION

The datasets and tool are available at https://doi.org/10.5281/zenodo.13891508.

Circulating serum miRNAs predict response to platinum chemotherapy in high-grade serous ovarian cancer

BACKGROUND

Platinum chemotherapy is the cornerstone of treatment for high-grade serous ovarian cancer (HGSOC); however, validated biomarkers that can accurately predict platinum response are lacking. Based on their roles in the underlying pathophysiology, circulating microRNAs are potential, noninvasive biomarkers in cancer. In the present study, we aimed to evaluate the circulating miRNA profiles of patients with HGSOC and to assess their potential utility as biomarkers to predict platinum response.

METHODS

Pretreatment serum samples collected from patients who received platinum chemotherapy for Stage III-IV HGSOC between 2008 and 2016 were analyzed using miRNA microarray. LASSO logistic regression analysis was used to construct predictive models for treatment-free interval of platinum (TFIp).

RESULTS

The median follow-up was 54.6 (range, 3.5-144.1) months. The comprehensive analysis of 2588 miRNAs was performed in serum samples of 153 eligible patients, and predictive models were constructed using a combination of circulating miRNAs with an area under the receiver operating characteristic curve of 0.944 for TFIp >1 month, 0.637 for TFIp ≥6 months, 0.705 for TFIp ≥12 months, and 0.938 for TFIp ≥36 months. Each predictive model provided a significant TFIp classification (p = 0.001 in TFIp >1 month, p = 0.013 in TFIp ≥6 months, p < 0.001 in TFIp ≥12 months, and p < 0.001 in TFIp ≥36 months).

CONCLUSION

Circulating miRNA profiles has potential utility in predicting platinum response in patients with HGSOC and can aid clinicians in choosing appropriate treatment strategies.

Early Cancer Detection via Multi-microRNA Profiling of Urinary Exosomes Captured by Nanowires

Multiple microRNAs encapsulated in extracellular vesicles (EVs) including exosomes, unique subtypes of EVs, differ in healthy and cancer groups of people, and they represent a warning sign for various cancer scenarios. Since all EVs in blood cannot be transferred from donor to recipient cells during a single blood circulation, kidney filtration could pass some untransferred EVs from blood to urine. Previously, we reported on the ability of zinc oxide nanowires to capture EVs based on surface charge and hydrogen bonding; these nanowires extracted massive numbers of microRNAs in urine, seeking cancer-related microRNAs through statistical analysis. Here, we report on the scalability of the nanowire performance capability to comprehensively capture EVs, including exosomes, in urine, extract microRNAs from the captured EVs in situ, and identify multiple microRNAs in the extracted microRNAs differing in noncancer and lung cancer subjects through machine learning-based analysis. The nanowire-based extraction allowed the presence of about 2500 species of urinary microRNAs to be confirmed, meaning that urine includes almost all human microRNA species. The machine learning-based analysis identified multiple microRNAs from the extracted microRNA species. The ensembles could classify cancer and noncancer subjects with the area under the receiver operating characteristic curve of 0.99, even though the former were staged early.

The miRNA and PD-1/PD-L1 signaling axis: an arsenal of immunotherapeutic targets against lung cancer

Lung cancer is a severe challenge to the health care system with intrinsic resistance to first and second-line chemo/radiotherapies. In view of the sterile environment of lung cancer, several immunotherapeutic drugs including nivolumab, pembrolizumab, atezolizumab, and durvalumab are currently being used in clinics globally with the intention of releasing exhausted T-cells back against refractory tumor cells. Immunotherapies have a limited response rate and may cause immune-related adverse events (irAEs) in some patients. Hence, a deeper understanding of regulating immune checkpoint interactions could significantly enhance lung cancer treatments. In this review, we explore the role of miRNAs in modulating immunogenic responses against tumors. We discuss various aspects of how manipulating these checkpoints can bias the immune system's response against lung cancer. Specifically, we examine how altering the miRNA profile can impact the activity of various immune checkpoint inhibitors, focusing on the PD-1/PD-L1 pathway within the complex landscape of lung cancer. We believe that a clear understanding of the host's miRNA profile can influence the efficacy of checkpoint inhibitors and significantly contribute to existing immunotherapies for lung cancer patients. Additionally, we discuss ongoing clinical trials involving immunotherapeutic drugs, both as standalone treatments and in combination with other therapies, intending to advance the development of immunotherapy for lung cancer.

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

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

Genetic landscape of homologous recombination repair and practical outcomes of PARPi therapy in ovarian cancer management

Background

Genetic studies of ovarian cancer (OC) have historically focused on BRCA1/2 mutations, lacking other studies of homologous recombination repair (HRR). Poly (ADP-ribose) polymerase inhibitors (PARPi) exploit synthetic lethality to significantly improve OC treatment outcomes, especially in BRCA1/2 deficiency patients.

Objectives

Our study aims to construct a mutation map of HRR genes in OC and identify factors influencing the efficacy of PARPi.

Design

A retrospective observational analysis of HRR gene variation data from 695 OC patients from March 2019 to February 2022 was performed.

Methods

The HRR gene variation data of 695 OC patients who underwent next-generation sequencing (NGS) in the First Affiliated Hospital of Zhengzhou University were retrospectively collected. Clinical data on the use of PARPi in these patients were also gathered to identify factors that may interfere with the efficacy of PARPi.

Results

Out of 127 pathogenic variants in the BRCA1/2 genes, 104 (81.9%) were BRCA1 mutations, and 23 (18.1%) were BRCA2 mutations. Among the 59 variants of uncertain significance (VUS), 20 (33.9%) were BRCA1, while 39 (66.1%) were BRCA2 mutations. In addition to BRCA1/2, HRR gene results showed that 9 (69%) of 13 were HRR pathway pathogenic variants; and 16 (1.7%) of 116 VUS were Food and Drug Administration (FDA)-approved mutated HRR genes. Notably, the treatment regimen significantly influenced the effectiveness of PARPi, especially when using first-line maintenance therapy, leading to enhanced progression-free survival (PFS) compared to alternative protocols.

Conclusion

Focusing on HRR gene mutations and supporting clinical research about PARPi in OC patients is crucial for developing precision treatment strategies and enhancing prognosis.

Comprehensive serum glycopeptide spectra analysis to identify early-stage epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is widely recognized as the most lethal gynecological malignancy; however, its early-stage detection remains a considerable clinical challenge. To address this, we have introduced a new method, named Comprehensive Serum Glycopeptide Spectral Analysis (CSGSA), which detects early-stage cancer by combining glycan alterations in serum glycoproteins with tumor markers. We detected 1712 glycopeptides using liquid chromatography-mass spectrometry from the sera obtained from 564 patients with EOC and 1149 controls across 13 institutions. Furthermore, we used a convolutional neural network to analyze the expression patterns of the glycopeptides and tumor markers. Using this approach, we successfully differentiated early-stage EOC (Stage I) from non-EOC, with an area under the curve (AUC) of 0.924 in receiver operating characteristic (ROC) analysis. This method markedly outperforms conventional tumor markers, including cancer antigen 125 (CA125, 0.842) and human epididymis protein 4 (HE4, 0.717). Notably, our method exhibited remarkable efficacy in differentiating early-stage ovarian clear cell carcinoma from endometrioma, achieving a ROC-AUC of 0.808, outperforming CA125 (0.538) and HE4 (0.557). Our study presents a promising breakthrough in the early detection of EOC through the innovative CSGSA method. The integration of glycan alterations with cancer-related tumor markers has demonstrated exceptional diagnostic potential.

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.

Sensitive fluorescence detection of miRNA-124 in cardiomyocytes under oxidative stress using a nucleic acid probe

MicroRNAs (miRNAs) are small noncoding RNAs of 18-25 bases. miRNAs are also important new biomarkers that can be used for disease diagnosis in the future. Studies have shown that miR-124 levels are significantly elevated during acute myocardial infarction (AMI) and play a key role in the cardiovascular system. A variety of methods have been established to detect myocardial infarction-related miRNAs. However, most require complex miRNA extraction and isolation, and these methods are virtually undetectable when RNA levels are low in the sample. It may lead to biased results. Thus, it is necessary to develop a technique that can detect miRNA without extracting it, which means that intracellular detection is of great significance. Here, we improved the traditional silicon spheres and obtained a biosensor that could effectively capture and detect specific noncoding nucleic acids through the layer-by-layer assembly method. The sensor is protected by hyaluronic acid so it can successfully escape the lysosome into the cell and achieve detection. With the help of a full-featured microplate reader, we determined that the detection limit of the biosensor could reach 1 fM, meeting the needs of intracellular detection. At the same time, we prepared an oxidative stress cardiomyocyte infarction model and successfully captured the overexpressed miR-124 in the infarcted cells to achieve in situ detection. This study could provide a new potential tool to develop miRNAs for sensitive diagnosis in AMI, and the proposed strategy implies its potential for biomedical research.

Validation of miRNA signatures for ovarian cancer earlier detection in the pre-diagnosis setting using machine learning approaches

Introduction

Effective strategies for early detection of epithelial ovarian cancer are lacking. We evaluated whether a panel of 14 previously established circulating microRNAs could discriminate between cases diagnosed <2 years after serum collection and those diagnosed 2-7 years after serum collection. miRNA sequencing data from subsequent ovarian cancer cases were obtained as part of the ongoing multi-cancer JanusRNA project, utilizing pre-diagnostic serum samples from the Janus Serum Bank and linked to the Cancer Registry of Norway for cancer outcomes.

Methods

We included a total of 80 ovarian cancer cases contributing 80 serum samples and compared 40 serum samples from cases with samples collected <2 years prior to diagnosis with 40 serum samples from cases with sample collection ≥2 to 7 years. We employed the extreme gradient boosting (XGBoost) algorithm to train a binary classification model using 70% of the available data, while the model was tested on the remaining 30% of the dataset.

Results

The performance of the model was evaluated using repeated holdout validation. The previously established set of miRNAs achieved a median area under the receiver operating characteristic curve (AUC) of 0.771 in the test sets. Four out of 14 miRNAs (hsa-miR-200a-3p, hsa-miR-1246, hsa-miR-203a-3p, hsa-miR-23b-3p) exhibited higher expression levels closer to diagnosis, consistent with the previously reported upregulation in cancer cases, with statistical significance observed only for hsa-miR-200a-3p (beta=0.14; p=0.04).

Discussion

The discrimination potential of the selected models provides evidence of the robustness of the miRNA signature for ovarian cancer.

Comparing preprocessing strategies for 3D-Gene microarray data of extracellular vesicle-derived miRNAs

BACKGROUND

Extracellular vesicle-derived (EV)-miRNAs have potential to serve as biomarkers for the diagnosis of various diseases. miRNA microarrays are widely used to quantify circulating EV-miRNA levels, and the preprocessing of miRNA microarray data is critical for analytical accuracy and reliability. Thus, although microarray data have been used in various studies, the effects of preprocessing have not been studied for Toray's 3D-Gene chip, a widely used measurement method. We aimed to evaluate batch effect, missing value imputation accuracy, and the influence of preprocessing on measured values in 18 different preprocessing pipelines for EV-miRNA microarray data from two cohorts with amyotrophic lateral sclerosis using 3D-Gene technology.

RESULTS

Eighteen different pipelines with different types and orders of missing value completion and normalization were used to preprocess the 3D-Gene microarray EV-miRNA data. Notable results were suppressed in the batch effects in all pipelines using the batch effect correction method ComBat. Furthermore, pipelines utilizing missForest for missing value imputation showed high agreement with measured values. In contrast, imputation using constant values for missing data exhibited low agreement.

CONCLUSIONS

This study highlights the importance of selecting the appropriate preprocessing strategy for EV-miRNA microarray data when using 3D-Gene technology. These findings emphasize the importance of validating preprocessing approaches, particularly in the context of batch effect correction and missing value imputation, for reliably analyzing data in biomarker discovery and disease research.

Uncovering miRNA-mRNA Regulatory Networks Related to Olaparib Resistance and Resensitization of BRCA2MUT Ovarian Cancer PEO1-OR Cells with the ATR/CHK1 Pathway Inhibitors

Resistance to olaparib is the major obstacle in targeted therapy for ovarian cancer (OC) with poly(ADP-ribose) polymerase inhibitors (PARPis), prompting studies on novel combination therapies to enhance olaparib efficacy. Despite identifying various mechanisms, understanding how OC cells acquire PARPi resistance remains incomplete. This study investigated microRNA (miRNA) expression in olaparib-sensitive (PEO1, PEO4) and previously established olaparib-resistant OC cell lines (PEO1-OR) using high-throughput RT-qPCR and bioinformatic analyses. The role of miRNAs was explored regarding acquired resistance and resensitization with the ATR/CHK1 pathway inhibitors. Differentially expressed miRNAs were used to construct miRNA-mRNA regulatory networks and perform functional enrichment analyses for target genes with miRNet 2.0. TCGA-OV dataset was analyzed to explore the prognostic value of selected miRNAs and target genes in clinical samples. We identified potential processes associated with olaparib resistance, including cell proliferation, migration, cell cycle, and growth factor signaling. Resensitized PEO1-OR cells were enriched in growth factor signaling via PDGF, EGFR, FGFR1, VEGFR2, and TGFβR, regulation of the cell cycle via the G2/M checkpoint, and caspase-mediated apoptosis. Antibody microarray analysis confirmed dysregulated growth factor expression. The addition of the ATR/CHK1 pathway inhibitors to olaparib downregulated FGF4, FGF6, NT-4, PLGF, and TGFβ1 exclusively in PEO1-OR cells. Survival and differential expression analyses for serous OC patients revealed prognostic miRNAs likely associated with olaparib resistance (miR-99b-5p, miR-424-3p, and miR-505-5p) and resensitization to olaparib (miR-324-5p and miR-424-3p). Essential miRNA-mRNA interactions were reconstructed based on prognostic miRNAs and target genes. In conclusion, our data highlight distinct miRNA profiles in olaparib-sensitive and olaparib-resistant cells, offering molecular insights into overcoming resistance with the ATR/CHK1 inhibitors in OC. Moreover, some miRNAs might serve as potential predictive signature molecules of resistance and therapeutic response.

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.

Current strategies for early epithelial ovarian cancer detection using miRNA as a potential tool

Ovarian cancer is one of the most aggressive and significant malignant tumor forms in the female reproductive system. It is the leading cause of death among gynecological cancers owing to its metastasis. Since its preliminary disease symptoms are lacking, it is imperative to develop early diagnostic biomarkers to aid in treatment optimization and personalization. In this vein, microRNAs, which are short sequence non-coding molecules, displayed great potential as highly specific and sensitive biomarker. miRNAs have been extensively advocated and proven to serve an instrumental part in the clinical management of cancer, especially ovarian cancer, by promoting the cancer cell progression, invasion, delayed apoptosis, epithelial-mesenchymal transition, metastasis of cancer cells, chemosensitivity and resistance and disease therapy. Here, we cover our present comprehension of the most up-to-date microRNA-based approaches to detect ovarian cancer, as well as current diagnostic and treatment strategies, the role of microRNAs as oncogenes or tumor suppressor genes, and their significance in ovarian cancer progression, prognosis, and therapy.

A miRNA-7704/IL2RB/AKT feedback loop regulates tumorigenesis and chemoresistance in ovarian cancer

Ovarian cancer is one of the most common gynecological tumors worldwide. Despite the availability of multiple treatments for ovarian cancer, its resistance to chemotherapy remains a significant challenge. miRNAs play crucial roles in the initiation and progression of cancer by affecting processes such as differentiation, proliferation, and chemoresistance. According to microarray and qPCR analyses, miR-7704 is significantly downregulated in cisplatin-resistant cells compared to parental cells. In this study, we found that miR-7704 inhibited the proliferation and promoted cisplatin sensitivity of ovarian cancer cells in vitro and in vivo. Moreover, ectopic expression of miR-7704 had the same effect as IL2RB knockdown. Further mechanistic studies revealed that miR-7704 played an inhibitory role by regulating IL2RB expression to inactivate the AKT signaling pathway. Furthermore, IL2RB reversed the miR-7704 mediated resistance to cisplatin in ovarian cancer. Based on these findings, miR-7704 and IL2RB show the potential as novel therapeutic targets for ovarian cancer.

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.

Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)

Artificial intelligence (AI) has emerged as a crucial technique for extracting high‑throughput information from various sources, including medical images, pathological images, and genomics, transcriptomics, proteomics and metabolomics data. AI has been widely used in the field of diagnosis, for the differentiation of benign and malignant ovarian cancer (OC), and for prognostic assessment, with favorable results. Notably, AI‑based radiomics has proven to be a non‑invasive, convenient and economical approach, making it an essential asset in a gynecological setting. The present study reviews the application of AI in the diagnosis, differentiation and prognostic assessment of OC. It is suggested that AI‑based multi‑omics studies have the potential to improve the diagnostic and prognostic predictive ability in patients with OC, thereby facilitating the realization of precision medicine.

Over-expression of mir-181a-3p in serum of breast cancer patients as diagnostic biomarker

BACKGROUND

Recently, the significance of epigenetics, particularly in breast cancer (BC), has become increasingly recognized. MicroRNAs (miRNAs), as an epigenetic factor, are offering valuable insights into various aspects of BC, such as its origins and clinical features. Therefore, studying the disruption of specific miRNAs through microarray and RNA-seq techniques is considered useful.

METHODS AND MATERIALS

We analyzed two microarray datasets (GSE106817 and GSE113486) in order to discover dysregulated miRNAs in the serum of BC patients. Subsequently, RNA-seq analysis was employed on the TCGA data. Two miRNAs, mir-450a-5p and mir-181a-3p, as novel miRNAs in BC studies, were selected for assessment in the serum of 100 BC patients versus 100 healthy female participants. The quantities of the miRNAs described above were determined through the utilization of RT-qPCR. Furthermore, ROC curve assessments were conducted for each individual miRNA. Next, an assessment was conducted to determine the prognostic significance of these miRNAs.

CONCLUSIONS

In summary, the utilization of microarray and RNA-seq analysis techniques has demonstrated that mir-450a-5p and mir-181a-3p exhibit elevated expression levels in the serum of BC patients. However, it is noteworthy that only mir-181a-3p displayed clinical dysregulation, as confirmed by RT-PCR findings. These miRNAs have been found to play a crucial role in modulating essential cellular processes and biological functions that contribute to cancer development. Furthermore, noteworthy outcomes have been observed for mir-181a-3p in relation to diagnostic and prognostic clinical characteristics.

Highly sensitive SERS platform for pathogen analysis by cyclic DNA nanostructure@AuNP tags and cascade primer exchange reaction

The capacity to identify small amounts of pathogens in real samples is extremely useful. Herein, we proposed a sensitive platform for detecting pathogens using cyclic DNA nanostructure@AuNP tags (CDNA) and a cascade primer exchange reaction (cPER). This platform employs wheat germ agglutinin-modified Fe3O4@Au magnetic nanoparticles (WMRs) to bind the E. coli O157:H7, and then triggers the cPER to generate branched DNA products for CDNA tag hybridization with high stability and amplified SERS signals. It can identify target pathogens as low as 1.91 CFU/mL and discriminate E. coli O157:H7 in complex samples such as water, milk, and serum, demonstrating comparable or greater sensitivity and accuracy than traditional qPCR. Moreover, the developed platform can detect low levels of E. coli O157:H7 in mouse serum, allowing the discrimination of mice with early-stage infection. Thus, this platform holds promise for food analysis and early infection diagnosis.

The role of liquid biopsy in epithelial ovarian cancer: State of the art

The clinical implementation of liquid biopsy has dramatically modified the analytical paradigm for several solid tumors. To date, however, only circulating free DNA (cfDNA) has been approved in clinical practice to select targeted treatments for patients with colorectal cancer (CRC), non-small cell lung cancer (NSCLC), and breast cancer (BC). Interestingly, emerging liquid biopsy analytes in peripheral blood, including circulating tumor cells (CTC), miRNA, and extracellular vesicles (EVs), have been shown to play a crucial role in the clinical management of solid tumor patients. Here, we review how these blood-based biomarkers may positively impact early diagnosis, prognosis, and treatment response in ovarian cancer (OC) patients.

miR‑146a‑5p and miR‑191‑5p as novel diagnostic marker candidates for ovarian clear cell carcinoma

Ovarian cancer is a malignant gynecologic disease rarely diagnosed in the early stages. Among the various types of ovarian cancer, clear cell carcinoma has a poor prognosis due to its malignant potential. MicroRNAs (miRNAs/miRs) regulate gene expression in cells by suppressing the translation of target genes or by degrading the target mRNA. miRNAs are also secreted from the cells in the blood, binding to proteins or lipids and assisting in cell-cell communication. Therefore, serum miRNAs may be considered potential diagnostic biomarkers for ovarian cancer. The present study investigated and identified specific miRNAs associated with ovarian clear cell carcinoma and compared them to those in ovarian endometrioma samples and healthy controls. CA125, an ovarian tumor marker, did not differ between patients with ovarian clear cell carcinoma, endometriosis or healthy controls. Subsequently, four miRNAs (miR-146a-5p, miR-191-5p, miR-484 and miR-574-3p) were analyzed. The expression levels of miR-146a-5p and miR-191-5p were significantly increased in the serum samples from patients with ovarian clear cell carcinoma compared with those in the healthy controls, but there was no significant difference compared with in patients with endometriosis. Furthermore, the bioinformatics analysis showed that CCND2 and NOTCH2 were the candidate target genes of miR-146a-5p and miR-191-5p. In conclusion, the results of the present study demonstrated that miR-146a-5p and miR-191-5p may be useful as early and non-invasive diagnostic tools in ovarian clear cell carcinoma. These miRNAs can help in distinguishing between ovarian clear cell carcinoma and ovarian endometrioma. To the best of our knowledge, no previous studies have screened any candidates specifically for ovarian clear cell carcinoma.

An Aptamer-Based Nanoflow Cytometry Method for the Molecular Detection and Classification of Ovarian Cancers through Profiling of Tumor Markers on Small Extracellular Vesicles

Molecular profiling of protein markers on small extracellular vesicles (sEVs) is a promising strategy for the precise detection and classification of ovarian cancers. However, this strategy is challenging owing to the lack of simple and practical detection methods. In this work, using an aptamer-based nanoflow cytometry (nFCM) detection strategy, a simple and rapid method for the molecular profiling of multiple protein markers on sEVs was developed. The protein markers can be easily labeled with aptamer probes and then rapidly profiled by nFCM. Seven cancer-associated protein markers, including CA125, STIP1, CD24, EpCAM, EGFR, MUC1, and HER2, on plasma sEVs were profiled for the molecular detection and classification of ovarian cancers. Profiling these seven protein markers enabled the precise detection of ovarian cancer with a high accuracy of 94.2 %. In addition, combined with machine learning algorithms, such as linear discriminant analysis (LDA) and random forest (RF), the molecular classifications of ovarian cancer cell lines and subtypes were achieved with overall accuracies of 82.9 % and 55.4 %, respectively. Therefore, this simple, rapid, and non-invasive method exhibited considerable potential for the auxiliary diagnosis and molecular classification of ovarian cancers in clinical practice.

High-Grade Serous Ovarian Cancer-A Risk Factor Puzzle and Screening Fugitive

High-grade serous ovarian cancer (HGSOC) is the most lethal tumor of the female genital tract. Despite extensive studies and the identification of some precursor lesions like serous tubal intraepithelial cancer (STIC) or the deviated mutational status of the patients (BRCA germinal mutation), the pathophysiology of HGSOC and the existence of particular risk factors is still a puzzle. Moreover, a lack of screening programs results in delayed diagnosis, which is accompanied by a secondary chemo-resistance of the tumor and usually results in a high recurrence rate after the primary therapy. Therefore, there is an urgent need to identify the substantial risk factors for both predisposed and low-risk populations of women, as well as to create an economically and clinically justified screening program. This paper reviews the classic and novel risk factors for HGSOC and methods of diagnosis and prediction, including serum biomarkers, the liquid biopsy of circulating tumor cells or circulating tumor DNA, epigenetic markers, exosomes, and genomic and proteomic biomarkers. The novel future complex approach to ovarian cancer diagnosis should be devised based on these findings, and the general outcome of such an approach is proposed and discussed in the paper.

Differential microRNA Expression Analysis in Patients with HPV-Infected Ovarian Neoplasms

This study aimed to identify microRNAs (miRNAs) whose expression levels are altered by high-risk human papillomavirus (HR-HPV) infection in women with epithelial ovarian neoplasms. MiRNA expression was quantified by real-time polymerase chain reaction, while HR-HPV DNA was quantified using digital-droplet PCR. Analysis of 11 miRNAs demonstrated significantly lower hsa-miR-25-5p expression in HPV-infected compared to uninfected ovarian tissues (p = 0.0405), while differences in miRNA expression in corresponding serum were statistically insignificant. The expression of hsa-miR-218-5p in ovarian tumors was significantly higher in high-grade serous ovarian carcinoma (HGSOC) cases than in other neoplasms (p = 0.0166). In addition, hsa-miR-218-5p was significantly upregulated, whereas hsa-miR-191-5p was significantly downregulated in tissues with stage III/IV FIGO (p = 0.0009 and p = 0.0305, respectively). Using unsupervised clustering, we identified three unique patient groups with significantly varied frequencies of HPV16/18-positive samples and varied miRNA expression profiles. In multivariate analysis, high expression of hsa-miR-16-5p was an independent prognostic factor for poor overall survival (p = 0.0068). This preliminary analysis showed the changes in miRNA expression in ovarian neoplasms during HPV infection and those collected from HGSOCs or patients with advanced disease. This prospective study can provide new insights into the pathogenesis of ovarian neoplasms and host-virus interactions.

Circ_0070203 Promotes Epithelial-mesenchymal Transition in Ovarian Serous Cystadenocarcinoma through miR-370-3p/TGFβR2 Axis

INTRODUCTION

Circular RNAs (circRNAs) are important biological molecules associated with the pathogenesis of multiple cancers.

OBJECTIVE

This work aimed to investigate the function and molecular mechanism of circ_0070203 in high-grade serous ovarian cystadenocarcinoma (HGSOC).

METHODS

circRNA microarray was conducted to detect the circ_0070203 expression in HGSOC tissues. Bioinformatics analysis was used to find the binding sites between circ_0070203, miR- 370-3p and TGFβR2. Real-time quantitative reverse transcription PCR (RT-qPCR) was executed to detect the expressions of circ_0070203, miR-370-3p and TGFβR2 in HGSOC tissues and SKOV3 cells. Dual-luciferase reporter gene assay was used to validate the relationships between miR-370-3p and circ_0070203 or TGFβR2. Besides, transwell assays were conducted to assess the migrative, invasive abilities of ovarian cancer (OC) cells. Western blotting was adopted to detect the expression of epithelial-mesenchymal transition (EMT)-related proteins. The related patents were also studied during the research.

RESULTS

Circ_0070203 and TGFβR2 were upregulated, while miR-370-3p was downregulated in FIGO stage III-IV HGSOC tissues and SKOV-3 cell lines. circ_0070203 overexpression changed the expression of other EMT-related proteins and enhanced the migrative, invasive abilities of OC cells, while silencing circ_0070203 worked oppositely. Mechanistically, circ_0070203 could upregulate TGFβR2 expression in OC cells via sponging miR-370-3p.

CONCLUSION

Circ_0070203 could promote the epithelial-mesenchymal transition, invasion, and metastasis of HGSOC via regulating the miR-370-3p/TGFβR2 axis. Our findings provided a potential biomarker for HGSOC therapy.

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.

Identification of stably expressed microRNAs in plasma from high-grade serous ovarian carcinoma and benign tumor patients

BACKGROUND

Ovarian cancer is a lethal gynecological cancer and no reliable minimally invasive early diagnosis tools exist. High grade serous ovarian carcinoma (HGSOC) is often diagnosed at advanced stages, resulting in poorer outcome than those diagnosed in early stage. Circulating microRNAs have been investigated for their biomarker potential. However, due to lack of standardization methods for microRNA detection, there is no consensus, which microRNAs should be used as stable endogenous controls. We aimed to identify microRNAs that are stably expressed in plasma of HGSOC and benign ovarian tumor patients.

METHODS AND RESULTS

We isolated RNA from plasma samples of 60 HGSOC and 48 benign patients. RT-qPCR was accomplished with a custom panel covering 40 microRNAs and 8 controls. Stability analysis was performed using five algorithms: Normfinder, geNorm, Delta-Ct, BestKeeper and RefFinder using an R-package; RefSeeker developed by our study group [1]. Among 41 analyzed RNAs, 13 were present in all samples and eligible for stability analysis. Differences between stability rankings were observed across algorithms. In HGSOC samples, hsa-miR-126-3p and hsa-miR-23a-3p were identified as the two most stable miRNAs. In benign samples, hsa-miR-191-5p and hsa-miR-27a-3p were most stable. In the combined HGSOC and benign group, hsa-miR-23a-3p and hsa-miR-27a-3p were identified by both the RefFinder and Normfinder analysis as the most stable miRNAs.

CONCLUSIONS

Consensus regarding normalization approaches in microRNA studies is needed. The choice of endogenous microRNAs used for normalization depends on the histological content of the cohort. Furthermore, normalization also depends on the algorithms used for stability analysis.

Identification of serum miR-1246 and miR-150-5p as novel diagnostic biomarkers for high-grade serous ovarian cancer

Epithelial ovarian cancer (EOC) is one of the leading cancers in women, with high-grade serous ovarian cancer (HGSOC) being the most common and lethal subtype of this disease. A vast majority of HGSOC are diagnosed at the late stage of the disease when the treatment and total recovery chances are low. Thus, there is an urgent need for novel, more sensitive and specific methods for early and routine HGSOC clinical diagnosis. In this study, we performed miRNA expression profiling using the NanoString miRNA assay in 34 serum samples from patients with HGSOC and 36 healthy women. We identified 13 miRNAs that were differentially expressed (DE). For additional exploration of expression patterns correlated with HGSOC, we performed weighted gene co-expression network analysis (WGCNA). As a result, we showed that the module most correlated with tumour size, nodule and metastasis contained 8 DE miRNAs. The panel including miR-1246 and miR-150-5p was identified as a signature that could discriminate HGSOC patients with AUCs of 0.98 and 1 for the training and test sets, respectively. Furthermore, the above two-miRNA panel had an AUC = 0.946 in the verification cohorts of RT-qPCR data and an AUC = 0.895 using external data from the GEO public database. Thus, the model we developed has the potential to markedly improve the diagnosis of ovarian cancer.

Early Detection of Ovarian Cancer

The risk of death from ovarian cancer is highly associated with the clinical stage at diagnosis. Efforts to implement screening for ovarian cancer have been largely unsuccessful, due to the low prevalence of the disease in the general population and the heterogeneity of the various cancer types that fall under the ovarian cancer designation. A practical test for early detection will require both high sensitivity and high specificity to balance reducing the number of cancer deaths with minimizing surgical interventions for false positive screens. The technology must be cost-effective to deliver at scale, widely accessible, and relatively noninvasive. Most importantly, a successful early detection test must be effective not only at diagnosing ovarian cancer but also in reducing ovarian cancer deaths. Stepwise or multimodal approaches among the various areas under investigation will likely be required to make early detection a reality.

Circulating miRNAs as Noninvasive Biomarkers for PDAC Diagnosis and Prognosis in Mexico

Among malignant neoplasms, pancreatic ductal adenocarcinoma (PDAC) has one of the highest fatality rates due to its late detection. Therefore, it is essential to discover a noninvasive, early, specific, and sensitive diagnostic method. MicroRNAs (miRNAs) are attractive biomarkers because they are accessible, highly specific, and sensitive. It is crucial to find miRNAs that could be used as possible biomarkers because PDAC is the eighth most common cause of cancer death in Mexico. With the help of microRNA microarrays, differentially expressed miRNAs (DEmiRNAs) were found in PDAC tissues. The presence of these DEmiRNAs in the plasma of Mexican patients with PDAC was determined using RT-qPCR. Receiver operating characteristic curve analysis was performed to determine the diagnostic capacity of these DEmiRNAs. Gene Expression Omnibus datasets (GEO) were employed to verify our results. The Prisma V8 statistical analysis program was used. Four DEmiRNAs in plasma from PDAC patients and microarray tissues were found. Serum samples from patients with PDAC were used to validate their overexpression in GEO databases. We discovered a new panel of the two miRNAs miR-222-3p and miR-221-3p that could be used to diagnose PDAC, and when miR-221-3p and miR-222-3p were overexpressed, survival rates decreased. Therefore, miR-222-3p and miR-221-3p might be employed as noninvasive indicators for the diagnosis and survival of PDAC in Mexican patients.

Unraveling the Possibilities: Recent Progress in DNA Biosensing

Due to the advantages of its numerous modification sites, predictable structure, high thermal stability, and excellent biocompatibility, DNA is the ideal choice as a key component of biosensors. DNA biosensors offer significant advantages over existing bioanalytical techniques, addressing limitations in sensitivity, selectivity, and limit of detection. Consequently, they have attracted significant attention from researchers worldwide. Here, we exemplify four foundational categories of functional nucleic acids: aptamers, DNAzymes, i-motifs, and G-quadruplexes, from the perspective of the structure-driven functionality in constructing DNA biosensors. Furthermore, we provide a concise overview of the design and detection mechanisms employed in these DNA biosensors. Noteworthy advantages of DNA as a sensor component, including its programmable structure, reaction predictility, exceptional specificity, excellent sensitivity, and thermal stability, are highlighted. These characteristics contribute to the efficacy and reliability of DNA biosensors. Despite their great potential, challenges remain for the successful application of DNA biosensors, spanning storage and detection conditions, as well as associated costs. To overcome these limitations, we propose potential strategies that can be implemented to solve these issues. By offering these insights, we aim to inspire subsequent researchers in related fields.