Integrated extracellular microRNA profiling for ovarian cancer screening

Serum Free Fatty Acid Changes Caused by High Expression of Stearoyl-CoA Desaturase 1 in Tumor Tissues Are Early Diagnostic Markers for Ovarian Cancer

Ovarian cancer has a poor prognosis and is difficult to detect in early stages. Therefore, developing new diagnostic markers for early-stage ovarian cancer is critical. Here, we developed a diagnostic marker for early-stage ovarian cancer on the basis of fatty acid metabolism characteristics of cancer cells. The expression of various fatty acid metabolizing enzymes such as stearoyl-CoA desaturase 1 (SCD1) was altered in early-stage ovarian cancer tissue compared with that in normal ovarian tissue. Changes in the expression of fatty acid metabolizing enzymes, particularly SCD1, in cancer tissues were found to alter concentrations of multiple free fatty acids (FFA) in serum. We were the first to show that fatty acid metabolic characteristics in tissues are related to the FFA composition of serum. Surprisingly, patients with stage I/II ovarian cancer also showed significant changes in serum levels of eight FFAs, which can be early diagnostic markers. Finally, using statistical analysis, an optimal early diagnostic model combining oleic and arachidic acid levels, fatty acids associated with SCD1, was established and confirmed to have higher diagnostic power than CA125, regardless of histology. Thus, our newly developed diagnostic model using serum FFAs may be a powerful tool for the noninvasive early detection of ovarian cancer.

SIGNIFICANCE

Measurement of serum FFA levels by changes in the expression of fatty acid metabolizing enzymes in tumor tissue would allow early detection of ovarian cancer. In particular, the SCD1-associated FFAs, oleic and arachidic acid, would be powerful new screening tools for early-stage ovarian cancer.

Methods of ex vivo analysis of tissue status in vascularized composite allografts

Vascularized composite allotransplantation can improve quality of life and restore functionality. However, the complex tissue composition of vascularized composite allografts (VCAs) presents unique clinical challenges that increase the likelihood of transplant rejection. Under prolonged static cold storage, highly damage-susceptible tissues such as muscle and nerve undergo irreversible degradation that may render allografts non-functional. Skin-containing VCA elicits an immunogenic response that increases the risk of recipient allograft rejection. The development of quantitative metrics to evaluate VCAs prior to and following transplantation are key to mitigating allograft rejection. Correspondingly, a broad range of bioanalytical methods have emerged to assess the progression of VCA rejection and characterize transplantation outcomes. To consolidate the current range of relevant technologies and expand on potential for development, methods to evaluate ex vivo VCA status are herein reviewed and comparatively assessed. The use of implantable physiological status monitoring biochips, non-invasive bioimpedance monitoring to assess edema, and deep learning algorithms to fuse disparate inputs to stratify VCAs are identified.

MicroRNA targeting: A novel therapeutic intervention for ovarian cancer

Ovarian cancer, a perilous form of cancer affecting the female reproductive system, exhibits intricate communication networks that contribute to its progression. This study aims to identify crucial molecular abnormalities linked to the disease to enhance diagnostic and therapeutic strategies. In particular, we investigate the role of microRNAs (miRNAs) as diagnostic biomarkers and explore their potential in treating ovarian cancer. By targeting miRNAs, which can influence multiple pathways and genes, substantial therapeutic benefits can be attained. In this review we want to shed light on the promising application of miRNA-based interventions and provide insights into the specific miRNAs implicated in ovarian cancer pathogenesis.

Immunomodulatory effects of one novel microRNA miR-63 in pearl oyster Pinctada fucata martensii

Novel microRNA miR-63 (novel-miR-63) from pearl oyster Pinctada fucata martensii (Pm-novel-miR-63) is a species-specific miRNA. Our previous research has shown that the expression of Pm-novel-miR-63 was significantly downregulated at 24 h after nucleus transplantation. In this study, we analyzed the function and regulatory role of Pm-novel-miR-63 in the immune response of pearl oysters. The results showed that Pm-novel-miR-63 expression increased after the stimulation of pathogen associated molecular patterns at 6-12 h, and the activity of immune and antioxidant enzymes in the serum decreased after Pm-novel-miR-63 overexpression. Transcriptome analysis revealed that Pm-novel-miR-63 participated in regulating transplantation immunity through the Notch and mRNA surveillance signaling pathways. Target prediction and dual luciferase analysis revealed that Pm-GDP-FucTP, Pm-CysLTR2, and Pm-RLR were the target genes of Pm-novel-miR-63. These results suggested that Pm-novel-miR-63 participated in regulating the immune response in pearl oysters and can serve as a new interference target to reasonably control excessive immune rejection in pearl culture.

Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach: application of Boruta

Introduction

In gynecologic oncology, ovarian cancer is a great clinical challenge. Because of the lack of typical symptoms and effective biomarkers for noninvasive screening, most patients develop advanced-stage ovarian cancer by the time of diagnosis. MicroRNAs (miRNAs) are a type of non-coding RNA molecule that has been linked to human cancers. Specifying diagnostic biomarkers to determine non-cancer and cancer samples is difficult.

Methods

By using Boruta, a novel random forest-based feature selection in the machine-learning techniques, we aimed to identify biomarkers associated with ovarian cancer using cancerous and non-cancer samples from the Gene Expression Omnibus (GEO) database: GSE106817. In this study, we used two independent GEO data sets as external validation, including GSE113486 and GSE113740. We utilized five state-of-the-art machine-learning algorithms for classification: logistic regression, random forest, decision trees, artificial neural networks, and XGBoost.

Results

Four models discovered in GSE113486 had an AUC of 100%, three in GSE113740 with AUC of over 94%, and four in GSE113486 with AUC of over 94%. We identified 10 miRNAs to distinguish ovarian cancer cases from normal controls: hsa-miR-1290, hsa-miR-1233-5p, hsa-miR-1914-5p, hsa-miR-1469, hsa-miR-4675, hsa-miR-1228-5p, hsa-miR-3184-5p, hsa-miR-6784-5p, hsa-miR-6800-5p, and hsa-miR-5100. Our findings suggest that miRNAs could be used as possible biomarkers for ovarian cancer screening, for possible intervention.

Changes in Multiple microRNA Levels with Antidepressant Treatment Are Associated with Remission and Interact with Key Pathways: A Comprehensive microRNA Analysis

Individual treatment outcomes to antidepressants varies widely, yet the determinants to this difference remain elusive. MicroRNA (miRNA) gene expression regulation in major depressive disorder (MDD) has attracted interest as a biomarker. This 4-week randomized controlled trial examined changes in the plasma miRNAs that correlated with the treatment outcomes of mirtazapine (MIR) and selective serotonin reuptake inhibitor (SSRI) monotherapy. Pre- and post- treatment, we comprehensively analyzed the miRNA levels in MDD patients, and identified the gene pathways linked to these miRNAs in 46 patients. Overall, 141 miRNA levels significantly demonstrated correlations with treatment remission after 4 weeks of MIR, with miR-1237-5p showing the most robust and significant correlation after Bonferroni correction. These 141 miRNAs displayed a negative correlation with remission, indicating a decreasing trend. These miRNAs were associated with 15 pathways, including TGF-β and MAPK. Through database searches, the genes targeted by these miRNAs with the identified pathways were compared, and it was found that MAPK1, IGF1, IGF1R, and BRAF matched. Alterations in specific miRNAs levels before and after MIR treatment correlated with remission. The miRNAs mentioned in this study have not been previously reported. No other studies have investigated treatment with MIR. The identified miRNAs also correlated with depression-related genes and pathways.

Identifying high-grade serous ovarian carcinoma-specific extracellular vesicles by polyketone-coated nanowires

Cancer cell-derived extracellular vesicles (EVs) have unique protein profiles, making them promising targets as disease biomarkers. High-grade serous ovarian carcinoma (HGSOC) is the deadly subtype of epithelial ovarian cancer, and we aimed to identify HGSOC-specific membrane proteins. Small EVs (sEVs) and medium/large EVs (m/lEVs) from cell lines or patient serum and ascites were analyzed by LC-MS/MS, revealing that both EV subtypes had unique proteomic characteristics. Multivalidation steps identified FRα, Claudin-3, and TACSTD2 as HGSOC-specific sEV proteins, but m/lEV-associated candidates were not identified. In addition, for using a simple-to-use microfluidic device for EV isolation, polyketone-coated nanowires (pNWs) were developed, which efficiently purify sEVs from biofluids. Multiplexed array assays of sEVs isolated by pNW showed specific detectability in cancer patients and predicted clinical status. In summary, the HGSOC-specific marker detection by pNW are a promising platform as clinical biomarkers, and these insights provide detailed proteomic aspects of diverse EVs in HGSOC patients.

Size-Controlled DNA Tile Self-Assembly Nanostructures Through Caveolae-Mediated Endocytosis for Signal-Amplified Imaging of MicroRNAs in Living Cells

Signal-amplified imaging of microRNAs (miRNAs) is a promising strategy at the single-cell level because liquid biopsy fails to reflect real-time dynamic miRNA levels. However, the internalization pathways for available conventional vectors predominantly involve endo-lysosomes, showing nonideal cytoplasmic delivery efficiency. In this study, size-controlled 9-tile nanoarrays are designed and constructed by integrating catalytic hairpin assembly (CHA) with DNA tile self-assembly technology to achieve caveolae-mediated endocytosis for the amplified imaging of miRNAs in a complex intracellular environment. Compared with classical CHA, the 9-tile nanoarrays possess high sensitivity and specificity for miRNAs, achieve excellent internalization efficiency by caveolar endocytosis, bypassing lysosomal traps, and exhibit more powerful signal-amplified imaging of intracellular miRNAs. Because of their excellent safety, physiological stability, and highly efficient cytoplasmic delivery, the 9-tile nanoarrays can realize real-time amplified monitoring of miRNAs in various tumor and identical cells of different periods, and imaging effects are consistent with the actual expression levels of miRNAs, ultimately demonstrating their feasibility and capacity. This strategy provides a high-potential delivery pathway for cell imaging and targeted delivery, simultaneously offering a meaningful reference for the application of DNA tile self-assembly technology in relevant fundamental research and medical diagnostics.

Current Understanding on Why Ovarian Cancer Is Resistant to Immune Checkpoint Inhibitors

The standard treatment of ovarian cancer (OC) patients, including debulking surgery and first-line chemotherapy, is unsatisfactory because of recurrent episodes in the majority (~70%) of patients with advanced OC. Clinical trials have shown only a modest (10-15%) response of OC individuals to treatment based on immune checkpoint inhibitors (ICIs). The resistance of OC to therapy is caused by various factors, including OC heterogeneity, low density of tumor-infiltrating lymphocytes (TILs), non-cellular and cellular interactions in the tumor microenvironment (TME), as well as a network of microRNA regulating immune checkpoint pathways. Moreover, ICIs are the most efficient in tumors that are marked by high microsatellite instability and high tumor mutation burden, which is rare among OC patients. The great challenge in ICI implementation is connected with distinguishing hyper-, pseudo-, and real progression of the disease. The understanding of the immunological, molecular, and genetic mechanisms of OC resistance is crucial to selecting the group of OC individuals in whom personalized treatment would be beneficial. In this review, we summarize current knowledge about the selected factors inducing OC resistance and discuss the future directions of ICI-based immunotherapy development for OC patients.

Identification of BRCA1/2 mutation female carriers using circulating microRNA profiles

Identifying germline BRCA1/2 mutation carriers is vital for reducing their risk of breast and ovarian cancer. To derive a serum miRNA-based diagnostic test we used samples from 653 healthy women from six international cohorts, including 350 (53.6%) with BRCA1/2 mutations and 303 (46.4%) BRCA1/2 wild-type. All individuals were cancer-free before and at least 12 months after sampling. RNA-sequencing followed by differential expression analysis identified 19 miRNAs significantly associated with BRCA mutations, 10 of which were ultimately used for classification: hsa-miR-20b-5p, hsa-miR-19b-3p, hsa-let-7b-5p, hsa-miR-320b, hsa-miR-139-3p, hsa-miR-30d-5p, hsa-miR-17-5p, hsa-miR-182-5p, hsa-miR-421, hsa-miR-375-3p. The final logistic regression model achieved area under the receiver operating characteristic curve 0.89 (95% CI: 0.87-0.93), 93.88% sensitivity and 80.72% specificity in an independent validation cohort. Mutated gene, menopausal status or having preemptive oophorectomy did not affect classification performance. Circulating microRNAs may be used to identify BRCA1/2 mutations in patients of high risk of cancer, offering an opportunity to reduce screening costs.

Application of Metal-Based Nanomaterials in In Vitro Diagnosis of Tumor Markers: Summary and Prospect

Cancer, which presents with high incidence and mortality rates, has become a significant health threat worldwide. However, there is currently no effective solution for rapid screening and high-quality treatment of early-stage cancer patients. Metal-based nanoparticles (MNPs), as a new type of compound with stable properties, convenient synthesis, high efficiency, and few adverse reactions, have become highly competitive tools for early cancer diagnosis. Nevertheless, challenges such as the difference between the microenvironment of detected markers and the real-life body fluids remain in achieving widespread clinical application of MNPs. This review provides a comprehensive review of the research progress made in the field of in vitro cancer diagnosis using metal-based nanoparticles. By delving into the characteristics and advantages of these materials, this paper aims to inspire and guide researchers towards fully exploiting the potential of metal-based nanoparticles in the early diagnosis and treatment of cancer.

Circulating microRNAs for Early Diagnosis of Ovarian Cancer: A Systematic Review and Meta-Analysis

In this study, we conducted a systematic review and meta-analysis to summarize and evaluate the global research potential of different circulating miRNAs as an early diagnostic biomarker for OC. A systematic literature search for relevant studies was conducted in June 2020 and followed up in November 2021. The search was conducted in English databases (PubMed, ScienceDirect). The primary search resulted in a total of 1887 articles, which were screened according to the prior established inclusion and exclusion criteria. We identified 44 relevant studies, of which 22 were eligible for the quantitative meta-analysis. Statistical analysis was performed using the Meta-package in Rstudio. Standardized mean differences (SMD) of relative levels between control subjects and OC patients were used to evaluate the differential expression. All studies were quality evaluated using a Newcastle-Ottawa Scale. Based on the meta-analysis, nine miRNAs were identified as dysregulated in OC patients compared to controls. Nine were upregulated in OC patients compared to controls (miR-21, -125, -141, -145, -205, -328, -200a, -200b, -200c). Furthermore, miR-26, -93, -106 and -200a were analyzed, but did not present an overall significant difference between OC patients and controls. These observations should be considered when performing future studies of circulating miRNAs in relation to OC: sufficient size of clinical cohorts, development of consensus guidelines for circulating miRNA measurements, and coverage of previously reported miRNAs.

Mutation detection of urinary cell-free DNA via catch-and-release isolation on nanowires for liquid biopsy

Cell-free DNA (cfDNA) and extracellular vesicles (EVs) are molecular biomarkers in liquid biopsies that can be applied for cancer detection, which are known to carry information on the necessary conditions for oncogenesis and cancer cell-specific activities after oncogenesis, respectively. Analyses for both cfDNA and EVs from the same body fluid can provide insights into screening and identifying the molecular subtypes of cancer; however, a major bottleneck is the lack of efficient and standardized techniques for the isolation of cfDNA and EVs from clinical specimens. Here, we achieved catch-and-release isolation by hydrogen bond-mediated binding of cfDNA in urine to zinc oxide (ZnO) nanowires, which also capture EVs by surface charge, and subsequently we identified genetic mutations in urinary cfDNA. The binding strength of hydrogen bonds between single-crystal ZnO nanowires and DNA was found to be equal to or larger than that of conventional hydrophobic interactions, suggesting the possibility of isolating trace amounts of cfDNA. Our results demonstrated that nanowire-based cancer screening assay can screen cancer and can identify the molecular subtypes of cancer in urine from brain tumor patients through EV analysis and cfDNA mutation analysis. We anticipate our method to be a starting point for more sophisticated diagnostic models of cancer screening and identification.

Three-dimensional ordered DNA network constructed by a biomarker pair for accurate monitoring of colorectal cancer

Precise and early screening of colorectal cancer (CRC) is one crucial yet challenging task for its treatment, and the analysis of multi-targets of CRC in a single assay with high accuracy is essential for pathological research and clinical diagnosis. Here, a CRC-related biomarker pair, microRNA-211 (miRNA-211) and H₂S, was detected by constructing a three-dimensional (3D) ordered DNA network. First, trace amount of miRNA-211 could initiate a hybridization chain reaction-based amplification process. A highly ordered 3D DNA network was formed based on the organized assembly of DNA-cube frameworks that were constructed by DNA origamis and Ag nanoparticles (NPs) encapsulated inside. In the presence of the H₂S, Ag NPs within the network can be etched to generate Ag₂S quantum dots, which could be better visualized in fluorescence in situ cell imaging. Using the 3D DNA ordered network as the sensing platform, it can acquire dual analysis of biomolecule (miRNA-211) and inorganic gas (H₂S) in vitro, overcoming the limitations of single type of biomarker detection in a single assay. This assay achieved a wide linearity range of H₂S from 0.05 to 10 μM, and exhibited a low limit of detection of 4.78 nM. This strategy allows us to acquire the spatial distributions of H₂S and miRNA expression levels in living CRC cells simultaneously, providing a highly sensitive and selective tool for early screening and monitoring of CRC.

MiR-423-5p is a novel endogenous control for the quantification of circulating miRNAs in human esophageal squamous cell carcinoma

Circulating miRNA expression is most commonly measured by qRT-PCR, however, the lack of a suitable endogenous control hinders people from evaluating the accurate changes in miRNA expression levels and developing the non-invasive biomarkers. In this study, we aimed to screen the specific, highly stable endogenous control in esophageal squamous cell carcinoma (ESCC) to overcome the obstacle. We selected "housekeeping" miRNAs according to the published database and initially acquired 21 miRNAs. Subsequently, we screened these miRNAs using GSE106817 and TCGA datasets according to specific inclusion criteria and evaluated the suitability of "candidate" miRNAs. Among these miRNAs, the average abundance of miR-423-5p was relatively high in serum. Notably, miR-423-5p expression in serum showed no significant difference between ESCC patients and healthy controls (n = 188, P = 0.29). Moreover, among these miRNAs, miR-423-5p was the most stable miRNA using the NormFinder algorithms. Overall, these results indicate that miR-423-5p, as a novel and optimal endogenous control, could be used to quantify circulating miRNAs in ESCC.

Network-Based and Machine-Learning Approaches Identify Diagnostic and Prognostic Models for EMT-Type Gastric Tumors

The microsatellite stable/epithelial-mesenchymal transition (MSS/EMT) subtype of gastric cancer represents a highly aggressive class of tumors associated with low rates of survival and considerably high probabilities of recurrence. In the era of precision medicine, the accurate and prompt diagnosis of tumors of this subtype is of vital importance. In this study, we used Weighted Gene Co-expression Network Analysis (WGCNA) to identify a differentially expressed co-expression module of mRNAs in EMT-type gastric tumors. Using network analysis and linear discriminant analysis, we identified mRNA motifs and microRNA-based models with strong prognostic and diagnostic relevance: three models comprised of (i) the microRNAs miR-199a-5p and miR-141-3p, (ii) EVC/EVC2/GLI3, and (iii) PDE2A/GUCY1A1/GUCY1B1 gene expression profiles distinguish EMT-type tumors from other gastric tumors with high accuracy (Area Under the Receiver Operating Characteristic Curve (AUC) = 0.995, AUC = 0.9742, and AUC = 0.9717; respectively). Additionally, the DMD/ITGA1/CAV1 motif was identified as the top motif with consistent relevance to prognosis (hazard ratio > 3). Molecular functions of the members of the identified models highlight the central roles of MAPK, Hh, and cGMP/cAMP signaling in the pathology of the EMT subtype of gastric cancer and underscore their potential utility in precision therapeutic approaches.

Construct of qualitative diagnostic biomarkers specific for glioma by pairing serum microRNAs

BACKGROUND

Serum microRNAs (miRNAs) are promising non-invasive biomarkers for diagnosing glioma. However, most reported predictive models are constructed without a large enough sample size, and quantitative expression levels of their constituent serum miRNAs are susceptible to batch effects, decreasing their clinical applicability.

METHODS

We propose a general method for detecting qualitative serum predictive biomarkers using a large cohort of miRNA-profiled serum samples (n = 15,460) based on the within-sample relative expression orderings of miRNAs.

RESULTS

Two panels of miRNA pairs (miRPairs) were developed. The first was composed of five serum miRPairs (5-miRPairs), reaching 100% diagnostic accuracy in three validation sets for distinguishing glioma and non-cancer controls (n = 436: glioma = 236, non-cancers = 200). An additional validation set without glioma samples (non-cancers = 2611) showed a predictive accuracy of 95.9%. The second panel included 32 serum miRPairs (32-miRPairs), reaching 100% diagnostic performance in training set on specifically discriminating glioma from other cancer types (sensitivity = 100%, specificity = 100%, accuracy = 100%), which was reproducible in five validation datasets (n = 3387: glioma = 236, non-glioma cancers = 3151, sensitivity> 97.9%, specificity> 99.5%, accuracy> 95.7%). In other brain diseases, the 5-miRPairs classified all non-neoplastic samples as non-cancer, including stroke (n = 165), Alzheimer's disease (n = 973), and healthy samples (n = 1820), and all neoplastic samples as cancer, including meningioma (n = 16), and primary central nervous system lymphoma samples (n = 39). The 32-miRPairs predicted 82.2 and 92.3% of the two kinds of neoplastic samples as positive, respectively. Based on the Human miRNA tissue atlas database, the glioma-specific 32-miRPairs were significantly enriched in the spinal cord (p = 0.013) and brain (p = 0.015).

CONCLUSIONS

The identified 5-miRPairs and 32-miRPairs provide potential population screening and cancer-specific biomarkers for glioma clinical practice.

Insight into the causality between basal metabolic rate and endometrial and ovarian cancers: Analysis utilizing systematic Mendelian randomization and genetic association data from over 331,000 UK biobank participants

BACKGROUND

Observational studies have demonstrated that basal metabolic rate (BMR) is associated with the risk of endometrial cancer (EC) and ovarian cancer (OC). However, it is unclear whether these associations reflect a causal relationship.

OBJECTIVE

To reveal the causality between BMR and EC and OC, we performed the first comprehensive two-sample Mendelian randomization (MR) analyses.

METHODS

Genetic variants were used as proxies of BMR. GWAS summary statistics of BMR, EC and OC were obtained from the UK Biobank Consortium, Endometrial Cancer Association Consortium and Ovarian Cancer Association Consortium respectively. The inverse variance weighted method was employed as the main approach for MR analysis. A series of sensitivity analyses were implemented to validate the robustness and reliability of the results.

RESULTS

BMR was significantly related to an increased risk of EC (OR_SD  = 1.49; 95% CI: 1.29-1.72; p-Value < .001) and OC (OR_SD  = 1.21; 95% CI: 1.08-1.35; p-Value < .001). Furthermore, the stratified analysis indicated that BMR was positively associated with endometrioid endometrial cancer (EEC) (OR_SD  = 1.45; 95% CI, 1.23-1.70; p-Value < .001), clear cell ovarian cancer(CCOC) (OR_SD  = 1.89; 95% CI:1.35-2.64; p-Value < .001) and endometrioid ovarian cancer risk (EOC) (OR_SD  = 1.45; 95% CI: 1.12-1.88; p-Value = .005). However, there were no significant associations of BMR with invasive mucinous ovarian cancer (IMOC), high-grade serous ovarian cancer (HGSOC) and low-grade serous ovarian cancer (LGSOC). The robustness of the above results was further verified in sensitivity analyses.

CONCLUSION

The MR study provided etiological evidence for the positive association of BMR with the risk of EC, EEC, OC, CCOC and EOC. But this study did not provide enough evidence suggesting the causal associations of BMR with IMOC, HGSOC and LGSOC.

Combination of serum human satellite RNA and miR-21-5p levels as a biomarker for pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis due to the difficulty of its diagnosis. Because human satellite II (HSATII) RNA, a satellite repeat RNA, is highly and specifically expressed in human PDAC, the serum HSATII RNA level may be a biomarker of PDAC. To measure the serum HSATII RNA level with high sensitivity and reproducibility, we previously developed a convenient method, tandem repeat amplification by nuclease protection (TRAP) combined with droplet digital PCR (ddPCR). Here, we refined the original method by simultaneously measuring the serum miR-21-5p level to enhance the detection of PDAC. The resulting PDAC-Index, constructed using serum HSATII RNA and miR-21-5p levels, discriminated patients with PDAC with high accuracy. We verified the clinical usefulness of the PDAC-Index as a supportive test in difficult-to-diagnose cases. The PDAC-Index has satisfactory diagnostic performance and may routinely be applied for detecting PDAC.

The role of urine and serum biomarkers in the early detection of ovarian epithelial tumours

Ovarian cancer (OC) is one of the leading causes of gynaecological cancer mortality in women worldwide. If detected at an early stage (I, II), OC has a 90% 5-year survival rate; nevertheless, symptoms are often hidden, leading to late-stage (III, IV) diagnosis and a poor prognosis. The current diagnostic procedures, such as a pelvic exam, transvaginal ultrasound, CA-125 blood tests, serum HE4 tests and multivariate index assays (MIA), are insufficient. Sadly, surgery is frequently required to confirm a positive diagnosis. Therefore, there has been an increased interest in different biomarkers using a non-invasive test as a tool for the earlier diagnosis of OC to resolve the need for precise and non-invasive diagnostic methods. This review article aims to investigate how biomarkers influence early OC detection and to emphasise the role of using a combination of serum biomarkers panel rather than a single biomarker. In addition, this review provides insights into the current serum biomarkers, urine biomarkers and other emerging biomarkers in the early detection of OC for better specificity and sensitivity and to improve the overall survival (OS) rate.

Liquid Biopsy for Oral Cancer Diagnosis: Recent Advances and Challenges

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

Identification of microRNA Signatures in Peripheral Blood of Young Women as Potential Biomarkers for Metal Allergy

MicroRNA (miRNA) is a short (19-24 nucleotide) endogenous non-protein RNA that exists in the body and controls the translation process from genes to proteins. It has become useful as a diagnostic tool and a potential treatment target in cancer research. To explore the function of miRNA in contact dermatitis, female participants with a positive metal allergy diagnosis (n = 3) were enrolled along with additional female participants with no medical history of metal allergy (n = 3). A patch test was performed on each participant. Peripheral blood was collected from all the participants before the patch test and at days 3 and 7 after starting the patch test. After total RNA was obtained from peripheral blood leukocytes and cDNA was generated, microarray analysis was performed to analyze the large-scale circulating miRNA profile. Real-time polymerase chain reaction (RT-PCR) was then used to clarify the overall target miRNA expression. Downregulation of hsa-let-7d-5p, hsa-miR-24-3p, hsa-miR-23b-3p, hsa-miR-26b-5p, and hsa-miR-150-5p was found on day 7. Certain miRNAs were confirmed using RT-PCR. These peripheral blood miRNAs could be diagnostic biomarkers for metal allergies.

Exosomes: A potential tool for immunotherapy of ovarian cancer

Ovarian cancer is a malignant tumor of the female reproductive system, with a very poor prognosis and high mortality rates. Chemotherapy and radiotherapy are the most common treatments for ovarian cancer, with unsatisfactory results. Exosomes are a subpopulation of extracellular vesicles, which have a diameter of approximately 30-100 nm and are secreted by many different types of cells in various body fluids. Exosomes are highly stable and are effective carriers of immunotherapeutic drugs. Recent studies have shown that exosomes are involved in various cellular responses in the tumor microenvironment, influencing the development and therapeutic efficacy of ovarian cancer, and exhibiting dual roles in inhibiting and promoting tumor development. Exosomes also contain a variety of genes related to ovarian cancer immunotherapy that could be potential biomarkers for ovarian cancer diagnosis and prognosis. Undoubtedly, exosomes have great therapeutic potential in the field of ovarian cancer immunotherapy. However, translation of this idea to the clinic has not occurred. Therefore, it is important to understand how exosomes could be used in ovarian cancer immunotherapy to regulate tumor progression. In this review, we summarize the biomarkers of exosomes in different body fluids related to immunotherapy in ovarian cancer and the potential mechanisms by which exosomes influence immunotherapeutic response. We also discuss the prospects for clinical application of exosome-based immunotherapy in ovarian cancer.

Diagnosis, Monitoring, and Prognosis of Liquid Biopsy in Cancer Immunotherapy

Liquid biopsy (LB), as a minimally invasive method of gleaning insight into the dynamics of diseases through a patient fluid sample, represents an interesting tool that can advise in disease monitoring, treatment selection, early diagnosis, evaluation of the response, and prognosis. Cancer immunotherapy is a breakthrough in cancer treatment, which is now recognized as the "fourth pillar" of cancer treatment, after surgery, chemotherapy, and radiotherapy. Liquid biopsy offers a different befalling for beneath invasive diagnosis, real-time accommodating monitoring, and analysis options, involving the isolation of circulating biomarkers, such as cell-free DNA (cfDNA), circulating tumor cells (CTCs), exosomes, and microRNAs (miRNAs). The biomarkers herein have great potential to allow the realization of liquid biopsy for predicting the immunotherapy response and precision medicine. Liquid biopsy offers an alternative, less invasive approach to select cancer patients who would benefit from immunotherapy and to monitor patients during their disease course. This review focuses on the use of liquid biopsy in the immunotherapy treatment of patients with cancer. In this review, we addressed the different promising liquid biopsy-based biomarkers in cancer patients that enable the selection of patients who benefit from immunotherapy and the monitoring of patients during this therapy.

Strategies for data normalization and missing data imputation and consequences for potential diagnostic microRNA biomarkers in epithelial ovarian cancer

MicroRNAs (miRNAs) are small non-coding RNA molecules regulating gene expression with diagnostic potential in different diseases, including epithelial ovarian carcinomas (EOC). As only a few studies have been published on the identification of stable endogenous miRNA in EOC, there is no consensus which miRNAs should be used aiming standardization. Currently, U6-snRNA is widely adopted as a normalization control in RT-qPCR when investigating miRNAs in EOC; despite its variable expression across cancers being reported. Therefore, our goal was to compare different missing data and normalization approaches to investigate their impact on the choice of stable endogenous controls and subsequent survival analysis while performing expression analysis of miRNAs by RT-qPCR in most frequent subtype of EOC: high-grade serous carcinoma (HGSC). 40 miRNAs were included based on their potential as stable endogenous controls or as biomarkers in EOC. Following RNA extraction from formalin-fixed paraffin embedded tissues from 63 HGSC patients, RT-qPCR was performed with a custom panel covering 40 target miRNAs and 8 controls. The raw data was analyzed by applying various strategies regarding choosing stable endogenous controls (geNorm, BestKeeper, NormFinder, the comparative ΔCt method and RefFinder), missing data (single/multiple imputation), and normalization (endogenous miRNA controls, U6-snRNA or global mean). Based on our study, we propose hsa-miR-23a-3p and hsa-miR-193a-5p, but not U6-snRNA as endogenous controls in HGSC patients. Our findings are validated in two external cohorts retrieved from the NCBI Gene Expression Omnibus database. We present that the outcome of stability analysis depends on the histological composition of the cohort, and it might suggest unique pattern of miRNA stability profiles for each subtype of EOC. Moreover, our data demonstrates the challenge of miRNA data analysis by presenting various outcomes from normalization and missing data imputation strategies on survival analysis.

Dynamic and static circulating cancer microRNA biomarkers - a validation study

For cancers and other pathologies, early diagnosis remains the most promising path to survival. Profiling of longitudinal cohorts facilitates insights into trajectories of biomarkers. We measured microRNA expression in 240 serum samples from patients with colon, lung, and breast cancer and from cancer-free controls. Each patient provided at least two serum samples, one prior to diagnosis and one following diagnosis. The median time interval between the samples was 11.6 years. Using computational models, we evaluated the circulating profiles of 21 microRNAs. The analysis yielded two sets of biomarkers, static ones that show an absolute difference between certain cancer types and controls and dynamic ones where the level over time provided higher diagnostic information content. In the first group, miR-99a-5p stands out for all three cancer types. In the second group, miR-155-5p allows to predict lung cancers and colon cancers. Classification in samples from cancer and non-cancer patients using gradient boosted trees reached an average accuracy of 79.9%. The results suggest that individual change over time or an absolute value at one time point may predict a disease with high specificity and sensitivity.

Preoperative serum microRNAs as potential prognostic biomarkers in ovarian clear cell carcinoma

OBJECTIVE

Ovarian clear cell carcinoma (OCCC) is a subtype of epithelial ovarian carcinoma with poor prognosis. However, no effective biomarkers have been established for predicting unfavorable events, including recurrence and poor prognoses. Serum microRNAs (miRNAs) have been increasingly reported to be useful in predicting a patient's condition and have been recognized as a potentially less-invasive source for liquid biopsy in cancer. Therefore, this study aimed to evaluate serum miRNA profiles from patients with OCCC and to establish biomarker for predicting the prognoses.

METHODS

The GSE106817, which included preoperative serum miRNA profiles of patients with ovarian tumors, was used, and clinical information was investigated. In all, 66 patients with OCCC were included, excluding those with other histological subtypes or insufficient prognostic information. Moreover, miRNA profiles of OCCC tissues were also examined.

RESULTS

The median follow-up period was 64.3 (8.0-153.3) months. Based on multivariable Cox regression analyses and the expression of miRNAs in OCCC tissues, miR-150-3p, miR-3195, and miR-7704 were selected as miRNA candidates associated with both progression-free survival (PFS) and overall survival (OS). Then, the prognostic index was calculated based on expression values of 3 serum miRNAs. Kaplan-Meier survival analysis indicated that the prognostic index was significantly predictive of PFS and OS (p=0.004 and p=0.012, respectively).

CONCLUSION

Preoperative serum miRNA profiles of miR-150-3p, miR-3195, and miR-7704 can be used to potentially predict the prognosis of patients with OCCC.

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

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

Localized DNA tetrahedrons assisted catalytic hairpin assembly for the rapid and sensitive profiling of small extracellular vesicle-associated microRNAs

The profiling of small extracellular vesicle-associated microRNAs (sEV-miRNAs) plays a vital role in cancer diagnosis and monitoring. However, detecting sEV-miRNAs with low expression in clinical samples remains challenging. Herein, we propose a novel electrochemical biosensor using localized DNA tetrahedron-assisted catalytic hairpin assembly (LDT-CHA) for sEV-miRNA determination. The LDT-CHA contained localized DNA tetrahedrons with CHA substrates, leveraging an efficient localized reaction to enable sensitive and rapid sEV-miRNA measurement. Based on the LDT-CHA, the proposed platform can quantitatively detect sEV-miRNA down to 25 aM in 30 min with outstanding specificity. For accurate diagnosis of gastric cancer patients, a combination of LDT-CHA and a panel of four sEV-miRNAs (sEV-miR-1246, sEV-miR-21, sEV-miR-183-5P, and sEV-miR-142-5P) was employed in a gastric cancer cohort. Compared with diagnosis with single sEV-miRNA, the proposed platform demonstrated a higher accuracy of 88.3% for early gastric tumor diagnoses with higher efficiency (AUC: 0.883) and great potential for treatment monitoring. Thus, this study provides a promising method for the bioanalysis and determination of the clinical applications of LDT-CHA.

Prediction of tissue-of-origin of early stage cancers using serum miRNomes

BACKGROUND

Noninvasive detection of early stage cancers with accurate prediction of tumor tissue-of-origin could improve patient prognosis. Because miRNA profiles differ between organs, circulating miRNomics represent a promising method for early detection of cancers, but this has not been shown conclusively.

METHODS

A serum miRNA profile (miRNomes)-based classifier was evaluated for its ability to discriminate cancer types using advanced machine learning. The training set comprised 7931 serum samples from patients with 13 types of solid cancers and 5013 noncancer samples. The validation set consisted of 1990 cancer and 1256 noncancer samples. The contribution of each miRNA to the cancer-type classification was evaluated, and those with a high contribution were identified.

RESULTS

Cancer type was predicted with an accuracy of 0.88 (95% confidence interval [CI] = 0.87 to 0.90) in all stages and an accuracy of 0.90 (95% CI = 0.88 to 0.91) in resectable stages (stages 0-II). The F1 score for the discrimination of the 13 cancer types was 0.93. Optimal classification performance was achieved with at least 100 miRNAs that contributed the strongest to accurate prediction of cancer type. Assessment of tissue expression patterns of these miRNAs suggested that miRNAs secreted from the tumor environment could be used to establish cancer type-specific serum miRNomes.

CONCLUSIONS

This study demonstrates that large-scale serum miRNomics in combination with machine learning could lead to the development of a blood-based cancer classification system. Further investigations of the regulating mechanisms of the miRNAs that contributed strongly to accurate prediction of cancer type could pave the way for the clinical use of circulating miRNA diagnostics.

LncRNA-miRNA-mRNA regulatory axes in endometrial cancer: a comprehensive overview

INTRODUCTION

Recent research on tumorigenesis and progression has opened up an array of novel molecular mechanisms in the form of interactions between cellular non-coding RNAs (long non-coding RNA[lncRNA]/microRNA [miRNA]) and coding transcripts that regulate health and disease. Endometrial cancer (EC) is a prominent gynecological malignancy with a high incidence rate and poorly known etiology and prognostic factors that hinder the success of disease management. The emerging role of lncRNA-miRNA-mRNA interactions and their dysregulation in the pathophysiology of EC has been elucidated in many recent studies.

METHODS

A thorough literature review was conducted to explore information about lncRNA-miRNA-mRNA axes in EC.

RESULTS

Several lncRNAs act as molecular sponges that sequester various tumor suppressor miRNAs to inhibit their function, leading to the dysregulation of their target mRNA transcripts that contribute to the EC regulation.

CONCLUSIONS

This review summarizes these networks of molecular mechanisms and their contribution to different aspects of endometrial carcinogenesis, leading to a better conceptualization of the molecular pathways that underlie the disease and helping establish novel diagnostic biomarkers and therapeutic intervention points to aid the curative intent of EC.

Recent advances of non-coding RNAs in ovarian cancer prognosis and therapeutics

Ovarian cancer (OC) is the third most common gynecological malignancy with the highest mortality worldwide. OC is usually diagnosed at an advanced stage, and the standard treatment is surgery combined with platinum or paclitaxel chemotherapy. However, chemoresistance inevitably appears coupled with the easy recurrence and poor prognosis. Thus, early diagnosis, predicting prognosis, and reducing chemoresistance are of great significance for controlling the progression and improving treatment effects of OC. Recently, much insight has been gained into the non-coding RNA (ncRNA) that is employed for RNAs but does not encode a protein, and many types of ncRNAs have been characterized including long-chain non-coding RNAs, microRNAs, and circular RNAs. Accumulating evidence indicates these ncRNAs play very active roles in OC progression and metastasis. In this review, we briefly discuss the ncRNAs as biomarkers for OC prognosis. We focus on the recent advances of ncRNAs as therapeutic targets in preventing OC metastasis, chemoresistance, immune escape, and metabolism. The novel strategies for ncRNAs-targeted therapy are also exploited for improving the survival of OC patients.

Small extracellular vesicle-associated miR-6068 promotes aggressive phenotypes of prostate cancer through miR-6068/HIC2/SIRT1 axis

Early diagnosis and treatment of patients with aggressive prostate cancer (PCa) remains a clinically unmet need. We aimed to determine the levels of small extracellular vesicle (sEV)-associated microRNAs (miRs); miR-4737, miR-6068, and miR-6076 in a large panel of PCa cells and delineate the biological significance of miR-6068 in promoting PCa cells. sEVs were isolated from the conditioned medium of PCa cells, followed by RNA extraction and quantitative Real-Time PCR analysis. Functional assays were performed, and the protein expression of hypermethylated in cancer 2 (HIC2), as a potential miR-6068 target gene, was evaluated in PCa tissues by immunohistochemistry. sEV-associated miR-6068, miR-4737, and miR-6076 levels displayed large and significant differences compared to normal cells. miR-6068 was explicitly upregulated in sEV of PC-3 and CWR-R1ca cells (P<0.010). Suppression of miR-6068 in CWR-R1ca cells decreased cell proliferation, colony formation, and cell migration. In contrast, upregulation of miR-6068 in RC77T/E cells decreased HIC2 levels and increased cell aggressive phenotypes. The overexpression of HIC2 in PCa tissues was primarily observed in the cytoplasm compared to benign prostatic hyperplasia (BPH) and normal tissues (P<0.0001). This study confirms the differential packaging of miR-4737, miR-6068, and miR-6076 in sEVs of PCa cells. MiR-6068 promotes PCa cells to acquire aggressive phenotypes by inhibiting the HIC2/Sirtuin 1 (SIRT1) axis.

A novel circulating miRNA panel for non-invasive ovarian cancer diagnosis and prognosis

Background

Ovarian cancer (OC) is an aggressive disease, primarily diagnosed in late stages with only 20% of patients surviving more than 5 years after diagnosis. There is a pending need to improve current diagnostics and prognostics.

Methods

In this study, we investigated total circulating cell-free microRNA (cf-miRNA) levels as well as a panel of cf-miRNAs in the plasma of OC patients (n = 100), patients with benign lesions (n = 45) and healthy controls (n = 99).

Results

High levels of cf-miRNAs correlated with unfavourable clinical features and were an independent prognosticator of patient survival. By mining NGS data, we identified a signature panel of seven individual cf-miRNAs which could distinguish controls from benign cases with an AUC of 0.77 and controls from cancer cases with an AUC of 0.87. Importantly, in combination with the current gold-standard marker, CA-125, the panel could predict early OC with an AUC of 0.93.

Conclusion

Our findings highlight the potential of cf-miRNA levels as well as individual cf-miRNAs for OC diagnosis and prognosis that warrants further clinical evaluation.

Predict ovarian cancer by pairing serum miRNAs: Construct of single sample classifiers

Objective

The accuracy of CA125 or clinical examination in ovarian cancer (OVC) screening is still facing challenges. Serum miRNAs have been considered as promising biomarkers for clinical applications. Here, we propose a single sample classifier (SSC) method based on within-sample relative expression orderings (REOs) of serum miRNAs for OVC diagnosis.

Methods

Based on the stable REOs within 4,965 non-cancer serum samples, we developed the SSC for OVC in the training cohort (GSE106817: OVC = 200, non-cancer = 2,000) by focusing on highly reversed REOs within OVC. The best diagnosis is achieved using a combination of reversed miRNA pairs, considering the largest evaluation index and the lowest number of miRNA pairs possessed according to the voting rule. The SSC was then validated in internal data (GSE106817: OVC = 120, non-cancer = 759) and external data (GSE113486: OVC = 40, non-cancer = 100).

Results

The obtained 13-miRPairs classifier showed high diagnostic accuracy on distinguishing OVC from non-cancer controls in the training set (sensitivity = 98.00%, specificity = 99.60%), which was reproducible in internal data (sensitivity = 98.33%, specificity = 99.21%) and external data (sensitivity = 97.50%, specificity = 100%). Compared with the published models, it stood out in terms of correct positive predictive value (PPV) and negative predictive value (NPV) (PPV = 96.08% and NPV=95.16% in training set, and both above 99% in validation set). In addition, 13-miRPairs demonstrated a classification accuracy of over 97.5% for stage I OVC samples. By integrating other non-OVC serum samples as a control, the obtained 17-miRPairs classifier could distinguish OVC from other cancers (AUC&gt;92% in training and validation set).

Conclusion

The REO-based SSCs performed well in predicting OVC (including early samples) and distinguishing OVC from other cancer types, proving that REOs of serum miRNAs represent a robust and non-invasive biomarker.

Present Status, Challenges, and Prospects of Dihydromyricetin in the Battle against Cancer

Dihydromyricetin (DHM) is a natural flavonoid compound extracted from Ampelopsis grossedentata that has been used for centuries in traditional Chinese medicine. DHM has attracted intensive attention due to its numerous beneficial activities, such as hepatoprotection, cardioprotection, antioxidant, and anti-inflammation. In addition, DHM inhibits the progression of cancers such as lung cancer, hepatocellular cancer, breast cancer, melanoma, and malignant reproductive systems through multiple mechanisms, including antiangiogenesis, antiproliferation, apoptosis, and inhibition of invasion and migration. Notably, DHM also activates autophagy at different levels, exerting a dual-regulatory effect on cancers. Mechanistically, DHM can effectively regulate mammalian target of rapamycin (mTOR), noncoding RNA-mediated signaling, phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) pathway, nuclear factor-κB (NF-κB), p53, and endoplasmic reticulum stress (ER stress)-driven signaling in different types of cancers. DHM has also been shown to have inhibitory effects on various regulators that trigger epithelial–mesenchymal transition (EMT). Furthermore, DHM exhibits a remarkable anticancer reversal ability when used in combination with drugs such as adriamycin, nedaplatin, and other drugs. However, the low bioavailability of DHM limits its potential applications, which are improved through structural modification and the exploration of novel dosage forms. Therefore, DHM may become a promising candidate for treating malignancies alone or combined with conventional anticancer strategies used in clinical practice.

Tetrahedral DNA Supported Walking Nanomachine for Ultrasensitive miRNA Detection in Cancer Cells and Serums

A three-dimensional DNA tetrahedral nanostructure is constructed to support a walker strand on top and multiple track strands around it via the assembly of triplex-forming oligonucleotide (TFO). This design facilitates the regeneration of the sensing interface by simply adjusting pH conditions. On the basis of the tetrahedral DNA supported walking nanomachine, ultrasensitive electrochemical analysis of miRNA (miR-141) is achieved. Target miRNA assists the formation of three-way junction nanostructure. It contains a duplex region (hybridized by track and walker strands) that could be specially recognized and digested by certain nicking endonuclease. As a result, walker strand and target miRNA are released and move around the attached tracks for continuous cleavage reactions, releasing a larger number of signal reporters. By measuring the variation of signal responses, ultrasensitive analysis of miRNA is achieved. The limit of detection (LOD) is calculated to be 4.9 aM, which is rather low. In addition, the proposed method is successfully applied for the detection of miRNA in cell and serum samples, which could distinguish pathological information from healthy controls.

Current and Emerging Methods for Ovarian Cancer Screening and Diagnostics: A Comprehensive Review

With a 5-year survival rate of less than 50%, ovarian high-grade serous carcinoma (HGSC) is one of the most highly aggressive gynecological malignancies affecting women today. The high mortality rate of HGSC is largely attributable to delays in diagnosis, as most patients remain undiagnosed until the late stages of -disease. There are currently no recommended screening tests for ovarian cancer and there thus remains an urgent need for new diagnostic methods, particularly those that can detect the disease at early stages when clinical intervention remains effective. While diagnostics for ovarian cancer share many of the same technical hurdles as for other cancer types, the low prevalence of the disease in the general population, coupled with a notable lack of sensitive and specific biomarkers, have made the development of a clinically useful screening strategy particularly challenging. Here, we present a detailed review of the overall landscape of ovarian cancer diagnostics, with emphasis on emerging methods that employ novel protein, genetic, epigenetic and imaging-based biomarkers and/or advanced diagnostic technologies for the noninvasive detection of HGSC, particularly in women at high risk due to germline mutations such as BRCA1/2. Lastly, we discuss the translational potential of these approaches for achieving a clinically implementable solution for screening and diagnostics of early-stage ovarian cancer as a means of ultimately improving patient outcomes in both the general and high-risk populations.

Identification of four serum miRNAs as potential markers to screen for thirteen cancer types

Introduction

Cancer consistently remains one of the top causes of death in the United States every year, with many cancer deaths preventable if detected early. Circulating serum miRNAs are a promising, minimally invasive supplement or even an alternative to many current screening procedures. Many studies have shown that different serum miRNAs can discriminate healthy individuals from those with certain types of cancer. Although many of those miRNAs are often reported to be significant in one cancer type, they are also altered in other cancer types. Currently, very few studies have investigated serum miRNA biomarkers for multiple cancer types for general cancer screening purposes.

Method

To identify serum miRNAs that would be useful in screening multiple types of cancers, microarray cancer datasets were curated, yielding 13 different types of cancer with a total of 3352 cancer samples and 2809 non-cancer samples. The samples were divided into training and validation sets. One hundred random forest models were built using the training set to select candidate miRNAs. The selected miRNAs were then used in the validation set to see how well they differentiate cancer from normal samples in an independent dataset. Furthermore, the interactions between these miRNAs and their target mRNAs were investigated.

Result

The random forest models achieved an average of 97% accuracy in the training set with 95% bootstrap confidence interval of 0.9544 to 0.9778. The selected miRNAs were hsa-miR-663a, hsa-miR-6802-5p, hsa-miR-6784-5p, hsa-miR-3184-5p, and hsa-miR-8073. Each miRNA exhibited high area under the curve (AUC) value using receiver operating characteristic analysis. Moreover, the combination of four out of five miRNAs achieved the highest AUC value of 0.9815 with high sensitivity of 0.9773, indicating that these miRNAs have a high potential for cancer screening. miRNA-mRNA and protein-protein interaction analysis provided insights into how these miRNAs play a role in cancer.

Discovering Common miRNA Signatures Underlying Female-Specific Cancers via a Machine Learning Approach Driven by the Cancer Hallmark ERBB

Big data processing, using omics data integration and machine learning (ML) methods, drive efforts to discover diagnostic and prognostic biomarkers for clinical decision making. Previously, we used the TCGA database for gene expression profiling of breast, ovary, and endometrial cancers, and identified a top-scoring network centered on the ERBB2 gene, which plays a crucial role in carcinogenesis in the three estrogen-dependent tumors. Here, we focused on microRNA expression signature similarity, asking whether they could target the ERBB family. We applied an ML approach on integrated TCGA miRNA profiling of breast, endometrium, and ovarian cancer to identify common miRNA signatures differentiating tumor and normal conditions. Using the ML-based algorithm and the miRTarBase database, we found 205 features and 158 miRNAs targeting ERBB isoforms, respectively. By merging the results of both databases and ranking each feature according to the weighted Support Vector Machine model, we prioritized 42 features, with accuracy (0.98), AUC (0.93–95% CI 0.917–0.94), sensitivity (0.85), and specificity (0.99), indicating their diagnostic capability to discriminate between the two conditions. In vitro validations by qRT-PCR experiments, using model and parental cell lines for each tumor type showed that five miRNAs (hsa-mir-323a-3p, hsa-mir-323b-3p, hsa-mir-331-3p, hsa-mir-381-3p, and hsa-mir-1301-3p) had expressed trend concordance between breast, ovarian, and endometrium cancer cell lines compared with normal lines, confirming our in silico predictions. This shows that an integrated computational approach combined with biological knowledge, could identify expression signatures as potential diagnostic biomarkers common to multiple tumors.

Multiple cancer type classification by small RNA expression profiles with plasma samples from multiple facilities

Liquid biopsy is expected to be a promising cancer screening method because of its low invasiveness and the possibility of detecting multiple types in a single test. In the last decade, many studies on cancer detection using small RNAs in blood have been reported. To put small RNA tests into practical use as a multiple cancer type screening test, it is necessary to develop a method that can be applied to multiple facilities. We collected samples of eight cancer types and healthy controls from 20 facilities to evaluate the performance of cancer type classification. A total of 2,475 cancer samples and 496 healthy control samples were collected using a standardized protocol. After obtaining a small RNA expression profile, we constructed a classification model and evaluated its performance. First, we investigated the classification performance using samples from five single facilities. Each model showed areas under the receiver curve (AUC) ranging from 0.67 to 0.89. Second, we performed principal component analysis (PCA) to examine the characteristics of the facilities. The degree of hemolysis and the data acquisition period affected the expression profiles. Finally, we constructed the classification model by reducing the influence of these factors, and its performance had an AUC of 0.76. The results reveal that small RNA can be used for the classification of cancer types in samples from a single facility. However, interfacility biases will affect the classification of samples from multiple facilities. These findings will provide important insights to improve the performance of multiple cancer type classifications using small RNA expression profiles acquired from multiple facilities.

Development and Validation of a Novel Circulating miRNA-Based Diagnostic Score for Early Detection of Hepatocellular Carcinoma

BACKGROUND

With the rise of liquid biopsy in oncology, circulating miRNAs have become one of the most promising noninvasive biomarkers for early detection of hepatocellular carcinoma (HCC). However, a reliable HCC-related circulating miRNA panel and corresponding diagnostic model remain to be explored.

METHODS

Five large public datasets related to intact miRNA profiles in the serum or tumors of HCC patients were included and divided into training cohorts (GSE113740 and TCGA-LIHC) and validation cohorts (GSE112264, GSE113486 and GSE106817). Compared with non-cancer controls and high-risk patients, key miRNAs dysregulated in both the serum and tumors of HCC patients were identified by differential expression analysis and overlapping analysis. The corresponding diagnostic model was constructed by LASSO logistic regression and evaluated by receiver operating characteristic curves and a nomogram with calibration plot.

RESULTS

A distinctive panel of HCC-related circulating miRNAs, including three upregulated miRNAs (miR-184, miR-532-5p, miR-221-3p) and three downregulated miRNAs (miR-5589-5p, let-7b-3p, miR-26b-3p), were rigorously screened out, all of which displayed significant discriminability between HCC patients and controls (all P < 0.05). In addition, a reliable six-circulating miRNA-based diagnostic score was constructed and displayed robust diagnostic ability for HCC (particularly for early-stage HCC) (AUC = 0.9535, P < 0.05) compared with that of the serum α-fetoprotein test. Importantly, its efficacy was sufficiently validated in three independent datasets (AUC = 0.9780/0.9961/0.9681, all P < 0.05). Furthermore, a visual nomogram based on the diagnostic score was correspondingly established to strengthen its clinical applicability.

CONCLUSION

The six-circulating miRNA-based diagnostic score may be a reliable noninvasive biomarker for early-stage HCC screening and dynamic monitoring of postoperative recurrence.

Non-Coding RNAs Delivery by Small Extracellular Vesicles and Their Applications in Ovarian Cancer

Ovarian cancer (OC) is the most fatal gynecological malignancy because of its early asymptomatic nature and acquired resistance to chemotherapy. Small extracellular vesicles (sEVs) are a heterogeneous group of biological vesicles with a diameter <200 nm released by cells under physiological or pathological conditions. sEVs-derived non-coding RNAs (ncRNAs) are the essential effectors in the biological environment. sEVs-ncRNAs have critical roles in tumor progression via regulating mRNA expression of target cells to affect cell signaling. In addition, the status of parental cells can be disclosed via analyzing the composition of sEVs-ncRNAs, and their “cargoes” with specific changes can be used as key biomarkers for the diagnosis and prognosis of OC. Accumulating evidence has demonstrated that sEVs-ncRNAs are involved in multiple key processes that mediate the development of metastasis and chemotherapeutic resistance in OC: epithelial–mesenchymal transition; tumorigenicity of mesenchymal stem cells; immune evasion; angiogenesis. The nanomedicine delivery system based on engineering sEVs is expected to be a novel therapeutic strategy for OC. Insights into the biological roles of sEVs-ncRNAs in the invasion, metastasis, immune regulation, and chemoresistance of OC will contribute to discovery of novel biomarkers and molecular targets for early detection and innovative therapy. In this review, we highlight recent advances and applications of sEVs-ncRNAs in OC diagnosis and treatment. We also outline current challenges and knowledge gaps.

Potential clinical utility of liquid biopsies in ovarian cancer

BACKGROUND

Ovarian cancer (OC) is the most lethal gynecologic malignancy worldwide. One of the main challenges in the management of OC is the late clinical presentation of disease that results in poor survival. Conventional tissue biopsy methods and serological biomarkers such as CA-125 have limited clinical applications. Liquid biopsy is a novel sampling method that analyzes distinctive tumour components released into the peripheral circulation, including circulating tumour DNA (ctDNA), circulating tumour cells (CTCs), cell-free RNA (cfRNA), tumour-educated platelets (TEPs) and exosomes. Increasing evidence suggests that liquid biopsy could enhance the clinical management of OC by improving early diagnosis, predicting prognosis, detecting recurrence, and monitoring response to treatment. Capturing the unique tumour genetic landscape can also guide treatment decisions and the selection of appropriate targeted therapies. Key advantages of liquid biopsy include its non-invasive nature and feasibility, which allow for serial sampling and longitudinal monitoring of dynamic tumour changes over time. In this review, we outline the evidence for the clinical utility of each liquid biopsy component and review the advantages and current limitations of applying liquid biopsy in managing ovarian cancer. We also highlight future directions considering the current challenges and explore areas where more studies are warranted to elucidate its emerging clinical potential.

Applications of Proteomics in Ovarian Cancer: Dawn of a New Era

The ability to identify ovarian cancer (OC) at its earliest stages remains a challenge. The patients present an advanced stage at diagnosis. This heterogeneous disease has distinguishable etiology and molecular biology. Next-generation sequencing changed clinical diagnostic testing, allowing assessment of multiple genes, simultaneously, in a faster and cheaper manner than sequential single gene analysis. Technologies of proteomics, such as mass spectrometry (MS) and protein array analysis, have advanced the dissection of the underlying molecular signaling events and the proteomic characterization of OC. Proteomics analysis of OC, as well as their adaptive responses to therapy, can uncover new therapeutic choices, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is an urgent need to better understand how the genomic and epigenomic heterogeneity intrinsic to OC is reflected at the protein level, and how this information could potentially lead to prolonged survival.

Comparative computational RNA analysis of cardiac-derived progenitor cells and their extracellular vesicles

Stem/progenitor cells, including cardiac-derived c-kit+ progenitor cells (CPCs), are under clinical evaluation for treatment of cardiac disease. Therapeutic efficacy of cardiac cell therapy can be attributed to paracrine signaling and the release of extracellular vesicles (EVs) carrying diverse cargo molecules. Despite some successes and demonstrated safety, large variation in cell populations and preclinical/clinical outcomes remains a problem. Here, we investigated this variability by sequencing coding and non-coding RNAs of CPCs and CPC-EVs from 30 congenital heart disease patients and used machine learning methods to determine potential mechanistic insights. CPCs retained RNAs related to extracellular matrix organization and exported RNAs related to various signaling pathways to CPC-EVs. CPC-EVs are enriched in miRNA clusters related to cell proliferation and angiogenesis. With network analyses, we identified differences in non-coding RNAs which give insight into age-dependent functionality of CPCs. By taking a quantitative computational approach, we aimed to uncover sources of CPC cell therapy variability.

Multi-omics approaches for biomarker discovery in early ovarian cancer diagnosis

Ovarian cancer (OC) is a heterogeneous disease with the highest mortality rate and the poorest prognosis among gynecological malignancies. Because of the absence of specific early symptoms, most OC patients are often diagnosed at late stages. Thus, improved biomarkers of OC for use in research and clinical practice are urgently needed. The last decade has seen increasingly rapid advances in sequencing and biotechnological methodologies. Consequently, multiple omics technologies, including genomic/transcriptomic sequencings and proteomic/metabolomic mass spectra, have been widely applied to analyze tissue- and liquid-derived samples from OC patients. The integration of multi-omics data has increased our knowledge of the disease and identified valuable OC biomarkers. In this review, we summarize the recent advances and perspectives in the use of multi-omics technologies in OC research and highlight potential applications of multi-omics for identifying novel biomarkers and improving clinical assessments.