Identification of the miRNA signature associated with survival in patients with ovarian cancer

Emerging biologic and clinical implications of miR-182-5p in gynecologic cancers

MicroRNAs (miRNAs) have emerged as important regulators of gene expression in various biological processes, including cancer. miR-182-5p has gained attention for its potential implications in gynecologic cancers, including breast, ovarian, endometrial, and cervical cancers. miR-182-5p dysregulation has been associated with multiple facets of tumor biology in gynecologic cancers, including tumor initiation, progression, metastasis, and therapeutic response. Studies have highlighted its involvement in key signaling pathways and cellular processes that contribute to cancer development and progression. In addition, miR-182-5p has shown potential as a diagnostic and prognostic biomarker, with studies demonstrating its correlation with clinicopathological features and patient outcomes. Furthermore, the therapeutic potential of miR-182-5p is being explored in gynecologic cancers. Strategies such as miRNA mimics or inhibitors targeting miR-182-5p have shown promise in preclinical and early clinical studies. These approaches aim to modulate miR-182-5p expression, restoring normal cellular functions and potentially enhancing treatment responses. Understanding the biologic and clinical implications of miR-182-5p in gynecologic cancers is crucial for the development of targeted therapeutic strategies and personalized medicine approaches. Further investigations are needed to unravel the specific target genes and pathways regulated by miR-182-5p. It is important to consider the emerging biologic and clinical implications of miR-182-5p in gynecologic cancers.

Insights into the genetic and epigenetic mechanisms governing X-chromosome-linked-miRNAs expression in cancer; a step-toward ncRNA precision

Sex chromosomes play a significant role in establishing sex-specific differences in gene expression, thereby contributing to phenotypic diversity and susceptibility to various diseases. MicroRNAs (miRNAs), which are small non-coding RNAs encoded by both the X and Y chromosomes, exhibit sex-specific regulatory characteristics. Computational analysis has identified several X-linked miRNAs differentially expressed in sex-specific cancers. This review aims to elucidate the genetic and epigenetic mechanisms that govern the sex-specific expression of X- and Y-linked miRNAs, with particular attention to their functional role in regulating diverse cellular processes in different cancer pathways. In addition, this review provides a comprehensive understanding of the targeted therapeutic interventions and critical insights into the potential clinical implications of targeting sex-specific miRNAs. In conclusion, this review opens new horizons for further research to effectively translate these findings into viable treatment options.

MicroRNAs in metabolism for precision treatment of lung cancer

The dysregulation of miRNAs in lung cancer has been extensively documented, with specific miRNAs acting as both tumor suppressors and oncogenes, depending on their target genes. Recent research has unveiled the regulatory roles of miRNAs in key metabolic pathways, such as glycolysis, the tricarboxylic acid cycle, fatty acid metabolism, and autophagy, which collectively contribute to the aberrant energy metabolism characteristic of cancer cells. Furthermore, miRNAs are increasingly recognized as critical modulators of the tumor microenvironment, impacting immune response and angiogenesis. This review embarks on a comprehensive journey into the world of miRNAs, unraveling their multifaceted roles, and more notably, their emerging significance in the context of cancer, with a particular focus on lung cancer. As we navigate this extensive terrain, we will explore the fascinating realm of miRNA-mediated metabolic rewiring, a phenomenon that plays a pivotal role in the progression of lung cancer and holds promise in the development of novel therapeutic strategies.

Evolutionary learning-derived lncRNA signature with biomarker discovery for predicting stage of colon adenocarcinoma

In recent years, long non-coding RNAs (lncRNAs) have emerged as potential regulators of biological processes and genes, with the potential to serve as valuable biomarkers for cancer diagnosis and prognosis prediction. This work proposes an evolutionary learning-based method, EL-COAD, to identify a robust lncRNA signature with biomarker discovery for predicting stages of colon adenocarcinoma (COAD). The COAD patient cohorts were obtained from both the Cancer Genome Atlas and Gene Expression Omnibus (gse17536) databases. EL-COAD incorporates a bi-objective combinatorial genetic algorithm with a support vector machine for selecting a minimal number of lncRNAs while maximizing prediction accuracy. EL-COAD identified a 15-lncRNA signature and achieved a five-fold cross-validation and area under receiver operating characteristic curve of 79.4% and 0.792, respectively. Utilising the 10 lncRNAs from the signature for an independent dataset gse17536, the Sequential Minimal Optimization model achieved a test accuracy of 64.15%. Furthermore, the lncRNAs of the signature were prioritized, with the top five being TMEM105, DUXAP8, APCDD1L-DT, PCAT6, and a novel transcript, ENSG00000226308. Furthermore, both Kyoto Encyclopedia of Genes and Genomes pathway and Disease Ontology analyses provided strong support for the viability of this model-independent signature, emphasising ENSG00000226308 as a promising biomarker.

Advances in applications of artificial intelligence algorithms for cancer-related miRNA research

MiRNAs are a class of small non-coding RNAs, which regulate gene expression post-transcriptionally by partial complementary base pairing. Aberrant miRNA expressions have been reported in tumor tissues and peripheral blood of cancer patients. In recent years, artificial intelligence algorithms such as machine learning and deep learning have been widely used in bioinformatic research. Compared to traditional bioinformatic tools, miRNA target prediction tools based on artificial intelligence algorithms have higher accuracy, and can successfully predict subcellular localization and redistribution of miRNAs to deepen our understanding. Additionally, the construction of clinical models based on artificial intelligence algorithms could significantly improve the mining efficiency of miRNA used as biomarkers. In this article, we summarize recent development of bioinformatic miRNA tools based on artificial intelligence algorithms, focusing on the potential of machine learning and deep learning in cancer-related miRNA research.

ExplORRNet: An interactive web tool to explore stage-wise miRNA expression profiles and their interactions with mRNA and lncRNA in human breast and gynecological cancers

Background

MicroRNAs (miRNAs) are key regulators of gene expression that have been implicated in gynecological and breast cancers. Understanding the cancer stage-wise expression patterns of miRNAs and their interactions with other RNA molecules in cancer is crucial to improve cancer diagnosis and treatment planning. Comprehensive web tools that integrate data on the transcriptome, circulating miRNAs, and their validated targets to derive beneficial conclusions in cancer research are lacking.

Methods

Using the Shiny R package, we developed a web tool called ExplORRNet that integrates transcriptomic profiles from The Cancer Genome Atlas and miRNA expression data derived from various sources, including tissues, cell lines, exosomes, serum, and plasma, available in the Gene Expression Omnibus database. Differential expression analyses between normal and tumor tissue samples as well as different stages of cancer, accompanied by gene enrichment and survival analyses, can be performed using specialized R packages. Additionally, a miRNA-messenger RNA (mRNA)-long non-coding RNA (lncRNA) networks are constructed to identify regulatory modules.

Results

Our tool identifies cancer stage-wise differentially regulated miRNAs, mRNAs, and lncRNAs in gynecological and breast cancers. Survival analysis identifies miRNAs associated with patient survival, and functional enrichment analysis provides insights into dysregulated miRNA-related biological processes and pathways. The miRNA-mRNA-lncRNA networks highlight interconnected regulatory molecular modules driving cancer progression. Case studies demonstrate the utility of the ExplORRNet for studying gynecological and breast cancers.

Conclusion

ExplORRNet is an intuitive and user-friendly web tool that provides a deeper understanding of dysregulated miRNAs and their functional implications in gynecological and breast cancers. We hope our ExplORRNet tool has potential utility among the clinical and basic researchers and will be beneficial to the entire cancer genomics community to encourage and facilitate mining the rapidly growing public databases to progress the field of precision oncology. The ExplORRNet is available at https://mirna.cs.ut.ee.

Prognostic microRNA signature for estimating survival in patients with hepatocellular carcinoma

OBJECTIVE

Hepatocellular carcinoma (HCC) is one of the leading cancer types with increasing annual incidence and high mortality in the USA. MicroRNAs (miRNAs) have emerged as valuable prognostic indicators in cancer patients. To identify a miRNA signature predictive of survival in patients with HCC, we developed a machine learning-based HCC survival estimation method, HCCse, using the miRNA expression profiles of 122 patients with HCC.

METHODS

The HCCse method was designed using an optimal feature selection algorithm incorporated with support vector regression.

RESULTS

HCCse identified a robust miRNA signature consisting of 32 miRNAs and obtained a mean correlation coefficient (R) and mean absolute error (MAE) of 0.87 ± 0.02 and 0.73 years between the actual and estimated survival times of patients with HCC; and the jackknife test achieved an R and MAE of 0.73 and 0.97 years between actual and estimated survival times, respectively. The identified signature has seven prognostic miRNAs (hsa-miR-146a-3p, hsa-miR-200a-3p, hsa-miR-652-3p, hsa-miR-34a-3p, hsa-miR-132-5p, hsa-miR-1301-3p and hsa-miR-374b-3p) and four diagnostic miRNAs (hsa-miR-1301-3p, hsa-miR-17-5p, hsa-miR-34a-3p and hsa-miR-200a-3p). Notably, three of these miRNAs, hsa-miR-200a-3p, hsa-miR-1301-3p and hsa-miR-17-5p, also displayed association with tumor stage, further emphasizing their clinical relevance. Furthermore, we performed pathway enrichment analysis and found that the target genes of the identified miRNA signature were significantly enriched in the hepatitis B pathway, suggesting its potential involvement in HCC pathogenesis.

CONCLUSIONS

Our study developed HCCse, a machine learning-based method, to predict survival in HCC patients using miRNA expression profiles. We identified a robust miRNA signature of 32 miRNAs with prognostic and diagnostic value, highlighting their clinical relevance in HCC management and potential involvement in HCC pathogenesis.

Non-coding RNAs in gynecologic cancer

The term "gynecologic cancer" pertains to neoplasms impacting the reproductive tissues and organs of women encompassing the endometrium, vagina, cervix, uterus, vulva, and ovaries. The progression of gynecologic cancer is linked to various molecular mechanisms. Historically, cancer research primarily focused on protein-coding genes. However, recent years have unveiled the involvement of non-coding RNAs (ncRNAs), including microRNAs, long non-coding RNAs (LncRNAs), and circular RNAs, in modulating cellular functions within gynecological cancer. Substantial evidence suggests that ncRNAs may wield a dual role in gynecological cancer, acting as either oncogenic or tumor-suppressive agents. Numerous clinical trials are presently investigating the roles of ncRNAs as biomarkers and therapeutic agents. These endeavors may introduce a fresh perspective on the diagnosis and treatment of gynecological cancer. In this overview, we highlight some of the ncRNAs associated with gynecological cancers.

Cellular signaling modulated by miRNA-3652 in ovarian cancer: unveiling mechanistic pathways for future therapeutic strategies

MicroRNAs (miRNAs) are small non-coding RNA molecules that play pivotal roles in regulating gene expression and have been implicated in the pathogenesis of numerous cancers. miRNA-3652, though relatively less explored, has recently emerged as a potential key player in ovarian cancer's molecular landscape. This review aims to delineate the functional significance and tumor progression role of miRNA-3652 in ovarian cancer, shedding light on its potential as both a diagnostic biomarker and therapeutic target. A comprehensive literature search was carried out using established databases, the focus was on articles that reported the role of miRNA-3652 in ovarian cancer, encompassing mechanistic insights, functional studies, and its association with clinical outcomes. This updated review highlighted that miRNA-3652 is intricately involved in ovarian cancer cell proliferation, migration, and invasion, its dysregulation was linked to altered expression of critical genes involved in tumor growth and metastasis; furthermore, miRNA-3652 expression levels were found to correlate with clinical stages, prognosis, and response to therapy in ovarian cancer patients. miRNA-3652 holds significant promise as a vital molecular player in ovarian cancer's pathophysiology. Its functional role and impact on tumor progression make it a potential candidate for diagnostic and therapeutic applications in ovarian cancer. Given the pivotal role of miRNA-3652 in ovarian cancer, future studies should emphasize in-depth mechanistic explorations, utilizing advanced genomic and proteomic tools. Collaboration between basic scientists and clinicians will be vital to translating these findings into innovative diagnostic and therapeutic strategies, ultimately benefiting ovarian cancer patients. Video Abstract.

The Role of MicroRNAs in Breast Cancer and the Challenges of Their Clinical Application

MicroRNAs (miRNAs) constitute a subclass of non-coding RNAs that exert substantial influence on gene-expression regulation. Their tightly controlled expression plays a pivotal role in various cellular processes, while their dysregulation has been implicated in numerous pathological conditions, including cancer. Among cancers affecting women, breast cancer (BC) is the most prevalent malignant tumor. Extensive investigations have demonstrated distinct expression patterns of miRNAs in normal and malignant breast cells. Consequently, these findings have prompted research efforts towards leveraging miRNAs as diagnostic tools and the development of therapeutic strategies. The aim of this review is to describe the role of miRNAs in BC. We discuss the identification of oncogenic, tumor suppressor and metastatic miRNAs among BC cells, and their impact on tumor progression. We describe the potential of miRNAs as diagnostic and prognostic biomarkers for BC, as well as their role as promising therapeutic targets. Finally, we evaluate the current use of artificial intelligence tools for miRNA analysis and the challenges faced by these new biomedical approaches in its clinical application. The insights presented in this review underscore the promising prospects of utilizing miRNAs as innovative diagnostic, prognostic, and therapeutic tools for the management of BC.

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

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

The downregulation of miR-509-3p expression by collagen type XI alpha 1-regulated hypermethylation facilitates cancer progression and chemoresistance via the DNA methyltransferase 1/Small ubiquitin-like modifier-3 axis in ovarian cancer cells

BACKGROUND

MicroRNAs are a group of small non-coding RNAs that are involved in development and diseases such as cancer. Previously, we demonstrated that miR-335 is crucial for preventing collagen type XI alpha 1 (COL11A1)-mediated epithelial ovarian cancer (EOC) progression and chemoresistance. Here, we examined the role of miR-509-3p in EOC.

METHODS

The patients with EOC who underwent primary cytoreductive surgery and postoperative platinum-based chemotherapy were recruited. Their clinic-pathologic characteristics were collected, and disease-related survivals were determined. The COL11A1 and miR-509-3p mRNA expression levels of 161 ovarian tumors were determined by real-time reverse transcription-polymerase chain reaction. Additionally, miR-509-3p hypermethylation was evaluated by sequencing in these tumors. The A2780CP70 and OVCAR-8 cells transfected with miR-509-3p mimic, while the A2780 and OVCAR-3 cells transfected with miR-509-3p inhibitor. The A2780CP70 cells transfected with a small interference RNA of COL11A1, and the A2780 cells transfected with a COL11A1 expression plasmid. Site-directed mutagenesis, luciferase, and chromatin immunoprecipitation assays were performed in this study.

RESULTS

Low miR-509-3p levels were correlated with disease progression, a poor survival, and high COL11A1 expression levels. In vivo studies reinforced these findings and indicated that the occurrence of invasive EOC cell phenotypes and resistance to cisplatin are decreased by miR-509-3p. The miR-509-3p promoter region (p278) is important for miR-509-3p transcription regulation via methylation. The miR-509-3p hypermethylation frequency was significantly higher in EOC tumors with a low miR-509-3p expression than in those with a high miR-509-3p expression. The patients with miR-509-3p hypermethylation had a significantly shorter overall survival (OS) than those without miR-509-3p hypermethylation. Mechanistic studies further indicated that miR-509-3p transcription was downregulated by COL11A1 through a DNA methyltransferase 1 (DNMT1) stability increase. Moreover, miR-509-3p targets small ubiquitin-like modifier (SUMO)-3 to regulate EOC cell growth, invasiveness, and chemosensitivity.

CONCLUSION

The miR-509-3p/DNMT1/SUMO-3 axis may be an ovarian cancer treatment target.

The Downregulation of miR-509-3p Expression by Collagen Type XI Alpha 1-Regulated Hypermethylation Facilitates Cancer Progression and Chemoresistance via the DNA Methyltransferase 1/Small Ubiquitin-like Modifier-3 Axis in Ovarian Cancer Cells

Background MicroRNAs are a group of small non-coding RNAs that are involved in development and diseases such as cancer. Previously, we demonstrated that miR-335 is crucial for preventing collagen type XI alpha 1 (COL11A1)-mediated epithelial ovarian cancer (EOC) progression and chemoresistance. Here, we examined the role of miR-509-3p in EOC. Methods The patients with EOC who underwent primary cytoreductive surgery and postoperative platinum-based chemotherapy were recruited. Their clinic-pathologic characteristics were collected, and disease-related survivals were determined. The COL11A1 and miR-509-3p mRNA expression levels of 161 ovarian tumors were determined by real-time reverse transcription-polymerase chain reaction. Additionally, miR-509-3p hypermethylation was evaluated by sequencing in these tumors. The A2780CP70 and OVCAR-8 cells transfected with miR-509-3p mimic, while the A2780 and OVCAR-3 cells transfected with miR-509-3p inhibitor. The A2780CP70 cells transfected with a small interference RNA of COL11A1, and the A2780 cells transfected with a COL11A1 expression plasmid. Site-directed mutagenesis, luciferase, and chromatin immunoprecipitation assays were performed in this study. Results Low miR-509-3p levels were correlated with disease progression, a poor survival, and high COL11A1 expression levels. In vivo studies reinforced these findings and indicated that the occurrence of invasive EOC cell phenotypes and resistance to cisplatin are decreased by miR-509-3p. The miR-509-3p promoter region (p278) is important for miR-509-3p transcription regulation via methylation. The miR-509-3p hypermethylation frequency was significantly higher in EOC tumors with a low miR-509-3p expression than in those with a high miR-509-3p expression. The patients with miR-509-3p hypermethylation had a significantly shorter overall survival (OS) than those without miR-509-3p hypermethylation. Mechanistic studies further indicated that miR-509-3p transcription was downregulated by COL11A1 through a DNA methyltransferase 1 (DNMT1) phosphorylation and stability increase. Moreover, miR-509-3p targets small ubiquitin-like modifier (SUMO)-3 to regulate EOC cell growth, invasiveness, and chemosensitivity. Conclusion The miR-509-3p/DNMT1/SUMO-3 axis may be an ovarian cancer treatment target.

Exosomal MicroRNA as Biomarkers for Diagnosing or Monitoring the Progression of Ovarian Clear Cell Carcinoma: A Pilot Study

Ovarian cancer is the most common cause of gynecological malignancy-related mortality since early-stage disease is difficult to diagnose. Advanced clear cell carcinoma of the ovary (CCCO) has dismal prognosis, and its incidence has been increasing in Japan, emphasizing the need for highly sensitive diagnostic and prognostic CCCO biomarkers. Exosomal microRNAs (miRNAs) secreted by tumor cells are known to play a role in carcinogenesis; however, their involvement in ovarian cancer is unclear. In this study, we performed expression profiling of miRNAs from exosomes released by five cell lines representing different histological types of ovarian cancer. Exosomes isolated from culture media of cancer and normal cells were compared for miRNA composition using human miRNA microarray. We detected 143 exosomal miRNAs, whose expression was ≥1.5-fold higher in ovarian cancer cells than in the control. Among them, 28 miRNAs were upregulated in cells of all histological ovarian cancer types compared to control, and three were upregulated in CCCO cells compared to other types. Functional analyses indicated that miR-21 overexpressed in CCCO cells targeted tumor suppressor genes PTEN, TPM1, PDCD4, and MASP1. The identified miRNAs could represent novel candidate biomarkers to diagnose or monitor progression of ovarian cancer, particularly CCCO.

MicroRNA signature for estimating the survival time in patients with bladder urothelial carcinoma

Bladder urothelial carcinoma (BLC) is one of the most common cancers in men, and its heterogeneity challenges the treatment to cure this disease. Recently, microRNAs (miRNAs) gained promising attention as biomarkers due to their potential roles in cancer biology. Identifying survival-associated miRNAs may help identify targets for therapeutic interventions in BLC. This work aims to identify a miRNA signature that could estimate the survival in patients with BLC. We developed a survival estimation method called BLC-SVR based on support vector regression incorporated with an optimal feature selection algorithm to select a robust set of miRNAs as a signature to estimate the survival in patients with BLC. BLC-SVR identified a miRNA signature consisting of 29 miRNAs and obtained a mean squared correlation coefficient and mean absolute error of 0.79 ± 0.02 and 0.52 ± 0.32 year between actual and estimated survival times, respectively. The prediction performance of BLC-SVR had a better estimation capability than other standard regression methods. In the identified miRNA signature, 14 miRNAs, hsa-miR-432-5p, hsa-let-7e-3p, hsa-miR-652-3p, hsa-miR-629-5p, and hsa-miR-203a-3p, hsa-miR-129-5p, hsa-miR-769-3p, hsa-miR-570-3p, hsa-miR-320c, hsa-miR-642a-5p, hsa-miR-496, hsa-miR-5480-3p, hsa-miR-221-5p, and hsa-miR-7-1-3p, were found to be good biomarkers for BLC diagnosis; and the six miRNAs, hsa-miR-652-5p, hsa-miR-193b-5p, hsa-miR-129-5p, hsa-miR-143-5p, hsa-miR-496, and hsa-miR-7-1-3p, were found to be good biomarkers of prognosis. Further bioinformatics analysis of this miRNA signature demonstrated its importance in various biological pathways and gene ontology annotation. The identified miRNA signature would further help in understanding of BLC diagnosis and prognosis in the development of novel miRNA-target based therapeutics in BLC.

Tracking the amino acid changes of spike proteins across diverse host species of severe acute respiratory syndrome coronavirus 2

Knowledge of the host-specific properties of the spike protein is of crucial importance to understand the adaptability of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) to infect multiple species and alter transmissibility, particularly in humans. Here, we propose a spike protein predictor SPIKES incorporating with an inheritable bi-objective combinatorial genetic algorithm to identify the biochemical properties of spike proteins and determine their specificity to human hosts. SPIKES identified 20 informative physicochemical properties of the spike protein, including information measures for alpha helix and relative mutability, and amino acid and dipeptide compositions, which have shown compositional difference at the amino acid sequence level between human and diverse animal coronaviruses. We suggest that alterations of these amino acids between human and animal coronaviruses may provide insights into the development and transmission of SARS-CoV-2 in human and other species and support the discovery of targeted antiviral therapies.

•

Differences exist in the amino acids within the S protein of diverse host species CoVs

•

We developed SPIKES to identify informative properties of S protein

•

SARS-CoV-2 variants have amino acid changes that alter infection and transmission

•

The SPIKES identified changes in S protein properties from animal to human host CoVs

Survival estimation in patients with stomach and esophageal carcinoma using miRNA expression profiles

Identifying a miRNA signature associated with survival will open a new window for developing miRNA-targeted treatment strategies in stomach and esophageal cancers (STEC). Here, using data from The Cancer Genome Atlas on 516 patients with STEC, we developed a Genetic Algorithm-based Survival Estimation method, GASE, to identify a miRNA signature that could estimate survival in patients with STEC. GASE identified 27 miRNAs as a survival miRNA signature and estimated the survival time with a mean squared correlation coefficient of 0.80 ± 0.01 and a mean absolute error of 0.44 ± 0.25 years between actual and estimated survival times, and showed a good estimation capability on an independent test cohort. The miRNAs of the signature were prioritized and analyzed to explore their roles in STEC. The diagnostic ability of the identified miRNA signature was analyzed, and identified some critical miRNAs in STEC. Further, miRNA-gene target enrichment analysis revealed the involvement of these miRNAs in various pathways, including the somatotrophic axis in mammals that involves the growth hormone and transforming growth factor beta signaling pathways, and gene ontology annotations. The identified miRNA signature provides evidence for survival-related miRNAs and their involvement in STEC, which would aid in developing miRNA-target based therapeutics.

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

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