Project description:A major obstacle to improving prognoses in ovarian cancer is the lack of effective screening methods for early detection. Circulating microRNAs (miRNAs) have been recognized as promising biomarkers that could lead to clinical applications. Here, to develop an optimal detection method, we use microarrays to obtain comprehensive miRNA profiles from 4046 serum samples, including 428 patients with ovarian tumors. A diagnostic model based on expression levels of ten miRNAs is constructed in the discovery set. Validation in an independent cohort reveals that the model is very accurate (sensitivity, 0.99; specificity, 1.00), and the diagnostic accuracy is maintained even in early-stage ovarian cancers. Furthermore, we construct two additional models, each using 9-10 serum miRNAs, aimed at discriminating ovarian cancers from the other types of solid tumors or benign ovarian tumors. Our findings provide robust evidence that the serum miRNA profile represents a promising diagnostic biomarker for ovarian cancer.

| S-EPMC6192980 | biostudies-literature

Plasma small extracellular vesicles microRNAs profile in advanced ovarian cancer

Project description:No residual disease after debulking Surgery (R0 resection) is the most critical independent prognostic factor for advanced ovarian cancer (AOC). Therefore, it is of paramount importance to preoperatively estimate the likelihood of R0 resection for choosing the best therapeutic strategy. Our study aimed to develop a non-invasive and reliable detection method for AOC patients with a high risk of residual disease. An integrated plasma small extracellular vesicles (sEVs) microRNA profiling was generated by RNA sequencing in AOC patients with no residual disease patients (R0) and residual disease (non-R0). We identified and validated a logistic model based on plasma sEVs miRNAs to predict residual disease in AOC patients.

2024-01-02 | GSE223126 | GEO

Plasma small extracellular vesicles microRNAs profile in advanced ovarian cancer

Project description:No residual disease after debulking Surgery (R0 resection) is the most critical independent prognostic factor for advanced ovarian cancer (AOC). Therefore, it is of paramount importance to preoperative estimate the likelihood of R0 resection for choosing the best therapeutic strategy. Our study aimed to develop a non-invasive and reliable detection method for AOC patients with a high risk of residual disease. An integrated plasma small extracellular vesicles (sEVs) microRNA profiling was generated by RNA sequencing in AOC patients with no residual disease patients (R0) and residual disease(non-R0). We identified and validated a logistic model based on plasma sEVs miRNAs to predict residual disease in AOC patients.

2023-04-01 | GSE166310 | GEO

Ovarian Cancer MicroRNA

Project description:Comparison of various ovarian tumors and ovarian cell lines. Keywords: Various ovarian tumors and cell lines. Samples from ovarian tumors and ovarian cell lines were examined for their microRNA expression patterns.

2008-06-29 | E-GEOD-10150 | biostudies-arrayexpress

Catalyzing Early Ovarian Cancer Detection: Platelet RNA-Based Precision Screening

Project description:Early detection of ovarian cancer is crucial for successful treatment, yet most cases are diagnosed at advanced stages due to a lack of effective screening. Recent advancements in RNA technology from platelets aid in early tumor detection. Here, we proposed our two-step method for assessing the existence of pelvic mass either located at ovaries or uterus with more than 99% specificity by utilizing exon-exon junction features with a sampling invariant normalization technique; then next our model finds the malignancy of detected mass with more than 99% negative predictive value to practically assist clinicians’ further investigation via combined features of exon-exon junctions, and hematology parameters. We diverged from traditional methods by employing ISR counts rather than gene expression levels to use splice junctions as features in our models. If integrated with current screening methods, our algorithm holds promise for identifying ovarian or endometrial cancer in its early stages.

2025-06-18 | GSE278917 | GEO

microRNA screening in human pancreatic adenocarcinoma

Project description:microRNA screening in human pancreatic adenocarcinoma

| PRJNA300855 | ENA

microRNA profiling of recurrent high grade ovarian carcinoma compared to primary ovarian cancer and normal ovary

Project description:To determine microRNA expression in chemoresistant ovarian cancer, we have employed whole microRNA microarray expression profiling as a discovery platform to identify genes with the potential to distinguish recurrent ovarian cancer. 8 recurrent ovarian cancer tissue and 8 primary ovarian cancer tissue and 4 normal ovarian tissue was used to identify miRNA profiling.

2017-06-01 | GSE83693 | GEO

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