Prognostic significance of non-coding RNAs related to the tumorigenic epithelial-mesenchymal transition (EMT) process among ovarian cancer patients: A systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Ovarian cancer is the seventh most prevalent cancer among women. It has high mortality and morbidity and imposes a great burden on healthcare systems worldwide. Unraveling the mechanisms behind the Epithelial-Mesenchymal Transition and finding a panel for predicting the prognosis of the disease may help find the appropriate treatment approaches for the management of the disease. The overarching aim of this systematic review was to define a panel of different types of EMT-associated non-coding RNAs (ncRNAs) with significant prognostic value in all types of ovarian cancers. Methods: We searched PubMed, Web of Science, Scopus, and Embase till Jun 2024 to retrieve relevant papers. Two independent reviewers screened papers, and discrepancies were resolved by consensus. Publications related to the dysregulation of different types of ncRNAs, including microRNAs, lncRNAs, and circRNAs, only in patients with ovarian cancer were included. The participation of ncRNAs in epithelial-mesenchymal transformation should be assessed via methods evaluating different EMT-related proteins. To assess the quality and risk of bias for the included case-control and cohort studies, refined Newcastle-Ottawa Scale (NOS) and Quadas-2 were recruited. A bivariate meta-analysis was performed to analyze extracted data. Results: A total of 37 studies with overall 42 non-coding RNAs (15 microRNA, 24 long non-coding RNAs, and 3 circular RNAs) were entered into the analysis. Overall diagnostic odds ratio for ncRNAs in lymph node metastasis, distant metastasis, TNM stage, and clinical stage were 4.19, 3.80, 6.52, and 3.97, respectively. Also, a hazard ratio of 1.39 (P = 0.32) for overall survival was observed. Bioinformatic analyses on the Pan-cancer database demonstrated a significant correlation between low expression of miRNA and high expression of lncRNAs with poor prognosis of ovarian cancer. Conclusion: Based on the results, the defined panel of ncRNAs can properly predict prognostic factors related to EMT in ovarian cancer without involving potentially invasive methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".