Exosomal microRNAs as potential circulating biomarkers in gastrointestinal tract cancers: A systematic review protocol (Preprint)
Bibliographic record
Abstract
BACKGROUND microRNAs (miRNAs, miRs) are being suggested as potential diagnostic and predictive biomarkers for gastrointestinal (GI) cancers. The encapsulation of miRNAs in nano-sized exosomes makes them more resistant to degradation. Therefore, they may be useful diagnostic biomarkers for the detection of cancer in body fluids, especially in the serum of cancer patients. OBJECTIVE The aim of this study is to systematically review primary studies and identify the miRNA profiles of serum exosomes of GI cancers. METHODS This systematic review will be reported in line with the Preferred Reporting Items for Systematic Reviews (PRISMA-P) guidance. Relevant studies will be identified through a comprehensive search of the following main electronic databases: PubMed, Web of Science, Embase, Scopus and Google Scholar (up to July 2017). Full copies of articles will be identified by a defined search strategy. The quality assessment of the included studies will be performed by the guidelines for evaluating prevalence studies. Evaluation of the heterogeneity among the primary studies will be performed by χ2 and I2 statistical tests. RESULTS The analysis of circulating exosomal miRNA profiles provides attractive screening and non-invasive diagnostic tools for the majority of solid tumours including GI cancers. There is limited information regarding the relationship between serum exosomal miRNA profiles and the pathological condition of patients with different GI cancers. Since there is no specific biomarker for GI cancers, we aim to suggest a number of circulating exosomal miRNA candidates as potential multifaceted GI cancer biomarkers for clinical utility. CONCLUSIONS The findings of this review may be used in clinical settings and in designing and developing of the novel non-invasive biomarkers for early diagnosis and pathological condition of patients with GI cancers. CLINICALTRIAL CRD 42017057129
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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 | 0.030 | 0.057 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.076 | 0.008 |
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".