MicroRNA-155 as Biomarker and Its Diagnostic Value in Breast Cancer: A Systematic Review
Bibliographic record
Abstract
The investigation of microRNAs (miRNAs) for the purpose of identifying biomarkers and new treatments for breast cancer has been gaining traction from scientists in recent years. Of all the miRNAs, miR-155 has been reportedly involved in breast cancer development as it regulates various cellular processes such as glucose uptake, proliferation, metastasis, and migration. Various efforts have been done towards researching miR-155 as a biomarker in breast cancer; however, the results were varied. The objective of the current systematic review is to compile and summarize information regarding miR-155 as a potential diagnostic biomarker for breast cancer. All eligible studies were found from SCOPUS and PubMed databases. Out of the 376 potential eligible records, only 26 original articles were selected for further assessment according to inclusion and exclusion criteria. The expressions of miR-155 in serum, plasma, biopsy, urine, nipple aspirate fluid, serum exosomes, and peripheral blood mononuclear cells were recorded and analyzed. Besides that, the expression of miR-155 was also correlated to clinicopathological features in breast cancer patients. The area under the curve (AUC) values from receiver operating characteristic (ROC) analysis used to evaluate diagnostic sensitivity and specificity of miR-155 as a diagnostic biomarker were also recorded. The limitations such as the small sampling size, the unemployment of internal controls for quantitative real-time polymerase chain reaction (RT-qPCR), and inconsistency of sensitivity as well as specificity values of miR-155 as a biomarker have been discussed. The present study proposed that miR-155 is a good diagnostic biomarker for breast cancer; however, further clinical research is required to assess the validity of miR-155 as a potential biomarker to translate the research outcomes into clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".