Accuracy of combined serum micro RNA 21 and micro RNA let 7g expression level in breast cancer diagnosis
Bibliographic record
Abstract
Micro RNAs are single-stranded non-coding RNA molecules that regulate biological processes by inhibiting post-transcriptional activity. Many diseases including breast cancer have uncontrolled levels of microRNA. The current work aims to highlight the importance of miRNA let7g and miRNA 21 expression in blood for early detection of breast cancer. Methodology: Using quantitative real-time polymerase chain reaction the serum expression levels of micro RNA 21 and micro RNA let7g were evaluated in 52 female patients with primary breast cancer and 28 matched healthy females as a control group. Results: Serum expression level of micro RNA 21was highly increased in breast cancer patients when compared to the control group, while the serum expression levels of micro RNA let7g recorded a lower level in breast cancer patients when compared to control group and these differences were statistically significant. On studying the relation between the expression levels of the measured parameters and the different clinicopathological characteristics; a positive significant correlation was found between CA15.3 and microRNA 21 levels. A negative significant correlation was present between miRNA let7g levels and HER2, as well as with molecular subtype prognostic ranking. Receiver operating characteristic curve of both markers were exploited at a cut off points 0.82 and 1.785 respectively and showed a 100% sensitivity, 67.8% specificity. Conclusion: The effectiveness of serum mi RNA21 and mi RNA let7g as diagnostic biomarkers for detecting breast cancer has been convincingly proven. This innovative idea could be used to create a complementary tool for disease diagnosis, prognosis, and screening.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".