MiR-708-5p as a Predictive Marker of Colorectal Cancer Prognosis
Bibliographic record
Abstract
Background: MicroRNAs (miRNA) are short non-coding RNA that act as negative regulators of gene expression. Altered levels of miR-708-5p have recently been described in many tumors, although its contribution in colorectal cancer (CRC) pathophysiology remains unclear. Methods/Patients: Quantitative real-time polymerase chain reaction was employed to evaluate the expression of miR-708-5p in 50 CRC and 20 paired adjacent noncancerous tissues. The relationship between miRNA levels and clinicopathological features was estimated using the Mann-Whitney test, and survival curves calculated by the Kaplan-Meier method. Additionally, in vitro assays were performed to investigate the possible role of miR-708-5p on CRC cell survival. Results: The expression level of miR-708-5p was significantly decreased in CRC tissues (3.79 fold-change, p=0.0112) when compared with non-neoplastic colon samples. Paired analysis in 20 CRC samples with their corresponding adjacent non-neoplastic tissue showed miR-708 downregulation in 60% of them. The same pattern was seen in DLD1 and HT-29 cell lines (~50-fold decrease). Interestingly, higher expression is observed in patients with poor prognosissuch as stage III/IV, relapse/metastasis and death, and shorter 5-year event free survival. Exogenous expression of miR-708 exerted a significant influence on clonogenicity in vitro. Conclusion: These results suggest that reduced miR-708-5p expression may contribute to the first stages of colorectal carcinogenesis. A shift in the regulation of miR-708-5p might operate in more severe stages of the disease. It seems that lower levels of miR-708 expression might connote less advanced disease and better prognosis. Further studies are needed to corroborate our results and better elucidate the role of miR-708 in CRC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".