Circulating miR‐26a and miR‐21 as biomarkers for glioblastoma multiform
Bibliographic record
Abstract
Glioblastoma multiform is the most common and lethal primary central nervous system tumor. Circulating microRNAs (miRNAs), present in cell-free bodily fluids, have been gaining importance as cancer biomarkers. The primary aim of this study was to assess whether circulating miRNA-128, -21, and -26a in glioblastoma patients can be used as diagnostic biomarkers. Venous blood samples were collected from 11 noncancerous volunteers and 15 glioblastoma patients pre- and post operation. Also, tissue tumor samples were obtained intra-operationally to assay consistency of miRNA levels in serum and tissue samples. Serum and tissue levels of miRNAs were determined by quantitative reverse transcription PCR. miR-21 and miR-26a were both significantly upregulated in pre- and postoperation serum samples of glioblastoma patients compared with the serum samples of noncancerous controls. We found that all three miR-128, -21, and -26a expression levels were reduced in postoperative serum samples compared with pre-operative serum samples, though this decrease was only significant for miR-26a. The serum miR-26a and miR-21 upregulation in glioblastoma patients compared to noncancerous controls and their downregulation in postoperative serum from glioblastoma patients suggest that these miRNAs could be used as serum-derived miRNA biomarkers for glioblastoma.
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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 | 0.000 |
| 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) | 0.001 | 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".