miRNA Associated With Glucose Transporters in Oral Squamous Cell Carcinoma: A Systematic Review
Bibliographic record
Abstract
Oral squamous cell carcinoma (OSCC) is a malignancy of the oral cavity with poor prognosis. Dysregulation in glycolytic pathways involving glucose transporters (GLUT) has been implicated in poor prognosis. Furthermore, GLUT expression in cancer cells is regulated by several miRNAs. However, there is a lack of data about miRNA involved in the regulation of GLUT in OSCC. The objective is to evaluate the role of miRNA in the regulation of GLUT in OSCC. Data sources include PubMed (MEDLINE), Scopus, and Web of Science. Studies evaluating the miRNA involved or associated with the regulation of GLUT in OSCC were included in the systematic review. Data pertaining to GLUT and associated miRNA expression were extracted from studies. Qualitative assessment was carried out for GLUT and miRNA. The Newcastle-Ottawa Scale was used for quality assessment. Ten study articles were included after analyzing 4675 papers. These studies evaluated the GLUT and miRNA expression between healthy and OSCC samples. There are variable expression patterns of GLUT in OSCC. Furthermore, it was dependent on miRNA. The GLUT1 and GLUT-3 were detected more frequently in OSCC, while no study reveals the expression of GLUT2, GLUT4, GLUT7, GLUT8, GLUT13, SGLT1, and SGLT2 with miRNA regulation. However, there was insufficient evidence on specific miRNA linked to GLUT1 or GLUT3 expression. There is evidence of the role of miRNA in the regulation of GLUT especially GLUT1 and GLUT3 in OSCC; however, a specific relation to miRNA was understudied. In the future, studies exploring a clearer understanding of the association between miRNA and the GLUT metabolic pathway in relation to OSCC are warranted. Furthermore, association of miRNA and GLUT with progression of disease, disease resistance, and prognosis is assessed for better treatment outcomes.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".