CD44 Overexpression as a Mediator of Drug Resistance in Oral Cancer: A Meta-Analysis Unveiling Molecular Underpinnings and Therapeutic Implications
Bibliographic record
Abstract
Background: Therapeutic resistance in oral cancer, with cancer stem cells (CSCs) playing a pivotal role, remains a major clinical challenge. CD44, a key CSC marker, has been implicated in multidrug resistance mechanisms through drug efflux, DNA repair, and anti-apoptotic pathways. This meta-analysis aims to systematically evaluate the association between CD44 expression and clinicopathological characteristics in oral cancer patients through analysis of controlled clinical studies. Methods: We systematically searched PubMed, EMBASE, Cochrane Library, Web of Science, CNKI, VIP, WanFang, and China Biomedical Literature Database from inception to September 2022. Controlled clinical studies examining CD44 overexpression in relation to clinicopathological characteristics and survival outcomes in oral cancer patients were included. Quality assessment was performed using the Newcastle-Ottawa Scale. Data were analyzed using RevMan 5.3 software. Results: Ten high-quality studies comprising 846 oral cancer patients were included. CD44 overexpression was significantly associated with advanced local tumor invasion (T category: OR: 1.38; 95% CI: 1.14-1.66; P<0.001; I²=25%). No significant associations were found with N category, histological grade, or vascular invasion. Pooled survival analysis was limited by heterogeneous reporting methods across studies. Conclusion: CD44 overexpression serves as a molecular marker of therapeutic resistance in oral cancer, particularly associated with local tumor progression. These findings highlight CD44-targeted therapies as promising strategies to overcome treatment resistance. Future large-scale, multicenter trials are needed to validate CD44 isoforms as prognostic biomarkers and therapeutic targets.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.037 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".