Salivary Sialic Acid Levels as a Biomarker for Early Detection of Oral Precancer and Oral Cancer: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: Salivary sialic acid (SSA) has been detected as biomarker in several cancers and the level of salivary sialic acids has been proven to have a potential diagnostic value in early detection of cancer. This systematic review aims to assess Salivary Sialic Acid (SSA) levels as a biomarker for early detection of oral precancer and oral cancer. DATA SOURCES: A comprehensive Literature search was conducted in various databases such as PubMed, Scopus, Google scholar and ProQuest. Quality assessment of articles was done by Newcastle Ottawa Quality Assessment Scale. RESULTS: A total of 22 studies were included in the systematic review and 14 articles were included for meta-analysis. Studies showed an increase in SSA levels in both oral precancer (SMD 1.79; 95% CI 0.41-3.18), and oral cancer (SMD 11.30; 95% CI -17.04 - 39.64). Total free and protein-bound sialic acid levels were increased in oral cancer group as compared to the healthy controls. The overall standard mean difference of FSA, PBSA, TSA among oral cancer and HC (SMD 23.83; 95% CI 9.22-38.44; p=0.02) and the data revealed statistically significant differences. The results of Meta-analysis revealed statistically significant differences between SSA levels of oral cancer and healthy group. CONCLUSION: Salivary sialic acid levels were observed to be consistently higher in oral cancer group compared to oral precancer and healthy group. However, a cut-off value of SSA levels for the early detection of oral cancer and precancer could not be established because of the limited and heterogeneous data. In order to translate the use of SSA levels into clinical practice, to utilize it as a sensitive and reliable biomarker, more standardized method of saliva processing and biochemical analysis are required with studies conducted on larger populations.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".