Identification of salivary volatile organic compounds as the potential diagnostic markers of oral cancer by the gas chromatography–mass spectrometry analysis
Bibliographic record
Abstract
BACKGROUND: Oral cancer (OC) is a major public health problem in the Indian subcontinent. As many as 90% of all OC cases are oral squamous cell carcinomas (OSCCs), often developing from oral potentially malignant disorders (OPMDs). Although the oral cavity is freely accessible, visual identification is often challenging. Biopsy and a microscopic examination is the only confirmatory diagnostic test. Recently, the analysis of volatile organic compounds (VOCs) has emerged as a new, non-invasive, rapid, and inexpensive strategy with promising potential in clinical diagnostics. The human VOCs produced in metabolic pathways, present in body fluids and the exhaled air, can be used for monitoring several oral diseases, including OC. OBJECTIVES: The aim of the present study was to determine the potential diagnostic capabilities of salivary VOCs in OC through identifying and comparing the salivary volatilomic profiles among OSCC and OPMD subjects, as well as healthy controls, using the gas chromatography-mass spectrometry (GC-MS) analysis. MATERIAL AND METHODS: Unstimulated saliva samples were collected from 35 OSCC subjects, 35 OPMD subjects and 40 healthy controls. The VOCs extracted from the ZSM-5/PDMS film were condensed with 100 μL of methanol, of which 1.0 μL was subjected to the GC-MS analysis. RESULTS: A total of 128 salivary VOCs were detected and identified among the OSCC and OPMD subjects and the healthy controls. Twenty-five metabolites were determined to be statistically significant in differentiating among the 3 groups. Organic acids, alcohols, ketones, alkanes, and acid amides were the major classes of VOCs in the OSCC subjects, while organic acids, alcohols, ketones, acid amides, heterocyclic compounds, and phenols constituted the VOC profile in the OPMD subjects. 1-chloro-dodecane and 1-tridecanol were significant VOCs observed among the controls. CONCLUSIONS: The study demonstrates that salivary VOC profiling can reveal distinct metabolomic alterations in OSCC and OPMDs, with several VOCs emerging as potential tumor-specific biomarkers. While these findings highlight the promise of VOC-based screening, larger studies are needed to validate these markers and establish their clinical applicability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".