Abstract 4131: Serum miRNA profiles of patients with oral cancer and pre-cancer
Bibliographic record
Abstract
Abstract Oral cancer is one of the most commonly diagnosed cancers worldwide. Those diagnosed with this disease are often diagnosed at late stage with poor 5-year survival rate and/or suffer from a high rate of local recurrence or second primary tumor. Novel approaches for early detection for treatment stratification are needed to improve these statistics. MicroRNAs (miRNAs), a group of small, non-coding RNAs, can be isolated from blood serum samples and have shown the potential to be used as a non-invasive detection method in several cancer types. The purpose of this study is to examine the expression profiles of circulating miRNAs in the serum of patients with high-risk oral lesions (HRLs; oral cancer/carcinoma in situ) and to explore their utility as potential oral cancer biomarkers. Methods: Serum samples were collected from 33 patients with HRL (17 oral cancer and 16 carcinoma in situ) undergoing intent-to-cure surgical treatment and from a demographically matched (for age, sex, and smoking status) normal control group (N=15). RNA was extracted from serum samples using a modified miRNeasy (Qiagen) protocol. The miRCURY LNA™ Universal RT miRNA PCR system was used to profile all serum samples. The system allowed for the profiling of 742 miRNAs plus required controls within a single experiment. Statistical tests were carried out using Statistica 64 and Microsoft Excel. Results: Using analysis of variance, we detected 37 miRNAs that showed a significant differential expression between the three groups (oral squamous cell carcinoma, carcinoma in situ, and controls). Among these 37 miRNAs, miR-223, miR-342-3p, miR-423-5p, and miR-451 were the most differentially expressed. Conclusions: By examining circulating miRNA expression profiles, we have identified potential biomarkers that can be obtained from a simple blood test. The differential expression of serum miRNAs between disease stages suggests the possible utility of these miRNAs as biomarkers for the early detection of oral cancer and its progression. Blood samples are easily collected from patients, and combining serum miRNA profiles with other screening techniques could greatly improve the sensitivity in oral cancer detection. Early detection to facilitate treatment decision making for those with oral cancer or premalignant disease would have a great impact on disease survival. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4131. doi:1538-7445.AM2012-4131
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".