Extracellular microRNAs as biomarkers for the detection of nasopharyngeal carcinoma
Bibliographic record
Abstract
Nasopharyngeal carcinoma (NPC) is a cancer of the upper region of the pharynx located behind the nose. Despite its low global incidence rate, this malignancy is endemic among primarily Chinese, South-East Asian, and Arctic descendants. microRNAs (miRNA) have been shown to be stable in circulation, with consistent aberrant expression profiles indicative of specific disease states – highlighting circulating miRNAs as attractive biomarkers. Preliminary testing for the feasibility of identifying a unique miRNA signature was conducted using Taqman Low Density Array (TLDA) cards. The profile of 754 miRNAs was assessed in serum samples from NPC patients, normal controls, and non-cancer oral/sinus inflammation patients. Differential expression of miR-151b, miR-450a-5p, miR-485-3p, and miR-885-5p were validated using qRT-PCR and assessed in matched NPC pre- and post-treatment serum samples. Additionally, extracellular vesicles (EVs) from NPC cell lines (HK1, Sune1) were collected via ultracentrifugation and the EV miRNA profiled via TLDA cards. 35 miRNAs were identified as significantly dysregulated in NPC serum compared to normal controls. Additionally, total of 46 miRNAs were identified as significantly dysregulated in non-cancer oral/sinus inflammation serum compared to normal controls. Cross-referencing these two lists, 12 miRNAs appear uniquely dysregulated only in NPC compared to controls. miR-485-3p was found to be downregulated in NPC serum, cells, and EVs. Moreover, expression of miR-485-3p, miR-151b, and miR-885-5p were significantly increased in NPC post-treatment samples. These results indicate the potential clinical utility and feasibility of establishing a simple, non-invasive, serum-based miRNA biomarker for the detection of NPC.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".