High TLR6 Expression Status Predicts a More Favorable Prognosis after Esophagectomy for Locally Advanced Thoracic Esophageal Squamous Cell Carcinoma
Bibliographic record
Abstract
Most so-called "beneficial bacteria" in gut microbiota are Gram-positive, and TLR6 recognizes the peptidoglycan (PGN) present in their cell walls. We hypothesized that a high TLR6 expression status predicts a more favorable prognosis after esophagectomy. We used an ESCC tissue microarray (TMA) to examine TLR6 expression status in ESCC patients and to determine whether TLR6 expression status correlates with prognosis after curative esophagectomy. We also examined whether PGN influences the cell proliferation activity of ESCC lines. Clinical ESCC samples from 177 patients tested for the expression of TLR6 were categorized as 3+ (n = 17), 2+ (n = 48), 1+ (n = 68), or 0 (n = 44). High TLR6 expression (3+ and 2+) correlated with significantly more favorable 5-year overall survival (OS) and disease-specific survival (DSS) after esophagectomy than a lower TLR6 expression (1+ and 0). Univariate and multivariate analyses showed that TLR6 expression status is an independent prognostic factor that affects 5-year OS. PGN significantly inhibited the cell proliferation activity of ESCC lines. This is the first study to show that high TLR6 expression status predicts a more favorable prognosis in locally advanced thoracic ESCC patients after curative esophagectomy. PGN released from "beneficial bacteria" seems to have potential to inhibit the cell proliferation activity of ESCC.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".