Agrin in the Muscularis Mucosa Serves as a Biomarker Distinguishing Hyperplastic Polyps from Sessile Serrated Lesions
Bibliographic record
Abstract
PURPOSE: Sessile serrated lesions (SSL) are precursors to colon carcinoma, and their distinction from other polyps, in particular hyperplastic polyps (HP), presents significant diagnostic challenges. We evaluated expression patterns in colonic polyps of previously identified colon carcinoma-associated extracellular matrix (ECM) proteins to identify markers distinguishing SSLs from other polyps. EXPERIMENTAL DESIGN: Gene-expression analyses of ECM proteins were performed using publicly available data on preneoplastic colonic polyps. In parallel, we evaluated by IHC the expression of agrin (AGRN) in over 400 colonic polyps, including HP, SSL with and without dysplasia, traditional serrated adenomas (TSA), and tubular adenomas (TA), and compared the consistency of standard histologic diagnosis of SSLs by experienced gastrointestinal pathologists with that of AGRN IHC. RESULTS: Differential gene expression analysis and IHC identified AGRN, serine peptidase inhibitor (SERPINE2), and TIMP metallopeptidase inhibitor 1 (TIMP1) elevated in SSLs and HPs but decreased in TAs and absent in normal colon. AGRN-positive basal laminae were noted in all TA, TSA, HP, and SSL in distinguishable patterns, whereas other polyps and normal mucosa were negative. SSL with or without dysplasia consistently showed IHC staining for AGRN in the muscularis mucosae, which was absent in HP, TSA, TA, and other polyps. In contrast, histologic evaluation showed only weak interobserver agreement (kappa value = 0.493) in distinguishing SSLs. CONCLUSIONS: Muscularis mucosae-based AGRN immunostaining is a novel biomarker to distinguish SSL from HP, TSA, and TA, with a specificity of 97.1% and sensitivity of 98.9% and can assist in diagnosis of morphologically challenging colonic polyps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| 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 | 0.002 |
| 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 teacher head, 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".