High SPINK4 Expression Predicts Poor Outcomes among Rectal Cancer Patients Receiving CCRT
Bibliographic record
Abstract
Background: Patients with rectal cancer can prospectively be favored for neoadjuvant concurrent chemoradiotherapy (CCRT) to downstage before a radical proctectomy, but the risk stratification and clinical outcomes remain disappointing. Methods: From a published rectal cancer transcriptome dataset (GSE35452), we highlighted extracellular matrix (ECM)-linked genes and identified the serine protease inhibitor Kazal-type 4 (SPINK4) gene as the most relevant among the top 10 differentially expressed genes associated with CCRT resistance. We accumulated the cases of 172 rectal cancer patients who received neoadjuvant CCRT followed by surgery and collected tumor specimens for the evaluation of the expression of SPINK4 using immunohistochemistry. Results: The results revealed that high SPINK4 immunoexpression was significantly related to advanced pre-CCRT and post-CCRT tumor status (both p < 0.001), post-CCRT lymph node metastasis (p = 0.001), more vascular and perineurial invasion (p = 0.015 and p = 0.023), and a lower degree of tumor regression (p = 0.001). In univariate analyses, high SPINK4 immunoexpression was remarkably correlated with worse disease-specific survival (DSS) (p < 0.0001), local recurrence-free survival (LRFS) (p = 0.0017), and metastasis-free survival (MeFS) (p < 0.0001). Furthermore, in multivariate analyses, high SPINK4 immunoexpression remained independently prognostic of inferior DSS and MeFS (p = 0.004 and p = 0.002). Conclusion: These results imply that high SPINK4 expression is associated with advanced clinicopathological features and a poor therapeutic response among rectal cancer patients undergoing CCRT, thus validating the prospective prognostic value of SPINK4 for those patients.
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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".