Co-expression of HER2/EGFRvIII/CD44 and Claudin 18.2/CD109 as Novel Prognostic Indicators in Stomach Adenocarcinoma
Bibliographic record
Abstract
Background: The heterogenous expression of human epidermal growth factor receptor (HER) family members may contribute to poor response to current therapies with HER inhibitors in cancer. This study aimed to explore the co-expression and prognostic significance of HER family members with epidermal growth factor receptor variant III (EGFRvIII), cluster of differentiation 44 (CD44), cluster of differentiation 109 (CD109), and claudin 18.2 (CLDN18.2) in patients with stomach cancer. Methods: The relative expression and prognostic significance of these biomarkers at different cut-off values were determined in 78 patients with stomach adenocarcinoma by immunohistochemistry. Results: Of the 78 cases, positive tumor staining was present for wild-type EGFR (13%), HER2 (82%), HER3 (9%), HER4 (33%), EGFRvIII (33%), CD44 (41%), CD109 (60%), and CLDN18.2 (40%). Furthermore, the expression of HER2 was accompanied with the co-expression of EGFR (9%), HER3 (8%), HER4 (27%), EGFRvIII (28%), CD44 (33%), CD109 (49%), and CLDN18.2 (32%). Interestingly, at the cut-off value ≥ 5% of tumor cells with positive staining, the co-expressions of HER2/EGFRvIII, EGFRvIII/CD44, and HER2/EGFRvIII/CD44 were associated with poor overall survival. Moreover, CLDN18.2 immunostaining of intensity of 3+, membranous expression of CD109, the co-expression of CD109/CLDN18.2 and CD109/EGFRvIII/CD44 were also associated with poorer overall survival and a higher risk of poor overall survival. All these remained as independent prognostic factors for survival in multivariate analysis. Conclusion: This study provides first comprehensive analysis of the novel biomarker combinations that are significantly associated with overall survival. Co-expression of HER2 with EGFRvIII, CD44, and CD109, plus membranous CD109 and high-intensity CLDN18.2, independently predicted poor survival in stomach adenocarcinoma, highlighting their potential as prognostic biomarkers. These biomarker combinations may represent potential therapeutic targets for novel combination therapies, and future studies should investigate their predictive value for the response to therapy.
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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 | 0.001 | 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) | 0.001 | 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 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".