Expression and Potential Prognostic Value of SOX9, MCL-1 and SPOCK1 in Gastric Adenocarcinoma
Bibliographic record
Abstract
Gastric cancer is a common malignancy and remains one of the leading causes of cancer-related deaths, though its incidence is in decline in most developed countries. One of the major challenges of treating gastric cancer is tumor heterogeneity, which portends a high degree of prognostic variance and the necessity for different treatment modalities. Tumor heterogeneity is at least in part due to divergent differentiation of tumor cells to clones harboring different molecular alterations. Here we studied the expression of emerging prognostic markers SOX9, MCL-1, and SPOCK1 (Testican-1) in a cohort of gastric cancer by immunohistochemistry and investigated how individual biomarkers and their combinations predict disease prognosis. We found frequent expression of SPOCK1 (in both nuclei and cytoplasm), MCL-1 and SOX9 in gastric cancer. In univariate analysis, nuclear SPOCK1 expression and pathologic TNM stage were negative prognostic markers in this cohort. In multivariate analysis, SOX9 expression stood out as a predictor of poor prognosis. Further subgroup analysis suggested prognostic value of SOX9 expression in poorly differentiated gastric adenocarcinoma. MCL-1 showed no prognostic role in this cohort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".