Downregulation of ALDOB is associated with poor prognosis of patients with gastric cancer
Bibliographic record
Abstract
OBJECTIVES: To examine the expression of ALDOB in gastric cancer (GC) tissue and to reveal its potential clinicopathological and prognostic significance. MATERIALS AND METHODS: We screened for genes that were differentially expressed between GC and nontumor tissues using a microarray, specifically the Affymetrix U133 Plus 2.0 Array platform. We then verified the transcriptional and translational levels of ALDOB by performing quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC). In addition, a merged data set based on the Gene Expression Omnibus was generated and a survival analysis performed. RESULTS: was downregulated (more than sevenfold) in GC compared with nontumor tissue. Both qRT-PCR and IHC validated the decrease of ALDOB in GC tissue. Moreover, we found that the expression of ALDOB was significantly related to tumor-invasion depth, lymph-node metastasis, distant metastasis, and TNM stage. The survival analysis, based on the IHC and merged data set, indicated that the overall survival was better in patients with high ALDOB expression. The Cox regression analysis showed that ALDOB expression was an independent prognostic factor for GC. CONCLUSION: The expression of ALDOB in GC tissue was significantly related to the clinicopathological features and prognosis of the disease, thus suggesting that ALDOB could act as a novel molecular marker for GC.
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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) | 0.001 | 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".