GLI1 expression in pancreatic ductal adenocarcinoma correlates the clinical significance and prognosis
Bibliographic record
Abstract
BACKGROUND: Glioma-associated oncogene homolog 1(GLI1) expression correlates with the clinical significance and prognosis of several cancers. However, the evaluation of the role GLI1 expression plays in pancreatic ductal adenocarcinoma (PDAC) clinicopathological features and outcomes still lacks. OBJECTIVE: The present study systemic reviewed the association of GLI1 expression and clinical significance as well as patients survival in PDAC. METHODS: We systematically searched the database of The Cochrane Library, PubMed, Embase, CNKI, Weipu data, and Wanfang data according to the inclusion and exclusion criteria. (The search ended on January 1, 2019; no language restrictions). The Newcastle-Ottawa Scale (NOS) scale was implemented to assess the quality of the literature and the Review Manager 5.3 Software was used to conduct a meta-analysis. Finally, 9 studies, a total of 1058 patients, have been included. RESULTS: GLI1 is more likely expressed in PDAC tissue rather than para-carcinoma tissue (OR = 2.86, 95%CI = 1.87-4.36, P < .00001). GLI1 expression is associated with the TNM stage (OR = 3.11, 95%CI = 2.01-4.79, P < .00001), perineural invasion (OR = 2.50, 95%CI = 1.28-4.91, P = .008), and lymphatic metastasis (OR = 2.73, 95%CI = 1.71-4.36, P < .0001). But the association with differentiation (OR = 1.20, 95%CI = 0.74-1.96, P = .46) and tumor size (OR = 2.41, 95%CI = 0.97-6.00, P = .06) was not significant. GLI1 expression is related to the worse overall survival in PDACs (HR = 1.68, 95%CI = 1.40-2.02, P < .00001). CONCLUSION: Positive GLI1 expression promotes the progression and metastasis of PDACs and plays an important role in the clinical significance and the patients survival.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".