The association of hypoxia-inducible factor-1α and hypoxia-inducible factor-2α protein expression with clinicopathological characteristics in papillary thyroid carcinoma: A meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To investigate the correlation of hypoxia-inducible factor-1α (HIF-1α) and hypoxia-inducible factor-2α (HIF-2α) protein expression with clinicopathologic characteristics in patients with papillary thyroid carcinoma (PTC) through a meta-analysis. METHODS: PubMed, Embase, Web of Science, Cochrane, CNKI, Wanfang, and VIP databases were searched from the establishment of the database to February 2023. The New castle-Ottawa Scale was used to evaluate the quality of the literature. Rev Man 5.3 and Stata14.0 were used to conduct a meta-analysis of the included studies. RESULTS: Twenty-eight articles with 2346 samples were included in the Meta-analysis. Compared with normal thyroid tissues, HIF-1α and HIF-2α proteins were highly expressed in PTC tumor tissues. High expression of HIF-1α protein was associated with tumor size (odds ratio [OR] = 4.50, 95% confidence interval [CI]: 2.88-7.04, P < .00001), lymph node metastasis (OR = 4.76, 95% CI: 3.78-5.99, P < .00001), TNM stage (OR = 3.67, 95% CI: 2.68-5.03, P < .00001), capsular invasion (OR = 2.30, 95% CI: 1.43-3.71, P = .0006 < .05), and extrathyroidal extension (OR = 10.96, 95% CI: 4.80-25.02, P < .00001). High expression of HIF-2α protein was associated with lymph node metastasis (OR = 4.18, 95% CI: 2.63-6.65, P < .00001), TNM stage (OR = 2.56, 95% CI: 1.36-4.82, P = .004 < .05), and capsular invasion (OR = 3.84, 95% CI: 1.66-8.88, P = .002 < .05). In addition, we concluded for the first time that there was a statistically significant difference in the expression of HIF-1α and HIF-2α in PTC patients (OR = 2.36, 95% CI: 1.26-4.42, P = .007 < .05). CONCLUSIONS: The high expression of HIF-1α and HIF-2α proteins is closely related to some clinicopathological parameters of PTC, and can provide potential biological indicators for the diagnosis and prognosis of PTC.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.042 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".