Prognostic role of HSF1 overexpression in solid tumors: a pooled analysis of 3,159 patients
Why this work is in the frame
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Bibliographic record
Abstract
Background and objective: HSF1 is reported to be overexpressed in various solid tumors and play a pivotal role in cancer progression. A meta-analysis was conducted to assess the potential prognostic role of HSF1 in patients with solid tumors. Methods: An extensive electronic search of three databases was performed for relevant articles. The pooled hazard ratios (HRs) or odds ratios with their corresponding 95% CI were calculated with a random-effects model. Heterogeneity and publication bias analyses were also conducted. Results: A total of 3,159 patients from 10 eligible studies were included into the analysis. The results showed that positive HSF1 expression was significantly correlated with poor overall survival in all tumors (HR=2.09; 95% CI: 1.62–2.70; P <0.001). Subgroup analysis revealed that there was a significant association between HSF1 overexpression and poor prognosis in esophageal squamous cell carcinoma (ESCC) (HR=1.83; 95% CI: 1.21–2.77; P =0.004), breast cancer (BC) (HR=1.52; 95% CI: 1.24–2.86; P <0.001), hepatocellular carcinoma (HR=3.02; 95% CI: 1.77–5.18; P <0.001), non-small-cell lung cancer (HR=2.19; 95% CI: 1.20–3.99; P =0.01), and pancreatic cancer (HR=2.58; 95% CI: 1.11–6.03; P =0.03) but not in osteosarcoma (HR=1.58; 95% CI: 0.47–5.35; P =0.46). In addition, HSF1 overexpression was significantly associated with some phenotypes of tumor aggressiveness including TNM stage, histological grade, lymph node metastasis, and vascular invasion. Conclusion: HSF1 overexpression may prove to be an unfavorable prognostic biomarker for solid tumor patients. Keywords: HSF1, solid tumors, prognosis, overall survival, meta-analysis
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it