High Expression of Heat Shock Protein Family D Member 1 Predicts Poor Prognosis of Esophageal Cancer
Bibliographic record
Abstract
Background: Heat shock protein family D (Hsp60) member 1 (HSPD1) has been reported as a potential survival-related biomarker in some cancers. However, the correlation between HSPD1 expression with prognosis and clinical features of esophageal cancer (EC) is poorly understood. Our research aimed to explore the clinical and prognostic significance of HSPD1 expression in EC patients. Methods: In our study, HSPD1 expression was detected by immunochemistry in 87 EC tissue specimens and 20 normal cancerous peripheral tissue specimens. Meanwhile, we also analyzed the expression of HSPD1 in EC by The Cancer Genome Atlas (TCGA) database. Then Chi-squared and Fisher's exact tests and Wilcoxon signed-rank test and logistic regression models were separately used to test the correlation between clinical characteristics and HSPD1 expression in our and TCGA cohort. Moreover, we evaluated the value of HSPD1 in prognosis by Kaplan-Meier curves and Cox analysis. Finally, gene set enrichment analysis (GSEA) was performed using the data accessed from TCGA. Results: The results showed that HSPD1 was overexpressed in EC, and the expression was related to histological type, histological grade, N classification, and clinical stage. Moreover, Kaplan-Meier curves and Cox analysis indicated that high expression of HSPD1 correlated with poor prognosis, and HSPD1 was an independent risk factor for EC. GSEA identified pathways involved in cysteine and methionine metabolism, spliceosome, selenoamino acid metabolism, mismatch repair, RNA degration, DNA replication, and cell cycle as differentially enriched in ECs with high HSPD1 expression. Conclusions: Our results suggest that HSPD1 is expressed at high levels in EC, and has potential to be used as a novel biomarker for the prognosis of patients with EC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".