Expression of Ezrin correlates with lung metastasis in Chinese patients with osteosarcoma
Bibliographic record
Abstract
PURPOSE: To determine the prognostic value of the expression of Ezrin, CD44 and Six1 genes in osteosarcoma tissues of Chinese patients. METHODS: Fluorescent quantitative real-time PCR was applied to study the mRNA levels of Ezrin, CD44 and Six1 genes in 32 osteosarcoma patient samples and 10 adjacent normal tissues and MG63 osteosarcoma cell lines. The analysis of relationships between pulmonary metastasis and overall survival time were carried out based on the clinical data. RESULTS: mRNA levels of Ezrin and Six1 genes in osteosarcoma tissues were higher than those in adjacent normal tissues (P=0.015, 0.025). The mRNA levels of Ezrin, CD44 and Six1 genes were closely correlated with Enneking GTM clinical staging, while no correlations were demonstrated between the mRNA level of these genes with sex, age, location or pathological types. In addition, we demonstrated that the high mRNA level of Ezrin gene was related to shorter lung metastasis-free and overall survival time of the Chinese patients with osteosarcoma (P < 0.001). CONCLUSION: Our data suggest that Ezrin, but not CD44 and Six1, could be a prognostic factor and a predictor of potential lung metastasis in osteosarcoma. Further large sample studies need to be done to confirm the potential value of Ezrin as a new therapeutic target.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".