Long non-coding RNA XIST expression as a prognostic factor in human cancers: A meta-analysis
Bibliographic record
Abstract
A large number of literature has shown that high expression of X inactive-specific transcript (XIST) is associated with poor prognosis and metastasis of cancer in patients. However, most of this literature is limited by the small sample sizes and discrete outcomes. Therefore, a meta-analysis was performed to investigate the relation between XIST expression and tumor node metastasis (TNM) stage, lymph node metastasis, distant metastasis, and overall survival of cancer patients. We searched for literature in PubMed, Embase, and Web of Science. The pooled hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to evaluate the association of XIST expression with prognosis and clinicopathological characteristics of cancer patients. Finally, a total of 14 articles involving 1123 patients were included in this meta-analysis. The results suggested that high expression of XIST has a significant relationship with a relatively poor overall survival for patients with malignant tumors (HR 1.82; 95% CI 1.32, 2.52; P = 0.0003). Moreover, high expression of XIST was significantly associated with poor TNM stage (OR 3.64; 95% CI 2.62, 5.07; P < 0.0001), lymph node metastasis (OR 2.39; 95% CI 1.65, 3.46; P < 0.0001) and distant metastasis (OR 2.84; 95% CI 1.90, 4.23; P < 0.0001). In conclusion, high expression of lncRNA XIST may be a predictive factor of poor prognosis in human cancers.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.050 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".