Clinical Value of Long Noncoding RNA HOTAIR as a Novel Biomarker in Digestive Cancers: A Meta-Analysis
Bibliographic record
Abstract
HOX transcript antisense intergenic RNA has been reported to serve as an important prognostic biomarker in several types of cancers. However, the clinical value of HOX transcript antisense intergenic RNA in digestive cancers remains unclear. Therefore, we tried to investigate the clinical role of expression of HOX transcript antisense intergenic RNA as a prognostic indicator in digestive cancers by a meta-analysis. Literature collection was performed by searching the PubMed, Embase, Web of Science, and Cochrane Library databases (up to October 7, 2017). A quantitative meta-analysis was conducted to assess the eligible articles on the prognostic value of HOX transcript antisense intergenic RNA in digestive cancers. The pooled hazard ratios or odds ratios with 95% confidence intervals were used to evaluate the association between expression of HOX transcript antisense intergenic RNA and clinical outcomes. A total of 1844 patients from 22 studies were included in this meta-analysis. The results found a significant association between expression of HOX transcript antisense intergenic RNA and poor overall survival in digestive cancers (pooled hazard ratio = 2.19, 95% confidence interval, 1.86-2.57, P < .001). Furthermore, subgroup analysis showed that tumor type, region, Newcastle-Ottawa scale, and sample size did not alter the predictive value of HOX transcript antisense intergenic RNA as an independent factor for patients' survival. In addition, we also revealed that the clinicopathological characteristics such as differentiation, lymph node metastasis, tumor node metastasis (TNM) stage, and distant metastasis were positively related to expression of HOX transcript antisense intergenic RNA digestive cancers. In conclusion, our results suggested high expression of HOX transcript antisense intergenic RNA was correlated with poor clinical outcomes and may serve as a novel prognostic biomarker for patients with digestive 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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.048 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.002 | 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".