Long noncoding RNA ROR as a novel biomarker for progress and prognosis outcome in human cancer: a meta-analysis in the Asian population
Bibliographic record
Abstract
Background: Long intergenic non-protein coding RNA, a regulator of reprogramming (ROR), has been found to play an oncogene role in various human malignant tumors. This meta-analysis aimed to synthesize available data to verify the association between clinical prognosis value and ROR expression level. Materials and methods: We performed a systematic search by using PubMed (Medline), Embase, Cochrane Library, ScienceDirect, Springer, and ISI Web of Knowledge from inception to November 15, 2017. Eleven studies with 903 patients were included in this meta-analysis according to the exclusion and inclusion criteria, and the quality of the publications was assessed by using the Newcastle-Ottawa Scale. Pooled odds ratios (OR) and hazard ratios (HR) with 95% CI were used to describe the effect. Results: The results showed that overexpression of ROR is positively associated with lymph node metastasis (OR=4.472, 95% CI: 3.212–6.225, Z=8.87, P =0.000), tumor invasion depth (OR=9.93, 95% CI: 5.33–18.47, Z=7.24, P <0.001), TNM stage (III/IV vs I/II, OR=2.96, 95% CI: 2.18–4.02, Z=6.95, P <0.001), distant metastasis (OR=3.142, 95% CI: 2.187–4.513, Z=6.20, P <0.001) respectively. Additionally, high expression of ROR was significantly correlated with unfavorable disease-free survival (DFS) (HR=2.74, 95% CI: 1.65–3.82, Z=4.93, P =0.000) and overall survival (OS) (HR=2.09, 95% CI: 1.64–2.54, Z=9.07, P <0.001). Subgroup analysis demonstrated that neither cancer type (digestive or respiratory system) nor sample size (more or less than 100) did not alter the prognostic value of ROR. Furthermore, we performed publication bias and sensitivity analysis in order to examine the stability of meta-analysis of ROR along with OS, which showed that the shape of the funnel plot was nearly symmetrical and the resulting pattern was not significantly influenced while disconnecting each suitable study. Conclusion: In accordance with these results, we suggested that the overexpression of long noncoding RNA ROR could act as a novel biomarker for predicting poor prognosis in different human cancers. Keywords: long noncoding RNA, regulator of reprogramming, ROR, prognosis, cancers, meta-analysis
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.046 |
| Bibliometrics | 0.004 | 0.006 |
| 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".