Prognostic and clinical significance of lncRNA NKILA in tumors: a meta-analysis
Bibliographic record
Abstract
Abstract Background lncRNA NKILA is a newly discovered long non-coding RNA, and some studies have shown that lncRNA NKILA has certain clinical prognostic value in malignant tumors. In this study, we used meta-analysis to integrate existing literature to further evaluate the clinic relevance between lncRNA NKILA expression level and cancers. Materials and Methods We conducted a meta-analysis based on literature reporting lncRNA NKILA expression in various cancers published before 25, May 2020 by PubMed, Embase, and Web of Science. The quality ratings of the included studies were assessed according to the Newcastle-Ottawa Scale. After rigorous screening, a total of 8 articles and 859 patients were included in our study. Hazard ratios (HRs) and odds ratios (ORs) were used to demonstrate the relationship between NKILA and prognosis by using Stata 15.0 software. Results The results show overexpression level of lncRNA NKILA is significantly associated with better overall survival (pooled HR = 0.45, 95%CI:0.35–0.59, P < 0.001, fixed-effects model). Furthermore, increased expression level of lncRNA NKILA was associated with negative lymph node metastasis (positive vs. negative,OR = 0.27, 95%CI:0.18–0.42, P < 0.001, fixed-effects model) and earlier clinical stage (TNM III/IV vs. I/II: OR = 0.34, 95%CI:0.25–0.46, p < 0.001, fixed-effects model). Conclusions Our study indicated lncRNA NKILA was a novel biomarker for prognosis in cancers. It could serve as a tumor-suppressive role.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.047 |
| Bibliometrics | 0.005 | 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) | 0.003 | 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".