The clinical prognostic value of lncRNA LINC00675 in cancer patients
Bibliographic record
Abstract
ABSTRACT: A newly discovered long non-coding RNA (lncRNA) is associated with the progression of a variety of tumors. The purpose of this meta-analysis is to explore further the relationship between clinicopathological features and the prognostic value of LINC00675 in caners.We searched the various database, including PubMed, Web of Science, Cochrane Library, Embase together with Wanfang, and China National Knowledge Infrastructure for articles on LINC00675 and clinicopathological characteristics and prognosis of patients with cancers before February 20, 2020. According to the inclusion and exclusion criteria, the studies that meet the criteria were systematically collected through search keywords. The Newcastle Ottawa document quality assessment system was used to evaluate the quality of documents. The required data from literature were extracted, and the hazard ratio (HR), odds ratio (OR), and 95confidence interval (CI) were calculated using stata12.0 software and RevMan5.3 software.A total of 5 studies covering 462 patients were included in this meta-analysis to evaluate the prognostic value of LINC00675 in cancers. Our results showed that high LINC00675 expression was significantly correlated with poor overall survival (OS) (HR = 1.60, 95% CI: 1.23-2.08, P = .0005). Additionally, upregulated expression of LINC00675 was significantly associated with tumor node metastasis stage (OR = 1.74, 95% CI: 1.18-2.58, P = .006) and distant metastasis (OR = 2.22, 95% CI: 1.21-4.08, P = .01).Our study suggests that LINC00675 could be used as a biomarker to evaluate the prognosis of cancer patients. More studies to further confirm that the clinical value of LINC00675 in cancers will be required.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.014 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".