Prognostic value of circulating long non-coding RNAs in colorectal cancer patients: a meta-analysis
Bibliographic record
Abstract
Objectives This meta-analysis aimed to evaluate the prognostic significance of circulating long non-coding RNAs (lncRNAs) in colorectal cancer (CRC).Methods A comprehensive literature search was conducted in databases (Embase, Web of Science, PubMed, and Cochrane Library) up to July 2022. The quality of included studies was assessed using the Newcastle-Ottawa Scale (NOS). Statistical analysis was performed with Review Manager 5.4 and Stata 17.0. Publication bias was assessed using Begg’s test, and sensitivity analysis was conducted to validate the meta-analysis results.Results Ten articles, comprising 1,473 CRC patients and 18 different circulating lncRNAs, were included. Thirteen circulating lncRNAs were found to be up-regulated in CRC patients, while five were down-regulated. High expression of circulating lncRNAs up-regulated in CRC patients was associated with shorter CRC OS (HR = 2.91, 95% CI: 1.17, 7.22; P = 0.02, I2 = 86%). Conversely, high expression of circulating lncRNAs down-regulated in CRC patients was linked to longer CRC OS (HR = 0.16, 95% CI: 0.07, 0.40; P < 0.0001, I2 = 0%) and improved DFS (HR = 0.52, 95% CI: 0.37, 0.74; P = 0.0002, I2 = 0%). Additionally, circulating lncRNA levels correlated with TNM staging, tumor location, and lymph node metastasis.Conclusion Circulating lncRNAs show promise as prognostic markers for CRC patients, but further studies are warranted to validate these findings.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.066 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".