Prognostic Value of Differentially Expressed LncRNAs in Triple-Negative Breast Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Breast cancer is the most common cancer in women worldwide and can be classified into multiple subtypes, including triple-negative breast cancer (TNBC). TNBC is more aggressive than other types of breast cancer and has a poor prognosis. However, excluding chemotherapy, the treatment of TNBC does not involve targeted therapy. The dysregulated expression of lncRNAs plays a vital role in the development of numerous cancers. Thus, the aim of this meta-analysis is to determine the functional roles of lncRNAs in TNBC. We performed a systematic search for articles related to TNBC using multiple online databases, including PubMed, EMBASE, Web of Science, and Science-Direct. We collated pooled hazard ratios with 95% confidence interval to estimate the prognostic value of lncRNAs. We assessed the quality of studies using the Newcastle-Ottawa scale. Data were collected from cohort studies that compared overall survival, disease-free survival, and relapse-free survival between patients with high and patients with low expression of lncRNAs. Using 2,192 samples from 21 studies, we observed a correlation between poor prognosis and the upregulation of 14 lncRNAs (LINC00173, HUMT, HOTAIR, LUCAT1, HIF1A-AS2, ZEB2-AS1, NAMPT-AS, DANCR, LINC01638, ZNF469-3, AFAP1-AS1, ANRIL, MALAT1, and HULC) and downregulation of four lncRNAs (MIR503HG, NEF, TC0NS_12_00002973, and GAS5). The pooled hazard ratios for the correlation between differentially expressed lncRNAs and overall, disease-free, and relapse-free survival were 2.38 (2.03-2.78), 2.19 (1.51-3.16), and 3.19 (0.81-12.53), respectively. This meta-analysis shows that the expression of candidate lncRNAs may reliably predict the prognosis of patients with TNBC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".