The Prognostic Role of Cyclin D1 in Multiple Myeloma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Purpose: Cyclin D1 has been identified as a proto-oncogene associated with the uncontrolled proliferation of tumor cells. This systematic review and meta-analysis aims to estimate the prognostic significance of cyclin D1 in multiple myeloma (MM) patients. Method: We searched for qualified data in PubMed, Embase, and Web of Science up to February 2020. Data quality was assessed by the Newcastle-Ottawa scale (NOS). Hazard ratios (HRs) and 95% confidence intervals (95% CIs) were used to evaluate the relationship between cyclin D1 expression and overall survival (OS), progression-free survival (PFS)/event-free survival (EFS) in patients with MM. Result: A total of 13 studies involving 961 patients were included. Overall, pooled analysis revealed significant heterogeneity between cyclin D1 expression and the prognosis of MM (OS, HR = 1.08, 95% CI: 0.71-1.64, I 2 = 67.9%; PFS/EFS, HR = 0.97, 95% CI: 0.49-1.93, I 2 = 85.8%). Subgroup analysis revealed that the prolongation of OS was relevant to increased expression of cyclin D1 in MM patients in the relapsed and refractory group (OS, HR = 0.46, 95% CI: 0.24-0.90). Another subgroup assessment of OS established that MM patients with CCND1 overexpression in the bortezomib group had longer survival time (HR = 0.30, 95% CI: 0.11-0.82), whereas, those overexpressing CCND1 in the conventional chemotherapy group had poor prognosis (HR = 2.19, 95% CI: 1.18-4.08). We also found that increased cyclin D1 expression correlated favorably with PFS in the autologous stem cell transplantation (ASCT) (HR = 0.45, 95% CI: 0.28-0.73) or reverse transcription-polymerase chain reaction (RT-PCR) group (HR = 0.41, 95% CI: 0.26-0.64). Conclusion: The result of this meta-analysis suggested that CCND1 overexpression might be a predictive biomarker for MM patients when treated with bortezomib, receiving ASCT, or in relapsed and refractory period.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".