Prognostic significance of cyclin-dependent kinase subunit 2 (CKS2) in malignant tumours: a meta-analysis and bioinformatic analysis
Bibliographic record
Abstract
Objectives This study aimed to systematically elucidate the prognostic significance of cyclin-dependent kinase subunit 2 (CKS2) expression in various cancers and its correlation with their clinicopathological characteristics. Design In this meta-analysis and bioinformatic analysis, articles were identified through searches of multiple databases and meta-analysed according to the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols. Data from The Cancer Genome Atlas were examined using UCSC Xena tools to further confirm the prognostic effect of CKS2. Data sources The PubMed, Embase, Web of Science and Cochrane Library databases were searched for articles published from their inception to 1 January 2023, using a combination of subject terms and free words, including ‘CKS2’, ‘cancer’, ‘tumor’, ‘neoplasm’, ‘carcinoma’, ‘malignancy’ and ‘prognosis’. Eligibility criteria The analysis included cohort or case–control studies, reported in English, with malignancy diagnoses confirmed by pathological methods, available HRs and 95% CIs for overall survival (OS) or extractable Kaplan-Meier curves, and a sample size of ≥20 patients. Reviews, commentaries, letters, conference reports, case reports, in vitro and animal studies, studies of CKS2 gene variants, studies with sample cases from public databases and studies with unavailable survival or duplicated data were excluded. Data extraction and synthesis Two researchers independently screened the articles, extracted the data and evaluated the quality of included studies using the Newcastle-Ottawa Scale. Meta-analysis and bioinformatic analyses were performed using the STATA and R software, respectively. Results The analysis included 13 retrospective studies encompassing 1348 cases across 10 cancer types. Nine studies involving 1124 patients examined the correlation between CKS2 expression levels and OS. A fixed-effects model analysis revealed a significant association between high CKS2 expression and reduced OS (HR=2.27, 95% CI=1.87 to 2.77, p<0.001). Furthermore, high CKS2 expression was significantly associated with advanced tumour stage (relative risk (RR) = 1.82, 95% CI=1.57 to 2.11, p<0.001), lymph node metastasis (RR=1.68, 95% CI=1.38 to 2.04, p<0.001), larger tumour size (RR=1.60, 95% CI=1.27 to 2.03, p<0.001) and lower differentiation grade (RR=1.57, 95% CI=1.29 to 1.90, p<0.001). CKS2 expression levels were not significantly correlated with patients’ age (RR=1.11, 95% CI=0.99 to 1.26, p=0.071) or sex (RR=0.98, 95% CI=0.90 to 1.07, p=0.653). An assessment of the articles showed no significant publication bias, confirming the robustness of these findings. The bioinformatic analysis further confirmed CKS2 upregulation in the examined cancer types and its association with poor OS in glioma (HR=1.97, 95% CI=1.78 to 2.18, p=3.70×10 −42 ), liver hepatocellular carcinoma (HR=1.56, 95% CI=1.31 to 1.86, p=3.50×10 −7 ) and lung adenocarcinoma (HR=1.27, 95% CI=1.10 to 1.48, p=1.70×10 −3 ). Conclusions Elevated CKS2 expression is associated with poor prognosis in a subset of malignant tumours, highlighting its potential as a prognostic marker. PROSPERO registration number CRD42023394038.
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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 | 0.027 | 0.044 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.058 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 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".