Is Sphingosine Kinase 1 Associated with Hematological Malignancy? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Sphingosine kinase 1 (SphK1) is a lipid enzyme whose role in the etiology of cancer has been well explored. Here, a systematic review and meta-analysis were conducted to evaluate the association of SphK1 expression with hematological malignancy. MATERIALS AND METHODS: Relevant studies were identified through electronic databases (PubMed, Scopus, Embase, and OVID) and evaluated based on predefined inclusion and exclusion criteria. Quality assessment using the Newcastle-Ottawa Scale (NOS) was conducted, and pooled odds ratio (OR) was calculated to assess the association between SphK1 expression and hematological malignancy. RESULTS: Nine studies meeting the inclusion criteria were included in the systematic review. These studies utilized various techniques to assess SphK1 expression in hematological malignancies. The quality assessment reported that the included studies were of moderate quality. Meta-analysis of eligible studies revealed a positive association between SphK1 expression and hematological malignancies at the protein level (OR = 52.37, 95% CI = 10.10 to 271.47, and P = 0.00001). The funnel plot indicated no publication bias among the included studies. However, the certainty of the evidence was low according to the GRADE assessment. CONCLUSION: Our study's findings support the link between SphK1 expression and hematological malignancies. SphK1 gene dysregulation may contribute to various malignancies, suggesting it could be a therapeutic target to improve patient outcomes. Further research is needed to understand SphK1's mechanistic role in hematological malignancies and its therapeutic potential.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".