Serine hydroxymethyltransferase 2 predicts unfavorable outcomes in multiple cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Serine hydroxymethyltransferase (SHMT) is critical for one-carbon unit metabolism and is increasingly reported to be associated with tumor patients’ outcomes. Thus, we designed and performed this meta-analysis to reveal its prognostic role and relationship with clinicopathological characteristics in human cancer. Methods: A systematic search of PubMed, Embase, Web of Science and Cochrane Library (CENTRAL) was carried out. Two reviewers independently screened all references for eligibility according to the inclusion criteria. The Newcastle-Ottawa Quality Assessment Scale was used to assess the quality and data was extracted for the meta-analysis. Results: Ten studies, composed of 1,942 patients in total, were included in this meta-analysis. Higher expression of SHMT2 means an unfavorable prognosis [overall survival: hazard ratio (HR) =2.14, 95% confidence interval (CI): 1.53 to 2.99; progression-free survival (PFS)/disease-free survival (DFS)/recurrence-free survival (RFS): HR =1.90, 95% CI: 1.31 to 2.76]. Furthermore, higher SHMT2 expression is associated with larger tumor size [odds ratio (OR) =2.09, 95% CI: 1.58 to 2.77], more lymph node invasions [OR =2.67, 95% CI: 1.78 to 4.00), and higher tumor node metastasis classification (TNM) stage (OR =2.23, 95% CI: 1.55 to 3.21). Higher expression of SHMT2 is also related to higher histopathological grade (OR =3.46, 95% CI: 1.46 to 8.27) and distant metastasis (OR =1.25, 95% CI: 0.32 to 4.90), however, with significant heterogeneity (I2=61%, P=0.08 for distant metastasis; I2=82%, P<0.001 for histopathological grade). The prognostic clinical role of SHMT1 in clinical patients has not been directly investigated yet. Discussion: SHMT2 may serve as a promising prognostic biomarker in various cancer, especially in the alimentary system. Further large-scale studies are warranted to verify the possible effect.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.039 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".