Circulating Proteins and Metabolite Biomarkers in Gastric Cancer: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Gastric cancer (GC) is often diagnosed at an advanced stage and thus patients have a poor prognosis. This implies that early detection of this cancer will improve patient prognosis and survival. This systematic review explored the association of circulating protein and metabolite biomarkers with GC development. METHODS: A literature search was conducted until November 2021 on Medline, Embase, Cochrane library, and Web of Science databases. Studies were included if they assessed circulating proteins and metabolites in blood, urine, or saliva and determined their association with GC risk. Quality of identified studies was determined using the Newcastle-Ottawa scale for cohort studies. Random and fixed effects meta-analyses were performed to calculate pooled odds ratio. RESULTS: A total of 53 studies were included. High levels of anti-Helicobacter pylORi IgG levels, pepsinogen I (PGI) <30 µg/L and serum pepsinogen I/ pepsinogen II (PGI/II) ratio<3 were positively associated with risk of developing GC (pooled odds ratio (OR): 2.70; 95% CI: 1.44-5.04, 5.96, 95% CI: 2.65-13.42 and 4.43; 95% CI: 3.04-6.47). In addition, an inverse relationship was found between ferritin, iron and transferrin levels and risk of developing GC (OR: 0.62; 95% CI: 0.38-1,0.97; 95% CI: 0.94-1 and 0.85; 95% CI: 0.76-0.94). However, there was no association between levels of glucose, cholesterol, vitamin C, vitamin B12, vitamin A, α-Carotene, β-Carotene, α-Tocopherol, γ-Tocopherol, and GC risk. CONCLUSION: The pooled analysis demonstrated that high levels of anti-Helicobacter pylORi IgG, PGI<30µg/L and serum PGI/II ratio <3 and low levels of ferritin, iron and transferrin were associated with risk of GC.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".