Association of Serum Immunoglobulin Levels with Solid Cancer: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: The nature of humoral immunity in carcinogenesis remains poorly understood. In this systematic review and meta-analysis, we aimed to evaluate the association of serum immunoglobulin classes with solid cancer and test our hypothesis that the immune escape of tumors is accompanied by dysregulated systemic immunoglobulin class-switching. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched the Cochrane Library, Embase, and MEDLINE/PubMed databases for observational studies investigating the association between serum immunoglobulins (IgA, IgG, and IgM) and histologically confirmed diagnosis of solid cancer in adults. We selected case-control studies, including more than 20 cases, and those explicitly stating that no form of anticancer treatment was administered prior to immunoglobulin measurement. No eligible cohort studies were identified. The primary summary measure was the standardized mean difference (SMD) with 95% confidence intervals (CI) calculated using a random effects model. RESULTS: Pooling 11 eligible studies comparing serum IgA levels in 1,351 patients and 560 control subjects revealed a statistically significant SMD (1.50; 95% CI, 0.96-2.04). Nonsignificant SMDs were observed for the 14 selected studies investigating serum IgG [SMD, -0.02 (95% CI, -0.22 to 0.18)] and for the 10 studies reporting serum IgM [SMD, 0.11 (95% CI, -0.10 to 0.32)]. Substantial heterogeneity between studies was observed despite sensitivity analysis by immunoglobulin measurement method, control matching, type of cancer, stage of disease, and sequential study exclusion. CONCLUSIONS: Serum immunoglobulin levels in patients diagnosed with solid cancer might be skewed toward class-switching to IgA, possibly reflecting Th2-polarized immunity. IMPACT: Further combinatorial analyses of serum immunoglobulin isotypes alongside other immune parameters in databases and observational studies are warranted.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.043 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".