Prognostic Relevance of Inflammatory Cytokines Il-6 and TNF-Alpha in Patients with Breast Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Although cytokines mediate inflammation and inflammation facilitates cancer progression, few studies have evaluated the association between specific cytokines and the prognostic value of breast cancer. Therefore, this study aims to address the following question: What is the prognostic relevance of serum IL-6 and TNF-alpha levels on overall survival and treatment response in women with breast cancer? A systematic review and meta-analysis of cohort studies was conducted. The databases consulted included PubMed/Medline, Web of Science, and EMBASE. A total of 1748 articles were identified, of which 10 were included in the review. A significant association was found between elevated levels of IL-6 and TNF-alpha with poor overall survival and poor treatment response. The meta-analysis showed an HR of 3.74 (95% CI: 1.84–7.6) for elevated IL-6 with high heterogeneity (I2: 61%; p = 0.07) and an HR of 3.13 (95% CI: 1.57–6.23) for TNF-alpha with low heterogeneity (I2: 0%; p = 0.9). The overall response rate was 75% (95% CI: 31–100%; I2: 92%). In conclusion, IL-6 and TNF-alpha emerge as prognostic inflammatory biomarkers in women with breast cancer and are associated with poor survival and poor treatment response. This study highlights the need to establish an international consensus on cutoff points and standardized determination methods to implement these biomarkers in clinical practice.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.042 |
| Bibliometrics | 0.007 | 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) | 0.002 | 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".