Tumor Necrosis Factor-Alpha and Its Association With Breast Cancer: A Systematic Review
Bibliographic record
Abstract
Background: Tumor necrosis factor-alpha (TNF-α) is a pro-inflammatory cytokine implicated in the pathogenesis and progression of various cancers, including breast cancer. Elevated TNF-α levels have been associated with cancer progression, metastasis, and treatment outcomes. This systematic review aimed to synthesize existing evidence on the relationship between TNF-α levels and breast cancer. Methods: A systematic search of observational studies published from inception to June 2024 was conducted in PubMed, ScienceDirect, Sage Journals, and Google Scholar to identify studies examining TNF-α levels in breast cancer patients compared to healthy controls, as well as its association with metastasis, response to chemotherapy, and survival outcomes. Inclusion criteria were applied to select eligible studies, resulting in nine studies that met the criteria for this review. Results: Eight eligible studies reported that breast cancer patients exhibited higher TNF-α levels than healthy controls. Two studies indicated that TNF-α levels were elevated in patients with metastatic breast cancer. Additionally, two studies found that patients with higher TNF-α levels tended to have a poorer response to chemotherapy. One study revealed that patients with elevated TNF-α levels had a lower mean survival time. Conclusions: Elevated TNF-α levels are significantly associated with breast cancer progression, metastasis, and poorer treatment outcomes. These findings underscore the potential of TNF-α as a biomarker for breast cancer prognosis and therapeutic response. Further research is warranted to explore the underlying mechanisms and validate TNF-α as a target for therapeutic intervention in breast cancer management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".