Tumor necrosis factor, lymphotoxin and cancer
Bibliographic record
Abstract
Initially TNF has been discovered as an anti-tumor factor, but it is now considered as one of the universal effectors of innate signaling implicating its key role in host defense and inflammation. Other physiological functions of TNF are primarily linked to organization of lymphoid tissues. TNF can exert deleterious effects on the organism when its local or systemic concentrations exceed certain levels. This is the main reason for the failure of TNF therapy in oncology. Moreover, in certain experimental models TNF to TNFRp55 signaling axis was found to play a pro-tumorigenic role. On the other hand, anti-TNF therapy proved to be beneficial in rheumatic and other autoimmune diseases. Taking into consideration the pivotal function of TNF in the immune system, it is obvious that such therapy cannot be entirely free of adverse effects including suppression of host defense and, possibly, predisposition to lymphomas. Lymphotoxins alpha and beta are the two related cytokines that exist in distinct trimeric forms which can signal through TNFR I and TNFR II, as well LTbetaR receptors, depending on the composition of the trimer. These signals have important functions in the development and homeostasis of the immune system. Importantly, there is a recently uncovered link between the LTalpha/LTbeta to LTbetaR signaling axis and cancer. Here we review the current status of the field with the focus on one particular issue: are TNF and lymphotoxins intrinsically anti-cancer or pro-tumorigenic.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".