Toll-like receptor signaling in colorectal cancer: Carcinogenesis to cancer therapy
Bibliographic record
Abstract
Toll-like receptors (TLRs) are germ line encoded innate immune sensors that recognize conserved microbial structures and host alarmins, and signal expression of major histocompatibility complex proteins, costimulatory molecules, and inflammatory mediators by macrophages, neutrophils, dendritic cells, and other cell types. These protein receptors are characterized by their ability to respond to invading pathogens promptly by recognizing particular TLR ligands, including flagellin and lipopolysaccharide of bacteria, nucleic acids derived from viruses, and zymosan of fungi. There are 2 major TLR pathways; one is mediated by myeloid differentiation factor 88 (MYD88) adaptor proteins, and the other is independent of MYD88. The MYD88-dependent pathway involves early-phase activation of nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 (NF-κB1) and all the TLRs, except TLR3, have been shown to activate this pathway. TLR3 and TLR4 act via MYD88-independent pathways with delayed activation of NF-κB signaling. TLRs play a vital role in activating immune responses. TLRs have been shown to mediate inflammatory responses and maintain epithelial barrier homeostasis, and are highly likely to be involved in the activation of a number of pathways following cancer therapy. Colorectal cancer (CRC) is one of the most common cancers, and accounts for almost half a million deaths annually worldwide. Inflammation is considered a risk factor for many common malignancies including cancers of the colorectum. The key molecules involved in inflammation-driven carcinogenesis include TLRs. As sensors of cell death and tissue remodeling, TLRs may have a universal role in cancer; stimulation of TLRs to activate the innate immune system has been a legitimate therapeutic strategy for some years. TLRs 3/4/7/8/9 are all validated targets for cancer therapy, and a number of companies are developing agonists and vaccine adjuvants. On the other hand, antagonists may favor inhibition of signaling responsible for autoimmune responses. In this paper, we review TLR signaling in CRC from carcinogenesis to cancer therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".