Triggering of TLR7 and TLR8 expressed by human lung cancer cells induces cell survival and chemoresistance
Bibliographic record
Abstract
Compelling evidence suggests that inflammation, cell survival, and cancer are linked, with a central role played by NF-kappaB. Recent studies implicate some TLRs in tumor development based on their ability to facilitate tumor growth; however, to our knowledge, involvement of neither TLR7 nor TLR78 has yet been demonstrated. Here we have demonstrated expression of TLR7 and TLR8, the natural receptors for single-stranded RNA, by tumor cells in human lung cancer in situ and in human lung tumor cell lines. Stimulation with TLR7 or TLR8 agonists led to activated NF-kappaB, upregulated expression of the antiapoptotic protein Bcl-2, increased tumor cell survival, and chemoresistance. Transcriptional analysis performed on human primary lung tumor cells and TLR7- or TLR8-stimulated human lung tumor cell lines revealed a gene expression signature suggestive of chronic stimulation of tumor cells by TLR ligands in situ. Together, these data emphasize that TLR signaling can directly favor tumor development and further suggest that researchers developing anticancer immunotherapy using TLR7 or TLR8 agonists as adjuvants should take into account the expression of these TLRs in lung tumor cells.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".