Metastatic Cells Can Escape the Proapoptotic Effects of TNF-α through Increased Autocrine IL-6/STAT3 Signaling
Bibliographic record
Abstract
The liver is a common site for cancer metastases in which the entrance of tumor cells has been shown to trigger a rapid inflammatory response. In considering how an inflammatory response may affect metastatic colonization in this setting, we hypothesized that tumor cells may acquire resistance to the proapoptotic and tumoricidal effects of TNF-α, a cytokine that is elevated in a proinflammatory tissue microenvironment. In this study, we investigated molecular mechanisms by which such resistance may emerge using tumor cells in which the overexpression of the type I insulin-like growth factor receptor (IGF-IR) enhanced the inflammatory and metastatic capacities of poorly metastatic cells in the liver. Mechanistic investigations in vitro revealed that IGF-IR overexpression increased cell survival in the presence of high levels of TNF-α, in a manner associated with increased autocrine production of interleukin-6 (IL)-6. In turn, tumor cell-derived IL-6 induced gp130 and IL-6R-dependent activation of STAT3, leading to reduced caspase-3 activation and apoptosis. We found that IL-6 production and cell death resistance were dose dependent with increasing TNF-α levels. In addition, RNA interference-mediated knockdown of either IL-6 or gp130 that established a blockade to autocrine STAT3 induction was sufficient to abolish the prosurvival effect of TNF-α and to inhibit liver metastasis. Taken together, our findings define an IGF-IR-mediated mechanism of cancer cell survival that is critical for metastatic colonization of the liver.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".