Differential interleukin-6/Stat3 signaling as a function of cellular context mediates Ras-induced transformation
Bibliographic record
Abstract
INTRODUCTION: Tyrosine phosphorylated signal transducer and activator of transcription 3 (pStat3) is present in numerous cancers and is required for mediating tumorigenesis. Autocrine and paracrine interleukin (IL)-6 signaling is the principal mechanism by which Stat3 is persistently phosphorylated in epithelial tumors including breast, lung, colon and gastric cancer. The Ras oncogene mediates cellular transformation without evidence of pStat3 in cultured cells. Recently, however non-tyrosine phosphorylated Stat3 was shown to have a transcriptional activating function, a role in mitochondrial function and to mediate cell migration.. Here we examined the role of Stat3 in Ras mediated transformation. METHODS: Ha-rasV12 transformed mammary epithelial cells (MCF10A) cells were transduced with a Stat3shRNA, IL-6shRNA and/or treated with inhibitors of Janus kinases (JAKs) to examine the role of the IL-6 signaling pathway in Ras mediated invasion and tumorigenesis. RESULTS: Cellular migration, invasion, anchorage independent growth and tumorigenesis were largely abrogated in the Stat3-reduced cells compared to control cells. Analysis of MCF10A-Ras tumors revealed high levels of pStat3 and interleukin-6. Tumors derived from transgenic MMTV-K-Ras mice were also found to express pStat3 and IL-6. MCF10A-Ras cells, when grown in a three-dimensional Matrigel culture system revealed the appearance of the junctional protein E-Cadherin as a consequence of reducing Stat3 levels or inhibiting Stat3 activity. Decreasing IL-6 levels in the MCF10A-Ras cells abrogated tumorigenesis and reduced cell migration. By isolating Ras-expressing primary tumors and serially passaging these cells in two-dimensional culture led to a decrease in IL-6 and pStat3 levels with the reappearance of E-Cadherin. CONCLUSIONS: The cellular and environmental context can lead to differential IL-6/pStat3 signaling and a dependency on this cytokine and transcription factor for migration, invasion and tumorigenesis.
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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) | 0.003 | 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".