Evidence for a JAK2/STAT3 proinflammatory and vasculogenic mimicry interrelated molecular signature in adipocyte-derived mesenchymal stromal/stem cells
Bibliographic record
Abstract
BACKGROUND: During obesity, the excessive accumulation of fat in tissue promotes dysregulated hormonal and cytokine homeostasis that triggers chronic inflammation, which is, in part, associated with an increased incidence of some cancers. This protumoral inflammatory environment is further exacerbated through the secretome of mature adipocytes, which promotes tumor angiogenesis. Emerging studies suggest that human adipocyte-derived mesenchymal stromal/stem cells (ADMSCs) may contribute to a complementary process supporting local angiogenesis termed vasculogenic mimicry (VM). The molecular mechanisms linking ADMSCs to VM and inflammation remain poorly understood. METHODS: PCR arrays. Transient gene silencing was performed by transfecting cells with a specific siRNA against STAT3. Protein lysates were harvested and used for Western blotting. Realtime cell migration was performed with the xCELLigence system. RESULTS: Our findings revealed that in vitro priming of ADMSCs with Cultrex led to the formation of 3D capillary-like structures and the acquisition of an inflammatory molecular signature. VM-derived ADMSCs share a common proinflammatory molecular signature similar to that induced in 2D ADMSC monolayers by tumor necrosis factor (TNF)-alpha and are characterized by upregulated expression of COX2, CCL2, CCL5, CXCL5, CXCL8, IL-6, SNAI1, and MMP9. Interestingly, pharmacological inhibition or gene silencing of the JAK2/STAT3 signaling pathway reduced chemotactic cell migration, in vitro VM and the expression of proinflammatory and invasive biomarkers. CONCLUSIONS: Overall, we provide novel evidence that inhibiting JAK2/STAT3-regulated VM can also alter the acquisition of a proinflammatory signature and prevent the contribution of ADMSCs to alternative tumor neovascularization processes.
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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) | 0.002 | 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".