M2‐polarized tumor‐associated macrophages promote epithelial‐mesenchymal transition via activation of the AKT3/PRAS40 signaling pathway in intrahepatic cholangiocarcinoma
Bibliographic record
Abstract
Tumor-associated macrophages (TAMs) have been considered as a major component of the tumor microenvironment. However, the crosstalk between M2-polarized tumor-associated macrophages (M2-TAMs) and intrahepatic cholangiocarcinoma (ICC) remains undetermined. In the present study, we aimed to clarify the role of M2-TAMs in ICC and the underlying mechanism. The in vitro assay demonstrated M2-TAMs promoted epithelial-mesenchymal transition (EMT) of ICC cells, resulting in enhanced cell invasion and metastasis ability. Moreover, M2-TAMs modulated the microenvironment of ICC by increasing the secretion of cytokines (GM-CSF, tumor necrosis factor-α [TNF-α], ICAM-1, interleukin-6 [IL-6], etc) and chemokines (CCL1, CCL3, etc). In addition, p-AKT (Ser473) and p-PRAS40 (Thr246) were upregulated in ICC cells when cocultured with M2-TAMs or treated with M2-TAMs secreted core cytokines (GM-CSF, TNF-α, ICAM-1, and IL-6). Consistently, AKT3 silencing (but not AKT1 silencing and AKT2 silencing) markedly inhibited phosphorylation of AKT and PRAS40 of ICC cells and inhibited the EMT process when cocultured with M2-TAMs. Taken together, the current data indicated that M2-TAMs promoted ICC cells EMT, partially through increasing secretion of cytokines and chemokines, thus modulating the microenvironment and activating the AKT3/PRAS40 signaling pathway.
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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".