A dialog between glioma and microglia that promotes tumor invasiveness through the CCL2/CCR2/interleukin-6 axis
Bibliographic record
Abstract
Glioma cells in situ are surrounded by microglia, suggesting the potential of glioma-microglia interactions to produce various outcomes. As chemokines are important mediators of cell-cell communication, we sought first to identify commonly expressed chemokines in 16 human glioma lines. We found CCL2 (macrophage chemoattractant protein-1) messenger RNA to be expressed by the majority of glioma lines. However, these lines did not express the CCL2 receptor, CCR2, which was found on microglia. Next, we overexpressed CCL2 in the U87 glioma line, which has low basal level of CCL2, to investigate the hypothesis that glioma-secreted CCL2 interacts with microglia to affect glioma growth. Stable clones with 10- to 12-fold elevation of CCL2 have similar growth rate and invasive capacity as vector controls when cultured in isolation. However, in coculture with microglia in a three-dimensional collagen gel matrix, the invasiveness of CCL2-overexpressing clones was increased. Gene array analyses were then undertaken and they revealed that interleukin (IL)-6 was consistently increased in the coculture. Recombinant IL-6 enhanced the invasiveness of glioma cells when these were cultured alone, whereas a neutralizing antibody to IL-6 attenuated the microglia-stimulated glioma invasiveness. Finally, we found that human glioma specimens in situ contained IL-6 immunoreactivity that was expressed on CD68+ cells. This study has uncovered a mechanism by which glioma cells exploit microglia for increased invasiveness. Specifically, glioma-derived CCL2 acts upon CCR2-bearing microglia, which then produces IL-6 to stimulate gliomas. The CCL2/CCR2/IL-6 loop is a potential therapeutic target for the currently incurable malignant gliomas.
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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) | 0.001 | 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".