THE ROLE OF ICAM1 IN TUMOR ASSOCIATED MACROPHAGES IN GLIOBLASTOMA TUMORIGENESIS
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is a fatal brain cancer in adults with ineffective treatment methods. Cell adhesion molecules (CAMs) are proteins that are expressed on the surface of cells and enable them to interact with one another and the surrounding microenvironment. Intracellular adhesion molecule 1 (ICAM-1) is a cell adhesion molecule expressed by various cell types. Preliminary data showed that ICAM-1 is associated with poorer overall and progression free survival in patients and ICAM-1 expression levels increase in recurrent tumors. TAMs enhance GBM tumor growth. The aim of this study was to determine if ICAM-1 expression on TAMs contributes to GBM tumorigenesis, specifically within the hypoxic tumor microenvironment. We hypothesized that ICAM1 facilitates TAM migration, proliferation, cell adhesion and phagocytosis, thus enhancing tumor growth. We found that upon incubation of human and mouse primary macrophages in hypoxia, ICAM-1 expression levels increased and were further exasperated upon culturing with tumor cell-conditioned medium. The migration, cell adhesion and phagocytosis of ICAM-1 deficient macrophages was lower than wild type macrophages, and effects were further exasperated upon culturing with tumor conditioned media and incubation in hypoxia. Intracranial injection of tumor cells into ICAM-1 deficient and wild type mice revealed that ICAM1 deficient mice survived longer with lower overall tumor volume than wild type mice, with survival and volume differences being rescued upon ICAM1 bone marrow reconstitution. In conclusion, the expression of ICAM1 in TAMs likely promotes GBM tumorigenesis and hypoxia further enhances the expression of ICAM-1 in macrophages which is associated with more aggressive GBM.
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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".