Bibliographic record
Abstract
Introduction: Glioblastoma (GBM) is an extremely aggressive brain tumor that poses significant challenges to clinical oncology. Previous research has discovered that intercellular adhesion molecule-1 (ICAM-1) is expressed in immune cells and is one of the most common molecules in the tumor microenvironment. However, the entire scope of ICAM-1 functions on immune cells in glioblastoma is still under investigation. This literature review aims to synthesize existing knowledge about the role of ICAM-1 on immune cells in GBM progression. Methods: This review summarizes research from 1980-2024 using PubMed, OVID Medline, Web of Science, and Google Scholar. The following keywords were used to identify the articles focusing on the role of ICAM-1 on immune cells in glioblastoma: “glioblastoma”, “ICAM-1”, “CD54”, “macrophages”, “lymphocytes”, “dendritic cells”, and “natural killer cells”. Studies that primarily focus on ICAM-1 expression and function within the tumor microenvironment were selected. Results: Immune cell expression of ICAM-1 in the GBM microenvironment may exhibit both pro- and anti-tumor effects. In tumor-associated macrophages, ICAM-1 upregulation regulates polarization and immunosuppression. In dendritic cells, decreased ICAM-1 expression may hinder anti-tumor responses by limiting T cell activation. The role of ICAM-1 in tumor-infiltrating lymphocytes remains unclear. In neutrophils, ICAM-1 upregulation may promote immune suppression by reducing T-cell activity. The decreased ICAM-1 levels on NK cells in GBM may lead to NK cell exhaustion. Discussion: The radio-chemotherapy has differential effects on ICAM-1 functions and, to some extent, affects the interpretation of findings. In turn, the alteration of ICAM-1 expression also influences the effectiveness of GBM radio-chemotherapy and the composition of the tumor microenvironment. The corticosteroid administration and tumor types are also factors affecting immune cell activity and composition. Conclusion: This review inspires innovative therapeutic strategies to improve treatment outcomes and patient prognosis for glioblastoma, as well as provides potential directions for future research on ICAM-1 for glioblastoma.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".