Microglia function in brain tumors.

J Neurosci Res

Department of Comparative Biosciences, School of Veterinary Medicine, University of Wisconsin, Madison, Wisconsin 53792-3232, USA.

Published: August 2005

Microglia play an important role in inflammatory diseases of the central nervous system (CNS). These cells have also been identified in brain neoplasms; however, as of yet their function largely remains unclear. More recent studies designed to characterize further tumor-associated microglia suggest that the immune effector function of these cells may be suppressed in CNS tumors. Furthermore, microglia and macrophages can secrete various cytokines and growth factors that may contribute to the successful immune evasion, growth, and invasion of brain neoplasms. A better understanding of microglia and macrophage function is essential for the development of immune-based treatment strategies against malignant brain tumors.

Download full-text PDF

Source
http://dx.doi.org/10.1002/jnr.20485DOI Listing

Publication Analysis

Top Keywords

brain tumors
8
tumors microglia
8
brain neoplasms
8
microglia
5
microglia function
4
brain
4
function brain
4
microglia play
4
play role
4
role inflammatory
4

Similar Publications

CDK5: Insights into its roles in diseases.

Mol Biol Rep

January 2025

Institute of Pathogenic Biology, Guilin Medical University, Guilin, 541199, China.

Cyclin-dependent kinase 5 (CDK5), a unique member of the CDK family, is a proline-directed serine/threonine protein kinase with critical roles in various physiological and pathological processes. Widely expressed in the central nervous system, CDK5 is strongly implicated in neurological diseases. Beyond its neurological roles, CDK5 is involved in metabolic disorders, psychiatric conditions, and tumor progression, contributing to processes such as proliferation, migration, immune evasion, genomic stability, and angiogenesis.

View Article and Find Full Text PDF

Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. Malignant tumours can develop quickly, pass through neighboring tissues, and extend to further brain regions or the central nervous system. In contrast, healthy tumors typically develop slowly and do not invade surrounding tissues.

View Article and Find Full Text PDF

Mutations in Sonic Hedgehog (SHH) signaling pathway genes, for example, (SUFU), drive granule neuron precursors (GNP) to form medulloblastomas (MB). However, how different molecular lesions in the Shh pathway drive transformation is frequently unclear, and mutations in the cerebellum seem distinct. In this study, we show that fibroblast growth factor 5 (FGF5) signaling is integral for many infantile MB cases and that expression is uniquely upregulated in infantile MB tumors.

View Article and Find Full Text PDF

Introduction: Medulloblastoma (MB) is the most common malignant childhood brain tumor. Molecular subgrouping of MB has become a major determinant of management in high-income countries. Subgrouping is still very limited in low- and middle-income countries (LMICs), and its relevance to management with the incorporation of risk stratification (low risk, standard risk, high risk, and very high risk) has yet to be evaluated in this setting.

View Article and Find Full Text PDF

Utilizing machine-learning techniques on MRI radiomics to identify primary tumors in brain metastases.

Front Neurol

January 2025

Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

Objective: To develop a machine learning-based clinical and/or radiomics model for predicting the primary site of brain metastases using multiparametric magnetic resonance imaging (MRI).

Materials And Methods: A total of 202 patients (87 males, 115 females) with 439 brain metastases were retrospectively included, divided into training sets (brain metastases of lung cancer [BMLC]  = 194, brain metastases of breast cancer [BMBC]  = 108, brain metastases of gastrointestinal tumor [BMGiT]  = 48) and test sets (BMLC  = 50, BMBC  = 27, BMGiT  = 12). A total of 3,404 quantitative image features were obtained through semi-automatic segmentation from MRI images (T1WI, T2WI, FLAIR, and T1-CE).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!