ArticleTranslational cancer research2026
Construction of an actin cytoskeleton-related gene signature for predicting prognosis and therapeutic response in glioblastoma: based on machine learning.
Article in Translational cancer research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Glioblastoma (GBM) is highly aggressive and prone to recurrence, resulting in extremely poor patient outcomes. Evidence suggests that dynamic regulation of the actin cytoskeleton plays a critical role in tumor cell proliferation, invasion, recurrence, and therapy resistance. However, the prognostic value and regulatory mechanisms of actin cytoskeleton-related genes in GBM remain unclear. This study aimed to identify key actin cytoskeleton related genes using machine learning, construct a robust gene signature for prognosis prediction, and explore its value in evaluating therapeutic response and underlying molecular mechanisms in GBM. Methods: Gene expression data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Chinese Glioma Genome Atlas (CGGA) were analyzed. Machine learning (ML) methods were used to identify key actin cytoskeleton-related genes and construct a gene signature. Immune profiling and multi-omics analyses were further applied to explore potential regulatory mechanisms. Results: Seven key genes- Conclusions: The actin cytoskeleton-based gene signature serves as an independent indicator of poor prognosis and may support precise prognostic assessment and personalized therapeutic strategies for GBM.
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