Ocaña-Tienda, Beatriz

Abstract Background Glioblastoma (GB) exhibits marked tumor microenvironmental heterogeneity, contributing to both therapy resistance and poor survival outcomes. Intercellular adhesion molecule 1 (ICAM1) is an inflammation-associated adhesion molecule implicated in immune-stromal interactions, but its clinical and biological significance in GB remains incompletely defined. Methods We performed ICAM1 immunohistochemistry, bulk RNA sequencing, DNA methylation analysis, single-nucleus RNA sequencing, and preoperative MRI-derived morphologic feature analysis across multi-institutional cohorts of treatment-naïve IDH-wildtype GB. Results High ICAM1 expression at both transcript and protein levels is associated with shorter overall survival, particularly within the mesenchymal transcriptional subtype. ICAM1 expression is inversely associated with promoter methylation. Single-nucleus RNA-sequencing analysis shows that ICAM1-high tumors are enriched for mesenchymal, hypoxia, stress-associated, and immunosuppressive myeloid programs, suggesting that ICAM1 reflects a multicompartment inflammatory tumor ecosystem. ICAM1 expression, tumor surface irregularity, and patient age are independently prognostic and minimally correlated, capturing complementary clinical, imaging, and molecular features of GB biology. When combined, our Clinical-Imaging-Molecular (CIM) framework refines outcome prediction across independent GB cohorts using routinely available clinical data, conventional imaging, and standard immunohistochemistry. Conclusions ICAM1 characterizes an immune-remodeled, stress-adapted GB ecosystem associated with poor clinical outcomes. Integrating patient age, surface irregularity, and ICAM1 expression yields a scalable and clinically accessible prognostic framework for GB, supporting tailored outcome prediction, particularly in low- and middle-resource settings without routine access to advanced molecular profiling.

Abstract BACKGROUND Glioblastoma (GBM) is a highly aggressive brain tumour with limited prognostic tools that are broadly implementable in routine clinical settings. Current models often depend on molecular platforms not universally accessible. We aimed to develop and validate a clinically practical prognostic model for GBM that integrates ICAM1 protein expression, imaging-derived tumor morphometrics, and clinical features. MATERIAL AND METHODS We analyzed ICAM1 expression in GBM at the DNA methylation, RNA, and protein levels using public datasets and institutional cohorts, confirming its association with tumor grade and poor survival. ICAM1 protein levels were then quantified via digital pathology in a multi-institutional cohort of 197 newly diagnosed, treatment-naïve GBM patients. Tumor surface irregularity (SIR) was calculated from pre-operative contrast-enhanced MRI using open-source imaging tools. Age at diagnosis was also recorded. These three variables—ICAM1 expression, SIR, and age—were integrated into a Clinical-Imaging-Molecular (CIM) model using multivariable Cox regression. The model was validated in an independent external GBM cohort (n = 20). Survival prediction was assessed using Kaplan-Meier curves, ROC analysis, and concordance index (c-index). RESULTS ICAM1 expression was associated with DNA hypomethylation and significantly correlated with reduced survival at transcriptomic and proteomic levels. Pathway analysis identified associations with immune activation and angiogenesis. In the clinical cohort, high ICAM1 expression, elevated tumor SIR, and older age independently predicted worse survival. The CIM model combining these factors stratified patients into distinct prognostic groups, achieving strong performance (c-index = 0.779, AUC = 0.778). External validation confirmed the model’s predictive utility (median survival difference: 9.1 months, P = 0.009). CONCLUSION We present a validated and clinically applicable prognostic model for GBM that integrates ICAM1 expression, imaging features, and age. The CIM model is scalable, interpretable, and feasible for implementation across academic and community settings, providing a robust tool for patient stratification, treatment planning, and trial design.