Pan-cancer and machine learning reveal the role of cell adhesion molecules in prognosis, immune remodeling, and anti-tumour therapy in lower-grade gliomas.

Cell adhesion molecules (CAMs) have emerged as pivotal regulators of tumor biology, influencing invasion, metastasis, and the tumor microenvironment (TME) through their roles in cell-cell and cell-matrix interactions. Despite their critical roles, the prognostic value and clinical potential of CAMs in lower-grade gliomas (LGGs) remain underexplored. In this study, we utilized machine learning algorithms and multi-omics data from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) to systematically evaluate CAM-associated features in LGGs. A novel CAM-related prognostic signature (CAMSig) was developed, incorporating 13 key genes, including CD58, ITGB1, and VCAM1. The CAMSig score effectively stratified patients into distinct risk groups with varying survival outcomes, showed improved prognostic accuracy compared to some traditional biomarkers such as IDH mutation and 1p/19q co-deletion in prognostic accuracy. Patients in the high-risk CAMSig group exhibited significant activation of tumor progression pathways, such as epithelial-mesenchymal transition (EMT) and PI3K-AKT signaling, along with substantial remodeling of the TME. These patients showed increased infiltration of immunosuppressive cells, including regulatory T cells (Tregs) and macrophages, as well as upregulation of immune checkpoint molecules such as PD-L1 and PD-L2, suggestive of an immunosuppressive microenvironment. Importantly, the CAMSig score also revealed differential therapeutic sensitivities. Patients with high CAMSig scores demonstrated reduced responsiveness to immune checkpoint blockade (ICB) therapy but increased sensitivity to chemotherapeutic agents, such as temozolomide. This study highlights the multifaceted role of CAMs in LGG and their potential impact across cancers, establishing the CAMSig score as a robust tool for prognostic assessment and personalized therapy guidance. By integrating insights into tumor progression, immune modulation, and therapeutic response, this research offers novel strategies for advancing the clinical management of LGG.