Artificial intelligence-based radiogenomics reveals the potential immunoregulatory role of COL22A1 in glioma and its induced autoimmune encephalitis.

Background: The tumor microenvironment plays a crucial role in the progression of both glioma and glioma-induced autoimmune encephalitis. However, there remains a significant lack of effective therapeutic targets for these diseases. Method: We collected 54 CT images of glioma patients and 54 glioma-induced autoimmune encephalitis patients, respectively. Radiomics features were extracted from tumors and encephalitis regions using Python, followed by dimensionality reduction via random forest and lasso regression, and construction of radiomics-based risk scores. Genomic data matched with clinical information were analyzed to identify key prognostic genes significantly associated with risk scores. Gene expression was validated by immunohistochemistry using our clinical samples. Immune infiltration was evaluated using five algorithms (MCP-counter, EPIC, TIMER, QUANT and IPS). The association between hub genes and immune checkpoint markers as well as immunoregulation-related genes was also analyzed using Spearman correlation. Results: We identified 980 radiomics features both in glioma and encephalitis patient images and selected four key features through lasso regression to build a radiomics-based risk score. COL22A1 was strongly correlated with the risk score and identified as the hub prognostic gene. COL22A1 expression was higher in glioblastoma tissues and cell lines, and correlated with clinical factors such as higher age, WHO grade, and IDH mutation status. Immune infiltration analysis indicated associations with diverse immune and stromal cell populations, including CD8 Conclusion: Our study highlights the critical role of COL22A1 in gliomas and glioma-Induced Autoimmune Encephalitis, demonstrating its strong association with poor prognosis and its significant involvement in tumor immune regulation.