Viruses are critical for the regulation of cancer development as well as therapy. Human adenovirus C (HadVC) has been detected in central nervous system and glioma tissue. The objective of current study was the development of a robust prognostic model based on HadVC infection (HadVCi)-relevant genes.The genome, transcriptome, and virome were systemically analyzed using TCGA dataset for training and two cohorts from CGGA and an immunotherapy trial cohort with 17 patients receiving anti-PD-1 treatment for validation. HadVCi-relevant gene selection from differentially expressed genes (DEGs) between HadVC-infected or non-infected glioma patients using LASSO regression was followed by Cox regression modeling to establish a prognostic HadVCi score. Kaplan-Meier and ROC curve analyses were performed to estimate the predictive capacity of HadVCi score. Chi-square/Spearman/Mann Whitney U test is utilized to identify its correlation to clinicopathological parameters, treatment responsiveness, and immune landscape. TMZ-resistant glioma cells were established and analyzed at the transcriptional level using RNA-seq data.HadVCi score =(-0.2526673*TRPC6) + (-0.2244276*RNF207) + (-0.0894468*SEC31B) + (-0.0190214* ZCRB1) + (-0.017122*DNPH1) + (0.0495818*CCDC34) + (0.1196349*PURG) + (0.1778997*LILRA5). The score possesses a strong ability to predict overall survival. Further analysis revealed higher HadVCi score is correlated with malignant phenotype, and poorer treatment responsiveness, such as TMZ-based chemotherapy and combined therapies. Additionally, transcriptomic analysis showed malignancy-, stemness-, and radioresistance-related gene activation in HadVCi group, which characterized the poor outcomes and limited sensitivity to standard therapy.The HadVCi score may be an effective tool for survival prediction and treatment guidance in patients with glioma.Copyright © 2023. Published by Elsevier Inc.