Abstract
Viruses are critical for the regulation of cancer development and for therapy. Human adenovirus C (HadVC) has been detected in central nervous system and glioma tissue. The objective of the present 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 The Cancer Genome Atlas dataset for training and 2 cohorts from the Chinese Glioma Genome Atlas and an immunotherapy trial cohort with 17 patients receiving anti-PD-1 treatment for validation. HadVCi-relevant gene selection from differentially expressed genes between HadVC-infected and non-HadVC-infected glioma patients using least absolute shrinkage and selection operator regression was followed by Cox regression modeling to establish a prognostic HadVCi score. Kaplan-Meier and receiver operating characteristic curve analyses were performed to estimate the predictive capacity of the HadVCi score. The χ2, Spearman, and Mann-Whitney U tests were used to identify the correlation with the clinicopathological parameters, treatment responsiveness, and immune landscape. Temozolomide-resistant glioma cells were established and analyzed at the transcriptional level using RNA sequencing data. The HadVCi score was (-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 a higher HadVCi score correlated with a malignant phenotype and poorer treatment responsiveness to temozolomide-based chemotherapy and combined therapies. Additionally, transcriptomic analysis showed malignancy-, stemness-, and radioresistant-related gene activation in the HadVCi group, which characterized the poor outcomes and limited sensitivity to standard therapy. The HadVCi score could be an effective tool for survival prediction and treatment guidance for patients with glioma.
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