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Updated: Aug 11, 2026

RNAscope for In situ Detection of Transcriptionally Active Human Papillomavirus in Head and Neck Squamous Cell Carcinoma
Published on: March 11, 2014
Single-cell transcriptomics uncovers HPV-driven immune evasion and establishes a TAB2-based prognostic signature in
Jiarui Mi1, Wenzhe Guo2, Li Li3
1Department of Gynecology, Affiliated Hospital of Hebei University, Baoding, Hebei 071000, China; Baoding Key Laboratory of Gynecological Tumor Diagnosis and Precise Treatment, Baoding, Hebei 071000, China.
Background:
Persistent human papillomavirus (HPV) infection is a key driver in cervical cancer, yet the molecular mechanisms linking HPV to immune escape remain incompletely understood.
Methods:
We integrated single-cell RNA sequencing (scRNA-seq), bulk transcriptome, and clinical data to analyze cervical cancer heterogeneity. scRNA-seq, inferCNV, and pseudotime analysis characterized HPV-associated cellular features and malignant transformation. hdWGCNA identified core gene modules. Univariate Cox, Lasso, and multivariate Cox regressions were used to construct a prognostic risk model. In vitro functional validation of TAB2 was also performed.
Results:
Eleven major cell populations were identified, with epithelial cells as primary HPV targets. HPV-related gene enrichment increased with disease progression. Malignant cells exhibited significantly higher immune escape scores than non-tumor cells, strongly correlating with HPV infection levels (P < 0.001). hdWGCNA identified three modules linked to immunosuppression, stress response, and proliferation. An eight-gene risk model showed stable prognostic performance across TCGA and GEO cohorts, served as an independent prognostic factor (P < 0.05), and correlated with WNT/TGFβ pathway activation and immune checkpoint genes. TAB2 knockdown significantly suppressed cervical cancer cell proliferation, migration, and invasion in vitro.
Conclusion:
This multi-omics analysis identifies transcriptional programs associated with HPV infection and immune escape in cervical cancer. The eight-gene risk model shows potential for prognosis prediction and patient stratification, offering a theoretical basis for individualized treatment. The mechanistic hypotheses generated warrant further experimental validation.
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