ArticleFrontiers in immunology2022
Mining of immunological and prognostic-related biomarker for cervical cancer based on immune cell signatures.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 7 citations in OpenAlex.
- Multi-omics integration identifies prognostic genes linked to adaptive immunity response and radiosensitivity in cervical cancer.Translational oncology · 2026Article
- CD155 links tumor immunotype to epithelial-directed precision therapy beyond checkpoint inhibition in cervical cancer.Journal for immunotherapy of cancer · 2026Article
- Pan-cancer analysis identifies FKBP10 as a regulator of tumor immunosuppression and therapeutic response.Translational oncology · 2026Article
- Stratification by Mutational Landscape Reveals Differential Immune Infiltration and Predicts the Recurrence and Clinical Outcome of Cervical Cancer.Phenomics (Cham, Switzerland) · 2025Article
- Development and Validation of an Anoikis-Related Gene Signature for Prognostic Prediction in Cervical Cancer.International journal of general medicine · 2025Article
- TBP activates DCBLD1 transcription to promote cell cycle progression in cervical cancer.Functional & integrative genomics · 2024Article
- Identification of an inflammatory response-related gene prognostic signature and immune microenvironment for cervical cancer.Frontiers in molecular biosciences · 2024Article
- Bioinformatics analysis of immune characteristics in tumors with alternative carcinogenesis pathways induced by human papillomaviruses.Virology journal · 2023Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Immunotherapy has changed the therapeutic landscape of cervical cancer (CC), but has durable anti-tumor activity only in a subset of patients. This study aims to comprehensively analyze the tumor immune microenvironment (TIME) of CC and to mine biomarkers related to immunotherapy and prognosis. Methods: The Cancer Genome Atlas (TCGA) data was utilized to identify heterogeneous immune subtypes based on survival-related immune cell signatures (ICSs). ICSs prognostic model was constructed by Cox regression analyses, and immunohistochemistry was conducted to verify the gene with the largest weight coefficient in the model. Meanwhile, the tumor immune infiltration landscape was comprehensively characterized by ESTIMATE, CIBERSORT and MCPcounter algorithms. In addition, we also analyzed the differences in immunotherapy-related biomarkers between high and low-risk groups. IMvigor210 and two gynecologic tumor cohorts were used to validate the reliability and scalability of the Risk score. Results: A total of 291 TCGA-CC samples were divided into two ICSs clusters with significant differences in immune infiltration landscape and prognosis. ICSs prognostic model was constructed based on eight immune-related genes (IRGs), which showed higher overall survival (OS) rate in the low-risk group ( Conclusion: This study comprehensively assessed the TIME of CC and constructed an ICSs prognostic model, which provides an effective tool for predicting patient's prognosis and accurate immunotherapy.
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