ArticleFrontiers in immunology2024
Multi-omics analysis and experiments uncover the function of cancer stemness in ovarian cancer and establish a machine learning-based model for predicting immunotherapy responses.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Construction of a novel signature based on CRISPR-Cas9 screening for prognostic prediction in breast cancer.BMC cancer · 2026Article
- A panel of machine learning approaches for diagnostic model development and validation in ovarian cancer.Translational cancer research · 2026Article
- The resilient subset: cancer stem cells at the core of immunotherapy resistance.Immunotherapy advances · 2026Review
- Cancer stem cells: landscape, challenges and emerging therapeutic innovations.Signal transduction and targeted therapy · 2025Review
- Leveraging machine learning models to evaluate immune infiltration in the ovarian cancer microenvironment: a single-cell analysis approach.Discover oncology · 2025Article
- Ovarian Cancer: Multi-Omics Data Integration.International journal of molecular sciences · 2025Review
- A comprehensive overview of ovarian cancer stem cells: correlation with high recurrence rate, underlying mechanisms, and therapeutic opportunities.Molecular cancer · 2025Review
- Incidence of severe adverse events in cancer patients after treatment with immune-checkpoint inhibitors during the COVID- 19 pandemic.BMC immunology · 2025Article
- Igniting Cold Tumors: Multi-Omics-Driven Strategies to Overcome Immune Evasion and Restore Immune Surveillance.Oncology research · 2025Review
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10 authors.
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Abstract
Background: The heterogeneity of cancer makes it challenging to predict its response to immunotherapy, highlighting the need to find reliable biomarkers for assessment. The sophisticated role of cancer stemness in mediating resistance to immune checkpoint inhibitors (ICIs) is still inadequately comprehended. Methods: Genome-scale CRISPR screening of RNA sequencing data from Project Achilles was utilized to pinpoint crucial genes unique to Ovarian Cancer (OV). Thirteen publicly accessible OV transcriptomic datasets, seven pan-cancer ICI transcriptomic cohorts, and one single-cell RNA dataset from melanoma patients treated with PD-1 were utilized to scale a novel cancer stemness index (CSI). An OV single-cell RNA dataset was amassed and scrutinized to uncover the role of Small Nuclear Ribonucleoprotein Polypeptide E (SNRPE) in the tumor microenvironment (TME). Vitro experiments were performed to validate the function of SNRPE in promoting proliferation and migration of ovarian cancer. Results: Through the analysis of extensive datasets on ovarian cancer, a specific gene set that impacts the stemness characteristics of tumors has been identified and we unveiled a negative correlation between cancer stemness, and benefits of ICI treatment in single cell ICI cohorts. This identified gene set underpinned the development of the CSI, a groundbreaking tool leveraging advanced machine learning to predict prognosis and immunotherapy responses in ovarian cancer patients. The accuracy of the CSI was further confirmed by applying PD1/PD-L1 ICI transcriptomic cohorts, with a mean AUC exceeding 0.8 for predicting tumor progression and immunotherapy benefits. Remarkably, when compared to existing immunotherapy and prognosis markers, CSI exhibited superior predictive capabilities across various datasets. Interestingly, our research unveiled that the amplification of SNRPE contribute to remodeling the TME and promoting the evasion of malignant cells from immune system recognition and SNRPE can server as a novel biomarker for predicting immunotherapy response. Conclusions: A strong relationship between cancer stemness and the response to immunotherapy has been identified in our study. This finding provides valuable insights for devising efficient strategies to address immune evasion by targeting the regulation of genes associated with cellular stemness.
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