ArticleFrontiers in oncology2021
Development of a Prognostic Model for Ovarian Cancer Patients Based on Novel Immune Microenvironment Related Genes.
Article in Frontiers in oncology, 2021. 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.
- PD-L1 is highly expressed in ovarian cancer and associated with cancer stem cells populations expressing CD44 and other stem cell markers.BMC cancer · 2023Article
- Article
- HAUS Augmin-Like Complex Subunit 1 Influences Tumour Microenvironment and Prognostic Outcomes in Glioma.Journal of oncology · 2022Article
- Identification of Nine M6A-Related Long Noncoding RNAs as Prognostic Signatures Associated with Oxidative Stress in Oral Cancer Based on Data from The Cancer Genome Atlas.Oxidative medicine and cellular longevity · 2022Article
- Application of Prognostic Models Based on Psoas Muscle Index, Stage, Pathological Grade, and Preoperative Carcinoembryonic Antigen Level in Stage II-III Colorectal Cancer Patients Undergoing Adjuvant Chemotherapy.Journal of oncology · 2022Article
- Identification of Novel Hypoxia Subtypes for Prognosis Based on Machine Learning Algorithms.Journal of oncology · 2022Article
- KLHL14: A Novel Prognostic Biomarker and Therapeutic Target for Ovarian Cancer.Journal of oncology · 2022Article
- Identification of Five m6A-Related lncRNA Genes as Prognostic Markers for Endometrial Cancer Based on TCGA Database.Journal of immunology research · 2022Article
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Authors and funding
7 authors.
Funding
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Abstract
Ovarian cancer (OV) has become the most lethal gynecological cancer. However, its treatment methods and staging system are far from ideal. In the present study, taking the advantage of large-scale public cohorts, we extracted a list of immune-related prognostic genes that differentially expressed in tumor and normal ovarian tissues. Importantly, an individualized immune-related gene based prognostic model (IPM) for OV patients were developed. Furthermore, we validated our IPM in Gene Expression Omnibus (GEO) repository and compared the immune landscape and pathways between high-risk and low-risk groups. The results of our study can serve as an important model to identify the immune subset of patients and has potential for use in immune therapeutic selection and patient management.
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