ArticleFrontiers in genetics2022
A Risk Model Developed Based on Homologous Recombination Deficiency Predicts Overall Survival in Patients With Lower Grade Glioma.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 3 citations in OpenAlex.
- A Risk Model Developed based on Homologous Recombination Deficiency Genes for Evaluating the Drug Sensitivity and Prognostic Prediction of Lung Adenocarcinoma.Current medicinal chemistry · 2025Article
- Mapping the Somatic Mutation Landscape of Familial NF2-Related Schwannomatosis using Whole-Exome Sequencing.International journal of medical sciences · 2025Article
- Multiomics analysis of homologous recombination deficiency across cancer types.Biomolecules & biomedicine · 2024Article
- Identification of molecular subtypes and a risk model based on inflammation-related genes in patients with low grade glioma.Heliyon · 2023Article
- Arachidonate lipoxygenases 5 is a novel prognostic biomarker and correlates with high tumor immune infiltration in low-grade glioma.Frontiers in genetics · 2023Article
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Authors and funding
6 authors at 3 institutions in 2 countries.
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Abstract
The role of homologous recombination deficiency (HRD) in lower grade glioma (LGG) has not been elucidated, and accurate prognostic prediction is also important for the treatment and management of LGG. The aim of this study was to construct an HRD-based risk model and to explore the immunological and molecular characteristics of this risk model. The HRD score threshold = 10 was determined from 506 LGG samples in The Cancer Genome Atlas cohort using the best cut-off value, and patients with high HRD scores had worse overall survival. A total of 251 HRD-related genes were identified by analyzing differentially expressed genes, 182 of which were associated with survival. A risk score model based on HRD-related genes was constructed using univariate Cox regression, least absolute shrinkage and selection operator regression, and stepwise regression, and patients were divided into high- and low-risk groups using the median risk score. High-risk patients had significantly worse overall survival than low-risk patients. The risk model had excellent predictive performance for overall survival in LGG and was found to be an independent risk factor. The prognostic value of the risk model was validated using an independent cohort. In addition, the risk score was associated with tumor mutation burden and immune cell infiltration in LGG. High-risk patients had higher HRD scores and "hot" tumor immune microenvironment, which could benefit from poly-ADP-ribose polymerase inhibitors and immune checkpoint inhibitors. Overall, this big data study determined the threshold of HRD score in LGG, identified HRD-related genes, developed a risk model based on HRD-related genes, and determined the molecular and immunological characteristics of the risk model. This provides potential new targets for future targeted therapies and facilitates the development of individualized immunotherapy to improve prognosis.
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