ArticleThe Journal of international medical research2022
Prediction of homologous recombination deficiency from cancer gene expression data.
Article in The Journal of international medical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Cross-cancer homologous recombination deficiency prediction from whole slide images using transfer learning.Scientific reports · 2026Article
- IdentifiHR predicts homologous recombination deficiency in high-grade serous ovarian carcinoma using gene expression.Communications medicine · 2026Article
- Predictive biomarkers for the efficacy of PARP inhibitors in ovarian cancer: an updated systematic review.BJC reports · 2025Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveHomologous recombination deficiency (HRD) is the main mechanism of tumorigenesis in some cancers. HRD causes abnormal double-strand break repair, resulting in genomic scars. Some scoring HRD tests have been approved as companion diagnostics of polyadenosine diphosphate-ribose polymerase (PARP) inhibitor treatment. This study aimed to build an HRD prediction model using gene expression data from various cancer types.
methodsThe cancer genome atlas data were used for HRD prediction modeling. A total of 10,567 cases of 33 cancer types were included, and expression data from 5128 out of 20,502 genes were included as predictors. A penalized logistic regression model was chosen as a modeling technique.
resultsThe area under the curve of the receiver operating characteristic curve of HRD status prediction was 0.98 for the training set and 0.93 for the test set. The accuracy of HRD status prediction was 0.93 for the training set and 0.88 for the test set.
conclusionsOur study suggests that the HRD prediction model based on penalized logistic regression using gene expression data can be used to select patients for treatment with PARP inhibitors.
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