{"title":"Genomic and transcriptomic data reveal molecular differences between homologous recombination deficiency subgroups in Chinese ovarian cancer patients.","authors":"Hongxia Wang, Wenhong Zhao, Wenhao Zhou, Na Wang, Yijie Li, Kaiyun Qin, Jingde Jia, Jiaqian Wang, Congcong Song, Yu Yu, Fenghua Zhang, Xu Cui, Lanlan Zhao, Haitao Luo, Zhengmao Zhang","doi":"10.1186/s40246-025-00806-w","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Ovarian cancer (OV) has the highest mortality rate among gynecological cancers and shows varied responses to chemotherapy combined with PARP inhibitors based on homologous recombination deficiency (HRD) subtypes.</p><p><strong>Methods: </strong>This study enrolled 143 Chinese OV patients to determine the HRD score grouping threshold using genomic features, dividing patients into HRD-high and HRD-low groups. Multi-omics sequencing was conducted on 70 patients receiving adjuvant chemotherapy with PARP inhibitors.</p><p><strong>Results: </strong>In this study, TP53 mutations enriched in the HRD-high group, while ARID1A, PIK3CA, and PTEN mutations were more common in the HRD-low group. HRD-high patients exhibited stronger immune activation, including elevated STAT1 expression, HLA signatures, and increased M1 macrophage infiltration, correlating with better prognosis. Additionally, peripheral blood analysis revealed higher bMSI and maxVAF levels in HRD-low patients compared to HRD-high patients, suggesting ctDNA as a potential tool for dynamic monitoring post-treatment.</p><p><strong>Conclusions: </strong>This study identified distinct molecular and immune profiles between HRD subgroups in Chinese ovarian cancer patients. Patients with HRD-high and STAT1 expression ≥ 74 suggests PARPi benefit.</p>","PeriodicalId":13183,"journal":{"name":"Human Genomics","volume":"19 1","pages":"104"},"PeriodicalIF":4.3000,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12398958/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Genomics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s40246-025-00806-w","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Ovarian cancer (OV) has the highest mortality rate among gynecological cancers and shows varied responses to chemotherapy combined with PARP inhibitors based on homologous recombination deficiency (HRD) subtypes.
Methods: This study enrolled 143 Chinese OV patients to determine the HRD score grouping threshold using genomic features, dividing patients into HRD-high and HRD-low groups. Multi-omics sequencing was conducted on 70 patients receiving adjuvant chemotherapy with PARP inhibitors.
Results: In this study, TP53 mutations enriched in the HRD-high group, while ARID1A, PIK3CA, and PTEN mutations were more common in the HRD-low group. HRD-high patients exhibited stronger immune activation, including elevated STAT1 expression, HLA signatures, and increased M1 macrophage infiltration, correlating with better prognosis. Additionally, peripheral blood analysis revealed higher bMSI and maxVAF levels in HRD-low patients compared to HRD-high patients, suggesting ctDNA as a potential tool for dynamic monitoring post-treatment.
Conclusions: This study identified distinct molecular and immune profiles between HRD subgroups in Chinese ovarian cancer patients. Patients with HRD-high and STAT1 expression ≥ 74 suggests PARPi benefit.
期刊介绍:
Human Genomics is a peer-reviewed, open access, online journal that focuses on the application of genomic analysis in all aspects of human health and disease, as well as genomic analysis of drug efficacy and safety, and comparative genomics.
Topics covered by the journal include, but are not limited to: pharmacogenomics, genome-wide association studies, genome-wide sequencing, exome sequencing, next-generation deep-sequencing, functional genomics, epigenomics, translational genomics, expression profiling, proteomics, bioinformatics, animal models, statistical genetics, genetic epidemiology, human population genetics and comparative genomics.