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Analysis of Omics Data Reveals Nucleotide Excision Repair-Related Genes Signature in Highly-Grade Serous Ovarian Cancer to Predict Prognosis.
Dai, Danian; Li, Qiang; Zhou, Pengfei; Huang, Jianjiang; Zhuang, Hongkai; Wu, Hongmei; Chen, Bo.
  • Dai D; Department of Vascular and Plastic Surgery, Guangdong Provincial People's Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China.
  • Li Q; Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
  • Zhou P; Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Stomatological Hospital of Chongqing Medical University, Chongqing, China.
  • Huang J; Department of Pathology, Guangdong Provincial People's Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China.
  • Zhuang H; Department of General Surgery, Guangdong Provincial People's Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China.
  • Wu H; Department of Pathology, Guangdong Provincial People's Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China.
  • Chen B; Department of Breast Cancer, Guangdong Provincial People's Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China.
Front Cell Dev Biol ; 10: 874588, 2022.
Article en En | MEDLINE | ID: mdl-35769257
ABSTRACT
Most of the high-grade serous ovarian cancers (HGSOC) are accompanied by P53 mutations, which are related to the nucleotide excision repair (NER) pathway. This study aims to construct a risk signature based on NER-related genes that could effectively predict the prognosis for advanced patients with HGSOC. In our study, we found that two clusters of HGSOC with significantly different overall survival (OS) were identified by consensus clustering and principal component analysis (PCA). Then, a 7-gene risk signature (DDB2, POLR2D, CCNH, XPC, ERCC2, ERCC4, and RPA2) for OS prediction was developed subsequently based on TCGA cohort, and the risk score-based signature was identified as an independent prognostic indicator for HGSOC. According to the risk score, HGSOC patients were divided into high-risk group and low-risk group, in which the distinct OS and the predictive power were also successfully verified in the GEO validation sets. Then we constructed a nomogram, including the risk signature and clinical-related risk factors (age and treatment response) that predicted an individual's risk of OS, which can be validated by assessing calibration curves. Furthermore, GSEA showed that the genes in the high-risk group were significantly enriched in cancer-related pathways, such as "MAPK signaling pathway", "mTOR signaling pathway", "VEGF signaling pathway" and so on. In conclusion, our study has developed a robust NER-related genes-based molecular signature for prognosis prediction, and the nomogram could be used as a convenient tool for OS evaluation and guidance of therapeutic strategies in advanced patients with HGSOC.
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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Guideline / Prognostic_studies / Risk_factors_studies Idioma: En Año: 2022 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Guideline / Prognostic_studies / Risk_factors_studies Idioma: En Año: 2022 Tipo del documento: Article