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Identification of a ferroptosis- and oxidative stress-associated gene signature for prognostic stratification of ovarian cancer.
Li, Shenyi; Cao, Tianyue; Wu, Tiantian; Xu, Jinfu; Shen, Cong; Hou, Shunyu; Wu, Yibo.
Affiliation
  • Li S; Human Reproductive and Genetic Center, Affiliated Hospital of Jiangnan University Wuxi 214122, Jiangsu, China.
  • Cao T; Department of Gynaecology, Suzhou Municipal Hospital, The Affiliated Suzhou Hospital of Nanjing Medical University, Gusu School, Nanjing Medical University Suzhou 215002, Jiangsu, China.
  • Wu T; State Key Laboratory of Reproductive Medicine, Department of Histology and Embryology, School of Basic Medical Sciences, Nanjing Medical University Nanjing 211166, Jiangsu, China.
  • Xu J; State Key Laboratory of Reproductive Medicine, Department of Histology and Embryology, School of Basic Medical Sciences, Nanjing Medical University Nanjing 211166, Jiangsu, China.
  • Shen C; NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract (Anhui Medical University) Hefei 230032, Anhui, China.
  • Hou S; State Key Laboratory of Reproductive Medicine, Center for Reproduction and Genetics, Suzhou Municipal Hospital, The Affiliated Suzhou Hospital of Nanjing Medical University, Gusu School, Nanjing Medical University Suzhou 215002, Jiangsu, China.
  • Wu Y; Department of Gynaecology, Suzhou Municipal Hospital, The Affiliated Suzhou Hospital of Nanjing Medical University, Gusu School, Nanjing Medical University Suzhou 215002, Jiangsu, China.
Am J Transl Res ; 15(4): 2645-2655, 2023.
Article in En | MEDLINE | ID: mdl-37193145
BACKGROUND: Studies have shown that ferroptosis- and oxidative stress-related genes (FORGs) perform crosstalk in ovarian cancer (OC). The specific role of FORGs in OC, however, remains unclear. We aimed to develop a molecular subtype and prognostic model associated with FORGs that could predict OC prognosis and evaluate the infiltration of tumor-associated immune cells. METHODS: Gene expression samples were collected from the GEO (GSE53963) and Cancer Genome Atlas (TCGA) databases. Kaplan-Meier analysis was used to evaluate prognostic efficacy. Unsupervised clustering was applied to identify molecular subtypes, which was followed by tumor immune cell infiltration and functional enrichment analyses. Subtype-related differentially expressed genes (DEGs) were identified and used to establish prognostic models. Associations between the model and immune checkpoint expression, stromal scores, and chemotherapy were investigated. RESULTS: OC patients were categorized into two FORG subtypes based on the expression characteristics of 19 FORGs. Molecular subtypes associated with patient prognosis, immune activity, and energy metabolism pathways were identified. Subsequently, DEGs in the two FORG subtypes were identified and used in prognostic models. We identified six signature genes (MEGF8, ECE1, SASH1, ARHGEF16, PLXNA1, and FCGBP) with LASSO analysis to assess the risk of OC. Patients in the high-risk group had poor prognoses and immunosuppression, while the risk scores were significantly associated with immune checkpoint expression, stromal scores, and chemotherapy sensitivity. CONCLUSIONS: Our novel clustering algorithm was used to create distinct clusters of OC patients and a prognostic model was developed that accurately predicted patient outcomes and chemotherapy responses. This approach offers effective precision medicine for OC patients.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: Am J Transl Res Year: 2023 Document type: Article Affiliation country: China Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: Am J Transl Res Year: 2023 Document type: Article Affiliation country: China Country of publication: United States