首页|Zhongnan Hospital of Wuhan University Reports Findings in Carcinomas (Somatic mu tation of targeted sequencing identifies risk stratification in advanced ovarian clear cell carcinoma)
Zhongnan Hospital of Wuhan University Reports Findings in Carcinomas (Somatic mu tation of targeted sequencing identifies risk stratification in advanced ovarian clear cell carcinoma)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Oncology - Carcinomas is the subject of a report. According to news originating from Hubei, People’s R epublic of China, by NewsRx correspondents, research stated, “Ovarian clear cell carcinoma (OCCC) is a unique subtype of epithelial ovarian cancer. Advanced OCC C display a poor prognosis.” Our news journalists obtained a quote from the research from the Zhongnan Hospit al of Wuhan University, “Therefore, we aimed to make risk stratification for pre cise medicine. We performed a large next generation sequencing (NGS) gene panel on 44 patients with OCCC in FIGO stage II-IV. Then, by machine learning algorith ms, including extreme gradient boosting (XGBoost), random survival forest (RSF), and Cox regression, we screened for feature genes associated with prognosis and constructed a 5-gene panel for risk stratification. The prediction efficacy of the 5-gene panel was compared with FIGO stage and residual disease by receiver o perating characteristic curve and decision curve analysis. The feature mutated g enes related to prognosis, selected by machine learning algorithms, include MUC1 6, ATM, NOTCH3, KMT2A, and CTNNA1. The 5-gene panel can effectively distinguish the prognosis, as well as platinum response, of advanced OCCC in both internal a nd external cohorts, with the predictive capability superior to FIGO stage and r esidual disease. Mutations in genes, including MUC16, ATM, NOTCH3, KMT2A, and CT NNA1, were associated with the poor prognosis of advanced OCCC.”
HubeiPeople’s Republic of ChinaAsiaCancerCarcinomasCyborgsEmerging TechnologiesGeneticsGynecologyHealt h and MedicineMachine LearningOncologyWomen’s Health
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