首页|China Academy of Chinese Medical Sciences Reports Findings in Ovarian Cancer (An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer)
China Academy of Chinese Medical Sciences Reports Findings in Ovarian Cancer (An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Oncology - Ovarian Cancer is the subject of a report.According to news reporting originating from Beijing,Peopl e's Republic of China,by NewsRx correspondents,research stated,"Ovarian cance r (OC) has the highest mortality rate among gynecological malignancies.Current treatment options are limited and ineffective,prompting the discovery of reliab le biomarkers." Our news editors obtained a quote from the research from the China Academy of Ch inese Medical Sciences,"Exosome lncRNAs,carrying genetic information,are prom ising new markers.Previous studies only focused on exosome-related genes and em ployed the Lasso algorithm to construct prediction models,which are not robust.420 OC patients from the TCGA datasets were divided into training and validatio n datasets.The GSE102037 dataset was used for external validation.LncRNAs asso ciated with exosomerelated genes were selected using Pearson analysis.Univaria te COX regression analysis was used to filter prognosis-related lncRNAs.The ove rlapping lncRNAs were identified as candidate lncRNAs for machine learning.Base d on 10 machine learning algorithms and 117 algorithm combinations,the optimal predictor combinations were selected according to the C index.The exosome-relat ed LncRNA Signature (ERLS) model was constructed using multivariate COX regressi on.Based on the median risk score of the training datasets,the patients were d ivided into high- and low-risk groups.Kaplan-Meier survival analysis,the time- dependent ROC,immune cell infiltration,immunotherapy response,and immune chec kpoints were analyzed.64 lncRNAs were subjected to a machine-learning process.Based on the stepCox (forward) combined Ridge algorithm,20 lncRNA were selected to construct the ERLS model.Kaplan-Meier survival analysis showed that the hig h-risk group had a lower survival rate.The area under the curve (AUC) in predic ting OS at 1,3,and 5 years were 0.758,0.816,and 0.827 in the entire TCGA coh ort.xCell and ssGSEA analysis showed that the low-risk group had higher immune cell infiltration,which may contribute to the activation of cytolytic activity,inflammation promotion,and T-cell co-stimulation pathways.The low-risk group had higher expression levels of PDL1,CTLA4,and higher TMB.The ERLS model can predict response to anti-PD1 and anti-CTLA4 therapy.Patients with low expressio n of PDL1 or high expression of CTLA4 and low ERLS exhibited significantly bette r survival prospects,whereas patients with high ERLS and low levels of PDL1 or CTLA4 exhibited the poorest outcomes."
BeijingPeople's Republic of ChinaAsi aAlgorithmsBiomarkersCancerCyborgsCytoplasmic StructuresDiagnostics and ScreeningDrugs and TherapiesEmerging TechnologiesExosomesGynecologyHealth and MedicineImmunologyImmunotherapyMachine LearningOncologyOrg anellesOvarian CancerTransport VesiclesWomen's Health
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