Abstract
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."
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