文献类型：期刊文献

英文题名：Integrating plasma exosomal miRNAs, ultrasound radiomics and tPSA for the diagnosis and prediction of early prostate cancer: a multi-center study

作者：Wang, Chao[1];Zhou, Chuan[1];Zhang, Yun-Feng[2];He, Han[1];Wang, Dong[2];Lv, Hao-Xuan[1];Yang, Zhi-jun[1];Wang, Jia[2];Ren, Yong-qi[2];Zhang, Wen-bo[2];Zhou, Feng-Hai[1,3]

第一作者：Wang, Chao

通信作者：Zhou, FH[1];Zhou, FH[2]

机构：[1]Lanzhou Univ, Clin Med Coll 1, Lanzhou 73000, Peoples R China;[2]Gansu Univ Chinese Med, Clin Med Coll 1, Lanzhou 730000, Peoples R China;[3]Gansu Prov Hosp, Dept Urol, Lanzhou 730000, Peoples R China

第一机构：Lanzhou Univ, Clin Med Coll 1, Lanzhou 73000, Peoples R China

通信机构：[1]corresponding author), Lanzhou Univ, Clin Med Coll 1, Lanzhou 73000, Peoples R China;[2]corresponding author), Gansu Prov Hosp, Dept Urol, Lanzhou 730000, Peoples R China.

年份：2024

外文期刊名：CLINICAL & TRANSLATIONAL ONCOLOGY

收录：;Scopus(收录号：2-s2.0-85202470613);WOS:【SCI-EXPANDED(收录号:WOS:001299695700001)】；

基金：We thank for GEO to share the gene expression data

语种：英文

外文关键词：Prostate cancer; Plasma exosomes; Early diagnosis; MiRNA; Prediction model

摘要：IntroductionThis multi-center study aims to explore the roles of plasma exosomal microRNAs (miRNAs), ultrasound (US) radiomics, and total prostate-specific antigen (tPSA) levels in early prostate cancer detection.MethodsWe analyzed the publicly available dataset GSE112264 to identify the differentially expressed miRNAs associated with prostate cancer. Then, PyRadiomics was used to extract image features, and least absolute shrinkage and selection operator (LASSO) was used to screen the data. Subsequently, according to strict inclusion and exclusion criteria, the internal dataset (n = 199) was used to construct a diagnostic model, and the receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), and DeLong test were used to evaluate its diagnostic performance. Finally, we used an external dataset (n = 158) for further validation.ResultsThe number of features extracted by PyRadiomics was 851, and the number of features screened by LASSO was 23. We combined the hsa-miR-320c, hsa-miR-944, radiomics, and tPSA features to construct a joint model. The area under the ROC curve of the combined model was 0.935. In the internal validation, the area under the curve (AUC) of the training set was 0.943, and the AUC of the test set was 0.946. The AUC of the external data set was 0.910. The calibration curve and decision curve were consistent with the performance of the combined model. There was a significant difference in the prediction ability between the combined prediction model and the single index prediction model, indicating the high credibility and accuracy of the combined model in predicting PCa.ConclusionsThe combined prediction model, consisting of plasma exosomal miRNAs (hsa-miR-320c and hsa-miR-944), US radiomics, and clinical tPSA, can be utilized for the early diagnosis of prostate cancer.