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Diagnostic Value of Plasma MicroRNAs for Lung Cancer Using Support Vector Machine Model

Aim: Small single-stranded non-coding RNAs (miRNAs) play an important role in carcinogenesis through degrading target mRNAs. However, the diagnostic value of miRNAs was not explored in lung cancers. In this study, a support-vector-machine (SVM) model for diagnosis of lung cancer was established base...

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Detalles Bibliográficos
Autores principales: Wang, Wei, Ding, Mingcui, Duan, Xiaoran, Feng, Xiaolei, Wang, Pengpeng, Jiang, Qingfeng, Cheng, Zhe, Zhang, Wenjuan, Yu, Songcheng, Yao, Wu, Cui, Liuxin, Wu, Yongjun, Feng, Feifei, Yang, Yongli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6775617/
https://www.ncbi.nlm.nih.gov/pubmed/31602261
http://dx.doi.org/10.7150/jca.30528
Descripción
Sumario:Aim: Small single-stranded non-coding RNAs (miRNAs) play an important role in carcinogenesis through degrading target mRNAs. However, the diagnostic value of miRNAs was not explored in lung cancers. In this study, a support-vector-machine (SVM) model for diagnosis of lung cancer was established based on plasma miRNAs biomarkers, clinical symptoms and epidemiology material. Methods: The expressions of plasma miRNA were examined with SYBR Green-based quantitative real-time PCR. Results: We identified that the expressions of 10 plasma miRNAs (miR-21, miR-20a, miR-210, miR-145, miR-126, miR-223, miR-197, miR-30a, miR-30d, miR-25), smoking status, fever, cough, chest pain or tightness, bloody phlegm, haemoptysis, were significantly different between lung cancer and control groups (P<0.05). The accuracies of the combined SVM, miRNAs SVM, symptom SVM, combined Fisher, miRNAs Fisher and symptom Fisher were 96.34%, 80.49%, 84.15%, 84.15%, 75.61%, and 80.49%, respectively; AUC of these six model were 0.976, 0.841, 0.838, 0.865, 0.750, and 0.801, respectively. The accuracy and AUC of combined SVM were higher than the other 5 models (P<0.05). Conclusions: Our findings indicate that SVM model based on plasma miRNAs biomarkers may serve as a novel, accurate, noninvasive method for auxiliary diagnosis of lung cancer.