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Construction of a specific SVM classifier and identification of molecular markers for lung adenocarcinoma based on lncRNA-miRNA-mRNA network

Authors Zhao J, Cheng W, He X, Liu Y, Li J, Sun J, Li J, Wang F, Gao Y

Received 7 September 2017

Accepted for publication 24 March 2018

Published 25 May 2018 Volume 2018:11 Pages 3129—3140


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 3

Editor who approved publication: Dr Ingrid Espinoza

Jingming Zhao,1 Wei Cheng,1 Xigang He,2 Yanli Liu,1 Ji Li,3 Jiaxing Sun,1 Jinfeng Li,1 Fangfang Wang,1 Yufang Gao4

1Department of Respiratory Medicine, The Affiliated Hospital of Qingdao University, Qingdao, P.R. China; 2Department of Respiratory Medicine, People’s Hospital of Rizhao Lanshan, Lanshan District, Rizhao, P.R. China; 3Department of Pharmacy, Qilu Hospital of Shandong University (Qingdao), Qingdao, P.R. China; 4Department of President’s Office, The Affiliated Hospital of Qingdao University, Qingdao, P.R. China

Background: Novel diagnostic predictors and drug targets are needed for LUAD (lung adenocarcinoma). We aimed to build a specific SVM (support vector machine) classifier for diagnosis of LUAD and identify molecular markers with prognostic value for LUAD.
Methods: The expression differences of miRNAs, lncRNAs and mRNAs between LUAD and normal samples were compared using data from TCGA (The Cancer Genome Atlas) database. A LUAD related miRNA-lncRNA-mRNA network was constructed, based on which feature genes were selected for the construction of LUAD specific SVM classifier. The robustness and transferability of SVM classifier were validated using gene expression profile datasets GSE43458 and GSE10072. Prognostic markers were identified from the network. A set of LUAD-related differentially expressed miRNAs, lncRNAs and miRNAs were identified and a LUAD related miRNA-lncRNA-mRNA network was obtained. The LUAD specific SVM classifier constructed on the basis of the network was robust and efficient for classification of samples from TCGA dataset and two independent validation datasets.
Results: Eight RNAs with prognostic value were identified, including hsa-miR-96, hsa-miR-204, PGM5P2 (phosphoglucomutase 5 pseudogene 2), SFTA1P (surfactant associated 1), RGS20 (regulator of G protein signaling 20), RGS9BP (RGS9-binding protein), FGB (fibrinogen beta chain) and INA (alpha-internexin). Among them, RGS20 and INA were regulated by hsa-miR-96. RGS20 was also regulated by hsa-miR-204, which was a potential target of SFTA1P.
Conclusion: The LUAD specific SVM classifier may serve as a novel diagnostic predictor. hsa-miR-96, hsa-miR-204, PGM5P2, SFTA1P, RGS20, RGS9BP, FGB and INA may serve as prognostic markers in clinical practice.

Keywords: lung adenocarcinoma, lncRNA-miRNA-mRNA network, SVM classifier, molecular marker, prognosis

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