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

Abstract

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

Acknowledgments

This work was supported by Chinese Medicine Science and Technology Development Project Fund of Shandong Province (project no 2017-200), Postdoctoral Applications Research Project Fund of Qingdao (project no 2016055) and The Affiliated Hospital of Qingdao University Youth Research Fund (2016).

Disclosure

All authors declared that they have no conflicts of interest in this work.