Construction of a specific SVM classifier and identification of molecular markers for lung adenocarcinoma based on IncRNA-miRNAmRNA network

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 ofmiRNAs, IncRNAs and mRNAs between LUAD and normal samples were compared using data from TCGA (The Cancer Genome Atlas) database. ALUAD related miRNA-lncRNA-mRNAnetwork was constructed, based on which feature genes were selected for the construction ofLUAD specific SVM classifier. The robustness and transferability ofSVM classifier were validatedusing gene expression profile datasets GSE43458 and GSE10072. Prognostic markers were identi fled from the network. Aset of LUAD-related differenlially expressed miRNAs. IncRN As and miRNAs were identified and aLUAD 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 (phosphoglucomutasc 5 pseudogcnc 2). SFTA1P (surfactant associated 1). RGS20 (regulator of G protein signaling 20), RGS9BP (RGS9-binding protein), FGB (fibrinogen beta chain) and INA (alplia-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.