Screening key prognostic factors and constructing survival prognostic risk prediction model based on ceRNA network in early lung adenocarcinoma

BACKGROUND: We aim to discover some prognostic factors, provide a basis for discovering molecular markers, and provide a basis for molecular features of early lung adenocarcinoma (LUAD) to predict patient prognosis. METHODS: Sequence data of LUAD were downloaded from The Cancer Genome Atlas (TCGA) database to screen out differentially expressed lncRNAs, miRNAs, and mRNAs (DERs). DERs were identified using R software’s limma package. The competitive endogenous RNA (ceRNA) network was constructed based on these RNAs. Univariate and multivariate Cox regression analysis on the RNAs in the ceRNA screened out independent prognostic-related RNAs to construct a prognostic risk score (PS) model. Combined with clinical data, we can calculate the survival rate of patients with early LUAD. RESULTS: There were 2,701 differentially expressed mRNAs (DEmRNAs), 47 differentially expressed lncRNAs (DElncRNAs), and 161 differentially expressed miRNAs (DEmiRNAs) identified in early LUAD. Based on these RNAs, 32 lncRNAs, 87 miRNAs, and 174 mRNAs participated in the ceRNA network. Twelve independently prognostic-related RNAs form an optimized combination. We developed a PS model based on these RNAs. Age, tumor recurrence and PS model status were independent survival prognostic clinical factors. Nomogram was established to predict the 3-year and 5-year survival rates. CONCLUSIONS: We successfully constructed a ceRNA regulatory network based on the DERs in early LUAD. It can help us clarify the molecular mechanism of early LUAD. Simultaneously, the prognostic-related RNAs in early LUAD were also screened out. This network could provide new bases for diagnoses and prognoses of patients with LUAD.