Molecular Subgroup Establishment and Signature Creation of lncRNAs Associated with Acetylation in Lung Adenocarcinoma

Background: The long non-coding RNAs (lncRNAs) have progressively been acknowledged as crucial intercessors of histone acetylation and play an authoritative role in lung adenocarcinoma (LUAD) prognosis. Despite this, it is still unclear whether acetylation-related lncRNAs (ARLs) could be utilized to forecast the overall survival (OS) of LUAD specimens. Methods The RNA-Seq and clinical information of 501 LUAD specimens and 56 corresponding paracancerous tissue specimens were downloaded from The Cancer Genome Atlas (TCGA). Through the differential analysis, Weighted correlation network analysis (WGCNA), Pearson correlation test and univariate Cox regression, we found out the prognosis associated ARLs and divided LUAD specimens into two molecular subclasses. Utilizing those ARLs, a novel signature was built by adopting Least absolute shrinkage and selection operator (LASSO) algorithm. ROC analysis and Kaplan–Meier survival curve were subsequently exercised to examine the predictive performance. Then based on the ARLs model, we exploited bioinformatics techniques to analyze the signaling pathways and biological roles, immunity, drug efficacy, and tumor somatic mutation. Next, univariate and multivariate Cox analysis were utilized to determine if the signature was an isolated predictor. Finally, ARL expression in LUAD was confirmed by quantitative real-time PCR (qRT-PCR). Results We triumphantly built a ARLs prognostic model with excellent predictive capability for LUAD, including ADAMTS9-AS2, AF131215.6, CYP4F26P, LINC00622, LINC00639, LINC00968, MIR22HG, MIR99AHG and WWC2-AS2. Univariate and multivariate Cox analysis illustrated that risk model was an isolated predictor for affecting the OS of LUAD. A nomogram showed robust prognostic validity. There were also diversities between subgroups in the field of immunity, biological functions, drug sensitivity and gene mutations. Conclusions Nine ARLs were identified as promising indicators of personalized prognosis and drug selection for people suffering with LUAD.