Construction and validation of a prognostic model of lncRNAs associated with RNA methylation in lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is a common type of lung cancer and one of the leading causes of cancer death worldwide. Long non-coding RNAs (lncRNAs) play a crucial role in tumors. The purpose of this study was to explore the expression of lncRNAs associated with RNA methylation modification and their prognostic value in LUAD. Methods: The RNA sequencing and clinical data were downloaded from The Cancer Genome Atlas dataset, and the messenger RNA and lncRNAs were annotated by Ensemble. The lncRNAs related to RNA methylation regulators (RMlncRNAs) were filtered by Pearson correlation analysis between differentially expressed lncRNAs and RNA methylation regulators. Univariate Cox regression analysis, multivariate Cox regression analysis, and least absolute shrinkage and selection operator regression analysis were used to construct a prognostic model. The receiver operating characteristic curve (ROC) was plotted to validate the predictive value of the prognostic model. Then, tumor mutational burden (TMB) and microsatellite instability were used to compare the immunotherapy response. Finally, to perform a drug sensitivity analysis, the half-maximal inhibitory concentration (IC50) of targeted drugs was calculated using pRRophetic package. Results: In total, 18 RMlncRNAs associated with the prognosis of LUAD patients were identified. Then, six feature lncRNAs (NFYC-AS1, OGFRP1, MIR4435-2HG, TDRKH-AS1, DANCR, and TMPO-AS1) were used to construct a prognostic model. The ROC curves for training, testing, and validation sets showed that the prognosis model was effective. The subindex based on the prognostic model had a high correlation with TMB. The high-risk group might be subject to greater immune resistance according to the comparison of Tumor Immune Dysfunction and Exclusion scores. Finally, the IC50 of 11 drugs had differences between high- and low-risk group, and only three of the drug's target genes (ERBB4, CASP8, and CD86) were differentially expressed. Conclusions: In conclusion, a prognostic model based on six feature lncRNAs (NFYC-AS1, OGFRP1, MIR4435-2HG, TDRKH-AS1, DANCR, and TMPO-AS1) was constructed by bioinformatics analysis, which might provide a new insight into the evaluation and treatment of LUAD.