Abstract:
Objective To explore the prognostic value and immune infiltration landscape of anoikis-related long noncoding RNAs (arlncRNAs) in lung adenocarcinoma.
Methods RNA-seq and clinical data of lung adenocarcinoma were downloaded from the TCGA database, and anoikis-related genes were obtained from the GeneCards and Harmonizome databases. Coexpression, differential, and WGCNA analyses were performed to screen differentially expressed arlncRNAs closely related to the occurrence of lung adenocarcinoma. A prognostic risk model was then constructed based on the arlncRNAs, and its predictive efficacy was further validated. Finally, consensus clustering was used to identify the molecular subtypes associated with anoikis in lung adenocarcinoma.
Results Seven prognostic arlncRNAs were identified, and the prognostic risk models established based on them had AUC values of ROC curves greater than 0.7. Survival and immune infiltration analyses revealed that low-risk patients had high overall survival and immune infiltration, implying that they experienced good immune treatment effects. Drug sensitivity analysis showed that the high-risk patients were more sensitive to commonly used chemotherapeutic agents than the low-risk patients. According to the expression of model genes, subtypes C1 and C2 were identified through consensus clustering, and C1 showed a good prognosis.
Conclusion The prognostic risk model based on the seven arlncRNAs can effectively predict the prognosis of lung adenocarcinoma patients. The results of immune-related and drug sensitivity analyses provide a reference for the precise individualized treatment of patients with lung adenocarcinoma.
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