Abstract
BACKGROUND: Lung cancer is the most widespread and fatal oncological disease, with lung adenocarcinoma (LUAD) being the predominant subtype, characterized by a poor long-term survival rate. Although the N7-methylguanosone (m7G) genes have been reported to be correlated with the prognosis of lung cancer, m7G gene-associated Long non-coding RNA (lncRNAs) have been poorly studied in LUAD. This study aimed to explore the prognostic value of an m7G-related lncRNAs model in LUAD. METHODS: Transcriptome and clinical data of LUAD patients were downloaded from the TCGA database. Pearson correlation analysis and Cox regression analysis were utilized to identify lncRNAs associated with m7G-related genes and prognosis. Then m7G-related prognostic model was constructed by LASSO regression analysis, with its accuracy and independence were validated by AUC curve, survival analysis and COX regression, respectively. Subsequently, a nomogram, KEGG analysis, GO analysis and immune infiltration analysis were applied successively to reveal potential prognosis of LUAD patients based on prognostic model. Finally, expression level of hub lncRNAs in the mode was detected by RT-qPCR, and the correlation analysis revealed the relationship between hub lncRNAs and clinical information of LUAD patients. RESULTS: The m7G-related lncRNA prognosis model for LUAD, consisting of 6 lncRNAs, stratified patients into high- and low- risk groups. High-risk patients were associated with poor prognosis, and the model's accuracy was confirmed by ROC curves, with the AUC of all-year approaching 0.737. Expression of AC007128.1 and HNRNPUP1 were higher in tumor tissues compared to normal tissue, while the other lncRNAs showed the opposite state, supporting the model's signature. Immune infiltration analysis indicated that patients in the low-risk group were closely related to better immune cell infiltration and more highly expressed immune checkpoint genes. CONCLUSIONS: The prognosis model of six m7G-related lncRNAs can accurately predict the OS of LUAD patients and provide valuable insights for treatment strategies.