Lung adenocarcinoma (LUAD), a malignant respiratory tumor with an extremely poor prognosis, has troubled the medical community all over the world. According to recent studies, fatty acid metabolism (FAM) and long non-coding RNAs (lncRNAs) regulation have shown exciting results in tumor therapy. In this study, the original LUAD patient data was obtained from the TCGA database, and 12 FAM-related lncRNAs (AL390755.1, AC105020.6, TMPO-AS1, AC016737.2, AC127070.2, LINC01281, AL589986.2, GAS6-DT, AC078993.1, LINC02198, AC007032.1, and AL021026.1) that were highly related to the progression of LUAD were finally identified through bioinformatics analysis, and a risk score model for clinical reference was constructed. The window explores the immunology and molecular mechanism of LUAD, aiming to shed the hoping light on LUAD treatment.

Lung adenocarcinoma (LUAD) is a malignant disease with an extremely poor prognosis, and there is currently a lack of clinical methods for early diagnosis and precise treatment and management. With the deepening of tumor research, more and more attention has been paid to the role of immune checkpoints (ICP) and long non-coding RNAs (lncRNAs) regulation in tumor development. Therefore, this study downloaded LUAD patient data from the TCGA database, and finally screened 14 key ICP-related lncRNAs based on ICP-related genes using univariate/multivariate COX regression analysis and LASSO regression analysis to construct a risk prediction model and corresponding nomogram. After multi-dimensional testing of the model, the model showed good prognostic prediction ability. In addition, to further elucidate how ICP plays a role in LUAD, we jointly analyzed the immune microenvironmental changes in LAUD patients and performed a functional enrichment analysis. Furthermore, to enhance the clinical significance of this study, we performed a sensitivity analysis of common antitumor drugs. All the above works aim to point to new directions for the treatment of LUAD.