A novel anoikis-related gene signature to predict the prognosis, immune infiltration, and therapeutic outcome of lung adenocarcinoma.

Background: Lung cancer is a highly aggressive disease and the leading cause of cancer-related deaths. Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer. As a type of programmed cell death, anoikis serves a key role in tumor metastasis. However, as few studies have focused on anoikis and prognostic indicators in LUAD, in this study, we constructed an anoikis-related risk model to explore how anoikis could influence the tumor microenvironment (TME), clinical treatment, and prognosis in LUAD patients; we aimed to provide new insight for future research. Methods: Using patient data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), we utilized the 'limma' package to select differentially expressed genes (DEGs) associated with anoikis and then they were divided into 2 clusters with consensus clustering. Risk models were constructed with least absolute shrinkage and selection operator (LASSO) Cox regression (LCR). Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curves were performed to assess the independent risk factors for different clinical characteristics, including age, sex, disease stage, grade, and their associated risk scores. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed to explore the biological pathways in our model. The effectiveness of clinical treatment was detected according to tumor immune dysfunction and exclusion (TIDE), The Cancer Immunome Atlas (TCIA), and IMvigor210. Results: Our model was found to divide LUAD patients into high- and low-risk groups well, in which high risk groups had poor overall survival (OS), indicating that risk score could be an independent risk factor to predict the prognosis of LUAD patients. Interestingly, we found that anoikis could not only influence the extracellular organization but also play great roles in immune infiltration and immunotherapy, which might provide a new insight for future research. Conclusions: The risk model constructed in this study can benefit to predict patient survival. Our results provided new potential treatment strategies.