Background: Lung adenocarcinoma (LUAD), a type of lung cancer, is one of the most aggressive and deadly malignancies worldwide. Malignant tumor cells exhibit strong anti-anoikis properties to achieve distant metastasis through the circulatory system. However, more research is needed to understand how anoikis is involved in the progression, metastasis and especially the prognosis of LUAD.
Methods: We obtained anoikis-related genes (ARGs) from two websites, Harmonizome and Genecards, and integrated them to select and model the genes associated with LUAD prognosis. In addition, we investigated differences in the immune cell microenvironment and pathways of enrichment analysis between subtypes. We finally constructed a nomogram based on ARGs and used decision curve analysis (DCA) to demonstrate that this model could help clinicians make clinical decisions.
Results: Sixty-four differentially expressed genes (DEGs) were found to be associated with survival, and of these, six were chosen to build a prognostic model. The time-dependent receiver operating characteristic (ROC) curves showed that the model had a satisfactory predictive ability. Enrichment analysis and immune microenvironment analysis revealed that the immune status and drug sensitivity of populations at high and low risk were different. We integrated the clinicopathological features of LUAD with the risk score to build the nomogram. The nomogram was shown to be a good predictor of short- and long-term survival in LUAD patients through DCA analysis.
Conclusions: This new model based on six ARGs and nomograms in our study could help patients with LUAD develop personalized treatment plans.
Keywords: Anoikis; bioinformatics; lung adenocarcinoma (LUAD); model; prognosis.
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