A novel anoikis‐related prognostic signature associated with prognosis and immune infiltration landscape in lung adenocarcinoma

Abstract

Abstract Background One of the most prevalent malignancies in the world is lung adenocarcinoma (LUAD), with a large number of people dying from lung cancer each year. Anoikis has a crucial function in tumor metastasis, promoting cancer cell shedding and survival from the primary tumor site. However, the role of anoikis in LUAD is still unclear. Methods The GeneCard database ( https://www.genecards.org/ ) was utilized to obtain anoikis‐related genes with correlation greater than 0.4. Differential analysis was employed to acquire differential genes. Univariate, multifactorial Cox analyses and the least absolute shrinkage and selection operator were then utilized to capture genes connected to overall survival time. These genes were used to build prognostic models. The predictive model was analyzed and visualized. Survival analysis was conducted on the model and risk scores were calculated. The TCGA samples were split into groups of low and high risk depending on risk scores. A Gene Expression Omnibus database sample was used for external verification. Immunization estimates were performed using ESTIMATE, CiberSort and single sample gene set enrichment analysis. The connection between the prognostic gene model and immune cells was analyzed. Drug susceptibility prediction analysis was performed. The clinical information for samples was extracted and analyzed. Results We selected six genes related to anoikis in LUAD to construct a prognosis model (CDC25C, ITPRIP, SLCO1B3, CDX2, CSPG4 and PIK3CG). Compared with cases of high‐risk scores, the overall survival of those with low risk was significantly elevated based on Kaplan–Meier survival analysis. Immune function analysis exhibited that different risk groups had different immune states. The results of ESTIMATE, CiberSort and single sample gene set enrichment analysis showed great gaps in immunization between patients in the two groups. The normogram of the risk score and the LUAD clinicopathological features was constructed. Principal component analysis showed that this model could effectively distinguish the two groups of LUAD patients. Conclusions We integrated multiple anoikis‐related genes to build a prognostic model. This investigation demonstrates that anoikis‐related genes can be used as a stratification element for fine therapy of individuals with LUAD.