Wnt signaling pathway-derived score for predicting therapeutic resistance and tumor microenvironment in lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Due to tumor heterogeneity, understanding the pathological mechanism of tumor progression helps to improve the diagnosis process and clinical treatment strategies of LUAD patients. Methods: The transcriptome pattern, mutant expression and complete clinical information were obtained from the cancer genome atlas (TCGA) database and microarray data from gene expression omnibus (GEO) database. Firstly, we used single sample Gene Set Enrichment Analysis (ssGSEA) to estimate the activation of Wnt signaling pathway in each sample. Consensus clustering algorithm was used to classify LUAD samples into different subgroups according to the transcription patterns of 152 Wnt signaling pathway related genes. Then, ESTIMATE, ssGSEA and Gene Set Variation Analysis (GSVA) algorithms were used to assess the biological pathways and immunocytes infiltration between different subtypes. LASSO-COX algorithm was conducted to construct prognostic model. Kaplan-Meier and multivariate Cox analysis were performed to evaluate the predictive performance of risk model. Gene features were further confirmed using external datasets. Finally, we conducted vitro assay for validating hub gene (LEF1). Results: Based on the transcription patterns of 152 Wnt signaling pathway related genes, four different subtypes of LUAD patients were screened out by consensus clustering algorithm. Subsequently, it was found that patients with cluster A and B had massive immunocytes infiltration, and the survival rate of patients with cluster B was better than that of other subgroups. According to the coefficients in the LASSO- Cox model and the transcriptome patterns of these 18 genes, the risk score was constructed for each sample. The degree of malignancy of LUAD patients with high-risk subgroup was remarkable higher than that of patients with low-risk subgroup (p < 0.001). Subsequently, five top prognostic genes (AXIN1, CTNNB1, LEF1, FZD2, FZD4.) were screened, and their expression values were different between cancer and normal tissues. FZD2 and LEF1 were negatively related to ImmunoScore, and AXIN1 was negatively related to ImmunoScore. The significant correlation between LUAD patient risk score and overall survival (OS) was verified in external datasets. In the A549 cell line, knockdown of LEF1 can reduce the invasive and proliferation ability of LUAD cells. Conclusion: A innovative 18 genes predictive feature based on transcriptome pattern was found in patients with lung adenocarcinoma. These investigations further promote the insight of the prognosis of lung adenocarcinoma and may contribute to disease management at risk stratification.