CAV1 是特发性肺纤维化合并肺癌患者的预后预测因子

The extremely high mortality of both lung cancer and Idiopathic pulmonary fibrosis (IPF) is a global threat. Early detection and diagnosis can reduce their mortality. Since fibrosis is a necessary process of cancer, identifying the common potential prognostic genes involved in these two diseases will significantly contribute to disease prevention and targeted therapy. Microarray datasets of IPF and lung cancer were extracted from the GEO database.

GEO2R was exploited to retrieve the differentially expressed genes (DEGs). The intersecting DEGs were obtained by the Venn tool. DAVID tools were used to perform GO and KEGG pathway enrichment analysis of DEGs. Then, the Kaplan-Meier plotter was employed to determine the prognostic value and verify the expression, pathological stage, and phosphorylation level of the hub gene in the TCGA and GTEx database.

Finally, the extent of immune cell infiltration in lung cancer was estimated by the TIMER2 tool. The Venn diagram revealed 1 upregulated gene and 15 downregulated genes from GSE32863, GSE43458, GSE118370, and GSE75037 of lung cancer, as well as GSE2052 and GSE53845 of IPF. CytoHubba identified the top three genes [TEK receptor tyrosine kinase (TEK), caveolin 1 (CAV1), and endomucin (EMCN)] as hub genes following the connectivity degree.

Survival analysis claimed the association of only TEK and CAV1 expression to both overall survival (OS) and first progression (FP). Pathological stage analyses revealed the relationship of only CAV1 expression to the pathological stage and the significant correlation of only CAV1 phosphorylation expression level for lung cancer.

Furthermore, a statistically positive correlation was observed between the immune infiltration of cancer-associated fibroblasts, endothelial, and neutrophils with the CAV1 expression in lung cancer, whereas the contradictory result was noted for the immune infiltration of T cell follicular helper. Early detection and diagnostic potential of lung cancer are ameliorated by the combined selection of key genes among IPF and lung cancer.