Screening the differentially expressed key genes in renal clear cell carcinoma by bioinformatics method

Objective To analyze the relationship of anilin actin binding protein (ANLN) and its related molecules with clear cell renal cell carcinoma (ccRCC) based on bioinformatics method.

Methods The expression profile data of ccRCC tissue samples and normal kidney tissue samples were obtained from GSE116251 and GSE168845 datasets of Gene Expression Omnibus (GEO) database. The differentially expressed microRNAs (DEmiRNAs) and differentially expressed genes (DEGs) were screened by R software, and functional analysis and signal pathway enrichment analysis were performed. The correlations of the expression levels of DEmiRNAs and their target genes with overall survival of patients were analyzed, and the target genes with the lowest P-value and their interacting miRNA were selected and analyzed with the clinical characteristics of ccRCC patients from the Tumor Genome Atlas (TCGA) database. Based on the Human Protein Map (HPA) database, the protein expression of the selected genes in ccRCC tissues and normal kidney tissues was verified by immunohistochemistry (IHC).

Results Three differentially expressed DEmiRNAs and 1 610 differentially expressed DEGs were selected by using R software. Survival analysis database results showed that ANLN gene and its interacting microRNA-200c-3p(miR-200c-3p)were related with the overall survival of patients with ccRCC. Enrichment analysis showed that DEmiRNA had unique functions in cell cyclic adenylate (cAMP) receptor mediated signaling, lysosome, phosphodiester hydrolase activity and cytoskeletal protein binding. Furthermore, DEmiRNAs were mainly enriched in 6 pathways including osteoclast differentiation, Staphylococcus aureus infection, cell adhesion molecules, complement and coagulation cascades, phagosome, and primary immunodeficiency. TCGA database analysis showed that there were statistically significant differences in the expression levels of ANLN and mir-200C-3p in patients with different TNM stages (P < 0.05). IHC outcomes showed that ANLN was up-regulated in ccRCC tissues compared with normal kidney tissue.

Conclusion MicroRNA-200c-3p and its targets gene ANLN may take part in the development of ccRCC and can be potential biomarkers for patients with ccRCC.