β-catenin regulates its target genes which are associated with proliferation, differentiation, migration and angiogenesis, and the dysregulation of Wnt/β-catenin signaling facilitates hallmarks of colorectal cancer (CRC). Identification of β-catenin targets and their potential roles in tumorigenesis has gained increased interest. However, the number of identified targets remains limited. The present study implemented a novel strategy, interrogating gene fitness profiles derived from large-scale RNA interference and CRISPR-CRISPR associated protein 9 screening data to identify β-catenin target genes in CRC cell lines. Using these data sets, pair wise gene fitness similarities were determined which highlighted a total of 13 genes whose functions were highly correlated with β-catenin. It was further demonstrated that the expression of these genes were altered in CRC, illustrating their potential roles in the progression of CRC. The present study further demonstrated that these targets could be used to predict disease-free survival in CRC. In conclusion, the findings provided novel approaches for the identification of β-catenin targets, which may become prognostic biomarkers or drug targets for the management of CRC.

The aim of the present study was to identify the potential treatment targets of peripheral arterial disease (PAD) and provide further insights into the underlying mechanism of PAD, based on a weighted gene co‑expression network analysis (WGCNA) method. The mRNA expression profiles (accession. no. GSE27034), which included 19 samples from patients with PAD and 18 samples from normal control individuals were extracted from the Gene Expression Omnibus database. Subsequently, the differentially expressed genes (DEGs) were obtained using the Limma package and the co‑expression network modules were screened using the WGCNA approach. In addition, the protein‑protein interaction network for the DEGs in the most significant module was constructed using Cytoscape software. Functional enrichment analyses of the DEGs in the most significant module were also performed using the Database for Annotation, Visualization and Integrated Discovery and Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology‑Based Annotation System, respectively. A total of 148 DEGs were identified in PAD, which were used to construct the WGCN, in which two modules (gray module and turquoise module) were identified, with the gray module exhibiting a higher gene significance (GS) value than the turquoise module. In addition, a co‑expression network was constructed for 60 DEGs in the gray module. The functional enrichment results showed that the DEGs in the gray module were enriched in five Gene Ontology terms and four KEGG pathways. For example, cyclin‑dependent kinase inhibitor 1A (CDKN1A), FBJ murine osteosarcoma viral oncogene homolog (FOS) and prostaglandin‑endoperoxide synthase 2 (PTGS2) were enriched in response to glucocorticoid stimulus. The results of the present study suggested that DEGs in the gray module, including CDKN1A, FOS and PTGS2, may be associated with the pathogenesis of PAD, by modulating the cell cycle, and may offer potential for use as candidate treatment targets for PAD.

This study aimed to investigate the role and potential mechanism of miR-22 in clear cell ovarian cancer (CCOC) progression. The gene expression profile of GSE16568, including 3 CCOC samples with miR-22 overexpression and 3 negative controls, was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened using the limma package in R. Gene Ontology (GO) and pathway enrichment analysis of DEGs were performed by using The Database for Annotation, Visualization and Integrated Discovery (DAVID). Furthermore, protein-protein interaction (PPI) network of the DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database. Besides, the miR-22 -mRNA interaction pairs were predicted to explore the critical genes involved in the cancer. Totally, 95 up-regulated DEGs and 51 down-regulated DEGs were identified. The DEGs were enriched in different GO terms and pathways. The up-regulated genes cyclin-dependent kinases (CDK6), MDM2 oncogene, E3 ubiquitin protein ligase (MDM2), and thrombospondin 1 (THBS1) were involved in the p53 signaling pathway. The up-regulated gene FBJ murine osteosarcoma viral oncogene homolog (FOS) was a hub protein in the PPI network of the DEGs. The down-regulated DEGs including lymphoid enhancer-binding factor 1 (LEF1) and v-myb avian myeloblastosis viral oncogene homolog (MYB) were mainly associated with immunity. Nine DEGs as target genes were identified to be recognized by miR-22. Our study suggested that several key genes such as CDK6, MDM2, LEF1, MYB, and FOS that involved in different pathways including p53 signaling pathway were associated with CCOC progression. miR-22 may play an essential role in cell migration and invasion in CCOC through targeting responsive genes.