Abstract LB-209: Novel pathway prediction for colorectal cancer to liver metastasis through bioinformatics

Abstract

Abstract Background: Liver metastases are the most common cause of colorectal cancer (CRC) related deaths, with no reliable clinical predictors. Therefore, identifying potential candidate predictors is crucial for clinical application and management. Methodology: The Gene Expression Omnibus (GEO) datasets GSE49355, GSE41258 and GSE81558 for genes and GSE54088 and GSE56350 for miRNAs were used to identify common differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) between paired primary CRC tissues and the corresponding liver metastatic tissues. Then, DEMs targets were identified from DEGs. These were verified in GEO datasets comprising gene, miRNA and miRNA exosome profiles of CRC patients with no distant metastases (M0) and distant metastases (M1); the interaction networks and pathways were also mapped. Results: There were 49 upregulated and 13 downregulated DEGs, and 16 downregulated and 14 upregulated DEMs; between DEGs and DEMs targets, there were five upregulated and four downregulated genes. MiR-20a was significantly high in M1, with the combination of MiR-20a, -495, -576-5p and -449a offering a superior prediction value (sensitivity of 83.3%, specificity of 63.8%, AUC of 0.725). In miRNA exosomes, MiR-576-5p was significantly low in M1, while MiR-20a and -449a were highly expressed, and combination of miR-20a, -495, -576-5p, -449a, -619 and -382 provided best predictive value (sensitivity=75.0%, specificity=88.2%, AUC=0.875). The miRNA target CDH2 was significantly highly expressed in M1 while KNG1 and MMP2 were lowly expressed. The miRNA to gene interaction model was demonstrated, and enriched pathways were; regulation of IGF transport and uptake by IGFBPs, extracellular matrix organization, signal transduction and immune system. Conclusion: This model predicts novel pathway for predicting CRC to liver metastases. This may be clinically applicable. Citation Format: Precious Takondwa Makondi, Chien-Yu Huang, Yu-Jia Chang. Novel pathway prediction for colorectal cancer to liver metastasis through bioinformatics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-209.