Application of Bioinformatics in Predicting the Efficacy of Digestive Tumour Immunotherapy Target TIM-3 and its Inhibitors.

ABSTRACT

Background: Our main objective is to apply bioinformatics in predicting the efficacy of digestive tumour immunotherapy target TIM-3 and its inhibitors. Methods: Our study used the gene expression omnibus (GEO) database to identify datasets associated with digestive tumours and the action of TIM-3. The GSE427729 dataset based on the GPL10192 platform. The dataset consisted of six samples of total RNA derived from TIM-3 control and knockdown RAW 264.7 cells. We used GEO2R tool to identify DEGs before performing Gene Ontology and identifying the kyoto encyclopedia of genes and genomes (KEGG) pathways. Lastly, we determined the PPI networks to identify hub genes. Results: Our study identified 57 differentially expressed genes based on an adjusted p-value of less than 0.05 and a log2 fold change of 2.0. There were 26 down-regulated genes with 31 up-regulated genes while 22, 404 genes were non-significant. The DEGs were enriched in biological pathways such as activating leukocytes, cells, and development of the immune system. Additionally, we identified four significant KEGG pathways that were implicated in digestive tumour immunotherapy and TIM-3; pathways of pancreatic cancer, NF-Kappa B signalling pathway, Toll-like receptor signalling pathway and C-type lectin receptor signalling pathway. The PPI networks identified 10 hub genes that were implicated in digestive tumour immunotherapy target TIM-3 (Myd88, Traf6, Irf7, Cdk4, Ccnd2, Mapkap1, Prr5, Mpp3, Serpinb6b and Pvrl3). Conclusion: Targeting these biological pathways, KEGG pathways, molecular functions and cellular processes can lead to novel therapeutic treatment and management in digestive tumours based on TIM-3 immunotherapy.