Integrative Bioinformatics Approaches to Screen Potential Prognostic Immune-Related Genes and Drugs in the Cervical Cancer Microenvironment - Zhao, Zitong; Li, Jigang; Li, He; Yuan Wu, Na-Yi; Ou-Yang, Peilin; Liu, Shan; Cai, Jingting; Wang, Jing (Frontiers in Genetics, 2020)
Cervical cancer is the leading cause of cancer-related deaths among women in developing countries. To better understand the correlation between tumor microenvironment (TME) and prognosis of cervical cancer, we screened 1367 differentially expressed genes (DEGs) in cervical cancer samples using both The Cancer Genome Atlas (TCGA) database and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm-derived immune scores. Then, we extracted 401 tumor immune microenvironment (TIME)-related DEGs that related to patients’ survival outcomes. Protein-protein interaction (PPI) network and functional enrichment analysis showed that the prognostic genes mainly participated in myeloid leukocyte activation, adaptive immune response regulation, and receptor signaling pathways. 79 key prognostic DEGs were obtained through PPI network. A TF-lncRNA-miRNA-mRNA regulatory network was contructed to explore the potential regulatory mechanism. 4 genes (CCR7, PD-1, ZAP70, and CD28) were validated in another independent cervical cancer cohort from the Gene Expression Omnibus (GEO) database. Finally, potential drugs for key prognostics DEGs were predicted using DrugBank. In conclusion, we obtained a list of potential prognostic TIME-related genes and potential predicted drugs by integrative bioinformatics approaches. A comprehensive understanding of prognostic genes within the TIME may provide new strategies for the treatment of cervical cancer.