The incidence of endometrial cancer (EC) is intensively increasing. However, due to the complexity and heterogeneity of EC, the molecular targeted therapy is still limited. The reliable and accurate biomarkers for tumor progression are urgently demanded. After normalizing the data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), we utilized limma and WGCNA packages to identify differentially expressed genes (DEGs). The copy number variations of candidate genes were investigated by cBioPortal. Enrichment pathways analysis was performed by ClueGO and CluePedia. The methylation status was explored by UALCAN. ROC curve and survival analysis were conducted by SPSS and Kaplan-Meier. Infiltration immune cells in microenvironment were analyzed by TISIDB. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were applied to explore potential biological pathways. Immunohistochemistry staining (IHC), cell proliferation, cell apoptosis, colony formation, migration, invasion and scratch-wound assays were performed to investigate the function of key genes . In this study, four expression profile datasets were integrated to identify candidate genes. Combined with WGCNA analysis, the top ten candidates were screened out, whose abnormal methylation patterns were extremely correlated with their expression level and they were associated with tumor grades and predicted poor survival. GSEA and GSVA demonstrated they were involved in DNA replication and cell cycle transition in EC. Gene silencing of TICRR and PPIF dramatically inhibited cell growth, migration and epithelial-mesenchymal transition (EMT) and enhanced progesterone sensitivity. Additionally, from DrugBank database, cyclosporine may be effective for PPIF targeted therapy. By integrative bioinformatics analysis and experiments, our study shed novel light on the molecular mechanisms of EC. TICRR and PPIF may promise to be potential therapeutic targets for endometrial cancer.