Research Paper Volume 12, Issue 4 pp 3371—3387
Identification of prognostic immune-related genes in the tumor microenvironment of endometrial cancer
- 1 Department of Gynecology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, China
received: October 25, 2019 ; accepted: January 27, 2020 ; published: February 19, 2020 ;https://doi.org/10.18632/aging.102817
How to Cite
Copyright © 2020 Chen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Endometrial cancer (EC) is one of the most common gynecologic malignancies. To identify potential prognostic biomarkers for EC, we analyzed the relationship between the EC tumor microenvironment and gene expression profiles. Using the ESTIMATE R tool, we found that immune and stromal scores correlated with clinical data and the prognosis of EC patients. Based on the immune and stromal scores, 387 intersection differentially expressed genes were identified. Eight immune-related genes were then identified using two machine learning algorithms. Functional enrichment analysis revealed that these genes were mainly associated with T cell activation and response. Kaplan-Meier survival analysis showed that expression of TMEM150B, CACNA2D2, TRPM5, NOL4, CTSW, and SIGLEC1 significantly correlated with overall survival times of EC patients. In addition, using the TIMER algorithm, we found that expression of TMEM150B, SIGLEC1, and CTSW correlated positively with the tumor infiltration levels of B cells, CD8+ T cells, CD4+ T cells, macrophages, and dendritic cells. These findings indicate that the composition of the tumor microenvironment affects the clinical outcomes of EC patients, and suggests that it may provide a basis for development of novel prognostic biomarkers and immunotherapies for EC patients.
EC: endometrial carcinoma; TME: tumor microenvironment; ECM: extracellular matrix; ESTIMATE: Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data; TIMER: Tumor Immune Estimation Resource; PFI: progression-free interval; OS: overall survival; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; KM: Kaplan-Meier; DEGs: differentially expressed genes; TCGA: The Cancer Genome Atlas; FPKM: Fragments Per Kilobase of transcript per Million mapped reads; TCGA-CDR: TCGA Pan-cancer Clinical Data Resource; MCODE: Molecular COmplex Detection; LASSO: Least Absolute Shrinkage and Selector Operation; ROC: time-dependent receiver operating characteristic.