Identification of the role of endoplasmic reticulum stress genes in endometrial cancer and their association with tumor immunity

Abstract

Background Endometrial cancer (EC) is one of the worldwide gynecological malignancies. Endoplasmic reticulum (ER) stress is the cellular homeostasis disturbance that participates in cancer progression. However, the mechanisms of ER Stress on EC have not been fully elucidated. Method The ER Stress-related genes were obtained from Gene Set Enrichment Analysis (GSEA) and GeneCards, and the RNA-seq and clinical data were downloaded from The Cancer Genome Atlas (TCGA). The risk signature was constructed by the Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis. The significance of the risk signature and clinical factors were tested by time-dependent receiver operating characteristic (ROC) curves, and the selected were to build a nomogram. The immunity correlation was particularly analyzed, including the related immune cells, pathways, and immune checkpoints. Functional enrichment, potential chemotherapies, and in vitro validation were also conducted. Result An ER Stress-based risk signature, consisting of TRIB3, CREB3L3, XBP1, and PPP1R15A was established. Patients were randomly divided into training and testing groups with 1:1 ratio for subsequent calculation and validation. Based on risk scores, high- and low-risk subgroups were classified, and low-risk subgroup demonstrated better prognosis. The Area Under Curve (AUC) demonstrated a reliable predictive capability of the risk signature. The majority of significantly different immune cells and pathways were enriched more in low-risk subgroup. Similarly, several typical immune checkpoints, expressed higher in low-risk subgroup. Patients of the two subgroups responded differently to chemotherapies. Conclusion We established an ER Stress-based risk signature that could effectively predict EC patients’ prognosis and their immune correlation.

Construction and verification of an endoplasmic reticulum stress-related prognostic model for endometrial cancer based on WGCNA and machine learning algorithms

“…Zhou and Zhang ( 39 , 40 ) also analyzed TCGA-UCEC data and created a prognostic model. While both our study and theirs rely on bioinformatic data analysis, our research appears to be more comprehensive and in-depth.…”

Independent organelle and organelle—organelle interactions: essential mechanisms for malignant gynecological cancer cell survival

“…Expression of mitochondria-related molecules and OXPHOS affect endometrial cancer cell migration, invasion, disease severity, and prognosis ( 72 – 75 ); regulation of mitochondria is thus a primary way to induce apoptosis in endometrial cancer treatment ( 76 – 80 ). Expressions of many lysosomal and ER biogenic genes and factors affect endometrial cancer prognosis, effectively predict immune associations, and can be used as biomarkers ( 81 , 82 , 84 , 85 , 87 ). Thus, the diversity of organelle genes and factors diversify potential endometrial cancer treatment approaches.…”

“…Impaired glucose metabolism accelerates dyslipidemia and promotes endometrial cancer progression due to ERS and hyperinsulinemia and saturated fatty acid-induced dyshomeostasis of Ca 2+ and ERS ( 83 ). Risk profiles including TRIB3, CREB3L3, XBP1, and PPP1R15A are effective for predicting prognosis and immune relevance in patients with endometrial cancer ( 84 ).…”