Prediction of Prognosis in Patients With Endometrial Carcinoma and Immune Microenvironment Estimation Based on Ferroptosis-Related Genes

Abstract

Background: Ferroptosis, a form of non-apoptotic cell death, has aroused worldwide interest in cancer researchers. However, the current study about the correlation between ferroptosis-related genes (FRGs) and endometrial cancer (EC) remains limited.Methods: First, the transcriptome profiling and clinical data of EC patients were downloaded from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) program as the training group and testing group, respectively. FRGs were acquired through literature mining. Then, we used R 4.1.1 software to screen the differently expressed FRGs from TCGA, which was also connected with the prognosis of EC patients. Subsequently, the risk score of each tumor sample was identified by LASSO regression analysis, and we classified these samples into the high- and low-risk groups in the light of the median risk score. Receiver operating characteristic (ROC) curve analysis and Kaplan-Meier analysis were performed to assess the accuracy of this signature. Significantly, the data from CPTAC was used to validate the prediction model externally. Furthermore, we evaluated the immune microenvironment in this model via single-sample gene set enrichment analysis (ssGSEA).Results: Among the 150 FRGs, 6 differentially expressed genes (DEGs) based on TCGA had a relationship with the prognosis of EC patients, namely, TP53, AIFM2, ATG7, TLR4, PANX1 and MDM2. The survival curve indicated a higher survival probability in the low-risk group. Moreover, the FRGs-based signature acted well in the prediction of overall survival (OS). The results of external verification confirmed the prediction model we established. Finally, ssGSEA revealed significant differences in the abundance of 16 immune cells infiltration and the activity of 13 immune functions between different risk groups.Conclusion: We identified a novel ferroptosis-related gene signature which could concisely predict the prognosis and immunotherapy in EC patients.

Analysis and verification of ferroptosis-related genes in pediatric hepatoblastoma

“…Hsiao et al reported that TRL4 is a target for suppression of proliferation and chemoresistance in HepG2 HB cells 18 . Consistence with our results, some researches revealed that TRL4 is a ferroptosis-signi cant adverse-selected gene promoting tumor poor prognosis in multiple models, like breast cancer 19 , gastric cancer 20 , endometrial carcinoma 21 , lung adenocarcinoma 22 . These suggested that TRL4 may involve in ferroptosis-related HB tumor progression.…”

A disulfidptosis-related lncRNAs cluster to forecast the prognosis and immune landscapes of ovarian cancer

“…Waterfall plots were constructed using the ‘maftools’ package in R to determine the frequency of point mutations in samples and examine the relationship between TMB and risk scores ( Liu S. et al, 2023 ). The effectiveness of targeted therapy was forecasted using the ‘pRRophetic’ package ( Liu et al, 2022 ). Sensitivity to different drugs was assessed using the semi-maximum inhibitory concentration index (IC50) to explore the therapeutic advantages for ovarian cancer patients ( Liu S. et al, 2023 ).…”

Crosstalk between ferroptosis and steroid hormone signaling in gynecologic cancers

“…Inhibiting SLC7A11 causes GSH depletion ( Dixon et al, 2012 ). Down-regulating system xc− by targeting TP53 ( Jiang et al, 2015 ; Liu S. et al, 2022 ), NFE2L2 ( Chen et al, 2017 ), BAP1 ( Zhang et al, 2018 ), BECN1 or OTUB1( Song et al, 2018 ), can reduce GSH synthesis, enhance ROS generation, and result in ferroptosis. GPX4 belongs to GSH peroxidases and it is an antiferroptotic molecular ( Seibt et al, 2019 ).…”