Development and validation of nomogram with tumor microenvironment-related genes and clinical factors for predicting overall survival of endometrial cancer

Abstract

Background: Tumor microenvironment (TME) has attracted lots of attention with its important role in the tumor development. This study aimed to explore TME-related genes of prognostic value in patients with endometrial cancer (EC) and establish a prediction model for EC. Methods: The RNA-Seq data and clinicopathological characteristics of 521 subjects were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified based on the immune and stromal scores, which were calculated by the ESTIMATE algorithm. Hub genes were initially screened using the Cytoscape and further selected through Cox regression. Gene correlation analysis was performed in TIMER database. A nomogram was constructed integrating prognosis-related hub genes and clinical factors and validated in the validation group. Risk stratification was performed based on the nomogram. Results: Three TME-related hub genes (CCR5, FCER1G, and ITGB2) were found with significant prognostic value for EC patients. The expression of CCR5, FCER1G, and ITGB2 were significantly correlated with various immune cells infiltration. Based on the Cox regression, a nomogram was constructed by integrating five predictors (stage, grade, immune score, expression of FCER1G, and ITGB2), with a C-index of 0.765. Discrimination of the model was confirmed in the validation group (C-index: 0.716). The calibration curves for the 3-and 5-year survival indicated good calibration. Patients in high-and low-risk groups presented significantly different survival outcomes (P<0.001) in both discovery and validation group. Conclusion: TME-related hub genes of prognostic value identified in our study may provide references for the mechanisms underlying EC development and the immunotherapy for EC. The prediction model may help assess the prognosis of EC patients.

Single cell and spatial transcriptomics analysis of kidney double negative T lymphocytes in normal and ischemic mouse kidneys

“…However, in another study the expression level of Fcer1g negatively correlated with myeloma progression, and high Fcer1g expression was suggested as a favorable biomarker in multiple myeloma patients 33 . A significant positive correlation was also identified between expression of Fcer1g and the infiltration levels immune cells using the immune infiltration analysis, implicating the role of this gene in regulating tumor immunology in endometrial cancer 34 . While human atlas protein data base displays highly expression of this gene in other kidney cell type, our data demonstrated within the T cell subset, DN T cells exhibited significantly higher expression levels of this gene in comparison to CD4 + and CD8 + T cell populations.…”

“…After, the union of overdispersed genes for each individual sample was taken resulting in a final set of 2327 overdispersed genes, which was used for subsequent deconvolution. The four normal and the four ischemic kidney samples were combined into two separate spatial datasets for reference-free deconvolution with STdeconvolve 34 (version 1.0.0), which resulted in a normal condition dataset of 2327 genes and 11,779 spots, and an ischemic condition dataset with 2327 genes and 10,970 spots. LDA models were trained on each combined dataset using a range of Ks from 6 to 26 and in each case, selected the model with K = 12, which was the highest K that also minimized both perplexity and the number of deconvolved cell types with mean spot proportions less than 0.05.…”

MCM10: An effective treatment target and a prognostic biomarker in patients with uterine corpus endometrial carcinoma

“…In recent years, based on tumour microenvironment‐ and immune‐related genes, several models predicting the prognosis of endometrial cancer have been constructed 41,42 . Considering that MCM10 was identified as a tumour immune microenvironment‐related gene and an independent risk factor for the OS of UCEC, TCGA data‐based prognosis prediction model for UCEC, incorporating MCM10 expression and some other independent risk factors, was firstly established in this study.…”

“…In recent years, based on tumour microenvironment-and immune-related genes, several models predicting the prognosis of endometrial cancer have been constructed. 41,42 Considering that MCM10 was identified as a tumour immune microenvironmentrelated gene and an independent risk factor for the OS of UCEC,…”

Construction and validation of a model based on immunogenic cell death-associated lncRNAs to predict prognosis and direct therapy for kidney renal clear cell carcinoma

“…We used the R programming language “RMS” to create a prognostic nomogram using age, risk group, and tumor stage as factors to predict patient outcomes at 1, 3, and 5 years [ 12 ]. We also determined the value of this nomogram in prognosis prediction.…”