MiRNA based tumor mutation burden diagnostic and prognostic prediction models for endometrial cancer

Abstract

Uterus Corpus Endometrial cancer (UCEC) is the sixth most common malignant tumor worldwide. In this research, we identified diagnostic and prognostic biomarkers to reflect patients' immune microenvironment and prognostic. Various data of UCEC patients from the TCGA database were obtained. Firstly, patients were divided into a high tumor mutation burden (TMB) level group and a low TMB level group according to the level of TMB. Then, differentially expressed miRNAs between the two groups were obtained. LASSO logistic regression analysis was used to construct a diagnostic model to predict the level of TMB. Univariate, multivariate, and LASSO regression analysis were used to construct a prognostic risk signature (PRS) to predict the prognosis of UCEC patients. Twenty-one miRNAs were used to construct a diagnostic model for predicting TMB levels. The AUC values of ROC curves for 21-miRNAbased diagnostic models were 0.911 in the training set, 0.827 in the test set, and 0.878 in the entire set. This diagnostic model showed positive correlation with TMB, PDL1 expression, and the infiltration of immune cells. In addition, three prognostic miRNAs were finally used to construct the PRS. The PRS was related to the expression of multiple immune checkpoints and the infiltration of multiple immune cells. Furthermore, the PRS can also reflect the response to some commonly used chemotherapy regimens. We have established a miRNA-based diagnostic model and a prognostic model that can predict the prognosis of UCEC patients and their response to chemotherapy and immunotherapy, thus providing valuable information on the choice of treatment regimen.

Construction of a miRNA-Based Nomogram Model to Predict the Prognosis of Endometrial Cancer

“…Dou et al found novel molecular associations with potential mechanistic implications [ 14 ], while Lu et al culminated the use of three prognostic miRNAs to construct a prognostic risk signature that correlates with the expression of multiple immune checkpoints. This involved the infiltration of multiple immune cells [ 41 ]. However, although some studies have analyzed endometrial cancer-related miRNAs (DOI: 10.1080/21655979.2021.1947940, DOI: 10.1016/j.gene.2019.01.046), our research screened the miRNA data of the latest endometrial cancer samples in TCGA, and finally screened out 12 significantly differentially expressed miRNAs, including those not found in previous studies.…”

Ferroptosis: Opportunities and Challenges in Treating Endometrial Cancer

“…It is well known that abnormal ferroptosis is an important reason behind the poor prognosis of endometrial cancer, as indicated by the evidence that molecular typing based on ferroptosis-associated genes showed good prognosis predictive value: 1) the ferroptosis score, based on thirteen ferroptosis-associated genes, was established, and OS of patients with a low score of ferroptosis was superior to that of those with high score of ferroptosis (AUC = 0.726) (Wang et al, 2021b); 2) a molecular typing of endometrial cancer, based on six ferroptosisassociated genes of HMOX1, KEAP1, HSBP1, SAT1, CISD1, and GPX4, showed good 1-, 3-, and 5-year prognostic predictive value (AUC = 0.705, 0.676, and 0.713) (Liu J. et al, 2021); 3) a ferroptosis-associated gene signature with eight genes of MDM2, GPX4, PRKAA2, PRNP, SLC11A2, ATP5MC3, PHKG2, and ACO1, showed better 1-, 3-, and 5-year prognostic predictive value (AUC = 0.676, 0.797, and 0.826) than the aforementioned two ferroptosis prognosis molecular typings (Weijiao et al, 2021). Ferroptosis prognosis molecular typing possessed a comparable or superior prognosis predictive value when compared with the previous prognosis molecular typings of endometrial cancer (Table 1) (Tang et al, 2019;Yang et al, 2021b;Coll-de la Rubia et al, 2021;Huang S. et al, 2021;Lu N. et al, 2021;Pang et al, 2021;Wang Z. et al, 2021). As indicated by the evidence, ferroptosis-associated genes-based molecular typing can be considered as an effective method to predict the prognosis of endometrial cancer.…”

Non-coding RNA in cancer drug resistance: Underlying mechanisms and clinical applications

“…The diagnostic panel was closely associated with tumor mutation burden, PDL1 expression, and the infiltration of immune cells. Moreover, the prognostic risk signature of the prognostic panel can be used to predict the response to some commonly used chemotherapy regimens ( 285 ).…”