Establishment of the Prognosis Predicting Signature for Endometrial Cancer Patient

Abstract

BackgroundNovel biomarkers provide clinicians more critical information on tumor genetic features and patients’ prognosis. Here, we aimed to establish prognosis-predicting signatures for endometrial carcinoma (EC) patients based on the miRNA information.Material/MethodsThe Cancer Genome Atlas (TCGA) website was available for dataset extraction. Prognosis-associated miRNAs were generated by univariate Cox regression test. Online websites were used to predict the targeted genes of these enrolled miRNAs. The miRNA-mRNA network was described by Cytoscape software, while the relevant signaling pathways of these targeted genes were enriched by Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses.ResultsThe miRNA-based overall survival (OS) and recurrence-free survival (RFS) predicting signatures were constructed by LASSO Cox regression analyses, respectively, by which, the endometrial carcinoma patients were separated into high- and low-risk groups in both the discovery and validation sets. Univariate Cox regression analyses suggested that these high-risk patients had elevated death and recurrence risk compared to low-risk patients. In addition, multivariate Cox regression analysis confirmed that our signatures were independent prognosticate factors with or without clinicopathological features for endometrial carcinoma patients. Moreover, the miRNA-mRNA network was displayed by Cytoscape software, and the pathway enrichment analyses found that the targeted genes of these enrolled miRNAs were enriched in tumor progression and drug resistance-related pathways.ConclusionsThe OS and RFS predicting classifiers serve as independent prognosis-associated determiners for EC patients.

The optimal cut‐off value of immunohistochemical parameter P53 for predicting recurrence of endometrial cancer

“…In multivariate analyses, not only was a high P53 expression detected to be associated with relapse, but ER and FIGO stage were also independent factors affecting prognosis, which was consistent with other reports 19‐21 . Although histological type and grade, myometrial invasion, cervical stromal invasion, postoperative adjuvant therapy, and the expression of PR and Ki‐67 had not shown a significant correlation with relapse in multivariate analysis, their predictive value for relapse in endometrial cancer cannot be denied.…”

Development of a novel immune-related lncRNA signature as a prognostic classifier for endometrial carcinoma

“…The prognostic evaluation of EnCa has always been a hot topic for concerned scholars. Construction of scoring models will contribute to quantification of the prognostic evaluation criteria, and an increasing number of studies are successfully establishing precedents in this regard [22][23][24]. In the current research, a series of systematic bioinformatics analyses were conducted, ultimately leading to the development of a novel 13-lncRNA signature associated with the clinical outcome of EnCa patients; this signature included AL080317.2, ERICH6-AS1, AC016877.3, MCCC1-AS1, AC120053.1, AC138932.5, ZNF433-AS1, SCARNA9, DBH-AS1, AL157932.1, AC073046.1, POC1B-AS1, and AP003419.3.…”

Low Expression of miR-375 and miR-190b Differentiates Grade 3 Patients with Endometrial Cancer

“…The first attempts to use the miRNA profile to predict OFS, PFS, and RFS have also been made [ 40 , 41 , 42 ]. Tang and colleagues used TCGA data to establish a prognosis signature based on the hazard ratio (HR) of each miRNA.…”

“…With this signature, the authors showed that patients in a high-risk group have poorer OS compared to the low-risk group. Unfortunately, this classification rule was less efficient than the clinical stage but was superior to histological type or neoplasm histological grade [ 42 ]. The potential of miRNA profile in predicting the course of the disease is high, however, the use of miRNA-based stratification should be assisted with genomic subtyping.…”