Predicting prognosis of endometrioid endometrial adenocarcinoma on the basis of gene expression and clinical features using Random Forest

Yin, Fufen; Shao, Xingyang; Zhao, Lijun; Li, Xiaoping; Zhou, Jingyi; Cheng, Yuan; He, Xijing; Shu, Lei; Li, Jiangeng; Wang, Jianliu

doi:10.3892/ol.2019.10504

Cited by 13 publications

(12 citation statements)

References 49 publications

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“…All samples were from patients between January 2008 and December 2012. Total RNA isolation and reverse transcription-quantitative PCR procedures were performed as previously described ( 23 ). This research was approved by the Institutional Ethics Committee (Human Research) of our hospital and informed consent was obtained from the patients.…”

Section: Methodsmentioning

confidence: 99%

Identification of a Metabolism-Related Signature for the Prediction of Survival in Endometrial Cancer Patients

Yuan

Tian

et al. 2021

Front. Oncol.

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ObjectiveEndometrial cancer (EC) is one of the most common gynecologic malignancies. The present study aims to identify a metabolism-related biosignature for EC and explore the molecular immune-related mechanisms underlying the tumorigenesis of EC.MethodsTranscriptomics and clinical data of EC were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Common differentially expressed metabolism-related genes were extracted and a risk signature was identified by using the least absolute shrinkage and selection operator (LASSO) regression analysis method. A nomogram integrating the prognostic model and the clinicopathological characteristics was established and validated by a cohort of clinical EC patients. Furthermore, the immune and stromal scores were observed and the infiltration of immune cells in EC cells was analyzed.ResultsSix genes, including CA3, HNMT, PHGDH, CD38, PSAT1, and GPI, were selected for the development of the risk prediction model. The Kaplan-Meier curve indicated that patients in the low-risk group had considerably better overall survival (OS) (P = 7.874e-05). Then a nomogram was constructed and could accurately predict the OS (AUC = 0.827, 0.821, 0.845 at 3-, 5-, and 7-year of OS). External validation with clinical patients showed that patients with low risk scores had a longer OS (p = 0.04). Immune/stromal scores and infiltrating density of six types of immune cells were lower in high-risk group.ConclusionsIn summary, our work provided six potential metabolism-related biomarkers as well as a nomogram for the prognosis of EC patients, and explored the underlying mechanism involved in the progression of EC.

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Section: Methodsmentioning

confidence: 99%

Identification of a Metabolism-Related Signature for the Prediction of Survival in Endometrial Cancer Patients

Yuan

Tian

et al. 2021

Front. Oncol.

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“…The glycolysis-related gene signature was further validated by our own clinical data including 24 EC samples from surgical patients in the Department of Obstetrics and Gynecology, Peking University People's Hospital. Total RNA isolation and RNA sequencing were performed as previously reported (Yin et al, 2019). The patients were followed-up by February 2018.…”

Section: External Validation Based On the Clinical Samplesmentioning

confidence: 99%

Comprehensive Analysis of the Glycolysis-Related Gene Prognostic Signature and Immune Infiltration in Endometrial Cancer

Yang

Cheng

et al. 2022

Front. Cell Dev. Biol.

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Glucose metabolic reprogramming and immune imbalance play important roles in the progression of cancers. The purpose of this study is to develop a glycolysis-related prognostic signature for endometrial cancer (EC) and analyze its relationship with immune function. The mRNA expression profiling of the glycolysis-related genes and clinical data of EC patients were downloaded from The Cancer Genome Atlas (TCGA). We identified a glycolysis-related gene prognostic signature for predicting the prognosis of EC by using The Least Absolute Shrinkage and Selection Operator (LASSO) regression and found the patients in the high-risk group had worse survival prognosis. Multivariate Cox regression analysis showed that the gene signature was an independent prognostic factor for EC. The ROC curve confirmed the accuracy of the prognostic signature (AUC = 0.730). Then, we constructed a nomogram to predict the 1–5 years survival rate of EC patients. The association between the gene signature and immune function was analyzed based on the “ESTIMATE” and “CIBERSORT” algorithm, which showed the immune and ESTIMATE scores of patients in the high-risk group were lower, while the low immune and ESTIMATE scores were associated with a worse prognosis of patients. The imbalance of immune cells was also found in the high-risk group. Further, the protein of CDK1, a gene in the signature, was found to be closely related to prognosis of EC and inhibition of CDK1 could inhibit migration and promote apoptosis of EC cells. This study reveals a link between glycolysis-related gene signature and immunity, and provides personalized therapeutic targets for EC.

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“…[33,34]. Integrating the clinicopathologic features with molecular biomarkers predictive of patient tumor behavior may provide more accurate risk stratification and improve patients' prognosis [35][36][37].…”

Section: The Cancer Genome Atlas (Tcga) Molecular Classification For Endometrial Cancermentioning

confidence: 99%

Endometrial Cancer Molecular Characterization: The Key to Identifying High-Risk Patients and Defining Guidelines for Clinical Decision-Making?

Baiden-Amissah

Annibali

Tuyaerts

et al. 2021

Cancers

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Endometrial carcinomas (EC) are the sixth most common cancer in women worldwide and the most prevalent in the developed world. ECs have been historically sub-classified in two major groups, type I and type II, based primarily on histopathological characteristics. Notwithstanding the usefulness of such classification in the clinics, until now it failed to adequately stratify patients preoperatively into low- or high-risk groups. Pieces of evidence point to the fact that molecular features could also serve as a base for better patients’ risk stratification and treatment decision-making. The Cancer Genome Atlas (TCGA), back in 2013, redefined EC into four main molecular subgroups. Despite the high hopes that welcomed the possibility to incorporate molecular features into practice, currently they have not been systematically applied in the clinics. Here, we outline how the emerging molecular patterns can be used as prognostic factors together with tumor histopathology and grade, and how they can help to identify high-risk EC subpopulations for better risk stratification and treatment strategy improvement. Considering the importance of the use of preclinical models in translational research, we also discuss how the new patient-derived models can help in identifying novel potential targets and help in treatment decisions.

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Predicting prognosis of endometrioid endometrial adenocarcinoma on the basis of gene expression and clinical features using Random Forest

Cited by 13 publications

References 49 publications

Identification of a Metabolism-Related Signature for the Prediction of Survival in Endometrial Cancer Patients

Identification of a Metabolism-Related Signature for the Prediction of Survival in Endometrial Cancer Patients

Comprehensive Analysis of the Glycolysis-Related Gene Prognostic Signature and Immune Infiltration in Endometrial Cancer

Endometrial Cancer Molecular Characterization: The Key to Identifying High-Risk Patients and Defining Guidelines for Clinical Decision-Making?

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