Multiomic Analysis of Methylation and Transcriptome Reveals a Novel Signature in Esophageal Cancer

Jin, Yiqi; Miao, Dong-liu

doi:10.1177/1559325820942075

Cited by 6 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some studies have incorporated the nutritional information and inflammatory profile of patients into the nomogram and successfully demonstrated that the developed nomogram has a better performance than the TNM staging system ( 6 , 21 , 22 ). In addition, other studies have incorporated genomic data, such as DNA methylation ( 23 ), long non-coding RNA ( 24 ), and autophagy-related gene data ( 25 ), into the nomogram and successfully shown that the developed nomogram has a better discrimination ability than the TNM staging system. However, no studies have incorporated ctDNA mutation information into nomogram models.…”

Section: Discussionmentioning

confidence: 99%

A clinical prognostic model for patients with esophageal squamous cell carcinoma based on circulating tumor DNA mutation features

Liu

Cheng

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

BackgroundFew predictive models have included circulating tumor DNA (ctDNA) indicators to predict prognosis of esophageal squamous cell carcinoma (ESCC) patients. Here, we aimed to explore whether ctDNA can be used as a predictive biomarker in nomogram models to predict the prognosis of patients with ESCC.MethodsWe included 57 patients who underwent surgery and completed a 5-year follow-up. With next-generation sequencing, a 61-gene panel was used to evaluate plasma cell-free DNA and white blood cell genomic DNA from patients with ESCC. We analyzed the relationship between the mutation features of ctDNA and the prognosis of patients with ESCC, identified candidate risk predictors by Cox analysis, and developed nomogram models to predict the 2- and 5-year disease-free survival (DFS) and overall survival (OS). The area under the curve of the receiver operating characteristic (ROC) curve, concordance index (C-index), calibration plot, and integrated discrimination improvement (IDI) were used to evaluate the performance of the nomogram model. The model was compared with the traditional tumor-nodes-metastasis (TNM) staging system.ResultsThe ROC curve showed that the average mutant allele frequency (MAF) of ctDNA variants and the number of ctDNA variants were potential biomarkers for predicting the prognosis of patients with ESCC. The predictors included in the models were common candidate predictors of ESCC, such as lymph node stage, angiolymphatic invasion, drinking history, and ctDNA characteristics. The calibration curve demonstrated consistency between the observed and predicted results. Moreover, our nomogram models showed clear prognostic superiority over the traditional TNM staging system (based on C-index, 2-year DFS: 0.82 vs. 0.64; 5-year DFS: 0.78 vs. 0.65; 2-year OS: 0.80 vs. 0.66; 5-year OS: 0.77 vs. 0.66; based on IDI, 2-year DFS: 0.33, p <0.001; 5-year DFS: 0.18, p = 0.04; 2-year OS: 0.28, p <0.001; 5-year OS: 0.15, p = 0.04). The comprehensive scores of the nomogram models could be used to stratify patients with ESCC.ConclusionsThe novel nomogram incorporating ctDNA features may help predict the prognosis of patients with resectable ESCC. This model can potentially be used to guide the postoperative management of ESCC patients in the future, such as adjuvant therapy and follow-up.

show abstract

Section: Discussionmentioning

confidence: 99%

A clinical prognostic model for patients with esophageal squamous cell carcinoma based on circulating tumor DNA mutation features

Liu

Cheng

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…EC is the most common malignant tumor of the digestive tract and has a poor prognosis. Studies have established several novel biomarkers to determine the prognosis of EC patients, such as miRNA signatures 7,8 , autophagy-related signatures 9 , m6A RNA methylation regulator-based signatures 10 , integrated mRNA-lncRNA signatures 11 , epigenetic signatures 12 , lymph node metastasis-associated gene signatures 13 , urinary metabolomic signatures 14 , and immune-related gene signatures 15 . Although RBPrelated signatures have been analyzed for a variety of cancers [16][17][18][19][20][21] , related analyses for EC are rare.…”

Section: Discussionmentioning

confidence: 99%

Genome-Wide Profiling of a Prognostic RNA-Binding Protein Signature in Esophageal Cancer

Zhang

Yang

et al. 2022

Preprint

View full text Add to dashboard Cite

Background the roles of RNA-binding proteins (RBPs) in esophageal cancer (EC) need elucidation. Materials and methods The gene expression profiles and clinical data of patients with EC were downloaded from the Xena database. Candidate genes were obtained by taking the union of RBP genes, KEGG pathway-related genes and differentially expressed RBP genes from cluster analysis. Hub genes were extracted by PPI network construction. Result We built a Cox proportional hazards regression model with 7 prognostic RBPs (TRMT2A, PDHA1, MPRIP, KRI1, IL17A, HSPA1A and HIST1H4J). The risk score of each patient in the internal and external dataset cohorts was calculated and then the patients were divided into two groups based on the median. There were significant differences between the RBP risk score and response to chemotherapy, with low-risk patients being more likely to achieve CR. Finally, the risk score was revealed to be significantly related to OS by univariate and multivariate analyses, and pathological stage could also be used independently to predict the prognosis of EC. Conclusion Our study indicated that the RBP signature could serve as a prognostic biomarker of EC and provide new insight into chemoresistance in EC patients.

show abstract

“…Therefore, DNA methylation may become one of the methods for early diagnosis of EAC. Recently, a study on EC established an epigenetic signature to evaluate the prognosis of EC [ 34 ]. However, the prognostic model for EAC alone is still lacking.…”

Section: Discussionmentioning

confidence: 99%

Identification of Prognostic Risk Model Based on DNA Methylation-Driven Genes in Esophageal Adenocarcinoma

Chen¹,

Wang

Zhou

et al. 2021

BioMed Research International

View full text Add to dashboard Cite

Background. DNA methylation is an important part of epigenetic modification, and its abnormality is closely related to esophageal adenocarcinoma (EAC). This study was aimed at using bioinformatics analysis to identify methylation-driven genes (MDGs) in EAC patients and establish a risk model as a biological indicator of EAC prognosis. Method. Downloaded EAC DNA methylation, transcriptome, and related clinical data from TCGA database. MethylMix was used to identify MDGs. R package clusterProfiler and the ConsensusPathDB online database were used to analyze the rich functions and pathways of these MDGs. The prognostic risk model was established by univariate Cox regression, Lasso regression, and multivariate Cox regression analysis. Finally each MDG in the model were carried out through the survival R package. Results. A total of 273 MDGs were identified, which were enriched in transcriptional regulation and embryonic organ morphogenesis. Cox regression analysis established a risk model consisting of GPBAR1, OLFM4, FOXI2, and CASP10. In addition, further survival analysis revealed that OLFM4 and its two related sites were significantly related to the EAC patients’ survival. Conclusion. In summary, this study used bioinformatics methods to identify EAC MDGs and established a reliable risk prognosis model. It provided potential biomarkers for the early treatment and prognosis evaluation of EAC.

show abstract

Multiomic Analysis of Methylation and Transcriptome Reveals a Novel Signature in Esophageal Cancer

Cited by 6 publications

References 34 publications

A clinical prognostic model for patients with esophageal squamous cell carcinoma based on circulating tumor DNA mutation features

A clinical prognostic model for patients with esophageal squamous cell carcinoma based on circulating tumor DNA mutation features

Genome-Wide Profiling of a Prognostic RNA-Binding Protein Signature in Esophageal Cancer

Identification of Prognostic Risk Model Based on DNA Methylation-Driven Genes in Esophageal Adenocarcinoma

Contact Info

Product

Resources

About