DNA methylation data‐based prognosis‐subtype distinctions in patients with esophageal carcinoma by bioinformatic studies

Chen, Hui; Qin, Qin; Xu, Zhipeng; Chen, Tingting; Yao, Xijuan; Xu, Bing; Sun, Xinchen

doi:10.1002/jcp.29999

Cited by 6 publications

(5 citation statements)

References 31 publications

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“…In tumor cells, proto-oncogenes are in a state of hypomethylation and activated, while tumor suppressor genes are in a state of hypermethylation and inhibited ( Kulis and Esteller, 2010 ; Gyorffy et al, 2016 ; Chen et al, 2021 ). Next, we explored whether some methylated CPG sites had DNA methylation abnormalities due to subtypes driven by the oncogenic signaling pathway.…”

Section: Resultsmentioning

confidence: 99%

Oncogenic signaling pathway dysregulation landscape reveals the role of pathways at multiple omics levels in pan-cancer

Wang

He²,

Wu³

et al. 2022

Front. Genet.

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Dysregulation of signaling pathways plays an essential role in cancer. However, there is not a comprehensive understanding on how oncogenic signaling pathways affect the occurrence and development with a common molecular mechanism of pan-cancer. Here, we investigated the oncogenic signaling pathway dysregulation by using multi-omics data on patients from TCGA from a pan-cancer perspective to identify commonalities across different cancer types. First, the pathway dysregulation profile was constructed by integrating typical oncogenic signaling pathways and the gene expression of TCGA samples, and four molecular subtypes with significant phenotypic and clinical differences induced by different oncogenic signaling pathways were identified: TGF-β+ subtype; cell cycle, MYC, and NF2− subtype; cell cycle and TP53+ subtype; and TGF-β and TP53− subtype. Patients in the TGF-β+ subtype have the best prognosis; meanwhile, the TGF-β+ subtype is associated with hypomethylation. Moreover, there is a higher level of immune cell infiltration but a slightly worse survival prognosis in the cell cycle, MYC, and NF2− subtype patients due to the effect of T-cell dysfunction. Then, the prognosis and subtype classifiers constructed by differential genes on a multi-omics level show great performance, indicating that these genes can be considered as biomarkers with potential therapeutic and prognostic significance for cancers. In summary, our study identified four oncogenic signaling pathway–driven patterns presented as molecular subtypes and their related potential prognostic biomarkers by integrating multiple omics data. Our discovery provides a perspective for understanding the role of oncogenic signaling pathways in pan-cancer.

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Section: Resultsmentioning

confidence: 99%

Oncogenic signaling pathway dysregulation landscape reveals the role of pathways at multiple omics levels in pan-cancer

Wang

He²,

Wu³

et al. 2022

Front. Genet.

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“…Among the 39 studies that adopted clustering-based workflows (5, 18-23, 29, 32-35, 37-39, 43, 45-47, 50, 53-57, 61, 62, 64, 69, 70, 75, 77-80, 83, 84, 87, 88), most started with a preliminary screening of CpG sites (Figure 4.1). The most frequently used pre-selection approach was selecting CpG sites that were significantly associated with prognosis by first performing univariable Cox regression analysis, and then performing multivariable Cox analysis with adjustment for clinical variables.…”

Section: Resultsmentioning

confidence: 99%

“…At the third stage, a reduced number of cluster-specific CpG sites were identified, mostly by selecting dmCpG sites across clusters, or by selecting dmCpG sites in the seed cluster (i.e., the cluster associated with good prognosis and containing a great number of dmCpGs). Four studies (50, 56, 62, 79) used weighted gene co-expression network analysis to first identify the co-expression modules having the greatest correlation with the seed cluster, and then to screen for CpGs in that module mostly correlated with the seed cluster.…”

Section: Resultsmentioning

confidence: 99%

“…None of the studies using TCGA data reported median follow-up time, so little can be said about the data quality and interpretability of the conducted study. Second, six studies failed to specify the type of survival outcomes (e.g., overall survival or disease-free survival) (43, 46, 47, 60, 62, 75), which further limits the interpretation of results.…”

Section: Discussionmentioning

confidence: 99%

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Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: a systematic review of epigenome-wide studies

Yuan

Edelmann

Fan

et al. 2022

Preprint

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Background: DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)- based analytic techniques might help overcome the challenges of analyzing high- dimensional data in relatively small sample sizes. This systematic review summarizes the current use of ML-based methods in epigenome-wide studies for the identification of DNA methylation signatures associated with cancer prognosis. Methods: We searched three electronic databases including PubMed, EMBASE, and Web of Science for articles published until 8 June 2022. ML-based methods and workflows used to identify DNA methylation signatures associated with cancer prognosis were extracted and summarized. Two authors independently assessed the methodological quality of included studies by a seven-item checklist adapted from relevant guidelines. Results: Seventy-six studies were included in this review. Three major types of ML- based workflows were identified: 1) unsupervised clustering, 2) supervised feature selection, and 3) deep learning-based feature transformation. For the three workflows, the most frequently used ML techniques were consensus clustering, least absolute shrinkage and selection operator (LASSO), and autoencoder, respectively. The systematic review revealed that the performance of these approaches has not 2 been adequately evaluated yet and that methodological and reporting flaws were common in the identified studies using ML techniques. Conclusions: There is great heterogeneity in ML-based methodological strategies used by epigenome-wide studies to identify DNA methylation markers associated with cancer prognosis. Benchmarking studies are needed to compare the relative performance of various approaches for specific cancer types. Adherence to relevant methodological and reporting guidelines are urgently needed.

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“…TCGA includes a large number of Infinium Human Methylation 450 BeadChip arrays of tumor samples, which provides available bioinformatics data for the study of tumor DNA methylation. Based on the DNA methylation data of cancers in TCGA, several studies have classified a variety of malignancies, such as biliary tract ( 25 ), breast ( 26 ), cervical ( 27 ), and esophageal ( 28 ) cancers. In this study, the transcriptome and DNA methylation data of LUAD were downloaded from TCGA.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis based on DNA methylation data identified in lung adenocarcinoma subgroups with different immune characteristics and clinical outcomes

Yu¹,

Huang²,

Lin³

et al. 2023

J Thorac Dis

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Background DNA methylation can be used to predict clinical outcomes and improve the classification of tumors. The present study aimed to develop a new lung adenocarcinoma (LUAD) classification system according to the immune cell gene-related methylation sites and to reveal the survival outcomes, clinical characteristics, immune cell infiltration, stem cell characteristics, and genomic variations of each molecular subgroup. Methods The DNA methylation sites of LUAD samples collected from The Cancer Genome Atlas (TCGA) database were analyzed, and the prognosis-related differential methylation sites (DMS) were screened. Consistent clustering of the samples was conducted using ConsensusClusterPlus, and the classification results were verified by principal component analysis (PCA). The survival and clinical results, immune cell infiltration, stemness, DNA mutation, and copy number variation (CNV) of each molecular subgroup were analyzed. Results A total of 40 DMS were obtained by difference and univariate COX analyses, and the TCGA LUAD samples were divided into three subgroups: cluster 1 (C1), cluster 2 (C2), and cluster 3 (C3). Among these subgroups, the overall survival (OS) of C3 was significantly higher than that of C1 and C2. Compared with C1 and C3, C2 had the lowest innate immune cell and adaptive immune cell infiltration scores; the lowest stromal score, immune score, and iconic immune checkpoint expression; and the highest expression of messenger RNA (mRNA) expression-based stemness indices (mRNAsi), DNA methylation-based stemness index (mDNAsi), and tumor mutational burden (TMB). Conclusions In this study, we proposed a LUAD typing system based on DMS, which was closely related to the survival, clinical features, immune characteristics, and genomic variations of LUAD, and may contribute to the development of personalized therapy for new specific subtypes.

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DNA methylation data‐based prognosis‐subtype distinctions in patients with esophageal carcinoma by bioinformatic studies

Cited by 6 publications

References 31 publications

Oncogenic signaling pathway dysregulation landscape reveals the role of pathways at multiple omics levels in pan-cancer

Oncogenic signaling pathway dysregulation landscape reveals the role of pathways at multiple omics levels in pan-cancer

Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: a systematic review of epigenome-wide studies

Bioinformatics analysis based on DNA methylation data identified in lung adenocarcinoma subgroups with different immune characteristics and clinical outcomes

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