Molecular subtypes based on DNA methylation predict prognosis in colon adenocarcinoma patients

Yang, Changshun; Xu, Xiaoqin; Li, Weihua

doi:10.18632/aging.102492

Cited by 44 publications

(46 citation statements)

References 29 publications

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“…To understand the mechanism of cancer, help guide therapy, and improve prognoses, it is vital to identify accurate subtypes. Several studies reported identifying subtypes based on DNA methylation, including colon adenocarcinoma [ 36 ], cervical cancer [ 37 ], glioblastoma [ 38 ], and bladder cancer [ 39 ]. This study divided BC into three distinct subtypes based on the immune-cell-related methylation profiles ( Figure 1 B–C).…”

Section: Discussionmentioning

confidence: 99%

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A Methylation-Based Reclassification of Bladder Cancer Based on Immune Cell Genes

Luo

Vögeli

2020

Cancers

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Background: Bladder cancer is highly related to immune cell infiltration. This study aimed to develop a new classification of BC molecular subtypes based on immune-cell-associated CpG sites. Methods: The genes of 28 types of immune cells were obtained from previous studies. Then, methylation sites corresponding to immune-cell-associated genes were acquired. Differentially methylated sites (DMSs) were identified between normal samples and bladder cancer samples. Unsupervised clustering analysis of differentially methylated sites was performed to divide the sites into several subtypes. Then, the potential mechanism of different subtypes was explored. Results: Bladder cancer patients were divided into three groups. The cluster 3 subtype had the best prognosis. Cluster 1 had the poorest prognosis. The distribution of immune cells, level of expression of checkpoints, stromal score, immune score, ESTIMATEScore, tumor purity, APC co_inhibition, APC co_stimulation, HLA, MHC class_I, Type I IFN Response, Type II IFN Response, and DNAss presented significant differences among the three subgroups. The distribution of genomic alterations was also different. Conclusions: The proposed classification was accurate and stable. BC patients could be divided into three subtypes based on the immune-cell-associated CpG sites. Specific biological signaling pathways, immune mechanisms, and genomic alterations were varied among the three subgroups. High-level immune infiltration was correlated with high-level methylation. The lower RNAss was associated with higher immune infiltration. The study of the intratumoral immune microenvironment may provide a new perspective for BC therapy.

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Section: Discussionmentioning

confidence: 99%

“…(3) Probes on the X and Y chromosomes were excluded from the analysis [ 96 ]. The remaining sites were imputed with the k-nearest neighbors (KNN) imputation procedure [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

A Methylation-Based Reclassification of Bladder Cancer Based on Immune Cell Genes

Luo

Vögeli

2020

Cancers

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“…With the development of RNA-seq technology, many classical analyses based on gene expression data have been reported for most cancers (6,(38)(39)(40). However, few cluster studies have been performed on PCa to explore the tumor metabolic characteristics.…”

Section: Discussionmentioning

confidence: 99%

Identification of Metabolism-Associated Prostate Cancer Subtypes and Construction of a Prognostic Risk Model

Zhang

Liang

et al. 2020

Front. Oncol.

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BackgroundDespite being the second most common tumor in men worldwide, the tumor metabolism-associated mechanisms of prostate cancer (PCa) remain unclear. Herein, this study aimed to investigate the metabolism-associated characteristics of PCa and to develop a metabolism-associated prognostic risk model for patients with PCa.MethodsThe activity levels of PCa metabolic pathways were determined using mRNA expression profiling of The Cancer Genome Atlas Prostate Adenocarcinoma cohort via single-sample gene set enrichment analysis (ssGSEA). The analyzed samples were divided into three subtypes based on the partitioning around medication algorithm. Tumor characteristics of the subsets were then investigated using t-distributed stochastic neighbor embedding (t-SNE) analysis, differential analysis, Kaplan–Meier survival analysis, and GSEA. Finally, we developed and validated a metabolism-associated prognostic risk model using weighted gene co-expression network analysis, univariate Cox analysis, least absolute shrinkage and selection operator, and multivariate Cox analysis. Other cohorts (GSE54460, GSE70768, genotype-tissue expression, and International Cancer Genome Consortium) were utilized for external validation. Drug sensibility analysis was performed on Genomics of Drug Sensitivity in Cancer and GSE78220 datasets. In total, 1,039 samples and six cell lines were concluded in our work.ResultsThree metabolism-associated clusters with significantly different characteristics in disease-free survival (DFS), clinical stage, stemness index, tumor microenvironment including stromal and immune cells, DNA mutation (TP53 and SPOP), copy number variation, and microsatellite instability were identified in PCa. Eighty-four of the metabolism-associated module genes were narrowed to a six-gene signature associated with DFS, CACNG4, SLC2A4, EPHX2, CA14, NUDT7, and ADH5 (p <0.05). A risk model was developed, and external validation revealed the strong robustness our risk model possessed in diagnosis and prognosis as well as the association with the cancer feature of drug sensitivity.ConclusionsThe identified metabolism-associated subtypes reflected the pathogenesis, essential features, and heterogeneity of PCa tumors. Our metabolism-associated risk model may provide clinicians with predictive values for diagnosis, prognosis, and treatment guidance in patients with PCa.

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“…With the progress of high-throughput gene sequencing technology, more and more genes have been put into practical use as speci c therapeutic targets and prognostic markers for malignant tumors (11,12).…”

Section: Introductionmentioning

confidence: 99%

A Four-Immune Gene Prognostic Risk Model for Colorectal Adenocarcinoma Based on the TCGA and ImmPort Data Sets

Xu¹,

Peng²,

An³

et al. 2020

Preprint

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Background: As one of the hot spots in oncology field, immune research provides new ideas for the diagnosis and treatment of tumors. Different histological types of colorectal cancer are different. Adenocarcinoma, as the type with the highest proportion, has a high research value. This study aims to build an immune gene prognostic risk model for colorectal adenocarcinoma to improve the diagnosis and prognosis prediction of colorectal adenocarcinoma.Methods: The differentially expressed immune genes could be obtained from the gene expression data downloaded from The Cancer Genome Atlas (TCGA) and the immune gene data downloaded from the ImmPort Database. Univariate COX and multivariate COX analyses were used to construct the immune gene prognostic risk model of and the clinical application potential of this model. The correlation between the model and the immune cells infiltration and the influence of each immune cell on the survival were analyzed.Results: 5975 differentially expressed genes were obtained, and 497 differentially expressed immune genes were selected by combining the information of immune genes. Among them, 36 immune genes were associated with prognosis, and 4 immune genes (THRB, IL1RL2, LGR6, LTB4R2) were included in the prognostic risk model of immune genes. Patients with higher Risk Score had shorter survival. Compared with gender, age and pathological stage, the model has better prediction potential. In addition, the model was correlated with Macrophages M0, Macrophages M1, T cells follicular helper and NK cells activated. Among them, T cells follicular helper and Macrophages M0 were related to the survival of patients.Conclusion: We developed a prognostic risk model containing four immune genes, THRB, IL1RL2, LGR6 and LTB4R2, which accurately described the prognosis of the patient, and affected the survival of patients by influencing the infiltration of Macrophages M0 and T cells follicular helper.

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Molecular subtypes based on DNA methylation predict prognosis in colon adenocarcinoma patients

Cited by 44 publications

References 29 publications

A Methylation-Based Reclassification of Bladder Cancer Based on Immune Cell Genes

A Methylation-Based Reclassification of Bladder Cancer Based on Immune Cell Genes

Identification of Metabolism-Associated Prostate Cancer Subtypes and Construction of a Prognostic Risk Model

A Four-Immune Gene Prognostic Risk Model for Colorectal Adenocarcinoma Based on the TCGA and ImmPort Data Sets

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