Integrative analysis of methylation and gene expression data in TCGA

Liu, Yihua; Qiu, Peng

doi:10.1109/gensips.2012.6507712

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…As we all know, the TCGA database is the world’s largest and most comprehensive gene chip database [ 18 ]. So far, TCGA researchers have analyzed large cohorts of over 30 human tumors through large-scale genome sequencing and integrated multi-dimensional analyses [ 19 ].…”

Section: Discussionmentioning

confidence: 99%

Analysis of SEC24D gene in breast cancer based on UALCAN database

Zhou²,

Wang

et al. 2019

Open Life Sciences

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ObjectiveTo analyze the expression and its clinical significance of the SEC24D gene in breast cancer.MethodsThe dataset of breast cancer were searched in the UALCAN database, and the data obtained were mined and combined with literature analysis.ResultsThe mRNA expression of the SEC24D gene in breast cancer tissues was significantly higher than that of breast normal tissues from the UALCAN database (P < 0.05). The promoter methylation levels of the SEC24D gene in breast cancer tissues were lower than that of breast normal tissues (P < 0.05). Survival analysis showed that the relapse-free survival of breast cancer patients with a higher expression of SEC24D gene was significantly worse than those patients with a lower expression of SEC24D (P < 0.05).ConclusionThe SEC24D gene has a high expression in breast cancer tissues and its expression level was related to the prognosis of breast cancer patients.

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Section: Discussionmentioning

confidence: 99%

Analysis of SEC24D gene in breast cancer based on UALCAN database

Zhou²,

Wang

et al. 2019

Open Life Sciences

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“…We obtained methylation data files from TCGA Colon Cancer project (COAD) version 1.23.0 ( , accessed 16 June 2018) that were filtered to include harmonized Illumina 450 K methylation data, BMI, age and gender from normal colon ( n = 15) and colon cancer ( n = 225) tissues using an R package designed for data retrieval, grouping and DMR analysis, TCGAbiolinksGUI [ 77 , 78 , 79 , 80 ]. The Illumina 450 K methylation array provided data on 485,000 CpG sites, which covered approximately 1.6% of all CpG sites, (0.01% of the entire genome), and methylation information on 99% of all known genes [ 81 , 82 , 83 , 84 ].…”

Section: Methodsmentioning

confidence: 99%

Obesity-Associated Differentially Methylated Regions in Colon Cancer

Milner

Chen

Grayson

et al. 2022

JPM

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Obesity with adiposity is a common disorder in modern days, influenced by environmental factors such as eating and lifestyle habits and affecting the epigenetics of adipose-based gene regulations and metabolic pathways in colorectal cancer (CRC). We compared epigenetic changes of differentially methylated regions (DMR) of genes in colon tissues of 225 colon cancer cases (154 non-obese and 71 obese) and 15 healthy non-obese controls by accessing The Cancer Genome Atlas (TCGA) data. We applied machine-learning-based analytics including generalized regression (GR) as a confirmatory validation model to identify the factors that could contribute to DMRs impacting colon cancer to enhance prediction accuracy. We found that age was a significant predictor in obese cancer patients, both alone (p = 0.003) and interacting with hypomethylated DMRs of ZBTB46, a tumor suppressor gene (p = 0.008). DMRs of three additional genes: HIST1H3I (p = 0.001), an oncogene with a hypomethylated DMR in the promoter region; SRGAP2C (p = 0.006), a tumor suppressor gene with a hypermethylated DMR in the promoter region; and NFATC4 (p = 0.006), an adipocyte differentiating oncogene with a hypermethylated DMR in an intron region, are also significant predictors of cancer in obese patients, independent of age. The genes affected by these DMR could be potential novel biomarkers of colon cancer in obese patients for cancer prevention and progression.

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