scholarly journals Integrated analysis of genome-wide DNA methylation and gene expression profiles in molecular subtypes of breast cancer

2013 ◽  
Vol 41 (18) ◽  
pp. 8464-8474 ◽  
Author(s):  
J.-K. Rhee ◽  
K. Kim ◽  
H. Chae ◽  
J. Evans ◽  
P. Yan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Molecular Subtypes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Integrated Analysis ◽  
Genome Wide

scholarly journals Integrated analysis of genome-wide DNA methylation and gene expression profiles identifies potential novel biomarkers of rectal cancer

Oncotarget ◽  
2016 ◽  
Vol 7 (38) ◽  
pp. 62547-62558 ◽  
Author(s):  
Jiufeng Wei ◽  
Guodong Li ◽  
Jinning Zhang ◽  
Yuhui Zhou ◽  
Shuwei Dang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Rectal Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Integrated Analysis ◽  
Genome Wide ◽  
Novel Biomarkers

scholarly journals Integrative analysis of DNA methylation and gene expression profiles identified potential breast cancer-specific diagnostic markers

2020 ◽  
Vol 40 (5) ◽  
Author(s):  
Xinhua Liu ◽  
Yonglin Peng ◽  
Ju Wang
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Diagnostic Model ◽  
Breast Cancer Patients ◽  
Gene Expressions ◽  
Normal Tissues

Abstract Breast cancer is a common malignant tumor among women whose prognosis is largely determined by the period and accuracy of diagnosis. We here propose to identify a robust DNA methylation-based breast cancer-specific diagnostic signature. Genome-wide DNA methylation and gene expression profiles of breast cancer patients along with their adjacent normal tissues from the Cancer Genome Atlas (TCGA) were obtained as the training set. CpGs that with significantly elevated methylation level in breast cancer than not only their adjacent normal tissues and the other ten common cancers from TCGA but also the healthy breast tissues from the Gene Expression Omnibus (GEO) were finally remained for logistic regression analysis. Another independent breast cancer DNA methylation dataset from GEO was used as the testing set. Lots of CpGs were hyper-methylated in breast cancer samples compared with adjacent normal tissues, which tend to be negatively correlated with gene expressions. Eight CpGs located at RIIAD1, ENPP2, ESPN, and ETS1, were finally retained. The diagnostic model was reliable in separating BRCA from normal samples. Besides, chromatin accessibility status of RIIAD1, ENPP2, ESPN and ETS1 showed great differences between MCF-7 and MDA-MB-231 cell lines. In conclusion, the present study should be helpful for breast cancer early and accurate diagnosis.

scholarly journals Integrated analysis of DNA methylation and gene expression profiles identified S100A9 as a potential biomarker in ulcerative colitis

2020 ◽  
Vol 40 (12) ◽  
Author(s):  
Shasha Su ◽  
Wenjie Kong ◽  
Jing Zhang ◽  
Xinguo Wang ◽  
Hongmei Guo
Keyword(s):  
Gene Expression ◽  
Ulcerative Colitis ◽  
Dna Methylation ◽  
Expression Profiles ◽  
Osteoclast Differentiation ◽  
Functional Enrichment ◽  
Functional Network ◽  
Integrated Analysis ◽  
Genome Wide ◽  
Potential Biomarker

Abstract Ulcerative colitis (UC) is a prevalent relapsing-remitting inflammatory bowel disease whose pathogenetic mechanisms remain elusive. In the present study, colonic biopsies samples from three UC patients treated in the Traditional Chinese Medicine Hospital and three healthy controls were obtained. The genome-wide mRNA and lncRNA expression of the samples were profiled through Agilent gene expression microarray. Moreover, the genome-wide DNA methylation dataset of normal and UC colon tissues was also downloaded from GEO for a collaborative analysis. Differential expression of lncRNA (DELs) and mRNAs (DEMs) in UC samples compared with healthy samples were identified by using limma Bioconductor package. Differentially methylated promoters (DMPs) in UC samples compared with controls were obtained through comparing the average methylation level of CpGs located at promoters by using t-test. Functional enrichment analysis was performed by the DAVID. STRING database was applied to the construction of gene functional interaction network. As a result, 2090 DEMs and 1242 DELs were screened out in UC samples that were closely associated with processes related to complement and coagulation cascades, osteoclast differentiation vaccinia, and hemorrhagic diseases. A total of 90 DEMs and 72 DELs were retained for the construction of functional network for the promoters of their corresponding genes were identified as DMPs. S100A9, HECW2, SOD3 and HIX0114733 showed high interaction degrees in the functional network, and expression of S100A9 was confirmed to be significantly elevated in colon tissues of UC patients compared with that of controls by qRT-PCR that was consistent with gene microarray analysis. These indicate that S100A9 could potentially be used as predictive biomarkers in UC.

scholarly journals Identifying Methylation-Driven Immune-related Molecular Subtypes Predicts the Prognosis of Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Qian Yan ◽  
Baoqian Ye ◽  
Boqing Wang ◽  
Wenjiang Zheng ◽  
Xiongwen Wang
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Cpg Sites ◽  
Prognosis Model ◽  
Immune Related Genes

Abstract The purpose of this study is to analyze the DNA methylation and gene expression profiles of immune-related CpG sites to identify the molecular subtypes and CpG sites related to the prognosis of HCC. In this study, the DNA methylation and gene expression datasets were downloaded from The Cancer Genome Atlas database, together with immune-related genes downloaded from the immunology database and analysis portal database to explore the prognostic molecular subtypes of HCC. By performing consistent clustering analysis on 830 immune-related CpG sites, we identified seven subgroups with significant differences in overall survival. Finally, 16 classifiers of immune-related CpG sites were constructed and used in the testing set to verify the prognosis of DNA methylation subgroups, and the results were consistent with the training set. Using the TIMER database, we analyzed 16 immune-related CpG sites expression with the abundance of six types of immune infiltrating cells and found that most are positively correlated with the level of infiltration of multiple immune cells in HCC. This study screened potential immune-related prognostic methylation sites and established a new prognosis model of HCC based on DNA methylation molecular subtype, which may help in the early diagnosis of HCC and developing more effective personalized treatments.

Prospective analysis of mRNA expression signatures as predictive tests in primary breast cancer

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 2531-2531
Author(s):  
J. Hannemann ◽  
H. Halfwerk ◽  
A. Velds ◽  
C. Loo ◽  
E. J. Rutgers ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Complete Remission ◽  
Gene Expression Profiling ◽  
Preoperative Chemotherapy ◽  
Expression Profiling ◽  
Primary Breast Cancer ◽  
Molecular Subtypes ◽  
Expression Profiles ◽  
Gene Expression Profiles

2531 Background: Preoperative chemotherapy is increasingly employed to treat primary breast cancer, allowing an ‘in vivo chemosensitivity test’. Markers which predict a pathological complete response are urgently needed to refine this strategy. This study was conducted to evaluate the use of gene expression profiling to predict response to neoadjuvant anthracycline- or taxane-based chemotherapy. Methods: Patients with operable or locally advanced HER2-negative breast cancer received preoperative chemotherapy: either dose- dense doxorubicin and cyclophosphamide (ddAC) or capecitabine and docetaxel (CD). Core needle biopsies were taken before treatment and gene expression profiling was performed using 35k oligo microarrays. Results: Gene expression profiles were obtained from pretreatment biopsies of 63 tumors. 27% of the patients achieved a (near) pathologic complete remission (pCR), 40% of the patients had a partial remission and 33% of the patients did not respond to chemotherapy. Based on the gene expression profiles, tumors were assigned to the previously identified “molecular subtypes” luminal, basal-like or ERBB2-like (Sorlie et al., PNAS 98: 10869, 2001). 13 out of 25 patients with a basal-like tumor (52%) achieved a complete remission, whereas for the luminal tumors a pCR was only obtained in 2 out of 29 patients. Using four published gene expression classifiers of response to chemotherapy, a reasonable separation between responders and non-responders could be observed for two of these. We also performed exploratory supervised classification analyses on our dataset to identify a novel classifier. This resulted in a classifier for response to therapy irrespective of the chemotherapy regimen used and a second classifier specifically associated with response to ddAC chemotherapy. We will perform validation of these classifiers in samples from patients that are currently being enrolled in the study. Conclusions: Basal-like tumors have a better response to neoadjuvant chemotherapy as compared to other tumor types. The identification of robust gene expression signatures for better response prediction may require larger patient groups and should probably be established separately for each of the molecular subtypes of breast cancer. No significant financial relationships to disclose.

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