Screening of significantly hypermethylated genes in breast cancer using microarray-based methylated-CpG island recovery assay and identification of their expression levels

Lian, Zhenqiang; Wang, Qi; Li, Wenping; Zhang, Anqin; Wu, Ling

doi:10.3892/ijo.2012.1464

Cited by 30 publications

(23 citation statements)

References 22 publications

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“…GDF15 was also reported as an epigenetic biomarker for detection of bladder cancer from DNA-Based analyses of Urine samples [ 24 ]. In a recent study of microarray-based methylated-CpG island recovery assay, hypermethylation and low expression level of ITGA4 were reported to be enriched in breast cancers [ 25 ]. Direct bisulfite sequencing also showed widespread methylation occurring in intragenic regions of the WT1, PAX6 and ITGA4 genes and in the promoter region of the OTX2 gene in breast cancer tissues [ 25 ].…”

Section: Resultsmentioning

confidence: 99%

Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection

et al. 2015

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BackgroundAn enduring challenge in personalized medicine is to select right drug for individual patients. Testing drugs on patients in large clinical trials is one way to assess their efficacy and toxicity, but it is impractical to test hundreds of drugs currently under development. Therefore the preclinical prediction model is highly expected as it enables prediction of drug response to hundreds of cell lines in parallel.MethodsRecently, two large-scale pharmacogenomic studies screened multiple anticancer drugs on over 1000 cell lines in an effort to elucidate the response mechanism of anticancer drugs. To this aim, we here used gene expression features and drug sensitivity data in Cancer Cell Line Encyclopedia (CCLE) to build a predictor based on Support Vector Machine (SVM) and a recursive feature selection tool. Robustness of our model was validated by cross-validation and an independent dataset, the Cancer Genome Project (CGP).ResultsOur model achieved good cross validation performance for most drugs in the Cancer Cell Line Encyclopedia (≥80 % accuracy for 10 drugs, ≥ 75 % accuracy for 19 drugs). Independent tests on eleven common drugs between CCLE and CGP achieved satisfactory performance for three of them, i.e., AZD6244, Erlotinib and PD-0325901, using expression levels of only twelve, six and seven genes, respectively.ConclusionsThese results suggest that drug response could be effectively predicted from genomic features. Our model could be applied to predict drug response for some certain drugs and potentially play a complementary role in personalized medicine.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-015-1492-6) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection

et al. 2015

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“…The fibroblast growth factor (FGF) family exhibits a broad range of mitogenic and cell survival activities. FGF12 was shown to be significantly methylated in breast and colorectal cancer [ 60 , 61 ]. On the other hand, it was found to be overexpressed in lung squamous cell carcinoma [ 62 ].…”

Section: Resultsmentioning

confidence: 99%

Identification of Novel Epigenetic Markers of Prostate Cancer by NotI-Microarray Analysis

et al. 2015

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A significant need for reliable and accurate cancer diagnostics and prognosis compels the search for novel biomarkers that would be able to discriminate between indolent and aggressive tumors at the early stages of disease. The aim of this work was identification of potential diagnostic biomarkers for characterization of different types of prostate tumors. NotI-microarrays with 180 clones associated with chromosome 3 genes/loci were applied to determine genetic and epigenetic alterations in 33 prostate tumors. For 88 clones, aberrations were detected in more than 10% of tumors. The major types of alterations were DNA methylation and/or deletions. Frequent methylation of the discovered loci was confirmed by bisulfite sequencing on selective sampling of genes: FGF12, GATA2, and LMCD1. Three genes (BHLHE40, BCL6, and ITGA9) were tested for expression level alterations using qPCR, and downregulation associated with hypermethylation was shown in the majority of tumors. Based on these data, we proposed the set of potential biomarkers for detection of prostate cancer and discrimination between prostate tumors with different malignancy and aggressiveness: BHLHE40, FOXP1, LOC285205, ITGA9, CTDSPL, FGF12, LOC440944/SETD5, VHL, CLCN2, OSBPL10/ZNF860, LMCD1, FAM19A4, CAND2, MAP4, KY, and LRRC58. Moreover, we probabilistically estimated putative functional relations between the genes within each set using the network enrichment analysis.

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“…NKX3-1, a prostate-specific tumor suppressor gene is the earliest known marker of prostate epithelium during embryogenesis and is subsequently expressed at all stages of prostate differentiation in vivo 39. Hypermethylated NFIX is identified in the breast cancer model40, its differential expression among ER positive tumors as stratified by HER2 status suggests the role of methylation in regulating such a phenotypic difference.…”

Section: Discussionmentioning

confidence: 99%

Exploring the intrinsic differences among breast tumor subtypes defined using immunohistochemistry markers based on the decision tree

Bai

Dai

2016

Sci Rep

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Exploring the intrinsic differences among breast cancer subtypes is of crucial importance for precise diagnosis and therapeutic decision-making in diseases of high heterogeneity. The subtypes defined with several layers of information are related but not consistent, especially using immunohistochemistry markers and gene expression profiling. Here, we explored the intrinsic differences among the subtypes defined by the estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 based on the decision tree. We identified 30 mRNAs and 7 miRNAs differentially expressed along the tree’s branches. The final signature panel contained 30 mRNAs, whose performance was validated using two public datasets based on 3 well-known classifiers. The network and pathway analysis were explored for feature genes, from which key molecules including FOXQ1 and SFRP1 were revealed to be densely connected with other molecules and participate in the validated metabolic pathways. Our study uncovered the differences among the four IHC-defined breast tumor subtypes at the mRNA and miRNA levels, presented a novel signature for breast tumor subtyping, and identified several key molecules potentially driving the heterogeneity of such tumors. The results help us further understand breast tumor heterogeneity, which could be availed in clinics.

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Screening of significantly hypermethylated genes in breast cancer using microarray-based methylated-CpG island recovery assay and identification of their expression levels

Cited by 30 publications

References 22 publications

Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection

Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection

Identification of Novel Epigenetic Markers of Prostate Cancer by NotI-Microarray Analysis

Exploring the intrinsic differences among breast tumor subtypes defined using immunohistochemistry markers based on the decision tree

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