Novel markers of gene methylation and expression in breast cancer

Kuznetsova, Ekaterina; Кекеева, Т. В.; Larin, S. S.; Zemlyakova, V. V.; Бабенко, О. В.; Немцова, М. В.; Zaletayev, D. V.; Стрельников, В. В.

doi:10.1134/s0026893307040061

Cited by 23 publications

(15 citation statements)

References 15 publications

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“…To our knowledge, hypermethylation of KCNH5 and KCNH8, two voltage-gated, ether a go-go (Eag 1) family potassium channels, has not been previously studied with regards to human cancers, although KCNH2 has been reported to be downregulated in breast cancer (54). The role of potassium channels in cancer has gained increasing interest in recent years as a possible target for tumor therapy (55, 56).…”

Section: Discussionmentioning

confidence: 99%

DNA hypermethylation of tumors from non-small cell lung cancer (NSCLC) patients is associated with gender and histologic type

Hawes¹,

Stern²,

Feng³

et al. 2010

Lung Cancer

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Section: Discussionmentioning

confidence: 99%

DNA hypermethylation of tumors from non-small cell lung cancer (NSCLC) patients is associated with gender and histologic type

Hawes¹,

Stern²,

Feng³

et al. 2010

Lung Cancer

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“…The numbers of breast cancer related genes identified by existing methods (Table 1) were 8, 8, 4, 3, and 2 for MOST, ORT, OS, the t -statistics, and COPA, respectively. However, our proposed method (LSOSS) has identified 9 breast cancer related genes: KCNH2,7 NEO1,8 MAGEA3,9 ENG,10 GABRG2,11 ATM,12 NUP88,13 CYP3A714 and PMP22 15. Although it should not be treated as a golden standard method for evaluating different statistical tools, this type of analysis generally validates the statistical results and highlights their biological relevance.…”

Section: Resultsmentioning

confidence: 75%

LSOSS: Detection of Cancer Outlier Differential Gene Expression

Wang

Rekaya

2010

Biomark�Insights

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Detection of differential gene expression using microarray technology has received considerable interest in cancer research studies. Recently, many researchers discovered that oncogenes may be activated in some but not all samples in a given disease group. The existing statistical tools for detecting differentially expressed genes in a subset of the disease group mainly include cancer outlier profile analysis (COPA), outlier sum (OS), outlier robust t-statistic (ORT) and maximum ordered subset t-statistics (MOST). In this study, another approach named Least Sum of Ordered Subset Square t-statistic (LSOSS) is proposed. The results of our simulation studies indicated that LSOSS often has more power than previous statistical methods. When applied to real human breast and prostate cancer data sets, LSOSS was competitive in terms of the biological relevance of top ranked genes. Furthermore, a modified hierarchical clustering method was developed to classify the heterogeneous gene activation patterns of human breast cancer samples based on the significant genes detected by LSOSS. Three classes of gene activation patterns, which correspond to estrogen receptor (ER)+, ER− and a mixture of ER+ and ER−, were detected and each class was assigned a different gene signature.

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“…Of interest, this study detected MEG3 co-expressed with two protein coding genes LAMC3 and C8orf58. Significant methylation was found in the promoter region of LAMC3 in breast cancer (Kuznetsova et al 2007). HAND2-AS1, also known as DEIN, was first identified by Voth et al (2007) displaying high expression level in stage IVS neuroblastoma.…”

Section: Discussionmentioning

confidence: 99%

Identification of important long non-coding RNAs and highly recurrent aberrant alternative splicing events in hepatocellular carcinoma through integrative analysis of multiple RNA-Seq datasets

Zhang

Liu²,

Zhang

et al. 2015

Mol Genet Genomics

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Hepatocellular carcinoma (HCC) is an aggressive and deadly cancer. The molecular pathogenesis of the disease remains poorly understood. To better understand HCC biology and explore potential biomarkers and therapeutic targets, we investigated the whole transcriptome of HCC. Considering the genetic heterogeneity of HCC, four datasets from four studies consisting of 15 pairs of HCC and adjacent normal samples were analyzed. We observed that the number of lncRNAs expressed in each HCC sample was consistently greater than the adjacent normal sample. Moreover, 15 lncRNAs were identified expressed in five to seven HCC tissues but were not detected in any adjacent normal tissue. Differential expression analysis detected 35 up- and 80 down-regulated lncRNAs in HCC samples compared with adjacent normal samples. In addition, five differentially expressed lncRNAs were predicted to play a role in oxidation and reduction process. With regard to splicing alterations, we identified nine highly recurrent differential splicing events belonging to eight genes USO1, RPS24, CCDC50, THNSL2, NUMB, FN1 (two events), SLC39A14 and NR1I3. Of them, splicing alterations of SLC39A14 and NR1I3 were reported for the association with HCC for the first time. The splicing dysregulation in HCC may be influenced by three splicing factors ESRP2, CELF2 and SRSF5 which were significantly down-regulated in HCC samples. This study revealed uncharacterized aspects of HCC transcriptome and identified important lncRNAs and splicing isoforms with the potential to serve as biomarkers and therapeutic targets for the disease.

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Novel markers of gene methylation and expression in breast cancer

Cited by 23 publications

References 15 publications

DNA hypermethylation of tumors from non-small cell lung cancer (NSCLC) patients is associated with gender and histologic type

DNA hypermethylation of tumors from non-small cell lung cancer (NSCLC) patients is associated with gender and histologic type

LSOSS: Detection of Cancer Outlier Differential Gene Expression

Identification of important long non-coding RNAs and highly recurrent aberrant alternative splicing events in hepatocellular carcinoma through integrative analysis of multiple RNA-Seq datasets

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