Abundant NDRG2 Expression Is Associated with Aggressiveness and Unfavorable Patients’ Outcome in Basal-Like Breast Cancer

Kloten, Vera; Schlensog, Martin; Eschenbruch, Julian; Gasthaus, Janina; Tiedemann, Janina; Mijnes, Jolein; Heide, Timon; Braunschweig, Till; Knüchel, Ruth; Dahl, Edgar

doi:10.1371/journal.pone.0159073

Cited by 17 publications

(17 citation statements)

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“…By integrating these analyses, we identified 131 genes which were characterized as PPS-specific CN gains and which were overexpressed at the transcript and protein levels in PPS High tumours. By assessing individual genes, we identified a number of known oncogenes including: BCLAF1 [40], YWHAB [41,42], NDRG2 [43], NFYB [44], PAX8 [45,46], PSMD14 [47]. A number of other genes that represent FDA approved drug targets including TOP1, PARP1, HDAC2 , and BRD4 were also included on this list of genes.…”

Section: Resultsmentioning

confidence: 99%

Integrative proteogenomic analyses of human tumours identifies ADNP as a novel oncogenic mediator of cell cycle progression in high-grade serous ovarian cancer with poor prognosis

et al. 2019

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BackgroundDespite toxic side effects and limited durable response, the current standard-of-care treatment for high grade serous ovarian cancer (HGSOC) remains platinum/taxane-based chemotherapy. Given that the overall prognosis has not improved drastically over the past several decades, there is a critical need to understand the underlying mechanisms that lead to tumour development and progression.MethodsWe utilized an integrative proteogenomic analysis of HGSOC tumours applying a poor prognosis gene expression signature (PPS) as a conceptual framework to analyse orthogonal genomic and proteomic data from the TCGA (n = 488) and CPTAC (n = 169) studies. Genes identified through in silico analyses were assessed in vitro studies to demonstrate their impact on proliferation and cell cycle progression.FindingsThese analyses identified DNA amplification and overexpression of the transcription factor ADNP (Activity Dependent Neuroprotector Homeobox) in poorly prognostic tumours. Validation studies confirmed the prognostic capacity of ADNP and suggested an oncogenic role for this protein given the association between ADNP expression and pro-proliferative signalling. In vitro studies confirmed ADNP as a novel and essential mediator of cell proliferation through dysregulation of cell cycle checkpoints.InterpretationWe identified ADNP as being amplified and overexpressed in poor prognosis HGSOC in silico analyses and demonstrated that ADNP is a novel and essential oncogene in HGSOC which mediates proliferation through dysregulation of cell cycle checkpoints in vitro.FundingThe National Cancer Institute of the National Institutes of Health, the V Foundation for Cancer Research and the New Jersey Commission for Cancer Research.

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Section: Resultsmentioning

confidence: 99%

Integrative proteogenomic analyses of human tumours identifies ADNP as a novel oncogenic mediator of cell cycle progression in high-grade serous ovarian cancer with poor prognosis

et al. 2019

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“…The differentially expressed proteins were more comprehensive and subtype-specific in the former data set, because of its larger the sample size and higher sequence coverage. Particularly, for the triple-negative subtype, which has been characterized by the high abundances of basal-like cytokeratins ( 56 ), SMOC1, S100B, GSTA1, SFRP1, S100A1, PTX3, SOX10, ANGPT2, COCH, as well as many other known markers such as SYNM1 ( 57 ), MFI2 ( 58 ), NDRG2 ( 59 ), CRYAB ( 60 ), and PLA2G4A ( 61 ) were found on top of the list of differential regulated proteins in the CPTAC data ( supplemental Table S3 ). Markers are located on the scatter plot based on the fold changes relative to the three subtypes (axes) in log-scale.…”

Section: Resultsmentioning

confidence: 99%

Covariation of Peptide Abundances Accurately Reflects Protein Concentration Differences

Zhang

Pirmoradian

Zubarev

et al. 2017

Molecular & Cellular Proteomics

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Most implementations of mass spectrometry-based proteomics involve enzymatic digestion of proteins, expanding the analysis to multiple proteolytic peptides for each protein. Currently, there is no consensus of how to summarize peptides' abundances to protein concentrations, and such efforts are complicated by the fact that error control normally is applied to the identification process, and do not directly control errors linking peptide abundance measures to protein concentration. Peptides resulting from suboptimal digestion or being partially modified are not representative of the protein concentration. Without a mechanism to remove such unrepresentative peptides, their abundance adversely impacts the estimation of their protein's concentration. Here, we present a relative quantification approach, Diffacto, that applies factor analysis to extract the covariation of peptides' abundances. The method enables a weighted geometrical average summarization and automatic elimination of incoherent peptides. We demonstrate, based on a set of controlled label-free experiments using standard mixtures of proteins, that the covariation structure extracted by the factor analysis accurately reflects protein concentrations. In the 1% peptide-spectrum match-level FDR data set, as many as 11% of the peptides have abundance differences incoherent with the other peptides attributed to the same protein. If not controlled, such contradicting peptide abundance have a severe impact on protein quantifications. When adding the quantities of each protein's three most abundant peptides, we note as many as 14% of the proteins being estimated as having a negative correlation with their actual concentration differences between samples. Diffacto reduced the amount of such obviously incorrectly quantified proteins to 1.6%. Furthermore, by analyzing clinical data sets from two breast cancer studies, our method revealed the persistent proteomic signatures linked to three subtypes of breast cancer. We conclude that Diffacto can facilitate the interpretation and enhance the utility of most types of proteomics data.

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“…N-myc has also been found to induce miR-17-92 expression in medulloblastomas [45]. NDRG2, N-myc downstream-regulated gene 2, has been found to be significantly higher in triple-negative breast cancers compared to other subtypes [46], again indicating that differences in expression and activity of transcription factors targeting the miR-17-92 promoter vary between breast cancer subtypes and can explain differences in miR-17-92 expression.…”

Section: Discussionmentioning

confidence: 99%

Differential expression of the miR-17-92 cluster and miR-17 family in breast cancer according to tumor type; results from the Norwegian Women and Cancer (NOWAC) study

Moi¹,

Braaten²,

Al-Shibli³

et al. 2019

J Transl Med

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Background MicroRNAs (miRNAs) are promising biomarkers due to their structural stability and distinct expression profile in various cancers. We wanted to explore the miRNA expression in benign breast tissue and breast cancer subgroups in the Norwegian Women and Cancer study. Methods Specimens and histopathological data from study participants in Northern Norway diagnosed with breast cancer, and benign tissue from breast reduction surgery were collected. Main molecular subtypes were based on surrogate markers; luminal A (ER+ and/or PR+, HER2− and Ki67 ≤ 30%), luminal B (ER+ and/or PR+, HER2− and Ki67 > 30% or ER+ and/or PR+ and HER2+), HER2 positive (ER− and PR− and HER2+) and triple-negative (ER−, PR− and HER2−). RNA was extracted from formalin-fixed paraffin-embedded (FFPE) tissue, and miRNAs were successfully analyzed in 102 cancers and 36 benign controls using the 7th generation miRCURY LNA microarray containing probes targeting all human miRNAs as annotated in miRBASE version 19.0. Validation with RT-qPCR was performed. Results On average, 450 miRNAs were detected in each sample, and 304 miRNAs were significantly different between malignant and benign tissue. Subgroup analyses of cancer cases revealed 23 miRNAs significantly different between ER+ and ER− tumors, and 47 miRNAs different between tumors stratified according to grade. Significantly higher levels were found in high grade tumors for miR-17-5p (p = 0.006), miR-20a-5p (p = 0.007), miR-106b-5p (p = 0.007), miR-93-5p (p = 0.007) and miR-25-3p (p = 0.015) from the paralogous clusters miR-17-92 and miR-106b-25. Expression of miR-17-5p (p = 0.0029), miR-20a-5p (p = 0.0021), miR-92a-3p (p = 0.011) and miR-106b-5p (p = 0.021) was significantly higher in triple-negative tumors compared to the rest, and miR-17-5p and miR-20a-5p were significantly lower in luminal A tumors. Conclusions miRNA expression profiles were significantly different between malignant and benign tissue and between cancer subgroups according to ER− status, grade and molecular subtype. miRNAs in the miR-17-92 cluster and miR-17 family were overexpressed in high grade and triple-negative tumors associated with aggressive behavior. The expression and functional role of these miRNAs should be further studied in breast cancer to explore their potential as biomarkers in diagnostic pathology and clinical oncology.

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Abundant NDRG2 Expression Is Associated with Aggressiveness and Unfavorable Patients’ Outcome in Basal-Like Breast Cancer

Cited by 17 publications

References 50 publications

Integrative proteogenomic analyses of human tumours identifies ADNP as a novel oncogenic mediator of cell cycle progression in high-grade serous ovarian cancer with poor prognosis

Integrative proteogenomic analyses of human tumours identifies ADNP as a novel oncogenic mediator of cell cycle progression in high-grade serous ovarian cancer with poor prognosis

Covariation of Peptide Abundances Accurately Reflects Protein Concentration Differences

Differential expression of the miR-17-92 cluster and miR-17 family in breast cancer according to tumor type; results from the Norwegian Women and Cancer (NOWAC) study

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