Unknown,Transcriptomics,Genomics,Proteomics

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CHAC1 mRNA expression is a strong prognostic biomarker in breast and ovarian cancer

ABSTRACT: Extracellular, cancer-specific methylated DNA has been shown to be a prognostic marker when detected in serum or plasma. In this study we investigated the effect of treating cancer cells with differentially methylated CpG DNA. When breast cancer cell lines were treated with methylated CpG DNA, a consistent upregulation of CHAC1 mRNA expression was observed. CHAC1 was recently described to be a novel component of the unfolded protein response pathway. To elucidate the role of CHAC1 mRNA expression in cancer in more detail, we analyzed expression of this gene in breast (n=107) and ovarian cancer (n=107) and found a strong correlation with tumor differentiation. Poorly differentiated tumors exhibited higher CHAC1 expression levels (p=0.004 for breast and p=0.031 for ovarian cancer). Additionally, hormone receptor (HR)-negative breast cancers (p<0.001) and advanced stage disease ovarian cancers (p=0.026) also demonstrated high CHAC1 mRNA levels. mRNA expression analysis of the two known CHAC1 isoforms showed a strong association of expression above the median with poor outcome in breast cancer patients in a multivariate analysis (isoform a: relative risk (RR) of death 3.2 (95% CI 1.6-6.5; p<0.01); RR of relapse 3.9 (95% CI 1.6-9.8; p<0.01); isoform b: relative risk (RR) of death 3.5 (95% CI 1.6-7.3; p<0.01); RR of relapse 6.6 (95% CI 2.4-18.5; p<0.01)). Univariate analysis in ovarian cancer showed that CHAC1 mRNA expression above the median was associated with a poor relapse free survival (p=0.03). In younger ovarian cancer patients (age < median age), a high CHAC1 mRNA expression was associated with overall survival (p=0.007) and relapse free survival (p=0.015). Finally, we show that downregulation of CHAC1 by small interfering RNA suppressed breast cancer cell migration and proliferation, whereas overexpression resulted in an observed increase in these cellular behaviours. This is the first report demonstrating that a gene (CHAC1) whose expression is triggered by methylated, but not unmethylated DNA, is involved in tumour biology. Human breast cancer cell lines (BT-20, Hs578T) were treated with methylated and unmethylated DNA oligos, and differentially expressed genes were identified between treatments with methylated and un-methylated oligos.

ORGANISM(S): Homo sapiens

SUBMITTER: Johannes Rainer

PROVIDER: E-GEOD-22865 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Elevated mRNA expression of CHAC1 splicing variants is associated with poor outcome for breast and ovarian cancer patients.

Goebel G G Berger R R Strasak A M AM Egle D D Müller-Holzner E E Schmidt S S Rainer J J Presul E E Parson W W Lang S S Jones A A Widschwendter M M Fiegl H H

British journal of cancer 20111122 1

<h4>Background</h4>The role of CHAC1 (cation transport regulator-like protein 1), a recently identified component of the unfolded protein response (UPR) pathway, in gynaecological cancers has not yet been characterised. Now, this work illustrates CHAC1 mRNA expression and associated clinical outcome in breast and ovarian cancer.<h4>Methods</h4>The prognostic value of CHAC1 and its two transcript variants was investigated in 116 breast and 133 ovarian tissues using quantitative real-time reverse- ...[more]

PMID: 22108517

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Project description:Extracellular, cancer-specific methylated DNA has been shown to be a prognostic marker when detected in serum or plasma. In this study we investigated the effect of treating cancer cells with differentially methylated CpG DNA. When breast cancer cell lines were treated with methylated CpG DNA, a consistent upregulation of CHAC1 mRNA expression was observed. CHAC1 was recently described to be a novel component of the unfolded protein response pathway. To elucidate the role of CHAC1 mRNA expression in cancer in more detail, we analyzed expression of this gene in breast (n=107) and ovarian cancer (n=107) and found a strong correlation with tumor differentiation. Poorly differentiated tumors exhibited higher CHAC1 expression levels (p=0.004 for breast and p=0.031 for ovarian cancer). Additionally, hormone receptor (HR)-negative breast cancers (p<0.001) and advanced stage disease ovarian cancers (p=0.026) also demonstrated high CHAC1 mRNA levels. mRNA expression analysis of the two known CHAC1 isoforms showed a strong association of expression above the median with poor outcome in breast cancer patients in a multivariate analysis (isoform a: relative risk (RR) of death 3.2 (95% CI 1.6-6.5; p<0.01); RR of relapse 3.9 (95% CI 1.6-9.8; p<0.01); isoform b: relative risk (RR) of death 3.5 (95% CI 1.6-7.3; p<0.01); RR of relapse 6.6 (95% CI 2.4-18.5; p<0.01)). Univariate analysis in ovarian cancer showed that CHAC1 mRNA expression above the median was associated with a poor relapse free survival (p=0.03). In younger ovarian cancer patients (age < median age), a high CHAC1 mRNA expression was associated with overall survival (p=0.007) and relapse free survival (p=0.015). Finally, we show that downregulation of CHAC1 by small interfering RNA suppressed breast cancer cell migration and proliferation, whereas overexpression resulted in an observed increase in these cellular behaviours. This is the first report demonstrating that a gene (CHAC1) whose expression is triggered by methylated, but not unmethylated DNA, is involved in tumour biology.

Project description:summary Ovarian cancer is the leading cause of death among gynecologic malignancies. This is partly due to a non-durable response to chemotherapy. Prediction of resistance to chemotherapy could be a key role in more personalized treatment. In the current study we aimed to examine if microRNA based predictors could predict resistance to chemotherapy in ovarian cancer, and to investigate if the predictors could be prognostic factors for progression free and overall survival. Methods Predictors of chemotherapy-resistance were developed based on correlation between miRNA expression and differences in measured growth inhibition in a variety of human cancer cell lines in the presence of Carboplatin, Paclitaxel and Docetaxel (deposited under accession number E-MTAB-327 in the ArrayExpress database of the European Bioinformatics Institute, 2012). These predictors were then, retrospectively, blindly validated in a cohort of 170 epithelial ovarian cancer patients treated with Carboplatin and Paclitaxel or Docetaxel as first line treatment. Results In a multivariate cox proportional analysis the predictors of chemotherapy-resistance were not able to predict time to progression after end of chemotherapy (hazard ratio: 0.64, 95% CI: 0.36 – 1.12, P=0.117). However, in a multivariate logistic analysis, where time to progression was considered as either more or less than 6 months, the predictors match clinical observed chemotherapy-resistance (odds ratio: 0.19, 95% CI: 0.05-0.73, P=0.015). Neither univariate nor multivariate, time-dependent, cox analysis for progression free survival (PFS) or overall survival (OS) in all 170 patients showed to match predicted resistance to chemotherapy (PFS: hazard ratio: 0.69, 95% CI: 0.40-1.19, P=0.183, OS: hazard ratio: 0.76, 95% CI: 0.42-1.40, P=0.386). Conclusion In the current study, microRNA based predictors of chemotherapy-resistance did not demonstrate any convincing correlation to clinical observed chemotherapy-resistance, progression free survival, or overall survival, in patients with epithelial ovarian cancer. However the predictors did reflect relapse more or less than 6 months.

Project description:Background. Although gene expression arrays have been used to generate molecular predictors of relapse and drug sensitivity in breast cancer, no large study describes genes and exons differentially expressed between breast cancer and benign lesions.<br><br> Methods. One hundred and sixty-five tumour samples were obtained by fine needle aspiration (FNA). cDNA were hybridized on Splice ArrayTM. A nearest centroid prediction rule was developed to classify lesions as malignant or benign on a training set, and its performance evaluated on an independent validation set. A two-way ANOVA model was used to identify probesets that present a differential expression between cancer and benign lesions while adjusting for scan dates. P-values were adjusted for False Discovery Rate.<br><br>Findings. Overall 120 breast cancers and 45 benign lesions were included in the study. A 1228-probeset molecular classifier for breast cancer diagnosis was generated from the training set (n=94). This signature accurately classified all samples (100% accuracy, 95% exact CI: 96-100%). In the validation set (n=71), the molecular predictor accurately classified 68 out of 71 tumours (96%, 95% CI: 88-99%). When the 165 samples were taken into account, 37 858 exon-probesets (5.4%) and 3733 genes (20%) were found to be differentially expressed between malignant and benign conditions (adjusted p-value<0.05). Pathway analyses showed that genes involved in spliceosome assembly were significantly enriched in malignant condition (permutation p=0.002). In the same population of 165 samples, 956 exon-probesets presented both a higher intensity and higher splice index in breast cancer, although located on unchanged genes.<br><br> Interpretation. The present study provides a thorough description of differentially expressed exons between breast cancer and benign lesions, and emphasizes the contribution of spliceosome and alternative transcripts to the molecular portrait of breast malignancy. This allowed the development of a molecular classifier for breast cancer diagnosis using FNA.

Project description:To identify and evaluate the prognostic ability of progression-free survival-related profile for advanced-stage ovarian cancer, advanced-stage serous ovarian cancer tissues from 110 patients who received primary surgery and a platinum/taxane-based chemotherapy were profiled using oligonucleotide microarrays of more than 40,000 transcripts. We first selected 88 genes by a univariate Cox proportional hazard analysis (p<0.01) and next optimized regression coefficients by ridge regression model using 10-fold cross-validation. The prognostic index was independently associated with PFS time compared to other clinical factors in multivariate analysis [hazard ratio (HR), 3.72; 95% confidence interval (CI), 2.66–5.43; p,0.0001]. In an external dataset, multivariate analysis revealed that this prognostic index was significantly correlated with PFS time (HR, 1.54; 95% CI, 1.20–1.98; p = 0.0008). Furthermore, the correlation between the prognostic index and overall survival time was confirmed in the two independent external datasets (log rank test, p = 0.0010 and 0.0008). In multivariable analysis, our prognostic index was independently associated with progression-free survival times compared to other clinical factors (p<0.001). Furthermore, the prognostic ability of our prognostic index was validated in external, publicly available dataset (n = 87), and was proved in multivariate analysis (p = 0.0032). These results suggest that disease progression or recurrence of advanced-stage serous ovarian cancer can be predicted by gene expression profile. The prognostic ability of our index based on the 88-gene expression profile in ridge regression Cox hazard model was shown to be independent of other clinical factors in predicting cancer prognosis across two distinct datasets. Further study will be necessary to improve predictive accuracy of the prognostic index toward clinical application for evaluation of the risk of recurrence in patients with advanced-stage serous ovarian cancer.

Dataset Information

CHAC1 mRNA expression is a strong prognostic biomarker in breast and ovarian cancer

Publications

Elevated mRNA expression of CHAC1 splicing variants is associated with poor outcome for breast and ovarian cancer patients.

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