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Multidrug resistance-linked gene signature predicts overall survival of patients with primary ovarian serous carcinoma.

ABSTRACT: PURPOSE:This study assesses the ability of multidrug resistance (MDR)-associated gene expression patterns to predict survival in patients with newly diagnosed carcinoma of the ovary. The scope of this research differs substantially from that of previous reports, as a very large set of genes was evaluated whose expression has been shown to affect response to chemotherapy. EXPERIMENTAL DESIGN:We applied a customized TaqMan low density array, a highly sensitive and specific assay, to study the expression profiles of 380 MDR-linked genes in 80 tumor specimens collected at initial surgery to debulk primary serous carcinoma. The RNA expression profiles of these drug resistance genes were correlated with clinical outcomes. RESULTS:Leave-one-out cross-validation was used to estimate the ability of MDR gene expression to predict survival. Although gene expression alone does not predict overall survival (OS; P = 0.06), four covariates (age, stage, CA125 level, and surgical debulking) do (P = 0.03). When gene expression was added to the covariates, we found an 11-gene signature that provides a major improvement in OS prediction (log-rank statistic P < 0.003). The predictive power of this 11-gene signature was confirmed by dividing high- and low-risk patient groups, as defined by their clinical covariates, into four specific risk groups on the basis of expression levels. CONCLUSION:This study reveals an 11-gene signature that allows a more precise prognosis for patients with serous cancer of the ovary treated with carboplatin- and paclitaxel-based therapy. These 11 new targets offer opportunities for new therapies to improve clinical outcome in ovarian cancer.

SUBMITTER: Gillet JP

PROVIDER: S-EPMC3376649 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Multidrug resistance-linked gene signature predicts overall survival of patients with primary ovarian serous carcinoma.

Gillet Jean-Pierre JP Calcagno Anna Maria AM Varma Sudhir S Davidson Ben B Bunkholt Elstrand Mari M Ganapathi Ram R Kamat Aparna A AA Sood Anil K AK Ambudkar Suresh V SV Seiden Michael V MV Rueda Bo R BR Gottesman Michael M MM

Clinical cancer research : an official journal of the American Association for Cancer Research 20120405 11

<h4>Purpose</h4>This study assesses the ability of multidrug resistance (MDR)-associated gene expression patterns to predict survival in patients with newly diagnosed carcinoma of the ovary. The scope of this research differs substantially from that of previous reports, as a very large set of genes was evaluated whose expression has been shown to affect response to chemotherapy.<h4>Experimental design</h4>We applied a customized TaqMan low density array, a highly sensitive and specific assay, to ...[more]

PMID: 22492981

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Project description:BackgroundHigh-grade serious ovarian carcinoma (HGSOC) is a subtype of ovarian cancer with a different prognosis attributable to genetic heterogeneity. The prognosis of patients with advanced HGSOC requires prediction by genetic markers. This study systematically analyzed gene expression profile data to establish a genetic marker for predicting HGSOC prognosis.MethodsThe RNA-seq data set and information on clinical follow-up of HGSOC were retrieved from Gene Expression Omnibus (GEO) database, and the data were standardized by DESeq2 as a training set. On the other hand, HGSOC RNA sequence data and information on clinical follow-up were retrieved from The Cancer Genome Atlas (TCGA) as a test set. Additionally, ovarian cancer microarray data set was obtained from GEO as the external validation set. Prognostic genes were screened from the training set, and characteristic selection was performed using the least absolute shrinkage and selection operator (LASSO) with 80% re-sampling for 5000 times. Genes with a frequency of more than 2000 were selected as robust biomarkers. Finally, a gene-related prognostic model was validated in both the test and GEO validation sets.ResultsA total of 148 genes were found to be significantly correlated with HGSOC prognosis. The expression profile of these genes could stratify HGSOC prognosis and they were enriched to multiple tumor-related regulatory pathways such as tyrosine metabolism and AMPK signaling pathway. AKR1B10 and ANGPT4 were obtained after 5000-time re-sampling by LASSO regression. AKR1B10 was associated with the metastasis and progression of several tumors. In this study, Cox regression analysis was performed to create a 2-gene signature as an independent prognostic factor for HGSOC, which has the ability to stratify risk samples in all three data sets (p < 0.05). The Gene Set Enrichment Analysis (GSEA) discovered abnormally active REGULATION_OF_AUTOPHAGY and OLFACTORY_TRANSDUCTION pathways in the high-risk group samples.ConclusionThis study resulted in the creation of a 2-gene molecular prognostic classifier that distinguished clinical features and was a promising novel prognostic tool for assessing the prognosis of HGSOC. RiskScore was a novel prognostic model which might be effective in guiding accurate prognosis of HGSOC.

Project description:Hepatocellular carcinoma (HCC) is one of the most common public health challenges, worldwide. Because of molecular complexity and tumor heterogeneity, there are no effective predictive models for prognosis of HCC. This underlines the unmet need for accurate prognostic models for HCC. Analysis of GSE14520 data from gene omnibus (GEO) database identified multiple differentially expressed mRNAs (DEMs) between HCC and normal tissues. After randomly stratifying the patients into the training and testing groups, we performed univariate, lasso, and multivariable Cox regression analyses to delineate the prognostic gene signature in training set. We then used Kaplan-Meier plot, time-dependent receiver operating characteristic (ROC), multivariable Cox regression analysis of clinical information, nomogram, and decision curve analysis (DCA) to evaluate the predictive and overall survival value of a novel five-gene signature (CNIH4, SOX4, SPP1, SORBS2, and CCL19) within and across sets, separately and combined. We also validated the prognostic value of the five-gene signature using The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC), GSE54236 and International Cancer Genome Consortium (ICGC) sets. Multivariable Cox regression analysis revealed that the five-gene signature and tumor node metastasis (TNM) stage were independent prognostic factors for overall survival of HCC patients in GSE14520 and TCGA-LIHC. Combining TNM stage clinical pathological parameters and nomogram greatly improved the prognosis prediction of HCC. Further gene set enrichment analysis (GSEA) revealed enrichment of KEGG pathways related to cell cycle in the high-risk group and histidine metabolism in the low-risk group. Finally, all these five mRNAs are overexpressed between 12 pairs of HCC and adjacent normal tissues by quantitative real-time PCR validation. In brief, a five-gene prognostic signature and a nomogram were identified and constructed, respectively, and further validated for their HCC prognostic value. The five-gene risk score together with TNM stage models could aid in rationalizing customized therapies in HCC patients.

Dataset Information

Multidrug resistance-linked gene signature predicts overall survival of patients with primary ovarian serous carcinoma.

Publications

Multidrug resistance-linked gene signature predicts overall survival of patients with primary ovarian serous carcinoma.

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