A 10‑gene expression signature of Notch pathway predicts recurrence in ovarian carcinoma

  • Authors:
    • Fang Chen
    • Naifu Liu
  • View Affiliations

  • Published online on: June 16, 2015     https://doi.org/10.3892/ol.2015.3382
  • Pages: 1704-1708
Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

Patients with ovarian carcinoma are at high risk of tumor recurrence. In the present study, 81 Notch pathway genes were selected to find recurrence‑related genes in The Cancer Genome Atlas dataset. A 10‑gene signature (FZD4, HES1, PSEN2, JAG2, PPARG, FOS, HEY1, CDC16, MFNG, and EP300) was identified and validated that is associated with recurrence‑free survival time, but not with overall survival time, in the TCGA dataset and in other two independent datasets, GSE9891 and GSE30161. This gene signature gave a significant performance in discriminating patients at high risk of recurrence from those at low risk, as measured by the area under the receiver operating characteristic curve. Cox proportional hazards regression analyses demonstrated that the prognostic value of this 10‑gene set is independent of other clinical variables in all three datasets. The potential as a biomarker for predicting high‑ and low‑risk subgroups for recurrence in ovarian cancer patients deserves further investigation in prospective patient cohorts in the future.

Introduction

Ovarian cancer is one of the most lethal malignant gynecological cancers worldwide (1). Compared with other cancers in women, ovarian carcinoma confers a relatively high risk of recurrence. Although there is a high initial response rate to standard surgery and chemotherapy, 30–40% of patients relapse within one year (2,3). Therefore, the prediction of patients at a high risk of recurrence may provide novel therapeutic avenues to improve their outcomes. Although common clinicopathological parameters, such as stage and histological grade, and several biomarkers were proposed for recurrence prediction, these factors demonstrated insufficient sensitivity and specificity (4). Thus, there is an urgent requirement to identify novel markers or models to increase the power of recurrence prediction for patients with ovarian carcinoma.

The Notch pathway and its abundant associated genes comprise a complicated network, which plays a significant role in the progressive growth of tumor cells in multiple cancer types (5). The Notch pathway alterations are prevalent and significantly associated with poor outcomes, including early recurrence in ovarian carcinoma (6,7). We speculate that Notch pathway associated molecular signatures may be useful for characterizing ovarian carcinomas at high risk of recurrence. In the present study, a 10-gene Notch pathway signature is defined that may assist in improved predictions of recurrence in ovarian carcinoma patients.

Materials and methods

Datasets

Three ovarian carcinoma gene expression datasets [The Cancer Genome Atlas (TCGA), GSE9891 and GSE30161] with documented recurrence information were selected for analysis in the present study (810). The expression data together with the curated and documented clinical metadata were extracted by the R curated Ovarian Data Bioconductor package, as previously described (11). Microarray platforms used in these datasets were Affy HT U133a (TCGA) and Affy U133 Plus 2.0 (GSE9891 and GSE30161). TCGA dataset comprises the whole-genome mRNA expression data of 522 ovarian carcinoma samples. The GSE9891 dataset comprises the gene expression microarray data of 275 ovarian carcinoma samples, including 40 early-stage and 257 late-stage tumors. The GSE30161 dataset was generated from 58 late-stage ovarian cancer samples, and 5 arrays were excluded due to the lack of complete recurrence information.

Finding of genes correlated with recurrence in TCGA ovarian carcinoma dataset

The expression data of a subset of 81 Notch pathway-associated genes (including core Notch pathway members, Notch pathway target genes, genes that crosstalk with Notch pathway and other genes involved in Notch pathway) were selected from TCGA ovarian carcinoma dataset. Cox proportional hazards model was used to test whether the gene expression of a particular gene significantly influenced recurrence using the BRB-Arraytools software (12). The tests were performed at a significance threshold of univariate tests of 0.05 using permutation tests. The number of permutation tests was set as 10,000.

Recurrence-free survival (RFS) and overall survival (OS) time prediction based on the supervised principal components method using the 10-Notch pathway gene signature

After subseting the gene expression data using the 10-Notch pathway gene signature, recurrence and survival risk prediction in TCGA, GSE9891 and GSE30161 datasets were performed based on principal components using the BRB-ArrayTools software. 10-fold cross validation was selected, and the number of principal components was set as 2. The prognostic index percentile was used to separate arrays into high- and low-risk groups.

Statistical analysis

Distributions of RFS and OS were assessed using the Kaplan-Meier curve method and evaluated by the log-rank test. Multivariate analyses of prognostic factors were based on the Cox proportional hazards model. The receiver operating characteristic (ROC) curve was constructed using R package survival ROC and determined by permutation testing. The difference in clinicopathological characteristics between the high- and low-risk subgroups was determined by χ2 test. All the statistical analyses were performed with Medcalc 11.4 Software unless otherwise specified. P<0.05 was considered to indicate a statistically significant difference.

Results

We hypothesized that Notch pathway genes correlated with recurrence may be clinically useful to differentiate between high- and low-risk ovarian carcinoma tumors. To investigate this, the BRB-Arraytool package was used to find Notch pathway genes with expression that was correlated with RFS time by univariate tests. As shown in Table I, using a significance threshold at 0.05, a list of 10 Notch pathway genes was determined to be associated with RFS time in TCGA ovarian carcinoma dataset.

Table I.

Notch pathway genes correlated with recurrence in The Cancer Genome Atlas ovarian carcinoma dataset.

Table I.

Notch pathway genes correlated with recurrence in The Cancer Genome Atlas ovarian carcinoma dataset.

Notch pathway genesGene namesAccession no.Hazard ratioFDRPermutation P-value
FZD4Frizzled family receptor 4NM_0121930.7860.04660.0010
HES1Hairy and enhancer of split 1, (Drosophila)NM_0055240.8740.2590.0091
PSEN2Presenilin 2 (Alzheimer disease 4)NM_0004470.4710.2590.0094
JAG2Jagged 2NM_0022260.8190.2860.0241
PPARGPeroxisome proliferator-activated receptor gammaNM_0050370.7410.2860.0284
FOSFBJ murine osteosarcoma viral oncogene homologNM_0052521.0940.2860.0266
HEY1 Hairy/enhancer-of-split related with YRPW motif 1NM_0010407080.8790.2860.0298
CDC16Cell division cycle 16NM_0010786451.2020.2860.0296
MFNGMFNG O-fucosylpeptide 3-β-N-acetylglucosaminyltransferaseNM_0011663431.3230.2860.0357
EP300E1A-binding protein p300NM_0014290.8180.3210.0429

[i] FDR, false discovery rate.

Using a principal components model based on these 10 Notch pathway genes as a signature, a predictor was generated and TCGA ovarian cancer samples were classified into high-risk (n=263) and low-risk (n=259) subgroups. As shown in Fig. 1A, in TCGA dataset, the high-risk ovarian carcinomas demonstrated a significantly shorter RFS time than the low-risk cases (hazard ratio, 1.3656; 95% confidence interval, 1.0739–1.7364; P=0.0104). In order to evaluate the performance of this novel gene signature, its performance in predicting recurrence in two other independent ovarian carcinoma datasets, GSE9891 and GSE30161, was further validated. As shown in Fig. 1B and C, the signature was independently predictive of recurrence in the two validation datasets. In addition, this gene signature gave a significant value for the area under the curve when discriminating between high- and low-risk ovarian carcinomas in all three datasets (TCGA, P=0.0167; GSE9891, P=0.04; GSE30161, P=0.01) (Fig. 1D–F).

Next, the association between the predicted recurrence risk subgroups and the known prognostic factors was analyzed. In GSE9891, but not the other two datasets, the high recurrence risk subgroup exhibited a significant association with stage and debulking level (Table II). The multivariate Cox proportional hazards regression analyses found that the prognostic value on recurrence of the 10-Notch gene signature was independent of other known predictors in all three datasets (Table III).

Table II.

Difference in clinicopathological characteristics between high- and low-risk recurrence subgroups defined by a 10-Notch pathway gene signature in 3 ovarian carcinoma gene expression datasets.a

Table II.

Difference in clinicopathological characteristics between high- and low-risk recurrence subgroups defined by a 10-Notch pathway gene signature in 3 ovarian carcinoma gene expression datasets.a

CharacteristicsLow riskHigh riskP-value
TCGA dataset 0.9589
Number259263
Grade
  1–2  33  34
  3224220
Stage 0.6486
  Early  18  22
  Late240239
Debulking 0.1067
  Optimal184167
  Suboptimal  52  68
GSE9891 dataset
Number1421330.0114
Grade
  1–2  68  44
  3  71  89
Stage 0.0008
  Early  31     9
  Late111123
Debulking 0.8884
  Optimal  41  41
  Suboptimal  83  77
GSE30161 dataset
Number  26  281.0000
Grade
  1–2  11  10
  3  14  15
Debulking 0.9485
  Optimal  14  15
  Suboptimal  10  13

a Cases with unavailable clinicopathological data were not included.

Table III.

Multivariate Cox proportional hazards regression analyses on recurrence-free survival time in 3 ovarian carcinoma gene expression datasets.

Table III.

Multivariate Cox proportional hazards regression analyses on recurrence-free survival time in 3 ovarian carcinoma gene expression datasets.

CovariateP-valueExp(b)95% CI of Exp(b)
TCGA dataset
  Grade0.56241.11120.7792–1.5848
  Stage0.01552.02911.1473–3.5887
  Debulking0.68010.94040.7032–1.2575
  Predicted risk0.00831.41191.0944–1.8215
GSE9891 dataset
  Grade0.65090.93770.7106–1.2373
  Stage0.00015.00272.3040–10.8624
  Debulking0.00100.58290.4228–0.8035
  Predicted risk0.03821.39841.0200–1.9171
GSE30161 dataset
  Grade0.78701.09970.5537–2.1841
  Debulking0.01600.42350.2113–0.8488
  Predicted risk0.00882.33121.2413–4.3781

[i] TCGA, The Cancer Genome Atlas; CI, confidence interval.

Finally, it was evaluated whether this 10-gene signature could predict OS time for ovarian carcinomas. In contrast to RFS, no significant difference was found in OS time between the high- and low- risk subgroups in all three datasets (Fig. 2).

Discussion

In the current study, 10 Notch pathway genes were identified to significantly correlate with recurrence in ovarian carcinoma. This 10-gene signature could classify ovarian carcinoma into high- and low-risk recurrence subgroups, and showed a significant performance to predict recurrence in independent cohorts. Moreover, the prognostic value of this gene signature is independent of the common clinicopathological predictors of ovarian cancers.

Prognostic gene signatures based on microarray data have been recently developed to identify subgroups with a more aggressive phenotype or poor outcomes in ovarian carcinoma. For example, Gillet et al (13) found 3 multidrug resistance gene signatures with a statistically significant correlation with OS and progression-free survival time. Cheon et al (14) identified and validated a 10-gene signature regulated by transforming growth factor-β signaling that is associated with poor OS time in patients with high-grade serous ovarian cancer. Compared with the previous gene signatures, the present 10-gene classifier exhibited a better performance on predicting recurrence, but not OS time for ovarian carcinoma. Moreover, for the first time, a Notch pathway gene signature is reported to have clinical significance in the prognosis prediction for patients with ovarian carcinoma. The use of this 10-Notch pathway gene signature should be investigated in prospective patient cohorts in the future.

Of the 10 Notch pathway genes identified in the present study, the functional and clinical significance has only been investigated in FZD4, HES1, PPARG and FOS. Only FZD4 has been found to be associated with recurrence in a previous study. Dai et al (15) found that DNA methylation and reduced expression of FZD4 are indicators of early disease relapse in ovarian tumors, which is consistent with the present finding that FZD4 gene expression is negatively associated with recurrence in ovarian carcinoma. Increased expression of HES1 and PPARG were validated to be predictors for poor OS in ovarian cancers (16,17). High expression of FOS was significantly associated with advanced clinical stage and chemoresistance (18). Considering that this 10-gene signature has clinical significance in classifying different subgroups with high and low recurrence risk, it is also possible that these Notch pathway genes may act as important mediators during ovarian carcinogenesis, and may represent novel therapeutic targets. Therefore, the biological significance of these 10 Notch signaling genes also deserves further investigation.

In conclusion, in the present study, a novel Notch pathway gene signature that is useful for predicting recurrence in ovarian cancers was developed. If prospectively validated, it would provide a reference for informing treatment decisions for patients with ovarian carcinoma. The biological significance of this signature and its potential as a biomarker also deserve further investigation in future studies.

References

1 

Lowe KA, Chia VM, Taylor A, O'Malley C, Kelsh M, Mohamed M, Mowat FS and Goff B: An international assessment of ovarian cancer incidence and mortality. Gynecol Oncol. 130:107–114. 2013. View Article : Google Scholar : PubMed/NCBI

2 

Foley OW, Rauh-Hain JA and del Carmen MG: Recurrent epithelial ovarian cancer: an update on treatment. Oncology (Williston Park). 27:288–294. 2982013.PubMed/NCBI

3 

Geurts SM, de Vegt F, van Altena AM, van Dijck JA, Tjan-Heijnen VC, Verbeek AL and Massuger LF: Considering early detection of relapsed ovarian cancer: a review of the literature. Int J Gynecol Cancer. 21:837–845. 2011. View Article : Google Scholar : PubMed/NCBI

4 

Santillan A, Garg R, Zahurak ML, Gardner GJ, Giuntoli RL II, Armstrong DK and Bristow RE: Risk of epithelial ovarian cancer recurrence in patients with rising serum CA-125 levels within the normal range. J Clin Oncol. 23:9338–9343. 2005. View Article : Google Scholar : PubMed/NCBI

5 

Takebe N, Nguyen D and Yang SX: Targeting notch signaling pathway in cancer: Clinical development advances and challenges. Pharmacol Ther. 141:140–149. 2014. View Article : Google Scholar : PubMed/NCBI

6 

Rose SL, Kunnimalaiyaan M, Drenzek J and Seiler N: Notch 1 signaling is active in ovarian cancer. Gynecol Oncol. 117:130–133. 2010. View Article : Google Scholar : PubMed/NCBI

7 

Jung SG, Kwon YD, Song JA, Back MJ, Lee SY, Lee C, Hwang YY and An HJ: Prognostic significance of Notch 3 gene expression in ovarian serous carcinoma. Cancer Sci. 101:1977–1983. 2010. View Article : Google Scholar : PubMed/NCBI

8 

Cancer Genome Atlas Research Network, . Integrated genomic analyses of ovarian carcinoma. Nature. 474:609–615. 2011. View Article : Google Scholar : PubMed/NCBI

9 

Tothill RW, Tinker AV, George J, Brown R, Fox SB, Lade S, Johnson DS, Trivett MK, Etemadmoghadam D, Locandro B, et al: Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome. Clin Cancer Res. 14:5198–5208. 2008. View Article : Google Scholar : PubMed/NCBI

10 

Ferriss JS, Kim Y, Duska L, Birrer M, Levine DA, Moskaluk C, Theodorescu D and Lee JK: Multi-gene expression predictors of single drug responses to adjuvant chemotherapy in ovarian carcinoma: Predicting platinum resistance. PLoS One. 7:e305502012. View Article : Google Scholar : PubMed/NCBI

11 

Ganzfried BF, Riester M, Haibe-Kains B, Risch T, Tyekucheva S, Jazic I, Wang XV, Ahmadifar M, Birrer MJ, Parmigiani G, et al: CuratedOvarianData: Clinically annotated data for the ovarian cancer transcriptome. Database (Oxford). 2013:bat0132013. View Article : Google Scholar : PubMed/NCBI

12 

Simon R, Lam A, Li MC, Ngan M, Menenzes S and Zhao Y: Analysis of gene expression data using BRB-ArrayTools. Cancer Inform. 3:11–17. 2007.PubMed/NCBI

13 

Gillet JP, Wang J, Calcagno AM, Green LJ, Varma S, Bunkholt Elstrand M, Trope CG, Ambudkar SV, Davidson B, et al: Clinical relevance of multidrug resistance gene expression in ovarian serous carcinoma effusions. Mol Pharm. 8:2080–2088. 2011. View Article : Google Scholar : PubMed/NCBI

14 

Cheon DJ, Tong Y, Sim MS, Dering J, Berel D, Cui X, Lester J, Beach JA, Tighiouart M, Walts AE, et al: A collagen-remodeling gene signature regulated by TGF-β signaling is associated with metastasis and poor survival in serous ovarian cancer. Clin Cancer Res. 20:711–723. 2014. View Article : Google Scholar : PubMed/NCBI

15 

Dai W, Teodoridis JM, Zeller C, Graham J, Hersey J, Flanagan JM, Stronach E, Millan DW, Siddiqui N, Paul J, et al: Systematic CpG islands methylation profiling of genes in the wnt pathway in epithelial ovarian cancer identifies biomarkers of progression-free survival. Clin Cancer Res. 17:4052–4062. 2011. View Article : Google Scholar : PubMed/NCBI

16 

Wang X, Fu Y, Chen X, Ye J, Lü B, Ye F, Lü W and Xie X: The expressions of bHLH gene HES1 and HES5 in advanced ovarian serous adenocarcinomas and their prognostic significance: A retrospective clinical study. J Cancer Res Clin Oncol. 136:989–996. 2010. View Article : Google Scholar : PubMed/NCBI

17 

Ivan C, Hu W, Bottsford-Miller J, Zand B, Dalton HJ, Liu T, Huang J, Nick AM, Lopez-Berestein G, Coleman RL, et al: Epigenetic analysis of the Notch superfamily in high-grade serous ovarian cancer. Gynecol Oncol. 128:506–511. 2013. View Article : Google Scholar : PubMed/NCBI

18 

Kang KW, Lee MJ, Song JA, Jeong JY, Kim YK, Lee C, Kim TH, Kwak KB, Kim OJ and An HJ: Overexpression of goosecoid homeobox is associated with chemoresistance and poor prognosis in ovarian carcinoma. Oncol Rep. 32:189–198. 2014.PubMed/NCBI

Related Articles

Journal Cover

September-2015
Volume 10 Issue 3

Print ISSN: 1792-1074
Online ISSN:1792-1082

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Chen F and Chen F: A 10‑gene expression signature of Notch pathway predicts recurrence in ovarian carcinoma. Oncol Lett 10: 1704-1708, 2015
APA
Chen, F., & Chen, F. (2015). A 10‑gene expression signature of Notch pathway predicts recurrence in ovarian carcinoma. Oncology Letters, 10, 1704-1708. https://doi.org/10.3892/ol.2015.3382
MLA
Chen, F., Liu, N."A 10‑gene expression signature of Notch pathway predicts recurrence in ovarian carcinoma". Oncology Letters 10.3 (2015): 1704-1708.
Chicago
Chen, F., Liu, N."A 10‑gene expression signature of Notch pathway predicts recurrence in ovarian carcinoma". Oncology Letters 10, no. 3 (2015): 1704-1708. https://doi.org/10.3892/ol.2015.3382