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. 2018 Mar 25;7(5):1896–1907. doi: 10.1002/cam4.1406

Functional transcriptomic annotation and protein–protein interaction analysis identify EZH2 and UBE2C as key upregulated proteins in ovarian cancer

Sandra Martínez‐Canales 1, Miguel López de Rodas 1, Miriam Nuncia‐Cantarero 1, Raquel Páez 1, Eitan Amir 2, Balázs Győrffy 3, Atanasio Pandiella 4, Eva María Galán‐Moya 1,✉, Alberto Ocaña 1,4,✉
PMCID: PMC5943485  PMID: 29575713

Abstract

Although early stage ovarian cancer is in most cases a curable disease, some patients relapse even with appropriate adjuvant treatment. Therefore, the identification of patient and tumor characteristics to better stratify risk and guide rational drug development is desirable. Using transcriptomic functional annotation followed by protein–protein interacting (PPI) network analyses, we identified functions that were upregulated and associated with detrimental outcome in patients with early stage ovarian cancer. Some of the identified functions included cell cycle, cell division, signal transduction/protein modification, cellular response to extracellular stimuli or transcription regulation, among others. Genes within these functions included AURKA, AURKB, CDK1, BIRC5, or CHEK1 among others. Of note, the histone‐lysine N‐methyltransferase (EZH2) and the ubiquitin‐conjugating enzyme E2C (UBE2C) genes were found to be upregulated and amplified in 10% and 6% of tumors, respectively. Of note, EZH2 and UBE2C were identified as principal interacting proteins of druggable networks. In conclusion, we describe a set of genes overexpressed in ovarian cancer with potential for therapeutic intervention including EZH2 and UBE2C.

Keywords: Clinical outcome, druggable proteins, EZH2, Ovarian cancer, protein–protein interaction, UBE2C

Introduction

Disseminated ovarian cancer is an incurable disease 1. However, if diagnosed in its early stage, resection and adjuvant chemotherapy can reduce the probability of the tumor to relapse and spread 2. Unfortunately, some patients with early stage ovarian cancer (mainly stage 1 and 2) are still at high risk of relapse, even after being treated with adequate surgical and adjuvant chemotherapy 2. In this context, the identification of patients who have high risk of recurrence is desirable as it can influence adjuvant treatment and guide future drug development.

Similar to other cancers, in ovarian cancer, different molecular mechanisms are responsible for cancer initiation and progression. Uncontrolled proliferation, migration, evasion from immunological regulation, or the capacity to generate new vessels are, among others, oncogenic hallmarks of ovarian cancer 3. Of note, agents that mitigate these functions, such as antimitotic chemotherapies, DNA damaging agents or anti‐angiogenic compounds, have reached the clinical practice 3, 4. Among agents that target classical deregulated functions such as cell division or proliferation, novel vulnerabilities with potential for therapeutic capacity are under evaluation, including protein modifications or epigenetic events. New drugs targeting the proteasome, ubiquitination, or bromodomains are currently under evaluation in several solid tumors 5.

In this context, it will be desirable to identify biological functions that are characteristically deregulated in ovarian cancer at a transcriptomic and proteomic level. Genomic signatures and protein–protein interacting networks could be used to select patients with higher risk of relapse in the long term. Furthermore, molecular elements involved in these biological functions could be potentially druggable, opening the door to evaluate new compounds against these alterations in the clinical setting. With this approach in mind, we have described genes and gene signatures associated with mitosis that predicted poor outcome specifically in patients with early stage ovarian tumors 6. However, we envision that an analysis based of functional genomics and protein–protein interactions could provide more robust prediction outcome in ovarian cancers, and a more general overview of the biological characteristics of this disease.

In this project using an in silico approach using public transcriptomic data, we identified deregulated functions in early stage ovarian cancer that were associated with worse outcome. Expression of some of these signatures identified patients at a higher risk. A protein–protein interaction analysis revealed hubs of proteins with oncogenic implications that could be inhibited pharmacologically. Of note, a relevant finding was the identification of the histone‐lysine N‐methyltransferase EZH2, and the ubiquitin‐conjugating enzyme E2C as key upregulated interacting proteins. In addition, these proteins were amplified in 10% and 6% of the ovarian tumors. The data presented opens the door to the further assessment of these signatures in clinical studies, and for the evaluation of novel therapies against the mentioned proteins or pathways.

Material and Methods

Transcriptomic and gene expression analyses

To identify differences at a transcriptomic level, we used a public dataset (GEO DataSet accession number: GSE14407) of mRNA levels from twelve isolated ovarian epithelial cell lines and twelve isolated serous ovarian cancer epithelial (CEPI) cells. Affymetrix CEL files were downloaded and analyzed with Affymetrix Transcriptome Analysis Console 3.0. Differential gene expression profile for both groups was performed using a minimum fourfold change. Oncomine™ Platform was used to confirm the GEO DataSet findings (https://www.oncomine.org/resource/login.html).

Evaluation of clinical outcome

The publicly available Kaplan–Meier (KM) Plotter Online Tool (http://kmplot.com/analysis/) was used to evaluate the relationship between gene expression levels and patient's clinical outcome in early stage ovarian cancer (stage I and II). Only genes significantly associated with detrimental outcome (Hazard Ratio ≥1 and P‐value ≤0.05) were used for subsequent analysis (n = 131). This tool was also used to determine progression‐free survival (PFS) and overall survival (OS) in functional combined analyses. All the analyses were performed independently by two authors (SMC and MLR) and reviewed by a third author (EMGM) (Accession date January 8th 2018). No discrepancies were observed.

Protein–protein interactions maps and functional evaluation

Using the String Online Tool (http://www.string-db.org), we constructed the interactome. The PPI map was based on the list of genes associated with poor PFS. Proteins showing less than two interactions were not considered. Subsequently, we performed a functional screening using Ensembl (http://www.ensembl.org), and Gene Ontology (GO) by biological function.

Selection of potential drug candidates

We used information from Selleckchem (http://www.selleckchem.com) and Genecards (http://www.genecards.org) to select potentially druggable genes. Then, as described above, we used the STRING tool to build the druggable ovarian cancer interactome. Based on interacting groups, we divided the PPI map in three functional clusters: cell cycle (n = 19), DNA damage (n = 4), and angiogenesis (n = 3). PPI hubs proteins were determined as those with a higher number of interactions than the average (Edges ≥17.2).

Identification of molecular alterations

We used data contained at cBioportal (http://www.cbioportal.org; TCGA Ovarian Serous Cystadenocarcinoma, n = 603) to identify potential copy number alterations (amplification or deletion), and the presence of mutations in the identified genes.

Results

Selection of deregulated genes and functional analyses

To identify deregulated functions in ovarian cancer cells, we used public transcriptomic data (GSE14407), to compare isolated serous ovarian cancer epithelial (CEPI) cells with isolated ovarian epithelial cell lines. Using a minimum fold change of four, we identified 2925 genes of which 131 were associated with poor clinical outcome (Fig. 1A and Table 1). The upregulation of the genes was confirmed using data from human samples contained at Oncomine (Table 1). Protein–protein interaction network showed 130 nodes and a cluster coefficient of 0.62 (Fig. S1).

Figure 1.

Figure 1

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Transcriptomic analyses comparing isolated serous ovarian cancer epithelial (CEPI) cells with isolated ovarian epithelial cells. (A) Identification of deregulated genes (fold change ≥4) which are associated with bad prognosis in CEPI. (B) Functional enrichment analyses identify cell cycle, cell division, signal transduction/protein modification, cellular response to extracellular stimuli and transcription regulation, as the most altered functions in CEPI.

Table 1.

List of deregulated genes associated with bad prognosis

Probe ID Transcript ID Gene symbol AFFYMETRIX ONCOMINE KMPLOTTER
PFS
Fold change P‐Value ANOVA Fold change P‐Value HR P‐Value
211767_at g13543688 GINS4 4.01 0.002866 2.404 1.11E‐05 2.87 (1.54–5.35) 0.0005
228729_at Hs.23960.0 CCNB1 4.03 8.24E‐07 6.152 5.33E‐06 2.85 (1.3–6.22) 0.0062
1569241_a_at Hs2.149839.1 ZNF93 4.06 0.004685 2.296 6.40E‐06 2.41 (1.14–5.08) 0.0176
205869_at g4506144 PRSS1 4.06 0.000394 2.008 2.12E‐07 1.91 (1.06–3.45) 0.0296
213100_at Hs.13350.0 UNC5B 4.07 0.000451 1.351 0.006 1.82 (1.01–3.3) 0.0432
216615_s_at Hs.2142.1 HTR3A 4.11 0.001305 3.505 4.76E‐16 2.23 (1.19–4.15) 0.0098
40020_at 4858618_RC CELSR3 4.13 9.48E‐08 1.502 7.89E‐07 3.86 (1.98–7.54) 0
219306_at g9910265 KIF15 4.17 0.000147 3.696 2.29E‐08 2.88 (1.54–5.38) 0.0005
209342_s_at g4185274 IKBKB 4.17 0.007424 1.31 2.12E‐04 2.44 (1.31–4.56) 0.0038
213759_at Hs.111554.1 ARL4C 4.21 0.008012 3.624 8.81E‐06 1.89 (1.06–3.39) 0.0289
206134_at g7657318 ADAMDEC1 4.21 0.003324 1.87 0.012 1.88 (1.04–3.39) 0.0337
219787_s_at g8922431 ECT2 4.23 0.000033 10.209 2.81E‐08 2.62 (1.42–4.83) 0.0014
210559_s_at g3126638 CDK1 4.23 0.000803 7.317 4.85E‐07 1.8 (1.01–3.23) 0.0447
204444_at g13699823 KIF11 4.25 0.000272 5.467 7.52E‐07 2.73 (1.48–5.03) 0.0008
209053_s_at Hs.110457.3 WHSC1 4.25 0.000004 3.805 3.67E‐10 3.03 (1.57–5.84) 0.0005
209198_s_at g13279139 SYT11 4.27 0.000005 2.974 1.43E‐07 3.4 (1.81–6.39) 0.0001
207156_at g10800131 HIST1H2AG 4.3 0.006192 1.554 5.81E‐06 1.96 (1.1–3.5) 0.0201
205544_s_at g4503026 CR2 4.34 0.027892 1.428 6.37E‐06 3.37 (1.74–6.53) 0.0001
203046_s_at g4507506 TIMELESS 4.36 4.84E‐07 3.434 2.30E‐09 1.86 (1.03–3.37) 0.0365
202870_s_at g4557436 CDC20 4.41 0.000004 11.259 2.44E‐06 3.87 (2.01–7.46) 0
202860_at g7662151 DENND4B 4.43 0.000019 1.563 3.19E‐04 3.3 (1.71–6.37) 0.0002
214933_at Hs.96253.2 CACNA1A 4.45 0.000001 2.563 1.47E‐05 2.6 (1.39–4.85) 0.0018
210587_at g13477368 INHBE 4.47 0.042977 1.32 7.38E‐05 2.22 (1.2–4.09) 0.0089
214005_at Hs.77719.1 GGCX 4.5 2.02E‐08 1.837 2.04E‐04 2.45 (1.35–4.46) 0.0025
205660_at g11321576 OASL 4.53 0.006553 2.449 9.95E‐05 2.06 (1.13–3.78) 0.0165
219454_at g13124887 EGFL6 4.54 0.000439 2.582 5.00E‐03 2.34 (1.27–4.31) 0.0049
212816_s_at Hs.84152.2 CBS 4.55 0.000561 2.658 1.00E‐03 2.06 (1.14–3.74) 0.0146
205394_at g4502802 CHEK1 4.6 0.000004 4.147 2.43E‐07 2.04 (1.13–3.66) 0.015
221436_s_at g13876383 CDCA3 4.65 0.001047 4.847 1.30E‐09 2.09 (1.15–3.77) 0.0128
207109_at g7657408 POU2F3 4.66 0.024312 1.741 4.43E‐04 2.81 (1.53–5.18) 0.0005
202219_at g5032096 SLC6A8 4.68 0.000195 1.94 1.76E‐07 2.17 (1.18–4) 0.0108
217025_s_at Hs.89434.1 DBN1 4.69 0.007175 2.141 1.84E‐06 2.11 (1.13–3.93) 0.0163
202338_at g4507518 TK1 4.73 0.00005 4.968 1.55E‐08 2.07 (1.13–3.77) 0.0156
222251_s_at Hs.28906.1 GMEB2 4.81 0.004936 1.339 3.27E‐04 5.5 (2.57–11.76) 0
210697_at g4454677 ZNF257 4.82 0.001284 1.673 1.66E‐05 2.06 (1.15–3.7) 0.0135
214339_s_at Hs.86575.2 MAP4K1 4.87 0.000074 1.866 1.13E‐05 2.07 (1.13–3.79) 0.0154
203022_at g5454009 RNASEH2A 4.94 0.000147 2.785 1.11E‐06 2.06 (1.13–3.76) 0.016
206280_at g4826670 CDH18 4.96 0.006096 1.396 0.004 2.05 (1.14–3.7) 0.0143
211343_s_at g180828 COL13A1 5 0.000822 1.318 3.17E‐04 2.05 (1.13–3.75) 0.0165
206513_at g4757733 AIM2 5.02 0.007612 1.547 6.09E‐04 2.73 (1.46–5.11) 0.0011
204994_at g11342663 MX2 5.03 0.001647 3.916 3.32E‐04 2.2 (1.19–4.06) 0.0098
205163_at g7019426 MYLPF 5.04 0.000789 1.397 4.94E‐06 1.97 (1.1–3.54) 0.0201
218726_at g8922180 HJURP 5.07 0.010918 5.547 2.20E‐09 1.95 (1.08–3.5) 0.023
239219_at Hs.221197.0 AURKB 5.1 0.001028 2.818 2.34E‐05 2.22 (1.04–4.76) 0.0353
202575_at g6382069 CRABP2 5.27 0.000004 3.216 9.08E‐05 2.08 (1.16–3.74) 0.0124
35160_at 4870487_RC LDB1 5.29 0.00032 1.5 1.00E‐03 2.35 (1.27–4.32) 0.0048
212556_at Hs.239784.0 SCRIB 5.31 5.24E‐07 2.578 8.65E‐07 2 (1.1–3.65) 0.021
203439_s_at g12653744 STC2 5.31 0.001003 2.509 1.53E‐06 1.85 (1.03–3.35) 0.0379
234040_at Hs.287543.0 HELLS 5.35 0.004925 2.352 3.86E‐05 2.39 (1.11–5.11) 0.0209
221125_s_at g7657250 KCNMB3 5.47 0.000016 1.637 2.06E‐06 2.03 (1.11–3.71) 0.0183
205569_at g7657660 LAMP3 5.48 0.040774 3.979 3.15E‐04 3.56 (1.85–6.85) 0.0001
213520_at Hs.31442.0 RECQL4 5.48 0.00012 1.358 0.002 2.61 (1.4–4.87) 0.0018
205034_at g4757931 CCNE2 5.49 0.00002 1.344 2.00E‐03 2.29 (1.27–4.11) 0.0046
222037_at Hs.154443.1 MCM4 5.52 0.013214 4.726 8.00E‐08 2.9 (1.55–5.41) 0.0005
218494_s_at g13236503 SLC2A4RG 5.64 0.000878 2.118 5.86E‐06 2.48 (1.33–4.62) 0.0031
212235_at Hs.301685.0 PLXND1 5.64 0.000117 1.496 4.67E‐04 2.05 (1.13–3.71) 0.0152
218296_x_at g8922469 MSTO1 5.72 0.000026 1.381 0.018 1.83 (1–3.33) 0.0458
218009_s_at g4506038 PRC1 5.74 9.19E‐07 7.214 5.32E‐08 2.85 (1.53–5.32) 0.0006
209680_s_at g12653842 KIFC1 5.78 0.000129 3.845 3.64E‐08 2.33 (1.28–4.24) 0.0046
202954_at g5902145 UBE2C 5.81 2.13E‐08 10.184 2.24E‐07 3.03 (1.62–5.66) 0.0003
205240_at g9558734 GPSM2 6.01 0.000545 3.965 2.97E‐08 1.81 (1.01–3.25) 0.0435
209262_s_at g12803666 NR2F6 6.05 2.41E‐07 1.61 2.18E‐05 2.26 (1.23–4.17) 0.0071
203632_s_at g7706450 GPRC5B 6.12 0.000022 1.672 0.004 2.22 (1.21–4.04) 0.0078
207165_at g7108350 HMMR 6.14 0.000011 3.819 1.48E‐10 2.35 (1.3–4.25) 0.0037
205046_at g4502780 CENPE 6.16 0.00057 2.711 1.59E‐07 2.55 (1.36–4.75) 0.0024
208394_x_at g13259505 ESM1 6.2 0.000021 1.496 0.009 2.05 (1.13–3.71) 0.0152
216237_s_at Hs.77171.1 MCM5 6.22 0.001843 1.795 7.33E‐05 2.33 (1.25–4.34) 0.0063
205449_at g9558738 SAC3D1 6.31 0.000029 1.891 3.53E‐05 2.19 (1.2–4) 0.0086
203099_s_at g4558755 CDYL 6.33 0.000004 1.889 4.26E‐05 2.1 (1.15–3.83) 0.013
210983_s_at g12751125 MCM7 6.48 0.008102 3.523 2.31E‐07 2.23 (1.21–4.1) 0.0084
210052_s_at g6073830 TPX2 6.5 2.90E‐08 13.887 1.65E‐07 2.55 (1.38–4.69) 0.0019
225846_at Hs.24743.1 ESRP1 6.53 0.000005 2.135 2.68E‐04 2.3 (1.04–5.06) 0.0335
218308_at g5454101 TACC3 6.54 0.000462 4.047 9.61E‐06 4.1 (2.04–8.24) 0
239570_at Hs.144137.0 RAB1A 6.76 0.000581 1.31 3.07E‐04 2.48 (1.1–5.6) 0.0242
203358_s_at g4758323 EZH2 6.84 0.000002 6.584 1.44E‐06 3.63 (1.93–6.8) 0
203806_s_at g4503654 FANCA 6.87 0.00001 1.793 7.55E‐05 2.69 (1.42–5.08) 0.0016
219502_at g8922721 NEIL3 6.91 0.000004 1.519 1.08E‐05 2.36 (1.3–4.28) 0.0035
208079_s_at g4507278 AURKA 7 3.64E‐08 6.504 6.53E‐08 2.95 (1.6–5.45) 0.0003
204709_s_at g13699831 KIF23 7 0.000061 4.68 2.17E‐06 2.7 (1.48–4.95) 0.0008
203755_at g5729749 BUB1B 7.09 6.83E‐10 8.04 2.56E‐07 2.86 (1.55–5.29) 0.0004
222039_at Hs.274448.1 KIF18B 7.2 6.26E‐09 2.135 4.91E‐06 2.4 (1.31–4.37) 0.0032
204822_at g4507718 TTK 7.21 7.51E‐07 15.153 2.06E‐09 2.52 (1.38–4.61) 0.0019
212023_s_at Hs.80976.1 MKI67 7.25 1.04E‐07 4.023 5.17E‐10 1.94 (1.07–3.51) 0.0256
204170_s_at g4502858 CKS2 7.39 6.82E‐08 5.956 3.85E‐05 2.06 (1.14–3.73) 0.0147
207183_at g5453665 GPR19 7.43 0.000063 2.901 8.95E‐09 3.07 (1.64–5.72) 0.0002
207828_s_at g4885132 CENPF 7.52 0.000002 3.811 1.75E‐06 2.64 (1.43–4.87) 0.0013
206157_at g4506332 PTX3 7.56 0.000006 2.79 0.004 2.93 (1.6–5.37) 0.0003
218039_at g7705950 NUSAP1 7.63 5.89E‐09 9.731 7.45E‐07 2.08 (1.16–3.75) 0.0123
203554_x_at g11038651 PTTG1 7.68 0.000002 5.99 1.80E‐05 3.34 (1.76–6.34) 0.0001
209891_at g9963834 SPC25 7.73 0.000027 2.928 9.73E‐24 2.45 (1.34–4.47) 0.0026
221520_s_at g12804484 CDCA8 7.78 0.000076 3.705 5.44E‐07 2.63 (1.41–4.91) 0.0016
218755_at g5032012 KIF20A 7.9 1.87E‐08 9.021 9.21E‐08 2.56 (1.37–4.78) 0.0021
201761_at g13699869 MTHFD2 7.92 0.000004 3.82 1.20E‐04 2.68 (1.45–4.94) 0.001
204649_at g4885624 TROAP 7.95 0.000014 3.096 5.11E‐08 2.83 (1.49–5.35) 0.0008
209408_at g1695881 KIF2C 8.01 2.31E‐08 2.834 6.75E‐11 2.43 (1.33–4.44) 0.003
201663_s_at g4885112 SMC4 8.12 0.008518 7.44 9.32E‐09 2.55 (1.38–4.71) 0.0019
218542_at g8922501 CEP55 8.28 0.000158 8.075 1.50E‐08 1.89 (1.05–3.4) 0.0304
222958_s_at Hs.133260.0 DEPDC1 8.47 0.000182 3.833 2.12E‐07 2.61 (1.19–5.7) 0.0127
222008_at Hs.154850.0 COL9A1 8.48 0.000545 1.946 2.30E‐16 1.93 (1.08–3.47) 0.0249
210512_s_at g3719220 VEGFA 8.51 6.97E‐09 2.741 1.17E‐07 3.37 (1.75–6.48) 0.0001
205733_at g4557364 BLM 8.53 0.000002 2.88 3.59E‐06 1.99 (1.1–3.59) 0.0205
236641_at Hs.116649.0 KIF14 8.88 0.000311 3.139 5.27E‐06 2.28 (1.01–5.13) 0.0414
204962_s_at g4585861 CENPA 8.9 0.000014 11.775 2.63E‐09 2.48 (1.35–4.57) 0.0026
202705_at g10938017 CCNB2 9.15 1.13E‐07 10.154 1.59E‐06 1.87 (1.04–3.37) 0.0329
218585_s_at g7705575 DTL 9.2 2.49E‐10 6.089 1.58E‐07 1.89 (1.06–3.38) 0.0289
38158_at 4852842_RC ESPL1 9.2 8.96E‐09 4.341 6.11E‐07 3.19 (1.69–6.04) 0.0002
213523_at Hs.9700.0 CCNE1 9.36 2.03E‐07 7.062 8.28E‐09 1.84 (1.02–3.33) 0.0407
222946_s_at g12652906 AUNIP 9.41 0.00175 2.956 7.47E‐08 2.11 (0.99–4.52) 0.0485
213075_at Hs.94795.0 OLFML2A 9.44 0.000481 1.423 3.00E‐03 1.86 (1.04–3.34) 0.0348
204825_at g7661973 MELK 9.59 0.000007 10.6 2.98E‐07 1.95 (1.08–3.5) 0.0233
212563_at Hs.30736.0 BOP1 9.61 0.000165 1.669 3.35E‐06 2.03 (1.11–3.69) 0.0186
204026_s_at g6857828 ZWINT 9.92 1.65E‐09 7.001 1.71E‐05 2.05 (1.14–3.7) 0.015
202580_x_at g11386144 FOXM1 10.06 0.000022 5.982 8.64E‐09 3.03 (1.6–5.73) 0.0003
205694_at g4507756 TYRP1 10.15 0.000772 1.624 2.09E‐34 1.79 (1–3.23) 0.0486
204584_at Hs.1757.0 L1CAM 10.27 0.000008 3.985 7.02E‐15 3.02 (1.63–5.58) 0.0002
218662_s_at g11641252 NCAPG 10.35 4.18E‐08 3.207 2.13E‐10 3.28 (1.76–6.14) 0.0001
204695_at Hs.1634.0 CDC25A 10.39 0.000002 2.633 2.49E‐05 2.39 (1.31–4.34) 0.0033
212807_s_at Hs.281706.1 SORT1 10.54 4.63E‐07 1.977 9.60E‐06 2.05 (1.12–3.75) 0.0172
202094_at Hs.1578.0 BIRC5 10.82 0.000018 4.83 2.20E‐10 2.85 (1.53–5.31) 0.0006
204558_at g4506396 RAD54L 11.24 0.000856 2.09 6.38E‐06 2.85 (1.53–5.32) 0.0006
218355_at g7305204 KIF4A 11.53 5.42E‐08 2.359 1.00E‐03 2.82 (1.51–5.27) 0.0007
219650_at g8923111 ERCC6L 12.29 6.30E‐07 2.245 8.13E‐06 2.24 (1.24–4.04) 0.0063
204437_s_at g9257206 FOLR1 12.72 0.000017 1.696 2.00E‐03 2.05 (1.13–3.7) 0.0155
203418_at g4502612 CCNA2 13.14 0.00196 4.795 2.28E‐07 1.82 (1.02–3.26) 0.0403
205242_at g5453576 CXCL13 16.29 0.0001 2.091 2.94E‐08 1.83 (1.02–3.28) 0.0411
205572_at g4557314 ANGPT2 19.81 0.000006 1.312 0.029 2.02 (1.12–3.63) 0.0164
212949_at Hs.1192.0 NCAPH 19.94 7.17E‐07 2.497 1.51E‐06 3.06 (1.62–5.78) 0.0003
206772_at g4826953 PTH2R 21.94 0.000027 5.579 8.58E‐10 3.14 (1.66–5.97) 0.0002
222962_s_at g11527601 MCM10 29.44 6.72E‐09 2.718 1.28E‐07 2.33 (1.06–5.1) 0.0296
207039_at g4502748 CDKN2A 45.1 0.000022 6.481 5.60E‐14 2.01 (1.11–3.63) 0.0186
206373_at g4507970 ZIC1 99.77 2.13E‐08 3.712 8.24E‐07 2.19 (1.22–3.93) 0.0073
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Functional gene signatures associated with poor outcome

Functional annotation of the identified genes demonstrated several altered functions (Fig. 1A and B). By selecting those more represented (with a more than 20% of total genes expression), we identified cell cycle, cell division, signal transduction/protein modification, cellular response to extracellular (EC) stimuli, and transcription regulation. Table S1 provides detailed information of all functions and genes included within each function.

Using the KM Plotter online tool, we explored the association with clinical outcome of genes within each function. We did so to observe the role of each group with clinical prognosis. Genes within the cell cycle and cell division were associated with detrimental PFS and OS (PFS: HR = 4.07 (95% CI 1.66–9.98), P = 0.00086 and OS: HR = 3.33 (95% CI 0.94–11.81), P = 0.048 for cell cycle and PFS: HR = 3.58 (95% CI 1.46–8.78), P = 0.0029 and OS HR =  3.52 (95% CI 0.99–12.46), P = 0.038, for cell division) (Fig. 2). Results in the same range were observed for signal transduction/protein modification (PFS HR = 3.73 (95% CI 1.52–9.14), P = 0.002 and OS HR =  3.33 (95% CI 0.94–11.81), P = 0.048) and for transcription regulation PFS data (PFS: HR = 3.69 (95% CI 1.51–9.03), P = 0.0022). Interestingly, a poorer outcome for OS was found for this latter group (OS: HR = 12.55 (95% CI 1.65–95.48), P = 0.0017) (Fig. 3). Finally, the group of genes within the cellular response to EC stimuli function showed the worse outcome for both PFS and OS (PFS: HR = 6.37 (95% CI 2.22–18.28), P = 7.7e‐05 and OS: HR = 13.25 (95% CI 1.74–100.79), (Fig. 3).

Figure 2.

Figure 2

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Association with progression‐free survival (PFS) and overall survival (OS) in stage I and II ovarian cancer of gene sets included in the cell cycle and cell division function.

Figure 3.

Figure 3

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Association with progression‐free survival (PFS) and overall survival (OS) in stage I and II ovarian cancer of gene sets included in the cellular response to extracellular (EC) stimuli, signal transduction/protein modification and transcription regulation function.

Druggable opportunities within the identified functions

The description of functional signatures has the advantage of identifying relevant molecular alterations that have a potential oncogenic role in this disease, and therefore are susceptible to be inhibited. To get insights into potential therapies for those patients harboring these signatures, we used the drug gene interaction database available in Genecards and confirmed by other sources as described in Material and Methods. We therefore selected 26 genes that could potentially be inhibited pharmacologically (Table S2). We next used the proteins coded by these genes to build a protein–protein interaction network. We found 223 interactions (edges) linking 26 proteins (nodes). As expected, the clustering coefficient in this druggable network was high (0.85), confirming that most of the proteins act as a functional unit. We identified three different functional clusters with special affinity: Cell cycle (n = 19 genes), DNA damage (n = 4 genes), and angiogenesis (n = 3 genes) (Fig. 4A). Of note, DNA damage was included as part of the cellular response to EC stimuli in our initial functional annotation, and angiogenesis was one of the functions identified in the functional annotation studies, although was less represented (Table S1). These results suggest an important role of this in the druggable PPI. Next, based on the number of interactions, we selected the hub proteins of the interactome, defined as those with a higher number of interactions than the average (Edges ≥17.2; n = 18) (Fig. 4B).

Figure 4.

Figure 4

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Protein–protein interaction (PPI) map of the 26 potential druggable targets. (A) Potentially druggable targets were used to construct a PPI network using the online tool STRING. Blue nodes represent proteins involved in cell cycle. Red and green nodes represent proteins associated with DNA damage and angiogenesis, respectively. The nodes indicate proteins coded by the identified druggable targets and edges indicate the number of interactions. The number of average interactions per node is represented by the node degree. The clustering coefficient indicates the average node density of the map. (B) List of hub proteins according the number of interactions (edges) in the druggable PPI network.

Some of the genes identified here have been described previously in ovarian cancer as deregulated, including AURKA, AURKB, CDK1, BIRC5, and CHEK1 among others 6. Of note, the histone‐lysine N‐methyltransferase EZH2 is a novel epigenetic target not previously described, and the ubiquitin‐conjugating enzyme E2C (UBE2C), which belongs to the ubiquitin ligase family of enzymes is also a potentially druggable protein with limited evaluation in ovarian cancer. Interestingly, these two genes strongly associate with worse prognosis for OS (Table S3)

Molecular alterations in the identified signatures

To complete our study, we used the cancer genomics database (cBioportal 7) to obtain information about copy number alterations or mutations of the identified druggable genes. Most of genes that code for the identified druggable hubs were found to be amplified in ovarian cancer (Table 2). Of note, the new potential targets EZH2 and UBE2C were amplified in around 10% and 6% of ovarian cancers, respectively. Deletions and mutations were present at a very low frequency. Amplifications of other genes such as RAD54L, AURKA, KIF2C, or BIRC5 were also observed.

Table 2.

Molecular alterations of the identified hub proteins

311 Ovarian serous cystadenocarcinoma samples
Gene Name Amplification Deletion Mutation
EZH2 10.30% 0.30% –
RAD54L 9.00% – 0.60%
AURKA 8.70% – –
KIF2C 6.40% – 0.30%
BIRC5 6.10% 0.60% –
UBE2C 5.80% – –
BLM 5.50% 0.30% 1.30%
CHEK1 3.90% 0.60% –
MKI67 3.50% 1.00% 1.30%
MCM7 3.20% – –
KIF4A 1.90% 0.30% 0.60%
CDK1 1.90% – 0.60%
TTK 1.60% 0.30% 0.60%
MELK 1.30% – 0.60%
KIF15 1.00% 0.30% 0.30%
CENPE 0.60% 1.30% 0.60%
AURKB 0.60% 0.60% –
KIF11 0.60% 0.30% –
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Discussion

In the present article, we describe functional gene signatures and PPI networks associated with adverse outcome in early stage ovarian cancer. These signatures and interacting protein networks provide information about druggable opportunities that could be validated preclinically.

As ovarian cancer is an incurable disease, the identification of oncogenic functions and protein interacting networks associated with detrimental outcome is expected to improve the therapeutic landscape of this disease. In early stage ovarian cancer, the identification of patients with worse outcome is even more relevant as it may help in the selection of patients for additional adjuvant therapy, and even guide the evaluation of novel therapies.

In our study, we have identified five functions linked with detrimental PFS and OS in early stage ovarian cancer. Within cell cycle and cell division, we found genes such as AURKA, AURKB, CDK1, BIRC5, and CHEK1 that are associated with control of mitosis and cell cycle regulation 8. Of note, some of these genes have been reported previously to be linked with detrimental outcome 6. Inhibitors against proteins coded by these genes, such as AURKA and B or CHEK1, are currently in clinical development, so our findings provide support for the specific development of those agents in ovarian cancer.

An interesting finding was the identification of protein modifications and transcription regulation as upregulated functions. Protein modification and degradation is a vulnerability of tumor cells as has been demonstrated by the clinical activity of proteasome inhibitors in some hematological malignancies 9, 10. Ubiquitination is a necessary pathway to target proteins for degradation 11. The ubiquitin‐conjugating enzyme E2C is required for the destruction of mitotic cyclins and for cell cycle progression 12. UBE2C has been found to be overexpressed in esophageal squamous cell carcinoma playing a role in cancer progression 13, 14, as well as, in other tumor types such as nonsmall cell lung cancer 15. However, there are no published data regarding the role of this protein in ovarian cancer. As this family of proteins can be inhibited pharmacologically 11, the study of such agents in ovarian cancer is warranted.

Other relevant findings include the identification of EZH2 as upregulated and involved in the PPI network. EZH2 has been associated with epithelial to mesenchymal transition in ovarian cancers 16. Of note, EZH2 inhibitors seem to be particularly active in malignant rhabdoid tumors, which are deficient in the Switch/Sucrose NonFermentable (SWI/SNF) chromatin remodeling complexes INI1 (SMARCB1). Of interest, a subgroup of ovarian tumors has a similar phenotype and has shown responses to inhibitors of this complex 17. In our study, we observe that EZH2 is a relevant component of the PPI network therefore confirming a potentially druggable vulnerability. Of note, drugs such as tazemetostat, a potent and selective EZH2 inhibitor is currently in phase II testing 18. Other molecular alteration includes RAD54L that is amplified in 9% of patients. The protein associated by this gene is involved in the homologous recombination repair of DNA double‐strand breaks 19. Finally, genes such as KIF2C or AURKA are involved in mitotic formation and chromosome segregation 20.

Our analysis highlights several druggable functions in early stage ovarian cancer for which new agents are currently in preclinical or clinical evaluation. However, we should acknowledge that our study has some limitations. This is an in silico analysis that need confirmatory studies using human samples. In addition, functional assessment has the limitation for the redundancy of functions, as many genes can be classified in many different annotations. Finally, there are limitations for the existed software that help identifying druggable opportunities mainly for redundancy.

In conclusion, we have identified biological functions and PPI networks that are prognostic in early stage ovarian cancer and may guide future drug development (Fig. 5). Some of the identified genes such as EZH2 or UBE2C have not been described previously in ovarian cancer but are amplified, linked with detrimental prognosis and potentially druggable, and warrant preclinical and clinical assessment.

Figure 5.

Figure 5

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Study graphical abstract.

Conflict of Interest

None declared.

Supporting information

Table S1. Functional classification of the deregulated genes.

Click here for additional data file. (1.4MB, pdf)

Table S2. List of potentially druggable genes.

Click here for additional data file. (245.5KB, doc)

Table S3. Association with progression free survival (PFS) and overall survival (OS) of the identified hub proteins.

Click here for additional data file. (16.8KB, docx)

Figure S1. Protein‐protein interaction network of the 130 deregulated genes associated with detrimental prognosis.

Click here for additional data file. (66.6KB, docx)

 

Click here for additional data file. (1.4MB, pdf)

 

Click here for additional data file. (39.2KB, docx)

Acknowledgments

This work has been funded by Instituto de Salud Carlos III (PI16/01121), Diputación de Albacete and CRIS Cancer Foundation (to AO) and the framework agreement between University of Castilla‐La Mancha and Albacete Provincial Council (UCLM‐Excma. Diputación de Albacete) in support to research activity (to EMGM). We would like to also thanks to the cancer associations AMUMA and ACEPAIN for supporting part of this work. EMGM is funded by the implementation research program of the UCLM (UCLM resolution date: 31/07/2014), with a contract for accessing the Spanish System of Science, Technology and Innovation‐Secti (cofunded by the European Commission/FSE funds).

Cancer Medicine 2018; 7(5):1896–1907

Contributor Information

Eva María Galán‐Moya, Email: EvaMaria.Galan@uclm.es.

Alberto Ocaña, Email: albertoo@sescam.jccm.es.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1. Functional classification of the deregulated genes.

Click here for additional data file. (1.4MB, pdf)

Table S2. List of potentially druggable genes.

Click here for additional data file. (245.5KB, doc)

Table S3. Association with progression free survival (PFS) and overall survival (OS) of the identified hub proteins.

Click here for additional data file. (16.8KB, docx)

Figure S1. Protein‐protein interaction network of the 130 deregulated genes associated with detrimental prognosis.

Click here for additional data file. (66.6KB, docx)

 

Click here for additional data file. (1.4MB, pdf)

 

Click here for additional data file. (39.2KB, docx)

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