Ovarian cancer stem cell-specific gene expression profiling and targeted drug prescreening

  • Authors:
    • Yuting Huang
    • Baohui Ju
    • Jing Tian
    • Fenghua Liu
    • Hu Yu
    • Huiting Xiao
    • Xiangyu Liu
    • Wenxin Liu
    • Zhi Yao
    • Quan Hao
  • View Affiliations

  • Published online on: January 13, 2014     https://doi.org/10.3892/or.2014.2976
  • Pages: 1235-1248
Metrics: HTML 0 views | PDF 0 views     Cited By (CrossRef): 0 citations


Cancer stem cells, with unlimited self-renewal potential and other stem cell characteristics, occur in several types of cancer, including ovarian cancer (OvC). Although CSCs can cause tumor initiation, malignant proliferation, relapse and multi-drug resistance, ways to eliminate them remain unknown. In the present study, we compared ovarian cancer stem cell (OVCSC) expression profiles in normal ovarian surface epithelium and ovarian cells from patients with advanced disease to identify key pathways and specific molecular signatures involved in OVC progression and to prescreen candidate small-molecule compounds with anti-OVCSC activity. Comparison of genome-wide expression profiles of OvC stemness groups with non-stemness controls revealed 6495, 1347 and 509 differentially expressed genes in SDC, SP1 and SP2 groups, respectively, with a cut-off of fold-change set at >1.5 and P<0.05. NAB1 and NPIPL1 were commonly upregulated whereas PROS1, GREB1, KLF9 and MTUS1 were commonly downregulated in all 3 groups. Most differentially expressed genes consistently clustered with molecular functions such as protein receptor binding, kinase activity and chemo-repellent activity. These genes regulate cellular components such as centrosome, plasma membrane receptors, and basal lamina, and may participate in biological processes such as cell cycle regulation, chemoresistance and stemness induction. Key Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways such as ECM receptor, ErbB signaling, endocytosis and adherens junction pathways were enriched. Gene co-expression extrapolation screening by the Connectivity Map revealed several small-molecule compounds (such as SC-560, disulfiram, thapsigargin, esculetin and cinchonine) with potential anti-OVCSC properties targeting OVCSC signature genes. We identified several key CSC features and specific regulation networks in OVCSCs and predicted several small molecules with potential anti-OVCSC pharmacological properties, which may aid the development of OVCSC-specific drugs.


Cancer stem cells (CSCs) have the ability to self-renew and generate heterogeneous lineages of cancer cells in a tumor. In contrast to other cell populations in a tumor, this small fraction of cells is highly tumorigenic. CSCs can survive chemotherapy and radiotherapy via efficient DNA repair and various drug-pumping mechanisms (1). Therefore, elimination of these rapidly replicating cancer cells is important to ensure successful cancer treatment. Failure of primary treatments to kill a sufficient number of CSCs can lead to relapse and metastasis, often with chemoresistance (2). In ovarian cancer (OvC), the third most common gynecological malignancy, primary cytoreductive surgery in combination with chemotherapy is initially effective. However, most patients develop drug resistance and eventually relapse within 18 months of treatment (3). Due to relapse and metastasis and the potential involvement of CSCs, the 5-year survival rate of patients with OvC continues to be <30% and their comprehensive mortality is the highest among all gynecological malignant tumors.

Ovarian cancer stem cells (OVCSCs) were first isolated and characterized after the discovery of CSCs in leukemia (4). Since then, techniques such as isolation of side population (SP) through flow cytometry and in vitro culture of ovarian multiple cellular spheroids that contain potential CSCs defined by morphology have been established to analyze the characteristics of OVCSCs. Although the Notch signaling pathway has been implicated in the development of chemoresistance in OVCSCs (5,6), small molecules targeting OVCSCs have not yet been screened. The development of such OVCSC-specific therapies may hold the key to preventing relapse and successfully treating patients who have aggressive, non-resectable OvC.

In cancer research, differentially expressed genes have been identified by comparing samples with and without CSCs by high-throughput technologies such as microarrays. Gene Ontology analysis of these differentially expressed genes may provide ontology terms to describe attributes in 3 biological domains, cellular component, molecular function and biological process, to interpret microarray data (7). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway query can systemically connect differentially expressed genes with other known information on the molecular interaction networks, especially in signaling pathways (8). Also, the Connectivity Map (CMAP), a massive repository of gene expression data, provides information on changes in gene expression in several cell lines when treated with >1,000 bioactive compounds (9,10). With these differentially expressed genes as query signatures of CSCs, the CMAP provides a novel resource to systematically screen for small molecules targeting CSC-specific genes.

In the present study, we used the CMAP to conduct a comprehensive analysis of multiple samples derived from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database to reveal several key pathways and OVCSC signature genes. Data on signature genes were then used to perform an OVCSC-specific drug prescreening based on co-expression extrapolation and predicted several small molecules with potential anti-OVCSC pharmacological properties, which may aid the development of OVCSC-specific drugs.

Materials and methods

Sources of whole-genome expression profiles of ovarian cancer

In the present study, we reanalyzed previously published raw data (Table I). Expression profiles of normal ovarian surface epithelium cells and ovarian cancer cells from patients with advanced disease were compared. The original CEL files by Rizzo et al (11) (GSE25191), Vathipadiekal et al (12)(GSE33874) and Wang et al (13) (GSE28799) were retrieved from the NCBI GEO database for OVCSC analysis. In brief, SPs of OvC were isolated from the serous epithelial OvC cell line IGROV1 and patient-derived ascites. Multiple cellular spheroids were isolated from the OVCAR-3 cell line on the basis of morphologic characteristics. RNA was extracted from all specimens that had OVCSC features and from non-stemness controls. Extracted RNA was pre-amplified, conjugated with fluorescent markers or biotin-labeled markers, and hybridized to an expression microarray chip. Raw data from Bonome et al (14) (GSE26712) were used to calibrate the OVCSC signature for drug prescreening. Serous epithelial OvC specimens were obtained independently by optimal debulking surgery from patients with previously untreated late-stage (III–IV) high-grade (2,3) OvC. Normal ovarian surface epithelium cells were obtained by cytobrushing and RNA from these cells was independently analyzed by microarrays.

Table I

Group designations for data derivation and input into the GeneSifter microarray analysis platform for differential expression analysis.

Table I

Group designations for data derivation and input into the GeneSifter microarray analysis platform for differential expression analysis.

NameControlExperimental groupSeries and platformRefs.
Groups to identify stemness featuresSDC3 specimens of OVCAR-3 cells3 specimens of OVCAR-3-derived multi-cellular spheroidsGSE28799a; HG-U133_Plus_2(13)
SP13 specimens of IGROV1-derived non-side population cells3 specimens of IGROV1-derived side population cellsGSE25191a; HG-U133_Plus_2(11)
SP210 specimens of patient ascites-derived total ovarian cancer cells10 specimens of patient ascites-derived side population cellsGSE33874a; HG-U133_Plus_2(12)
Calibration group for drug screeningOVC8 specimens of normal ovarian surface epithelium8 specimens of ovarian cancer cells derived from patients with advanced diseaseGSE26712b; HG-U133A(14)

a All samples were used for analysis.

b Eight randomly selected samples (GSM657519, GSM657520, GSM657522, GSM657524, GSM657525, GSM657526, GSM657527 and GSM657528) from 10 normal controls, and 8 samples from stage III–IV patients who received optimal postoperative assessment but due to short overall survival eventually succumbed to the disease (GSM657540, GSM 657547, GSM 657568, GSM 657575, GSM 657636, GSM 657641, GSM 657647 and GSM 657697) out of raw microarray data from 185 patients with advanced OvC.

Preprocessing and normalization of array data

Four groups of raw data were normalized to probe level by using the RMA algorithm for background correction. Quantile normalization and multi-chip model mid-value fitting were performed. The final normalized data were output to the GeneSifter microarray analysis platform (http://www.geospiza.com/Products/AnalysisEdition.shtml; Geospiza, Inc., Seattle, WA, USA) by groups after logarithmic transformation.

Analysis of differentially expressed genes

In the pair wise mode, normalized microarray data were analyzed to obtain differentially expressed probes by groups and probes were then assigned to genes. The following analysis parameters were used: statistics (t-test); correction (Benjamini and Hochberg); fold-change (lower, 1.5; upper, none); and quality options (one group must pass). The Exclude Control Probes option was chosen to output both upregulated and downregulated genes with P<0.05.

Analysis of biological significance

Differentially expressed genes previously identified in the SDC, SP1 and SP2 groups were output to the Gene Ontology (GO) tool to determine their significance with respect to their biological processes, molecular functions and cellular components. These genes were also output to the KEGG pathway mapping tool to determine commonly enriched pathways. The Z score was used to test enrichment of GO terms and KEGG pathways in the differentially expressed gene list. A Z score of >2 or <-2 was considered statistically significant.

CMAP drug screening with cancer stem cell signature

To converge differentially expressed genes with ovarian cancer stem cell characteristics, analysis was performed by integrating upregulated and downregulated probes from each OVCSC group (SDC, SP1 and SP2) calibrated with the OvC group in the Venny tool, commonly upregulated and downregulated genes in OVCSC groups were identified in the following combinations: OVC/SDC + OVC/SP1 + OVC/SP2 + OVC/SDC/SP1 + OVC/SDC/SP2 + OVC/SP1/SP2 + OVC/SDC/SP1/SP2.

Calibration groups were introduced for drug prescreening for necessity and sufficiency:


The CMAP database was constructed by using thousands of HG-U133A (low-density array) expression data, but in the present study all OVCSC-related signatures were derived from HG-U133_Plus_2 (high-density array). If a low-density-based filtering is not performed, the OVCSC signature will not be compatible for CMAP querying. In addition, this filtering may reduce the accuracy or significance of differentially expressed signatures for cancer stemness groups compared with that for non-stemness controls, but there were almost no intentional bias between these groups.


If a calibration group with differentially expressed genes for normal ovarian surface epithelium with respect to OVCSCs containing ovarian cancer cells derived from patients with advanced disease is not introduced, the query signature will only represent massively proliferating non-stemness cancer cells over CSCs in the CAMP screening. As the small molecules with the potential of reversing the expression signature may activate CSCs by turning them into highly proliferating cancer cells, this calibration group was introduced.

CMAP drug screening by the COXEN method with the ovarian cancer stem cell signature

By using the input of OVCSC-specific query signature genes, a preliminary list of anti-OVCSC small molecules was obtained. To further screen the potential drugs targeting OVCSCs, more stringent criteria were applied by restricting the number of repeat experiments to >3 times, selecting molecules with negative enrichment score (representing the potential effectiveness), using P<0.01 for statistical tests, and setting the proportion of effective rate to >50%.


Differentially expressed genes in OVCSC cells

By using the GeneSifter software, differentially expressed genes with a fold-change >1.5 compared with non-stemness controls and P<0.05 was identified for each OVCSC group. Compared with non-stemness controls, there were 6,495 (3,252 upregulated and 3,243 downregulated), 1,347 (765 upregulated and 582 downregulated) and 509 (44 upregulated and 465 downregulated) differentially expressed genes in the SDC, SP1 and SP2 groups, respectively. NAB1 (NGFI-A binding protein 1) and NPIPL1 (nuclear pore complex interacting protein-like 1) were commonly upregulated in SDC and SP1. PROS1 (protein S α), GREB1 (growth regulation by estrogen in breast cancer 1), PLCL1 (phospholipase C-like 1), MTUS1 (mitochondrial tumor suppressor 1), PPM1D (protein phosphatase 1D magnesium-dependent), CDC42EP3 (CDC42 effector protein Rho GTPase binding 3) and AMPD (adenosine monophosphate deaminase isoform E) were commonly downregulated in all OVCSC groups.

Analysis of biological significance

Enrichment analysis of differentially expressed genes revealed that 2,052, 1,005 and 1,581 biological processes were enriched in cells from SDC, SP1 and SP2 groups, respectively, as compared with non-stemness controls. Some biological processes were enriched in all OVCSC groups, such as tolerance induction, cell cycle regulation, stemness maintenance and anti-apoptosis (Table II). Distribution patterns of the involved biological processes, cellular components, and molecular functions were similar among the 3 groups (Fig. 1).

Table II

Enriched and shared biological processes from commonly upregulated or downregulated genes by 2 or 3 stemness groups.

Table II

Enriched and shared biological processes from commonly upregulated or downregulated genes by 2 or 3 stemness groups.

Biological processSDCSP1SP2
Enriched by upregulated genes
 Histone H3-K27 demethylation4.124.31
 Histone H4-K20 demethylation4.124.31
 Regulation of phosphatidylinositol 3-kinase activity4.022
 Histone H3-K9 demethylation3.644.09
 Negative regulation of insulin-like growth factor receptor signaling pathway3.612.51
 Tolerance induction3.043.56
 Negative regulation of BMP signaling pathway by extracellular sequestering of BMP2.926.26
 Positive regulation of tolerance induction2.792
 Regulation of tolerance induction2.792
 Organ formation2.742.2
 Response to growth factor stimulus2.732.28
 Histone lysine demethylation2.22.83
 Myoblast cell fate commitment2.152.51
 Histone H3-K36 demethylation2.152.51
 Positive regulation of T-cell tolerance induction2.152.51
 Smooth muscle tissue development2.042.7
 Bone development2.962.86
 Regulation of fat cell differentiation2.885.73
 Regulation of Wnt receptor signaling pathway2.513.58
 Negative regulation of response to stimulus2.143.39
 Regulation of canonical Wnt receptor signaling pathway2.12.06
 Regulation of BMP signaling pathway3.123.39
 Negative regulation of BMP signaling pathway2.964.26
 BMP signaling pathway2.542.39
 Regulation of transforming growth factor β receptor signaling pathway2.162.67
 Interphase of mitotic cell cycle2.113.03
Enriched by downregulated genes
 Embryo development2.132.292.72
 Negative regulation of transferase activity5.3722.41
 Positive regulation of gene expression2.52.213.53
 Progesterone receptor signaling pathway3.663.172.98
 Regulation of toll-like receptor 3 signaling pathway3.214.244.01
 Regulation of transcription2.122.215.16
 Regulation of transcription from RNA polymerase II promoter2.373.055.07
 RNA polymerase II transcriptional preinitiation complex assembly2.842.582.42
 Epithelial cell maturation4.144.48
 Endodermal digestive tract morphogenesis2.957.61
 Negative regulation of tyrosine phosphorylation of STAT protein2.193.17
 Hippo signaling cascade4.643.27
 Membranous septum morphogenesis4.185.01
 Tolerance induction to lipopolysaccharide2.957.22
 Transmembrane receptor protein tyrosine kinase signaling pathway2.843.44
 Cell differentiation5.544.86
 Canonical Wnt receptor signaling pathway5.373.07
 Cellular developmental process5.374.64
 Cell development5.273.7
 Regulation of Wnt receptor signaling pathway5.263.02
 Positive regulation of canonical Wnt receptor signaling pathway5.224.52
 Epithelial cell maturation involved in prostate gland development5.124.01
 Epithelial cell differentiation involved in prostate gland development4.836.2
 Wnt receptor signaling pathway4.763.03
 Positive regulation of Wnt receptor signaling pathway4.424.79
 Inactivation of MAPK activity4.243.73
 Apoptosis in bone marrow4.185.01
 Regulation of apoptosis in bone marrow4.185.01
 Positive regulation of cell proliferation3.993.52
 Negative regulation of MAP kinase activity3.963.09
 Negative regulation of protein serine/threonine kinase activity3.933.29
 Cellular response to stimulus3.75.21
 Positive regulation of cell cycle3.343.17
 Cell motility3.223.37
 Regulation of response to stimulus3.25.12
 Positive regulation of response to stimulus3.174.8
 Apoptotic mitochondrial changes3.162.67
 Cell-cell adhesion3.133.05
 Regulation of developmental process3.095.22
 Positive regulation of release of cytochrome c from mitochondria3.094.22
 Epithelial cell differentiation3.083.1
 Release of cytochrome c from mitochondria3.073.36
 Urogenital system development3.053.09
 Positive regulation of mitotic cell cycle3.052.33
 Fibroblast growth factor receptor signaling pathway involved in positive regulation of cell proliferation2.957.22
 Negative regulation of CD40 signaling pathway2.957.22
 Negative regulation of toll-like receptor 3 signaling pathway2.957.22
 Regulation of CD40 signaling pathway2.957.22
 Regulation of release of cytochrome c from mitochondria2.943.27
 Response to stimulus2.913.91
 Response to external stimulus2.874.43
 Glandular epithelial cell differentiation2.845.2
 Regulation of MAP kinase activity2.792.93
 Response to chemical stimulus2.733.41
 Epithelial cell development2.632.67
 Fibroblast migration2.613.4
 Regulation of S phase2.52.41
 Cell proliferation2.444.93
 Regulation of cell proliferation2.435.55
 Epithelium development2.412.43
 Programmed cell death2.396.5
 Regulation of cell differentiation2.344.12
 Cell death2.325.8
 Regulation of binding2.314.01
 Cell-cell adhesion mediated by integrin2.282.04
 MAPKKK cascade2.233.8
 Regulation of cell migration2.214.07
 Morphogenesis of an epithelium2.133.31
 Regulation of cell motility2.134.02
 Epithelial cell differentiation involved in mammary gland alveolus development4.244.01
 Negative regulation of cell growth3.232.93
 Developmental maturation3.232.2
 Erythrocyte differentiation3.163.89
 Regulation of toll-like receptor 4 signaling pathway2.845.67
 Positive regulation of focal adhesion assembly2.842.67
 Positive regulation of macrophage differentiation2.842.67
 Cellular response to transforming growth factor β stimulus2.582.42
 Regulation of histone modification2.442.44
 Cell maturation2.442.21
 Response to transforming growth factor β stimulus2.372.21
 Regulation of epithelial to mesenchymal transition2.233.44
 Lymph vessel development2.192.04
 Regulation of histone methylation2.194.49
 Regulation of myeloid cell differentiation2.183.56
 Response to growth factor stimulus2.162.95
 Negative regulation of histone modification2.034.22

Enrichment analysis also revealed that 218, 137 and 99 cellular components were enriched in SDC, SP1 and SP2 groups, respectively. Cellular components such as membrane structures of drug resistance components, cell division components, and cell adhesion structures were enriched in all 3 groups (Table III).

Table III

Enriched and shared cellular components from significantly upregulated or downregulated genes by 2 or 3 stemness groups.

Table III

Enriched and shared cellular components from significantly upregulated or downregulated genes by 2 or 3 stemness groups.

Cellular componentSDCSP1SP2
Enriched by upregulated genes
 Coated membrane3.412.11
 Membrane coat3.412.11
 Plasma membrane-enriched fraction3.212.37
 1-Phosphatidylinositol-4-phosphate 3-kinase, class IA complex2.966.57
 Basal lamina2.772.75
 Polar microtubule2.623.06
 Nuclear envelope2.042.22
Enriched by downregulated genes
 Apicolateral plasma membrane5.042.032.7
 Apical junction complex4.452.092.76
 Occluding junction4.432.413.13
 Tight junction4.432.413.13
 Intracellular part4.462.03
 mRNA cap binding complex3.42.06
 RNA cap binding complex3.42.06
 Plasma membrane2.942.09
 Ruffle membrane2.854.04
 Receptor complex2.773.6
 Leading edge membrane2.572.54
 Plasma membrane part2.452.02
 Junctional sarcoplasmic reticulum membrane2.213.01
 Excitatory synapse2.213.01
 I-κβ/NF-κβ complex2.213.01
 Nuclear lumen2.192.4
 Basolateral plasma membrane2.123.19
 Neuromuscular junction2.082.14
 Synaptobrevin 2-SNAP-25-syntaxin-1a-complexin I complex5.215.05
 Synaptobrevin 2-SNAP-25-syntaxin-1a complex4.184.04
 Viral capsid4.184.04
 Virion part3.553.43
 Apical part of cell2.732.03
 Golgi lumen2.462.35

In total, 597, 315 and 253 molecular functions were enriched in cells from SDC, SP1 and SP2 groups, respectively. Commonly enriched molecular functions in OVCSC cells included chemorepellent activity, growth factor receptor activity, epigenetic molecular functions and kinase activity (Table IV).

Table IV

Enriched and shared molecular functions from significantly upregulated or downregulated genes by 2 or 3 stemness groups.

Table IV

Enriched and shared molecular functions from significantly upregulated or downregulated genes by 2 or 3 stemness groups.

Molecular functionSDCSP1SP2
Enriched by upregulated genes
 Inositol 1,4,5-trisphosphate-sensitive calcium-release channel activity5.123.49
 Dioxygenase activity4.414.48
 Oxidoreductase activity, acting on single donors with incorporation of molecular oxygen4.414.48
 Oxidoreductase activity, acting on single donors with incorporation of molecular oxygen, incorporation of 24.414.48
 Inositol-1,4,5-trisphosphate receptor activity4.262.94
 Androsterone dehydrogenase activity4.262.94
 trans-1,2-dihydrobenzene-1,2-diol dehydrogenase activity4.262.94
 Androsterone dehydrogenase (A-specific) activity4.184.38
 Glutaminase activity4.184.38
 Histone demethylase activity (H3-K27 specific)4.184.38
 Histone demethylase activity (H4-K20 specific)4.184.38
 Oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen, 2-oxogl3.93.33
 Histone demethylase activity (H3-K9 specific)3.662.56
 Metal ion binding3.572.09
 L-ascorbic acid binding3.482.12
 Protein binding3.132.66
 Calcium-dependent cysteine-type endopeptidase inhibitor activity2.956.36
 Poly(G) RNA binding2.956.36
 Poly-glutamine tract binding2.956.36
 Chemorepellent activity2.612.94
 Insulin binding2.612.94
 S100 β binding2.612.94
 Protein tyrosine kinase activity2.243.18
 Bile acid binding2.192.56
 Platelet-derived growth factor receptor binding2.094.44
 Transmembrane receptor protein kinase activity4.452.13
 Transmembrane receptor protein tyrosine kinase activity3.332.5
 Virion binding2.279.32
 Receptor tyrosine kinase binding2.044.72
Enriched by downregulated genes
 Fibroblast growth factor receptor activity3.673.143
 Protein anchor3.224.214.03
 Thiamine transmembrane transporter activity4.195.25
 Thiamine uptake transmembrane transporter activity4.195.25
 Uptake transmembrane transporter activity4.195.25
 Reduced folate carrier activity2.623.58
 General transcriptional repressor activity2.294.73
 Protein serine/threonine phosphatase inhibitor activity2.193.14
 Phosphatidylserine binding2.062.01
 Heparan sulfate proteoglycan binding2.062.01
 Interleukin-1, type II, blocking receptor activity4.195.03
 Epidermal growth factor receptor activity3.224.03
 Oncostatin-m receptor activity3.224.03
 Epinephrine binding3.212.68
 β2-adrenergic receptor activity2.967.25
 Calcium-dependent protein kinase c activity2.967.25
 Calcium-dependent protein serine/threonine kinase activity2.967.25
 Cytoskeletal regulatory protein binding2.967.25
 Endoribonuclease activity, cleaving siRNA-paired mRNA2.967.25
 GTP cyclohydrolase activity2.967.25
 GTP cyclohydrolase i activity2.967.25
 Protein channel activity2.967.25
 Interleukin-1 binding2.864
 Interleukin-1 receptor activity2.855.22
 RNA polymerase II transcription factor binding2.852.43
 Sodium channel regulator activity2.852.43
 Actin filament binding2.842.97
 Protein tyrosine kinase activity2.822.19
 Adenylate cyclase binding2.623.42
 Norepinephrine binding2.623.42
 Insulin receptor binding2.582.07
 Transmembrane receptor protein tyrosine kinase activity2.542.53
 Co-SMAD binding2.542.22
 RNA cap binding2.542.22
 Small conjugating protein ligase activity2.512.81
 Map kinase tyrosine/serine/threonine phosphatase activity2.443.6
 Protein dimerization activity2.413.03
 Growth factor binding2.43.47
 Ubiquitin thiolesterase activity2.372.68
 Acid-amino acid ligase activity2.352.38
 Transcription activator activity2.322.31
 Transmembrane receptor protein kinase activity2.312.78
 Adrenergic receptor activity2.292.05
 Map kinase phosphatase activity2.263.44
 Ligase activity, forming carbon-nitrogen bonds2.212.01
 Cyclohydrolase activity2.193
 Prostaglandin e receptor activity2.193
 Protein serine/threonine kinase activator activity2.193
 Transforming growth factor β receptor, pathway-specific cytoplasmic mediator activity2.193
 Ubiquitin-protein ligase activity2.013.03
 A1 adenosine receptor binding5.255.03
 Retinoic acid binding4.732.05
 AMP deaminase activity4.214.03
 Phosphatidylinositol-4-phosphate 3-kinase activity4.214.03
 Adenosine receptor binding3.143
 N-acetylglucosamine 6-O-sulfotransferase activity3.143
 Wnt-protein binding2.572.42
 Thyroid hormone receptor activity2.562.43
 Transcription regulator activity2.45
 Cation:chloride symporter activity2.354.84
 Monovalent cation:hydrogen antiporter activity2.352.22
 Syntaxin binding2.342.2
 Transcription repressor activity2.34.66
 Steroid hormone receptor activity2.294.2
 Prostaglandin receptor activity2.172.05
 Ligand-dependent nuclear receptor activity2.175.03

With regard to signaling pathways, 57, 31 and 33 KEGG pathways were enriched in cells from SDC, SP1 and SP2 groups, respectively. Key KEGG pathways such as the ErbB pathway, ECM-receptor pathway, endocytosis pathway and adherens junction pathway were enriched in all 3 groups (Table V).

Table V

The shared KEGG pathways of differentially expressed genes in ovarian cancer stemness groups.

Table V

The shared KEGG pathways of differentially expressed genes in ovarian cancer stemness groups.

SDC, SP1 and SP2SDC and SP1SDC and SP2SP1 and SP2
ErbB signaling pathwayMetabolic pathwaysPathways in cancerT-cell receptor signaling pathway
Prostate cancerFocal adhesionEndocytosisAmoebiasis
ECM-receptor interactionMAPK signaling pathwayNOD-like receptor signaling pathway
Dilated cardiomyopathyAdherens junctionDorso-ventral axis formation
Hypertrophic cardiomyopathyEpithelial cell signaling in Helicobacter pylori infectionLysine biosynthesis
Small-cell lung cancerB-cell-receptor signaling pathway
Arrhythmogenic right ventricular cardiomyopathyHedgehog signaling pathway
PeroxisomePathogenic Escherichia coli infection
Systemic lupus erythematosus
Glycosaminoglycan biosynthesis-keratan sulfate
Glycosylphosphatidylinositol (GPI)-anchor biosynthesis
D-Glutamine and D-glutamate metabolism
Cancer stem cell signature-specific drug prescreening

The list of differentially expressed genes identified does not necessarily represent the cancerous features of OVCSCs. CSCs are highly quiescent whereas differentiated cancer cells are highly proliferative. Genes responsible for the quiescent state were also included in the above lists; however, they may not be ideal targets for therapy, as reversion of the quiescent state of OVCSCs may translate to the massive production of cancerous cells, which is undesirable. Therefore, it is necessary to focus on the cancerous features of OVCSCs. Compared with the normal ovarian surface epithelium, there were 2,669 upregulated and 3,384 downregulated genes in cells derived from patients with advanced OvC (Fig. 2). This set of genes represented cancerous features, and these genes were considered candidates for the OVCSC signature. With this calibration set, genes unrelated to the cancerous features of OVCSCs, such as those responsible for the quiescent state, were removed from the list of differentially expressed genes. Compared with non-stemness controls, there were 225 upregulated and 373 downregulated probes in the SDC group, 23 upregulated and 22 downregulated probes in the SP1 group and 5 upregulated and 38 downregulated probes in the SP2 group. There were 2 shared upregulated probes and 6 shared downregulated probes between cells from SP1 and SDC groups; 7 shared downregulated probes between cells from the SDC and SP2 groups, and 1 commonly downregulated probe between cells from the SP1 and SP2 groups compared with non-stemness controls (Fig. 3; Table VI). An OVCSC signature was generated by combining the 255 upregulated tags (23+2+225+5) and the 447 downregulated tags (22+6+373+7+38+1).

Table VI

The probes from shared upregulated and downregulated genes in ovarian cancer stem cell groups and the calibration group.

Table VI

The probes from shared upregulated and downregulated genes in ovarian cancer stem cell groups and the calibration group.

GroupProbeGene nameDirectionSDC ratio (P-value)SP1 ratio (P-value)SP2 ratio (P-value)OVC ratio (P-value)
SDC, SP1 and OVC211139_s_atNGFI-A binding protein 1 (EGR1 binding protein 1) NAB1Up1.6 (0.000524207987825496)2.19 (0.00487987405631332)1.79 (0.000889300306837179)
215921_atNuclear pore complex interacting protein-like 1Up1.58 (0.00945304058490303)2.09 (0.0202576342575334)1.61 (0.0014333481542029)
207808_s_atProtein S (α)Down7.84 (0.0000735247070136605)1.76 (0.00346916670529939)12.32 (1.44641461217268E-08)
205862_atGREB1 proteinDown3.22 (0.000988605674703398)2.53 (0.0137390333141558)4.36 (0.0000851847027384107)
213931_atInhibitor of DNA binding 2, dominant negative helix-loop-helix proteinDown2.85 (0.000472661276918831)3.32 (0.00185745595436645)3.17 (0.000972847067933643)
203543_s_atKruppel-like factor 9Down4.98 (4.04319757064431E-07)1.58 (0.0229481071140268)1.92 (2.24515014734723E-06)
201566_x_atInhibitor of DNA binding 2, dominant negative helix-loop-helix proteinDown7.24 (0.0000951288294734348)2.05 (0.0411757137373141)1.8 (0.0095330740950358)
205934_atPhospholipase C-like 1Down1.54 (0.00586645102487323)2.14 (0.0491165384831927)1.55 (0.00197953042446589)
SDC, SP2 and OVC212096_s_atMitochondrial tumor suppressor 1Down2.44 (6.10988330229413E-06)1.68 (0.0291768466561271)6.61 (2.61683134224164E-06)
209288_s_atCDC42 effector protein (Rho GTPase binding) 3Down2.05 (0.000033372563900896)2.33 (0.027635397819083)5.37 (5.86407256617617E-06)
204224_s_atGTP cyclohydrolase 1Down1.55 (0.000854582086891518)1.84 (0.0394499654614312)2.54 (0.0324455426569389)
204566_atProtein phosphatase 1D magnesium-dependent, δ isoformDown1.61 (0.00187102894792315)1.72 (0.0472030826782719)2.44 (5.76098014017778E-06)
218182_s_atClaudin 1Down4.85 (0.0000344349818731975)1.55 (0.0482489441482889)1.87 (0.00689245472667143)
209286_atCDC42 effector protein (Rho GTPase binding) 3Down1.9 (0.0133367282355993)1.94 (0.0158631362361945)1.66 (0.00733852668541466)
205659_atHistone deacetylase 9Down1.88 (9.57664107206817E-06)2.2 (0.0335751161813233)1.5 (0.00774593390013839)
SP1, SP2, and OVC207992_s_atAdenosine monophosphate deaminase (isoform E)Down1.94 (0.0304265496975559)2.23 (0.0288974231347468)2.02 (0.0000150036714772332)

OVCSC signatures of 255 upregulated probes and 447 downregulated probes were analyzed by the CMAP for a drug prescreening. Of the 6,100 proceeded instances, it was predicted that 1,500 had the potential to promote the OVCSC signature, as indicated by their positive enrichment scores, whereas 1,419 could have anti-OVCSC effects, as reflected by their negative enrichment scores. By further filtering the 1,419 molecules with negative scores with the described criteria, 18 remained as the most promising therapeutic small-molecule candidates to target OVCSCs, for example SC-560, disulfiram (DS), thapsigargin, esculetin, cinchonine, alvespimycin and tanespimycin (Table VII).

Table VII

Eighteen therapeutic small-molecule drugs with potential OVCSC-specific targeting abilities in ovarian cancer.

Table VII

Eighteen therapeutic small-molecule drugs with potential OVCSC-specific targeting abilities in ovarian cancer.

Connectivity Map nameMeanNEnrichmentP-valueSpecificity% Non-nullCategory
Sc-560−0.523−0.9270.000620.0121100COX-1 inhibitor
Puromycin−0.484−0.8650.000620.0373100Protein synthesis inhibitor
Pralidoxime−0.364−0.8480.000970100Cholinesterase reactivator
Disulfiram−0.45−0.8460.000240.0072100Proteasome inhibitor
Thapsigargin−0.343−0.8410.008090.1161100Non-competitive SERCA inhibitor
Phenazone−0.393−0.8330.009310.0231100Analgesic and antipyretic
Lycorine−0.295−0.7620.001440.0880Protein synthesis inhibitor
Benzthiazide−0.174−0.7560.007160.020450Diuretic and antihypertensive
Naltrexone−0.225−0.7350.00270.022560Opioid receptor antagonist
Atropine oxide−0.215−0.7140.004170.006260
Pyrimethamine−0.285−0.7030.005050.0280DHFR inhibitor
Diethylstilbestrol−0.276−0.6410.006120.016483Synthetic nonsteroidal estrogen
Alvespimycin−0.2412−0.5010.002550.079758Hsp90 inhibitor
Tanespimycin−0.1962−0.48200.067253Hsp90 inhibitor


OvC has the highest mortality rates of all gynecological malignancies and the 5-year survival rates of patients with OvC remain poor (15). CSC theory can consistently illustrate many clinical and pathological features of OvC, and they have been well supported by the isolation and identification of CSCs in side populations, multi-cellular spheroids from tumor bulk (16) and OvC cell lines (4). CSCs, which often consist of a low fraction, have been established to have a general connection to OvC progression, such as relapse, migration and drug resistance. Efforts have been made to study the characteristics of CSCs in detail and to effectively eliminate them.

By comparing the genome-wide expression profiles of OVCSC-specific clinical and experimental specimens from the GEO database, we identified the differentially expressed genes shared by 3 OVCSC groups. Most of the OVCSC signature genes were closely related with respect to CSC properties and OvC progression. NGFI-A binding protein 1 has been reported to be upregulated in several OVCSC groups (17). By inhibiting the transcription factor EGR-1, NAB1 downregulates the transcription of aminolevulinic acid synthase 1 (ALAS1), leading to increase in the intracellular level of heme via feedback regulation (17). High levels of heme, together with iron-mediated oxidative stress and inflammation in patients with endometriosis, can accelerate the progression of OvC (18). In the present study, genes that were downregulated in all OVCSC groups included PROS1 (protein S α), GREB1, KLF9 (Kruppel like factor 9), MTUS1 (mitochondrial tumor suppressor 1), PPM1D (protein phosphatase 1D magnesium-dependent) and inhibitor of DNA binding 2 (ID2). The downregulation of gene PROS1, a member of the HNF4α tumor suppressor network, can promote cell proliferation and finally leads to tumor progression (19). In breast cancer, estrogen-receptor-negative cancer cells with low levels of GREB1 expression (20) have more stem cell features than other cancer cells (21). Overexpression of KLF9, a differentiation-related transcription factor, promoted differentiation of malignant glioma stem cell spheres and inhibited their proliferation in vivo in a xenotransplantation model (22). The downregulation of KLF9 in OVCSCs suggests that its absence may be required to maintain stemness. We also confirmed downregulation of the potential tumor suppressor gene MTUS1 in OVCSCs, which is consistent with a previous study showing that downregulation of MTUS1 and Claudin-1 (CL-1) in various human tumors is related to active proliferation, poor differentiation and poor clinical outcomes (23). PPM1D, a highly expressed oncogene in various human tumors, can inactivate CHK1 and p53 via dephosphorylation, leading to cisplatin resistance in cancer cells. Therefore, this gene is a potential target for treatment of OvC (24,25). ID2 is a new drug target, as high expression of ID2 can maintain the pluripotency of neural stem cell spheroids by direct inhibition of p53 (26). However, both PPM1D and ID2 were downregulated in the OVCSC groups, which may be due to the concealment and quiescence of CSCs, but this requires further investigation.

To identify the CSC features shared by 3 OVCSC groups, detailed Gene Ontology analysis was performed by using the GeneSifter software using domains of biological processes, cellular components and molecular functions. Differentially expressed genes were significantly enriched in biological features such as tolerance induction, cell cycle regulation, stemness maintenance and anti-apoptosis (all processes involved in OvC progression), which can easily distinguish OVCSCs from other OvC cells. By analyzing the enriched signaling pathways involving differentially expressed genes, we found that OVCSCs were different from other OvC cells in the ECM-receptor pathway, focal adhesion pathway and adherens junction signaling pathway. This finding is consistent with findings from other studies showing a strong correlation between these signaling pathways and epithelial stem cell proliferation, cancer invasion and migration and staged tumor progression (27,28). Moreover, some of these pathways have important functions in CSCs. The ErbB signaling pathway mediates epithelial-mesenchymal transitions in breast cancer, and is enriched in CD44+/CD24 breast CSCs (2931). During Helicobacter pylori infection, normal gastrointestinal stem cells are disrupted through epithelial cell signaling and the downstream STAT and WNT signaling pathways, leading to the genesis and progression of gastrointestinal tumors (32). These signaling pathways may also cooperate in colonic glands epithelial tumor stem cells. Pathogenic Escherichia coli infection correlates with the formation and progression of an epithelial colonic tumor, indicating that the same pathway is also active in OVCSCs (33). Endocytosis-related pathways are enriched in OVCSCs and represent endocytosis mediated by OVCSC-specific surface markers such as CD133 and CD44 (34).

Following characterization of signature genes in OVCSCs, co-expression extrapolation was performed with the CMAP, and small-molecule compounds with potential anti-OVCSC pharmacological properties were identified. Notably, some of these compounds [such as SC-560, disulfiram (DS), thapsigargin, esculetin, cinchonine, alvespimycin and tanespimycin] have been tested in other tumors. As a selective inhibitor of cyclooxygenase-1, SC-560 significantly inhibits cell proliferation and arrests cells in the G0/G1 phase. SC-560 can induce autophagy of colon cancer cells in vitro (35) and SC-560 effectively suppresses tumor growth in animals xenografted with the ovarian cancer cell line SKOV-3 by inhibiting cell proliferation and promoting apoptosis (36). Taken together, our results and those from previous studies on OVCSCs suggest that SC-560 is a promising target for OvC treatment. The presence of the recently identified CSC marker aldehyde dehydrogenase (ALDH) has been validated in many solid tumors, including breast, colon cancer and OvC (37). Disulfiram (DS), an ALDH protease inhibitor, might suppress the migration of glioma stem cells and be used as adjuvant treatment after resection and chemotherapy (38). A recent study confirmed that DS effectively inhibits the formation of breast cancer stem cell spheres while promoting the cytotoxic effect of taxol on breast cancer cell lines by simultaneously inducing reactive oxygen species and inhibiting the NF-κB signaling pathway. Thapsigargin has been tested in clinical trials for its high efficacy in targeting CSC-specific signaling pathways (39). The natural compound esculetin effectively inhibits Ras/ERK1/2-mediated in vitro proliferation of colon cancer cells (40). In vivo studies also indicate that esculetin can significantly enhance the chemotherapy effects of cisplatin by regulating the expression of p53/Akt/phosphatase while reducing side-effects such as induced nephrotoxicity and acute leucopenia (41). The alkaloid cinchonine may reverse multi-drug resistance (MDR), which is an important mechanism for chemotherapy failure. In 2001, Furusawa et al (42) found that cinchonine enhances doxorubicin-induced apoptosis in multi-drug-resistant P388 leukemia cells. A recent study on cervical cancer confirmed that cinchonine not only reverses the multi-drug-resistant properties of tumor cells but also has synergistic effects on taxol-induced apoptosis (43). Therefore, cinchonine may also help overcome MDR in OVCSCs. It has recently been shown that the opioid antagonist naltrexone (NTX) effectively inhibits the proliferation of OvC in vitro and in xenograft tumor models. NTX can significantly enhance the efficacy of cisplatin and effectively inhibit tumor progression while alleviating the cytotoxic effects of chemotherapy (44). The heat shock protein 90 (Hsp90) inhibitors alvespimycin and tanespimycin have synergistic and sensitizing effects with cisplatin and other chemotherapy drugs used to treat breast, bladder cancer, and nervous gliomas via their specific anti-CSC effects (4547). In clinical trials, several Hsp90 inhibitors have been effective in promoting targeted therapies of OvC. The above compounds provide new options to avoiding chemotherapy failure by specifically targeting CSCs (48). In the present study, we compared gene expression profiles from several OVCSC samples with their non-stemness cancer controls. Our study revealed that OVCSCs related differentially expressed genes, enriched Gene Ontology properties and key signaling pathways, and generated an OVCSC-specific signature for screening the small-molecule compounds with potential anti-OVCSC pharmacological properties. Thus, this approach may provide new insights into developing specific drugs that target OVCSCs.


The present study was supported by the Youth Program of Tianjin Nature Science Foundation (no. 13JCQNJC10700), the Tianjin Technology Support Program of International Science and Technology Cooperation (09ZCZDSF03800) and the 973 Program (Grant 2009CB918903).



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March 2014
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Huang, Y., Ju, B., Tian, J., Liu, F., Yu, H., Xiao, H. ... Hao, Q. (2014). Ovarian cancer stem cell-specific gene expression profiling and targeted drug prescreening. Oncology Reports, 31, 1235-1248. https://doi.org/10.3892/or.2014.2976
Huang, Y., Ju, B., Tian, J., Liu, F., Yu, H., Xiao, H., Liu, X., Liu, W., Yao, Z., Hao, Q."Ovarian cancer stem cell-specific gene expression profiling and targeted drug prescreening". Oncology Reports 31.3 (2014): 1235-1248.
Huang, Y., Ju, B., Tian, J., Liu, F., Yu, H., Xiao, H., Liu, X., Liu, W., Yao, Z., Hao, Q."Ovarian cancer stem cell-specific gene expression profiling and targeted drug prescreening". Oncology Reports 31, no. 3 (2014): 1235-1248. https://doi.org/10.3892/or.2014.2976