Increased epithelial stem cell traits in advanced endometrial endometrioid carcinoma
© Chang et al; licensee BioMed Central Ltd. 2009
Received: 4 May 2009
Accepted: 16 December 2009
Published: 16 December 2009
It has been recognized cancer cells acquire characters reminiscent of those of normal stem cells, and the degree of stem cell gene expression correlates with patient prognosis. Lgr5(+) or CD133(+) epithelial stem cells (EpiSCs) have recently been identified and these cells are susceptible to neoplastic transformation. It is unclear, however, whether genes enriched in EpiSCs also contribute in tumor malignancy. Endometrial endometrioid carcinoma (EEC) is a dominant type of the endometrial cancers and is still among the most common female cancers. Clinically endometrial carcinoma is classified into 4 FIGO stages by the degree of tumor invasion and metastasis, and the survival rate is low in patients with higher stages of tumors. Identifying genes shared between advanced tumors and stem cells will not only unmask the mechanisms of tumor malignancy but also provide novel therapeutic targets.
To identify EpiSC genes in late (stages III-IV) EECs, a molecular signature distinguishing early (stages I-II) and late EECs was first identified to delineate late EECs at the genomics level. ERBB2 and CCR1 were genes activated in late EECs, while APBA2 (MINT2) and CDK inhibitor p16 tumor suppressors in early EECs. MAPK pathway was significantly up in late EECs, indicating drugs targeting this canonical pathway might be useful for treating advanced EECs. A six-gene mini-signature was further identified to differentiate early from advanced EECs in both the training and testing datasets. Advanced, invasive EECs possessed a clear EpiSC gene expression pattern, explaining partly why these tumors are more malignant.
Our work provides new insights into the pathogenesis of EECs and reveals a previously unknown link between adult stem cells and the histopathological traits of EECs. Shared EpiSC genes in late EECs may contribute to the stem cell-like phenotypes shown by advanced tumors and hold the potential of being candidate therapeutic targets and novel prognosis biomarkers.
Tumor development, progression, and prognosis remain at the front position of medical research. Two hypotheses of the origin of cancer have existed for many decades. One hypothesis postulates that adult stem or precursor cell is the cell of origin for cancer, whereas the other declares a somatic cell can be mutated and then be dedifferentiated or be reprogrammed to regain properties associated with both cancer cells and stem cells [1–3]. The discovery of a subpopulation of tumor stem cells (TSCs) in leukemia and solid cancers has strengthened the stem cell hypothesis . Glioblastomas also possess characters and gene expression patterns of local neural stem cells (NSCs) , and artificially introducing cancer-associated mutations into stem or lineage-restricted precursor cells can indeed turn them into cancer initiating cells and all mice received mutations developed medulloblastomas [6, 7]. Another example that the adult stem cell represents the cell of origin of cancer has recently been made in chronic myeloid leukemia (CML): by restricting BCR-ABLp210 expression to mouse Sca1(+) hematopoietic stem cells, it is sufficient to induce CML formation that recapitulates the human disease . These evidences support the idea that mutations of stem cells may initiate the carcinogenic process of certain, although not necessary all, tumors.
On the other hand, the importance of somatic or tumor cell mutation and dedifferentiation has not been excluded completely. It has been recognized that during malignant transformation, cancer cells acquire genetic mutations that override the normal mechanisms controlling cellular proliferation. Human tumor cells can be created from healthy somatic cells with defined genetic elements . Even though cancers were originated from mutated stem cells, newly acquired mutations in tumors still contribute in cell malignancy and therapy resistance. It has been recognized that cancer cells acquire characters reminiscent of those of normal stem cells. Clinically cancer cells with poor differentiated pathological grading usually have worse therapy response than those with well differentiated morphology. The degree of embryonic gene re-expression correlates with pivotal tumor features and patient prognosis [10, 11]. It is known that colon cancers adopt a broad program encompassing embryonic colon development . In poorly differentiated breast cancer, gliomas and bladder carcinoma, an embryonic stem cell (ESC)-like gene expression signature is exhibited and the degree of ESC program recapitulation correlates with tumor stages and patient survival . Recent studies demonstrated that Snail, a potent oncogene which can induce epithelial-mesenchymal transition (EMT), contributes to the acquisition of stem cell traits in breast cancer cells [14, 15]. Pre-existing cancerous lesions may become more malignant by the accumulation of new oncogenic mutations (such as Snail) that can induce cell dedifferentiation. Identifying genes shared between transformed cells, especially the more malignant ones, and stem cells will help to unmask the pathogenesis of tumors, as well as provide us with novel therapeutic targets and prognosis biomarkers.
Endometrial carcinoma of the female genital tract can be divided into two forms: endometrial endometrioid carcinoma (EEC; Type I) which account for 70-80% of cases and are estrogen-related; whereas the Type II tumors (papillary serous or clear cell tumors) account for 20% of cases unrelated to estrogen stimulation . Clinically endometrial carcinoma is classified into 4 FIGO stages by the degree of invasion and metastasis: stage I tumors are limit to the uterine body and stage II tumors extend to the uterine cervix. Both stages are considered as less invasive, although stage IIB cases are characterized by a less favorable prognosis. In contrast, tumors of stages III-IV are invasive: for stage III there is regional tumor spread and for stage IV there is bulky pelvic disease or distant spread . Approximately 72% of endometrial carcinomas are stage I, 12% are stage II, 13% are stage III, and 3% are stage IV . The survival rate is also low in patients with higher stages of tumors: 80-90% in stage I, 70-80% in stage II, 40-60% in stage III, and 20% in stage IV . Identifying genes abundant in late EECs can not only unmask the mechanisms of tumor malignancy but also provide us with novel therapeutic targets. Recently Lgr5- or CD133-positive crypt stem cells of the intestinal track were identified and these cells were proven to be one of the original cells of intestinal cancer [18, 19]. OLFM4 is also a new, robust marker for stem cells in human intestine and marks a subset of colorectal cancer cells . Disruption of beta-catenin in cells positive for CD133 resulted in a gross disruption of crypt architecture and a disproportionate expansion of CD133(+) cells at the crypt base . It is unclear, however, whether genes high expressed in epithelial stem cells (EpiSCs) also contribute in tumor invasiveness, malignancy and therapy resistance. A broad description of stem cell traits reminiscent in EECs is therefore crucial.
In this study we dealt with the molecular bases of endometrial cancer and assessed the expression of epithelial precursor genes in advanced EEC. To examine the shared genes between EpiSC and late EECs, we first need to unmask the gene compositions in different stages of EECs. For this purpose we applied gene expression microarray and machine learning algorithms to filtrate genes differentially expressed in early (stages I-II) and late (stages III-IV) EECs. After obtaining genes unique in EECs of different stages, we then related transcriptional programs in EpiSCs and late EECs. This approach helped to discover a total of 217 probe sets differentiating EECs of different stages, and, moreover, showed late EECs possess a clear EpiSC gene expression pattern, partly explaining why these tumors are more malignant and fatal.
Molecular signatures of early and late stage EECs
Characteristics of 34 EEC patients used in the training cohort.
(Isolation site: Endometrium)
(Isolation site: outside endometrium)
Characteristics of another 15 early EEC patients used in the testing set.
The discrimination ability of these 678 probe sets were evaluated by a supervised machine learning strategy, which combines the weighted voting algorithm and leave-one-out cross validation (LOOCV) [23–25]. An error rate of 12.1% (2 out of 24 early cancers and 2 out of 9 late samples; P < 0.001 by permutation test) was found (Figure 1C and Additional file 1). However, we found the top 217 features (ranked by the weighted value of each probe set ) is the largest panel to have better discrimination ability than that of the 678-probeset signature (error rate 6.1% vs. 12.1%; Figure 1C, upper panel): 2 out of 24 early EEC tissues are classified into the late group while all 9 late ones are correct (Figure 1D). MDS analysis supports the superior classification power of these 217 probe sets: only 2 early samples express late EECs gene signatures and are grouped together with the late cases (Figure 1E). When applying these 217 probe sets on another independent testing data set containing 15 early EEC cases, 1 out of 15 early tissues (error rate 6.7%; P < 0.001 by permutation test) is misgrouped (Figure 1F).
In-depth exploration of EEC-related genes
Up-regulated known genes in late stage EECs.
Probe Set ID
ADAM metallopeptidase domain 17
adenosine A3 receptor
aldolase A, fructose-bisphosphate
chromosome 20 open reading frame 24
chromosome 20 open reading frame 52
CAP, adenylate cyclase-associated protein 1
capping protein (actin filament), gelsolin-like
chemokine (C-C motif) receptor 1
chitinase 3-like 1 (cartilage glycoprotein-39)
cystatin A (stefin A)
cleavage stimulation factor, subunit 1, 50kDa
docking protein 5
dual specificity phosphatase 16
ER degradation enhancer mannosidase a-like 2
v-erb-b2 erythroblastic leukemia viral oncogene
IgG Fc fragment, IIa, receptor (CD32)
IgG Fc fragment, IIb, receptor (CD32)
IgG Fc fragment, IIc, receptor (CD32)
IgG Fc fragment, IIIb, receptor (CD16b)
G protein pathway suppressor 1
lysophosphatidic acid receptor 5
hypothetical protein LOC130576
similar to RIKEN cDNA C030006K11 gene
mitochondrial ribosomal protein L52
macrophage scavenger receptor 1
neuronal PAS domain protein 2
oxysterol binding protein-like 2
programmed cell death 10
Polyhomeotic like 3 (Drosophila)
peptidase inhibitor 3, skin-derived (SKALP)
Presenilin 1 (Alzheimer diseas 3)
SUMO1/sentrin/SMT3 specific peptidase 2
solute carrier family 35, member E1
solute carrier family 9 (Na/H exchanger), 7
sorting nexin 6
Sjogren's syndrome/scleroderma autoantigen 1
translocase of inner mitochondrial 8 homolog B
transducin-like enhancer of split 3
V-set and immunoglobulin domain containing 4
Williams-Beuren syndrome region 16
zinc metallopeptidase (STE24 homolog, yeast)
zinc finger, HIT type 2
Down-regulated known genes in late stage EECs.
Probe Set ID
ATP-binding cassette, sub-family B, 11
AF4/FMR2 family, member 3
Rho guanine nucleotide exchange factor 7
ADP-ribosylation factor-like 17 pseudogene 1
ATPase, Class V, type 10A
Chromosome 9 open reading frame 3
CD99 molecule-like 2
CDC14 cell division cycle 14 homolog A
CDP-diacylglycerol synthase 2
craniofacial development protein 1
Component of oligomeric golgi complex 8
C-terminal binding protein 2
dynein, axonemal, heavy polypeptide 6
family with sequence similarity 82, A
FERM, RhoGEF & pleckstrin domain protein 1
Formin binding protein 1
Guanine nucleotide binding protein, β 5
GTF2I repeat domain containing 2
glucuronidase, beta pseudogene 1
Histone deacetylase 3
Hypermethylated in cancer 2
kelch repeat and BTB domain containing 6
kinesin family member 21A
LSM11, U7 small nuclear RNA associated
leucine zipper transcription factor-like 1
Methylcrotonoyl-Coenzyme A carboxylase 2
nuclear casein kinase and CDK substrate 1
OCIA domain containing 1
Pericentriolar material 1
Pleckstrin homology domain containing, A5
polymerase (RNA) III polypeptide E
Protein kinase C, epsilon
RNA binding motif protein 24
Ribosomal protein L27a
Smith-Magenis syndrome region, candidate 7
Suppressor of zeste 12 homolog pseudogene
thyroid hormone receptor, beta
Tight junction protein 1 (zona occludens 1)
transmembrane protein 101
Transmembrane protein 4
Vacuolar protein sorting 13 homolog D
WD repeat domain 4
sterile alpha motif and leucine zipper kinase AZK
Up-regulated biological modules in late EECs.
Regulation of catalytic activity
DUSP16, CAP1, ADORA3, ERBB2, GPS1, PSEN1
Immune system process
AQP9, FCGR2A, FCGR2B, FCGR2C, FCGR3B, CCR1, ERBB2, VSIG4
CAP1, ADORA3, ERBB2, CCR1
Regulation of MAP kinase activity
DUSP16, ERBB2, GPS1
Cell surface receptor linked signal transduction
TLE3, CAP1, SENP2, ADORA3, ERBB2, LPAR5, CCR1, ADAM17, PSEN1, SNX6
Membrane organization and biogenesis
CAP1, ZMPSTE24, MSR1, TIMM8B
Up-regulated known genes in early stage EECs.
Probe Set ID
amyloid beta (A4) precursor protein-binding A2
chromosome 10 open reading frame 136
chromosome 6 open reading frame 12
coiled-coil domain containing 144C
cyclin-dependent kinase inhibitor 2A (p16)
centrosomal protein 350kDa
Contactin 3 (plasmacytoma associated)
Chromosome X open reading frame 33
dynein, axonemal, heavy polypeptide 10
developmental pluripotency associated 4
ectonucleoside tri-P diphosphohydrolase 3
v-erb-a erythroblastic leukemia viral oncogene
establishment of cohesion 1 homolog 2
G protein-coupled receptor 37
heparan sulfate 6-O-sulfotransferase 3
Similar to cell division cycle 10 homolog
hypothetical protein LOC642691
roundabout, axon guidance receptor, 2
similar to Leucine-rich repeat protein SHOC-2
stearoyl-CoA desaturase 5
SEC22 vesicle trafficking protein homolog B
Solute carrier family 17, member 1
solute carrier family 17, member 6
ST7 overlapping transcript 4 (non-coding RNA)
Spermatid perinuclear RNA binding protein
toll-like receptor 10
ubiquitin-fold modifier conjugating enzyme 1
vang-like 1 (van gogh, Drosophila)
vacuolar protein sorting 33 homolog A
Williams Beuren syndrome region 19
Wilms tumor 1 associated protein
Zinc finger, A20 domain containing 1
zinc finger, SWIM-type containing 6
Down-regulated known genes in early stage EECs.
Probe Set ID
1-acylglycerol-3-phosphate O-acyltransferase 1
autocrine motility factor receptor
ATPase, Class VI, type 11B
C1q and tumor necrosis factor related protein 1
cerebral endothelial cell adhesion molecule 1
discs, large homolog-associated protein 4
insulin-like growth factor binding protein 5
kin of IRRE like (Drosophila)
LIM domain only 4
mediator of RNA polymerase II transcription 12
O-linked N-acetylglucosamine transferase
opioid receptor, sigma 1
peroxisomal biogenesis factor 5
Phospholipid scramblase 1
Protein phosphatase 1, regulatory 3E
RNA binding motif protein 39
retinoid × receptor, beta
SIN3 homolog B, transcription regulator (yeast)
transportin 2 (importin 3, karyopherin b 2b)
ubiquitin specific peptidase 11
To gain more insights into the functional consequences of differential gene expression, we performed gene set enrichment analysis for the filtrated genes. Signature probe sets were subjected into the Gene Ontology (GO) database search to find statistically over-represented functional groups within these genes. The biological processes being statistically overrepresented (P < 0.05) in late stage-enriched genes are shown in Table 5. These predominant processes include those pertaining to immune system process, second-messenger-mediated signaling (genes also involved in cyclic nucleotide second messenger (P = 0.0306) are bold), MAP kinase activity (genes also involved in the inactivation of MAPK activity (P = 0.0459) are bold), membrane organization and biogenesis, regulation of catalytic activity (genes also involved in the positive regulation of catalytic activity (P = 0.0182) are bold), and cell surface receptor-linked signal transduction are significantly up (Table 5).
For genes enriched in early EECs, CDKN2A (P16) tumor suppressor was found to be reverse correlated with EEC prognosis  (Table 6, bold). Another tumor suppressor is APBA2 (amyloid beta (A4) precursor protein-binding, family A, member 2; also known as MINT2), which is frequently methylated and silent in colorectal carcinoma and gastric carcinoma . Hypermethylation of GPR37 is also frequently found in acute myeloid leukemia . In terms of oncogenes, ROBO2 (roundabout, axon guidance receptor, 2), a receptor of the SLIT2 axon guidance and cell migration growth factor, is associated with poor prognosis of breast cancer . ESCO2 (establishment of cohesion 1 homolog 2) is tightly correlated with BRCA1-dependent and various cell-type specific carcinogenesis , and DAPP4 pluripotent factor is enriched in seminomas . VANGL1 (also known as KITENIN or STB2) acts as an executor in colon cancer cells with regard to cell motility and thereby controls cell invasion, which may contribute to promoting metastasis . The abundant expression of known oncogenes in early EECs also suggests the early EEC cases contain high percentage of epithelial tumor cells instead of merely stromal and myometrial contaminations.
A six-gene signature distinguishing early and late EECs
Gene annotations of the six-gene signature.
Probe Set ID
Homo sapiens, clone IMAGE:5759975, mRNA
ATP-binding cassette, B (MDR/TAP), 11
amyloid beta (A4) precursor protein-binding A2
LIM domain only 4
Hypothetical protein LOC647065
Re-activation of epithelial stem cell genes in advanced EECs
EEC still ranks one of the most fatal female cancers worldwide and disease progression very often accompany with worse clinical outcomes and treatment failure. Identifying genes or canonical pathways associated with advanced cancer can help to unmask the mechanisms of tumor malignancy as well as provide us with novel drug targets. It has been recognized clinically that cancer cells, especially the advanced and metastatic ones, possess characters reminiscent of those of normal stem cells. The degree of stem cell gene expression correlates with pivotal tumor features and patient prognosis [10, 11, 13]. Hence, identifying shared genes between late EECs and stem cells will provide new insights into cancer biology, as well as new prognosis markers and therapeutic targets. In this study, we identified a 217-probeset signature which could distinguish late (stages III-IV) from early (stages I-II) EECs (Figure 1). More low stage disease array data than high stage ones were obtained, which may partly due to the fact that the early diagnosis takes place in almost 90% of EEC clinically. We combined primary and metastatic late EEC samples in one group since their molecular profiles are indistinguishable (not shown). Prostate EpiSCs were used as a comparative group since array data for endometrial stem cells is not available yet. Nevertheless, prostate CD133+ cells are still epithelial stem cells and therefore good controls. Other EpiSC data should reproduce part of our findings.
Our results reveal a previously unaware link between genes associated with EpiSC identity and the histopathological traits of EECs. It is possible that these genes contribute to the stem cell-like phenotypes of late EECs. A total of 26 EpiSC genes were found overexpressed in late EECs (Figure 5C), and genes down-regulated in late EECs (Figure 2; 77 probe sets) are also absence in EpiSCs (Figure 5D). Among those 26 overexpressed genes there are famous oncogenes or stemness genes (Figure 5C, underlined). ADAM17 (A Disintegrin and A Metalloproteinase 17), also known as tumor necrosis factor-alpha converting enzyme (TACE) or less commonly CD156q, is a therapeutic target in multiple diseases since major contemporary pathologies like cancer, inflammatory and vascular diseases seem to be connected to its cleavage abilities . CAP1 (adenylate cyclase-associated protein 1) overexpressed in pancreatic cancers is involved in cancer cell motility . CAPG (capping protein (actin filament), gelsolin-like) also contributes in the motility of pancreatic cancer cells . PDCD10 (CCM3) is involved in cerebral cavernous malformations (CCM)  and is found to interact with Ste20-related kinase MST4 to promote cell growth and transformation via modulation of the ERK pathway . PSEN1 (presenilin 1) is involved in apoptosis, overexpressed in high-risk patients with stage I non-small cell lung cancer (NSCLC), and is in a prognosis signature of NSCLC patients . SENP2 (SUMO-specific protease 2) is highly expressed in trophoblast cells that are required for placentation, and targeted disruption of SENP2 in mice reveals its essential role in development of all three trophoblast layers via modulating the Mdm2-p53 pathway . The appearance of these known oncogenes or stemness genes in our data supports the reliability of our gene lists. The roles of EpiSC genes in both epithelial stem cell biology and EEC malignancy will be addressed further.
Several genes were previous suggested to be tumor suppressors. CSTA (cystatin A, or stefin A), a cysteine proteinases inhibitor, is implicated in preventing local and metastatic tumor spread of cancers. The risk of disease recurrence and disease-related death was thus higher in patients with low CSTA in patients with squamous cell carcinoma of the head and neck . NPAS2 (neuronal PAS domain protein 2) is a circadian gene as well as a putative tumor suppressor involved in DNA damage response . PHC3 (polyhomeotic homolog 3), a component of the hPRC-H complex, associates with E2F6 during G0 and is lost in osteosarcoma tumors . Validating their expression in different stages of EECs by further immunohistochemstry study will not only provide novel malignancy mechanisms but will also present new drug targets.
In the past few years, much effort has been put to explore the mechanisms and additional molecular markers for predicting prognosis of EECs by using high-throughput genomics technology. Gene expression microarray (GEM) is a popular platform among all of those high-throughput genomics techniques. In this study we applied GEM and machine learning algorithms to filtrate out a 217-probeset signature for disease diagnosis. Many of the filtrated genes have been linked to tumor progression and malignancy, supporting the reliability of our array data. Moreover, we narrowed down this 217-probeset profile to a six-gene mini-signature for the differentiation of early to late EECs in the training set. This signature can be validated by an independent testing cohort (Figure 4). Owing to the small gene number of this signature, it is now possible to check their mRNA levels in patient tissues by real-time PCR in regular clinical labs. Recently a five-gene profile and a five-microRNA signature are identified for the prediction of clinical outcomes in non-small-cell lung cancer [49, 50]. Whether our six-gene signature can be correlated with relapse-free and overall survival among patients with EEC is unclear and awaited to be elucidated. Also, whether the protein expression levels of these 6 genes correlate with those of mRNAs is unclear. Since most of the patients in either training or testing data set were Caucasian (Table 1), whether this gene signature can be applied in patients with various genetic backgrounds should also be studied.
In our datasets we noticed that few early EEC cases expressed already late EEC genes and therefore could not be classified correctly (Figs. 1, 2). Since patients with late and metastatic EEC tend to have poor prognosis, whether these unusual early cases possess worse clinical outcomes is an interesting issue. It has been suggested that prognosis potential of human tumors is inherited in early lesions. For example, the gene expression patterns in metastatic colorectal carcinoma are readily distinguishable from those associated with in situ tumors [24, 51]. A subset of primary tumors resembled metastatic tumors with respect to this gene-expression signature [24, 51]. Very recently Varmus and colleagues showed that when untransformed mouse mammary cells were introduced into the systemic circulation of a mouse, those cells can bypass transformation at the primary site, form long-term residence in the lungs but do not form ectopic tumors . Husemann et al. also observed that systemic spread can be an early step in breast cancer. Tumor cells can disseminate systemically from earliest epithelial alterations and form and micrometastasis in bone marrow and lungs . Therefore, release from dormancy of early-disseminated cancer cells may frequently account for metachronous metastasis. The metastatic potential of human tumors is encoded in the bulk of a primary tumor and, at least in a subset of patients, metastatic capability in cancers is an inherent feature. Our EEC gene signatures therefore hold the potential of being a novel prognosis panel. More advanced therapy and clinical follow-up should be applied on early stage patients with molecular feature similar to that of EpiSC.
In advanced EECs, tumor tissues express more genes abundant in CD133+ EpiSC and acquired a stem cell trait (Figure 5). The expression of these EpiSC genes in late EECs may due to the re-expression of EpiSC features in late stage EECs, i.e., further mutations and stem cell gene reactivation in certain early EECs. The intermediate EpiSC gene expression level in early EECs supports this point (Figure 5A &5C-D). Recent studies demonstrated that EMT contributes to the acquisition of stem cell traits in cancer cells and the induction of EMT inducer Snail results in stemness gene expression [14, 15]. Whether EMT also contributes in EEC progression and metastasis is an interesting issue to follow. However, we did not rule out the possibility that certain late EECs may arise from an independent rapidly progressing cancer utilizing stemness molecular pathways. According to the tumor stem cell theory, cancer cells may be originated from different cancer stem cells acquiring distinctive oncogenic mutations. Certain early EECs have the capacity to progress to late stage disease may due to a mechanism that they arose from the same mutated progenitor cells as late EECs. The observation that several early EEC cases express EpiSC genes already (Figure 1D &5C) favors the later hypotheses. These 2 situations may both exist in vivo, but our profiling work cannot favor any of them yet. Nevertheless, genes filtrated here will provide clinicians novel prognosis markers and therapeutic targets.
In summary, here we reveal distinct epithelial stem cell traits and gene expression patterns in late EECs and some of these genes hold the potential of being novel drug targets. Drugs targeting MAP kinase pathway, for example, may be applied for the treatment of late EEC since this canonical pathway is significantly up in late EECs (Table 5). Since applying a statistical analysis of gene ontology terms is the reliance on prior knowledge of the biological activity of each differentially expressed gene, the enrichment of genes associated with specific pathways may be a consequence of intense research in such areas. Hence, new canonical pathways may still exist and may serve as candidate therapeutic targets. Function of the filtrated KIAA (such as KIAA0323, Figure 5C) and LOC series of anonymous ESTs (such as C20orf24, Figure 5C) in Tables 3, 4, 5, 6, 7 should be studied and their roles in tumor malignancy, chemoresistance and EpiSC stemness are awaited to be elucidated. Further studies to prove the prognosis values and therapeutic potentials of the identified genes, especially those also present in epithelial stem cells, should lead to a better understanding of EEC and EpiSC biology and the susceptibilities of late EECs to treatment.
Microarray data sets
All array data were implemented by the Affymetrix™ HG-U133 Plus 2.0 GeneChip. Array data of normal CD133+ epithelial stem cells, which were used as a normal counterpart of cancer stem cells , isolated from benign prostatic hyperplasia were downloaded from the ArrayExpress database at the European Bioinformatics Institute (http://www.ebi.ac.uk/microarray-as/ae/; Accession No. E-MEXP-993; array data files 1325504978.cel, 1325505459.cel and 1325505089.cel were used).
The gene expression profiles of EEC tissues of different stages were generated by the International Genomics Consortium (IGC) under the expO (Expression Project for Ontology) project and were downloaded from Gene Expression Omnibus (GEO http://www.ncbi.nlm.nih.gov/geo/; GSE2109). EEC array data were divided into training (n = 33; incl. all 4 stages) and testing cohorts (n = 15) (details in Table 1). Array data of normal endometrium controls were from a Human body index dataset in GEO (GSE7307).
Array data processing
Feature selection was performed as previously described . Briefly, the default robust multichip average (RMA) settings were used to background correct, normalize and summarize all expression values using the 'affy' package of the Bioconductor suite of software http://www.bioconductor.org/ for the R statistical programming language. A t-statistic was calculated as normal for each gene and a p-value then calculated using a modified permutation test in the "LIMMA" package . To control the multiple testing errors, a false discovery rate (FDR) algorithm was then applied to these p-values to calculate a set of q-values: thresholds of the expected proportion of false positives, or false rejections of the null hypothesis [22, 54]. Gene annotation was performed by the ArrayFusion web tool http://microarray.ym.edu.tw/tools/arrayfusion/. Gene enrichment analysis was performed by the Gene Ontology (GO) database using the DAVID Bioinformatics Resources 2008 interface http://david.abcc.ncifcrf.gov/, a graph theory evidence-based method to agglomerate gene or protein identifiers [56, 57].
The discrimination power of filtrated genes was evaluated by a machine-learning approach combining the weighted voting algorithm  and leave-one-out cross-validation (LOOCV). This approach has been integrated in our Java tool http://microarray.ym.edu.tw/tools/set/. In brief, the uploaded genes are ranked according to the absolute values of corresponding signal-to-noise scores  in a descending order. Genes are included into a signature one at a time based on the order of ranking. The error rate for each new signature is estimated by the weighted voting algorithm and LOOCV and can be monitored by an error rate distribution plot . Based on the error rate information, we then selected an appropriate composition of discriminating genes with the lowest error rate. Once a signature is defined, the result of prediction strength (PS) analysis for each sample was shown. The PS values range from -1 to +1, where higher absolute values reflect stronger predictions . An overview of the results for samples in different groups was then illustrated by a PS plot .
Classical multidimensional scaling (MDS) is performed by the standard function of the R program to provide a visual impression of how the various sample groups are related. The average linkage distance between samples is calculated by the Pearson correlation subtracted from unity to provide bounded distances in the range (0, 2), as described in our previous study . The distance between two groups of samples is calculated using the average linkage measure (the mean of all pair-wise distances (linkages) between members of the two groups concerned). The standard error of the average linkage distance between two groups (the standard deviation of pair-wise linkages divided by the square root of the number of linkages) is quoted when inter-group distances are compared in the text.
Staining was performed on formalin-fixed, paraffin-embedded specimens using anti-ERBB2 primary antibody (DAKO, Carpinteria, CA, USA). Scoring was performed as following. 0: undetectable staining or membrane staining in <10% of the tumor cells. 1+: faint and incomplete membrane staining in >10% of the tumor cells; 2+: weak to moderate complete membrane staining in >10% of the tumor cells; 3+: strong complete membrane staining observed in >10% of the tumor cells. ERBB2 protein expression was categorized as negative (scores 0 and 1+), or positive (scores 2+ and 3+) .
The authors acknowledge the efforts of IGC and expO. This work is supported by the Mackay Memorial Hospital (MMH-HB-97-05), the National Health Research Institute (NHRI-EX97-9704BI), the National Science Council (NSC97-3111-B-010-004 and NSC98-2320-B-010-020-MY3), Taipei Veterans General Hospital Research Fund, VGHUST Joint research Program, Tsou's Foundation (V98ER2-003), and Yang-Ming University (a grant from Ministry of Education, Aim for the Top University Plan).
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