Gene expression profiling predicts a three-gene expression signature of endometrial adenocarcinoma in a rat model
© Karlsson et al; licensee BioMed Central Ltd. 2009
Received: 07 October 2008
Accepted: 08 May 2009
Published: 08 May 2009
In the Western world, endometrial cancers are the most common gynaecological neoplastic disorders among women. Initial symptoms are often vague and may be confused with several other conditions or disorders. Thus, there is a need for an easy and reliable diagnostic tool. The objective of this work was to identify a gene expression signature specific for endometrial adenocarcinomas to be used for testing potential endometrial biomarkers.
Changes in expression between endometrial adenocarcinomas and non-/pre-malignant endometrium from the BDII EAC rat model were compared in cDNA microarray assays. By employing classification analysis (Weka) on the expression data from approximately 5600 cDNA clones and TDT analysis on genotype data, we identified a three-gene signature (Gpx3, Bgn and Tgfb3). An independent analysis of differential expression, revealed a total of 354 cDNA clones with significant changes in expression. Among the 10 best ranked clones, Gpx3, Bgn and Tgfb3 were found.
Taken together, we present a unique data set of genes with different expression patterns between EACs and non-/pre-malignant endometrium, and specifically we found three genes that were confirmed in two independent analyses. These three genes are candidates for an EAC signature and further evaluations of their involvement in EAC tumorigenesis will be undertaken.
Endometrial carcinomas (ECs) are the most frequently occurring malignancies in the genital tract among women in the Western world. As in most other cancer diseases, neoplastic progression to EC is very complex, and involves high penetrance genes as well as intricate interactions of multiple low penetrance genes . ECs can be divided into two broad categories based on morphology; Type I endometrial cancer, which accounts for approximately 70–80% of all ECs, follows the estrogen-related pathway and frequently develops in the setting of complex atypical hyperplasias as malignant precursors. The type II category of endometrial tumors is non-endometroid carcinomas which arise from endometrial polyps or from endometrial intra-epithelial carcinoma. Endometrial adenocarcinoma (EAC) is the most common type I EC and originates from the glandular cells of the uterus surface epithelium. Type I tumors occur pre-dominantly in pre- or peri-menopausal women, whereas the more aggressive type II tumors occur in post-menopausal women and carry a high mortality rate [1–3]. Successful treatment is dependent on accurate and early diagnosis. In EAC, however, early presenting symptoms are usually vague and easily confused with other conditions and hence there is a need for an easy and reliable diagnostic tool.
Due to the genetic heterogeneity among the human population and the complexity of tumor etiology, it is attractive to use inbred animal model systems in studies of carcinogenesis. The BDII/Han inbred rat model, first described in 1987, is genetically well characterized and has since then become a useful model for studies of human endometrial adenocarcinoma. More than 90% of the female BDII/Han virgins spontaneously develop EC during their life time, where the majority of the neoplasms are EACs. The endometrial carcinogenesis in the females of the BDII rat strain is hormone dependent and represents an outstanding model for spontaneous hormonal carcinogenesis. [4–6]
Genomic approaches such as gene expression profiling by DNA microarrays, provide unprecedented tools to handle the complexity of cancer at the transcriptional level. In the present study, we have applied different statistical and classification approaches on global expression data to identify potential classifiers, i.e. sets of marker genes whose expression profiles can be used to differentiate between EAC tumors and non/pre-malignant endometrial lesions. Furthermore, we have used genotype data from the female progenies of BDII crosses [7, 8] to investigate whether the genes with significantly differential expression are associated to potential susceptibility regions. In this study, a unique dataset has been identified that can act as a starting point to establish a panel of endometrial cancer biomarkers and to explore the role of the identified genes in endometrial carcinogenesis.
Gene function classification
Identification of gene classifiers
The overall classification results obtained with the models derived by the machine learning algorithms in Weka are summarized in Supplementary Table 1. Each of the 44 applicable algorithms was applied in 29 training and test cycles. In each cycle, a classification was generated using 28 samples as training data and leaving out a single sample for testing (leave-one-out cross validation). The resulting cross-validated average classification accuracy was 97.1%. The algorithm ZeroR serves as a base-line, since it simply classifies every sample as belonging to the majority class, which here results in an accuracy of 59%. All algorithms performed much better than the base-line, with the majority (38 of 43 algorithms) reaching accuracies of 97% (a single misclassification) or 100%.
Potential marker genes most frequently occurring as the best classifiers derived by Weka.
Ensemble gene Id
Glutathione peroxidase 3
The significance analysis revealed 354 genes to be differentially expressed between non/pre-malignant and endometrial samples. We choose to investigate the locations in the genome of the 50 genes with the highest differential expression. By means of TDT analysis on samples on the backcross progeny (n = 39), we could only identify one gene (Tgfb3) with significant association to a susceptibility region (6q31, ~65 cM) in the BN background. In this region, all animals from crosses with the BN background that developed tumors were homozygous for the BDII alleles. We chose to include genes in the proximity (within 10 Mbp or a 20 cM region), which means that there is at least 80% probability that the loci are inherited together.
Gene expression profiling, in combination with statistical and classification methods, provides a powerful method of analyzing the complexity of cancer etiology. Genome-wide analysis of the expression patterns of neoplastic and non-/pre-malignant cells provides new information which can lead to identification of cellular pathways that might be affected by malignant transformation. In addition, global gene expression profiling facilitates the identification of new diagnostic markers and potential therapeutic targets.
In the present work, we have compared the expression patterns of EAC tumor cell lines with non-/pre-malignant endometrial samples. In total, 28 specimens were investigated, out of which 16 cell lines originated from EAC tumors and 12 from non-/pre-malignant lesions. Based on pathology, the tumors that developed in the N1 and F2 progenies were initially classified as EAC or other uterine tumors (Karlsson et al. 2007). However, in some cases, no malignant cells were detected in the removed cell mass when pathologically characterized. These tissue samples represent normal or pre-malignant endometrium, and hence provide new valuable information concerning the developmental stages during carcinogenesis. In a previous study, we re-classified the samples predicated on the expression patterns in combination with observed cell morphology/physiology. In the re-classification the expression of genes regulated by the TGF-β signaling pathway as well as global expression data were used, and two major groups with distinct differences in the expression patterns were recognized . One group contained mostly samples earlier classified as EACs and only few earlier classified as non-/pre-malignant cell lines. This group was subsequently characterized as EACs, since the morphology and physiology of these cell lines (including those formerly classified as non-/pre-malignant), are in accordance with the typical properties of malignant cells in culture. The second group contained mostly samples previously classified as non-/pre-malignant, and a minority of EACs. This group was subsequently classified as non-/pre-malignant samples, since all cell lines in this group shared similar expression patterns, and displayed more or less normal morphological/physiological characteristics when cultured in vitro.
As expected, many genes showed differential expression between non-/pre-malignant endometrium and endometrial adenocarcinomas. In total, we found 354 genes that were significantly differentially expressed between the groups. In Figure 1, we present a clustering of the 50 genes with most significant differential expression between EACs and non-/pre-malignant samples. Generally, the genes that were found to be differentially expressed were down-regulated in the EAC group whereas the majority of the genes were more or less normally expressed in the non-/pre-malignant samples. The fact that the majority of the differentially expressed genes are down-regulated in the tumors implies that these genes are disruptive in EAC and may exhibit tumor suppressor features.
We also examined the gene functions of the top 50 genes using DAVID , were 31 genes were found to be involved in cellular processes known to be frequently disturbed in malignant cells (Figure 2a). Thus, the results from the gene function classification support the candidate gene selection from the significance analysis. The fact that several of these genes were involved in more than one process reflects the complexity of the cancer etiology (Figure 2b). These 31 genes could be considered a basis for an EAC signature.
When we performed the TDT analysis on genotyping data from the genome-wide screen with microsatellite markers, we could pin-point one gene, transforming growth factor beta 3, Tgfb,3 that was located in a susceptibility region identified in progenies from backcrosses with the BN background. Independent of tumor grade (malignant or non-/pre-malignant cell lines), we found a significant chi-square value from the TDT analysis (p < 0.05). Moreover, Tgfb3 was also found among the top 10 genes with differential expression between endometrial tumors and non-/pre-malignant endometrium. This result indicates that Tgfb3 is a susceptibility gene candidate and thus that the BDII strain might harbor an SNP involved in the initiation of EAC which will be further investigated. However, the expression data only correlated with grade of tumor as the gene was down-regulated in EAC and up-regulated in non-/pre-malignant cell lines regardless of the genetic background. An explanation for the differences might be that Tgfb3 expression is correlated to different genetic changes occurring during tumor development/progression. Since TGFβ3 is involved in multiple cellular processes the effects of genetic variation on mRNA expression may vary during tumor progression due to interactions with other affected genes. Thus, if the hypothesized genetic change in Tgfb3 is inherited, the effect on the expression level might be different in early and late stages of EAC.
To investigate whether more specific signature genes that differentiate between normal/pre-malignant endometrium and endometrial adenocarcinomas could be created, we used the classifier tool Weka, which contains a number of different machine learning algorithms. A majority of the algorithms applied performed very well in deriving classifiers that could classify the samples classes, with an average accuracy of 97.1%, and several algorithms performed at an accuracy of 100%. Depending on the type of classifier derived, it may or may not be possible to identify which individual gene was the most important for the classification result. For example, tree-based classifiers select a small subset of genes and place the most important of these at the root of the tree, whereas multi-layer perceptrons use all genes as input, and consequently the importance of each gene cannot be readily determined. In our results, 16 algorithms derived classifiers where the most important gene could be determined, and the two genes that were most frequently identified as top genes in the classifiers were Gpx3 (nine classifiers) and Bgn (seven classifiers). Based on the content of these classifiers, Gpx3 and Bgn are the best individual marker genes and should be investigated for their potential value in diagnosis of human endometrial adenocarcinoma. The value of Gpx3 and Bgn as signature genes is strengthened by the fact that both of them are within the 10 most significant genes in the microarray differential expression analysis described above.
Plasma glutathione peroxidase, Gpx3, exhibits a critical role in detoxifying reactive oxidative species and maintaining the genetic integrity of mammalian cells. Gpx3 has been found to be either deleted or highly methylated in exon 1 in prostate cancer cell lines [11, 12] and in Barrett's tumorigenesis  and has been suggested to exhibit tumor suppressor activity. The tumor suppressor activity of Gpx3 is thought to be associated with its ability to repress the expression of Met. Expression data of Met in the present work implies that samples exhibiting an up-regulation of Gpx3 also show a low expression of Met. However, additional experimenting is essential to further evaluate the implications of loss of expression of Gpx3 in EAC carcinogenesis.
Gene expression of Biglycan, Bgn, is regulated by TGF-β signaling (dependent on functional smad2 signaling). We have previously shown down-regulation of several genes (several of these are among the top 50 genes presented herein) regulated by the TGF-β signaling pathway, indicating a disruptive TGF-β signaling. This result further strengthens our hypothesis of a dysfunctional TGF-β pathway in rat endometrial tumors. Furthermore, it has been shown that exogenously administered BGN induced pancreatic cancer cells to arrest in the G1 phase of the cell cycle, indicating a direct inhibiting effect on proliferation of BGN in cancerous cells .
In lung adenocarcinomas, the transcription of Tgfb3 has been suggested to be stimulated by collagen 1 through the PI3K/ERK pathway . Our previous results have shown down-regulation of several pro-collagens  which might explain the decreased mRNA expression of Tgfb3 in our model. Interestingly, it has been shown that TGFB3 expression is up-regulated in human EAC and that TGFβ-3 exerts promoting effects on invasiveness , which is contradictive to our results. However, given the background that Van Themsche and co-workers used two commercial cell lines (i.e. KLE and HEC-1A) originating from elderly women (64 and 71, respectively), it seems that they represent the more invasive and non-estrogen dependent type II endometrial carcinoma, whereas our model represents estrogen-dependent endometrial carcinoma. This indicates that TGF-β signaling in the more aggressive type II endometrial carcinoma has tumor promoting effects, whereas it has been shown that in the estrogen-dependent type I endometrial carcinoma, the TGF-β signaling pathway is disrupted [17–19] and thus has a tumor inhibiting role. Further studies are needed to clarify the implications of down-regulated expression of Tgfb 3 in rat EAC.
Summary of results from Weka, gene function analysis and TDT analysis.
Gene function analysis
Materials and methods
Animal crosses and tumor material
The animal material was derived from crosses between BDII/Han females and males from two non-susceptible rat strains, BN/Han and SPRDCu3/Han, to produce an F1 progeny. Subsequently, an F2 offspring was produced by brother/sister mating of the F1 progeny and N1 progeny was produced by backcrossing the males to BDII females. The female progeny was palpated twice each week for identification of uterine tumors. Animals suspected to have tumors were euthanized and the tumor tissue surgically removed, subjected to pathological characterization and used to establish cell cultures (for details, see Helou et al. 2001). Here, we have investigated two groups of samples, i.e. cell lines established from tissues pathologically classified as endometrial tumors and cell lines established from tissues from normal/pre-malignant endometrium.
The raw expression data of the microarray experiments were obtained from experiments described in Karlsson et al. 2007. Briefly, total RNA was extracted from harvested EAC and non/pre-malignant cell lines and labeled with Cy3 during cDNA synthesis. Universal rat reference RNA (Stratagene) was used as a common RNA reference (transcribed and labeled with Cy5) and thus hybridized to all arrays. We have employed the two-channel cDNA microarray format using the 18K (6000 clones in triplicates) rat 70 mer oligonucleotide arrays. The arrays were printed at the Swegene DNA microarray resource center in Lund, Sweden, by a BioRobotics MicroGrid 2 arrayer (Cambridge, UK) together with a split pin system. Each probe in the probe set (Rat 70 mer oligonucleotide set, ver 1.0, OPERON) was printed in triplicates at random positions on the arrays. The arrays were scanned in an Agilent Microarray scanner (Agilent technologies, USA). In total, 45 samples were arrayed. In the present work, we have focused the analysis on two groups of samples, constituting 29 arrays. These two groups represent early and more advanced stages of EAC and were therefore selected for the analyses in the present work, and is described more in detail in the discussion .
The quality control of the GenePix Extraction results was performed using methods available from Bioconductor packages (marray, limma, arrayQuality) in the statistical software package R. Intensities of negative and positive controls were inspected, as well as spatial effects, M/A plots and signal-to-noise distributions. No unexpected results were found, thus indicating an overall high quality of hybridizations and scanning. Subsequent to image acquisition and analysis, the microarray data were up-loaded into the BioArray Software Environment (BASE)  for analysis. The data were cleaned to eliminate bad quality spots and subsequently print-tip lowess- and scale normalized. The replicates were merged during the normalization procedures. Spots that were present on less than 30 arrays (of the total 45) were rejected. The data was also subjected to variation filtering, i.e. all position-reporter pairs with standard deviation (SD) smaller than 0.8 were rejected. After cleaning, filtering and normalization procedures, 4336 probes/reporters out of 6000 remained. Significant differences in expression for reporters between the groups were assessed by applying Student's t test with a significance threshold of 0.05 and correction for multiple testing. Comparisons were made between EAC cell lines vs non/pre-malignant endometrial cell lines. The p values were adjusted using the False Discovery Rate (FDR) procedure .
In the classification analysis, fold change values extracted from the microarray data were imported into the Waikato environment for knowledge analysis (Weka, version 3.4.12) . For each of the 28 samples, a "flag" (1 or 0) was set to signify group membership (cell lines from EAC tumors and non/pre-malignant lesions, respectively). The Weka software includes 70 different machine learning algorithms, each of which can be used to generate a classifier by learning from examples to distinguish between groups. We excluded all algorithms requiring discrete-valued input data, since these are not applicable to real-valued gene expression data sets. For each of the remaining 44 algorithms, the 28 samples were repeatedly divided into a training- and test set, using the leave-one-out cross validation method  and the average classification accuracy was recorded.
Transmission disequilibrium test (TDT)
TDT statistics for the backcross progeny was performed on genotype data (Falck et al. manuscript in preparation) from microsatellite markers located adjacent to chromosomal regions harboring the identified classifiers and the top 50 genes with the most significant differential expression between the two groups. The TDT statistic for backcross progeny is defined as (H-A)2/(H+A), where H is the number of heterozygous animals and A is the number of animals homozygous for the BDII allele. The test has a χ2 distribution with one degree of freedom. TDT statistics were calculated for markers adjacent to each gene in the EAC tumors versus non/pre-malignant lesions, and for differences between the two backgrounds, BDII/BN and BDII/SPRD, respectively.
Gene function classification
A web-accessible program, the Database for Annotation, Visualization and Integrated Discovery, DAVID, was used to obtain an overview of the gene functions of the 50 genes with the highest differential expression between endometrial tumors and non-/pre-malignant endometrium. DAVID provides tools for functional annotation of genes and gene functional classification, in which large lists of genes can be rapidly reduced into functionally related groups of genes to help unravel the biological content.  We wanted to investigate whether these genes were involved in pathways/processes contributing to the cancer phenotype (increased proliferation, increased apoptosis etc) and therefore selected cellular processes typically involved in cancer development.
Validation of the microarray data by semi-quantitative RT-PCR
Gene expressions for several genes associated with the TGF-beta pathway (Col1a1, Col1a2, Col3a1, Col5a1, Ltbp2, Ctgf, Tgfb1i1) were verified in a previous study . In this study, gene expression changes detected in the microarray were validated on subset of tumor samples (n = 10) by semi-quantitative RT-PCR for Tgfb3, Inhba, Wfdc1, Gpx3, Acta1, Serpine, Cpe, Bgn, Ssg1, Lox1, Bnip3I and Cnn. Primer sets specific for these genes were constructed using the Primer3 program in the Center for Genome Research at the Whitehead Institute http://frodo.wi.mit.edu/. The primers were designed to amplify across two or more different exons, thus ruling out DNA amplification. RT-PCR was performed on 500 ng of total RNA using Omniscript RT Kit according to the manufacturer's protocol (Qiagen, UK). Total 500 ng of total RNA was retro-transcribed to cDNA using 1× RT buffer, 2 mM dNTP mix, 1 μM Oligo-dT primer, 10 units RNA inhibitor, 4 units Omniscripts Reverse Transcriptase and RNAse free water for volume adjustment to 20 μl. PCR Amplification was performed in a 25 μl reaction mix using 10 μl of the RT reaction. The running conditions were as follow: 94°C for 2 min, 24–28 cycles of 30 sec at 94°C, 30 sec at 59°C, and 1 min at 72°C and followed by final extension of 72°C for 5 min.
Availability and requirements
The microarray data is available at: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13003
This work was supported by the Swedish National Research School in Bioinformatics and Genomics, Erik Philip-Sörensen Foundation, and the Nilsson-Ehle foundation. We also thank the Swegene DNA Microarray Resource center for their exceptional service and support. We are grateful to Karin Lilja for excellent technical assistance.
- Gruber SB, Thompson WD: A population-based study of endometrial cancer and familial risk in younger women. Cancer and steroid hormone study group. Cancer Epidemiol Biomarkers Prev. 1996, 5 (6): 411-417.PubMedGoogle Scholar
- Prat J: Prognostic parameters of endometrial carcinoma. Hum Pathol. 2004, 35 (6): 649-662. 10.1016/j.humpath.2004.02.007.View ArticlePubMedGoogle Scholar
- Bokhman JV: Two pathogenetic types of endometrial carcinoma. Gynecologic Oncology. 1983, 15: 10-17. 10.1016/0090-8258(83)90111-7.View ArticlePubMedGoogle Scholar
- Deerberg F, Kaspareit J: Endometrial carcinoma in BD II/Han rats: model of a spontaneous hormone-dependent tumor. J Natl Cancer Inst. 1987, 78 (6): 1245-1251.PubMedGoogle Scholar
- Kaspareit-Rittinghausen J, Deerberg F, Rapp K: Mortality and incidence of spontaneous neoplasms in BDII/Han rats. Z Versuchstierkd. 1987, 30 (5–6): 209-216.PubMedGoogle Scholar
- Vollmer G: Endometrial cancer: experimental models useful for studies on molecular aspects of endometrial cancer and carcinogenesis. Endocr Relat Cancer. 2003, 10 (1): 23-42. 10.1677/erc.0.0100023.View ArticlePubMedGoogle Scholar
- Roshani L, Mallon P, Sjostrand E, Wedekind D, Szpirer J, Szpirer C, Hedrich HJ, Klinga-Levan K: Genetic analysis of susceptibility to endometrial adenocarcinoma in the BDII rat model. Cancer Genet Cytogenet. 2005, 158 (2): 137-141. 10.1016/j.cancergencyto.2004.08.034.View ArticlePubMedGoogle Scholar
- Roshani L, Wedekind D, Szpirer J, Taib Z, Szpirer C, Beckmann B, Riviere M, Hedrich HJ, Klinga-Levan K: Genetic identification of multiple susceptibility genes involved in the development of endometrial carcinoma in a rat model. Int J Cancer. 2001, 94 (6): 795-799. 10.1002/ijc.1553.View ArticlePubMedGoogle Scholar
- Karlsson S, Holmberg E, Askerlund A, Levan KK: Altered transforming growth factor-beta pathway expression pattern in rat endometrial cancer. Cancer Genet Cytogenet. 2007, 177 (1): 43-50. 10.1016/j.cancergencyto.2007.05.010.View ArticlePubMedGoogle Scholar
- Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA: DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003, 4 (5): P3-10.1186/gb-2003-4-5-p3.View ArticlePubMedGoogle Scholar
- Yu YP, Yu G, Tseng G, Cieply K, Nelson J, Defrances M, Zarnegar R, Michalopoulos G, Luo JH: Glutathione peroxidase 3, deleted or methylated in prostate cancer, suppresses prostate cancer growth and metastasis. Cancer Res. 2007, 67 (17): 8043-8050. 10.1158/0008-5472.CAN-07-0648.View ArticlePubMedGoogle Scholar
- Lodygin D, Epanchintsev A, Menssen A, Diebold J, Hermeking H: Functional epigenomics identifies genes frequently silenced in prostate cancer. Cancer Res. 2005, 65 (10): 4218-4227. 10.1158/0008-5472.CAN-04-4407.View ArticlePubMedGoogle Scholar
- Lee OJ, Schneider-Stock R, McChesney PA, Kuester D, Roessner A, Vieth M, Moskaluk CA, El-Rifai W: Hypermethylation and loss of expression of glutathione peroxidase-3 in Barrett's tumorigenesis. Neoplasia. 2005, 7 (9): 854-861. 10.1593/neo.05328.PubMed CentralView ArticlePubMedGoogle Scholar
- Weber CK, Sommer G, Michl P, Fensterer H, Weimer M, Gansauge F, Leder G, Adler G, Gress TM: Biglycan is overexpressed in pancreatic cancer and induces G1-arrest in pancreatic cancer cell lines. Gastroenterology. 2001, 121 (3): 657-667. 10.1053/gast.2001.27222.View ArticlePubMedGoogle Scholar
- Shintani Y, Maeda M, Chaika N, Johnson KR, Wheelock MJ: Collagen I promotes epithelial-to-mesenchymal transition in lung cancer cells via transforming growth factor-beta signaling. Am J Respir Cell Mol Biol. 2008, 38 (1): 95-104. 10.1165/rcmb.2007-0071OC.PubMed CentralView ArticlePubMedGoogle Scholar
- Van Themsche C, Mathieu I, Parent S, Asselin E: Transforming growth factor-beta3 increases the invasiveness of endometrial carcinoma cells through phosphatidylinositol 3-kinase-dependent up-regulation of X-linked inhibitor of apoptosis and protein kinase c-dependent induction of matrix metalloproteinase-9. J Biol Chem. 2007, 282 (7): 4794-4802. 10.1074/jbc.M608497200.View ArticlePubMedGoogle Scholar
- Gold LI, Parekh TV: Loss of growth regulation by transforming growth factor-beta (TGF-beta) in human cancers: studies on endometrial carcinoma. Semin Reprod Endocrinol. 1999, 17 (1): 73-92. 10.1055/s-2007-1016214.View ArticlePubMedGoogle Scholar
- Sakaguchi J, Kyo S, Kanaya T, Maida Y, Hashimoto M, Nakamura M, Yamada K, Inoue M: Aberrant expression and mutations of TGF-beta receptor type II gene in endometrial cancer. Gynecol Oncol. 2005, 98 (3): 427-433. 10.1016/j.ygyno.2005.04.031.View ArticlePubMedGoogle Scholar
- Parekh TV, Gama P, Wen X, Demopoulos R, Munger JS, Carcangiu ML, Reiss M, Gold LI: Transforming growth factor beta signaling is disabled early in human endometrial carcinogenesis concomitant with loss of growth inhibition. Cancer Res. 2002, 62 (10): 2778-2790.PubMedGoogle Scholar
- Saal LH, Troein C, Vallon-Christersson J, Gruvberger S, Borg A, Peterson C: BioArray Software Environment (BASE): a platform for comprehensive management and analysis of microarray data. Genome Biol. 2002, 3 (8): SOFTWARE0003-10.1186/gb-2002-3-8-software0003.PubMed CentralView ArticlePubMedGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B. 1995, 57: 289-300.Google Scholar
- Frank E, Hall M, Trigg L, Holmes G, Witten IH: Data mining in bioinformatics using Weka. Bioinformatics. 2004, 20 (15): 2479-2481. 10.1093/bioinformatics/bth261.View ArticlePubMedGoogle Scholar
- Lachenbruch PaM A: Estimation of error rates in discriminat analysis. Techometrics. 1968, 10: 1-11. 10.2307/1266219.View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.