Daniel R. Rhodes *^{, 1}, Jianjun Yu *^{, 1}, K. Shanker ³, Nandan Deshpande³, Radhika Varambally *, Debashis Ghosh² , Terrence Barrette *, Akhilesh Pandey ¶, and Arul M. Chinnaiyan *||** ^@

Departments of *Pathology, ¹Bioinformatics, ²Biostatistics, and ||Urology and **Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109;
³Institute of Bioinformatics, Bangalore 560 066, India; and
¶McKusick-Nathans Institute of Genetic Medicine and Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, MD 21205

^@ To whom correspondence should be addressed.
Arul M. Chinnaiyan, E-mail: arul@umich.edu

Abstract:
Introduction:
Methods:
Results and Discussion:
Figure 1. Comparative meta-profiling flow diagram:
Figure 2. Meta-signature of neoplastic transformation:
...
Figure 3. Meta-signature of undifferentiated cancer:
Table 1. Universal meta-signature Training data sets:
Table 2. Undifferentiated meta-signature Training data sets:
Table 3. Universal meta-signature Test data sets:
Table 4. Undifferentiated meta-signature Test data sets:
References:
PubMed Related Articles:
Additional References:
Further Topics:
Other Links:
Further Information:

Many studies have used DNA microarrays to identify the gene expression signatures of human cancer, yet the critical features of these often unmanageably large signatures remain elusive. To address this, we developed a statistical method, comparative meta-profiling, which identifies and assesses the intersection of multiple gene expression signatures from a diverse collection of microarray data sets. We collected and analyzed 40 published cancer microarray data sets, comprising 38 million gene expression measurements from >3,700 cancer samples. From this, we characterized a common transcriptional profile that is universally activated in most cancer types relative to the normal tissues from which they arose, likely reflecting essential transcriptional features of neoplastic transformation. In addition, we characterized a transcriptional profile that is commonly activated in various types of undifferentiated cancer, suggesting common molecular mechanisms by which cancer cells progress and avoid differentiation. Finally, we validated these transcriptional profiles on independent data
sets.

In this report, we attempt to identify common transcriptional programs of neoplastic transformation and progression across a wide range of cancer types. To establish a framework for such analysis, we adopted and modified a method, termed meta-analysis of microarrays, which was previously used to validate analogous
prostate cancer microarray studies against one another (25). This method avoids many of the pitfalls that complicate the comparison of disparate microarray data sets by comparing statistical measures of differential expression generated independently from each data set rather than actual gene expression measurements. Here, we present a similar method, termed comparative meta-profiling, aimed not at validating analogous data sets, but at comparing and assessing the intersection of many cancer type-specific gene expression data sets, with the goal of identifying cancer type-independent, and likely essential, transcriptional profiles of neoplastic transformation and progression.

Methods

Data Collection, Processing, and Storage. Microarray data sets were downloaded from public web sites or provided by the authors upon request. Data are available at http://www.oncomine.org/meta  . Data were
of two general types, two channel ratio data and single channel intensity data, and were usually provided in single composite file format. All available data were included in processing and analysis, except for negative single channel intensity values. All data sets were log transformed and median centered per array, and the standard deviations were normalized to one per array. Studies were named by the following convention:
FirstAuthor_TissueTypeProfiled (e.g., Dhanasekaran_Prostate). To facilitate multistudy analysis, microarray features were mapped to Unigene Build 159. Data and initial data analyses were stored in an ORACLE 8.1 relational database.

Initial Data Analysis. For each of the 40 microarray data sets present in the database, we reviewed the samples profiled. Thirty-four studies had at least four samples corresponding to both classes of one analysis of interest and were further analyzed. Analyses of interest included: cancer versus respective normal tissue, high grade
(undifferentiated) cancer versus low grade (differentiated cancer) cancer, poor outcome (metastases, recurrence, or cancer-specific death) cancer versus good outcome (long-term or recurrence-free survival) cancer, metastasis versus primary cancer, and subtype 1 versus subtype 2. After the assignment of samples to classes, each gene was assessed for differential expression with Student’s t test using TOTAL ACCESS STATISTICS 2002 (FM, Vienna, VA). t tests were conducted both as two-sided for differential expression analysis and one-sided for overexpression analysis. To account for multiple hypothesis testing, Q values (26) (estimated false discovery rates) were calculated as

Q = (estimated no. of false positives)/(no. of called positives at a given P value)

Q = (P  x n)/i,

where P is P value, n is the total number of genes, and i is the sorted rank of P value.

Meta-Profiling. The purpose of meta-profiling is to address the hypothesis that a selected set of differential expression signatures shares a significant intersection of genes (a meta-signature), thus inferring a biological relatedness. The automated method proceeds as follows: (i) a set of S similar differential expression analyses are selected for meta-profiling; (ii) an overexpression direction (e.g., cancer > normal) and a significance threshold (T) are chosen to define differential expression signatures from the selected analyses (T_DEFAULT = 0.10); (iii) genes are sorted by the number of signatures in which they are present; (iv) the number of genes present in each possible number of signatures is tallied (N₀, N₁, N₂...N_S); (v) random permutations are performed (steps iii and iv) in which the actual Q values are randomly assigned to genes per study, so that the genes in each signature change at random, but the number of genes in each signature remain the same. This simulation generates a tally of the number of genes present in each possible number of random signatures (E₀, E₁, E₂...E_S); (vi) the significance of intersection among the true signatures is assessed by the minimum
meta-false discovery rate (mFDR_MIN) calculated as

mFDR_MIN = MINIMUM([E_i + 1]/[N_i ]) for i = 0 to S.

(vii) If mFDR_MIN < 0.10, a meta-signature is defined as those genes that are significantly differentially expressed (Q < T) in at least j of S analyses, where j is equal to i when mFDR_MIN was defined; (viii) if no meta-signature is defined by using T_DEFAULT, steps ii through vii are repeated as T is systematically lowered by 50% at each iteration until either a meta-signature is defined or the number of genes in two or more signatures reaches 0, in which case the result is negative. This assures that a meta-signature is not missed because
of an overly liberal Q value threshold. The meta-profiling algorithm was implemented in PERL.

Class Prediction. To assess the classification accuracy of the meta-signatures, a leave-one-out voting classifier was applied. To predict the class of a particular sample, that sample was removed from the data set, and the remaining samples were used to calculate the two class means for each gene in the signature. The left out sample’s gene expression values were compared to the class means. The class mean in which the left out sample’s value was closest to received a vote. The votes were tallied, and the prediction was defined as the
class with the most votes. A Fisher’s exact test was used to assess the significance of the classification. The meta-signatures and class prediction results were visualized by using TREEVIEW (27)
( http://rana.lbl.gov/eisensoftware.htm ).

Results and Discussion

Data and Primary Analysis. As of May 1, 2003, we cataloged information on 152 cancer microarray studies by searching the literature. This catalog and the results from this report are available to explore via our companion web resource, ONCOMINE ( http://www.oncomine.org/meta ). Of these published studies, 40 data sets were
publicly available and compiled; in total, 37,901,459 gene measurements from 3,762 microarray experiments. Most data sets were of two general formats, either single-channel intensity data, usually corresponding to Affymetrix microarrays, or dual-channel ratio data, usually corresponding to spotted cDNA microarrays, and in
the majority of cases, a single composite data file was provided by the study authors and incorporated into our database. Although many sophisticated analytical and statistical approaches have been applied to microarray normalization and differential expression analysis, we sought a single approach that would be simple in application yet robust to the heterogeneous data formats, experimental platforms, and experimental designs. We first applied a global normalization procedure to all data sets (see Methods). Second, by studying the samples profiled in each of the 40 data sets, we defined potential two class differential expression analyses relevant to the processes of neoplastic transformation and progression. These included cancer versus respective normal tissue, high-grade (undifferentiated) cancer versus low-grade (differentiated cancer) cancer, poor outcome (metastases, recurrence, or cancer-specific death) cancer versus good outcome (long-term or recurrence-free survival) cancer, metastatic cancer versus primary cancer, and cancer subtype 1 (e.g., estrogen receptor positive) versus subtype 2 (e.g., estrogen receptor negative) and were identified in 34 data sets (Fig. 4, which is published as supporting information on the PNAS web site). Based on these classifications, we conducted 81 sets of analyses by defining two classes of samples, calculating a Student’s t statistic, P value (false positive rate), and Q value (false discovery rate) for each microarray feature (see Methods) (26). The majority of cancer vs. normal (36 of 40), differentiation (8 of 11), metastases vs. primary (3 of 3), and cancer subtype (15 of 16) analyses identified large sets of differentially expressed genes (Q < 0.10), whereas only 3 of 11 outcome analyses did, two of which compared poor outcome breast cancer with favorable outcome (14, 15), and one which compared poor outcome diffuse large B cell lymphoma with favorable outcome (28). Fig. 4 summarizes the 81 analyses and the number of significant differentially expressed genes identified in each at varying significance thresholds. These data can be analyzed with our companion web resource, ONCOMINE
( http://www.oncomine.org/meta ).

Comparative Meta-Profiling Method. Because it is generally agreed that microarray data from distinct experimental platforms, often using distinct reference samples, are not directly comparable, we developed a method that instead compares statistical measures (Q values) generated independently from each data set (25). To compare statistical measures across data sets, our method requires that analogous hypotheses have been tested in each data set (e.g., genes differentially expressed between normal tissue and cancer tissue). To identify and assess the intersection of multiple differential expression signatures, so-called meta-signatures, we applied our automated method, comparative meta-profiling (Fig. 1, see Methods). The method is as follows: (i) a set of analogous differential expression analyses are selected for meta-profiling, (ii)a direction and significance threshold are set to define differential expression signatures from the precomputed differential expression
analyses (e.g., overexpressed in cancer relative to normal, Q<0.10), (iii) genes are sorted based on the number of signatures in which they are present, and (iv) a meta-signature is defined if there are significantly more genes intersecting a given number of signatures than would be expected by chance, as defined by a random simulation. A statistical measure, the minimum meta-false discovery rate (mFDR_MIN) is used to assess the degree of intersection among gene expression signatures (see Methods).

Meta-Signature of Neoplastic Transformation. We began by meta-profiling 36 neoplastic transformation signatures from 21 data sets (overexpressed in cancer relative to respective normal tissue, Q < 0.10), which span 12 tissue types including breast, prostate, colon, lung, liver, brain, ovary, pancreas, uterus, salivary gland, bladder, and B lymphocytes. We hypothesized that if a meta-signature existed, the genes in the signature would reflect essential transcriptional features of cancer, independent of tissue of origin or initial transforming mechanism. At the significance threshold of Q<0.10, 183 genes were present in at least 10 of 36 signatures, 67 genes in at least 12 signatures, and one gene in 18 signatures. In a random simulation, in which genes were randomly assigned to signatures while maintaining the number of genes in each signature, no genes were present in 10 or more signatures, indicating that the 183 genes present in at least 10 signatures represented a statistically significant multicancer-type meta-signature (mFDR_MIN = 0.0055).

Fig. 1. Comparative meta-profiling flow diagram (see Methods for details).

Fig. 2. Meta-signature of neoplastic transformation.

(A) Sixty-seven genes overexpressed in cancer relative to normal tissue counterpart in at least 12 of 39 ‘‘cancer vs. normal’’ signatures. Twelve distinct cancer types were selected for the figure. White boxes signify either not present or not significant. Red boxes signify significant overexpression in cancer relative to normal tissue (Q < 0.10), the shade of red indicating the percentage of cancer samples that had an expression value greater than the 90th percentile of normal samples.
...

To assess the universality of the meta-signature, the top 67 genes were used to predict cancer vs. normal status in 39 analyses using a leave-one-out voting classifier (see Methods and Table 1, which is published as supporting information on the PNAS web site). The signature was a significant predictor (P < 0.05) in 29 of 39 analyses (from 19 of 21 data sets), and was marginally predictive (P < 0.10) in 3 of 39 analyses (from 3 of 21 data sets) (Fig. 2B). The seven analyses in which the profile was not an accurate classifier were from a single multicancer data set (30). This data set was the largest in the database, providing 13 of the 39 cancer vs. normal analyses, of which six were predicted significantly. For each of the seven analyses that were not predicted significantly, there was a similar (i.e., same cancer type) analysis from an independent study that was predicted significantly. Taken together, 20 of 21 data sets suggest that the genes in this cancer meta-signature are differentially overexpressed in most, if not all, available cancer types relative to the normal tissue from which they arose.

The classification accuracy and statistical significance of the universal meta-signature in the training data sets. The class of each sample was predicted using a leave-one-out voting classifier (see Methods). n is the total number of samples that were predicted. Accuracy is the fraction of total samples that were classified correctly.
P values were generated using Fisher's exact test. Significant P values (P < 0.05) are marked with an asterisk and highly significant P values (P < 0.01) are marked with double asterisks.

The existence of a general cancer meta-signature may not be entirely surprising, because all cancer types share the common features of unregulated cell proliferation and invasion, and it would follow that the genes that are essential to these processes would be highly expressed in multiple cancer types. On the other hand, however, it is interesting that a small number of genes are almost universally activated, given the vast array of transforming mechanisms that are known to initiate cancer and the variety of tissue types represented in this analysis. Activation of these genes may represent convergence on the essential transcriptional features of neoplastic transformation. From a clinical standpoint, pharmacological agents that target these essential features of cancer might have broad application. For example, TOP2A, a gene present in 18 cancer vs. normal signatures representing 10 types of cancer, encodes the enzyme topoisomerase II, which is critical for DNA replication and is targeted by numerous chemotherapeutic agents (31). Furthermore, agents targeting the proteasome complex, of which three members were identified in the meta-signature, have also shown promise. These agents are in clinical trials and have been shown to induce apoptosis and sensitize cancer cells to traditional tumoricidal agents (32). The widespread activation of genes that encode successfully targeted proteins suggests that other genes in the meta-signature may play equally critical roles in carcinogenesis, and may serve as novel therapeutic targets.

Meta-Signature of Undifferentiated Cancer. We next sought to identify meta-signatures that characterize cancer progression as defined by histological, pathological, or clinical criteria, similar in concept to a report that identified a metastasis signature common to multiple types of primary tumors (19). As described above, only 3
of 10 outcome-based analyses identified significant differentially expressed genes, and two were of the same cancer type, making it infeasible to attempt to define an outcome meta-signature. However, 8 of 11 differentiation analyses, spanning seven types of cancer, identified significant differential expression signatures (differentially expressed in undifferentiated cancers relative to well differentiated cancers of the same origin, Q < 0.10). Undifferentiated cancers of different tissue types all fail to recapitulate their normal tissue architecture, instead maintaining a disordered state of increased cellular proliferation and invasion. Furthermore, undifferentiated cancers are associated with aggressive behavior and poor patient outcomes. Thus, we hypothesized that if an undifferentiated meta-signature existed, it might suggest common transcriptional mechanisms by which cancer cells avoid differentiation, or dedifferentiate. Meta-profiling was performed on seven ‘‘undifferentiated vs. well differentiated’’ signatures spanning six cancer types (overexpressed in undifferentiated cancers relative to well differentiated cancers, Q < 0.10). Sixty-nine genes were present in at least four of seven signatures, whereas just one gene was significant in four of seven signatures by chance (mFDR_MIN = 0.030). Twenty-four genes were present in five signatures, and six genes were present in six of seven signatures, whereas zero genes were significant in five or more by chance, thus defining an undifferentiated meta-signature common to multiple types of cancer. Fig. 3 displays the 69 genes present in at least four of seven signatures.

Fig. 3. Meta-signature of undifferentiated cancer. Sixty-nine genes that are overexpressed in undifferentiated cancer relative to well differentiated cancer (Q < 0.10) in at least four of seven signatures representing six types of cancer. See Fig. 2 legend for description.

To assess the generality of the undifferentiated meta-signature, a leave-one-out voting classifier was used to predict ‘‘high grade vs. low grade’’ status in all 11 differentiation analyses (Table 2 and Fig. 5, which are published as supporting information on the PNAS web site). The meta-signature was a significant predictor in six of the seven analyses in which differentially expressed genes were originally identified (P =0.001). The one analysis that was not predicted accurately was Singh_Prostate (P = 0.75). In the four remaining analyses, in which no significant differentially expressed genes were originally identified, the meta-signature was a significant predictor in one analysis (Welsh_Ovarian, P = 0.005), marginally predictive in two analyses (Dhanaskearan_Prostate, Welsh_Prostate, P < 0.15), and not predictive in one analysis (Garber_Lung, P = not
applicable). Taken together, it appears that this meta-signature is common to undifferentiated breast cancer, lung cancer, ovarian cancer, bladder cancer, and medulloblastoma, and may be marginally associated with undifferentiated prostate cancer.

The classification accuracy and statistical significance of the undifferentiated meta-signature in the training data sets. The class of each sample was predicted using a leave-one-out voting classifier (see Methods). n is the total number of samples that were predicted. Accuracy is the fraction of total samples that were classified correctly.
P values were generated using Fisher's exact test. Significant P values (P < 0.05) are marked with an asterisk and highly significant P values (P < 0.01) are marked with double asterisks.

Independent Data Set Validation of Meta-Signatures. To confirm the validity and biological relevance of the meta-signatures, we tested their discriminative power on 12 independent data sets that became recently available and were collected after the initial discovery of the metasignatures (28, 39–49) (see supporting information). To validate the universal cancer meta-signature, we analyzed nine independent data sets representing nine distinct cancer types, three of which were not represented in the original analysis [adrenocortical carcinoma (40), pilocytic astrocytoma (41), meningioma (39)]. Table 3, which is published as supporting information on the PNAS web site, shows that in seven of the nine data sets, including all three data sets representing new cancer types, the metasignature significantly discriminated between cancer and respective normal tissue (Fisher’s Exact Test, P < 0.05). In the two other data sets, LaTulippe_Prostate (45) and Rosenwald_Lymphoma (28), the metasignature made many more correct than incorrect predictions; however, the discrimination did not reach statistical significance (P = 0.085 and 0.115, respectively).

The classification accuracy and statistical significance of the universal meta-signature in the test data sets. The class of each sample was predicted using a leave-one-out voting classifier (see Methods). n is the total number of samples that were predicted. Accuracy is the fraction of total samples that were classified correctly.
P values were generated using Fisher's exact test. Significant P values (P < 0.05) are marked with an asterisk and highly significant P values (P < 0.01) are marked with double asterisks.

To assess the discriminative power of the undifferentiated meta-signature, we identified five independent data sets that included low- and high-grade cancer samples (Table 4, which is published as supporting information on the PNAS web site). In three of five data sets [Katua_Astrocytoma (44), Schaner_Ovarian (48), and
Sotiriou_Breast (49)] the meta-signature significantly discriminated between low- and high-grade cancer samples (Table 1, all P <0.01), whereas in the remaining two data sets the signature was not predictive (P > 0.5). In these two data sets [Mutter_Endometrium (46), Powell_Lung (47)], no genes were found to be significantly
differentially expressed between high- and low-grade cancers (Q < 0.10) and only a small number of cases defined each class (low grade: n =4, high grade: n =3). Similar to the results in the training set, this signature seems to perform well in data sets that identified significant gene expression differences, but poorly in those that do not. It is unclear whether this represents cancer types for which the undifferentiated meta-signature is not present, or if it was not detected because of technical issues in particular data sets. Regardless, the signature appears to define a wide variety of undifferentiated cancer types both in the training and test sets and likely points to common transcriptional mechanisms by which cancer cell avoid differentiation. In summary, this validation on independent microarray data sets confirms that the meta-signatures represent common gene expression programs that may be important to the processes of neoplastic transformation and progression.

The classification accuracy and statistical significance of the undifferentiated meta-signature in the test data sets. The class of each sample was predicted using a leave-one-out voting classifier (see Methods). n is the total number of samples that were predicted. Accuracy is the fraction of total samples that were classified correctly.
P values were generated using Fisher's exact test. Significant P values (P < 0.05) are marked with an asterisk and highly significant P values (P < 0.01) are marked with double asterisks.

We thank Douglas Gibbs for hardware support. This work was funded by pilot funds from the Dean’s Office, Department of Pathology, the American Cancer Society RSG-02-179-01, Cancer Center Support Grant
5P30 CA46592, the Specialized Program of Research Excellence in Prostate Cancer (P50 CA69568), and the Bioinformatics Program. D.R.R. is a fellow of the Medical Scientist Training Program, and A.M.C. is a Pew Biomedical Scholar.

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In this new paper by Daniel R. Rhodes, Jianjun Yu, K. Shanker, Nandan Deshpande, Radhika Varambally, Debashis Ghosh, Terrence Barrette, Akhilesh Pandey, and Arul M. Chinnaiyan, we see that no transcription signature, indeed no single gene expression, is sufficient to distinguish all neoplastic states within humans. It may indicate that human cancer is really more than 100 diseases,  and that human neoplasms may be best distinguished by analyzing noncoding RNA species rather than by analyzing messenger RNA species.

1:  Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T,
Pandey A, Chinnaiyan AM.
 Large-scale meta-analysis of cancer microarray data identifies common
transcriptional profiles of neoplastic transformation and progression.
Proc Natl Acad Sci U S A. 2004 Jun 7 [Epub ahead of print]
PMID: 15184677 [PubMed - as supplied by publisher]

2:  Rhodes DR, Barrette TR, Rubin MA, Ghosh D, Chinnaiyan AM.
 Meta-analysis of microarrays: interstudy validation of gene expression profiles
reveals pathway dysregulation in prostate cancer.
Cancer Res. 2002 Aug 1;62(15):4427-33.
PMID: 12154050 [PubMed - indexed for MEDLINE]

3:  Hoang CD, D'Cunha J, Kratzke MG, Casmey CE, Frizelle SP, Maddaus MA, Kratzke
RA.
 Gene expression profiling identifies matriptase overexpression in malignant
mesothelioma.
Chest. 2004 May;125(5):1843-52.
PMID: 15136399 [PubMed - in process]

4:  Hayashi S.
 Prediction of hormone sensitivity by DNA microarray.
Biomed Pharmacother. 2004 Jan;58(1):1-9.
PMID: 14739056 [PubMed - in process]

5:  Logsdon CD, Simeone DM, Binkley C, Arumugam T, Greenson JK, Giordano TJ,
Misek DE, Kuick R, Hanash S.
 Molecular profiling of pancreatic adenocarcinoma and chronic pancreatitis
identifies multiple genes differentially regulated in pancreatic cancer.
Cancer Res. 2003 May 15;63(10):2649-57. Erratum in: Cancer Res. 2003 Jun
15;63(12):3445.
PMID: 12750293 [PubMed - indexed for MEDLINE]

6:  Luo J, Isaacs WB, Trent JM, Duggan DJ.
 Looking beyond morphology: cancer gene expression profiling using DNA
microarrays.
Cancer Invest. 2003;21(6):937-49. Review.
PMID: 14735697 [PubMed - indexed for MEDLINE]

7:  Choi JK, Choi JY, Kim DG, Choi DW, Kim BY, Lee KH, Yeom YI, Yoo HS, Yoo OJ,
Kim S.
 Integrative analysis of multiple gene expression profiles applied to liver
cancer study.
FEBS Lett. 2004 May 7;565(1-3):93-100.
PMID: 15135059 [PubMed - in process]

8:  Zhao LP, Prentice R, Breeden L.
 Statistical modeling of large microarray data sets to identify
stimulus-response profiles.
Proc Natl Acad Sci U S A. 2001 May 8;98(10):5631-6.
PMID: 11344303 [PubMed - indexed for MEDLINE]

9:  Fu LM, Fu-Liu CS.
 Multi-class cancer subtype classification based on gene expression signatures
with reliability analysis.
FEBS Lett. 2004 Mar 12;561(1-3):186-90.
PMID: 15013775 [PubMed - indexed for MEDLINE]

10:  Glanzer JG, Haydon PG, Eberwine JH.
 Expression profile analysis of neurodegenerative disease: advances in
specificity and resolution.
Neurochem Res. 2004 Jun;29(6):1161-8.
PMID: 15176473 [PubMed - in process]

11:  Welsh JB, Zarrinkar PP, Sapinoso LM, Kern SG, Behling CA, Monk BJ, Lockhart
DJ, Burger RA, Hampton GM.
 Analysis of gene expression profiles in normal and neoplastic ovarian tissue
samples identifies candidate molecular markers of epithelial ovarian cancer.
Proc Natl Acad Sci U S A. 2001 Jan 30;98(3):1176-81.
PMID: 11158614 [PubMed - indexed for MEDLINE]

12:  Glanzer JG, Eberwine JH.
 Expression profiling of small cellular samples in cancer: less is more.
Br J Cancer. 2004 Mar 22;90(6):1111-4. Review.
PMID: 15026786 [PubMed - indexed for MEDLINE]

13:  Neumann NF, Galvez F.
 DNA microarrays and toxicogenomics: applications for ecotoxicology?
Biotechnol Adv. 2002 Dec;20(5-6):391-419.
PMID: 14550024 [PubMed]

14:  Risinger JI, Maxwell GL, Chandramouli GV, Jazaeri A, Aprelikova O,
Patterson T, Berchuck A, Barrett JC.
 Microarray analysis reveals distinct gene expression profiles among different
histologic types of endometrial cancer.
Cancer Res. 2003 Jan 1;63(1):6-11.
PMID: 12517768 [PubMed - indexed for MEDLINE]

15:  Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T,
Pandey A, Chinnaiyan AM.
 ONCOMINE: a cancer microarray database and integrated data-mining platform.
Neoplasia. 2004 Jan-Feb;6(1):1-6.
PMID: 15068665 [PubMed - in process]

16:  Wu TD.
 Large-scale analysis of gene expression profiles.
Brief Bioinform. 2002 Mar;3(1):7-17.
PMID: 12002225 [PubMed - indexed for MEDLINE]

17:  Wong YF, Selvanayagam ZE, Wei N, Porter J, Vittal R, Hu R, Lin Y, Liao J,
Shih JW, Cheung TH, Lo KW, Yim SF, Yip SK, Ngong DT, Siu N, Chan LK, Chan CS,
Kong T, Kutlina E, McKinnon RD, Denhardt DT, Chin KV, Chung TK.
 Expression genomics of cervical cancer: molecular classification and prediction
of radiotherapy response by DNA microarray.
Clin Cancer Res. 2003 Nov 15;9(15):5486-92.
PMID: 14654527 [PubMed - in process]

18:  King HC, Sinha AA.
 Gene expression profile analysis by DNA microarrays: promise and pitfalls.
JAMA. 2001 Nov 14;286(18):2280-8.
PMID: 11710894 [PubMed - indexed for MEDLINE]

19:  Best CJ, Leiva IM, Chuaqui RF, Gillespie JW, Duray PH, Murgai M, Zhao Y,
Simon R, Kang JJ, Green JE, Bostwick DG, Linehan WM, Emmert-Buck MR.
 Molecular differentiation of high- and moderate-grade human prostate cancer by
cDNA microarray analysis.
Diagn Mol Pathol. 2003 Jun;12(2):63-70.
PMID: 12766610 [PubMed - indexed for MEDLINE]

20:  Luo J, Duggan DJ, Chen Y, Sauvageot J, Ewing CM, Bittner ML, Trent JM,
Isaacs WB.
 Human prostate cancer and benign prostatic hyperplasia: molecular dissection by
gene expression profiling.
Cancer Res. 2001 Jun 15;61(12):4683-8.
PMID: 11406537 [PubMed - indexed for MEDLINE]

21:  Haviv I, Campbell IG.
 DNA microarrays for assessing ovarian cancer gene expression.
Mol Cell Endocrinol. 2002 May 31;191(1):121-6. Review.
PMID: 12044926 [PubMed - indexed for MEDLINE]

22:  Lee HK, Hsu AK, Sajdak J, Qin J, Pavlidis P.
 Coexpression analysis of human genes across many microarray data sets.
Genome Res. 2004 Jun;14(6):1085-94.
PMID: 15173114 [PubMed - in process]

23:  Ghosh D, Barette TR, Rhodes D, Chinnaiyan AM.
 Statistical issues and methods for meta-analysis of microarray data: a case
study in prostate cancer.
Funct Integr Genomics. 2003 Dec;3(4):180-8. Epub 2003 Jul 22.
PMID: 12884057 [PubMed - in process]

24:  Li Y, Ali S, Philip PA, Sarkar FH.
 Direct comparison of microarray gene expression profiles between
non-amplification and a modified cDNA amplification procedure applicable for
needle biopsy tissues.
Cancer Detect Prev. 2003;27(5):405-11.
PMID: 14585328 [PubMed - in process]

25:  Rickman DS, Bobek MP, Misek DE, Kuick R, Blaivas M, Kurnit DM, Taylor J,
Hanash SM.
 Distinctive molecular profiles of high-grade and low-grade gliomas based on
oligonucleotide microarray analysis.
Cancer Res. 2001 Sep 15;61(18):6885-91.
PMID: 11559565 [PubMed - indexed for MEDLINE]

26:  Smid-Koopman E, Blok LJ, Chadha-Ajwani S, Helmerhorst TJ, Brinkmann AO,
Huikeshoven FJ.
 Gene expression profiles of human endometrial cancer samples using a
cDNA-expression array technique: assessment of an analysis method.
Br J Cancer. 2000 Jul;83(2):246-51.
PMID: 10901378 [PubMed - indexed for MEDLINE]

27:  Bicciato S, Luchini A, Di Bello C.
 PCA disjoint models for multiclass cancer analysis using gene expression data.
Bioinformatics. 2003 Mar 22;19(5):571-8.
PMID: 12651714 [PubMed - indexed for MEDLINE]

28:  Benito M, Parker J, Du Q, Wu J, Xiang D, Perou CM, Marron JS.
 Adjustment of systematic microarray data biases.
Bioinformatics. 2004 Jan 1;20(1):105-14.
PMID: 14693816 [PubMed - indexed for MEDLINE]

29:  Bueno R, Loughlin KR, Powell MH, Gordon GJ.
 A diagnostic test for prostate cancer from gene expression profiling data.
J Urol. 2004 Feb;171(2 Pt 1):903-6.
PMID: 14713850 [PubMed - indexed for MEDLINE]

30:  Boussioutas A, Li H, Liu J, Waring P, Lade S, Holloway AJ, Taupin D,
Gorringe K, Haviv I, Desmond PV, Bowtell DD.
 Distinctive patterns of gene expression in premalignant gastric mucosa and
gastric cancer.
Cancer Res. 2003 May 15;63(10):2569-77.
PMID: 12750281 [PubMed - indexed for MEDLINE]

31:  Shai R, Shi T, Kremen TJ, Horvath S, Liau LM, Cloughesy TF, Mischel PS,
Nelson SF.
 Gene expression profiling identifies molecular subtypes of gliomas.
Oncogene. 2003 Jul 31;22(31):4918-23.
PMID: 12894235 [PubMed - indexed for MEDLINE]

32:  Li X, Rao S, Wang Y, Gong B.
 Gene mining: a novel and powerful ensemble decision approach to hunting for
disease genes using microarray expression profiling.
Nucleic Acids Res. 2004 May 17;32(9):2685-94. Print 2004.
PMID: 15148356 [PubMed - indexed for MEDLINE]

33:  Goldsmith ZG, Dhanasekaran N.
 The microrevolution: applications and impacts of microarray technology on
molecular biology and medicine (review).
Int J Mol Med. 2004 Apr;13(4):483-95. Review.
PMID: 15010847 [PubMed - indexed for MEDLINE]

34:  Ghosh D.
 Mixture models for assessing differential expression in complex tissues using
microarray data.
Bioinformatics. 2004 Feb 26 [Epub ahead of print]
PMID: 14988124 [PubMed - as supplied by publisher]

35:  Culhane AC, Perriere G, Higgins DG.
 Cross-platform comparison and visualisation of gene expression data using
co-inertia analysis.
BMC Bioinformatics. 2003 Nov 21;4(1):59.
PMID: 14633289 [PubMed - in process]

36:  Holter NS, Mitra M, Maritan A, Cieplak M, Banavar JR, Fedoroff NV.
 Fundamental patterns underlying gene expression profiles: simplicity from
complexity.
Proc Natl Acad Sci U S A. 2000 Jul 18;97(15):8409-14.
PMID: 10890920 [PubMed - indexed for MEDLINE]

37:  Brodsky L, Leontovich A, Shtutman M, Feinstein E.
 Identification and handling of artifactual gene expression profiles emerging in
microarray hybridization experiments.
Nucleic Acids Res. 2004 Mar 03;32(4):e46.
PMID: 14999086 [PubMed - indexed for MEDLINE]

38:  Hoos A, Cordon-Cardo C.
 Tissue microarray profiling of cancer specimens and cell lines: opportunities
and limitations.
Lab Invest. 2001 Oct;81(10):1331-8. Review.
PMID: 11598146 [PubMed - indexed for MEDLINE]

39:  McCarroll SA, Murphy CT, Zou S, Pletcher SD, Chin CS, Jan YN, Kenyon C,
Bargmann CI, Li H.
 Comparing genomic expression patterns across species identifies shared
transcriptional profile in aging.
Nat Genet. 2004 Feb;36(2):197-204. Epub 2004 Jan 18.
PMID: 14730301 [PubMed - indexed for MEDLINE]

40:  Ono K, Tanaka T, Tsunoda T, Kitahara O, Kihara C, Okamoto A, Ochiai K,
Takagi T, Nakamura Y.
 Identification by cDNA microarray of genes involved in ovarian carcinogenesis.
Cancer Res. 2000 Sep 15;60(18):5007-11.
PMID: 11016619 [PubMed - indexed for MEDLINE]

41:  von Heydebreck A, Huber W, Poustka A, Vingron M.
 Identifying splits with clear separation: a new class discovery method for gene
expression data.
Bioinformatics. 2001;17 Suppl 1:S107-14.
PMID: 11472999 [PubMed - indexed for MEDLINE]

42:  Elek J, Park KH, Narayanan R.
 Microarray-based expression profiling in prostate tumors.
In Vivo. 2000 Jan-Feb;14(1):173-82.
PMID: 10757075 [PubMed - indexed for MEDLINE]

43:  Zak DE, Pearson RK, Vadigepalli R, Gonye GE, Schwaber JS, Doyle FJ 3rd.
 Continuous-time identification of gene expression models.
OMICS. 2003 Winter;7(4):373-86.
PMID: 14683610 [PubMed - in process]

44:  Harkin DP.
 Uncovering functionally relevant signaling pathways using microarray-based
expression profiling.

Oncologist. 2000;5(6):501-7. Review.
PMID: 11110602 [PubMed - indexed for MEDLINE]

45:  Staege MS, Hattenhorst UE, Neumann UE, Hutter C, Foja S, Burdach S.
 DNA-microarrays as tools for the identification of tumor specific gene
expression profiles: applications in tumor biology, diagnosis and therapy.
Klin Padiatr. 2003 May-Jun;215(3):135-9.
PMID: 12838936 [PubMed - indexed for MEDLINE]

46:  Otsuka M, Hoshida Y, Kato N, Moriyama M, Taniguchi H, Arai M, Mori M, Seki
N, Omata M.
 Liver chip and gene shaving.
J Gastroenterol. 2003 Mar;38 Suppl 15:89-92. Review.
PMID: 12698879 [PubMed - indexed for MEDLINE]

47:  Macgregor PF.
 Gene expression in cancer: the application of microarrays.
Expert Rev Mol Diagn. 2003 Mar;3(2):185-200. Review.
PMID: 12647995 [PubMed - indexed for MEDLINE]

48:  Macgregor PF, Squire JA.
 Application of microarrays to the analysis of gene expression in cancer.
Clin Chem. 2002 Aug;48(8):1170-7. Review.
PMID: 12142369 [PubMed - indexed for MEDLINE]

49:  Tureci O, Ding J, Hilton H, Bian H, Ohkawa H, Braxenthaler M, Seitz G,
Raddrizzani L, Friess H, Buchler M, Sahin U, Hammer J.
 Computational dissection of tissue contamination for identification of colon
cancer-specific expression profiles.
FASEB J. 2003 Mar;17(3):376-85.
PMID: 12631577 [PubMed - indexed for MEDLINE]

50:  Mycko MP, Papoian R, Boschert U, Raine CS, Selmaj KW.
 Microarray gene expression profiling of chronic active and inactive lesions in
multiple sclerosis.
Clin Neurol Neurosurg. 2004 Jun;106(3):223-9.
PMID: 15177772 [PubMed - in process]

51:  Misra J, Schmitt W, Hwang D, Hsiao LL, Gullans S, Stephanopoulos G,
Stephanopoulos G.
 Interactive exploration of microarray gene expression patterns in a reduced
dimensional space.
Genome Res. 2002 Jul;12(7):1112-20.
PMID: 12097349 [PubMed - indexed for MEDLINE]

52:  Chang HY, Sneddon JB, Alizadeh AA, Sood R, West RB, Montgomery K, Chi JT,
Rijn Mv M, Botstein D, Brown PO.
 Gene Expression Signature of Fibroblast Serum Response Predicts Human Cancer
Progression: Similarities between Tumors and Wounds.
PLoS Biol. 2004 Feb;2(2):E7. Epub 2004 Jan 13.
PMID: 14737219 [PubMed - in process]

53:  Grimm MO, Hartmann FH, Schulz WA.
 [Microarrays]
Urologe A. 2004 Apr 27 [Epub ahead of print] German.
PMID: 15138693 [PubMed - as supplied by publisher]

54:  Kendziorski CM, Newton MA, Lan H, Gould MN.
 On parametric empirical Bayes methods for comparing multiple groups using
replicated gene expression profiles.
Stat Med. 2003 Dec 30;22(24):3899-914.
PMID: 14673946 [PubMed - indexed for MEDLINE]

55:  Sok JC, Kuriakose MA, Mahajan VB, Pearlman AN, DeLacure MD, Chen FA.
 Tissue-specific gene expression of head and neck squamous cell carcinoma in
vivo by complementary DNA microarray analysis.
Arch Otolaryngol Head Neck Surg. 2003 Jul;129(7):760-70.
PMID: 12874079 [PubMed - indexed for MEDLINE]

56:  Hanash SM.
 Global profiling of gene expression in cancer using genomics and proteomics.
Curr Opin Mol Ther. 2001 Dec;3(6):538-45. Review.
PMID: 11804268 [PubMed - indexed for MEDLINE]

57:  Malek RL, Irby RB, Guo QM, Lee K, Wong S, He M, Tsai J, Frank B, Liu ET,
Quackenbush J, Jove R, Yeatman TJ, Lee NH.
 Identification of Src transformation fingerprint in human colon cancer.
Oncogene. 2002 Oct 17;21(47):7256-65.
PMID: 12370817 [PubMed - indexed for MEDLINE]

58:  Nguyen DV, Rocke DM.
 Multi-class cancer classification via partial least squares with gene
expression profiles.
Bioinformatics. 2002 Sep;18(9):1216-26.
PMID: 12217913 [PubMed - indexed for MEDLINE]

59:  Yang D, Zakharkin SO, Page GP, Brand JP, Edwards JW, Bartolucci AA, Allison
DB.
 Applications of Bayesian statistical methods in microarray data analysis.
Am J Pharmacogenomics. 2004;4(1):53-62.
PMID: 14987122 [PubMed - in process]

60:  Claudio JO, Masih-Khan E, Stewart AK.
 Insights from the gene expression profiling of multiple myeloma.
Curr Hematol Rep. 2004 Jan;3(1):67-73. Review.
PMID: 14695854 [PubMed - indexed for MEDLINE]

61:  Zarrinkar PP, Mainquist JK, Zamora M, Stern D, Welsh JB, Sapinoso LM,
Hampton GM, Lockhart DJ.
 Arrays of arrays for high-throughput gene expression profiling.
Genome Res. 2001 Jul;11(7):1256-61.
PMID: 11435408 [PubMed - indexed for MEDLINE]

62:  Sgroi DC, Teng S, Robinson G, LeVangie R, Hudson JR Jr, Elkahloun AG.
 In vivo gene expression profile analysis of human breast cancer progression.
Cancer Res. 1999 Nov 15;59(22):5656-61.
PMID: 10582678 [PubMed - indexed for MEDLINE]

63:  Hong X, Li Y, Hussain M, Sarkar FH.
 Gene expression profiling reveals novel targets of estramustine phosphate in
prostate cancer cells.
Cancer Lett. 2004 Jun 25;209(2):187-95.
PMID: 15159021 [PubMed - in process]

64:  Fargiano AA, Desai KV, Green JE.
 Interrogating mouse mammary cancer models: insights from gene expression
profiling.
J Mammary Gland Biol Neoplasia. 2003 Jul;8(3):321-34.
PMID: 14973376 [PubMed - in process]

65:  Sarkar IN, Planet PJ, Bael TE, Stanley SE, Siddall M, DeSalle R, Figurski
DH.
 Characteristic attributes in cancer microarrays.
J Biomed Inform. 2002 Apr;35(2):111-22.
PMID: 12474425 [PubMed - indexed for MEDLINE]

66:  Mecham BH, Klus GT, Strovel J, Augustus M, Byrne D, Bozso P, Wetmore DZ,
Mariani TJ, Kohane IS, Szallasi Z.
 Sequence-matched probes produce increased cross-platform consistency and more
reproducible biological results in microarray-based gene expression
measurements.
Nucleic Acids Res. 2004 May 25;32(9):e74.
PMID: 15161944 [PubMed - indexed for MEDLINE]

67:  Li Y, Li Y, Tang R, Xu H, Qiu M, Chen Q, Chen J, Fu Z, Ying K, Xie Y, Mao
Y.
 Discovery and analysis of hepatocellular carcinoma genes using cDNA
microarrays.
J Cancer Res Clin Oncol. 2002 Jul;128(7):369-79. Epub 2002 Jun 20.
PMID: 12136251 [PubMed - indexed for MEDLINE]

68:  Cheung KH, White K, Hager J, Gerstein M, Reinke V, Nelson K, Masiar P,
Srivastava R, Li Y, Li J, Zhao H, Li J, Allison DB, Snyder M, Miller P, Williams
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 YMD: a microarray database for large-scale gene expression analysis.
Proc AMIA Symp. 2002;:140-4.
PMID: 12463803 [PubMed - indexed for MEDLINE]

69:  Harrington CA, Rosenow C, Retief J.
 Monitoring gene expression using DNA microarrays.
Curr Opin Microbiol. 2000 Jun;3(3):285-91. Review.
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70:  Menges M, Hennig L, Gruissem W, Murray JA.
 Genome-wide gene expression in an Arabidopsis cell suspension.
Plant Mol Biol. 2003 Nov;53(4):423-42.
PMID: 15010610 [PubMed - indexed for MEDLINE]

71:  van Steensel B, Henikoff S.
 Epigenomic profiling using microarrays.
Biotechniques. 2003 Aug;35(2):346-50, 352-4, 356-7. Review.
PMID: 12951776 [PubMed - indexed for MEDLINE]

72:  Smid M, Dorssers LC, Jenster G.
 Venn Mapping: clustering of heterologous microarray data based on the number of
co-occurring differentially expressed genes.
Bioinformatics. 2003 Nov 1;19(16):2065-71.
PMID: 14594711 [PubMed - indexed for MEDLINE]

73:  Kohlmann A, Schoch C, Schnittger S, Dugas M, Hiddemann W, Kern W, Haferlach
T.
 Pediatric acute lymphoblastic leukemia (ALL) gene expression signatures
classify an independent cohort of adult ALL patients.
Leukemia. 2004 Jan;18(1):63-71.
PMID: 14603332 [PubMed - indexed for MEDLINE]

74:  Boon K, Edwards JB, Siu IM, Olschner D, Eberhart CG, Marra MA, Strausberg
RL, Riggins GJ.
 Comparison of medulloblastoma and normal neural transcriptomes identifies a
restricted set of activated genes.
Oncogene. 2003 Oct 23;22(48):7687-94.
PMID: 14576832 [PubMed - indexed for MEDLINE]

75:  Pabon C, Modrusan Z, Ruvolo MV, Coleman IM, Daniel S, Yue H, Arnold LJ Jr.
 Optimized T7 amplification system for microarray analysis.
Biotechniques. 2001 Oct;31(4):874-9.
PMID: 11680719 [PubMed - indexed for MEDLINE]

76:  Davis RE, Staudt LM.
 Molecular diagnosis of lymphoid malignancies by gene expression profiling.
Curr Opin Hematol. 2002 Jul;9(4):333-8. Review.
PMID: 12042708 [PubMed - indexed for MEDLINE]

77:  Hennig L, Menges M, Murray JA, Gruissem W.
 Arabidopsis transcript profiling on Affymetrix GeneChip arrays.
Plant Mol Biol. 2003 Nov;53(4):457-65.
PMID: 15010612 [PubMed - indexed for MEDLINE]

78:  Chan WC, Huang JZ.
 Gene expression analysis in aggressive NHL.
Ann Hematol. 2001;80 Suppl 3:B38-41.
PMID: 11757704 [PubMed - indexed for MEDLINE]

79:  Fox MS, Ares VX, Turek PJ, Haqq C, Reijo Pera RA.
 Feasibility of global gene expression analysis in testicular biopsies from
infertile men.
Mol Reprod Dev. 2003 Dec;66(4):403-21.
PMID: 14579417 [PubMed - in process]

80:  Tsai WC, Tsai ST, Ko JY, Jin YT, Li C, Huang W, Young KC, Lai MD, Liu HS,
Wu LW.
 The mRNA profile of genes in betel quid chewing oral cancer patients.
Oral Oncol. 2004 Apr;40(4):418-26.
PMID: 14969821 [PubMed - indexed for MEDLINE]

81:  Shao Y, Yang SB, Wang MW, Wu BY, You WD, Li H.
 [Gene expression profile of human adenocarcinoma by cDNA microarray and
clustering]
Zhonghua Yi Xue Yi Chuan Xue Za Zhi. 2004 Apr;21(2):110-5. Chinese.
PMID: 15079790 [PubMed - in process]

82:  Ma XJ, Salunga R, Tuggle JT, Gaudet J, Enright E, McQuary P, Payette T,
Pistone M, Stecker K, Zhang BM, Zhou YX, Varnholt H, Smith B, Gadd M, Chatfield
E, Kessler J, Baer TM, Erlander MG, Sgroi DC.
 Gene expression profiles of human breast cancer progression.
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