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PLoS ONE. 2007; 2(11): e1149.
Published online Nov 7, 2007. doi:  10.1371/journal.pone.0001149
PMCID: PMC2065803

Common Human Cancer Genes Discovered by Integrated Gene-Expression Analysis

Oliver Hofmann, Academic Editor

Abstract

Background

Microarray technology enables a standardized, objective assessment of oncological diagnosis and prognosis. However, such studies are typically specific to certain cancer types, and the results have limited use due to inadequate validation in large patient cohorts. Discovery of genes commonly regulated in cancer may have an important implication in understanding the common molecular mechanism of cancer.

Methods and Findings

We described an integrated gene-expression analysis of 2,186 samples from 39 studies to identify and validate a cancer type-independent gene signature that can identify cancer patients for a wide variety of human malignancies. The commonness of gene expression in 20 types of common cancer was assessed in 20 training datasets. The discriminative power of a signature defined by these common cancer genes was evaluated in the other 19 independent datasets including novel cancer types. QRT-PCR and tissue microarray were used to validate commonly regulated genes in multiple cancer types. We identified 187 genes dysregulated in nearly all cancerous tissue samples. The 187-gene signature can robustly predict cancer versus normal status for a wide variety of human malignancies with an overall accuracy of 92.6%. We further refined our signature to 28 genes confirmed by QRT-PCR. The refined signature still achieved 80% accuracy of classifying samples from mixed cancer types. This signature performs well in the prediction of novel cancer types that were not represented in training datasets. We also identified three biological pathways including glycolysis, cell cycle checkpoint II and plk3 pathways in which most genes are systematically up-regulated in many types of cancer.

Conclusions

The identified signature has captured essential transcriptional features of neoplastic transformation and progression in general. These findings will help to elucidate the common molecular mechanism of cancer, and provide new insights into cancer diagnostics, prognostics and therapy.

Introduction

Cancer is one of the leading causes of death in Western countries resulting in one of every four deaths. More than 100 types of cancer with different incidence have been diagnosed in various organs or tissues. Cancer is associated with multiple genetic and regulatory aberrations in the cell. To capture these abnormalities, DNA microarrays, which permit the simultaneous measurement of expression levels of tens of thousands of genes, have been increasingly utilized to characterize the global gene-expression profiles of tumor cells and matched normal cells of the same origin. Over the past years, the global gene-expression profiles of various cancers have been analyzed and many gene-expression signatures that are associated with cancer progression, prognosis and response to therapy have been described [1][19]. However, such studies are typically specific to certain tumors. The cancer type-specific signatures from these studies show little overlap in gene constitutions and biologically important pathways. Decades of research in molecular oncology have yielded few useful tumor-specific molecular markers, due to limitations with sample availability, identification, acquisition, integrity, and preparation [20]. Cancer is a highly heterogeneous disease, both morphologically and genetically. It remains a challenge to capture an essential, common transcriptional feature of neoplastic transformation and progression.

To extract maximum value from the recent accumulation of publicly available cancer gene-expression data, it is necessary to evaluate, integrate and inter-validate multiple datasets. Comprehensive analyses of a myriad of published datasets make it possible to find common cancer genes and essential functional consequences that are associated with tumor initiation and progression in general. Systematic characterization of expression changes in biological pathways among different types of cancer will eventually lead to a better understanding of which perturbations in the cell give rise to cancer. These findings will provide multiple clinical directions for cancer diagnostics, prognostics and therapy on the basis of the gene expression signature of patients. In the present study, we described an integrated gene-expression analysis of 2,186 samples from 39 different studies to identify and validate a cancer gene signature that is independent of tumor types and can identify cancer patients for a wide variety of human malignancies.

Results

Common gene expression changes in various cancer types

We first analyzed gene expression profiles of 1,223 human samples (343 normal tissues and 880 tumor tissues) from the training datasets 1–20 containing 20 different types of cancers (Table S1). The commonness of gene expression in these 20 cancer types was statistically assessed by permutation analyses (p<10−5). In total, 187 genes commonly affected in cancer were identified. Of these, 117 were up-regulated and 70 were down-regulated in nearly all cancerous tissue samples, regardless of their tissue of origin (Table 1 and and2).2). With the bioinformatics tool, FatiGO (http://fatigo.bioinfo.cnio.es), we found 142 out of 187 cancer genes were significantly associated with at least one Gene Ontology (GO) category. Several functional categories have been shown to be important for carcinogenesis and cancer progression (Table S2). For example, 11 genes (BFAR, CARD4, SPP1, SNCA, BAX, STAT1, CLU, GULP1, BID, CIDEA and PPP2R1B) control programmed cell death; 8 genes (TTK, RECK, BAX, STAT1, NME1, CCNB2, E2F3 and PPP2R1B) are involved in regulation of the cell cycle; 8 genes (TAP1, APOL2, SPP1, CLU, PSMB8, TAPBP, HLA-F and TNFSF13B) play roles in the immune response; and 6 genes (TTK, SPP1, NME1, NAP1L1, NPM1 and TNFSF13B) regulate cell proliferation. In addition, genes that are involved in protein transport, M phase cell cycle, secretory pathway and DNA repair are consistently up-regulated in a large majority of cancer types.

Table 1
Common up-regulated genes in human malignancies
Table 2
Common down-regulated genes in human malignancies

Validation of common cancer genes by QRT-PCR and TMA

To validate the microarray gene expression results from the integrated gene-expression analysis, the relative expression levels of 32 of the 187 cancer genes were determined by QRT-PCR analysis using completely independent samples from three each of breast, lung, prostate, colon and cervical cancer and their matched normal tissue. We confirmed the expression results for most of these selected genes (fold change >1.5, p≤0.05 and consistency >60%) (Table 3). The top 10 genes with absolute fold change >4, p≤0.05 and consistency >85% were further confirmed using another 18 matched tumor and normal samples from breast, lung and cervical cancer patients. In the expanded analysis, the expression levels of these 10 genes were still significantly different with absolute fold change >4, p≤0.05 and consistency >85%, except genes SPP1 and NDRG2, which exhibited slightly decreased consistency (Figure 1).

Figure 1
QRT-PCR analysis for the selected top ten scoring genes in expanded samples.
Table 3
QRT-PCR analysis of selected common cancer genes in initial screens

Tissue microarray analysis (TMA) was also performed for three randomly picked common cancer genes (SPP1, BID and CLU) to determine if mRNA changes were correlated with changes in protein expression in cancer patients. The tissue microarray contains 200 tumor samples with 50 samples from each of four cancer types (colon, breast, ovarian, and lung). Analysis of SPP1 protein expression in tumor and normal tissues indicated that SPP1 is present in the cytoplasm and nucleus of cells. Most samples from colon adenocarcinoma, breast adenocarcinoma, ovary adenocarcinoma, and lung cancer showed intermediate to strong cytoplasmic SPP1 staining in tumor cells, but the staining in normal tissues was much weaker. The average scores for tumor and normal tissues are 11.1±1.8 and 1.8±1.5 in colon adenocarcinoma (p = 0.0005), 10.9±1.7 and 4.5±3.8 in breast adenocarcinoma (p = 0.043), 11.7±1.0 and 3.3±4.2 in ovary adenocarcinoma (p = 0.028), and 9.0±2.2 and 3.5±2.0 in lung cancer (p = 0.0005), respectively (Figure 2 and Figure S1). Positive cytoplasmic staining of clusterin (CLU) was present on both tumor and normal cells in lung, breast, and ovary tissue. However, as compared with normal tissues, the clusterin decreased in lung cancer (5.6±2.1 vs. 10.8±1.6, p = 0.005), breast cancer (7.1±2.7 vs. 10.5±1.7, p = 0.017), and ovarian cancer (6.8±3.0 vs. 10.5±1.7, p = 0.011) (Figure 3A, B and D). Colon cancer showed much less positive CLU staining than other types of tumor and there was no significant difference between colon tumor and normal colon tissues (Figure 3C). BID protein was significantly upregulated in colon cancer, lung cancer and breast cancer, but not in ovarian cancer (data not shown). The average scores of immunoreactive staining in tumor and normal tissues are 11.2±1.9 vs. 2.0±1.4 (p = 0.00015), 10.4±2.2 vs. 2.5±2.2 (p = 0.0002) and 11.5±1.4 vs. 6.5±1.9 (p = 0.010) for these three cancer types, respectively (Figure 3E–G). Semiquantitative analysis indicates that most of samples from different types of cancer have strong, high percentage of BID immunoreaction, while normal tissues only show low to medium level of staining; CLU tends to be down-regulated in tumors (Figure 3H). The results demonstrate that protein level is largely consistent with the mRNA expression of these three genes.

Figure 2
Immunostaining analysis of SPP1 expression in normal and tumor tissues.
Figure 3
CLU and BID immunostaining in tumor and normal tissues.

Common cancer pathways in various cancer types

We further surveyed a listing of 1,687 biological pathways which include metabolic pathways, protein interaction networks, signal transduction pathways, and gene regulatory networks to examine if several genes within a specific pathway act in a cumulative manner to influence neoplastic transformation and progression. The richness of significantly differentially expressed genes in a given pathway was again evaluated by 100,000 permutation tests in the training datasets. Pathway analysis showed that significant differentially expressed genes (p<0.01) were mostly enriched in the glycolysis pathway, cell cycle checkpoint II pathway and plk3 pathway, which included 24, 10 and 10 genes, respectively (p<10−5). Interestingly, most of the genes involved in these three pathways are up-regulated in a large majority of cancerous tissues as compared with normal tissues (Figure S2), suggesting the prevalence of gene hyperactivation and amplification in human malignancies.

Confirmation of the common gene expression pattern in independent datasets

Next, we determined to validate our gene expression signature and see if we could distinguish cancer samples from normal samples in completely independent datasets. The discriminative power of the 187-gene expression signature in normal and tumor samples was tested by clustering analysis using oligonucleotide gene expression data obtained from 19 completely independent datasets. Datasets 21 to 38, used for validation, were comprised of 211 normal and 492 tumor tissues from 14 different cancer types. In most of these 18 datasets, samples were classified into two groups, one comprising of most normal samples and another for most tumor samples, based on our 187 gene signature (Figure S3). The overall accuracy of correct classification is, on average, 92.64% ranging from 78% to 100% (Table 4). It should be noted that dataset 30 and 31 are slightly different from the other datasets due to heterogeneity of cancer samples. All the samples in these two datasets were classified into two big groups: one containing tumor samples only; the other group containing both tumors and normals, which can be clearly distinguished as two subgroups. Specifically, in dataset 30, eight myeloma cell lines formed one group with inclusion of one plasma cell leukemia (PCL); in the other group, eight normal plasma cell samples and eight samples from patients with multiple myeloma (MM) or PCL were clearly subdivided. In dataset 31, six metastatic prostate cancer samples were grouped together, while normal tissues and primary prostate cancers were in the other group, with six normal tissues and one primary prostate cancer in one subgroup, and six primary prostate cancers in the other subgroup. In clustering analysis for tumor samples of different subtypes with gene expression data, it is not unusual that some tumor subgroups are closer to the normal group, but are clearly distinguished from the normal group.

Table 4
Confirmation of the common gene expression pattern in independent datasets

Dataset 39 included 180 tumor samples, spanning 14 common tumor types, and 81 normal tissue samples. Among 14 tumor types, uterine adenocarcinoma, leukemia and pleural mesothelioma were not present in the 20 training datasets. In clustering analyses, samples are clustered into three groups: tumor group I composing of 57 tumors and 20 normal tissues, normal group composing of 11 tumors and 53 normal tissues, and tumor group II composing of 112 tumor and 8 normal tissues (Figure 4). The accuracy of classification is 85%. All of central nervous system cancer and most of pancreatic adenocarcinoma were classified into tumor group I, while all of leukemia and most of lymphomas were classified into tumor group II. Notably, the 187-gene signature performs well (81–100%) in classifying new cancer types (such as uterine adenocarcinoma, leukemia and pleural mesothelioma). It is also worth noting that there only ~31–60% of genes from this 187 gene signature that were used in clustering analyses in each of validation datasets due to the specificity of platforms, sample availability and missing values in microarray experiments. Furthermore, the set of genes used for clustering analyses are in part different among validation datasets, depending on the availability of gene-expression data in a specific study. This demonstrated the robustness, utility and ubiquity of our gene signature. Lastly, we also attempted to classify these 261 samples using the expression profiles of 28 common cancer genes confirmed by the QRT-PCR analysis with fold change >3 and consistency >60%. The refined 28-gene signature still achieved ~80% accuracy of classification (Figure S4). It should be noted that dataset 39 used an old microarray system of Affymetrix FL 6800 gene chip with a total of 7,289 probes. The numbers of probes used in the above two clustering analyses for the dataset 39 were 72 and 19, corresponding to 59 and 15 genes, respectively.

Figure 4
Hierarchical clustering of gene-expression profiles for 187 common cancer genes in dataset 39 with mixed cancer types.

Discussion

DNA microarray-based gene-expression classification enables a standardized, objective assessment of oncological diagnosis and prognosis and provides complementary information to current clinical protocols [20]. However, such studies are typically specific to certain types of cancer, and the obtained expression profiles have limited use due to inadequate validation in large patient cohorts. In this study, we identified a gene signature for molecular cancer classification through an integrative gene-expression analysis of 20 different types of common cancer. This signature contains 187 genes whose aberrant expression was observed in nearly all cancerous tissue samples, regardless of their tissue of origin. To illustrate the utility and robustness of this signature, we determined its discriminative power on another 19 completely independent datasets. The accuracy of classification is about 92.6% by using this common cancer signature. Interestingly, a different subset of genes that account for 31–60% of the 187-gene signature can rigorously identify cancer patients for a wide variety of human malignancies. More importantly, this signature also performs well in the prediction of novel cancer types that were not represented in the integrative analysis in training datasets. This confirms that the identified signature is cancer type-independent and has captured certain of the essential transcriptional features of neoplastic transformation and progression in general. However, it remains unknown whether all of these genes in our signature are involved in the development of cancer. Some of them may be an indication of something going on in the body that is accompanying the disease process; while others are genes pe se that promote tumorigenesis and cancer progression. We also compared our signature with two other signatures in independent datasets that were previously used in those studies [21], [22]. The overall accuracy of correct classification using our signature is, on average, 95% ranging from 90% to 100%; while the overall accuracy of Rhode's and Xu's is 89% and 93%, respectively (Table S3). The two previous signatures were determined either from the same set of genes in a single microarray platform [21] or common genes among different platforms [22]. The analyzed genes only represent a subset of genes on the genome (about 25%) and were highly over-represented in their signatures; while the other genes that are not presented in the analyzed platform or are not common among platforms were missed in their signatures. In the present study, the proposed method for determining gene signature is straightforward and independent of different microarray platforms (for example various uncommercial cDNA chips and Affymetrix chips). Therefore, we can utilize information of all the genes in a specific microarray study. Our study also highlights the importance of the large sample size in microarray analyses for identifying and validating prognostic signatures. In this study, we pooled a total of 2,186 samples from 39 independent microarray studies for classifier discovery and validation. The results from this large-scale integrative gene-expression analysis should be more robust and reliable than each of potentially under-powered individual studies.

We also identified several common pathways where altered expression of several genes act in a common pathway to influence tumor development. These pathways include the glycolysis, cell cycle checkpoint II and plk3 pathways. We found that many of the genes within each of pathways were up-regulated in various types of tumor tissues as compared with normal tissues. The perturbation of expression of multiple genes within these pathways may be a common characteristic of neoplastic transformation and progression in malignant tumors. Therapeutic manipulation of these pathways may provide a universal strategy for treatment of many types of cancer. For example, cancer cells often generate energy through glycolytic fermentation rather than oxidative phosphorylation. It is possible that the lack of oxidative phosphorylation limits the production of proapoptotic superoxide. Three enzymes of the 187 gene profile, TPI, PGK1, and ENO1, which are involved in the glycolytic pathway, were also found to be significantly overexpressed in the HER-2/neu-positive breast tumors [23]. Overexpression of these enzymes may well relate to the increased requirements of both energy and protein synthesis/degradation pathways in the rapidly growing tumors. This pathway was proposed to be significant in tumorigenesis more than 70 years by Warburg [24].

The genes identified in our signature could be the prime targets of cancer therapy and prevention, since they are dysregulated in many types of cancer. Characterization of these common genes should provide opportunities for elucidating certain of the more general mechanisms of cancer initiation and progression. Cancer gene therapy classically involves delivery of tumor suppressor, apoptosis-inducing or suicide genes directly into tumor cells. Arrest of tumor cell proliferation is the ultimate objective of anticancer therapy. Interestingly, in our data, the identified common genes that are involved in regulation of cell proliferation are all up-regulated in different types of tumor tissues (Table S2). These genes include TTK, SPP1, NME1, NAP1L1, NPM1 and TNFSF13B. Osteopontin (SPP1) is a gene that regulates cell proliferation. Many studies have shown that SPP1 is highly expressed in several malignancies. Abundant secretion of SPP1 acts as a marker for breast and prostrate cancer, osteosarcoma, glioblastoma, squamous cell carcinoma and melanoma [25]. Cells from SPP1 knockout mice show impaired colony formation in soft agar and slower tumor growth in vivo in comparison with tumors in wild-type mice [26]. In our QRT-PCR analysis, SPP1 was overexpressed in 18 out of 22 samples from five different types of tumor tissues (Figure 1). The tissue microarray analysis further demonstrated that this increased mRNA expression level of SPP1 was significantly correlated with protein level in cancer patients (Figure 2). Thus, SPP1 may be a promising common target of cancer therapy and prevention.

BH3-interacting domain death agonist (BID) and clusterin (CLU) are two other potential therapeutic targets that are involved in programmed cell death. BID contains only the BH3 domain, which is required for its interaction with the Bcl-2 family proteins and for its pro-death activity. BID is susceptible to proteolytic cleavage by caspases, calpains, Granzyme B and cathepsins [27]. BID is important to cell death mediated by these proteases and thus is the sentinel to protease-mediated death signals [28]. Protease-cleaved BID is able to induce multiple mitochondrial dysfunctions, including the release of the inter-membrane space proteins, cristae reorganization, depolarization, permeability transition and generation of reactive oxygen species. Thus BID is a molecular bridge linking various peripheral death pathways to the central mitochondria pathway. Recent studies further indicated that BID may function as more than just a proapoptotic killer molecule. BID not only promotes cell cycle progression into S phase but also involves the maintenance of genomic stability by engaging at mitotic checkpoints [27]. This protein has diverse functions that are important to both the life and death of the cell. A recent study showed that BID increased in brain tumor, gliomas, prostate cancer, ovarian cancer and colon cancer [29]. CLU is a sulphated glycoprotein, implicated in various cell functions involved in carcinogenesis and tumor progression, including cell cycle regulation, cell adhesion, DNA repair and apoptosis. Several studies show greatly reduced expression of CLU in tumors compared with normal tissue, including testicular tumor, von Hippel-Lindau (pVHL)-defective renal tumor, esophageal squamous cell carcinoma[30][34]. The reduction in the overall CLU level appears because the CLU positive stromal compartments of the normal mucosa are lost in tumor [35]. CLU plays a negative role in epithelial cell proliferation and lack of CLU increases the susceptibility to tumorigenesis after carcinogenic challenge. The under-expression of CLU was immediately apparent in highly malignant MD PR317 prostate adenocarcinoma cells using laser microdissection technique and serial analysis of gene expression [36]. Both our QRT-PCR and tissue microarray analyses confirmed the upregulation of BID and downregulation of CLU in most of cancerous tissues (Figure 3).

Stem cells are the very earliest cells of the embryo that divide and differentiate to form mature organs and tissues. Small numbers of normal stem cells persist into adulthood and function to maintain and repair healthy tissues. It has been recently established that, like normal tissues, human tumors are initiated and maintained by stem cells. Cancer stem cells exist as a minority population within the tumor and share many genetic and biologic characteristics of normal stem cells. Some genes overexpressed in cancer tissues identified in our signature were found highly expressed in embryonic stem cells. For example, EPRS, NPM1, STAT1 and LSM4 are higher expressed in human embryonic stem cell lines compared with human universal RNA [37], [38]. CCNB1, FBXO2, NME2, SNRPF, DDX21, SLC38A4, PSMA2, PSMA3 and AP1S2 are also higher expressed in human embryonic stem cell lines [37], [38], members of these gene families such as CCNB2, FBXO32, NME1, SNRPB, DDX39, SLC38A1, PSMA4, PSMA7 and AP1S1 are observed in our signature. Particularly, two genes in our signature, DNMT1 and TAPBP, are listed on SuperArray GEArray S Series Human Stem Cell Gene Array, which is designed to profile the expression of genes known to be important for the identification, growth and differentiation of stem cells (Catalog number HS-601.2, Superarray, Frederick, MD; http://www.superarray.com/home.php). Our gene signature also includes several genes related to tissue development, such as regulation of developmental process (FNDC3B, SPP1 and TTL), embryonic development (ADAM10) and organ development (NCL, SPP1, SFXN1, BAX, ADAM12, NRP2 and NME1) (http://fatigo.bioinfo.cnio.es).

In summary, we defined a cancer-type-independent gene signature predictive of cancer status for a wide variety of human malignancies. This signature has captured the essential transcriptional transition of normal cell behavior to uncontrolled cell growth in malignant tumors and thus has significant implications in cancer diagnostics, prognostics and therapy. These genes should prove applicable to not only understand the common molecular mechanism of cancer, and cancer diagnosis, but also serve as potential molecular targets as well.

Materials and Methods

Data collection and processing

Microarray datasets were obtained from public databases. Data were of two general types, dual channel ratio data corresponding to spotted cDNA microarrays and single channel intensity data corresponding to Affymetrix microarrays. Thirty nine studies had 634 normal and 1,552 cancer samples in total (Table S1). All these previous microarray studies were originally designed for the identification of differentially expressed genes between normal and malignant tumor tissues for that specific type of cancer. Pathology reports were the basis for classify the normal and tumor tissue, and benign and malignant tumors in these studies. Datasets 1–20 which represent 20 different common cancer types such as bladder, breast, colon, endometrial, kidney, liver, lung, melanoma, lymphoma, pancreatic, prostate and thyroid cancer were used to identify common cancer genes and pathways, and datasets 21–39 were used for extensive validation. The chosen training datasets were normally larger than validation datasets except several very recently released datasets. All of the expression values were base-two log transformed. To facilitate multi-study analysis, Unigene cluster ID and gene names were assigned to all of the cDNA clones and Affymetrix probes based on the NCBI Unigene Build 198 (http://www.ncbi.nlm.nih.gov/entrez/query.fcgidbunigene).

Detection of differentially expressed genes

We used two-sample permutation t test for identifying differentially expressed genes (DEGs), which was implemented in R package permax (http://www.r-project.org/), for each of the datasets 1–20. To obtain the robust results, 10,000 permutations were performed to calculate an empirical p value in the analysis of DEGs in each type of cancer.

Common cancer biomarkers

To identify common biomarkers in different types of cancer, we used a permutation procedure to examine if DEGs (p<0.01) are statistically consistently up-regulated or down-regulated in different types of cancer. Specifically, we first determined how often a DEG is consistently up-regulated or down-regulated in different types of cancer in the original datasets. Then, we reshuffled cancer status (normal and tumor) and created 100,000 replicates for each type of cancer. In each replicate, we conducted the analysis of DEGs as described in the section “Detection of differentially expressed genes”, and record the number of cancer types in which a DEG is consistently up-regulated or down-regulated. Finally, the probability for observing a common biomarker in different types of cancer by random chance is calculated as,

equation image

where N is the number of permutations, n is the number of cancer types in which a DEG is consistently up-regulated or down-regulated in the original datasets, and ci is the number of cancer types in which a DEG is consistently up-regulated or down-regulated in i-th permutation, i = 1,2,….N.

Pathway analysis

Sets of genes that act in concert to carry out a specific function were also identified in different types of cancer. Gene sets we used are listed as c2 for curated gene sets in the Molecular Signature Database (MSigDB, http://www.broad.mit.edu/gsea/msigdb/msigdb_index.html). We also employed permutation analysis to examine if DEGs (p<0.01) are statistically significantly enriched in a given pathway. Specifically, we first record the number of DEGs in each of 1,687 pathways in original datasets. Then, we randomly reshuffled cancer status to create 100,000 replicates. In each replicate, we conducted the analysis of DEGs as described in the section “Detection of differentially expressed genes” and record the number of DEGs. The probability for observing the richness of DEGs in a given pathway by chance is calculated as,

equation image

where N is the number of permutations, n is the number of DEGs observed in a given pathway in the original dataset, and mi is the number of DEGs observed in each randomly permuted replicate, i = 1,2,….20. This analysis was performed separately in each type of cancer.

Clustering analysis

Hierarchical clustering based on centered Pearson correlation coefficient algorithm and average linkage method was used to show the expression patterns of common cancer genes in datasets 21 to 39. The datasets were normalized by standardizing each row (gene) to mean 0 and variance 1. The clustering analysis was performed by using CLUSTER and TREEVIEW software (http://rana.lbl.gov/EisenSoftware.htm). Classification accuracy for each of these datasets was calculated.

QRT-PCR Analysis

Using 15 pairs of tumor and matched adjacent normal tissues from five cancer types (breast, lung, prostate, colon-recta and cervical cancer), three pairs for each cancer type, the relative expressions of 32 randomly chosen common cancer genes from the identified signature gene set were initially screened by QRT-PCR analysis, as described in a previous study [39]. These frozen tissues were acquired from Tissue Procurement Core at Washington University Siteman Cancer Center (St. Louis, Missouri, United States). Primers for the QRT-PCR analysis (Table S4) were designed using Primer Express software version 2.0 (Applied Biosystems, Foster City, CA). Amplification of each target gene was performed with SYBR Green master mix in BIO-RAD Single Color Real-Time PCR Detection system according to the manufacture protocols. The control gene β-Actin and target genes were amplified with equal efficiencies. The method for assessing if two amplicons have the same efficiency is to look at how ΔCT (CT,target–CT,β-Actin, where CT is cycle number at which the fluorescence signal exceeds background) varies with template dilution, which is described in detail elsewhere [40]. The fold change of gene expression in normal tissues relative to tumor tissues was calculated as 2−ΔΔCT (ΔΔCT = ΔCT normal–ΔCT tumor). One tailed Z-test was performed to determine statistical significance between normal and tumor groups. The average fold change of each gene and the consistency of the regulation tendency with the microarray data were also calculated. According to these three characteristics, the ten highest scoring genes were selected for further QRT-PCR confirmation using 18 matched tumor and normal samples from breast, lung and cervical cancer patients, three pairs for each cancer type.

Tissue microarray

Tissue microarray (TMA) slides were purchased from the NCI Tissue Array Research Program (http://ccr.cancer.gov/tech_initiatives/tarp/). All samples were formalin-fixed, paraffin-embedded tissues. Limited demographic and pathology information was available at the NCI website (http://ccr.cancer.gov/tech_initiatives/tarp). The TMA slides contained four tumor types including colon adenocarcinoma, breast adenocarcinoma, ovary adenocarcinoma, lung cancer, and normal tissues, each from a distinct patient. The normal tissues were not paired with the tumor tissues on the slides. The tissue of origin for all samples was confirmed by experienced surgical pathologists. Each TMA slide contained 200 tissue samples of 0.6 mm and was ready for use in immunohistochemistry, but only among which there were limited normal tissues. In order to have enough normal tissue samples for comparison, we obtained additional normal tissue slides from Tissue Procurment Core at Washington University in St. Louis School of Medicine according to the approved protocol by Washington University in St Louis Human Studies Committee, such that the total number of normal tissues was 65. All slides were deparaffinized and rehydrated before antigen retrieval which was applied in microwave for 20 minutes with citrate buffer, pH 6.0. After blocking in 10% of normal goat serum in PBS, all primary antibodies were incubated overnight at 4°C, including SPP1 (Novocastra Laboratories, Newcastle, UK, clone 15G12, dilution 1[ratio]100), BID (BD Tranduction Laboratories, San Jose, CA, clone 7, dilution 1[ratio]200) and CLU (Upstate Biotech, Lake Placid, NY, clone 41D, dilution 1[ratio]1000). The appropriate secondary biotinylated IgG (1[ratio]500) was used, followed by ABC method (Vectastain ABC Elite Kit, Vector Lab, Burlingame, CA) and diaminobenzidine (DAB) (Sigma, St. Louis, MO) was used as chromogen. For negative control, the primary antibody was omitted with normal serum. The percentage of positive cancer cells was scored on a semiquantitative scale as 0 (0%), 1 (1–20%), 2 (20–50%), 3 (50–75%) and 4 (over 75%). Intensity was scored as 1 (weak), 2 (middle) and 3 (strong). Results were calculated by multiplying the score of percentage of positive cells (P) by the intensity (I). The maximum score is 12. The evaluation of immunostaining results was performed independently by two investigators. Student's t test was used to assess the significance of expression difference from normal and tumor tissues.

Supporting Information

Figure S1

The immunostaining images of SPP1 in various cancer tissue microarray. The sections from normal tissues are shown in box A. Additional sample information was available at the NCI website (http://ccr.cancer.gov/tech_initiatives/tarp).

(2.31 MB PDF)

Figure S2

Three pathways enriched in various cancer types. (A) glycolysis pathway, (B) cell cycle checkpoint II pathway and (C) plk3 pathway. Dark red, overexpressed in tumors (p<0.01); light red, overexpressed in tumors (p>0.01); dark green, underexpressed in tumors (p<0.01); light green, underexpressed in tumors (p>0.01); white, missing data.

(0.61 MB PDF)

Figure S3

Hierarchical clustering of gene-expression profiles for 187 common cancer genes in datasets 21–38. Normal tissues were marked black and tumor tissues were marked red. The accuracy of classification is, on average, 92.64% ranging from 78% to 100%.

(2.19 MB PDF)

Figure S4

Hierarchical clustering of gene-expression profiles in dataset 39 using 28 common cancer genes confirmed by the QRT-PCR analysis with fold change >3 and consistency >60%. Samples are also clustered into three groups: tumor group I composing of 123 tumors and 18 normal tissues, tumor group II composing of 46 tumor and 24 normal tissues, and normal group composing of 39 normal tissues and 11 tumors. The accuracy of classification is 80%.

(0.15 MB PDF)

Table S1

Datasets in the integrated gene-expression analyses

(0.17 MB DOC)

Table S2

Functional categories of common up- and down-regulated cancer genes

(0.07 MB DOC)

Table S3

A comparison of several signatures in independent datasets

(0.03 MB DOC)

Table S4

Oligonucleotide primers and probes used for real-time PCR Analysis

(0.05 MB DOC)

Acknowledgments

The cancer tissues and matched normal tissues were procured from Tissue Procurement Core at Washington University Siteman Cancer Center (St. Louis, Missouri, United States).

Footnotes

Competing Interests: The authors have declared that no competing interests exist.

Funding: This work was supported by NIH grant R01CA58554 (MY).

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