Skip to main content
Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

A Microarray-Based Gene Expression Analysis to Identify Diagnostic Biomarkers for Unknown Primary Cancer

  • Issei Kurahashi,

    Affiliation Department of Planning, Information, and Management, University of Tokyo, Tokyo, Japan

  • Yoshihiko Fujita,

    Affiliation Department of Genome Biology, Kinki University School of Medicine, Osaka-Sayama, Japan

  • Tokuzo Arao,

    Affiliation Department of Genome Biology, Kinki University School of Medicine, Osaka-Sayama, Japan

  • Takayasu Kurata,

    Affiliation Department of Medical Oncology, Kinki University School of Medicine, Osaka-Sayama, Japan

  • Yasuhiro Koh,

    Affiliation Division of Drug Discovery and Development, Shizuoka Cancer Center Research Institute, Shizuoka, Japan

  • Kazuko Sakai,

    Affiliation Department of Genome Biology, Kinki University School of Medicine, Osaka-Sayama, Japan

  • Koji Matsumoto,

    Affiliation Medical Oncology Division, Hyogo Cancer Center, Akashi, Japan

  • Maki Tanioka,

    Affiliation Medical Oncology Division, Hyogo Cancer Center, Akashi, Japan

  • Koji Takeda,

    Affiliation Department of Clinical Oncology, Osaka City General Hospital, Osaka, Japan

  • Yuichi Takiguchi,

    Affiliation Department of Medical Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan

  • Nobuyuki Yamamoto,

    Affiliation Division of Thoracic Oncology, Shizuoka Cancer Center, Shizuoka, Japan

  • Asuka Tsuya,

    Affiliation Department of Clinical Oncology, Osaka City General Hospital, Osaka, Japan

  • Nobuaki Matsubara,

    Affiliation Division of Oncology and Hematology, National Cancer Center Hospital East, Chiba, Japan

  • Hirofumi Mukai,

    Affiliation Division of Oncology and Hematology, National Cancer Center Hospital East, Chiba, Japan

  • Hironobu Minami,

    Affiliation Division of Medical Oncology/Hematology, Kobe University Graduate School of Medicine, Kobe, Japan

  • Naoko Chayahara,

    Affiliation Division of Medical Oncology/Hematology, Kobe University Graduate School of Medicine, Kobe, Japan

  • Yasuhiro Yamanaka,

    Affiliation Department of Medical Oncology, Tochigi Cancer Center, Utsunomiya, Japan

  • Keisuke Miwa,

    Affiliation Department of Medical Oncology, International Medical Center-Comprehensive Cancer Center, Saitama Medical University, Saitama, Japan

  • Shin Takahashi,

    Affiliation Department of Clinical Oncology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan

  • Shunji Takahashi,

    Affiliation Division of Medical Oncology, Cancer Institute Hospital, Tokyo, Japan

  • Kazuhiko Nakagawa,

    Affiliation Department of Medical Oncology, Kinki University School of Medicine, Osaka-Sayama, Japan

  •  [ ... ],
  • Kazuto Nishio

    Affiliation Department of Genome Biology, Kinki University School of Medicine, Osaka-Sayama, Japan

  • [ view all ]
  • [ view less ]



The biological basis for cancer of unknown primary (CUP) at the molecular level remains largely unknown, with no evidence of whether a common biological entity exists. Here, we assessed the possibility of identifying a common diagnostic biomarker for CUP using a microarray gene expression analysis.


Tumor mRNA samples from 60 patients with CUP were analyzed using the Affymetrix U133A Plus 2.0 GeneChip and were normalized by asinh (hyperbolic arc sine) transformation to construct a mean gene-expression profile specific to CUP. A gene-expression profile specific to non-CUP group was constructed using publicly available raw microarray datasets. The t-tests were performed to compare the CUP with non-CUP groups and the top 59 CUP specific genes with the highest fold change were selected (p-value<0.001).


Among the 44 genes that were up-regulated in the CUP group, 6 genes for ribosomal proteins were identified. Two of these genes (RPS7 and RPL11) are known to be involved in the Mdm2–p53 pathway. We also identified several genes related to metastasis and apoptosis, suggesting a biological attribute of CUP.


The protein products of the up-regulated and down-regulated genes identified in this study may be clinically useful as unique biomarkers for CUP.


Patients with cancer of unknown primary (CUP) present with metastatic disease for which the primary site cannot be found, despite extensive standard investigation. The prognosis of patients with CUP is usually poor for those receiving empiric treatments. The median survival period is 3–9 months even when newer combination treatment regimens are administered [1][5]. The survival of patients with CUP can be improved if the primary site can be identified and a site-specific therapy can be applied [6], [7].

Clinically, CUPs exhibit common characteristics, such as rapid progression, early dissemination and a silent primary tumor, with signs and symptoms related to the metastatic site(s) [8]. The primary tumor may either have a slow growth pattern or may become involuted and undetectable. Existence of such common properties prompts us to hypothesize that there may be potential biological markers that elucidate CUP as a whole. Gene expression analysis is one of the means by which to identify genes characteristic to CUP.

Several studies using gene expression microarrays have demonstrated that the expression levels of thousands of genes can be used as a “molecular fingerprint” to classify a multitude of tumor types [9][15]. We are presently involved in a multicenter clinical study to predict the primary site of CUP based on the analysis of gene expression patterns. The analysis interprets the expression of ∼22,000 genes in each specimen by applying normalization and classification algorithms to gene expression data from a microarray. The similarity of each tumor specimen's gene expression pattern is then compared to the patterns for tumors from 24 known primary sites covered by the test. This study enabled the identification of genes that exhibited a unique expression pattern in CUP. Here, we present several genes encoding metastasis-and apoptosis-related proteins thus identified that may biologically characterize CUP.

Materials and Methods

Ethic Statement

All the patients provided written informed consent. Study approval was obtained from independent ethics committees of Kinki University, Shizuoka Cancer Center, Hyogo Cancer Center, Osaka City General Hospital, Chiba University, National Cancer Center Hospital East, Kobe University, Tochigi Cancer Center, Saitama Medical University, Tohoku University, and Cancer Institute Hospital. The study was undertaken in accordance with the Declaration of Helsinki.

Study Design

This study originated from currently ongoing multicenter, randomized, phase 2 prospective trial for the treatment of untreated CUP based on prediction of the primary site using data from a DNA chip. The patients had been diagnosed as having CUP between November 2008 and November 2010 at one of 13 centers of the West Japan Oncology Group (WJOG), a Japanese non-profit organization for conducting oncological clinical trials. The laboratory analyses were performed at 2 centers in Japan (Kinki University, Osaka-Sayama and Mitsubishi Chemical Medience Corporation, Tokyo).


All eligible patients had undergone a standard investigation for CUP. They were categorized into unfavorable subsets of CUP. Diagnoses of histologically or cytologically confirmed adenocarcinoma, poorly differentiated carcinoma, or squamous cell carcinoma were permitted. In each of the patients, a primary site had not been identified after a complete medical history, physical examination, chemistry profile, computed tomography (CT) scan of the chest, abdomen, and pelvis, mammography in women, measurements of the prostate-specific antigen (PSA) level in men, and a directed workup of any symptomatic areas. Patients in the following categories were excluded: women with adenocarcinoma involving only the axillary lymph nodes or the peritoneal cavity, patients with squamous cell carcinoma involving only cervical lymph nodes or inguinal lymph nodes, patients with poorly differentiated carcinoma consistent with a germ cell tumor (isolated midline structures, multiple pulmonary nodules, or elevated levels of β-human chorionic gonadotropin or α-human chorionic gonadotropin-fetoprotein), men with an elevated plasma PSA level or PSA-positive staining in a tumor, patients with a single, small, potentially resectable tumor, and patients with neuroendocrine carcinomas.

Sample Collection

Fresh frozen samples obtained from 60 patients with CUP were used for the analysis. All the samples were tested without knowledge of either the clinical characteristics or the subsequent response to treatment, except for the sex of the patient and the site of biopsy (mostly lymph nodes or ascites fluid).

Assay Procedure

RNA was extracted from the samples using an Isogene kit (Nippon Gene, Toyama, Japan). Spectrophotometry was used to assess whether an adequate total RNA concentration and purity was present. In general, the protocol for processing the RNA, amplifying and labeling fragments, hybridizing material on the microarray, and scanning was similar to the standard Affymetrix protocol for GeneChip® expression analysis. Affymetrix GeneChip® Human Genome U133 Plus 2.0 was used on an Affymetrix 3000 or 3000Dx GeneChip instrument (fluidics station and scanner) running Gene-Chip operating software to generate gene expression data (.CEL files).

Database Submission of Microarray Data

The microarray data were deposited in the Gene Expression Omnibus (GEO) database: The GEO accession number for the platform is GSE42392, samples GSM1038716-GSM 1038775.

Data Analysis

All the microarray data were normalized using asinh (hyperbolic arc sine) transformation, which is a modified version of Huber's normalization with variance stabilization [16], [17], and also a part of generalized log transformation (glog) [18]. Interinstitutional and array-to-array biases were corrected by subtracting their specific effects that were estimated by the mixed model [19]. The equation for asinh transformation is Igk/I.k, where I represents the expression value, g represents the gene, k represents the array, and the dot indicates the mean. The resulting asinh-transformed values, representing the relative expression of each gene, were used in further analyses.

The raw microarray datasets for 2,364 cancers of several primary types and 10 normal lymph nodes were obtained from the Gene Expression Omnibus (GEO) (Table 1). These datasets were normalized and used to construct gene-expression profiles specific to each type of cancer (n = 24) as well as an overall profile for cancer with known primary (CKP). The normal lymph node dataset was used as a reference. The data quality of CUP samples was monitored to ensure that data analysis of CUP samples was comparable to that of samples of CKP collected from GEO. Only the samples whose GAPDH, a housekeeping control gene, at 5′-terminal region (AFFX-HUMGAPDH/M33197_5_at) showed a minimum expression>500, and with the ratio of expression intensity (GAPDH at the 3′-region/5′-region)<3 were chosen.

Table 1. Number of cases for each cancer type and GEO series used for gene expression profiles.

The gene-expression profile specific to CUP was constructed using 30 CUP samples as training data and another 30 samples as test data (odd and even numbered cases, respectively). Of the 22,215 genes that were measured using both CUP samples (this work) and CKP samples (publicly accessed), a total of 5,645 genes with a present call for every sample were selected for further analysis. To identify CUP specific genes, the gene-expression profiles specific to CUP (training datasets) and normal lymph node were compared using t-tests. A histogram of the p-values is shown in Figure 1. The p-values for most of the genes were less than 0.001; when we selected the top 100 genes according to their p-values, the false discovery rate (FDR) was 4.56×10−12 [20]. To validate whether the genes identified using the CUP training datasets were significantly specific to CUP, the linear discriminant analysis (LDA) using these genes was performed for the CUP test datasets and the accuracy was estimated as described [21]. Heatmaps and a cluster dendrogram were then constructed using the Ward method [22].


Gene Expression Profile of CUP and Known Primary Cancers

A total of 237 genes were found to be either up-regulated or down-regulated by more than 2-fold between the normal lymph node and 30 CUP samples (training datasets). Of these, 59 genes with more than a 2.5-fold change (44 up-regulated and 15 down-regulated genes) are listed in Table 2. We designated the gene sets consisting of these CUP associated genes with >2 fold and >2.5 fold up-regulation or down-regulation as MCUP(2.0) and MCUP(2.5), respectively. Using these probe sets in MCUP(2.5), linear discriminant analysis (LDA) was performed for the CUP training datasets together with 2,364 cancers of various known types and 10 normal lymph nodes. As expected, all 2,404 samples were correctly discriminated. When the remaining 30 CUP samples (test datasets) were assessed using LDA that was modeled with the training datasets, 26 out of the 30 CUP samples were assigned correctly to “CUP”, while only the 4 samples were predicted as "the other cancer". Thus, the accuracy of CUP was validated to be 86.7%, indicating that the 59 genes selected were of statistically significance as having biological attributes of CUP.

Table 2. Genes identified as being up-regulatred or down-regulated in CUP.

Figure 2 shows the supervised clustering of all 60 CUP samples performed together with 2,364 cancers of various known types and 10 normal lymph nodes using the 59 genes. The CUP samples were split into 2 groups with lung adenocarcinoma (LAC) clustered in between (right most part of the heat map). The larger group consisted of 42 samples, while the smaller consisted of 15 samples. Only 3 CUP samples were not included in any of these groups and instead were included in the clusters for normal lymphoma, brain tumors, and ovarian cancer, respectively. These were among the 4 samples that were predicted as “the other cancer” in the LDA. The VAPA gene, which was overexpressed in most of the cancer samples but not in CUP or LAC, revealed a striking contrast between CUP/LAC and other samples, which may have influenced the clustering analysis. When we re-analyzed the data after excluding the VAPA gene, the grouping for CUP was unchanged, but the smaller group with 15 samples was no longer clustered with LAC (Figure S1). The mean gene expression profiles (GEPs) for CUP, normal lymphoma, and 24 known cancer types were compared to create a dendrogram representing the quantified relations among CUP and the known cancer types, which again showed the clustering of CUP together with LAC (Figure S2).

Figure 2. Heatmap representing the expressions of 59 genes with significant different expression in CUP compared with other cancer types or normal lymph nodes.

Genes are indicated on the right. The colored bar above the heatmap represents the different cancer types, and the legend key is on the left. On the heatmap, red represents up-regulated genes and green represents down-regulated genes, relative to the expression levels in normal lymph nodes, with the scale shown in the upper left corner. The gene expression profiling datasets for normal lymph nodes and 24 known cancer types other than CUP were obtained from publicly available sources, as described in the Materials and Methods.

Selection of CUP Associated Genes

Although the functions were diverse or unknown for the 44 up-regulated genes in the MCUP(2.5) datasets (Table 2), we found that 14 genes (S100A4, PRG1, S100A6, GSTP1, EIF5A, LGALS1, S100A11, PRKDC, VIM, CST3, TIMP1, YWHAZ, NEDD8, STK17A) could be characterized after a search using the keywords “metastasis” and “apoptosis”. Some of these genes were associated with the epithelial-to-mesenchymal transition (EMT), a function that has been increasingly recognized as a key step in cancer metastasis [23].

In the MCUP(2.5) dataset, 15 genes were down-regulated. Of these genes, we focused on CD24, KRAS and DICER1. The known functions of the above-mentioned up-regulated and down-regulated genes will be discussed in detail below.

Relative Expression of Up-Regulated Ribosomal Proteins

In the MCUP(2.5) dataset, we also identified 6 ribosomal proteins (RPL18A, RPS7, RPL11, RPS10, RPL36, and RPLP2). We found 11 more genes for ribosomal proteins (RPL24, RPL35, RPL35A, RPS20, RPL13A, RPL28, RPS26, RPS14, RPL27A, RPL19, and RPL29) in the MCUP(2.0) dataset. Ribosomal proteins are assembled into small and large ribosomal subunits. The small 40 S and large 60 S ribosomal subunits contain approximately 32 and 47 ribosomal proteins (known as RPS and RPL proteins), respectively [24]. The increased expression of ribosomal proteins has been associated with increased proliferation and growth; in some cases, however, increased expression has also been shown to suppress tumorigenesis [25], [26].

To examine whether ribosomal protein genes can be used as biomarkers to discriminate CUP from other cancer types, the mean GEPs for a total of 77 ribosomal protein genes were compared using clustering for CUP, normal lymphoma, and 24 known cancer types (Figure 3). The ribosomal protein genes that were up-regulated in CUP were also up-regulated in LAC.

Figure 3. Heatmap representing the expression of 77 ribosomal protein genes in CUP, normal lymph nodes, and other cancer types.

Ribosomal protein genes are indicated on the right. On the heatmap, purple represents up-regulated genes and green represents down-regulated genes, relative to the expression levels in normal lymph nodes, with the scale shown in the upper left. The genes that were exclusively overexpressed in CUP and lung adenocarcinoma are highlighted.

The relative mRNA expression levels of 4 ribosomal protein genes that were up-regulated in CUP (RPS7, RPL11, RPS10, and RPL36) were compared with the levels in normal lymphoma and 24 known cancer types (Figure 4). The 42 CUP samples that consistently contained large amounts of these mRNAs belonged to the larger CUP cluster, while the remaining 15 sample that showed relatively smaller amounts of these mRNAs belonged to the smaller cluster, as shown in Figure 2. As expected, the increased expressions of these mRNAs were also observed in LAC, but not in the other cancer types (Figure 4).

Figure 4. Relative expression levels for 4 ribosomal proteins.

The relative expression levels of (A)RPS7, (B)RPL11, (C)RPS10, and (D)RPL36 were compared using individual CUP samples (n = 60), the mean expression levels of known cancer types, and a normal lymph node samples (n = 25). The asinh-transformed values for each gene were used for the calculations.


Accumulating data sets from gene-expression microarray analyzed for various types of tumors have enabled the establishment of organ- and tumor-specific expression profiles that improve precise prediction of primary site of CUP [9], [10], [14], [15]. Our official phase 2 study to corroborate the feasibility of CUP prediction using our algorithm is currently ongoing and will provide genes that exhibit unique expression pattern in CUP. A compelling theory to explain CUP is that the primary cancer is microscopic and may disappear because of marked apoptosis after seeding metastases that are able to proliferate into more significant tumors in different tissues [27]. As a high metastasis potential and vulnerability to apoptosis would explain the properties of CUP well, we first searched for genes related to metastasis and apoptosis among all the genes that were up-regulated by more than 2.5-fold in the CUP samples (MCUP(2.5) dataset).

Of the 14 up-regulated genes that were found (S100A4, PRG1, S100A6, GSTP1, EIF5A, LGALS1, S100A11, PRKDC, VIM, CST3, TIMP1, YWHAZ, NEDD8, STK17A), three (S100A4, S100A6, S100A11) belong to a group of S100 proteins involved in the Ca 2+ signaling network and regulate a variety of intracellular activities including cell growth and motility [28]. The expressions of these genes are observed in several epithelial tumors and have been linked to metastasis [29], [30]. S100A4, together with VIM, has also been used as an EMT marker [31]. The overexpression of EIF5A induces the EMT, thereby promoting the tumor metastasis of colorectal and hepatocellular carcinoma [32]. Serglycin, a gene product of PRG1, is a proteoglycan that has been functionally identified as a significant regulator of metastasis in nasopharyngeal carcinoma (NPC) [33]. The elevated expression of Serglycin in NPC cells can mediate the level of vimentin (VIM) expression, which is not only a marker of the EMT, but also has an important role in the regulation of cellular migration [31], [34]. Lewis lung carcinoma cells in mice show metastasis to the lung when the cells express Galectin-1 (Gal-1), a large carbohydrate-binding protein encoded by LGALS1, suggesting novel targeting strategies for Gal-1 in cancer [35].

Both metastatic cells and drug-resistant cells have similar gene expression patterns of survival-related molecules, suggesting that metastatic cancer may be difficult to treat because of resistance to anticancer drugs. DNA-dependent protein kinase (DNA-PK), a gene product of PRKDC, is one of the proteins up-regulated in several metastatic and drug-resistant cancer cells [36]. Because the up-regulation of DNA-PK was observed in the CUP patients in our cohort, who had never been treated with chemotherapy, DNA-PK may indicate essential resistance, rather than acquired resistance, to chemotherapy. GSTP1 has also been postulated in several cancer types to enhance the metastatic potential and the development of resistance to drugs that induce reactive oxygen species (ROS), such as paclitaxel and cisplatin [37], [38]. Other genes up-regulated in CUP also reveal a significant role in chemoresistance and may be linked to the metastatic potential. Breast cancer cells overexpressing TIMP-1, a well-known inhibitor of matrix metalloproteinase, exhibit a reduced sensitivity to the chemotherapeutic drugs paclitaxel and epirubicin through the activation of transcription factor NF-κB [39]. The knocked-down expression of 14-3-3 ζ, a gene product of YWHAZ, sensitizes head and neck cancer cells to chemotherapy [40]. A small molecule inhibitor of NEDD8 activating enzyme (NAE) may be active against tumors that are resistant to other chemotherapeutic agents [41].

Unlike the hitherto described genes, cystatin C (CST-3) and STK17A function as direct pro-apoptotic factors by antagonizing TGF-β signaling and by modulating ROS, respectively. Cystatin C has been shown to interact with the TGF-β type II receptor, thereby preventing TGF-β binding and subsequent EMT induction [42]. TGF-β has been accepted as a main initiator of EMT; however, NF-κB was recently found to promote EMT in some cells that are unresponsive to TGF-β because they lack functional SMAD4, representing an alternative pathway leading to EMT that can replace TGF-β signaling [43]. NF-κB signaling may predominately induce EMT in CUP. Both TIMP-1, which can activate NF-κB, and vimentin, which is activated by NF-κB, were among the genes (proteins) that were up-regulated in CUP as described above, making this hypothesis more likely [39], [43]. STK17A is up-regulated in response to oxidative stress in a p53-dependent manner [44]. Since STK17A is known as a positive regulator of the apoptotic pathway and its expression level in colorectal carcinomas is enhanced in lesions with lymph node metastasis, the apoptotic process could be involved in the node metastasis of carcinomas, including CUP [45].

Of the 15 down-regulated genes in the MCUP(2.5) dataset, CD24, KRAS and DICER1 are of particular interest. CD24 is the most widely used marker, together with CD44, for identifying tumor-initiating cells in breast carcinomas. CD44+/CD24−/low breast cancer cells have the ability to metastasize, since the enrichment of these stem-like cells is significantly observed in patients with positive lymph nodes [46]. A subset of kras mutant cancer cells exhibit “kras addiction” and have a differentiated epithelial phenotype. The induction of EMT has been shown to convert kras-dependent cancer cells to kras-independent cells, which do not require the continued expression of kras [47]. Dicer1 functions as a haploinsufficient tumor suppressor gene [48]. Frequent loss of one allele of Dicer1 has been observed in several different tumor types causing a global reduction of steady-state micro RNA levels that could be functionally suppressive to the oncogenesis and metastasis of CUP.

The increased expression of several ribosomal proteins was found in CUP. Whether these changes in expression are causally related to the generation of CUP is unknown. In some cases, the overexpression of ribosomal proteins, including RPL5, RPL11, RPL23 and RPS7 has been shown to suppress tumorigenesis [49], [50]. These proteins activate p53 by binding to MDM2 and inhibiting MDM2-mediated p53 ubiquitination and degradation in response to nucleolar stress (also called ribosomal stress). RPL11 and RPS7 were recently shown to be required for p53 activation induced by DNA-damaging agents [51], suggesting that these ribosomal proteins may play a crucial role in p53 activation in response to diverse stressors. Furthermore, neddylation, the process by which the ubiquitin-like protein NEDD8 is conjugated to its target, is essential for RPL11's role in the mediation of p53 signaling [49]. Interestingly, these two ribosomal proteins and NEDD8 were included in our MCUP(2.5) dataset. The tumor suppressor function performed by these proteins may be related to the vulnerability to apoptosis that CUP (at the primary site) exhibits as one of its properties.

For functional analyses of the identified genes, overexpression or knockdown experiments using appropriate cell lines would be plausible to pursue if the gene of interest confers change in growth or in metastatic ability to the cells. The metastatic process can be evaluated in vitro by monitoring cell invasion through Matrigel and adhesion of cells to plates, etc. Synthetic inhibitors specific to Gal-1, DNA-PK and 14-3-3 ζ have been developed [52][54]. Thus, it will be intriguing to investigate the effect of these inhibitors on the cells overexpressing the respective gene in vitro or in vivo, which may lead to targeted therapy for CUP.

To our surprise, the gene expression profile (GEF) of CUP closely resembled that of lung adenocarcinoma (LAC), which may simply reflect the relatively high metastatic potential of LAC. In a study using 18F-fluoro-2-deoxyglucose positron emission tomography (FDG-PET), the most commonly detected location of the primary tumor in patients with CUP was the lung [55]. In CUP, the primary cancer and its metastasis (-ses) behave very differently in respect to proliferation, leading to the assumption that the molecular profiles of CUP specimens from the two sites would differ. We are unable to compare these differences because the primary cancer is unidentifiable. A differential gene expression analysis using primary and metastatic tumor tissues from advanced lung cancer patients may provide some clues to this question.

In conclusion, we identified several genes that were up-regulated in CUP and that may contribute to the acquisition of a metastatic phenotype as well as resistance to anticancer drugs in many cases. Proapoptotic factors were also identified. The combinational effects of the multiple functions of genes that are highly expressed in CUP could be involved in regulating CUP behaviors, such as apoptosis and metastasis. Immunohistochemical-based or PCR-based validation of the candidate genes is needed to refine the molecular classification of CUP.

Supporting Information

Figure S1.

Heatmap constructed as in Figure 1 but excluding the VAPA gene.


Figure S2.

Cluster dendrogram for each cancer type. Clustering analysis was done using the Ward method and 77 ribosomal protein genes.



We thank Takuya Wada for the microarray scanning and data analysis, Tomoko Kitayama for technical support, and Marco DeVelasco for critically reading the manuscript. This work was supported by NPO West Japan Oncology Group.

Author Contributions

Conceived and designed the experiments: IK TK YK K. Nakagawa K. Nishio. Performed the experiments: YF YK KS. Analyzed the data: IK YF TA. Contributed reagents/materials/analysis tools: TK K. Matsumoto MT KT YT NY AT NM H. Mukai H. Minami NC YY K. Miwa Shin Takahashi Shunji Takahashi. Wrote the paper: IK YF K. Nishio.


  1. 1. Greco FA, Burris HA, Litchy S, Barton JH, Bradof JE, et al. (2002) Gemcitabine, carboplatin, and paclitaxel for patients with carcinoma of unknown primary site: a Minnie Pearl Cancer Research Network study. J Clin Oncol 20: 1651–6.
  2. 2. Pavlidis N, Briasoulis E, Hainsworth J, Greco FA (2003) Diagnostic and therapeutic management of cancer of an unknown primary. Eur J Cancer 39: 1990–2005.
  3. 3. Hainsworth JD, Spigel DR, Farley C, Thompson DS, Shipley DL, et al. (2007) Phase II trial of bevacizumab and erlotinib in carcinomas of unknown primary site: the Minnie Pearl Cancer Research Network J Clin Oncol. 25: 1747–52.
  4. 4. Schneider BJ, El-Rayes B, Muler JH, Philip PA, Kalemkerian GP, et al. (2007) Phase II trial of carboplatin, gemcitabine, and capecitabine in patients with carcinoma of unknown primary site. Cancer 110: 770–5.
  5. 5. Pimiento JM, Teso D, Malkan A, Dudrick SJ, Palesty JA (2007) Cancer of unknown primary origin: a decade of experience in a community-based hospital. Am J Surg 194: 833–7 (Disc 7–8)..
  6. 6. Abbruzzese JL, Abbruzzese MC, Lenzi R, Hess KR, Raber MN (1995) Analysis of a diagnostic strategy for patients with suspected tumors of unknown origin. J Clin Oncol 13: 2094–103.
  7. 7. Varadhachary GR, Raber MN, Matamoros A, Abbruzzese JL (2008) Carcinoma of unknown primary with a colon-cancer profile-changing paradigm and emerging definitions. Lancet Onc 9: 596–9.
  8. 8. Abbruzzese JL, Abbruzzese MC, Hess KR, Hess KR, Raber MN, et al. (1994) Unknown primary carcinoma: natural history and prognostic factors in 657 consecutive patients. J Clin Oncol 12: 1272–80.
  9. 9. Dennis JL, Hvidsten TR, Wit EC, Komorowski J, Bell AK, et al. (2005) Markers of adenocarcinoma characteristic of the site of origin: development of a diagnostic algorithm. Clin Cancer Res 11: 3766–72.
  10. 10. Bloom G, Yang IV, Boulware D, Kwong KY, Coppola D, et al. (2004) Multi-platform, multi-site, microarray-based human tumor classification. Am J pathol 164: 9–16.
  11. 11. Giordano TJ, Shedden KA, Schwartz DR, Kuick R, Taylor JM, et al. (2001) Organ-specific molecular classification of primary lung, colon, and ovarian adenocarcinomas using gene expression profiles. Am J Pathol 159: 1231–8.
  12. 12. Ramaswamy S, Tamayo P, Rifkin R, Mukherjee S, Yeang CH, et al. (2001) Multiclass cancer diagnosis using tumor gene expression signatures. Proc Natl Acad Sci USA 98: 15149–54.
  13. 13. Shedden KA, Taylor JM, Giordano TJ, Kuick R, Misek DE, et al. (2003) Accurate molecular classification of human cancers based on gene expression using a simple classifier with a pathological tree-based framework. Am J Pathol 163: 1985–95.
  14. 14. Su AI, Welsh JB, Sapinoso LM, Kern SG, Dimitrov P, et al. (2001) Molecular classification of human carcinomas by use of gene expression signatures. Cancer Res 61: 7388–93.
  15. 15. Tothill RW, Kowalczyk A, Rischin D, Bousioutas A, Haviv I, et al. (2005) An expression-based site of origin diagnostic method designed for clinical application to cancer of unknown origin. Cancer Res 65: 4031–40.
  16. 16. Huber W, von Heydebreck A, Sultmann H, Poustka A, Vingron, et al (2002) Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18: S96–S104 (Suppl 1)..
  17. 17. Kurahashi I, Ito Y, Matsuyama Y, Ohashi Y, Nishio K (2007) Evaluation of normalization methods for cDNA macroarray data (in Japanese). J Japan Statist Soc 36: 147–63.
  18. 18. Durbin B, Rocke DM (2003) Estimation of transformation parameters for microarray data. Bioinformatics 19: 1360–7.
  19. 19. Laird NM, Ware JH (1982) Random-Effects Models for Longitudinal Data. Biometrics 38: 963–74.
  20. 20. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Statist Soc 57: 289–300.
  21. 21. Hastie T, Tibshirani R, Friedman J (2009) The Element of Statistical Learning: Data mining, Inference, and Prediction (ed 2).New York, NY: Springer.
  22. 22. Anderberg MR (1973) Cluster analysis for applications. New York, NY: Academic Press.
  23. 23. Mani SA, Guo W, Liao MJ, Eaton EN, Ayyanan A, et al. (2008) The epithelial-mesenchymal transition generates cells with properties of stem cells. Cell 133: 704–15.
  24. 24. Rodnina MV, Wintermeyer W (2009) Recent mechanistic insights into eukaryotic ribosomes. Curr Opin Cell Biol 21: 435–43.
  25. 25. Ruggero D, Pandolfi PP (2003) Does the ribosome translate cancer? Nat Rev Cancer 3: 179–92.
  26. 26. Dai MS, Lu H (2008) Crosstalk between c-Myc and ribosome in ribosomal biogenesis and cancer. J Cell Biochem 105: 670–7.
  27. 27. Park JS, Yim JJ, Kang WJ, Chung JK, Yoo CG, et al. (2011) Detection of primary sites in unknown primary tumors using FDG-PET or FDG-PET/CT. BMC Res Notes 4: 56.
  28. 28. Schafer BW, Heizmann CW (1996) The S100 family of EF-hand calcium-binding proteins: functions and pathology. Trends Biochem Sci 21: 134–40.
  29. 29. Maelandsmo GM, Florenes VA, Mellingsaeter T, Hovig E, Kerbel RS, et al. (1997) Differential expression patterns of S100A2, S100A4 and S100A6 during progression of human malignant melanoma. Int J cancer 74: 464–9.
  30. 30. Melle C, Ernst G, Schimmel B, Bleul A, von Eggeling F (2008) Colon-derived liver metastasis, colorectal carcinoma, and hepatocellular carcinoma can be discriminated by the Ca(2+)-binding proteins S100A6 and S100A11. PloS one 3: e3767.
  31. 31. Strutz F, Okada H, Lo CW, Danoff T, Carone RL, et al. (1995) Identification and characterization of a fibroblast marker: FSP1. J Cell Biol 130: 393–405.
  32. 32. Tang DJ, Dong SS, Ma NF, Xie D, Chen L, et al. (2010) Overexpression of eukaryotic initiation factor 5A2 enhances cell motility and promotes tumor metastasis in hepatocellular carcinoma. Hepatology 51: 1255–63.
  33. 33. Li XJ, Ong CK, Cao Y, Xiang YQ, Shao JY, et al. (2011) Serglycin is a theranostic target in nasopharyngeal carcinoma that promotes metastasis. Cancer Res 71: 3162–72.
  34. 34. Ivaska J, Pallari HM, Nevo J, Eriksson JE (2007) Novel functions of vimentin in cell adhesion, migration, and signaling. Exp Cell Res 313: 2050–62.
  35. 35. Banh A, Zhang J, Cao H, Bouley DM, Kwok S, et al. (2011) Tumor galectin-1 mediates tumor growth and metastasis through regulation of T-cell apoptosis. Cancer Res 71: 4423–31.
  36. 36. Um JH, Kwon JK, Kang CD, Kim MJ, Ju DS, et al. (2004) Relationship between antiapoptotic molecules and metastatic potency and the involvement of DNA-dependent protein kinase in the chemosensitization of metastatic human cancer cells by epidermal growth factor receptor blockade. J Pharmcol Exp Ther 11: 1062–70.
  37. 37. Raj L, Ide T, Gurkar AU, Foley M, Schenone M, et al. (2011) Selective killing of cancer cells by a small molecule targeting the stress response to ROS. Nature 475: 231–4.
  38. 38. Jiffar T, Yilmaz T, Lee J, Hanna E, El-Naggar A, et al. (2011) KiSS1 mediates platinum sensitivity and metastasis suppression in head and neck squamous cell carcinoma. Oncogene 30: 3163–73.
  39. 39. Fu ZY, Lv JH, Ma CY, Yang DP, Wang T (2011) Tissue inhibitor of metalloproteinase-1 decreased chemosensitivity of MDA-435 breast cancer cells to chemotherapeutic drugs through the PI3K/AKT/NF-κB pathway. Biomed Pharmacother 65: 163–7.
  40. 40. Macha MA, Matta A, Chauhan S, Siu KM, Ralhan R (2010) 14-3-3 zeta is a molecular target in guggulsterone induced apoptosis in head and neck cancer cells. BMC cancer 10: 655.
  41. 41. Soucy TA, Dick LR, Smith PG, Milhollen MA, Brownell JE (2010) The NEDD8 conjugation pathway and its relevance in cancer biology and therapy. Genes & Cancer 1: 708–16.
  42. 42. Sokol JP, Neil JR, Schiemann BJ, Schiemann WP (2005) The use of cystatin C to inhibit epithelial-mesenchymal transition and morphological transformation stimulated by transforming growth factor-beta. Breast Cancer Res 7: R844–853.
  43. 43. Maier HJ, Schmidt-Strassburger U, Huber MA, Wiedemann EM, Beug H, et al. (2010) NF-kappaB promotes epithelial-mesenchymal transition, migration and invasion of pancreatic carcinoma cells. Cancer lett 295: 214–28.
  44. 44. Mao P, Hever MP, Niemaszyk LM, Haghkerdar JM, Yanco EG, et al. (2011) Serine/threonine kinase 17A is a novel p53 target gene and modulator of cisplatin toxicity and reactive oxygen species in testicular cancer cells. J Biol Chem 286: 19381–91.
  45. 45. Bandres E, Catalan V, Sola I, Honorato B, Cubedo E, et al. (2004) Dysregulation of apoptosis is a major mechanism in the lymph node involvement in colorectal carcinoma. Oncol Rep 12: 287–92.
  46. 46. Al-Hajj M, Wicha MS, Benito-Hernandez A, Morrison SJ, Clarke MF (2003) Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci USA 100: 3983–8.
  47. 47. Singh A, Greninger P, Rhodes D, Koopman L, Violette S, et al. (2009) A gene expression signature associated with "K-Ras addiction" reveals regulators of EMT and tumor cell survival. Cancer Cell 15: 489–500.
  48. 48. Kumar MS, Pester RE, Chen CY, Lane K, Chin C, et al. (2009) Dicer1 functions as a haploinsufficient tumor suppressor. Genes Dev 23: 2700–4.
  49. 49. Sun XX, Wang YG, Xirodimas DP, Dai MS (2010) Perturbation of 60 S ribosomal biogenesis results in ribosomal protein L5- and L11-dependent p53 activation. J Biol Chem 285: 25812–21.
  50. 50. de Marval PM, Lutfeali S, Jin JY, Leshin B, Selim MA, et al. (2011) CYLD inhibits tumorigenesis and metastasis by blocking JNK/AP1 signaling at multiple levels. Cancer Prev Res 4: 851–9.
  51. 51. Zhu Y, Poyurovsky MV, Li Y, Biderman L, Stahl J, et al. (2009) Ribosomal protein S7 is both a regulator and a substrate of MDM2. Molecular Cell 35: 316–26.
  52. 52. Iurisci I, Cumashi A, Sherman AA, Tsvetkov YE, Tinari N, et al. (2009) Synthetic inhibitors of Galectin-1 and -3 selectively modulate homotypic cell aggregation and tumor cell apoptosis. Anticancer Res 29: 403–10.
  53. 53. Zhao Y, Thomas HD, Batey MA, Cowell IG, Richardson CJ, et al. (2006) Preclinical evaluation of a potent novel DNA-dependent protein kinase inhibitor NU7441. Cancer Res 66: 5354–62.
  54. 54. Matta A, Siu KM, Ralhan R (2012) 14-3-3 zeta as novel molecular target for cancer therapy. Expert Opin Ther Targets 16: 515–23.
  55. 55. Kwee TC, Kwee RM (2009) Combined FDG-PET/CT for the detection of unknown primary tumors: systematic review and meta-analysis. Eur Radiol 19: 731–44.