Gene expression profiling of cancer stem cell in human lung adenocarcinoma A549 cells (original) (raw)

Abstract

Background

The studies on cancer-stem-cells (CSCs) have attracted so much attention in recent years as possible therapeutic implications. This study was carried out to investigate the gene expression profile of CSCs in human lung adenocarcinoma A549 cells.

Results

We isolated CSCs from A549 cell line of which side population (SP) phenotype revealed several stem cell properties. After staining the cell line with Hoechst 33342 dye, the SP and non-side population (non-SP) cells were sorted using flow cytometric analysis. The mRNA expression profiles were measured using an Affymetrix GeneChip® oligonucleotide array. Among the sixty one differentially expressed genes, the twelve genes inclusive three poor prognostic genes; Aldo-keto reductase family 1, member C1/C2 (AKR1C1/C2), Transmembrane 4 L six family member 1 nuclear receptor (TM4SF1), and Nuclear receptor subfamily 0, group B, member 1 (NR0B1) were significantly up-regulated in SP compared to non-SP cells.

Conclusion

This is the first report indicating the differences of gene expression pattern between SP and non-SP cells in A549 cells. We suggest that the up-regulations of the genes AKR1C1/C2, TM4SF1 and NR0B1 in SP of human adenocarcinoma A549 cells could be a target of poor prognosis in anti-cancer therapy.

Background

Cancer stem cell hypothesis is the tumoral cells which have stem cell features such as self-renewal, high migration capacity, drug resistance, and aberrant differentiation which constitute the heterogeneous population of tumor [1,2]. Tissue-specific stem cells are defined by their ability to self-renew and to produce the well differentiated and functional cells within an organ. Differentiated cells are generally short-lived; in skin and blood for example, they are produced from a small pool of long-lived stem cells that last throughout the life [3-6]. Therefore, stem cells are necessary for tissue development, replacement, and repair [7]. On the other hands, the longevity of stem cells make them susceptible to accumulating genetic damage and thereby representing the growth root for cancer recurrence following treatment [8]. It was reported that some of the tumor stem cells can survive chemotherapy and support re-growth of the tumor mass [9].

Cancer stem cells (CSCs) were first identified in 1990s in hematological malignancies, mainly acute myelogenous leukemia (AML) and also in other subtypes like AML M0, M1, M2, M4, and M5, chronic myeloid leukemia (CML), acute lymphoblastic leukemia (ALL), and multiple myeloma [10,11]. CSCs are also known in solid tumors like breast, brain, lung, prostrate, testis, ovary, stomach, colon, skin, liver, and pancreas [12-17]. A character of stem cells, termed "side population (SP)", has been identified using Hoechst 33342 dye. The flow cytometric analysis makes sorting possible either to SP or non-SP cells. The SP cells have been isolated from various types of adult tissue where they demonstrate stem cell activity [18-23]. The findings of these previous studies suggest that the SP phenotype represents a common feature of stem cells.

We performed our work on human lung adenocarcinoma A549 cells (of which SP phenotype revealed several stem cell properties [24]) to identify the genes, which make the CSCs of poor prognostic phenotype and evaluate the gene expression intensities of SP and non-SP cells using oligonucleotide micro-array. The reasons why the A549 cell line was selected, because it has a relatively high proportion of SP cells compared to other cell lines [25] and is more chemo-resistant particularly to platinum drugs [26].

Results

The distinct gene regulations in SP cells

We sorted A549 cell line to SP and non-SP cells (Fig. 1) and compared the gene expression intensities of both cells. Official symbols and gene names were used in accordance with the symbol and name lists approved by HUGO (Human genome organization) Gene Nomenclature Committee (Table 1) [27]. Following data analysis, 12 genes were considered as up-regulated in SP cells (TM4SF1 has 2 probe ID) (fold changes are shown in Table 2), whereas, 49 genes were down-regulated (Fig. 2). Since we focused on distinct gene regulations, the student's _t'_-test was not employed to prevent loss of up-regulated genes in all of three chip data, though it had large chip variations.

Figure 1.

Sorting of SP and non-SP cells by FACSVantage SE.

Table 1.

The approved gene symbols and names in reference to HUGO Gene Nomenclature

Table 2.

Gene list up-regulated in SP cells compared to non-SP cells

Figure 2.

Gene clustering of up-regulated genes in SP and non-SP cells. After normalizing each chip to the 50th percentile of the measurements taken that chip, gene-probes scored less than 0.1 either in SP or non-SP were excluded from data analysis. Only matched up-regulated genes in SP compared to non- SP cells are selected in each step of chip data analysis. The 12 genes and 46 genes were considered as up-regulated in SP and non-SP cells, respectively.

Validation of gene regulations

To confirm the fold changes of AKR1C1 in chip data, quantitative real time – reverse transcriptase PCR was employed. The relative fold changes in SP compared to non-SP cells were 3.11 ± 0.92 and 2.88 ± 0.17 in microarray and qrt- rtPCR, respectively (Fig. 3).

Figure 3.

Relative fold changes of AKR1C1/C2 gene in SP and non-SP cells. The fold changes of AKR1C1/C2 between SP and non-SP cells were compared using GeneChip data and quantitative real time-reverse transcriptase PCR. The data (n = 3) were presented as mean ± SD.

Discussion

Based on the cancer stem cell hypothesis, we assumed that the up-regulation of certain genes that are related to poor prognosis (high migration capacity or drug resistance) in SP of cancer cells could be a target for therapeutic index. In the present study, we found some genes that are related to drug resistance, such as AKR1C1/C2 and NR0B1, or cancer metastasis, such as TM4SF1, were up-regulated in SP cells of human lung adenocarcinoma A549 cell line. Furthermore, the up-regulated gene, ABCG2, has been noticed to be as an indicator for sorting SP cells by Hoechst 33342 staining [24]. It was reported that ABCG2 pumping out the drugs was associated with multi-drug resistance in many cancers [28,29] and/or effects higher levels of DNA repair and hence lowered the ability to apoptosis [30].

AKR1C belongs to a superfamily of monomeric, cytosolic NADP(H)-dependent oxidoreductases that catalyzes the metabolic reduction and either activate or inactivate several xenobiotics [31,32]. In humans, at least four isoforms of AKR1C (AKR1C1~4) have been identified [33]. AKR1C1/AKR1C3 was known to inactivate progesterone, which could alter the progesterone/estrogen ratio in certain cancers [34,35]. Additionally, AKR1C1/C2 inhibitors were reported as potential anti-neoplastic agents [36,37]. The high expression of AKR1C was considered as a poor prognostic factor in patients with non-small-cell lung cancer [38], and it was enriched in hepatocellular carcinoma than normal hepatic cells [39]. A previous study evaluated the relationship between the AKR1C and drug resistance and revealed that the over expression of AKR1C1/C2 led to drug resistance in non-small-cell lung cancer cells [26]. A public database (Gene Expression Omnibus (GEO), NCBI) [40] has shown significant up-regulation of AKR1C1 in smokers (Figure 4A). Similar trends were also reported in lung cancer patients [41,42]. This means that smoking can alter the regulation of certain genes related to poor prognosis such as AKR1C1. Moreover, AKR1C1 was suggested as a marker of stem-like cells in thyroid cancer cell lines, though it was not proved by the authors [41].

Figure 4.

The up-regulation of AKR1C1 and TM4SF1 in smokers (derived from GEO public database). AKR1C1 (A) and TM4SF1 (B) were increased in current smoker compared to former or never smokers. TM4SF1 was increased in metastatic form compared to primary form of colon cancer (C).

From the data of GEO, the TM4SF1 gene, which is believed to be involved in cancer invasion and metastasis [44] was also up-regulated in smokers (Figure 4B), and metastatic form of colon cancer patients compared to primary form of colon cancer patients (Figure 4C). TM4SF1 was also suggested as a possible marker of stem-like cells in thyroid cancer cells [41]. However, we could not find out the GEO data that match to tumor genesis of the up-regulated NR0B1, though up-regulated NR0B1 gene was required for the transformed phenotype of certain sarcoma [45].

Conclusion

It is still unclear how the cancer stem cells express poor prognostic phenotype in cancer, but we found for the first time that several genes related to chemo-resistance and metastasis are up-regulated in SP cells compared to non-SP cells. Therefore, enrichment of AKR1C1/C2, TM4SF1 and NR0B1 in SP of A549 cells might be a target of poor prognosis in cancer therapy.

Methods

Cell culture

A549 cells, a representative human lung adenocarcinoma cell line, were obtained from American Type Culture Collection (ATCC, VA, USA). The cells were cultured on F-12K nutrient mixture, Kaighn's modification (1×) liquid (Gibco, CA, USA) supplemented with 10% fetal bovine serum (FBS) (Genetron Life Technology, INC., CA, USA) and 1% of penicillin/streptomycin (P/S) (Gibco, CA, USA) on standard plastic tissue culture dishes (SPL Life Sciences, Seoul, Republic of Korea) and incubated in an atmosphere of 95% air/5% CO2 at 37°C.

Fluorescence activated cell sorting (FACS) for SP and non-SP cells

The cells were detached with trypsin (Gibco, CA, USA), washed with phosphate buffer solution (PBS)/2% FBS, and resuspended at 1 × 106 cells per ml in pre-warmed Hanks' balanced salt solution (HBSS; Gibco, CA, USA)/10% FBS. Hoechst 33342 dye (Sigma, St. Louis, MO, USA) was then added to this portion (final concentration: 5 μg/ml), and incubation continued for 90 min at 37°C. After washing with PBS/2% FBS, the cells re-suspended in ice-cold HBSS/10% FBS were labeled with 2 μg/ml propidium iodide (Sigma, St. Louis, MO, USA) to distinguish live from dead cells prior to analysis. SP analysis and sorting were done using a FACSVantage SE (BD Biosciences, CA, USA). The Hoechst 33342 dye excited at 350 nm using UV laser and the DM610 SP was used to distinguish the red from the blue fluorescence signals. The EF675-LP (Hoechst Red) and BP450/20 nm (Hoechst Blue) filters were then installed in front of the PMT detector.

RNA isolation and oligonucleotide microarray analysis

We prepared total RNA from approximately 2 × 106 SP or non-SP cells with Qiagen RNeasy Mini kit (Qiagen, CA, USA) according to the instruction of the manufacturer. The RNAs were subjected to GeneChip® expression array in full commercial service with two-cycle target labeling (SeouLin Bioscience, Seoul, Republic of Korea). Briefly, cDNA were synthesized from total RNA using T7-Oligo (dT) primers. Using that cDNA, biotinylated cRNA was then synthesized. Fifteen μg of the labeled cRNA was hybridized to a Human Genome U133 Plus 2.0 Array (Affymetrix, CA, USA). Array image was scanned and analyzed using Genechip operating software (GCOS) (Affymetrix, CA, USA).

Quantitative real-time rt-PCR (qrt-rtPCR)

To validate the fold changes in the expression intensity of SP and non-SP cells in microarray data, we performed qrt-rtPCR using SYBR Premix Ex Taq Perfect Real Time kit (TaKaRa Bio, Ohtsu, Japan) in a SmartCycler (Cepheid, CA, USA) according to the manufacturer's instruction. The cycle threshold value, which was determined using second derivative, was used to calculate the normalized expression of the indicated genes using Smartcycler Software (Cepheid, CA, USA). The following primer pairs were used: GAPDH (as an internal control); F-primer 5'CGACCACTTTGTCAAGCTCA3' and R-primer 5'AGGGGAGATTCAGTGTGGTG3', AKR1C1;AKR1C2; F-primer 5'GTGGAAGCTGACCAGGTTGT3' and R-primer 5'AAGCCGTGTTCTTTCTGCTG3'.

Microarray data analysis

We used GeneSpring GX 7.3.1 software (Agilent Technologies, CA, USA) to normalize and analyze the microarray data. Following normalization of each chip to the 50th percentile of the measurements taken that chip, probes (genes) scored less than 0.1 both in SP and non-SP cells were excluded from the data analysis. Only matched up-regulated genes in SP and non-SP cells were selected from each step of chip (gene expression microarray) data analysis. More than 2 fold changes in all chip data were considered as up-regulated. Standard curve method was used to analyze qrt-rtPCR for confirmation of fold changes in chip data.

Competing interests

The author(s) declare that they have no competing interests.

Authors' contributions

DS and HS conceived the study. DS, JS, HC and HY carried out the sample preparation, gene expression analyses and quantitative real time RT-PCR. DK contributed to stem cell preparation. KS, IC, JK, AMAE and HS participated in the design, reviewed all data, and contributed in the preparation of the manuscript. All authors read and approved the final manuscript.

Acknowledgments

Acknowledgements

This work was supported by MOEHRD Basic Research Promotion Fund (KRF-2005-003-E00265) and the BK21 Program of Ministry of Education, Republic of Korea.

Contributor Information

Dong-Cheol Seo, Email: sdc97@hanmail.net.

Ji-Min Sung, Email: lava0331@hanmail.net.

Hee-Jung Cho, Email: hjcho@konkuk.ac.kr.

Hee Yi, Email: yahhee@hanmail.net.

Kun-Ho Seo, Email: bracstu3@konkuk.ac.kr.

In-Soo Choi, Email: ischoi@konkuk.ac.kr.

Dong-Ku Kim, Email: dokukim@hanmail.net.

Jin-Suk Kim, Email: jskim@konkuk.ac.kr.

Abd El-Aty AM, Email: abdelaty44@hotmail.com.

Ho-Chul Shin, Email: hshin@konkuk.ac.kr.

References

1. Graziano A, d'Aquino R, Tirino V, Desiderio V, Rossi A, Pirozzi G. The stem cell hypothesis in head and neck cancer. J Cell Biochem. 2007. DOI 10.1002/jcb.21436. [DOI] [PubMed]
2. Reya T, Morrison SJ, Clarke MF, Weissman IL. Stem cells, cancer, and cancer stem cells. Nature. 2001;414:105–111. doi: 10.1038/35102167. [DOI] [PubMed] [Google Scholar]
3. Dexter TM, Spooncer E. Growth and differentiation in the hemopoietic system. Annu Rev Cell Biol. 1987;3:423–441. doi: 10.1146/annurev.cb.03.110187.002231. [DOI] [PubMed] [Google Scholar]
4. Jones PH. Epithelial stem cells. Bioessays. 1997;19:683–690. doi: 10.1002/bies.950190808. [DOI] [PubMed] [Google Scholar]
5. Watt FM. Epidermal stem cells: markers, patterning and the control of stem cell fate. Philos Trans R Soc Lond B Biol Sci. 1998;353:831–837. doi: 10.1098/rstb.1998.0247. [DOI] [PMC free article] [PubMed] [Google Scholar]
6. Orkin SH. Diversification of haematopoietic stem cells to specific lineages. Nat Rev Genet. 2000;1:57–64. doi: 10.1038/35049577. [DOI] [PubMed] [Google Scholar]
7. Fuchs E, Segre JA. Stem cells: a new lease on life. Cell. 2000;100:143–155. doi: 10.1016/S0092-8674(00)81691-8. [DOI] [PubMed] [Google Scholar]
8. Clarke RB. Isolation and characterization of human mammary stem cells. Cell Prolif. 2005;38:375–386. doi: 10.1111/j.1365-2184.2005.00357.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
9. Dean M, Fojo T, Bates S. Tumor stem cells and drug resistance. Nat Rev Cancer. 2005;5:275–84. doi: 10.1038/nrc1590. [DOI] [PubMed] [Google Scholar]
10. Pan CX, Zhu W, Cheng L. Implications of cancer stem cells in the treatment of cancer. Future Oncol. 2006;2:723–731. doi: 10.2217/14796694.2.6.723. [DOI] [PubMed] [Google Scholar]
11. Bonnet D, Dick JE. Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat Med. 1997;3:730–737. doi: 10.1038/nm0797-730. [DOI] [PubMed] [Google Scholar]
12. Kim CF, Jackson EL, Woolfenden AE, Lawrence S, Babar I, Vogel S, Crowley D, Bronson RT, Jacks T. Identification of bronchioalveolar stem cells in normal lung and lung cancer. Cell. 2005;121:811–813. doi: 10.1016/j.cell.2005.06.004. [DOI] [PubMed] [Google Scholar]
13. Singh SK, Hawkins C, Clarke ID, Squire JA, Bayani J, Hide T, Henkelman RM, Cusimano MD, Dirks PB. Identification of human brain tumor initiating cells. Nature. 2004;19:396–401. doi: 10.1038/nature03128. [DOI] [PubMed] [Google Scholar]
14. Al-Hajj M, Wicha MS, Benito-Hernandez A, Morrison SJ, Clarke MF. Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci USA. 2003;100:3983–3988. doi: 10.1073/pnas.0530291100. [DOI] [PMC free article] [PubMed] [Google Scholar]
15. Al-Hajj M, Clarke MF. Self-renewal and solid tumor stem cells. Oncogene. 2004;23:7274–7282. doi: 10.1038/sj.onc.1207947. [DOI] [PubMed] [Google Scholar]
16. Woodward WA, Chen MS, Behbod F, Rosen JM. On mammary stem cells. Cell Sci. 2005;118:3585–3594. doi: 10.1242/jcs.02532. [DOI] [PubMed] [Google Scholar]
17. Moore KA, Lemischka IR. Stem cells and their niches. Science. 2006;311:1880–1885. doi: 10.1126/science.1110542. [DOI] [PubMed] [Google Scholar]
18. Goodell MA, Brose K, Paradis G, Conner AS, Mulligan RC. Isolation and functional properties of murine hematopoietic stem cells that are replicating in vivo. J Exp Med. 1996;183:1797–1806. doi: 10.1084/jem.183.4.1797. [DOI] [PMC free article] [PubMed] [Google Scholar]
19. Sumitra B, John DJ, Ani VD, Wallace BT, Charles K, Jackson J, Shantaram J, Iqbal A. Direct identification and enrichment of retinal stem cells/progenitors by hoechst dye efflux assay. Invest Ophthalmol Vis Sci. 2003;44:2764–2773. doi: 10.1167/iovs.02-0899. [DOI] [PubMed] [Google Scholar]
20. Jackson KA, Mi T, Goodell MA. Hematopoietic potential of stem cells isolated from murine skeletal muscle. Proc Natl Acad Sci USA. 1999;96:14482–14486. doi: 10.1073/pnas.96.25.14482. [DOI] [PMC free article] [PubMed] [Google Scholar]
21. Hulspas R, Quesenberry PJ. Characterization of neurosphere cell phenotypes by flow cytometry. Cytometry. 2000;40:245–250. doi: 10.1002/1097-0320(20000701)40:3<245::AID-CYTO10>3.0.CO;2-5. [DOI] [PubMed] [Google Scholar]
22. Asakura A, Rudnicki MA. Side population cells from diverse adult tissues are capable of in vitro hematopoietic differentiation. Exp Hematol. 2002;30:1339–1345. doi: 10.1016/S0301-472X(02)00954-2. [DOI] [PubMed] [Google Scholar]
23. Lechner A, Leech CA, Abraham EJ, Nolan AL, Habener JF. Nestin-positive progenitor cells derived from adult human pancreatic islets of Langerhans contain side population (SP) cells defined by expression of the ABCG2 (BCRP1) ATP-binding cassette transporter. Biochem Biophys Res Commun. 2002;293:670–674. doi: 10.1016/S0006-291X(02)00275-9. [DOI] [PubMed] [Google Scholar]
24. Ho MM, Ng AV, Lam S, Hung JY. Side population in human lung cancer cell lines and tumors is enriched with stem-like cancer cells. Cancer Res. 2007;15:4827–4833. doi: 10.1158/0008-5472.CAN-06-3557. [DOI] [PubMed] [Google Scholar]
25. Scharenberg CW, Harkey MA, Torok-Storb B. The ABCG2 transporter is an efficient Hoechst 33342 efflux pump and is preferentially expressed by immature human hematopoietic progenitors. Blood. 2002;15:507–512. doi: 10.1182/blood.V99.2.507. [DOI] [PubMed] [Google Scholar]
26. Deng HB, Adikari M, Parekh HK, Simpkins H. Ubiquitous induction of resistance to platinum drugs in human ovarian, cervical, germ-cell and lung carcinoma tumor cells overexpressing isoforms 1 and 2 of dihydrodiol dehydrogenase. Cancer Chemother Pharmacol. 2004;54:301–307. doi: 10.1007/s00280-004-0815-0. [DOI] [PubMed] [Google Scholar]
27. HUGO (Human genome organization) http://www.hugo-international.org/
28. Zhou S, Schuetz JD, Bunting KD, Colapietro AM, Sampath J, Morris JJ, Lagutina I, Grosveld GC, Osawa M, Nakauchi H, Sorrentino BP. The ABC transporter Bcrp1/ABCG2 is expressed in a wide variety of stem cells and is a molecular determinant of the side-population phenotype. Nat Med. 2001;7:1028–1034. doi: 10.1038/nm0901-1028. [DOI] [PubMed] [Google Scholar]
29. Maliepaard M, Van Gastelen MA, De Jong LA, Pluim D, Van Waardenburg RC, Ruevekamp-Helmers MC, Floot BG, Schellens JH. Overexpression of the BCRP/MXR/ABCP gene in a topotecan-selected ovarian tumor cell line. Cancer Res. 1999;15:4559–4563. [PubMed] [Google Scholar]
30. Lou H, Dean M. Targeted therapy for cancer stem cells: the patched pathway and ABC transporters. Oncogene. 2007;26:1357–1360. doi: 10.1038/sj.onc.1210200. [DOI] [PubMed] [Google Scholar]
31. Smithgall TE, Harvey RG, Penning TM. Oxidation of the trans-3,4-dihydrodiol metabolites of the potent carcinogen 7,12-dimethylbenz(a)anthracene and other benz(a)anthracene derivatives by 3 alpha-hydroxysteroid-dihydrodiol dehydrogenase: effects of methylsubstitution on velocity and stereochemical course of trans-dihydrodiol oxidation. Cancer Res. 1988;1:1227–1232. [PubMed] [Google Scholar]
32. Timmer-Bosscha H, Mulder NH, de Vries EG. Modulation of cis-diamminedichloroplatinum(II) resistance: a review. Br J Cancer. 1992;66:227–238. doi: 10.1038/bjc.1992.249. [DOI] [PMC free article] [PubMed] [Google Scholar]
33. Burczynski ME, Harvey RG, Penning TM. Expression and Characterization of Four Recombinant Human Dihydrodiol Dehydrogenase Isoforms: Oxidation of trans-7,8-Dihydroxy-7,8-dihydrobenzo[a]pyrene to the Activated o-Quinone Metabolite Benzo[a]pyrene-7,8-dione. Biochemistry. 1998;37:6781–6790. doi: 10.1021/bi972725u. [DOI] [PubMed] [Google Scholar]
34. Rizner TL, Smuc T, Rupreht R, Sinkovec J, Penning TM. AKR1C1 and AKR1C3 may determine progesterone and estrogen ratios in endometrial cancer. Mol Cell Endocrinol. 2006;27:126–135. doi: 10.1016/j.mce.2005.10.009. [DOI] [PubMed] [Google Scholar]
35. Mukherjee K, Syed V, Ho SM. Estrogen-induced loss of progesterone receptor expression in normal and malignant ovarian surface epithelial cells. Oncogene. 2005;24:4388–4400. doi: 10.1038/sj.onc.1208623. [DOI] [PubMed] [Google Scholar]
36. Bauman DR, Rudnick SI, Szewczuk LM, Jin Y, Gopishetty S, Penning TM. Development of Nonsteroidal Anti-inflammatory Drug Analogs and Steroid Carboxylates Selective for Human Aldo-Keto Reductase Isoforms: Potential Antineoplastic Agents That Work Independently of Cyclooxygenase Isozymes. Mol Pharmacol. 2005;67:60–68. doi: 10.1124/mol.104.006569. [DOI] [PubMed] [Google Scholar]
37. Wang HW, Lin CP, Chiu JH, Chow KC, Kuo KT, Lin CS, Wang LS. Reversal of inflammation-associated dihydrodiol dehydrogenases (AKR1C1 and AKR1C2) overexpression and drug resistance in nonsmall cell lung cancer cells by wogonin and chrysin. Int J Cancer. 2007;120:2019–2027. doi: 10.1002/ijc.22402. [DOI] [PubMed] [Google Scholar]
38. Hsu NY, Ho HC, Chow KC, Lin TY, Shih CS, Wang LS, Tsai CM. Overexpression of dihydrodiol dehydrogenase as a prognostic marker of non-small cell lung cancer. Cancer Res. 2001;15:2727–2731. [PubMed] [Google Scholar]
39. Yoon SY, Kim JM, Oh JH, Jeon YJ, Lee DS, Kim JH, Choi JY, Ahn BM, Kim SS, Yoo HS, Kim TS, Kim NS. Gene expression profiling of human HBV- and/or HCV-associated hepatocellular carcinoma cells using expressed sequence tags. Int J Oncol. 2006;29:315–327. [PubMed] [Google Scholar]
40. NCBI, GEO http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE994
41. Mitsutake N, Iwao A, Nagai K, Namba H, Ohtsuru A, Saenko V, Yamashita S. Characterization of side population in thyroid cancer cell lines: cancer stem-like cells are enriched partly but not exclusively. Endocrinology. 2007;148:1797–1803. doi: 10.1210/en.2006-1553. [DOI] [PubMed] [Google Scholar]
42. Spira A, Beane J, Shah V, Liu G, Schembri F, Yang X, Palma J, Brody JS. Effects of cigarette smoke on the human airway epithelial cell transcriptome. Proc Natl Acad Sci USA. 2004;101:10143–10148. doi: 10.1073/pnas.0401422101. [DOI] [PMC free article] [PubMed] [Google Scholar]
43. GEO http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1323
44. Kao YR, Shih JY, Wen WC, Ko YP, Chen BM, Chan YL, Chu YW, Yang PC, Wu CW, Roffler SR. Tumor-associated antigen L6 and the invasion of human lung cancer cells. Clin Cancer Res. 2003;9:2807–2816. [PubMed] [Google Scholar]
45. Kinsey M, Smith R, Lessnick SL. NR0B1 is required for the oncogenic phenotype mediated by EWS/FLI in Ewing's sarcoma. Mol Cancer Res. 2006;4:851–859. doi: 10.1158/1541-7786.MCR-06-0090. [DOI] [PubMed] [Google Scholar]