1476-4598-6-75 1476-4598 Research <p>Gene expression profiling of cancer stem cell in human lung adenocarcinoma A549 cells</p> Seo Dong-Cheol sdc97@hanmail.net Sung Ji-Min lava0331@hanmail.net Cho Hee-Jung hjcho@konkuk.ac.kr Yi Hee yahhee@hanmail.net Seo Kun-Ho bracstu3@konkuk.ac.kr Choi In-Soo ischoi@konkuk.ac.kr Kim Dong-Ku dokukim@hanmail.net Kim Jin-Suk jskim@konkuk.ac.kr El-Aty AM Abd abdelaty44@hotmail.com Shin Ho-Chul hshin@konkuk.ac.kr

Department of Veterinary Pharmacology and Toxicology College of Veterinary Medicine, Konkuk University, 1 Hwayang-dong, Kwangjin-gu, Seoul 143-701, Republic of Korea

Department of Public Health, College of Veterinary Medicine, Konkuk University, 1 Hwayang-dong, Kwangjin-gu, Seoul 143-701, Republic of Korea

Department of Infectious Diseases, College of Veterinary Medicine, Konkuk University, 1 Hwayang-dong, Kwangjin-gu, Seoul 143-701, Republic of Korea

Cell and Gene Therapy Research Institute, Pochon CHA University, CHA General Hospital, Seoul 135-081, Republic of Korea

 Molecular Cancer 1476-4598 2007 6 1 75 http://www.molecular-cancer.com/content/6/1/75 18034892 10.1186/1476-4598-6-75 06 9 2007 22 11 2007 22 11 2007 2007 Seo et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 12. 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 3456. 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 1011. CSCs are also known in solid tumors like breast, brain, lung, prostrate, testis, ovary, stomach, colon, skin, liver, and pancreas 121314151617. 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 181920212223. 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.

<p>Figure 1</p>

Sorting of SP and non-SP cells by FACSVantage SE

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

 <p>Table 1</p>

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

Gene symbol

Gene name

AKR1C1; AKR1C2

aldo-keto reductase family 1, member C1 (dihydrodiol dehydrogenase 1; 20-alpha (3-alpha)-hydroxysteroid dehydrogenase); aldo-keto reductase family 1, member C2 (dihydrodiol dehydrogenase 2; bile acid binding protein; 3-alpha hydroxysteroid dehydrogenase, type III)

TM4SF1

transmembrane 4 L six family member 1

NR0B1

nuclear receptor subfamily 0, group B, member 1

LRPPRC

leucine-rich PPR-motif containing

SFRS3

splicing factor, arginine/serine-rich 3

ABCG2

ATP-binding cassette, sub-family G (WHITE), member 2

Unidentified 1

adult retina protein

KRT4

keratin 4

ZNF567

zinc finger protein 567

IL6R

interleukin 6 receptor

PAMCI

peptidylglycine alpha-amidating monooxygenase COOH-terminal interactor

ZNF267

zinc finger protein 267

NEFL

neurofilament, light polypeptide 68 kDa

SFMBT2

Scm-like with four mbt domains 2

FSTL1

follistatin-like 1

TMEPAI

transmembrane, prostate androgen induced RNA

COL5A1

collagen, type V, alpha 1

SLC6A15

solute carrier family 6, member 15

COL1A1

collagen, type I, alpha 1

NTRK3

neurotrophic tyrosine kinase, receptor, type 3

CDH2

cadherin 2, type 1, N-cadherin (neuronal)

ANXA8

annexin A8

THBD

thrombomodulin

RAB3B

RAB3B, member RAS oncogene family

ADAM19

ADAM metallopeptidase domain 19 (meltrin beta)

COL4A2

collagen, type IV, alpha 2

IGFBP7

insulin-like growth factor binding protein 7

COL4A1

collagen, type IV, alpha 1

IFI16

interferon, gamma-inducible protein 16

GLIPR1

GLI pathogenesis-related 1 (glioma)

TGM2

transglutaminase 2 (C polypeptide, protein-glutamine-gamma-glutamyltransferase)

COL4A1

collagen, type IV, alpha 1

IGFBP7

insulin-like growth factor binding protein 7

MYL9

myosin, light chain 9, regulatory

Unidentified 2

CDNA FLJ44429 fis, clone UTERU2015653

MATN2

matrilin 2

TNFAIP6

tumor necrosis factor, alpha-induced protein 6

FRMD5

FERM domain containing 5

RUNX2

runt-related transcription factor 2

TMEPAI

transmembrane, prostate androgen induced RNA

GLIPR1

GLI pathogenesis-related 1 (glioma)

NPTX1

neuronal pentraxin I

GLIPR1

GLI pathogenesis-related 1 (glioma)

SPARC

secreted protein, acidic, cysteine-rich (osteonectin)

COL5A1

collagen, type V, alpha 1

COL1A1

collagen, type I, alpha 1

B4GALT1

UDP-Gal:betaGlcNAc beta 1,4- galactosyltransferase, polypeptide 1

Unidentified 3

-

TNS1

tensin 1

SPOCK1

sparc/osteonectin, cwcv and kazal-like domains proteoglycan (testican) 1

MOBKL2B

MOB1, Mps One Binder kinase activator-like 2B (yeast)

ID2

inhibitor of DNA binding 2, dominant negative helix-loop-helix protein

C4orf18

chromosome 4 open reading frame 18

COL5A1

collagen, type V, alpha 1

TAGLN

transgelin

CXCR7

chemokine (C-X-C motif) receptor 7

Unidentified 4

Transcribed locus, moderately similar to XP_517655.1 PREDICTED: similar to KIAA0825 protein [Pan troglodytes]

COL5A1

collagen, type V, alpha 1

MOBKL2B

MOB1, Mps One Binder kinase activator-like 2B (yeast)

TAGLN

transgelin

SPARC

secreted protein, acidic, cysteine-rich (osteonectin)

 <p>Figure 2</p>

Gene clustering of up-regulated genes in SP and non-SP cells

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.

 <p>Table 2</p>

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

Probe ID

Genbank

Gene symbol

Fold change (Mean ± SD)

217626_at

BF508244

AKR1C1; AKR1C2

3.11 ± 0.92

238168_at

AI760128

TM4SF1

2.77 ± 0.66

206645_s_at

NM_000475

NR0B1

2.78 ± 0.81

1557360_at

CA430402

LRPPRC

2.72 ± 0.37

215033_at

AI189753

TM4SF1

2.72 ± 0.46

232392_at

BE927772

SFRS3

2.51 ± 0.08

209735_at

AF098951

ABCG2

2.51 ± 0.37

238476_at

AA481560

Unidentified 1*

2.43 ± 0.18

213240_s_at

X07695

KRT4

2.43 ± 0.28

235648_at

AA742659

ZNF567

2.47 ± 0.75

205945_at

NM_000565

IL6R

.36 ± 0.24

210335_at

AF056209

PAMCI

2.31 ± 0.29

219540_at

AU150728

ZNF267

2.20 ± 0.27

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).

<p>Figure 3</p>

Relative fold changes of AKR1C1/C2 gene in SP and non-SP cells

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 2829 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 3132. 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 3435. Additionally, AKR1C1/C2 inhibitors were reported as potential anti-neoplastic agents 3637. 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 4142. 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.

<p>Figure 4</p>

The up-regulation of AKR1C1 and TM4SF1 in smokers (derived from GEO public database)

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.

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.

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