Genes and molecular pathways related to radioresistance of oral squamous cell carcinoma cells

Authors


Abstract

To identify genes associated with radioresistant oral squamous cell carcinoma (OSCC), we compared gene expression signatures between OSCC cell lines exhibiting radioresistance and cells with radiosensitivity after X-ray irradiation in a dose-dependent manner using Affymetrix GeneChip analysis with Human Genome-U133 plus 2.0 GeneChip. The microarray data identified 167 genes that were significantly overexpressed in radioresistant cells after X-ray irradiation. Among the genes identified, 40 were mapped to 3 highly significant genetic networks identified by the Ingenuity Pathway Analysis tool. Gene ontology analysis showed that cancer-related function had the highest significance. The 40 genes included 25 cancer-related genes that formed 1 network and were categorized by function into growth and proliferation, apoptosis, and adhesion. Furthermore, real-time quantitative reverse transcriptase–polymerase chain reaction showed that the mRNA expression levels of the 25 genes were higher in radioresistant cells than in radiosensitive cells in a dose-dependent manner and in a time-dependent manner. Our results suggest that the identified genes help to elucidate the molecular mechanisms of the radioresistance of OSCC and could be radiotherapeutic molecular markers for choosing the appropriate radiotherapy for this disease. © 2007 Wiley-Liss, Inc.

Radiation therapy has played an important role in controlling tumor growth in many patients with cancer. In patients with oral squamous cell carcinoma (OSCC), radiation therapy is currently the standard adjuvant treatment. However, radiation therapy is sometimes ineffective, because cancer cells can be refractory to radiation therapy. A relationship between radioresistance and expression of several genes, i.e., RAS,1RAF12 and BCL2,3 has been reported. It also has been proposed that the Ras/PI3K/Act pathway is associated with radioresistance both in vivo and in vitro in several human cancers, i.e., those of the larynx,4 uterine cervix,4 head and neck,5 bladder,4, 6, 7, 8 colon,4, 7, 8 breast4, 9 and fibrosarcoma.8 Moreover, recent studies have identified genes related to the radioresistance and radiosensitivity of human cancers10, 11, 12, 13, 14, 15 in comprehensive gene expression profiles by microarray analysis.

In oral malignancies including OSCCs, COX-2,16 glycerol,17 14-3-3 sigma protein,18 DNA-PK complex protein,19 DNA contact mutation of p5320 and NF- kappaB21 may be related to radioresistance. Although these findings have achieved a partial understanding of the molecular mechanisms responsible for cellular radiosensitivity, the entire process remains to be clarified. Because the complex mechanism of radioresistance cannot be explained by a small number of genes, it is necessary to analyze simultaneous expression levels of thousands of genes. In this context, the emerging microarray technology provides the ability to comparatively analyze mRNA expression of thousands of genes in parallel. However, little is known about comprehensive gene expression profiles related to the radioresistance of OSCC using microarray analysis.22

Our previous study showed that the radiosensitivity of OSCC cell lines differs greatly in their response to X-ray radiation as assessed by clonogenic survival assay.22 In the current study, we performed microarray analysis using high-density Affymetrix Human Genome-U133 plus 2.0 GeneChip arrays containing 54,675 probe sets (Affymetrix, Santa Clara, CA) to compare gene expression patterns among the cell lines with or without radioresistance after X-ray irradiation. Furthermore, the genes identified were analyzed for network and gene ontology by Ingenuity Pathway Analysis (IPA) software (Ingenuity Systems, Mountain View, CA) to identify networks of interacting genes and other functional groups.23 In addition, further selected genes confirmed the microarray data by real-time quantitative reverse transcriptase–polymerase chain reaction (qRT–PCR).

Abbreviations:

A calls, absent calls; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; IPA, ingenuity pathway analysis; OSCC, oral squamous cell carcinoma; P calls, present calls; qRT-PCR, quantitative reverse transcriptase–polymerase chain reaction; SD, standard deviation.

Material and methods

Cell lines and culture conditions

The human OSCC-derived cell lines, HSC2 and HSC3 (Human Science Research Resources Bank, Osaka, Japan), were prepared for this study. The cells were maintained in Dulbecco's modified Eagle's medium F-12 HAM (Sigma Chemical, St. Louis, MO) supplemented with 10% heat-inactivated fetal bovine serum and 50 units/ml penicillin and streptomycin. All cultures were grown at 37°C in a humidified atmosphere of 5% carbon dioxide for routine growth.

Irradiation using X-rays

The cells were plated in 75-mm2 tissue culture dishes containing 10 ml of medium, allowed to grow to 70–80% confluence, and then irradiated with 3 single radiation doses (2, 4 and 8 Gy) using X-ray irradiation equipment (MBR-1520R-3; Hitachi, Tokyo, Japan) from a distance of 55 cm.

Isolation of RNA

Total RNA was extracted with TRIzor reagent (Invitrogen Life Technologies, Carlsbad, CA) from X-ray irradiated and nonirradiated cells 1, 2, 4, 8 and 12 hr after X-ray irradiation, according to the manufacturer's instructions. The quality of the total RNA was determined by Bioanalyzer (Agilent Technologies, Palo Alto, CA).

Hybridization of RNAs to oligonucleotide arrays and data analysis

For microarray analysis, 4 hr after X-ray irradiation was selected as the time point to monitor the early response of OSCC cells for X-ray irradiation and to identify differentially expressed early genes that mediate cellular events, such as proliferation and apoptosis. We used Human Genome U133 plus 2.0 GeneChip oligonucleotide arrays. This GeneChip containing 54,675 probe sets analyzes the expression level of over 47,000 transcripts and variants, including 38,500 well-characterized human genes. Microarray analysis compared the most radioresistant cells and the most radiosensitive cells. For hybridization, 20 μg of total RNA per sample was prepared according to the manufacturer's protocols. Fragmented cRNA (15 μg of each) was hybridized to the Human Genome oligonucleotide arrays. The arrays were stained with phycoerythrin–streptavidin and the single intensity was amplified by treatment with a biotin-conjugated antistreptavidin antibody, followed by a second staining using phycoerythrin–streptavidin. The arrays stained a second time were scanned using the GeneChip Scanner 3000 (Affymetrix). Expression data were analyzed using GeneChip Operating Software 1.1 (Affymetrix) and GeneSpring 6.1 (Silicon Genetics, Redwood City, CA). The arrays were normalized by Operating Software 1.1 (Affymetrix) to determine the probe set intensities and “Present” (P) or “Absent” (A) calls. The Expression data of P calls indicated to have reliability. On the other hand, the data of A calls were unreliable.

Network, gene ontology and canonical pathway analysis

Under the criteria combining P calls and fold changes, which suggested a mean enhance in expression level at least 5-fold or more by X-ray irradiation at all doses (0, 2, 4 and 8 Gy) in the radioresistant cell lines, HSC2, compared with the radiosensitive cell lines, HSC3 in GeneChip analysis by GeneSpring 6.1 data mining software (Silicon Genetics, Redwood City, CA), the significantly altered 167 genes were selected and used for the network generation and pathway analysis. Gene accession numbers and mRNA expression values were imported into the IPA software. The genes were categorized based on molecular functions using the software. The identified genes also were mapped to genetic networks in the IPA database and ranked by score. The score reflects the probability that a collection of genes equal to or greater than the number in a network could be achieved by chance alone. A score of 3 indicates that there is a 1/1,000 chance that the focus genes in a network are there by random chance. Therefore, scores of 3 or higher have a 99.9% confidence level of not having been generated by random chance alone. This score was used as the cut-off for identifying gene networks. Moreover, relationships between the network generated in IPA and the known pathways which were associated with metabolism and signaling were investigated by Canonical pathway analysis.

Preparation of cDNA

Total RNA was extracted using TRIzor reagent from X-ray irradiated and nonirradiated HSC2 and HSC3 cells. Total RNA samples were extracted at 1, 2, 4, 8 and 12 hr after 2 Gy of X-ray irradiation to determine the time-dependent effects; the physical doses used in clinical radiotherapy and samples were extracted at 4 hr after 2, 4 and 8 Gy of X-ray irradiation to determine the dose-dependent effects, according to the manufacturer's instructions. Five micrograms of total RNA of each sample was reversed transcribed to cDNA using Ready-To-Go You-Prime First-Strand Beads (Amersham Biosciences, Little Chalfort, Buckinghamshire, UK) and oligo (dT) primer (Sigma Genosys, Ishikari, Japan), according to the manufacturer's protocol.

Analysis of mRNA expression by real-time quantitative reverse transcriptase–polymerase chain reaction (qRT–PCR)

Real-time qRT–PCR was performed to verify the microarray data with a single method using a LightCycler FastStart DNA Master SYBR Green I kit (Roche Diagnostics GmbH, Mannheim, Germany), according to the procedure provided by the manufacturer. Oligonucleotides used as primers and the predicted sizes of amplified PCR products are listed in Table I. Using LightCycler apparatus, we carried out PCR reactions in a final volume of 20 μl of a reaction mixture consisting of 2 μl of FirstStart DNA Master SYBR Green I mix, 3 mM MgCl2 and 1 μl of the primers according to the manufacturer's instructions. Subsequently, the reaction mixture was loaded into glass capillary tubes and subjected to initial denaturation at 95°C for 10 min, followed by 35–45 rounds of amplification at 95°C (10 sec) for denaturation, 58–65°C (10 sec) for annealing, and 72°C for extension, with a temperature slope of 20°C/sec, performed in the LightCycler. The transcript amount for the genes differentially expressed in the microarray analysis was estimated from the respective standard curves and normalized to the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) transcript amount determined in corresponding samples.

Table I. List of the Primers Used for the Quantitative RT-PCR
Gene nameForward primerReverse primerSize (bp)
  1. bp, base pair.

PTHLH5′-AAAGGGGACCTTGAACCTAT-3′5′-GGCAATAAAGTAGGGTCCTT-3′259
ID15′-AGAACCGCAAGGTGAGCAA-3′5′-TTCCGAGTTCAGCTCCAACTG-3′85
ID35′-CTGCCCACTTGACTTCACCAA-3′5′-TTCAGGCTACAAGTTCACAGTCCTT-3′85
FGFR35′-TGGCTCAGGGTGGTCTCTTCT-3′5′-AGCAACCAGGTGTCTTTATTTTTCG-3′125
VAV35′-CCCTCCTTAGCCCCTCCTAA-3′5′-CTCTTGGTTTTGCCCTGGTC-3′139
PTGS25′-CTCAAACATGATGTTTGCATTCTTT-3′5′-GCTGGCCCTCGCTTATGAT-3′78
PLD15′-CCATTGTTGGCCTCTCTCTTCA-3′5′-TGCCCATGGAGCTTACATT-3′253
FGFBP15′-CCCCAGGGAGCACATCAA-3′5′-GCTCCAAGTCTCTCCACAGAACTC-3′153
JAG15′-CCAAATCCTGTAAGAATCTCATTGC-3′5′-AGATACAGCGATAACCATTAACCAAA-3′155
PEG105′-GACTCCGGCTTTGACACAACA-3′5′-AACGCTGGAGCCACCAGTAA-3′99
S100P5′-AGACAAGGATGCCGTGGATAA-3′5′-GAAGTCCACCTGGGCATCTC-3′69
ICAM25′-ATTCAACAGCACGGCTGACA-3′5′-CAGGCTCATAGATCTCCAACATCT-3′136
MMP135′-TTCCCACAGTGCCTATTGATAC-3′5′-ATCAACAGTGTCTCTGAGCACAA-3′104
MME5′-TTGTTCGACCCCTATTCTGC-3′5′-AGCAGCACAGACCCAAACTT-3′293
SNCA5′-TCTTTGCTCCCAGTTTCTTGAG-3′5′-TGAAAGGGAAGCACCGAAAT-3′183
TNFSF105′-GGACAGACCTGCGTGCTGAT-3′5′-ACAAGCAATGCCACTTTTGGA-3′134
PLAGL15′-AAGGGAAATGCTAAAGTAAACC-3′5′-GGCAAGAGTGCTATTCCCAAAG-3′158
SERPINB55′-TTTAGCTGACAACAGTGTGAACGA-3′5′-CATCTGCACTGGTTTGGTGTCT-3′153
TIMP35′-TTCCCTGCGTCCATAAA-3′5′-TGACATCGCTTTGCGAAAGA-3′102
FBLN15′-CCTTCGAGTGCCCTGAGAACTA-3′5′-GTTAAGTTACATGAGGAAGTGGCTCAA-3′153
RUNX35′-CTGCACTCATCTGATGTAAAACCAT-3′5′-AAGCAAACGATAGTGCAAAGCA-3′83
DAB25′-AGCTATTGCAAATGAGGGAAGA-3′5′-GGAGCAAAACTGACTGAAAAAA-3′83
SYK5′-ATGACCCCGCTCTTAAAGATGA-3′5′-CGCACGATGTACGGGTTGT-3′76
ROBO15′-TGGTGAACACCAGCCTTT-3′5′-GTGGGCCATGAACCAAAT-3′163
PVRL35′-GTGACCAATTCCCTTGGTCAA-3′5′-TCTGTTGCTAGATCCTCGATGTC-3′133
GAPDH5′-CATCTCTGCCCCCTCTGCTGA-3′5′-GGATGACCTTGCCCACAGCCT-3′305

Statistical analysis

The Mann-Whitney U test was used to determine the statistical significance of the associations between the mRNA expression levels of the most radioresistant cell cells and the most radiosensitive cells. The criterion for statistical significance was p < 0.05.

Results

Microarray analysis of radiosensitive and radioresistant cell lines

To evaluate cell survival after irradiation, a clonogenic survival assay was used as previously described. Previous study showed that the most radioresistant cell line was HSC2 and the most radiosensitive cell line was HSC3 in 6 OSCC cell lines.22

The gene expression profiles of OSCC cells exposed to X-ray irradiation were analyzed using the high-throughput microarray, which contains 54,675 oligonucleotide-based probe sets. The results of microarray analysis showed that the expression levels of 167 genes (220 probes) were elevated at least 5-fold or more by X-ray irradiation at all doses (0, 2, 4 and 8 Gy) in the radioresistant cell lines, HSC2, compared with radiosensitive cell lines, HSC3.

Network and gene ontology analysis

We carried out genetic network analysis of the 167 genes (220 probes) with elevated expression at least 5-fold or more by X-ray irradiation at all doses (0, 2, 4 and 8 Gy) in radioresistant cell compared with radiosensitive cell in GeneChip results. Three networks (Table II) were identified by network analysis using the IPA tool. These networks indicated functional relationships between gene products based on known interactions in the literature. The IPA tool then associated these networks with known biologic pathways. Three networks were highly significant in that they had more of the identified genes present than would be expected by chance. These networks were associated with the cancer, cellular movement, cell cycle, proliferation, cell death and tissue development (Table II). Each network was characterized by different functions. They were merged via overlapping genes (Fig. 1). Of the 167 genes, 40 were mapped to genetic networks identified by the IPA tool. GeneChip results of 40 focus genes in genetic networks (Fig. 1) were showed in Table III. These results were directly correlated with the intensity of the node color (Red), which indicated the degree of up-regulation of focus genes in radioresistant cell (HSC2) compared with radiosensitive cell (HSC3) by microarray analysis in Figure 1. Gene ontology analysis was also performed using the IPA tool. A total of 46 functions were identified as high level functions (Table IV). The cancer-related function had the highest p value (p = 7.54e-5–4.63e-2). Furthermore, to investigate the network of 30 cancer-related genes, we performed network analysis. Consequently, we identified 1 network (Fig. 2) that included 25 cancer-related genes (Table V). These genes, which were part of the network of the 40 genes related to the radioresistance of OSCC, were categorized by function into growth and proliferation, apoptosis and adhesion.

Figure 1.

Networks of genes related to radioresistance of OSCC. The IPA tool was used to analyze the identified genes (n = 167). Three networks were identified and merged by overlapping genes (green, network I; blue, network II; and orange, network III). The genes written in bold letters with shaded node were identified by microarray analysis and the other genes were those related to the regulated genes based on the network analysis. The intensity of node color indicates the degree of up-regulation (red) in radioresistant cell (HSC2) compared with radiosensitive cell (HSC3) in microarray analysis. The meaning of Edge Labels and Node Shapes are indicated in this figure. The genes in black frame were overlapping genes. [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com.]

Figure 2.

Shaded genes in this network were identified by microarray analysis. These genes were up-regulated in radioresistant cells compared with radiosensitive cells and related to both cancer function and radioresistance of OSCC in gene ontology analysis. The asterisks indicate that the genes are associated with the function of proliferation. The daggers indicate that the genes are associated with the function of antiapoptosis.

Table II. Genetic Networks in the OSCC Cell Lines
NetworkGenesFunctionsScore1
  • Underlining indicates genes identified by microarray analyses; other genes were either not on the expression array or did not change significantly.

  • 1

    A score >3 was considered significant.

ICOL14A1, COL4A1, CRKL, E2F4, EFEMP1, ESR1, F12, FBLN1, ID1, ID3, IFI16, JAG1, MMP9, MMP13, MYOD1, NDN, PIAS1, PIAS2, PLAGL1, PTPRCAP, PVRL3, SERPINB5, SLC3A1, SLC3A2, SLC7A8, SYK, TFPI2, TIMP3, TP53, TP73L, VIL2, WAS, WASPIP, ZAP70, ZNF9ICancer, cellular movement, cell cycle23
IIARF1, CGB, CRIP2, CRLF1, DAB2, DLG4, DOK4, EP300, EPHA4, ETV4, FYB, FYN, GRB2, GRK4, HTR2C, LIN7C, MYOD1, PEG10, PIAS2, PIAS3, PLAGL1, PLD1, PRKCA, PTGS2, RUNX3, SCAP1, SCAP2, SIAH1, SLC1A1, SNCA, SRC, USF1, VAV3, VIPR1, WASBehavior, cell cycle, cellular growth and proliferation23
IIIA2M, AR, CDKN2A, CEBPA, COL4A2, CREBBP, E2F1, EGR2, FGF1, FGF2, FGFBP1, FGFR1, FGFR2, FGFR3, FOXO1A, GSTT1, ICAM1, ICAM2, JUN, MAGED1, MME, MMP9, MSX2, MYOD1, NDN, NFE2L2, NFKB1, PTHLH, ROBO1, SMARCA2, SP1, SPP1, TNFSF10, TP73, VIL2Cell death, cellular growth and proliferation, Tissue development13
Table III. Gene Chip Results of Forty Focus Genes in Genetic Networks
NetworkAffymetrix No.SymbolFold change1
0 Gy2 Gy (4 hr)4 Gy (4 hr)8 Gy (4 hr)
  • 1

    Fold overexpression for GeneChip data of radioresistant cell line (HSC2) compared to radiosensitive cell line (HSC3).

I211981_atCOL4A187.424.68.917.6
201843_s_atEFEMP19.611.410.78.8
201787_atFBLN16.311.311.110.1
208937_s_atID120.039.641.074.7
207826_s_atID38.325.835.732.4
209098_s_atJAG17.57.66.86.0
205959_atMMP1331.9182.2212.9307.9
207002_s_atPLAGL177.3103.16.9159.4
213325_atPVRL37.236.172.810.7
204855_atSERPINB59.29.111.613.9
202752_x_atSLC7A88.05.711.916.6
207540_s_atSYK7.716.414.528.5
201150_s_atTIMP35.5187.319.310.7
202664_atWASPIP60.422.37.47.8
206059_atZNF916.1224.326.642.5
II205387_s_atCGB5.029.99.618.0
206315_atCRLF17.011.539.917.1
201278_atDAB213.010.657.886.9
227449_atEPHA46.427.439.918.2
211795_s_atFYB15.612.38.228.2
207307_atHTR2C22.763.825.55.5
212094_atPEG1074.516.6125.395.6
207002_s_atPLAGL177.3103.16.9159.4
226636_atPLD113.25.911.526.7
204748_atPTGS219.835.112.218.6
204198_s_atRUNX325.727.921.410.6
213664_atSLC1A113.68.68.512.8
204466_s_atSNCA5.418.616.721.1
218807_atVAV314.16.810.514.9
205019_s_atVIPR112.447.533.120.9
III211964_atCOL4A2158.7179.850.2129.2
205014_atFGFBP19.011.413.016.6
204379_s_atFGFR317.194.77.568.5
203815_atGSTT119.2101.2103.858.5
213620_s_atICAM274.99.164.010.3
209014_atMAGED117.962.4227.949.9
203435_s_atMME10.820.642.453.3
206300_s_atPTHLH20.718.612.417.8
213194_atROBO1207.7342.535.657.4
202688_atTNFSF105.78.412.88.0
Table IV. Gene Ontology of Identified Genes
Molecular functionp valueGene (n)
Cancer7.54e−5 to 4.63e−230
Cellular growth and proliferation7.54e−5 to 4.45e−230
Tumor morphology7.54e−5 to 4.45e−210
Cellular Movement8.12e−5 to 4.63e−224
Embryonic development8.12e−5 to 4.45e−211
Tissue morphology8.12e−5 to 4.45e−221
Connective tissue development and function1.87e−4 to 4.45e−29
Tissue development1.87e−4 to 4.45e−227
Cell-to-cell signaling and interaction2.17e−4 to 4.45e−231
Endocrine system disorders4.81e−4 to 2.70e−23
Hematological disease4.81e−4 to 3.58e−27
Organ development4.81e−4 to 4.45e−224
Respiratory system development and function4.81e−4 to 2.70e−25
Skeletal and muscular system development and function5.33e−4 to 4.45e−29
Cardiovascular system development and function1.19e−3 to 4.45e−210
Amino acid metabolism1.27e−3 to 4.45e−24
Molecular transport1.27e−3 to 4.45e−25
Organismal development1.36e−3 to 4.29e−210
Cell cycle1.66e−3 to 4.45e−25
Cell morphology1.66e−3 to 4.19e−211
Cellular development1.66e−3 to 4.45e−211
Lipid metabolism1.66e−3 to 4.45e−25
Cell death1.90e−3 to 4.89e−225
Cellular function and maintenance2.19e−3 to 4.45e−210
Hematological system development and function2.19e−3 to 4.45e−29
Immune response2.19e−3 to 4.45e−26
Reproductive system disease2.19e−3 to 4.63e−212
Cellular compromise3.49e−3 to 4.45e−28
Immune and lymphatic system development and function3.51e−3 to 4.19e−28
Organismal injury and abnormalities5.05e−3 to 2.70e−23
Cellular assembly and organization5.94e−3 to 4.06e−215
Developmental disorder9.07e−3 to 4.55e−24
Digestive system development and function9.07e−3 to 3.58e−25
Endocrine system development and function9.07e−3 to 3.58e−23
Genetic disorder9.07e−3 to 1.81e−24
Metabolic disease9.07e−3 to 1.81e−22
Nucleic acid metabolism9.07e−3 to 9.07e−31
Organ morphology9.07e−3 to 4.81e−28
Reproductive system development and function9.07e−3 to 4.45e−27
Skeletal and muscular disorders9.07e−3 to 1.81e−23
Cell signaling1.81e−2 to 4.45e−22
Connective tissue disorders1.81e−2 to 1.81e−21
Protein synthesis1.81e−2 to 1.81e−21
Protein trafficking3.58e−2 to 3.58e−22
DNA replication, recombination, and repair4.45e−2 to 4.45e−21
Small molecular biochemistry4.45e−2 to 4.45e−21
Table V. Twenty-Five Cancer-Related Genes
SymbolFunctionDescription
PTHLHProliferation, antiapoptosis, cellular movement, cell-to-cell signalingParathyroid hormone-like hormone
ID1Proliferation, antiapoptosis, cellular movement, cell-to-cell signalingInhibitor of DNA binding 1, dominant negative helix-loop-helix protein
ID3Proliferation, apoptosis, cellular movement, cell-to-cell signalingInhibitor of DNA binding 3, dominant negative helix-loop-helix protein
FGFR3Proliferation, antiapoptosisFibroblast growth factor receptor 3 (achondroplasia, thanatophoric dwarfism)
VAV3Proliferation, antiapoptosis, cellular movementVav 3 oncogene
PTGS2Proliferation, antiapoptosis, cellular movement, cell-to-cell signalingProstaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase)
PLD1Proliferation, antiapoptosis, cellular movement, cell-to-cell signalingPhospholipase D1, phosphatidylcholine-specific
JAG1Proliferation, antiapoptosis, cellular movement, cell-to-cell signalingJagged 1 (Alagille syndrome)
FGFBP1ProliferationFibroblast growth factor binding protein 1
PEG10Proliferation, antiapoptosisPaternally expressed 10
S100PProliferation, antiapoptosis, cellular movementS100 calcium binding protein P
ICAM2Antiapoptosis, cellular movement, cell-to-cell signalingIntercellular adhesion molecule 2
MMP13Proliferation, cellular movement, cell-to-cell signalingMatrix metalloproteinase 13 (collagenase 3)
MMEApoptosis, cellular movement, cell-to-cell signalingMembrane metallo-endopeptidase (neutral endopeptidase, enkephalinase, CALLA, CD10)
SNCAApoptosis, cell-to-cell signalingSynuclein, alpha (non A4 component of amyloid precursor)
TNFSF10Apoptosis, cellular movement, cell-to-cell signalingTumor necrosis factor (ligand) superfamily, member 10
PLAGL1Antiproliferation, apoptosisPleomorphic adenoma gene-like 1
SERPINB5Antiproliferation, apoptosis, cellular movement, cell-to-cell signalingSerine (or cysteine) proteinase inhibitor, clade B (ovalbumin), member 5
TIMP3Antiproliferation, apoptosis, cellular movementTissue inhibitor of metalloproteinase 3 (Sorsby fundus dystrophy, pseudoinflammatory)
FBLN1Antiproliferation, cellular movementFibulin 1
RUNX3Antiproliferation, cellular movementRunt-related transcription factor 3
DAB2antiproliferation, cell-to-cell signalingHomo sapiens cDNA FLJ35517 fis, clone SPLEN2000698.
SYKAntiproliferation, apoptosis, cellular movement, cell-to-cell signalingSpleen tyrosine kinase
ROBO1Adhesion, formation, cell-to-cell signalingRoundabout, axon guidance receptor, homolog 1 (Drosophila)
PVRL3Adhesion, formation, cell-to-cell signalingPoliovirus receptor-related 3

Validation of microarray data by real-time qRT–PCR analysis

To verify the microarray data, we analyzed the levels of mRNAs of the 25 genes by real-time qRT-PCR. The results of the quantitative assessment of expression of the 25 genes after X-ray irradiation are shown in Figure 3. The results were in good agreement with those from the microarray data in a dose-dependent manner. The expression levels of these 25 genes tended to be elevated in radioresistant cells compared with radiosensitive cells not only in a dose-dependent manner but also in a time-dependent manner. Of the 25 genes, 13 had functions of proliferation and antiapoptosis. Another 10 genes had functions of antiproliferation and apoptosis. The remaining 2 genes were unrelated to proliferation and apoptosis (Table V).

Figure 3.

Quantification of the mRNA levels of 25 genes 4 hr after X-ray irradiation with 0, 2, 4 and 8 Gy and at 6 time points (0, 1, 2, 4, 8 and 12 hr) after X-ray irradiation with 2 Gy in OSCC cell lines by real-time qRT-PCR analysis. The mRNA levels at the time point of 0 hr shows the expression of the nonirradiated cell lines. The results are normalized as a ratio of each specific mRNA signal to the GAPDH gene signal within the same RNA sample and then expressed values. The asterisks indicate significant differences in mRNA expression levels between radioresistant cells (HSC2) and radiosensitive cell (HSC3) at each dose and time point (p < 0.05, Mann-Whitney U-test). A significant elevation is seen in radioresistant cells in 11 of the 25 genes compared with radiosensitive cells not only in a dose-dependent manner but also in a time-dependent manner by real-time qRT-PCR analysis. Data are expressed as the mean ± standard deviation (SD).

Furthermore, among the 25 genes, 11 genes (PEG10, ROBO1, ICAM2, TIMP3, DAB2, MMP13, PLAGL1, ID1, PVRL3, ID3 and FGFR3) had a significant (p < 0.05) elevation of radioresistant cells compared with the radiosensitive cells. The data were expressed as the mean ± standard deviation (SD) of 2 independent experiments with samples in triplicate.

Canonical pathway analysis

Several canonical pathways associated with the cancer-related network (Fig. 2) were found. These were Parkinson's signaling, notch signaling, IL-2 signaling, phospholipid degeneration, FGF signaling, prostaglandin metabolism, and B cell receptor signaling. In these canonical pathways, only FGF signaling pathway (Fig. 4) included the gene (FGFR3) which was confirmed the significant enhancement of gene expression in radioresistant cells (HSC2) compared with the radiosensitive cells (HSC3) by real-time qRT-PCR. FGF signaling pathway showed that the FGFRs (included FGFR3) elevations contributed to cell growth and angiogenesis.

Figure 4.

This figure show the canonical pathway of fibroblast growth factor (FGF) signaling. Shaded nodes indicate up-regulated genes in radioresistant cell (HSC2) compared with radiosensitive cell (HSC3). Endoplasmic reticulum is abbreviated to ER. The pathway suggests that increasing of FGFRs (included FGFR3) expression in radioresistant cell (HSC2) read to cell growth and angiogenesis.

Discussion

Radiotherapy, an inevitable component of modern cancer management, is a major treatment modality that can potentially provide a cure for patients with OSCC.24 The success or failure of radiotherapy can be affected by the radiosensitivity of the tumor target and the limits imposed on treatment by the radiosensitivity of normal tissues. Recently, several studies have successfully used microarrays to identify and classify a set of human genes that are radiosensitive to X-ray irradiation.10, 11, 12, 13, 14, 15

In the current study, we identified 167 genes with significantly elevated expression in radioresistant cells at all doses (0, 2, 4, 8 Gy) using the high-throughput microarray that contains 54,675 oligonucleotide-based probe sets. When we analyzed these genes regarding functional network using IPA software, we detected 3 genetic networks (Fig. 1) that included 40 genes with higher expression levels in radioresistant cells than in radiosensitive cells. In addition, gene ontology analysis identified 1 network (Fig. 2) that included 25 cancer-related genes that had the highest p value (p = 7.54e-5–4.63e-2). To further evaluate the validity of the 25 genes selected as radioresistant genes, we carried out qRT-PCR analysis. These genes were higher in radioresistant cells than radiosensitive cells not only in a dose-dependent manner but also in a time-dependent manner, and the genes were categorized by function into proliferation, apoptosis, and adhesion. The functions of cell proliferation, apoptosis, DNA repair, and cell cycle have been reported as the radioresistant-related functions.4, 11 We considered that proliferation and apoptosis were noteworthy functions in the 25 cancer-related genes. Ionizing radiation has been proposed to activate both proliferative and antiproliferative signal transduction pathways, the balance of which determines cell fate,25 suggesting that X-ray irradiation may activate functions of apoptosis and antiapoptosis. Thus, it was reasonable to suppose that the functions of proliferation and antiapoptosis were important for the radioresistance of cancer. Among the 25 genes identified, the 13 genes associated with proliferation and antiapoptosis were PTHLH,26, 27ID1,28, 29ID3,30, 31FGFR3,32VAV3,33PTGS2,34PLD1,35, 36JAG1,37FGFBP1,38, 39PEG10,40, 41, 42S100P,43, 44ICAM245 and MMP13.46, 47, 48 In particular, PTGS2 (known as COX-2) was reported to be linked to radioresistance of human glioblastoma,49 esophageal cancer,13 laryngeal cancer,50 OSCC,16 and lung cancer.51 The remaining 12 genes identified have not been reported to be correlated with radioresistance. PTGS2 overexpression leads to increased PGE2 production, resulting in increased cellular proliferation,52 and tends to be resistance to apoptosis by inducing Bcl-2 expression.53 Those studies reported that PTGS2 expression may play a role in radioresistance via proliferative and antiapoptotic pathways. Therefore, we postulated that the other 12 genes are connected to the radioresistance of cancer as well as the mechanism of radioresistance in PTGS2. Of them, 6 genes (ID1, ID3, FGFR3, PEG10, ICAM2 and MMP13) had a significant (p < 0.05) elevation in radioresistant cells compared with radiosensitive cells in a dose-dependent manner and in a time-dependent manner in real-time qRT-PCR analysis (Fig. 3). Therefore, these 6 genes will be useful to guide the choice of appropriate and effective cancer therapy. Especially, FGFR3 was emphasized the relation with the radioresistance by the results of canonical pathway analysis.

In conclusion, this comprehensive gene expression profiling-assisted pathway analysis provided an appealing approach for effectively identifying candidate genes and pathways involved in cellular radioresistance. We suggest that the pathway that includes the 25 genes is related to the radioresistance of OSCC owing to the balance of proliferation, antiproliferation, apoptosis, and antiapoptosis. Moreover, these 25 genes may contribute to a basic understanding of the molecular mechanism underlying the tumor radioresistance to X-ray irradiation in OSCC. The highlight of our study was the detection of a few genes related to cell proliferation and antiapoptosis, i.e., ID1, ID3, FGFR3, PEG10, ICAM2 and MMP13, the expression levels of which were substantially increased in radioresistant cells compared with radiosensitive cells. These genes may help to disclose the molecular mechanisms of the radioresistance of OSCC and could be radiotherapeutic molecular markers for choosing the appropriate radiotherapy in this disease.

Acknowledgements

We thank Lynda C. Charters for editing this manuscript.

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