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International Journal of Clinical and Experimental Pathology logoLink to International Journal of Clinical and Experimental Pathology
. 2013 Aug 15;6(9):1734–1746.

Array analysis for potential biomarker of gemcitabine identification in non-small cell lung cancer cell lines

Hai-Hong Zhang 1, Zhi-Yi Zhang 1, Chun-Li Che 2, Yi-Fang Mei 2, Yu-Zhi Shi 2
PMCID: PMC3759480  PMID: 24040438

Abstract

Gemcitabine is one of the most widely used drugs for the treatment of advanced Non-small cell lung cancer (NSCLC), but modest objective response rate of patients to gemcitabine makes it necessary to identify novel biomarkers for patients who can benefit from gemcitabine-based therapy and to improve the effect of clinical therapy. In this work, 3 NSCLC cell lines displaying different sensitivities to gemcitabine were applied for mRNA and microRNA (miR) expression chips to figure out the biomarkers for gemcitabine sensitivity. Genes whose expression increased dramatically in sensitive cell lines were mainly enriched in cell adhesion (NRP2, CXCR3, CDK5R1, IL32 and CDH2) and secretory granule (SLC11A1, GP5, CD36 and IGF1), while genes with significantly upregulated expression in resistant cell line were mainly clustered in methylation modification (HIST1H2BF, RAB23 and TP53) and oxidoreductase (TP53I3, CYP27B1 and SOD3). The most intriguing is the activation of Wnt/β-catenin signaling in gemcitabine resistant NSCLC cell lines. The miR-155, miR-10a, miR-30a, miR-24-2* and miR-30c-2* were upregulated in sensitive cell lines, while expression of miR-200c, miR-203, miR-885-5p, miR-195 and miR-25* was increased in resistant cell line. Genes with significantly altered expression and putatively mediated by the expression-changed miRs were mainly enriched in chromatin assembly (MAF, HLF, BCL2, and IGSF3), anti-apoptosis (BCL2, IGF1 and IKBKB), protein kinase (NRP2, PAK7 and CDK5R1) (all the above genes were upregulated in sensitive cells) and small GTPase mediated signal transduction (GNA13, RAP2A, ARHGAP5 and RAB23, down-regulated in sensitive cells). Our results might provide potential biomarkers for gemcitabine sensitivity prediction and putative targets to overcome gemcitabine resistance in NSCLC patients.

Keywords: Non-small cell lung cancer, gemcitabine, gene expression profiles, miR expression profiles

Introduction

Non-small cell lung cancer (NSCLC), the leading cause of cancer-related death in the world, accounts for about 80-85% of all cases of lung cancer [1]. Gemcitabine is one of the most widely used drugs for the treatment of NSCLC. Based on two clinical trials [2,3], the cisplatin plus gemcitabine regimen has become a commonly used combination for advanced NSCLC. The favorable tolerability profile of gemcitabine makes it an ideal choice for evaluation as maintenance therapy [4]. When used as first-line monotherapy, the objective response rate to gemcitabine is 16-22% [5-7]. As the majority of patients do not response to gemcitabine, it is urgent to figure out the biomarkers that influence the sensitivity to gemcitabine for individual therapy.

A substantial number of potential biomarkers for sensitivity or resistance to gemcitabine have been proposed, including gene - expression of ribonucleotide reductase subunit 1 (RRM1) [8-10], cN - II nucleotidase [11], multidrug resistance protein 5 (ABCC5) [12], BRCA1 [10], human equilibrate transporter - 1 [13], transforming growth factor beta-induced protein (TGFBI) [14], Rad51 [15] and clusterin (CLU) [16]. Owing to the different experimental materials and methods used in each research, the results varied case by case, sometimes were contradictory. For example, it was reported that the mRNA expression level of RRM2 in tissues of NSCLC patients was markedly correlated to response to gemcitabine [10], but another study suggested that no significant change in the expression of RRM2 was observed between H358, H460 cell lines and their corresponding gemcitabine-resistant cell lines [8], respectively. Therefore, it is necessary to investigate systemically the effect of gene expressions on chemosensitivity to gemcitabine.

MicroRNAs (miRs) are 19 - 23 nucleotides long RNAs found in multiple organisms that regulate gene expression and have been shown to play important roles in tumorigenesis. In the context of lung cancer, numerous studies have shown that tumor suppressor genes and oncogenes that play crucial roles in lung tumor development and progression are targets of miRs regulation. It has been documented that miRs regulate cell viability and drug sensitivity in lung cancer [17]. In the A549 model, miR-29b down-regulation of DMNT3b reduced promoter methylation of tumor suppressor genes such as Cell adhesion molecule 1 (CADM1), Ras associated (RalGDS/AF-6) domain family member 1 (RASSF1), and Fragile histidine triad gene (FHIT), increasing their expression [18]. Yet, there are few reports that miRs expression is directly related to the sensitivity to gemcitabine in NSCLC cell lines.

In this work, we employed 3 NSCLC cell lines, which are hypersensitive, moderate sensitive and resistant, respectively to gemcitabine. The mRNA and miR expression chips were carried out and bioinformatics analysis was performed to figure out the biomarkers for gemcitabine sensitivity. The results showed that hundreds of genes and 10 miRs were found to be markedly differentially expressed and the relationships between these expression-altered genes and miRs were investigated.

Materials and methods

Cell culture

NSCLC cell lines, NCI-H1975, NCI-H460 and SK-MES-1, were purchased from ATCC and maintained in DMEM medium supplemented with 10% FBS (Hyclone), penicillin (100 IU/ml) and Streptomycin (100 μg/ml) (Life Technologies) in a humidified atmosphere of 95% air and 5% CO2 at 37°C. Cells in the exponential growth phase were used for all the experiments.

IC50 detection of NSCLC cell lines to gemcitabine by MTS assay

NCI-H1975, NCI-H460 and SK-MES-1 cells (4×103) were grown in 100 μl of DMEM medium containing serum per well in a 96-well plate. After 24 h, the cells were treated with gemcitabine (0, 2, 6.3, 20, 63, 200, 630, 2000 nmol/L, respectively) for 72 h. Every treatment was triplicate in the same experiment. Then 20 μl of MTS (CellTiter 96 AQueous One Solution Reagent; Promega) was added to each well for 1 h at 37°C. After incubation, the absorbance was read at a wavelength of 490 nm according to the manufacturer’s instructions. The IC50 calculation was performed with GraphPad Prism 5.0 software.

Microarray analysis: gene expression profile, miR expression profile

The basal gene expression profiles of NCI-H1975, NCI-H460 and SK-MES-1 cell lines were derived from Cell line genetic (mutation and copy number) and gene expression data used for EN analysis on Cancer Centre of Sanger Institute website (http://www.cancerrxgene.org/downloads/).

Total RNA from NCI-H1975, NCI-H460 and SK-MES-1 cell lines was processed and hybridized to Affymetrix GeneChip® miRNA 2.0 Array, which recognizes 1,105 separate human miRs in accordance with the Sanger Institute miRBase version 15. Each sample was duplicated for miR expression profile.

Genes that expression level increased or decreased by higher than 50% gradually from NCI-H1975 to NCI-H460 to SK-MES-1 were thought as significantly altered genes. MiRs whose expression level altered by higher than 100% (P<0.05) gradually from NCI-H1975 to NCI-H460 to SK-MES-1 were considered as significantly altered miRs. Gene set enrichment analysis (GSEA) was performed for the differentially expressed genes with DAVID 6.7 online software and Ingenuity Pathway Analysis (IPA).

miR target prediction and miRNA target correlation

miR target prediction was performed with miRWalk online software. The comparative analysis was done by 5 prediction programs: miRanda, miRDB, miRWalk, RNA22 and TargetScan. Genes predicted by greater than or equal to 3 programs were selected as putative target of some miR. The selected putative genes were done intersection with genes that expression level altered bigger than 50% gradually from NCI-H1975 to NCI-H460 to SK-MES-1. Candidate genes that predicted by miRs of increased expression were done intersection with down-regulated genes determined by microarray, while candidate genes that mediated by miRs of decreased expression were done intersection with upregulated genes determined by microarray. The overlapped genes that mediated by at least 2 miRs were selected to construct miR-gene networks with the aid of Cytoscape 2.8 software.

Quantitative real-time PCR (qPCR)

Total RNA above isolated was synthesized to cDNA using PrimeScript RT reagent kit with gDNA Eraser (Takara, RR074A) for RT-PCR with mixture of oligo-dT and Random Primer (9 mer). The primers used for qPCR validation were list in Table 1. Real-time qPCR was performed on CFX-96 (Bio-lab), with endogenous control hActb. Gene expression was calculated relative to expression of hActb endogenous control and adjusted relative to expression in SK-MES-1 cells.

Table 1.

Primers for qPCR validation

GENE Forward Reverse
Actb GCATCCCCCAAAGTTCACAA GGACTTCCTGTAACAACGCATCT
EIF2S1 GAAGGCGTATCCGTTCTATCAAC AGCAACATGACGAAGAATGCTAT
PITPNB CAACAAAACCCACATTTCATACATC CAGTGCCCAGAGAGTAAACATCA
CYB5A AGCTGGAGGTGACGCTACTGA ACATTTCCCTGGCATCTGTAGAG
CDK4 AAAGCCTCTCTTCTGTGGAAACTC GCAGCCCAATCAGGTCAAAG
HDGF CTCTTCCCTTACGAGGAATCCA CCTTGACAGTAGGGTTGTTCTC
IFITM2 ATGAACCACATTGTGCAAACCT CCCAGCATAGCCACTTCCT
EIF5A CAAGGAGATTGAGCAGAAGTACGA TGGCAGACAGCACCGTGAT
GOLM1 GGCCCCAGAGATCGTTTGA CTCTGTAACTTCTAGGCCCCATTTT
MYC GGCGAACACACAACGTCTTG TGGTCACGCAGGGCAAA
PTEN GCAGTTGGCTAAGAGAGGTTTCC CTGAGCATTCCCTCCATTCCSK
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Statistical analysis

R2 values were calculated using Pearson’s correlation coefficient. The significant difference was calculated using Student’s t-test.

Results

The IC50s of gemcitabine examination in three NSCLC cell lines

Three NSCLC cell lines, NCI-H1975, NCI-H460 and CTGAGCATTCCCTCCATTCCSK-MES-1, were treated with 7 different concentrations of gemcitabine as indicated for 72 h. Then the cell viability was determined by MTS assay (Figure 1) and IC50s of these three cell lines were calculated. IC50 dose of NCI-H1975, NCI-H460 and SK-MES-1 cells to gemcitabine at 72 h were 471 (R2=0.97), 64 (R2=0.98) and 4.7 nmol/L (R2=0.90), respectively. According to data reported in DTP Data Search (http://dtp.nci.nih.gov/dtpstandard/dwindex/index.jsp), the mean IC50 of NCI-60 cell panel to gemcitabine is 92-240 nmol/L, NCI-H1975 was resistant to gemcitabine, NCI-H460 was moderately sensitive to gemcitabine, while SK-MES-1 was hypersensitive to gemcitabine.

Figure 1.

Figure 1

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Three NSCLC cells showed different sensitivity to gemcitabine. MTS assay was used to determine cell viability of NCI-H1975, NCI-H460 and SK-MES-1 cells treated with gemcitabine (0, 2, 6.3, 20, 63, 200, 630, 2000 nmol/L, respectively) for 72 h. Every treatment was triplicate in the same experiment. Error bars represent the standard deviation (SD).

Differential gene expression profiles of the three NSCLC cell lines

To find differential gene expression profiles of 3 NSCLC cell lines, cell line genetic (mutation and copy number) and gene expression data used for EN analysis on Cancer Centre of Sanger Institute website (http://www.cancerrxgene.org/downloads/) were download and used for analysis. Genes whose expression increased or decreased by higher than 50% gradually from SK-MES-1 to NCI-H460 to NCI-H1975 were considered as significantly altered genes. There were 1,797 genes of increased expression and 3,042 genes of decreased expression from SK-MES-1 to NCI-H460 to NCI-H1975. When the selection criterion was set as higher than 100%, there were 159 genes of increased expression and 716 genes of decreased expression. The most significantly differentially-expressed genes were BCL11B, MAP2, BCL2, IGF1, MYCN, BCL10, POLE3 and ZNF468 (Table 2). Interestingly, TP53, Myc and PTEN are upregulated in gemcitabine-resistant NSCLC cell lines. Expression of TP53, Myc and PTEN was increased by 4.0-, 2.6- and 2.0-fold in NCI-H460, increased by 8.0-, 11.2- and 3.1-fold in NCI-H1975, respectively, compared to that in SK-MES-1.

Table 2.

The most significantly differentially-expressed genes in 3 NSCLC cell lines

gene Relative expression/Fold Description
H1975 H460 SK-MES-1
BCL11B 1.0 22.0 40.9 B-cell CLL/lymphoma 11B
CBX4 1.0 21.4 43.2 chromobox homolog 4
MAP2 1.0 16.1 39.3 microtubule-associated protein 2
HLF 1.0 15.0 37.0 hepatic leukemia factor
MPDZ 1.0 13.5 43.3 multiple PDZ domain protein
ATXN7L1 1.0 13.4 31.3 ataxin 7-like 1
RAPGEF1 1.0 12.8 26.0 Rap guanine nucleotide exchange factor (GEF) 1
BCL2 1.0 4.1 6.5 B-cell CLL/lymphoma 2
IGF1 1.0 2.6 8.1 insulin-like growth factor 1
MYCN 1.0 1.6 6.5 v-myc myelocytomatosis viral related oncogene, neuroblastoma derived (avian)
BCL10 1.0 -1.7 -2.9 B-cell CLL/lymphoma 10
SUCLG2 1.0 -2.4 -5.5 succinate-CoA ligase, GDP-forming, beta subunit
RAB23 1.0 -2.4 -34.2 RAB23, member RAS oncogene family
POLE3 1.0 -2.5 -6.4 polymerase (DNA directed), epsilon 3, accessory subunit
LHFPL2 1.0 -2.9 -8.2 lipoma HMGIC fusion partner-like 2
RPS6KA3 1.0 -3.2 -8.9 ribosomal protein S6 kinase, 90 kDa, polypeptide 3
CPD 1.0 -3.5 -9.5 carboxypeptidase D
LIPG 1.0 -4.9 -16.2 lipase, endothelial
KCTD14 1.0 -6.4 -22.4 potassium channel tetramerization domain containing 14
ZNF468 1.0 -6.8 -15.9 zinc finger protein 468
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“-” in Relative expression columns means down-regulated compared to that in NCI-H1975.

GSEA were done to the differential gene expression profiles of 3 NSCLC cell lines with DAVID 6.7 online software (Figure 2 and Table 3). The 716 genes with decreased expression were mainly enriched in cell adhesion (NRP2, CXCR3, CDK5R1, IL32 and CDH2), disease mutation (SCN1A, FGFR3, NFKBIA, MAF and JAK2), ECM-receptor interaction (COL4A2, COL4A1 and ITGA8) and cell motion (NRP2, CDK5R1, CXCR3, CDH2, POU4F1, IGF1, ISL1, JAK2 and BMP7). The 159 expression-increased genes were mainly enriched in histone-nucleosome-chromatin assembly (H1F0 and HIST1H2AC), methylation modification (HIST1H2AC, RAB8A and TP53), response to extracellular stimulus (MUC1, TP53, and IGFBP2) and tight junction (CSNK2A1 and GNAI1).

Figure 2.

Figure 2

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GSEA of genes highly expressed in gemcitabine sensitive cell lines and resistant cell line, respectively, according to pathway/cell process using online software DAVID 6.7. The black column represents the upregulated pathway/cell process in sensitive cell lines, while the grey column represents the upregulated pathway/cell process in resistant cell line.

Table 3.

GSEA for differentially-expressed genes

pathway/cell process Up/Down* Count P value Genes
cell adhesion down 56 1.25E-07 NRP2, IGFBP7, CLDN5, CXCR3, CDH22, CDH2, CNTNAP2, CDK5R1, COL2A1, IL32, LY6D, COL7A1, COL8A2, LAMA4, DSG3, CDON, etc
disease mutation down 85 4.46E-06 FGF8, SLC7A7, IHH, SOX10, CYP11A1, NCF2, TF, SCN1A, FGFR3, ABCD1, SOX3, NFKBIA, COL2A1, FOXP3, TSC2, JAK2, MAPK8IP1, etc.
ECM-receptor interaction down 14 2.66E-05 IBSP, COL4A2, COL4A1, TNXB, HSPG2, COL2A1, GP9, LAMA4, GP5, CD36, GP1BB, ITGA8, SV2A, SV2C
secretory granule down 17 9.26E-04 TF, A2M, ARC, ICA1, NCF2, SEMG1, IGF1, GP9, AZU1, SLC11A1, GP5, CD36, F5, CAPN11, FGA, GP1BB, CHGB
cell motion down 32 0.002 NRP2, DRD1, CDK5R1, PLXNA3, CXCR3, CDH2, DOCK2, CXCR4, TEKT2, POU3F2, POU4F1, SPON2, VNN2, EFNB3, IGF1, ISL1, CD34, JAK2, BMP7, etc.
nucleosome up 6 1.24E-04 H1F0, HIST1H2AC, HIST1H2BD, H2BFS, HIST1H2BE, HIST1H2BF, HIST1H3H
methylation up 8 0.002 HIST1H2AC, RAB8A, HIST1H2BD, H2BFS, HIST1H2BE, HIST1H2BF, RAB23, TP53, HIST1H3H
oxidoreductase up 11 0.01 TP53I3, CYP27B1, EHHADH, AKR1B1, HSD17B6, OXNAD1, PRDX2, CP, DHRS7B, SC5DL, SOD3
response to extracellular stimulus up 7 0.01 MUC1, CYP27B1, LIPG, TP53, CP, IGFBP2, AQP3
Tight junction up 6 0.01 CSNK2A1, CLDN4, GNAI1, EXOC4, CASK, TJP2
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FDR<20%.

*

up means upregulated in gemcitabine resistant cell lines, while down means down-regulated in gemcitabine resistant cell lines.

And then, the 875 genes (159 upregulated genes and 716 down-regulated genes in gemcitabine-resistant cell line) were applied to ingenuity pathway analysis (IPA) systems. IPA results showed that Wnt/β-catenin signaling, tight junction signaling, aryl hydrocarbon receptor (AHR) signaling and p38MAPK signaling were dramatically altered. Within these altered signaling, Wnt/β-catenin signaling and p38MAPK signaling were predicted to be activated and repressed, respectively. The upregulation of β-catenin (CTNNB1, increased by 2.4-fold in NCI-H1975 compared to that in SK-MES-1), downregulation of SOX (including SOX3, SOX10 and SOX14, decreased by 52.5-, 26.2- and 8.8-fold in NCI-H1975 compared to that in SK-MES-1, respectively.) and predicted inactivation of NLK by downregulation of MAP3K7IP1 (TAB1, decreased by 21.1-fold in NCI-H1975 compared to that in SK-MES-1) concurrently activated the β-catenin/LEF/CBP transcription complex and hence promote the expression of a series of downstream genes, including OCT4, Myc, Cyclin D1, TCF1, MMP7 et al (Figure 3). Highly expression of OCT4 may confer the cancer cell pluripotency.

Figure 3.

Figure 3

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Wnt/β-catenin signaling was activated in gemcitabine resistant NSCLC cell lines. The 875 expression-altered genes were applied to IPA. IPA results showed that Wnt/β-catenin signaling was one of the most significantly activated pathway in gemcitabine resistant NSCLC cell lines. P value is 0.002 and 15 genes in the 875 gene-set were involved in this signaling pathway.

The confirmation of microarray data by qPCR

10 genes whose expression level increased gradually from SK-MES-1 to H460 to H1975 were selected to perform qPCR to validate the microarray data. The qPCR results showed that expression trends of majority of genes were consistent with the microarray data, except for that of EIF2S1 and PIPTNB (Figure 4). Although the expression of these two genes were decreased gradually from H1975 to H460 to SK-MES-1, but the expression changes had no significant difference (EIF2S1: p=0.06 and 0.3, respectively, for H1975 and H460 compared to SK-MES-1; PIPTNB: p=0.06 and 0.01, respectively, for H1975 and H460 compared to SK-MES-1). In spite of this, expression trends of majority of genes (8/10) were consistent with the microarray data, suggesting that online data from Sanger Institute is reliable for gene expression profile analysis. Intriguingly, the upregulation of Myc and PTEN in gemcitabine-resistant NSCLC cell lines was validated.

Figure 4.

Figure 4

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qPCR validation of microarray results. 10 genes whose expression increased in gemcitabine resistant cell line were chosen for qPCR. The expression of each gene was adjusted relative to expression in SK-MES-1 cells. Error bars represent the standard deviation (SD).

The miR expression patterns in the three NSCLC cell lines

Differential expression analysis showed that there were only 10 miRs whose expression altered higher than 100% (p<0.05) gradually from NCI-H1975 to NCI-H460 to SK-MES-1 (Table 4). Within these miRs, expression of miR-155, miR-10a, miR-30a, miR-24-2* and miR-30c-2* was down-regulated in resistant cell lines, while expression of miR-200c, miR-203, miR-885-5p, miR-195 and miR-25* was increased gradually from SK-MES-1 to NCI-H460 to NCI-H1975. The putative targets of these 10 miRs were also partially list in Table 4.

Table 4.

The most differentially expressed miRs in 3 NSCLC cell lines

miR H460 vs H1975 SK-MES-1 vs H1975 Hits** Putative prediction targets
Fold change P Fold change P
miR-155 +4.4 0.005 +1699.6 0.0002 60 ARHGAP29, BCL10, CCNJL, CHM, DDAH1, EXOC5, FDX1, GOLPH3L
miR-10a +4.7 0.03 +40.6 0.001 41 CHM, DDAH1, ELOVL6, EXOC5, FDX1, GOLPH3L, IMPAD1, OSMR, RAP2A, SSR1, ZNF264
miR-24-2* +5.1 0.003 +12.1 0.001 30 ARHGAP5, ENTPD7, IMPAD1, SEMA5A, SSR1, SUCLG2, ZNF264
miR-30c-2* +2.5 0.11 +8.2 0.03 108 ARHGAP5, CHM, DDX51, FDX1, GNA13, GOLPH3L
miR-30a +2.7 0.02 +6.4 0.002 82 ARHGAP29, BCL10, CCNJL, DDAH1, GNA13, IMPAD1,
miR-200c -117.2 0.0002 -1719.0 0.0001 147 AKAP13, BCL2, BCL11B, CDK5R1, DISC1, FGF23, IGF1,
miR-203 -33.8 0.001 -218.9 0.0003 160 AKAP13, ATRX, BCL2, BCL11B, CNTNAP2, DISC1, HLF, IGF1,
miR-885-5p -12.7 0.0002 -30.3 0.001 81 AKAP13, BCL2, DISC1, DZIP1, IGSF3, IKBKB, MMP16
miR-195 -4.7 0.01 -26.2 0.002 190 AKAP13, BCL2, BCL11B, CDK5R1, FGF23, IGF1, IGSF3, IKBKB, MMP16, PAK7
miR-25* -2.1 0.01 -5.9 0.0003 58 ASB1, CA5B, CCNL2, CDON, DZIP1, KLK7, MYCN, POLH
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“+” in fold change columns means upregulated in sensitive cell lines; “-” in fold change columns means down-regulated in sensitive cell lines.

**

hits means amount of overlapped genes between predicted target genes of some miR and genes whose expression was increased or decreased bigger than 50% gradually from H1975 to H460 to SK-MES-1. For miR-24-2*, miR-30c-2* and miR-25*, genes predicted by greater than or equal to 2 programs were selected as putative targets.

MiRs-genes network construction

To observe the interactions between significantly altered miRs and genes in these 3 NSCLC cell lines, the predicted targets of the 10 miRs were done intersections, respectively, with genes whose expression significantly altered to find the putative targets of these miRs in NSCLC cell lines. Candidate genes predicted by miRs of increased expression were done intersection with down-regulated genes determined by microarray, while candidate genes mediated by miRs decreased expression were done intersection with upregulated genes determined by microarray. Amounts of overlapped genes for each miR were showed in Table 4. Expression of 604 genes was mediated by at least one of the 10 miRs. And then the putative targets of each miR of increased or decreased expression were done intersection with the other four miRs of increased or decreased expression, respectively. The overlapped genes mediated by more than or equal to 2 miRs were used for miRs-genes network construction with the aid of Cytoscape 2.8 software (Figure 5). Within these genes, expression of 95 genes decreased gradually and that of 42 genes increased gradually from SK-MES-1 to H460 to H1975. Within the 95 genes, amounts of genes whose decreased expression was mediated by miRs of increased expression were as follow: only one gene (KCNMA1) by 5 miRs; 14 genes (AKAP13, BCL2, IKBKB, MMP16, etc.) by 4 miRs; 29 genes (BCL11B, IGF1, IGSF3, KSR1, PAK7, etc.) by 3 miRs; the remaining was mediated by 2 miRs. Within the 42 genes, amounts of genes whose increased expression was mediated by miRs of decreased expression were as follow: 2 genes (IMPAD1 and ZNF264) by 5 miRs; 2 genes (SEMA5A and SUCLG2) by 4 miRs; 16 genes (CHM, GNA13, POLE3, RAB23, etc.) by 3 miRs; the remaining 22 genes (BCL10, RAP2A, etc.) was mediated by 2 miRs of decreased expression. The genes of increased or decreased expression were applied for functional analysis (Table 5). The 95 genes of decreased expression were mainly enriched in chromosomal rearrangement (MAF, HLF, BCL2, IGSF3, CNTNAP2 and DISC1), protein kinase activity (NRP2, PAK7, PRKCQ, RNASEL, CDK5R1, PLK2, AKAP13, IKBKB and KSR1), cell-cell junction (SCN1A, INADL, MPDZ, TLN2 and ASH1L), Proto-oncogene (MAF, HLF, BCL2, AKAP13 and MYCN) and anti-apoptosis (PAK7, BCL2, CBX4, IGF1 and IKBKB). The 42 genes of increased expression were mainly enriched in small GTPase mediated signal transduction (GNA13, RAP2A, ARHGAP5, RAB23 and ARHGAP29), oncostatin-M receptor activity (OSMR and LIFR) and myosin pathway (GNA13, ARHGAP5).

Figure 5.

Figure 5

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MiRs-genes network. Genes whose expression was putatively mediated by more than or equal to 2 miRs were selected to construct network with corresponding miRs with the aid of Cytoscape 2.8. The yellow square represents miR, and the pink cycle represents mediated gene. A: Network of miRs whose expression was increased with genes whose expression was decreased in gemcitabine sensitive NSCLC cell lines. There were 42 genes in this figure. B: Network of miRs whose expression was increased with genes whose expression was decreased in gemcitabine resistant NSCLC cell lines. There were 95 genes in this figure.

Table 5.

GSEA for differentially expressed genes mediated by significantly altered miRs

pathway/cell process up/down* P value FDR/% Genes
chromosomal rearrangement down 0.008 9.2 MAF, HLF, BCL2, IGSF3, CNTNAP2, DISC1
protein kinase activity down 0.01 13.5 NRP2, PAK7, PRKCQ, RNASEL, CDK5R1, PLK2, AKAP13, IKBKB, KSR1
cell-cell junction down 0.02 17.8 SCN1A, INADL, MPDZ, TLN2, ASH1L
Proto-oncogene down 0.02. 20.9 MAF, HLF, BCL2, AKAP13, MYCN
anti-apoptosis down 0.02 26.3 PAK7, BCL2, CBX4, IGF1, IKBKB
small GTPase mediated signal transduction up 0.006 8.5 GNA13, RAP2A, ARHGAP5, RAB23, ARHGAP29
oncostatin-M receptor activity up 0.007 7.6 OSMR, LIFR
myosin Pathway up 0.03 14.5 GNA13, ARHGAP5
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*

up means upregulated in gemcitabine resistant cell lines, while down means down-regulated in gemcitabine resistant cell lines.

Discussion

Gemcitabine is a widely used drug in the treatment of advanced NSCLC, yet the modest objective response rate in patients to gemcitabine suggests identifying novel biomarkers for gemcitabine sensitivity prediction could do benefit to patients with gemcitabine-based therapy and improving the therapy effect. In this work, we used DNA microarray for gene and miR expression profile analysis to find the differentially expressed genes and miRs in 3 NSCLC cell lines. Hundreds of genes and 10 miRs were found to be markedly differentially expressed and the relationships between these expression-altered genes and miRs were investigated.

3 NSCLC cell lines, NCI-H1975, NCI-H460 and SK-MES-1, are resistant, moderate sensitive and hypersensitive, respectively, to gemcitabine. In these 3 cell lines, there are 875 significantly differentially-expressed genes (expression difference>100%). Within these genes, expression of 716 genes decreased and that of 159 genes increased gradually from gemcitabine sensitive cell lines (NCI-H460 and SK-MES-1) to resistant cell line (NCI-H1975). It was suggested that resistance to treatment is strongly related to tumor cell adhesion to the extracellular matrix [14], and some genes associated to cell adhesion, such as transforming growth factor beta-induced protein (TGFBI) [14] and DSP [19], have been reported that their ectopic expression increases the sensitivity of NSCLC cells to apoptosis induced by gemcitabine. Our data showed the most significantly down-regulated pathway in the 716 genes was cell adhesion, and expression of 56 members of this pathway markedly increased in relatively sensitive cell lines (H460 and SK-MES-1), suggesting the importance of this pathway in mediating sensitivity of NSCLC cells to gemcitabine. Moreover, it was reported that pancreatic tumor cells with a low secretory activity show a better response to gemcitabine [20], while our data showed that sensitive NSCLC cell lines had higher level of genes linked to secretory granule. This difference may be caused by the intrinsic characteristic of the two types of cancer and need to be further studied.

Genes highly expressed in gemcitabine resistant NSCLC cell line were not only potential biomarkers but also putative targets to overcome gemcitabine resistance by knockdown of these genes. These 159 genes were mainly enriched in histone-nucleosome-chromatin assembly (H1F0 and HIST1H2AC), methylation modification (HIST1H2AC, RAB8A and TP53), oxidoreductase (TP53I3, CYP27B1 and EHHADH), response to extracellular stimulus (MUC1, TP53, and IGFBP2) and tight junction (CSNK2A1 and GNAI1). Previous study has showed that methylation of Wnt antagonists like WIF and secreted frizzled related proteins plays roles in NSCLC stem cell maintenance and drug resistance [21]. Recently, it was demonstrated that methylation of TMS1 [22] and DKK3 [23] made pancreatic cancer cell resistant to gemcitabine. Since methylation of some genes can influence gemcitabine resistance, increased expression of genes linked to methylation modification, showed in our results, may enable NSCLC cells resistant to gemcitabine. Furthermore, it was proposed that silencing of oxidoreductase-linked genes, such as Heme oxygenase-1 in cholangiocarcinoma cells [24] and ribonucleotide reductase in pancreatic carcinoma cells [25], sensitized cancer cells to gemcitabine. These previous reports are in accordance to our data, which showed that oxidoreductases were overexpressed in gemcitabine resistant NSCLC cell line, H1975, and therefore may confer NSCLC cells resistance to gemcitabine.

In this work, two types of software, DAVID 6.7 and IPA, were used for GSEA. Results by DAVID 6.7 have been discussed above. IPA results showed some different pattern with DAVID 6.7, except for the tight junction signaling, which was showed to be significantly altered in resistant cell lines following both DAVID 6.7 and IPA analysis. IPA suggested that Wnt/β-catenin signaling and p38MAPK signaling were predicted to be activated and repressed, respectively, in gemcitabine resistant cell lines. Wnt/β-catenin signaling is demonstrated to play crucial roles in caner initiation and progression, its activation confers cancers resistance to multiple drugs, including chemotherapy [26-28] and targeted therapy drugs [29-32]. So its activation in gemcitabine-resistant NSCLC cell line is not surprising. Furthermore, activation of Wnt/β-catenin signaling confers cancer cells stemness and hence acquires multi-drug resistance [29,33]. In our IPA results (Figure 3), upregulation of OCT4, a stemness marker, may be the reason for gemcitabine resistance. Taken together, Wnt/β-catenin signaling may serve as a potential therapeutic target for NSCLC patients resistant to gemcitabine or other therapy, which should be further studied in more NSCLC cell lines and patients.

It has been reported that upregulated miR-155 increases chemo-sensitivity to cisplatin in human Caski cervical cancer cells [34]. MiR-10a was showed that its inhibition enhanced sensitivity to apoptosis in myelodysplastic syndrome [35]. MiR-30a was proposed that it sensitizes tumor cells to cis-platinum via suppressing beclin 1-mediated autophagy [36], but another report showed that overexpression of miR-30a in A549 lung cancer or BEAS-2B normal lung cell lines does not alter drug sensitivity to doxorubicin and cisplatin [37]. MiR-200c restores paclitaxel sensitivity of cancer cells [38] and was showed be expression-decreased in prostate cancer cells resistant to paclitaxel [39]. Dai et al reported that miR-195 was upregulated in docetaxel resistant head and neck squamous cell carcinoma cells [40], but another study showed miR-195 sensitizes human hepatocellular carcinoma cells to 5-FU by targeting BCL-w [41]. There were no report on effects of miR-24-2*, miR-30c-2*, miR-203, miR-885-5p and miR-25* on drug sensitivity. Our data also partially supported the above results, but for more detailed mechanism of actions, it is necessary to validate our data by functional experiments.

The highly expressed genes putatively mediated by significantly expression-decreased miRs in gemcitabine sensitive cell lines, H460 and SK-MES-1, were mainly enriched in chromosomal rearrangement, protein kinase activity, cell-cell junction, proto-oncogene and anti-apoptosis. The highly expressed genes putatively mediated by markedly down-regulated miRs in gemcitabine resistant cell line, H1975, were mainly enriched in small GTPase mediated signal transduction, oncostatin-M receptor activity and myosin pathway. BCL2, linked to anti-apoptosis and chromosomal rearrangement, was highly expressed in sensitive cell lines in our data, suggesting its lower expression is associated with gemcitabine resistance. This result is in line with the report that lower expression of BNIP3, a Bcl-2 family protein, may play an important role in the poor response to gemcitabine treatment in pancreatic cancer patients [42]. Furthermore, many studies have proposed that inhibition of protein kinase activity can sensitize NSCLC cells to gemcitabine, but our data showed that gemcitabine sensitive cell lines, H460 and SK-MES-1, harbored higher expression of 9 protein kinase genes (NRP2, PAK7, PRKCQ, RNASEL, CDK5R1, PLK2, AKAP13, IKBKB and KSR1) putatively mediated by some miRs. This inconsistency may be caused by the intrinsic characteristic of gemcitabine. Gemcitabine, a deoxycytidine analog, is phosphorylated by deoxycytidine kinase (DCK) to active nucleotides. It was suggested that increased DCK and thymidine kinase 2 activities enhanced the sensitivity of NSCLC cells to gemcitabine [43]. So, it is possible that the upregulated 9 protein kinases in sensitive NSCLC cell lines were associated with the activation of gemcitabine. Moreover, previous study has shown that inhibition of RAS, member of small GTPases and kinases, chemosensitized PANC-1 and SW480 cells to gemcitabine [44]. Our data showed that the genes linked to small GTPase mediated signal transduction were down-regulated and putatively repressed by some upregulated miRs in gemcitabine sensitive NSCLC cell lines. Additionally, amount (=604) of genes putatively mediated by altered miRs was far less than that (=4839) of genes differentially expressed in NSCLC cell lines, suggesting that gene expression alterations mediated by miRs only play partial roles in regulating gemcitabine sensitivity of NSCLC cells, there must be some genes themselves that are critical in regulating gemcitabine sensitivity of NSCLC cells.

Collectively, our data showed that hundreds of genes and 10 miRs were markedly differentially expressed in 3 NSCLC cell lines with different sensitivities to gemcitabine. Genes whose expression were increased dramatically in sensitive cell lines were mainly enriched in cell adhesion and secretory granule, while genes whose expression were increased significantly in resistant cell lines were mainly enriched in methylation modification and oxidoreductase. The most intriguing is the activation of Wnt/β-catenin signaling in gemcitabine resistant NSCLC cell lines. Expression of miR-155, miR-10a, miR-30a, miR-24-2* and miR-30c-2* was upregulated in sensitive cell lines, while expression of miR-200c, miR-203, miR-885-5p, miR-195 and miR-25* was increased in resistant cell line. After miR target prediction and miRNA target correlation, genes whose expression altered significantly and putatively mediated by expression-altered miRs were mainly enriched in chromatin assembly (upregulated in sensitive cells), anti-apoptosis (upregulated in sensitive cells), protein kinase (upregulated in sensitive cells) and small GTPase mediated signal transduction (down-regulated in sensitive cells). The above pathways and the involved genes are worth of further study by functional validation in more NSCLC cell lines and tissue samples, which might provide valuable biomarker(s) for Gemcitabine resistance prediction in clinical therapy.

Disclosure of conflict of interest

None.

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