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International Journal of Clinical and Experimental Pathology logoLink to International Journal of Clinical and Experimental Pathology
. 2018 May 1;11(5):2377–2388.

Quantitative proteomic study of mitoxantrone-resistant NCI-H460 cell-xenograft tumors

Li Su 1, Shuang Cui 2, Hongying Zhen 3, Jiao Liu 1
PMCID: PMC6958271  PMID: 31938350

Abstract

Mitoxantrone is one kind of chemical therapy medicine for cancer but certain kinds of cancer cells are chemical-resistant to it. In this research, we analyzed the quantitative proteomic difference between tumors in vivo xenograft by mitoxantrone-resistant (M group) and wild NCI-H460 cells (N group). Protein expression profiling in combination with pathway analysis was deployed to investigate molecular events associated with the tumors using a label-free quantitative proteomic approach. A total of 173 proteins were significantly differentially expressed in mitoxantrone-resistant tumors. Bioinformatics analysis using the cytoscape platform indicated that biological processes, including actin-mediated cell contraction, muscle system process, muscle filament sliding, and muscle contraction, are involved in mitoxantrone-resistance. As KEGG pathway enrichment analysis has shown, systemic lupus erythematosus, alcoholism, viral carcinogenesis, and tight junction are strongly regulated with chemical-resistance. By protein-protein interaction analysis, three protein clusters were found using k-means clustering algorithm. Dysregulation results can be verified by Western blotting. Further studies into the molecular functions of dysregulated proteins will help to provide new perspectives regarding chemoresistance for non-small cell lung cancers.

Keywords: Quantitative proteomics, bioinformatics, protein network, mitoxantrone, multi-drug resistance

Introduction

Lung cancer is the leading cause of cancer-related mortality worldwide, with a 5-year survival rate of only 18% [1]. The rate is still increasing. Non-small cell lung cancer (NSCLC) comprises 85-90% of lung cancer diagnoses [2], with 5-year survival < 20% [3,4], especially large cell lung cancer. As one of the most important therapies, chemotherapy has not been as useful for large cell lung cancer compared with small cell lung cancer. The mechanism of resistance is not very clear.

Recent advances in analytical techniques present a new opportunity to examine the networks and offer a new view of pathologies and therapy targets. Proteomics is a collective study of all expressed proteins in cells, tissues, or biological fluids at a given time. It can reveal information not only on individual proteins but also their interplay in cellular components, biological processes, pathways, and special biochemical functions [5-7]. Liquid chromatography coupled with mass spectrometry (LC-MS) is a high-throughput experimental platform to measure thousands of proteins from complex biological samples, simultaneously [8]. Label-free quantitative proteomics can achieve a good balance between quantitative precision and number of quantified features [9]. It is reliable, versatile, and a cost-effective alternative compared to labeled quantitation [10].

There are many hypotheses to explain chemoresistance of cancers, such as cancer stem cells, micro-environments, etc. Detailed network perspective associated with mitoxantrone-resistance tumors remains unclear, however. In this research, we analyzed the proteomics of mitoxantrone-resistant and wild NCI-H460 cells xenografts tumors in vivo of female athymic nude mice (BALB/C). We also analyzed the bioinformatics and protein-protein interaction networks of dysregulated proteins. Detailed elucidations are as follows.

Materials and methods

3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS), ammonium bicarbonate, sodium deoxycholate, and iodoacetamide were purchased from Sigma (St. Louis, MO, USA). Tris-(2-carboxyethyl) phosphine (TCEP) was acquired from Thermo Scientific (Rockford, Il, USA). Modified sequencing-grade trypsin was obtained from Promega (Madison, WI, USA). All mobile phases and solutions were prepared with HPLC grade solvents (i.e. water, acetonitrile, methanol, and formic acid) from Fisher. All other reagents were from commercial suppliers and of standard biochemical quality.

Cell lines and generation of chemoresistant cancer cells

Cancer cell line NCl-H460 was used. The chemotherapeutic drug mitoxantrone (MXR, 20 ng/mL) was used, a mutant from previous research [11]. In conclusion, the drug treatments were repeated twice or three times, mimicking the clinical regimen that patients with cancers would receive. This strategy ensured that more than 95% of cells underwent apoptosis or senescence with senescent cells eventually dying, thereby selecting the most resistant clones.

In vivo xenografts

Female athymic nude mice (BALB/C, 4-6 weeks of age) were used and all experimental procedures were performed according to protocols approved by the Peking University Health Science Center Animal Care and Use Committee. Each mouse was inoculated subcutaneously in both the side of flank with 5 × 106 H460 or H460/MTX cells suspended in 0.1 mL of serum-free medium containing 50% Matrigel (BD Biosciences, Bedford, MA). At 20 days after inoculation, all mice were sacrificed.

Protein preparation

After scarification, only 1 mm3 of each tumor was used to prepare protein samples. In brief, they were grinded and ultrasonic oscillated in individual tubes. Tissue homogenates were lysed in RIPA buffer (Applygen, Beijing, China). After centrifugation (9000 rpm, 5 min, 4°C), supernatant of total proteins was removed to new tubes and protein concentration was determined by BCA assay (Pierce, Thermo Fisher Scientific, MA, USA). Each group was analyzed in triplicate.

Sample preparation for LC-MS

Protein samples (50 μg) from each group were processed, according to manufacturer protocol, for filter-aided sample preparation (FASP) [12]. Briefly, proteins were concentrated using Vivacon 500 filtration tubes (Cat No. VNO1HO2, Sartorius Stedim Biotech, UK), mixed with 100 μL of 8 M urea in 0.1 M Tris/HCL (pH 8.5) buffer, and centrifuged at 14,000 g and 4°C for 15 minutes. This step was performed twice, after which 10 μL of 0.05 M TCEP in water was added to the filters and samples were incubated at 37°C for 1 hour. Then, 10 μL of 0.1 M iodoacetamide (IAA) was added to the filters, afterwards the samples were incubated in darkness for 30 minutes. Filters were washed twice with 200 μL of 50 mM NH4HCO3. Finally, 1 μg of trypsin in 100 μL of 50 mM NH4HCO3 was added to each filter. The protein to enzyme ratio was 50:1. Samples were incubated overnight at 37°C and released peptides were collected by centrifugation.

LC-MS analysis

LC-MS experiments were performed on a nano-flow HPLC system (Easy-nLC 1000, Thermo Fisher Scientific, Waltham, MA, USA) connected to a Q-Exactive HF mass spectrometer (Thermo Fisher Scientific), equipped with a Nanospray Flex Ion Source (Thermo Fisher Scientific). One μg peptide mixtures (5 μL) were separated using a home-made reversed phase C18 column (75 μm I.D. × 20 mm, 3 μm particle size) at a flow rate of 300 nL/min. Chromatographic separation was performed with a 90 minute gradient of 2% to 40% acetonitrile in 0.1% formic acid. The electrospray voltage was maintained at 2.2 kV and capillary temperature was set at 275°C. Q-Exactive HF was operated in data-dependent mode to simultaneously measure full scan MS spectra (m/z 300-1800) in the Orbitrap with a mass resolution of 60,000 at m/z 400. After full-scan survey, the 20 most abundant ions detected in the full-MS scan were measured in the Orbitrap using HCD mode.

Protein identification and quantification

Data analysis was performed with MaxQuant software (version 1.6.0.16) (http://www.maxquant.org/) [13]. For protein identification, MS/MS data were submitted to the Uniprot human protein database using the Andromeda search engine [14] with the following settings: trypsin cleavage; fixed modification of carbamidomethylation of cysteine; variable modifications of methionine oxidation; a maximum of two missed cleavages; false discovery rate of 0.01 at both peptide and protein level. Other parameters were set as default. These results were imported into Microsoft excel for further analysis. Label-free quantitation (LFQ) was also performed in MaxQuant. Minimum ratio count for LFQ was set to 2 and match-between-runs option was enabled. Other parameters were set as default. A 2-fold change in expression and a p-value of Student’s t-test of 0.05 were used as a combined threshold to define biologically dysregulated proteins.

Bioinformatics analysis

Principal component analysis (PCA) and hierarchical clustering analysis were performed using MetaboAnalyst 3.0 web service (http://www.metaboanalyst.ca/). For bioinformatics analysis, the 172 differentially expressed proteins were used as inputs. Protein-protein interaction networks were constructed by STRING web service (http://www.string-db.org/). DAVID web service (https://david.ncifcrf.gov/) was used to retrieve the Gene Ontology Consortium (GOC, http://geneontology.org/) annotation results and KEGG pathway enrichment results.

Western blotting

The same protein samples for LC-MS analysis were also used for Western blotting assay. After the addition of sample loading buffer, protein samples of each group were separated using 10% SDS-PAGE and subsequently transferred to PVDF membrane (Millipore). The membrane was incubated in fresh blocking buffer (0.1% Tween 20 in Tris-buffered saline, pH 7.4, containing 5% non-fat dried milk) at room temperature for 1 hour and then probed with monoclonal mouse anti-RAD50 antibody (Abcam, Cambridge, UK) and mouse anti-glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody (Zhongshan Gold Bridge Biotechnology Co. Ltd, China) in blocking buffer at 4°C overnight. Membranes were washed three times for 5 minutes each using PBST (PBS containing 0.1% Tween-20), then incubated with appropriate horseradish peroxidase (HRP)-conjugated secondary antibody at room temperature for 1 hour. Then, it was washed three more times in PBST buffer. The membrane was finally incubated with ECL substrate solution (ECL Kit, Perkinelmer) for 5 minutes, according to manufacturer instructions, and visualized with autoradiographic film.

Statistical analysis

Results are expressed as mean ± S.E.M. Statistical evaluation was performed using Student’s t-test (for comparing two value sets). P < 0.05 was considered statistically significant (*P < 0.05: **P < 0.01).

Results

Many proteins expressed different degrees between two kinds of tumors

From histogram results of raw abundance of proteins before and after logarithm transformation, as shown in Figure 1, protein expression revealed a normal distribution pattern after log transformation confirming the reliability of proteomics analysis. Statistical analysis with Perseus software was performed to select proteins that were differentially expressed between two kinds of tumors, using the following criteria: fold change > 2 or < 0.5, p-value < 0.01 (using Student’s t-test). Among thousands of proteins, there were 173 proteins dysregulated between the two kinds of tumors (Table 1). 52 proteins were downregulated in the M groups, during which there were twenty proteins only detected in the N groups. The other 121 proteins were highly upregulated and 83 of them only detected in the M groups.

Figure 1.

Figure 1

Histogram results of raw abundance of proteins before and after logarithm transformation. Histogram graph represents the protein expression distribution before (A) and after (B) logarithm (base 2) transformation. X-axis represents the intensities of proteins and Y-axis represents number of proteins. Protein expression shows a normal distribution pattern after log transformation.

Table 1.

Identification of differentially expressed proteins in mitoxantrone-resistant (M) and wild tumors NCI-H460 (N) cells xenografts tumors using LC-MS/MS

Majority Protein ID Protein names Gene names Ratio (M/N) -log10 t-test p-value
P09917 Arachidonate 5-lipoxygenase ALOX5 0 6.21
E9PMR4 Tetraspanin CD151 0 3.27
F8W8G8 Collagen alpha-5(VI) chain COL6A5 0 3.72
P56537 Eukaryotic translation initiation factor 6 EIF6 0 4.55
Q8NEZ5 F-box only protein 22 FBXO22 0 3.68
Q9Y5Y0 Feline leukemia virus subgroup C receptor-related protein 1 FLVCR1 0 8.17
P09471 Guanine nucleotide-binding protein G(o) subunit alpha GNAO1 0 5.44
P46734 Dual specificity mitogen-activated protein kinase kinase 3 MAP2K3 0 5.61
Q14696 LDLR chaperone MESD MESDC2 0 5.49
Q96T76 MMS19 nucleotide excision repair protein homolog MMS19 0 3.73
Q9UBG0 C-type mannose receptor 2 MRC2 0 3.09
Q86UY8 5-nucleotidase domain-containing protein 3 NT5DC3 0 4.65
Q96RS6 NudC domain-containing protein 1 NUDCD1 0 4.33
O95486 Protein transport protein Sec24A SEC24A 0 3.69
B1AMS2 Septin-6 septin 6 0 4.84
Q9H936 Mitochondrial glutamate carrier 1 SLC25A22 0 5.45
J3KTL8 Structural maintenance of chromosomes flexible hinge domain-containing protein 1 SMCHD1 0 4.01
Q13813-3 Spectrin alpha chain, non-erythrocytic 1 SPTAN1 0 6.82
F8WF27 Transmembrane 4 L6 family member 1 TM4SF1 0 1.38
P67936 Tropomyosin alpha-4 chain TPM4 0 4.02
Q99878 Histone H2A type 1-J HIST1H2AJ 0.05 1.70
Q7LBC6 Lysine-specific demethylase 3B KDM3B 0.18 1.71
P06396 Gelsolin GSN 0.22 1.48
P20827 Ephrin-A1 EFNA1 0.22 1.55
P16402 Histone H1.3 HIST1H1D 0.23 2.09
D6RFM0 Ubiquitin-conjugating enzyme E2 D2 UBE2D2 0.23 1.50
P12532 Creatine kinase U-type, mitochondrial CKMT1A 0.23 2.18
Q9C0B1 Alpha-ketoglutarate-dependent dioxygenase FTO FTO 0.25 1.4
P11182 Lipoamide acyltransferase component of branched-chain alpha-keto acid dehydrogenase complex, mitochondrial DBT 0.26 1.36
P16401 Histone H1.5 HIST1H1B 0.26 6.04
Q9H0E9 Bromodomain-containing protein 8 BRD8 0.26 1.96
P10124 Serglycin SRGN 0.27 4.09
K7EKP1 Apolipoprotein C-I APOC1 0.30 1.54
E7EQB2 Lactotransferrin LTF 0.33 2.60
P16403 Histone H1.2 HIST1H1C 0.36 2.88
Q71UI9 Histone H2A.V H2AFV 0.36 1.79
Q16778 Histone H2B type 2-E HIST2H2BE 0.37 1.45
A8MYE6 Integrin beta ITGB2 0.39 3.69
P11215 Integrin alpha-M ITGAM 0.41 3.8
P01008 Antithrombin-III SERPINC1 0.41 4.00
P98088 Mucin-5AC MUC5AC 0.41 6.71
Q5TEC6 Histone H3 HIST2H3PS2 0.43 1.52
P12955 Xaa-Pro dipeptidase PEPD 0.44 4.40
P05164 Myeloperoxidase MPO 0.45 6.02
B4DR52 Histone H2B HIST1H2BN 0.46 3.99
O75531 Barrier-to-autointegration factor BANF1 0.47 1.52
P62805 Histone H4 HIST1H4A 0.47 4.19
P00352 Retinal dehydrogenase 1 ALDH1A1 0.47 4.62
Q6FI13 Histone H2A type 2-A HIST2H2AA3 0.47 3.77
P05204 Non-histone chromosomal protein HMG-17 HMGN2 0.48 2.65
P00747 Plasminogen PLG 0.48 2.73
E9PBJ0 Mucin-5B MUC5B 0.49 5.83
Q01081 Splicing factor U2AF 35 kDa subunit U2AF1 2.03 3.68
P22087 rRNA 2-O-methyltransferase fibrillarin FBL 2.04 6.04
A6NN80 Annexin A6 ANXA6 2.05 6.17
O00499 Myc box-dependent-interacting protein 1 BIN1 2.09 1.78
F5H7V9 Tenascin TNC 2.11 4.36
P04264 Keratin, type II cytoskeletal 1 KRT1 2.19 4.25
Q8NI27 THO complex subunit 2 THOC2 2.25 1.90
Q9UBR2 Cathepsin Z CTSZ 2.35 4.63
Q96PZ0 Pseudouridylate synthase 7 homolog PUS7 2.46 1.32
P50402 Emerin EMD 2.48 1.32
F5H6U7 Vesicle transport protein GOT1B GOLT1B 2.63 1.93
Q96FQ6 Protein S100-A16 S100A16 2.67 4.29
Q13642 Four and a half LIM domains protein 1 FHL1 2.82 2.89
P60903 Protein S100-A10 S100A10 2.96 4.09
P68133 Actin, alpha skeletal muscle ACTA1 3.42 4.73
P11217 Glycogen phosphorylase, muscle form PYGM 3.58 4.01
P41223 Protein BUD31 homolog BUD31 3.92 1.32
Q8NBW7 ER lumen protein-retaining receptor 1 KDELR1 3.95 2.18
P23786 Carnitine O-palmitoyltransferase 2, mitochondrial CPT2 4.00 1.33
Q8N0U8 Vitamin K epoxide reductase complex subunit 1-like protein 1 VKORC1L1 4.01 1.40
P50238 Cysteine-rich protein 1 CRIP1 4.06 1.42
Q7Z4H3 HD domain-containing protein 2 HDDC2 4.10 1.34
Q9H2W6 39S ribosomal protein L46, mitochondrial MRPL46 4.29 1.35
P14618 Pyruvate kinase PKM PKM; PKM2 4.31 1.46
Q9NVP1 ATP-dependent RNA helicase DDX18 DDX18 4.44 1.58
P98160 Basement membrane-specific heparan sulfate proteoglycan core protein HSPG2 4.54 1.60
Q8IYB8 ATP-dependent RNA helicase SUPV3L1, mitochondrial SUPV3L1 4.54 1.51
O00139 Kinesin-like protein KIF2A KIF2A 4.61 1.53
O00767 Acyl-CoA desaturase SCD 4.86 1.73
Q86UX7 Fermitin family homolog 3 FERMT3 5.00 1.69
F5H4G7 Importin subunit alpha-6 KPNA6 5.01 1.72
B4E0V0 Pyridoxine-5-phosphate oxidase PNPO 6.44 2.03
Q86TD4 Sarcalumenin SRL 8.12 2.67
P07451 Carbonic anhydrase 3 CA3 9.19 5.59
E9PR30 40S ribosomal protein S30 FAU 25.28 4.61
P12882 Myosin-1 MYH1 34.78 5.65
P13929 Beta-enolase ENO3 40.44 2.95
P06732 Creatine kinase M-type CKM 140.97 3.70
F5GYC1 ATP-binding cassette sub-family D member 3 ABCD3 #DIV/0! 4.67
P35609 Alpha-actinin-2 ACTN2 #DIV/0! 3.46
Q9UKV8 Protein argonaute-2 AGO2 #DIV/0! 3.30
Q9BT22 Chitobiosyldiphosphodolichol beta-mannosyltransferase ALG1 #DIV/0! 3.85
Q5VY93 Rho guanine nucleotide exchange factor 2 ARHGEF2 #DIV/0! 3.01
Q96BM9 ADP-ribosylation factor-like protein 8A ARL8A #DIV/0! 6.74
O14983 Sarcoplasmic/endoplasmic reticulum calcium ATPase 1 ATP2A1 #DIV/0! 4.43
Q93084 Sarcoplasmic/endoplasmic reticulum calcium ATPase 3 ATP2A3 #DIV/0! 4.26
P46100 Transcriptional regulator ATRX ATRX #DIV/0! 5.97
C9JGJ9 B-cell receptor-associated protein 29 BCAP29 #DIV/0! 4.71
O75934 Pre-mRNA-splicing factor SPF27 BCAS2 #DIV/0! 3.05
P46736 Lys-63-specific deubiquitinase BRCC36 BRCC3 #DIV/0! 3.00
F5GX99 Caseinolytic peptidase B protein homolog CLPB #DIV/0! 5.06
P09497 Clathrin light chain B CLTB #DIV/0! 6.07
A6NLH6 Protein cornichon homolog 4 CNIH4 #DIV/0! 6.35
E9PJL7 Alpha-crystallin B chain CRYAB #DIV/0! 3.11
Q13363 C-terminal-binding protein 1 CTBP1 #DIV/0! 2.08
P07858 Cathepsin B CTSB #DIV/0! 6.02
Q9BVQ8 Probable ATP-dependent RNA helicase DDX49 DDX49 #DIV/0! 2.81
P00374 Dihydrofolate reductase DHFR #DIV/0! 5.94
Q8IYJ9 Dual specificity protein phosphatase 3 DUSP3 #DIV/0! 3.78
P49770 Translation initiation factor eIF-2B subunit beta EIF2B2 #DIV/0! 4.70
P11171 Protein 4.1 EPB41 #DIV/0! 3.36
C9JAG1 Ethanolaminephosphotransferase 1 EPT1 #DIV/0! 4.80
Q14315 Filamin-C FLNC #DIV/0! 7.06
A8MQB8 Fragile X mental retardation protein 1 FMR1 #DIV/0! 3.83
Q14C86 GTPase-activating protein and VPS9 domain-containing protein 1 GAPVD1 #DIV/0! 5.79
O00461 Golgi integral membrane protein 4 GOLIM4 #DIV/0! 2.86
B7WNW7 HEAT repeat-containing protein 3 HEATR3 #DIV/0! 5.08
O43719 HIV Tat-specific factor 1 HTATSF1 #DIV/0! 3.71
O15357 Phosphatidylinositol 3,4,5-trisphosphate 5-phosphatase 2 INPPL1 #DIV/0! 4.52
Q68E01 Integrator complex subunit 3 INTS3 #DIV/0! 4.29
Q14573 Inositol 1,4,5-trisphosphate receptor type 3 ITPR3 #DIV/0! 3.70
H0Y8E4 Kinase D-interacting substrate of 220 kDa KIDINS220 #DIV/0! 3.38
O75112 LIM domain-binding protein 3 LDB3 #DIV/0! 5.79
P21397 Amine oxidase [flavin-containing] A MAOA #DIV/0! 4.94
Q9P015 39S ribosomal protein L15, mitochondrial MRPL15 #DIV/0! 5.32
Q96DV4 39S ribosomal protein L38, mitochondrial MRPL38 #DIV/0! 3.92
Q13405 39S ribosomal protein L49, mitochondrial MRPL49 #DIV/0! 6.04
B8ZZU9 Bifunctional methylenetetrahydrofolate dehydrogenase/cyclohydrolase, mitochondrialdehydrogenase MTHFD2 #DIV/0! 4.31
Q14324 Myosin-binding protein C, fast-type MYBPC2 #DIV/0! 3.66
Q9UKX2 Myosin-2 MYH2 #DIV/0! 6.62
Q9Y623 Myosin-4 MYH4 #DIV/0! 2.50
P12883 Myosin-7 MYH7 #DIV/0! 3.79
Q96A32 Myosin regulatory light chain 2, skeletal muscle isoform MYLPF #DIV/0! 3.06
S4R3I5 NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 3 NDUFA3 #DIV/0! 3.63
H0Y9M8 NADH dehydrogenase [ubiquinone] iron-sulfur protein 4, mitochondrial NDUFS4 #DIV/0! 5.69
C9J808 MKI67 FHA domain-interacting nucleolar phosphoprotein NIFK #DIV/0! 5.67
P46087 Probable 28S rRNA (cytosine(4447)-C(5))-methyltransferase NOP2 #DIV/0! 4.01
Q5TFE4 5-nucleotidase domain-containing protein 1 NT5DC1 #DIV/0! 5.57
Q9NPF4 Probable tRNA N6-adenosine threonylcarbamoyltransferase OSGEP #DIV/0! 3.74
J3KNQ4 Alpha-parvin PARVA #DIV/0! 5.65
Q15118 [Pyruvate dehydrogenase (acetyl-transferring)] kinase isozyme 1, mitochondrial PDK1 #DIV/0! 3.54
P15259 Phosphoglycerate mutase 2 PGAM2 #DIV/0! 2.44
Q969N2 GPI transamidase component PIG-T PIGT #DIV/0! 4.81
P24928 DNA-directed RNA polymerase II subunit RPB1 POLR2A #DIV/0! 4.36
E9PG73 Peptidyl-prolyl cis-trans isomerase G PPIG #DIV/0! 4.12
P50336 Protoporphyrinogen oxidase PPOX #DIV/0! 4.02
P51888 Prolargin PRELP #DIV/0! 3.10
P17252 Protein kinase C alpha type PRKCA #DIV/0! 5.20
A2A2V1 Major prion protein PRNP #DIV/0! 4.91
E7EVX8 U4/U6 small nuclear ribonucleoprotein Prp31 PRPF31 #DIV/0! 4.24
O95758 Polypyrimidine tract-binding protein 3 PTBP3 #DIV/0! 3.83
Q92878 DNA repair protein RAD50 RAD50 #DIV/0! 6.15
H0YAE9 Ribonuclease T2 RNASET2 #DIV/0! 4.24
D6RD69 GTP-binding protein SAR1b SAR1B #DIV/0! 5.07
Q86TU7 Histone-lysine N-methyltransferase setd3 SETD3 #DIV/0! 5.76
O15374-4 Monocarboxylate transporter 5 SLC16A4 #DIV/0! 4.55
O00186 Syntaxin-binding protein 3 STXBP3 #DIV/0! 4.09
E7EMB1 Switch-associated protein 70 SWAP70 #DIV/0! 2.97
P57105 Synaptojanin-2-binding protein SYNJ2BP #DIV/0! 2.64
E9PF19 Transducin beta-like protein 2 TBL2 #DIV/0! 4.88
Q92544 Transmembrane 9 superfamily member 4 TM9SF4 #DIV/0! 5.47
P02585 Troponin C, skeletal muscle TNNC2 #DIV/0! 7.89
P48788 Troponin I, fast skeletal muscle TNNI2 #DIV/0! 3.21
H9KVA2 Troponin T, fast skeletal muscle TNNT3 #DIV/0! 3.65
Q8NFQ8 Torsin-1A-interacting protein 2 TOR1AIP2 #DIV/0! 3.40
Q6ZMU5 Tripartite motif-containing protein 72 TRIM72 #DIV/0! 3.69
B4DEB8 Tetraspanin-7 TSPAN7 #DIV/0! 4.35
Q6PGP7 Tetratricopeptide repeat protein 37 TTC37 #DIV/0! 2.32
Q9BZX2 Uridine-cytidine kinase 2 UCK2 #DIV/0! 3.72
Q5MNZ6 WD repeat domain phosphoinositide-interacting protein 3 WDR45B #DIV/0! 3.56
Q5BJH7 Protein YIF1B YIF1B #DIV/0! 4.52

An overview of proteomics analysis of the tumors is shown in Figure 2A, a PCA score plot of the two groups in terms of PC1 (X-axis) and PC2 (Y-axis). The two groups are presented by red (group N) and green (group M). Figure 2B shows volcano plot of the 2,406 proteins quantified. We determined the fold change in protein expression as X-axis represents fold change (group M/N) and Y-axis represents log10 (p-value). Up- and down-regulated proteins are colored in pink. Expression levels of the 172 proteins in all samples are shown in the heat map generated by hierarchical cluster analysis, displaying a clear difference in mitoxantrone-resistant (M) and wild NCI-H460 (N) cell xenograft tumors (Figure 3A).

Figure 2.

Figure 2

Overview of proteomic analysis of the tumors. A: PCA score plot of the two groups in terms of PC1 (X-axis) and PC2 (Y-axis). The two groups are presented by red (group B) and green (group LA). B: Volcano plot of the 2,406 proteins quantified. X-axis represents fold change (group M/N), and Y-axis represents -log10 (p-value). Up- and down-regulated proteins are colored pink.

Figure 3.

Figure 3

Expression pattern and bioinformatics analysis of significantly dysregulated proteins. A: Heat map representing the hierarchical clustering analysis results of the dysregulated proteins. Red represents upregulated proteins in group M and green represents downregulated proteins. B: Top 15 items of the gene ontology enrichment analysis in terms of cellular component. X-axis represents -log10 (p-value). C: Top 15 items of the gene ontology enrichment analysis in terms of biological process. D: Top 15 items of the gene ontology enrichment analysis in terms of molecular function. E: KEGG pathway enrichment analysis.

Bioinformatics analysis

To better understand the regulative network influenced by treatment, we analyzed the expression pattern and bioinformatics analysis of significantly dysregulated proteins using dysregulated proteins as inputs. Figure 3B shows the top 14 items of gene ontology enrichment analysis in terms of cellular components. X-axis represents -log10 (p-value). The cellular locations of these dysregulated proteins were mainly at sarcomere, contractile fiber part, contractile fiber, myofibril, and organelle part et al. Figure 3C shows the top 14 items of gene ontology enrichment analysis in terms of biological processes. Dysregulated proteins influenced by treatment were related with several biological processes. Bioinformatics analysis, using the cytoscape platform, indicated that biological processes, including actin-mediated cell contraction, muscle system process, muscle filament sliding, and muscle contraction, are involved in mitoxantrone-resistance. Figure 3D shows the top 14 items of gene ontology enrichment analysis in terms of molecular function. Their functions were included in binding, anion binding, carbohydrate derivative binding, small molecule binding, and structural molecule activity et al. As KEGG pathway enrichment analysis shows in Figure 3E, systemic lupus erythematosus, alcoholism, viral carcinogenesis, and tight junction were strongly regulated with mitoxantrone-resistance.

To construct a protein-protein interaction network associated with two kinds of tumors, we matched the 173 significantly differently expressed proteins with regulatory and data protein-protein interaction networks were constructed by STRING web service (http://www.string-db.org/) (Figure 4). Each node represents a protein and each line represents one kind of known interaction. Three protein clusters were found, using k-means clustering algorithm, and are represented by different node colors (green, red, and blue).

Figure 4.

Figure 4

Protein-protein interaction network constructed using dysregulated proteins. Each node represents a protein and each line represents one kind of known interaction. Three protein clusters are found using k-means clustering algorithm and represented by different node colors (green, red and blue).

Western blot verification of differentially expressed protein RAD50

RAD 50 is one of the important proteins dysregulated between the two groups. We verified its different expression by Western blot and got the same conclusion of its upregulation in the mitoxantrone-resistant cell xenografts group (Figure 5).

Figure 5.

Figure 5

Western blotting of RAD50 expression in vivo xenograft tumors. The band intensity shows that mitoxantrone-resistant cells xenograft results in upregulation of RAD50 compared with wild NCI-H460 cells xenograft. Data are means ± SD of the results from three independent experiments, **P < 0.01.

Discussion

Tumor cell xenografts in athymic nude mice are popular models for cancer research. Animal models enable us to further understand molecular and regulatory mechanisms of tumors such as occurrence, development, and multidrug resistance (MDR). Compared with cellular studies in vitro, animal models of MDR have more clinical value. Chemoresistant cell xenografts are usually used as MDR models in vivo [15,16]. NCI-H460 is a kind of non-small cell lung cancer (NSCLC) cell line and the mitoxantrone-resistance cell line is usually used for MDR mechanism in vitro. Deep research on mitoxantrone-resistance cell xenografts will be very helpful in understanding the MDR mechanism of NSCLC.

High throughput proteomics by liquid chromatography mass spectrometry (LC-MS) has become core instrumentation offering highly relevant information towards biology including protein composition, post-translational modifications, and protein dynamics, due to its performance and sensitivity [17,18]. Using network analysis, bioinformatic analysis of pathway levels and protein-protein interaction is popular and helpful for biological and medicine research such as diagnosing disease phenotype by identification of disease-specific biomarkers for cancer [19], neurodegeneration diagnosis [20], and other diseases [21]. For example, Diederick discovered novel biomarkers for prostate cancer progression using LC-MS mode [22]. Therefore, it is a powerful tool for connecting genotypes to phenotypes.

Bioinformatics is a useful tool for analyzing high throughput data from genomics, proteomics, metabolomics, and lipidomics [23]. There are many kinds of databases online, such as DIP, BIND, Intact, KEGG, and STRING. Each of them can provide different protein-protein interaction information on metabolic and signaling pathways or multiple organisms. All of these can provide useful and important information in understanding complex pathogeneses or biological phenomena. In this research, we identified 173 dysregulated proteins between tumors forming from mitoxantrone-resistant and wild NCI-H460 cell xenografts. Bioinformatics analysis showed that they belong to three protein-protein interaction clusters and all of them can interact by weak or hard connections. There are some important proteins which lie in the cores and connect to other proteins, including Rad 50. Rad 50 is involved in many biological processes including single-organism metabolic process, cellular component organization, cellular component organization or biogenesis, and response to stress. It can detect damage both of nuclear DNA and virus DNA and then induce immune responses [24]. We also verified upregulation of Rad50 in mitoxantrone-resistant tumors by Western blot analysis, demonstrating the reliability of proteomics results. Nevertheless, there are so many different proteins that clear function researches are needed to attain a deep understanding of chemoresistant mechanisms of cancer therapies. These will also be helpful in finding new targets for clinical therapies.

Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (30700206, 31700898).

Disclosure of conflict of interest

None.

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