Original Article Identification of Novel Proteins in Chemoresistant Lung Cancer Cells by Quantitative Proteomics

Int J Clin Exp Pathol 2018;11(3):1101-1111 www.ijcep.com /ISSN:1936-2625/IJCEP0069378

Original Article Identification of novel proteins in chemoresistant lung cancer cells by quantitative proteomics

Li Su1, Jiao Liu1, Hongying Zhen2

1Center of Medical and Health Analysis, Peking University, Beijing, China; 2The Department of Cell Biology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China Received November 18, 2017; Accepted December 2, 2017; Epub March 1, 2018; Published March 15, 2018

Abstract: Lung cancer is the leading cause of cancer-related deaths worldwide, with a five-year survival rate of only 18%. Non-small cell lung cancer (NSCLC) in addition to large cell lung cancer, comprise 85%-90% of all lung cancer diagnoses. Chemoresistance of the cancer cells is one of the reasons for the poor survival rate. In this research, we used mitoxantrone-induced resistant (MXR) NCl-H460 cells to find the mechanism of chemoresistance. We found that the MXR-resistant cells had high single-cell clonogenic ability like cancer stem cells. From the quantitative proteomics study, we found that the MXR-resistant cells high upregulated many metabolism and stem cell-related proteins, such as STAT3 and ALDH. The high level expression of histone 3.1 showed the possibility of genetic chang- ing of resistant cells. Using Western blot assays, we confirmed enhancement of EZH2 in MXR-resistant NCl-H460 cells. Therefore, the EZH2-STAT3 pathway has an important role in the MXR-resistant NCI-H460 cancer cells. Both EZH2 and STAT3 can be used as new target proteins for chemotherapy in the treatment of large cell lung cancer.

Keywords: Multidrug resistance, mitoxantrone, proteomics, bioinformatics, STAT3, cancer stem cells

Introduction of the resistance is not very clear. Cancer stem cells (CSCs) are one of the important reasons. Cancer incidence and mortality have been increasing in China, making cancer the leading Cancer stem cells, also called tumor-initiating cause of death since 2010 and a major public cells, have a high capacity for self-renewal and health problem. Lung cancer is the leading multi-lineage differentiation and are believed to cause of cancer-related deaths worldwide, with be responsible for tumor development, recur- rence and dissemination, as well as the acquisi- a five-year survival rate of only 18% [1]. In China tion of drug resistance [6]. These “stemness” also, lung cancer is the most common and has properties are governed by pathways such as the poorest survival rate [2]. STAT3, NANOG, NOTCH, WNT, and HEDGEHOG, There are two kinds of lung cancer, one is called which are highly dysregulated in CSCs due to genetic and epigenetic changes of cancers [7]. small cell lung cancer (SCLC), which is sensitive The proteins of these pathways are thought of to chemotherapy and has a high survival rate. as the CSCs markers. Different CSCs show dif- The other one is called non-small cell lung can- ferent dysregulation of the pathways. In this cer (NSCLC), which comprises 85%-90% of lung research, we used the non-small cell lung can- cancer diagnoses [3]. Despite drastic treat- cer cell lines, NCI-H460, to find the main dys- ment strategies, including radiotherapy, che- regulated proteins during the mitoxantrone- motherapy, and even immune approaches, the induced chemoresistance by quantitative five-year survival rate for all NSCLC is still < proteomics. We found 60 proteins that were 20% [4, 5]. Chemotherapy is not as useful for dysregulated which might mediate the mixto- large cell lung cancer as it is with small cell lung xantrone-resistance of the NCI-H460 cells, cancer. Multidrug resistance is one of the rea- including upregulation of ALDH1 and the EZH2- sons for its poor survival rate. The mechanism STAT3 pathway, which can be used as the new EZH2-STAT3 pathway and mitoxantrone-resistance in NCl-H460 cells protein targets of large cell lung cancer th- China) and the protein concentration was erapy. determined by BCA assay (Pierce, Thermo Fisher Scientific, MA, USA). Each group was Materials and methods analyzed in triplicate.

3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxy- Sample preparation for LC-MS phenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS), ammonium bicarbonate, sodium deoxycholate, Protein samples (50 μg) from each group were and iodoacetamide were purchased from Sigma processed according to the manufacturer’s (St. Louis, MO, USA). Tris-(2-carboxyethyl) phos- protocol for filter-aided sample preparation phine (TCEP) was acquired from Thermo Sci- (FASP) [10]. In brief, proteins were concentrat- entific (Rockford, Il, USA). Modified sequencing- ed using Vivacon 500 filtration tubes (Cat No. grade trypsin was obtained from Promega (Ma- VNO1HO2, Sartorius Stedim Biotech, UK), dison, WI, USA). All mobile phases and solutions mixed with 100 μL of 8 M urea in 0.1 M Tris/ were prepared with HPLC grade solvents (i.e. HCL (pH 8.5) buffer, and centrifuged at 14,000 water, acetonitrile, methanol, and formic acid) g and 4°C for 15 minutes. This step was per- from Fisher. All other reagents were from com- formed twice, after which 10 μL of 0.05 M TCEP mercial suppliers and were of standard bio- in water was added to the filters, and samples chemical quality. were incubated at 37°C for 1 h. Then, 10 μL of 0.1 M iodoacetamide (IAA) was added to the Cell lines and generation of chemoresistant filters, then the samples were incubated in cancer cells darkness for 30 minutes. Filters were washed twice with 200 μL of 50 mM NH HCO . Finally, Cancer cell line, NCl-H460 was used. Ch- 4 3 1 μg of trypsin in 100 μL of 50 mM NH HCO emotherapeutic drug, mitoxantrone (MXR, 20 4 3 was added to each filter. The protein to enzyme ng/mL) was used, along with a mutant from the ratio was 50:1. Samples were incubated over- previous research [8]. Drug treatments were night at 37°C and released peptides were col- repeated two or three times, which mimicked lected by centrifugation. the clinical regimen that patients with cancers would receive. This strategy ensured that more LC-MS analysis than 95% of cells underwent apoptosis or senescence with the senescent cells eventually LC-MS experiments were performed on a nano- dying, thereby selecting the most resistant flow HPLC system (Easy-nLC 1000, Thermo clones. Fisher Scientific, Waltham, MA, USA) connect- ed to a LTQ-Orbitrap Elite mass spectrometer Single-cell clonogenic assay (Thermo Fisher Scientific), equipped with a To assess self-renewal in vitro, cells were sort- Nanospray Flex Ion Source (Thermo Fisher ed by flow cytometry (FACS Aria II) using the Scientific). 1 μg peptide mixtures (5 μL) were single-cell sorting model, seeded into 6-well separated using a home-made reversed-phase plates (50, 100 or 200 cells per well), and cul- C18 column (150 μm I.D. × 150 mm, 3 μm of tured in a 10% fetal bovine serum (FBS)- particle size) at a flow rate of 300 nL/min. containing RPMI-1640 medium. Cell clones Chromatographic separation was performed were counted after fourteen days when a clone with a 60 min gradient of 2% to 40% acetoni- reached 100-200 cells [8, 9]. trile in 0.1% formic acid. The electrospray voltage was maintained at 2.2 kV, and the capillary Protein preparation temperature was set at 275°C. The LTQ- Orbitrap was operated in data-dependent After determining the density of the harvested mode to simultaneously measure full scan MS NaCl-H460 (N group) and MXR-chemoresistant spectra (m/z 300-1800) in the Orbitrap with a cells (M group), aliquots containing 107 cells mass resolution of 60,000 at m/z 400. After a were collected and placed into individual tubes full scan survey, the fifteen most abundant ions and washed with PBS. After centrifugation detected in the full MS scan were measured in (1000 rpm, 5 min, room temperature), cells the LTQ-Orbitrap, using collision-induced disso- were lysed in RIPA buffer (Applygen, Beijing, ciation (CID).

1102 Int J Clin Exp Pathol 2018;11(3):1101-1111 EZH2-STAT3 pathway and mitoxantrone-resistance in NCl-H460 cells

Western blotting

The same protein samples used for the LC-MS analysis were also used for the Western blotting assay. After the addition of the sample loading buffer, protein samples of each group were separated using 10% SDS-PAGE and were subsequently transferred to the 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 h and Figure 1. Colony forming efficiency. The MXR-che- then probed with monoclonal mouse anti-STAT3 moresistant cells had higher colony forming effi- antibody (Abcam, Cambridge, UK), mouse anti- ciency compared with the normal NCl-H460 cells EZH2 antibody (BD Bioscience, America), and (P=0.0186). mouse anti-glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody (Zhongshan Gold Bridge Biotechnology Co. Ltd, China) in blocking Protein identification and quantification buffer at 4°C overnight. The membrane was Data analysis was performed with MaxQuant washed three times for 5 minutes each using software (version 1.6.0.16) (http://www.max- PBST (PBS containing 0.1% Tween-20), then quant.org/) [11]. For protein identification, MS/ incubated with the appropriate horseradish MS data were submitted to the Uniprot human peroxidase (HRP)-conjugated secondary anti- protein database using the Andromeda search body at room temperature for 1 h, and washed engine [12] with the following settings: trypsin three more times in PBST buffer. The mem- cleavage; fixed modification of carbamidometh- brane was finally incubated with ECL substrate ylation of cysteine; variable modifications of solution (ECL Kit, Perkinelmer) for 5 minutes, methionine oxidation; a maximum of two according to the manufacturer’s instructions, missed cleavages; false discovery rate of 0.01 and visualized with autoradiographic film. at both peptide and protein level. Other param- Statistical analysis eters were set as default. The results were imported into Microsoft Excel for further analy- Results are expressed as mean ± S.E.M. St- sis. Label-free quantitation (LFQ) was also per- atistical evaluation was performed using the formed in MaxQuant. The Minimum ratio count Student’s t-test (for comparing two value sets). for LFQ was set to 2, and the match-between- P < 0.05 was considered statistically significant runs option was enabled. Other parameters (*P < 0.05: **P < 0.01). were set as default. A 2-fold change in expression and a p-value of the Student’s t-test of Results 0.05 were used as a combined threshold to define biologically dysregulated proteins. Chemoresistant cancer cells showed in- creased self-renewal in vitro Bioinformatic analysis One of the key characteristics of the stem cell is Principal component analysis (PCA) and hierar- its ability to self-renew. Although it is still not chical clustering analysis (HCA) were performed clear whether the molecular mechanism of con- using MetaboAnalyst 3.0 web service (http:// trolling self-renewal is similar between normal www.metaboanalyst.ca/). For bioinformatic and cancer stem cells, it is believed that self- analysis, the 44 differentially-expressed pro- renewal in cancer stem cells is enhanced. To teins were used as inputs. Protein-protein inter- address this, we first compared the self-renew- action networks were constructed by STRING al ability of MXR-chemoresistant to untreated web service (http://www.string-db.org/). DAVID NCl-H460 cancer cells by using a single-cell web service (https://david.ncifcrf.gov/) was clonogenic assay. As shown in Figure 1, the used to retrieve the Gene Ontology Consortium MXR-chemoresistant cells had a higher colony (GOC, http://geneontology.org/) annotation forming efficiency compared with the normal result and KEGG pathway enrichment result. NCl-H460 cells (P=0.0186).

1103 Int J Clin Exp Pathol 2018;11(3):1101-1111 EZH2-STAT3 pathway and mitoxantrone-resistance in NCl-H460 cells

Figure 2. The intensity of analyzed proteins. The intensity of the proteins followed a normal distribution after transformation and statistical analysis (N: NCl-H460 cells; M: MXR-chemoresistant cells).

Figure 3. PCA analysis. PCA analysis showed obvious clustering of the three biological samples in each group, and the two different groups were clearly separated in PCA score plot (A). Scatter plots of the intensities of the proteins identified in each sample were drawn to present the protein expression correlation between each sample by calculating the Pearson correlation coefficient, and it can be seen that the coefficient factors between samples in the same group was larger than those between samples in different groups (B).

Proteomic analysis reveals MXR-chemoresis- sity of proteins was following a normal distribu- tant related proteins tion after transformation, proper for the following statistical analysis. PCA analysis showed A total of 2,621 proteins were identified using obvious clustering of the three biological sam- the label-free proteomics analysis. A logarith- ples in each group, and the two different groups mic transformation (base 2) of the LFQ intensi- were clearly separated in PCA score plot (Figure ty of the proteins was performed prior to the 3A). Scatter plots of the intensities of the pro- data analysis. As shown in Figure 2, the inten- teins identified in each sample were drawn to

1104 Int J Clin Exp Pathol 2018;11(3):1101-1111 EZH2-STAT3 pathway and mitoxantrone-resistance in NCl-H460 cells

Table 1. 60 significantly dysregulated proteins in the MXR-resistant group compared with the non- treatment group

Protein names Gene names Ratio (MXR/Ctrl) -LOG10 t-test p-value WD repeat and HMG-box DNA-binding protein 1 WDHD1 0 6.0237 Lysophosphatidylcholine acyltransferase 1 LPCAT1 0 5.8732 WD repeat-containing protein 26 WDR26 0 5.4641 CD59 glycoprotein CD59 0 5.1510 Poly(A) RNA polymerase, mitochondrial MTPAP 0 4.9642 Exosome complex component RRP4 EXOSC2 0 4.5968 Cleavage and polyadenylation specificity factor subunit 2 CPSF2 0 4.5528 Tetraspanin-13 TSPAN13 0 4.0101 Ethanolaminephosphotransferase 1 EPT1 0 3.9967 Non-specific lipid-transfer protein SCP2 0 3.9038 Nucleolar protein 10 NOL10 0 3.8409 cAMP-specific 3, 5-cyclic phosphodiesterase 4D; cAMP-specific 3, 5-cyclic PDE4D; PDE4B; PDE4A 0 3.8263 phosphodiesterase 4B; cAMP-specific 3, 5-cyclic phosphodiesterase 4A Kinesin-like protein KIF11 KIF11 0 3.7670 Dynein light chain Tctex-type 1 DYNLT1 0 3.5621 Voltage-gated hydrogen channel 1 HVCN1 0 3.2688 Zinc finger protein 638 ZNF638 0 2.4180 Squamous cell carcinoma antigen recognized by T-cells 3 SART3 0 2.3620 Phosphoenolpyruvate carboxykinase [GTP], mitochondrial PCK2 0.1762 1.5276 Cysteine--tRNA ligase, cytoplasmic CARS 0.1971 1.5340 Putative oxidoreductase GLYR1 GLYR1 0.2008 1.7501 40S ribosomal protein S15 RPS15 0.2027 1.5106 F-actin-capping protein subunit beta CAPZB 0.2273 1.4280 Single-stranded DNA-binding protein, mitochondrial SSBP1 0.2301 1.5725 Carnitine O-palmitoyltransferase 2, mitochondrial CPT2 0.2355 1.4041 Phosphoglycolate phosphatase PGP 0.2475 1.3234 Fermitin family homolog 3 FERMT3 0.2564 3.4766 Sideroflexin; Sideroflexin-3 SFXN3 0.2639 1.3037 Asparagine synthetase [glutamine-hydrolyzing] ASNS 0.3098 3.1839 Argininosuccinate synthase ASS1 0.3313 4.5488 Aldo-keto reductase family 1 member C3 AKR1C3 0.3434 4.7750 Keratin, type II cytoskeletal 8 KRT8 0.3614 3.8501 Keratin, type I cytoskeletal 19 KRT19 0.3728 3.5060 Tryptophan--tRNA ligase, cytoplasmic; T1-TrpRS;T2-TrpRS WARS 0.4464 3.7500 Aldo-keto reductase family 1 member B10 AKR1B10 0.4655 6.0122 Retinal dehydrogenase 1 ALDH1A1 2.1123 3.3048 CON__P15497 (Protein ID) 2.3398 1.5219 Mortality factor 4-like protein 1 MORF4L1 2.4857 1.3475 DnaJ homolog subfamily C member 8 DNAJC8 2.9007 1.5390 Galactokinase GALK1 3.8321 1.3176 Phosphoribosyl pyrophosphate synthase-associated protein 1 PRPSAP1 4.0973 1.4142 Replication protein A 32 kDa subunit RPA2 4.1573 1.4274 Glypican-1; Secreted glypican-1 GPC1 4.2429 1.4418 Adenosine 3-phospho 5-phosphosulfate transporter 1 SLC35B2 5.0160 1.7268 Ras-related protein Rab-3C RAB3C 5.1119 4.3081 Histone H3.1 HIST1H3A 18.1103 2.2845 Cullin-4A CUL4A #DIV/0! 6.0121 Urokinase plasminogen activator surface receptor PLAUR #DIV/0! 5.8256 Integrin alpha-5; Integrin alpha-5 heavy chain; Integrin alpha-5 light chain ITGA5 #DIV/0! 5.6616 Neuromodulin GAP43 #DIV/0! 5.5693 Dystonin DST #DIV/0! 5.0345 Acyl-coenzyme A thioesterase 13; Acyl-coenzyme A thioesterase 13, ACOT13 #DIV/0! 4.8015 N-terminally processed ADP/ATP translocase 1 SLC25A4 #DIV/0! 4.7343

1105 Int J Clin Exp Pathol 2018;11(3):1101-1111 EZH2-STAT3 pathway and mitoxantrone-resistance in NCl-H460 cells

Polymerase delta-interacting protein 2 POLDIP2 #DIV/0! 4.7027 Charged multivesicular body protein 4b CHMP4B #DIV/0! 4.5955 Follistatin-related protein 1 FSTL1 #DIV/0! 4.5647 General transcription factor 3C polypeptide 3 GTF3C3 #DIV/0! 4.4946 Signal transducer and activator of transcription 3; Signal transducer and STAT3 #DIV/0! 4.1672 activator of transcription Glucose 1, 6-bisphosphate synthase PGM2L1 #DIV/0! 2.2821 Polyadenylate-binding protein 2 PABPN1 #DIV/0! 2.1123 28 kDa heat- and acid-stable phosphoprotein PDAP1 #DIV/0! 1.4271

Figure 4. Hierarchical clustering and correlation analysis. Hierarchical clustering analysis of the 60 dysregulated proteins was performed and shown as heat map (A). Correlation analysis of the 60 dysregulated proteins were performed and shown in (B). Both MXR-chemoresistant cells and the non-treated cells had excellent correlation in each groups. They were different cell lines. present the protein expression correlation be- performed and shown as a heat map (Figure tween each sample by calculating the Pearson 4A). Correlation analysis of the 60 dysregulated correlation coefficient, and it can be seen that proteins was performed and shown in Figure the coefficient factors between samples in 4B. the same group was larger than those between samples in different groups (Figure 3B). Bioinformatic analysis reveals a regulatory Statistical analysis with Excel software was network relevant to MXR-chemoresistance then performed to select proteins that were significantly dysregulated following treatment, To better understand the regulatory network using the following criteria: fold change > 2, influenced by the treatment, we constructed a p-value < 0.05 (using the Student’s t-test). As a protein-protein interaction network using the result, we identified 60 significantly dysregulat- dysregulated proteins as inputs. As shown in ed proteins in the MXR-chemoresistant group Figure 5, a PPI containing 30 proteins was con- compared with the non-treatment group. Of structed. GO analysis and KEGG pathway these, 26 proteins were upregulated, and 15 of enrichment were also performed, as shown in them were only detected in the MXR-resistant Figure 6. The dysregulated proteins influenced cells. The other 34 proteins were downregulat- by treatment were related with several biologi- ed (Table 1), half of which were only detected in cal processes including retinoid metabolic pro- the non-treated group. Hierarchical clustering cess, farnesol catabolic process and viral pro- analysis of the 60 dysregulated proteins was cess, having the molecular function of retinal

1106 Int J Clin Exp Pathol 2018;11(3):1101-1111 EZH2-STAT3 pathway and mitoxantrone-resistance in NCl-H460 cells

Figure 5. Bioinformatic analysis. The cellular location of these dysregulated proteins were mainly in extracellular exosomes, nucleoplasm and cytosol. dehydrogenase activity, geranylgeranyl reduc- 1950 s, starting with daunorubicin. Mitoxantr- tase activity, and indanol dehydrogenase activ- one hydrochloride was initially approved by the ity, etc. The cellular location of these dysregu- FDA for the treatment of acute myeloid leuke- lated proteins was primarily at extracellular mia (AML) in 1987 and has been approved for exosome, nucleoplasm and cytosol. As shown the treatment of numerous cancers due to its in the KEGG pathway enrichment analysis outstanding antitumor activity [16-18]. It is an result, the PPAR signaling pathway was strongly antineoplastic agent for the second phase of related with the treatment. cancer therapy, such as breast and prostate cancers, lymphomas, and leukemias [18]. Ho- Western blot detects differentially expressed wever, multidrug resistance (MDR) lies in many STAT3 signaling cancer cells, which presents a major obstacle to the long-term efficacy of chemotherapeutic STAT3 has a well known relationship with can- treatments. Prolonged exposure to chemother- cer cells. It is highly expressed in many kinds of apeutic drugs has been shown to render some cancers, such as pancreatic cancer. It is also cancers insensitive to chemotherapy [19]. Ca- accompanied with cancer development [13- ncer stem cells may contribute to the chemore- 15]. It was upregulated in the MXR-resistant NCl-H460 cells (Figure 7). We also found that sistance. But the cancer stem cells are hetero- the EZH2 was overexpressed in the same kind geneously dysregulated in different cancers. In of cells. the present study we found that MXR-resistant large lung cancer cells, MXR-HCI-H460 cells, Discussion had the stem cell features, such as high single- cell clonogenic ability, and had many differen- In the context of cancer therapy, anthracene- tially expressed proteins based on the proteo- diones have been used beginning in the mid- mics and bioinformatics assays.

1107 Int J Clin Exp Pathol 2018;11(3):1101-1111 EZH2-STAT3 pathway and mitoxantrone-resistance in NCl-H460 cells

Figure 6. GO analysis and KEGG pathway enrichment performance. The dysregulated proteins influenced by treatment were related with several biological process including retinoid metabolic process, farnesol catabolic process, and viral process, having the molecular function of retinal dehydrogenase activity, geranylgeranyl reductase activity, and indanol dehydrogenase activity, etc. PPAR signaling pathway was strongly related with treatment.

1108 Int J Clin Exp Pathol 2018;11(3):1101-1111 EZH2-STAT3 pathway and mitoxantrone-resistance in NCl-H460 cells

Figure 7. Western blot verification of differentially expressed of STAT3 and EZH2.

As the proteomics data shows, there are 60 dif- governed by pathways such as STAT3, NANOG, ferent proteins between the cell lines. In con- NOTCH, WNT, and HEDGEHOG, which are highly clusion, 26 proteins were upregulated in the dysregulated in CSCs due to genetic and epi- chemoresistant cells and 34 were downregu- genetic changes [7, 24]. The JAK/STAT signaling lated. The 26 proteins were mainly related to is widely involved in many chronic diseases, the stem cells, such ALDH1 and STAT3. The such as neurodegenerative diseases [25], STAT3 pathway has not been mentioned in the hepatic gluconeogenesis [26], and especially lung cancer cells before. It is one kind of trans- on cancers [27]. STAT3 may play a central role fection factor which has an important influence during the promotion and maintenance of a on cancer development [13-15]. It belongs to stem cell phenotype. So, controlling STAT3 the JAK-Src family and relates not only to the activity should inhibit tumor progression. The cellular factor receptors but also the GPCR cou- Janus kinases (JAKs) and signal transducer and pled signaling, such as S1PR1 [20, 21], activator of transcription (STAT) proteins, par- Prokineticin 2 [22], and angiotensin II [23]. ticularly STAT3, are among the most promising They can promote each other and result in can- new targets for cancer therapy [22]. cer growth. Many researchers have proven that EZH2 can regulate STAT3 activity through pro- STAT3 is related to cancer stem cells [7]. It is a tein methylation at lysine residues of STAT3. In very well known proto-oncogene. Maybe giving the published research, this pathway was clari- the chemical on both, the STAT3 and the other fied in leukemia and pancreatic cancers, but target protein, can improve the lung cancer sur- not in lung cancers. We first ensured the STAT3 vival rate. had been upregulated in the MXR-resistant From the proteomic analysis we can conclu- NCl-H460 cells, although we detected incre- de the mitoxantrone-induced chemoresistant ased EZH2 in chemoresistant NCl-H460 cells by W.B. However, the proteomic assay didn’t cells upregulate many proteins related to extra- detect changes in EZH2, which may mean that cellular exosomes or metabolic processes, wh- the lysis method was too gentle. The functional ich may mediate the tumor invasion or prolifer- disruption of STAT3 modifying enzymes, such ation. From the colony forming efficiency, we as EZH2, may serve as a promising therapeutic found that MXR-resistant cells have more self- strategy for human cancers. renewal ability, which may be mediated by the cancer stem cells. We found that some kinds of Of course, there still remains the need for more cell growth signaling pathways were upregulat- deep research on the STAT3 pathway in lung ed, such as JAK-STAT3, ALDH, and so forth, in cancers. This work stills gives us some indica- the MXR-resistant NCl-H460 cells, not only by tion for STAT3 as drug target for a lung cancer the proteomics but also by the western blot cure. assays. Acknowledgements Cancer stem cells (CSCs), also called tumor- initiating cells (TICs), possess “stemness” This work was supported, in part, by the properties including self-renewal, differentia- National Natural Science Foundation of China tion, and proliferative potential. All of these are (30700206, 31700898).

1109 Int J Clin Exp Pathol 2018;11(3):1101-1111 EZH2-STAT3 pathway and mitoxantrone-resistance in NCl-H460 cells

Disclosure of conflict of interest [11] Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.- None. range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 2008; Address correspondence to: Dr. Li Su, Center of 26: 1367-72. Medical and Health Analysis, Peking University, 38 [12] Cox J, Neuhauser N, Michalski A, Scheltema Xueyuan Road, Haidian District, Beijing 100191, RA, Olsen JV, Mann M. Andromeda: a peptide China. Tel: +86-10-8280-5034; Fax: +86-10-8280- search engine integrated into the MaxQuant 5034; E-mail: [email protected]; Dr. Hongying environment. J Proteome Res 2011; 10: 1794- 805. Zhen, Department of Cell Biology, School of Basic [13] Yuan J, Zhang F, Niu R. Multiple regulation Medical Science, Peking University Health Science pathways and pivotal biological functions of Center, Beijing, China. Tel: +86-10-8280-2164; STAT3 in cancer. Sci Rep 2015; 5: 17663. E-mail: [email protected] [14] Yu H, Lee H, Herrmann A, Buettner R, Jove R. Revisiting STAT3 signaling in cancer: new and References unexpected biological functions. Nat Rev Can- cer 2014; 14: 736-46. [1] Siegel RL, Miller KD, Jemal A. Cancer statistics, [15] Geiger JL, Grandis JR, Bauman JE. The STAT3 2017. CA Cancer J Clin 2017; 67: 7-30. pathway as a therapeutic target in head and [2] Chen W, Zheng R, Baade PD, Zhang S, Zeng H, neck cancer: barriers and innovations. Oral Bray F, Jemal A, Yu XQ, He J. Cancer statistics Oncol 2016; 56: 84-92. in China, 2015. CA Cancer J Clin 2016; 66: [16] Murdock KC, Child RG, Fabio PF, Angier RB, 115-32. Wallace RE, Durr FE, Citarella RV. Antitumor [3] Kanaan Z, Kloecker GH, Paintal A, Perez CA. agents. 1. 1,4-Bis[(aminoalkyl)amino]-9,10- Novel targeted therapies for resistant ALK-re- anthracenediones. J Med Chem 1979; 22: arranged non-small-cell lung cancer: ceritinib 1024-1030. and beyond. Onco Targets Ther 2015; 8: 885- [17] Faulds D, Balfour JA, Chrisp P, Langtry HD. Mi- 92. toxantrone. A review of its pharmacodynamic [4] Mostafa AA, Morris DG. Immunotherapy for and pharmacokinetic properties, and thera- lung cancer: has it finally arrived? Front Oncol peutic potential in the chemotherapy of can- 2014; 4: 288. cer. Drugs 1991; 41: 400-49. [5] Jung MJ, Rho JK, Kim YM, Jung JE, Jin YB, Ko [18] Evison BJ, Sleebs BE, Watson KG, Phillips DR, YG, Lee JS, Lee SJ, Lee JC, Park MJ. Upregula- Cutts SM. Mitoxantrone, more than just anoth- tion of CXCR4 is functionally crucial for mainte- er topoisomerase II poison. Inc Med Res Rev nance of stemness in drug-resistant non-small 2016; 36: 52-91. cell lung cancer cells. Oncogene 2013; 32: [19] Tennvall J, Tallroth E, el Hassan A, Lundell G, 209-21. Akerman M, Biörklund A, Blomgren H, Löwha- [6] Kubo T, Takigawa N, Osawa M, Harada D, gen T, Wallin G. Anaplastic thyroid carcinoma. Ninomiya T, Ochi N, Ichihara E, Yamane H, Tan- Doxorubicin, hyperfractionated radiotherapy imoto M, Kiura K. Subpopulation of small-cell and surgery. Acta Oncol 1990; 29: 1025-28. lung cancer cells expressing CD133 and CD87 [20] Lee H, Deng J, Kujawski M, Yang C, Liu Y, Her- show resistance to chemotherapy. Cancer Sci rmann A, Kortylewski M, Horne D, Somlo G, 2013; 104: 78-84. Forman S, Jove R, Yu H. STAT3-induced S1PR1 [7] Lathia JD, Liu H. Overview of cancer stem cells expression is crucial for persistent STAT3 acti- and stemness for community oncologists. Tar- vation in tumors. Nat Med 2010; 16: 1421-8. get Oncol 2017; 12: 387-99. [21] Smith GS, Kumar A, Saba JD. Sphingosine [8] Wang XQ, Ongkeko WM, Chen L, Yang ZF, Lu P, phosphate lyase regulates murine embryonic Chen KK, Lopez JP, Poon RT, Fan ST. Octamer stem cell proliferation and pluripotency 4 (Oct4) mediates chemotherapeutic drug re- through an S1P2/STAT3 signaling pathway. sistance in liver cancer cells through a poten- Biomolecules 2013; 3: 351-368. tial Oct4-AKT-ATP-binding cassette G2 path- [22] Xin H, Lu R, Lee H, Zhang W, Zhang C, Deng J, way. Hepatology 2010; 52: 528-39. Liu Y, Shen S, Wagner KU, Forman S, Jove R, Yu [9] Mackenzie IC. Stem cell properties and epithe- H. G-protein-coupled receptor agonist BV8/ lial malignancies. Eur J Cancer 2006; 42: prokineticin-2 and STAT3 protein form a feed- 1204-12. forward loop in both normal and malignant my- [10] Wisniewski JR, Zougman A, Nagaraj N, Mann eloid cells. J Biol Chem 2013; 288: 13842-9. M. Universal sample preparation method for [23] Zheng L, Jia X, Zhang C, Wang D, Cao Z, Wang proteome analysis. Nat Methods 2009; 6: J, Du X. Angiotensin II in atrial structural re- 359-62. modeling: the role of Ang II/JAK/STAT3 signal-

1110 Int J Clin Exp Pathol 2018;11(3):1101-1111 EZH2-STAT3 pathway and mitoxantrone-resistance in NCl-H460 cells

ing pathway. Am J Transl Res 2015; 7: 1021- [26] Ding Y, Zhang D, Wang B, Zhang Y, Wang L, 31. Chen X, Li M, Tang Z, Wang C. APPL1-mediated [24] Koury J, Zhong L, Hao J. Targeting signaling activation of STAT3 contributes to inhibitory ef- pathways in cancer stem cells for cancer treat- fect of adiponectin on hepatic gluconeogene- ment. Stem Cells Int 2017; 2017: 2925869. sis. Mol Cell Endocrinol 2016; 433: 12-9. [25] Eufemi M, Cocchiola R, Romaniello D, Correani [27] Zheng X, Xu M, Yao B, Wang C, Jia Y, Liu Q. IL-6/ V, Di Francesco L, Fabriz C, Maras B, Schinina STAT3 axis initiated CAFs via up-regulating ME. Acetylation and phosphorylation of STAT3 TIMP-1 which was attenuated by acetylation of are involved in the responsiveness of microglia STAT3 induced by PCAF in HCC microenviron- to beta amyloid. Neurochem Int 2015; 81: 48- ment. Cell Signal 2016; 28: 1314-24. 56.

1111 Int J Clin Exp Pathol 2018;11(3):1101-1111