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 changing 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
Cancer incidence and mortality have been increasing in China, making cancer the leading cause of death since 2010 and a major public health problem. Lung cancer is the leading cause of cancer-related deaths worldwide, with a five-year survival rate of only 18% [1]. In China also, lung cancer is the most common and has the poorest survival rate [2].
There are two kinds of lung cancer, one is called small cell lung cancer (SCLC), which is sensitive to chemotherapy and has a high survival rate. The other one is called non-small cell lung cancer (NSCLC), which comprises 85%-90% of lung cancer diagnoses [3]. Despite drastic treatment strategies, including radiotherapy, chemotherapy, and even immune approaches, the five-year survival rate for all NSCLC is still < 20% [4,5]. Chemotherapy is not as useful for large cell lung cancer as it is with small cell lung cancer. Multidrug resistance is one of the reasons for its poor survival rate. The mechanism of the resistance is not very clear. Cancer stem cells (CSCs) are one of the important reasons.
Cancer stem cells, also called tumor-initiating cells, have a high capacity for self-renewal and multi-lineage differentiation and are believed to be responsible for tumor development, recurrence and dissemination, as well as the acquisition of drug resistance [6]. These “stemness” properties are governed by pathways such as STAT3, NANOG, NOTCH, WNT, and HEDGEHOG, which are highly dysregulated in CSCs due to genetic and epigenetic changes of cancers [7]. The proteins of these pathways are thought of as the CSCs markers. Different CSCs show different dysregulation of the pathways. In this research, we used the non-small cell lung cancer cell lines, NCI-H460, to find the main dysregulated proteins during the mitoxantrone-induced chemoresistance by quantitative proteomics. We found 60 proteins that were dysregulated which might mediate the mixtoxantrone-resistance of the NCI-H460 cells, including upregulation of ALDH1 and the EZH2-STAT3 pathway, which can be used as the new protein targets of large cell lung cancer therapy.
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 were of standard biochemical quality.
Cell lines and generation of chemoresistant cancer cells
Cancer cell line, NCl-H460 was used. Chemotherapeutic drug, mitoxantrone (MXR, 20 ng/mL) was used, along with a mutant from the previous research [8]. Drug treatments were repeated two or three times, which mimicked the clinical regimen that patients with cancers would receive. This strategy ensured that more than 95% of cells underwent apoptosis or senescence with the senescent cells eventually dying, thereby selecting the most resistant clones.
Single-cell clonogenic assay
To assess self-renewal in vitro, cells were sorted by flow cytometry (FACS Aria II) using the single-cell sorting model, seeded into 6-well plates (50, 100 or 200 cells per well), and cultured in a 10% fetal bovine serum (FBS)-containing RPMI-1640 medium. Cell clones were counted after fourteen days when a clone reached 100-200 cells [8,9].
Protein preparation
After determining the density of the harvested NaCl-H460 (N group) and MXR-chemoresistant cells (M group), aliquots containing 107 cells were collected and placed into individual tubes and washed with PBS. After centrifugation (1000 rpm, 5 min, room temperature), cells were lysed in RIPA buffer (Applygen, Beijing, China) and the 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 the manufacturer’s protocol for filter-aided sample preparation (FASP) [10]. In brief, 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 h. Then, 10 μL of 0.1 M iodoacetamide (IAA) was added to the filters, then 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 LTQ-Orbitrap Elite mass spectrometer (Thermo Fisher Scientific), equipped with a Nanospray Flex Ion Source (Thermo Fisher Scientific). 1 μg peptide mixtures (5 μL) were separated using a home-made reversed-phase C18 column (150 μm I.D. × 150 mm, 3 μm of particle size) at a flow rate of 300 nL/min. Chromatographic separation was performed with a 60 min gradient of 2% to 40% acetonitrile in 0.1% formic acid. The electrospray voltage was maintained at 2.2 kV, and the capillary temperature was set at 275°C. The LTQ-Orbitrap 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 a full scan survey, the fifteen most abundant ions detected in the full MS scan were measured in the LTQ-Orbitrap, using collision-induced dissociation (CID).
Protein identification and quantification
Data analysis was performed with MaxQuant software (version 1.6.0.16) (http://www.maxquant.org/) [11]. For protein identification, MS/MS data were submitted to the Uniprot human protein database using the Andromeda search engine [12] 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. The results were imported into Microsoft Excel for further analysis. Label-free quantitation (LFQ) was also performed in MaxQuant. The Minimum ratio count for LFQ was set to 2, and the match-between-runs option was enabled. Other parameters were set as default. A 2-fold change in expression and a p-value of the Student’s t-test of 0.05 were used as a combined threshold to define biologically dysregulated proteins.
Bioinformatic analysis
Principal component analysis (PCA) and hierarchical clustering analysis (HCA) were performed using MetaboAnalyst 3.0 web service (http://www.metaboanalyst.ca/). For bioinformatic analysis, the 44 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 result and KEGG pathway enrichment result.
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 then probed with monoclonal mouse anti-STAT3 antibody (Abcam, Cambridge, UK), mouse anti-EZH2 antibody (BD Bioscience, America), and mouse anti-glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody (Zhongshan Gold Bridge Biotechnology Co. Ltd, China) in blocking buffer at 4°C overnight. The membrane was washed three times for 5 minutes each using PBST (PBS containing 0.1% Tween-20), then incubated with the appropriate horseradish peroxidase (HRP)-conjugated secondary antibody at room temperature for 1 h, and washed three more times in PBST buffer. The membrane was finally incubated with ECL substrate solution (ECL Kit, Perkinelmer) for 5 minutes, according to the manufacturer’s instructions, and visualized with autoradiographic film.
Statistical analysis
Results are expressed as mean ± S.E.M. Statistical evaluation was performed using the Student’s t-test (for comparing two value sets). P < 0.05 was considered statistically significant (*P < 0.05: **P < 0.01).
Results
Chemoresistant cancer cells showed increased self-renewal in vitro
One of the key characteristics of the stem cell is its ability to self-renew. Although it is still not clear whether the molecular mechanism of controlling self-renewal is similar between normal and cancer stem cells, it is believed that self-renewal in cancer stem cells is enhanced. To address this, we first compared the self-renewal ability of MXR-chemoresistant to untreated NCl-H460 cancer cells by using a single-cell clonogenic assay. As shown in Figure 1, the MXR-chemoresistant cells had a higher colony forming efficiency compared with the normal NCl-H460 cells (P=0.0186).
Figure 1.
Colony forming efficiency. The MXR-chemoresistant cells had higher colony forming efficiency compared with the normal NCl-H460 cells (P=0.0186).
Proteomic analysis reveals MXR-chemoresistant related proteins
A total of 2,621 proteins were identified using the label-free proteomics analysis. A logarithmic transformation (base 2) of the LFQ intensity of the proteins was performed prior to the data analysis. As shown in Figure 2, the intensity of proteins was following a normal distribution after transformation, proper for the following statistical 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 (Figure 3A). 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 (Figure 3B). Statistical analysis with Excel software was then performed to select proteins that were significantly dysregulated following treatment, using the following criteria: fold change > 2, p-value < 0.05 (using the Student’s t-test). As a result, we identified 60 significantly dysregulated proteins in the MXR-chemoresistant group compared with the non-treatment group. Of these, 26 proteins were upregulated, and 15 of them were only detected in the MXR-resistant cells. The other 34 proteins were downregulated (Table 1), half of which were only detected in the non-treated group. Hierarchical clustering analysis of the 60 dysregulated proteins was performed and shown as a heat map (Figure 4A). Correlation analysis of the 60 dysregulated proteins was performed and shown in Figure 4B.
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 g roups 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).
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 phosphodiesterase 4B; cAMP-specific 3, 5-cyclic phosphodiesterase 4A | PDE4D; PDE4B; PDE4A | 0 | 3.8263 |
| 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, N-terminally processed | ACOT13 | #DIV/0! | 4.8015 |
| ADP/ATP translocase 1 | SLC25A4 | #DIV/0! | 4.7343 |
| 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 activator of transcription | STAT3 | #DIV/0! | 4.1672 |
| 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.
Bioinformatic analysis reveals a regulatory network relevant to MXR-chemoresistance
To better understand the regulatory network influenced by the treatment, we constructed a protein-protein interaction network using the dysregulated proteins as inputs. As shown in Figure 5, a PPI containing 30 proteins was constructed. GO analysis and KEGG pathway enrichment were also performed, as shown in Figure 6. The dysregulated proteins influenced by treatment were related with several biological processes 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. The cellular location of these dysregulated proteins was primarily at extracellular exosome, nucleoplasm and cytosol. As shown in the KEGG pathway enrichment analysis result, the PPAR signaling pathway was strongly related with the treatment.
Figure 5.
Bioinformatic analysis. The cellular location of these dysregulated proteins were mainly in extracellular exosomes, nucleoplasm and cytosol.
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.
Western blot detects differentially expressed STAT3 signaling
STAT3 has a well known relationship with cancer cells. It is highly expressed in many kinds of cancers, such as pancreatic cancer. It is also accompanied with cancer development [13-15]. It was upregulated in the MXR-resistant NCl-H460 cells (Figure 7). We also found that the EZH2 was overexpressed in the same kind of cells.
Figure 7.
Western blot verification of differentially expressed of STAT3 and EZH2.
Discussion
In the context of cancer therapy, anthracenediones have been used beginning in the mid-1950 s, starting with daunorubicin. Mitoxantrone hydrochloride was initially approved by the FDA for the treatment of acute myeloid leukemia (AML) in 1987 and has been approved for the treatment of numerous cancers due to its outstanding antitumor activity [16-18]. It is an antineoplastic agent for the second phase of cancer therapy, such as breast and prostate cancers, lymphomas, and leukemias [18]. However, multidrug resistance (MDR) lies in many cancer cells, which presents a major obstacle to the long-term efficacy of chemotherapeutic treatments. Prolonged exposure to chemotherapeutic drugs has been shown to render some cancers insensitive to chemotherapy [19]. Cancer stem cells may contribute to the chemoresistance. But the cancer stem cells are heterogeneously dysregulated in different cancers. In the present study we found that MXR-resistant large lung cancer cells, MXR-HCI-H460 cells, had the stem cell features, such as high single-cell clonogenic ability, and had many differentially expressed proteins based on the proteomics and bioinformatics assays.
As the proteomics data shows, there are 60 different proteins between the cell lines. In conclusion, 26 proteins were upregulated in the chemoresistant cells and 34 were downregulated. The 26 proteins were mainly related to the stem cells, such ALDH1 and STAT3. The STAT3 pathway has not been mentioned in the lung cancer cells before. It is one kind of transfection factor which has an important influence on cancer development [13-15]. It belongs to the JAK-Src family and relates not only to the cellular factor receptors but also the GPCR coupled signaling, such as S1PR1 [20,21], Prokineticin 2 [22], and angiotensin II [23]. They can promote each other and result in cancer growth. Many researchers have proven that STAT3 is related to cancer stem cells [7]. It is a very well known proto-oncogene. Maybe giving the chemical on both, the STAT3 and the other target protein, can improve the lung cancer survival rate.
From the proteomic analysis we can conclude the mitoxantrone-induced chemoresistant cells upregulate many proteins related to extracellular exosomes or metabolic processes, which may mediate the tumor invasion or proliferation. From the colony forming efficiency, we found that MXR-resistant cells have more self-renewal ability, which may be mediated by the cancer stem cells. We found that some kinds of cell growth signaling pathways were upregulated, such as JAK-STAT3, ALDH, and so forth, in the MXR-resistant NCl-H460 cells, not only by the proteomics but also by the western blot assays.
Cancer stem cells (CSCs), also called tumor-initiating cells (TICs), possess “stemness” properties including self-renewal, differentiation, and proliferative potential. All of these are governed by pathways such as STAT3, NANOG, NOTCH, WNT, and HEDGEHOG, which are highly dysregulated in CSCs due to genetic and epigenetic changes [7,24]. The JAK/STAT signaling is widely involved in many chronic diseases, such as neurodegenerative diseases [25], hepatic gluconeogenesis [26], and especially on cancers [27]. STAT3 may play a central role during the promotion and maintenance of a stem cell phenotype. So, controlling STAT3 activity should inhibit tumor progression. The Janus kinases (JAKs) and signal transducer and activator of transcription (STAT) proteins, particularly STAT3, are among the most promising new targets for cancer therapy [22].
EZH2 can regulate STAT3 activity through protein methylation at lysine residues of STAT3. In the published research, this pathway was clarified in leukemia and pancreatic cancers, but not in lung cancers. We first ensured the STAT3 had been upregulated in the MXR-resistant NCl-H460 cells, although we detected increased EZH2 in chemoresistant NCl-H460 cells by W.B. However, the proteomic assay didn’t detect changes in EZH2, which may mean that the lysis method was too gentle. The functional disruption of STAT3 modifying enzymes, such as EZH2, may serve as a promising therapeutic strategy for human cancers.
Of course, there still remains the need for more deep research on the STAT3 pathway in lung cancers. This work stills gives us some indication for STAT3 as drug target for a lung cancer cure.
Acknowledgements
This work was supported, in part, by the National Natural Science Foundation of China (30700206, 31700898).
Disclosure of conflict of interest
None.
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