Overexpression of STMN1 is associated with the prognosis of meningioma patients

Neuroscience Letters 654 (2017) 1–5

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Research article

Overexpression of STMN1 is associated with the prognosis of meningioma patients Haifeng Wang a,1 , Wenchen Li a,1 , Guangming Wang a,1 , Shuyan Zhang a,1 , Li Bie a,b,∗ a b

Department of Neurosurgery of the First Clinical Hospital, Jilin University, Changchun, China Department of Pathology & Laboratory Medicine, University of California, Irvine, CA, USA

h i g h l i g h t s • • • •

There is a part of the pathological type of the meningioma has a malignant tendency and patients have poor prognosis. STMN1 plays an important role in the maintenance, metastasis, invasion, and differentiation of malignant tumor cells. High expression of STMN1, along with high Ki-67 scores and WHO grades, act as independent prognostic factors for survival times. STMN1 represents a potential biomarker for predicting meningioma progression.

a r t i c l e

i n f o

Article history: Received 23 April 2017 Received in revised form 13 June 2017 Accepted 14 June 2017 Keywords: Meningioma Stathmin Biomarker Prognosis

a b s t r a c t There is a small part of the pathological type of the meningioma has a malignant tendency and patients have the poor prognosis. Looking for effective biomarkers to predict the degree of malignancy of the tumors, will help us to better manage the patient and guide the treatment. The present study aims at investigating the prognostic value of the expression of Stathmin in a series of meningiomas of different grade. We integrated eight published microarray datasets of meningiomas to screen grade biomarkers in meningiomas patients using the WebArrayDB platform. We focused on Stathmin, Using formalinfixed paraffin-embedded (FFPE) tumor samples, we corroborated the relationship between Stathmin and patient outcomes using qRT-PCR for gene expression. We also found expression of Stathmin that atypical/anaplastic meningiomas have higher expression than benign meningiomas (p < 0.01). No correlation between Stathmin expression and age, gender and tumor extent of resection was found (p > 0.05). Moreover, increased Stathmin expression was correlated to higher meningioma grade and shorter disease-free survival (DFS) of meningioma patients with Simpson I resection. Stathmin might be promising targets to improve the cure rates in meningiomas. © 2017 Elsevier B.V. All rights reserved.

1. Introduction Meningiomas are thought to arise from arachnoidal cap cells of intracranial neoplasms in adulthood, and they account for 35.8% of all primary central nervous system (CNS) tumors in the United States [1]. They are currently classified into several histotypes and three grades of malignancy according to the criteria of the World Health Organization (WHO) classification scheme for tumors in

Abbreviations: CNS, central nervous system; DFS, disease-free survival; FFPE, formalin-fixed paraffin-embedded; H&E, hematoxylin and eosin; qRT-PCR, quantitative real-time polymerase chain reaction. ∗ Corresponding author at: Department of Neurosurgery of the First Clinical Hospital, Jilin University, Changchun, China. E-mail address: [email protected] (L. Bie). 1 These authors contributed equally to this work. http://dx.doi.org/10.1016/j.neulet.2017.06.020 0304-3940/© 2017 Elsevier B.V. All rights reserved.

the central nervous system (CNS). Pathological grade has been associated with significantly greater rates of morbidity and mortality, even with the provision of multimodality treatments [2]. Although atypical (WHO II) and anaplastic (WHO III) meningiomas only account for 10% of all meningiomas [3], they are more likely than WHO I meningiomas to display malignant behavior, characterized by invasion, growth, recurrence, and poor prognosis, after surgical resection [4]. Characterization of the genetic or epigenetic processes associated with malignant transformation may provide insights that inform the development of novel diagnostic and therapeutic tools to address this aggressive tumor subtype [5]. Previous studies have identified several biomarkers that correlate with the progression of meningiomas. Insulin-like growth factor binding protein 2 (IGFPB2) amplification, tumor protein 73 (TP 73) methylation, lysine-specific demethylase 5C (KDM5C) mutation, and vascular

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H. Wang et al. / Neuroscience Letters 654 (2017) 1–5

endothelial growth factor (VEGF) amplification, among other proteins, were observed more frequently in samples from aggressive rather than non-aggressive tumors [6–8]. Although meningioma research at the molecular level has progressed greatly, effective assessments of biomarkers are still lacking for clinical practice. Microarray techniques could aid in analyzing the genomic landscape to identify prognostic biomarkers for the survival of patients with meningiomas. However, analyses of microarray datasets require large numbers of samples to reduce bias. Unfortunately, the number of samples from malignant tumors has been very limited in past studies. Therefore, it will be important to generate data from larger cohorts for robust detection of differences by WHO grade. In our study, we analyzed microarray data integrated from multiple array sources using the WebArrayDB platform [9] to compensate for inadequate sample sizes. Among the genes upregulated in WHO II and WHO III groups, we focused on the significantly upregulated gene stathmin 1 (STMN1). STMN1 also plays an important role in the maintenance, metastasis, invasion, and differentiation of malignant tumor cells [10,11], which can affect the curative effect of some chemotherapy drugs on microtubules [12]. The correlation between STMN1 and tumor histological grade has already been documented in other CNS neoplasias [13–15]. In the present study, for the first time, we investigated the mRNA expression levels of STMN1 in meningiomas of different pathological grades and determined the correlation between STMN1 levels and survival time in Simpson I resection patients. STMN1 represents a potential biomarker for predicting meningioma progression.

2. Materials and methods 2.1. Patient samples Samples from 73 cases of meningioma were collected from Department of Neurosurgery, 1 st Affiliated Hospital of Jilin University, from 2005 to 2010. Meningioma tumors were obtained from 42 females and 31 males (at surgery: mean age, 49.9 ± 14.7 years; range, 18–78 years). No patient had a pretreatment history before surgery; however, the Simpson grade of the extent of survival resection was available in all cases [16], as were follow-up data on recurrences and disease-free survival (DFS). Recurrence was defined as the detection of a recurrent tumor by radiological investigations of patients with a previous complete surgical excision (Simpson grade I). All patients were histologically reviewed according to the WHO 2007 classification scheme for CNS tumors [2]. The meningiomas samples comprised 41 benign (WHO grade I), 23 atypical (WHO grade II), and 9 anaplastic (WHO grade III) samples. This study was approved by the Ethics Committee of 1st Affiliated Hospital of Jilin University (IRB 00008484).

ratios (A260 /280 ) were measured using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies). 2.3. Real-time quantitative reverse transcription-PCR (qRT-PCR) analysis The relative expression levels of STMN1 were analyzed by realtime qRT-PCR using the SYBR Green approach. cDNA synthesis was performed using a SuperScript II Reverse Transcriptase Kit (Invitrogen) with 300 ng total RNA according to the manufacturer’s protocol. All gene expression assays were run on an ABI Prism 7900HT Sequence Detection System (Applied Biosystems, Foster City, CA, USA) using the standard settings. All assays were prepared with 1 × SYBR Green PCR Supermix (BioPioneer, San Diego, CA, USA). mRNA expression levels were normalized to that of the housekeeping gene for ␤-actin. Relative fold changes were calculated using the Pfaffl method [17] for each gene after correction for the ␤-actin level. The primers were synthesized by Takara as follows: STMN1 sense: 5 - TGTCGCTTG TCTTCTATTCACCAT 3 ; STMN1 antisense: 5 - CTTTTGACCGAG GGCTGAGA -3 ; ˇ-actin sense: 5 -CCACGAAACTACCTTCAACTCCA-3 ; ˇ-actin antisense 5 GTGATCTCCTTCTGCATCCTGTC-3 . The absence of primer dimers was demonstrated by agarose gel electrophoresis. Each sample was run in triplicate. 2.4. Microarray dataset analysis We performed an integrated microarray analysis to select genes associated with meningioma progression. Eight independent external microarray datasets containing all 231 meningioma samples were analyzed, including 158 benign (WHO grade I), 60 atypical (WHO grade II), and 13 anaplastic (WHO grade III) samples. The microarray datasets were analyzed using the WebArrayDB crossplatform analysis suite to screen candidate genes (Table 1) [9]. Data were analyzed with an ANCOVA model to correlate gene expression with meningioma WHO grade in the WebArrayDB platform. Genes were sorted in ascending order according to the p values associated with WHO tumor grade. 2.5. Statistical analysis Variables are presented as means ± standard deviations. For comparisons of different groups, the ANOVA test was used. Cox proportional hazards model for multivariate survival analysis was used to assessing predictors of survival. DFS was assessed by the Kaplan-Meier method, and survival curves were calculated. A p value less than 0.05 was considered statistically significant. The statistical analysis was performed using SPSS software v.17.0 (SPSS Inc., Chicago, IL, USA).

2.2. Total RNA extraction and quality assessment

3. Results

Total RNA was extracted from FFPE using a QuickExtract FFPE RNA Extraction Kit protocol (Epicentre). Hematoxylin and eosin (H&E) sections from FFPE specimens were reviewed by a pathologist to select the most informative blocks. Four 10 mm-thick sections per FFPE block were cut, followed by one H&E control slide. The tumor area selected for analysis was marked on the control slide to ensure, as far as possible, that greater than 80% of neoplastic cells were within that area of a section. RNA deparaffinization, extraction, and purification were performed using the QuickExtract FFPE RNA Extraction Kit protocol (Epicentre). The concentration of isolated RNA was measured with a Qubit RNA BR assay (Invitrogen, Carlsbad, CA, USA) using a Qubit 2.0 fluorometer. Purity absorbance

3.1. Clinical characteristics and WHO grades A total of 73 meningioma patients who were treated within the defined study period were included in the study. The mean age at diagnosis was 49.9 years (range 18–78 years), and most patients had undergone Simpson I resections (49/73, 67.1%). In patients who had Simpson I resections, those in the atypical/anaplastic group had higher recurrence rates than those in the benign meningioma group (85.7% vs. 25.0%, p < 0.01). Moreover, the atypical/anaplastic group had higher Ki-67 scores (cut-off score, 4%) than those in the group of patients with benign meningiomas (87.5% vs. 12.2%, p < 0.01) (Table 2).

H. Wang et al. / Neuroscience Letters 654 (2017) 1–5

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Table 1 Independent meningioma RNA expression microarray datasets (n = 8). GEO ID

Platform

Sample type

WHO I

WHO II

WHO III

GSE4780 GSE16581 GSE43290 GSE54934 GSE55609 GSE66463 GSE77259 GSE32197

HU133 plus 2.0 arrays HU133 plus 2.0 arrays HG-U133A arrays Human Gene 1.0 ST HU133 plus 2.0 arrays Human Gene 1.0 ST Human Gene 1.0 ST HU133 plus 2.0 arrays

Frozen sample Frozen sample Frozen sample Frozen sample Frozen sample Frozen sample Frozen sample Frozen sample

25 43 34 20 18 7 10 1

19 19 12 2 3 1 4

1 9 2

1

Table 2 Clinical characteristics of patients (n = 73). WHO I (n = 41) No. Age at diagnosis (ys) Median (ys) Range (ys) Gender Male Female Extent of Resection Simpson I Simpson II Simpson III Ki-67 ≤4% >4% Recurrent (Simpson I)

WHO II (n = 23) %

No.

WHO III (n = 9) %

No.

p value % p > 0.05

49.4 18–78

49.9 21–72

51.8 23–74 p > 0.05

18 23

43.9 56.1

9 14

39.1 60.9

4 5

44.4 55.6

28 9 4

68.3 22.0 9.8

15 5 3

65.2 21.7 13.0

6 2 1

66.7 22.2 11.1

36 5 7

87.8 12.2 25.0

4 19 10

17.4 82.6 66.7

0 9 5

0 100 83.3

p > 0.05

p < 0.01

p < 0.01

p < 0.05.

3.2. Gene selection from microarray datasets To select genes of interest, eight independent microarray datasets were used (Table 1). Genes were sorted in ascending order according to the p values associated with WHO tumor grade. Of the top 10 genes, including in neurofibromatosis 2 (NF2), telomerase reverse transcriptase (TERT), insulin like growth factor binding protein 2 (IGFBP2), LIM domain only 4 (LMO4), CD44, histone cluster 1 H1 family member c (HIST1H1C), STMN1, homeobox A7 (HOXA7), adenylate cyclase 3 (ADCY3) and NDRG family member 2 (NDRG2), STMN1 had not been reported to be correlated with meningioma invasion and progression [18,19]. Therefore, we focused our attention on STMN1. 3.3. The expression of STMN1 analysis among different WHO grade patients STMN1 mRNA expression in meningiomas of all grades was assessed by qRT-PCR. Table 3 shows that mRNA levels of STMN1 were significantly different among meningioma samples (p < 0.05). Moreover, we found that expression levels of STMN1 in atypical/anaplastic meningiomas were higher than in benign meningiomas (p < 0.05). No correlation between STMN1 expression and age, gender, or tumor extent of resection was found (p > 0.05). 3.4. Analysis of survival time in meningioma patients with Simpson I grade meningiomas

Fig. 1. mRNA levels of STMN1 and DFS among tumor samples with Simpson I grade.

In the search for a model to describe the relationship between survival and expression of STMN1 using maximally selected logrank statistics, a cut-point model was determined most suitable, and it was used to delineate two subgroups of patients with meningiomas (cutoff point = 2.97). Applying the STMN1 expression level cut-off score to a Kaplan-Meier survival curve estimation revealed a decreased probability of survival for patients with tumors expressing high levels of this gene (p < 0.01) (Fig. 2). Two subgroups exhibited significantly different outcomes from patients with Simpson I. 4. Discussion

Fig. 1 shows that mRNA levels of STMN1 and DFS among tumor samples with Simpson I grade. We performed a Cox regression analysis to evaluate the expression of STMN1 in patients with Simpson I meningiomas. Multivariate survival analyses indicated that high expression of STMN1, along with high Ki-67 scores and WHO grades, act as independent prognostic factors for survival times (Table 4).

In the past 10 years, there have been exciting developments in molecular genetic research on meningiomas [20]. The resulting information has led to an increased interest in genetics-based diagnostics and treatments [18]. In the present study, we analyzed data from eight microarrays to screen samples from different

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Table 3 Comparison of mRNA expression of STMN1 in tumor tissue by qRT-PCR and microarray analysis. Gene

STMN1

Unique ID

200783 s at

Relative expression levels of mRNA

Gene origin

I (n = 41)

II (n = 23)

III (n = 9)

1.73 ± 0.69

3.68 ± 0.51

3.94 ± 0.87

p value

qRT-PCR Microarray

I vs II

I vs III

II vs III

3.63E-03 7.98E-04

1.06E-03 6.17E-04

5.17E-02 4.54E-02

p < 0.05.

Table 4 Multivariate analysis on prognosis of the patients with Simpson’s I (n = 49). Parameter

HR

95% CI

p value

Age (>50) Gender WHO Grade Location STMN1 Ki-67

2.912 1.405 3.763 1.859 4.119 5.269

2.043–4.375 0.597–2.149 2.314–6.355 1.375–3.236 1.647–7.983 2.493–8.326

0.067 0.245 0.012 0.397 0.009 0.006

p < 0.05, HR: hazard ratio.

Fig. 2. Kaplan-Meier estimates of disease-free survival (DFS) in relation to STMN1 expression as determined by qRT-PCR. Cutoff value for dichotomization of STMN1 expression and p values were determined by maximally selected log-rank statistics. A High STMN1 expression appeared to be strongly associated with poor overall survival in meningioma patients (p < 0.01).

WHO grade meningiomas. Among the top 10 genes analyzed, mRNA expression levels of STMN1 were significantly correlated with meningioma pathological grades. Moreover, the recent proteomics study of meningiomas has been reported that STMN1 is significant differentially expressed in WHO grades of meningiomas [21]. In our study, we further validated this biomarker in clinical samples by qRT-PCR. In our study, STMN1 expression in meningiomas was associated with the pathological grade of tumors, with the highest levels of expression in anaplastic meningiomas and lowest levels of expression in benign meningiomas. Furthermore, increased STMN1 expression correlated with higher meningioma grades and shorter DFS times in patients with meningiomas with Simpson I resections. To our knowledge, this is the first report indicating that STMN1 is a prognostic factor in meningioma patients. STMN1 is a microtubule labile protein, also known as oncoprotein 18 (Op 18), and is distributed throughout the cytoplasm [22]. By regulating the kinetics of the microtubule system to control the cell cycle, cell proliferation, and differentiation, death can be induced by this protein [23,24]. To date, many studies have reported that STMN1 is highly expressed in a variety of human malignancies, including neurological tumors, respiratory tumors [25], digestive system tumors [26,27], gynecological tumors [28], and urinary tract tumors [29,30]. The high level of STMN1 expression closely correlates with the malignant behavior and clinical features of tumors [31]. In our study, we also found that STMN1 expression was significantly increased in atypical/anaplastic samples vs. benign samples (p < 0.01) Therefore, STMN1 expression can be used as an independent prognostic indicator for survival (p < 0.01). STMN1 regulates the complex processes of tumor formation, progression, and metastasis and interferes with microtubule dynamics in cancer cells. The epithelial-mesenchymal transition (EMT) plays a positive role in the progression and recurrence

of malignant tumors. Studies have shown that the microtubuledestabilizing activity of STMN1 contributes to EMT via the STMN1-microtubule-EMT (S-M-E) axis during cancer development [32]; (2) STMN1 expression and/or phosphorylation and regulation of cellular proliferation in cancers. Knockdown of STMN1 leads to esophageal carcinoma cell cycle arrest in the G2/M phase and reduces their viability and colony-forming ability [33]. (3) STMN1 shows anti-apoptotic activity that prompts the progress of tumor cells. Research shows that knockdown of STMN1 inhibits the proliferation of glioma cells, induces apoptosis, arrests the glioma stem cell (GSC) cycle in the G2/M phase, and suppresses the migration and invasion of these cells [14]. (4) STMN1 expression regulates the motility of tumor cells. One study reported that STMN1 silencing significantly impedes cell proliferation and mobility of neuroblastoma cells [34]. Inhibition of STMN1 expression can not only reduce the proliferation of tumor cells but also can enhance the sensitivity of antimicrotubule chemotherapy drugs. Paclitaxel stabilizes microtubule structure, promotes microtubule polymerization, and blocks mitotic spindle formation, leading to cell mitosis stagnation in the G2 /M phase, and ultimately apoptosis. Vinblastine affects microtubule protein polymerization and stops mitosis. Although the two mechanisms of drug action are different, they both affect the dynamic balance of microtubules to stop mitosis. Studies have shown that tumor cells with high levels of STMN1 expression are less susceptible to paclitaxel and vinblastine [35,36]. Alli et al. speculate that STMN1 underlies the anti-paclitaxel mechanism that blocks microtubule polymerization and reduces its binding to paclitaxel. Although microtubule depolymerization increases in combination with vinblastine, only a small fraction of the tumor cells that highly express STMN1 enter mitosis, reducing the efficacy of vinblastine [12]. At present, adjuvant therapies for malignant meningiomas, including radiotherapy and chemotherapy, have a very limited effect. These patients often have poor prognoses. STMN1 represents a new therapeutic target; therefore, its mechanism of action should be studied further. 5. Conclusion In summary, we found upregulated expression of STMN1 in the atypical/anaplastic meningioma group, relative to that in the benign meningioma group. STMN1, therefore, is a promising target to improve cure rates in meningioma cases. These provisional findings, however, require confirmation with a larger set of patient samples.

H. Wang et al. / Neuroscience Letters 654 (2017) 1–5

Conflict of interest The authors declare no competing financial interests. Acknowledgments This work was supported by the National Natural Science Foundation of China (grant numbers 81201980 and 81572476) and the Natural Science Foundation of Jilin Province, China (number 20130522028JH). The data sets supporting the conclusion of this article are included within the article. Any request for data and material may be sent to the corresponding author. References [1] Q.T. Ostrom, H. Gittleman, P. Farah, A. Ondracek, Y. Chen, Y. Wolinsky, N.E. Stroup, C. Kruchko, J.S. Barnholtz-Sloan, CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2006–2010, Neuro Oncol. 15 (Suppl. 2) (2013) ii1–ii56. [2] D.N. Louis, H. Ohgaki, O.D. Wiestler, W.K. Cavenee, P.C. Burger, A. Jouvet, B.W. Scheithauer, P. Kleihues, The 2007 WHO classification of tumours of the central nervous system, Acta Neuropathol. 114 (2) (2007) 97–109. [3] V.R. Kshettry, Q.T. Ostrom, C. Kruchko, O. Al-Mefty, G.H. Barnett, J.S. Barnholtz-Sloan, Descriptive epidemiology of World Health Organization grades II and III intracranial meningiomas in the United States, Neuro Oncol. 17 (8) (2015) 1166–1173. [4] B.P. Walcott, B.V. Nahed, P.K. Brastianos, J.S. Loeffler, Radiation treatment for WHO grade II and III meningiomas, Front. Oncol. 3 (2013) 227. [5] F. Gao, L. Shi, J. Russin, L. Zeng, X. Chang, S. He, T.C. Chen, S.L. Giannotta, D.J. Weisenberger, G. Zada, W.J. Mack, K. Wang, DNA methylation in the malignant transformation of meningiomas, PLoS One 8 (1) (2013) e54114. [6] J. Vengoechea, A.E. Sloan, Y. Chen, X. Guan, Q.T. Ostrom, A. Kerstetter, D. Capella, M.L. Cohen, Y. Wolinsky, K. Devine, W. Selman, G.H. Barnett, R.E. Warnick, C. McPherson, E.A. Chiocca, J.B. Elder, J.S. Barnholtz-Sloan, Methylation markers of malignant potential in meningiomas, J. Neurosurg. 119 (4) (2013) 899–906. [7] Y. Nakane, A. Natsume, T. Wakabayashi, S. Oi, M. Ito, S. Inao, K. Saito, J. Yoshida, Malignant transformation-related genes in meningiomas: allelic loss on 1p36 and methylation status of p73 and RASSF1A, J. Neurosurg. 107 (2) (2007) 398–404. [8] P.K. Brastianos, P.M. Horowitz, S. Santagata, R.T. Jones, A. McKenna, G. Getz, K.L. Ligon, E. Palescandolo, P. Van Hummelen, M.D. Ducar, A. Raza, A. Sunkavalli, L.E. Macconaill, A.O. Stemmer-Rachamimov, D.N. Louis, W.C. Hahn, I.F. Dunn, R. Beroukhim, Genomic sequencing of meningiomas identifies oncogenic SMO and AKT1 mutations, Nat. Genet. 45 (3) (2013) 285–289. [9] Y. Lee, J. Liu, S. Patel, T. Cloughesy, A. Lai, H. Farooqi, D. Seligson, J. Dong, L. Liau, D. Becker, P. Mischel, S. Shams, S. Nelson, Genomic landscape of meningiomas, Brain Pathol. 20 (4) (2009) 751–762. [10] C.I. Rubin, G.F. Atweh, The role of stathmin in the regulation of the cell cycle, J. Cell. Biochem. 93 (2) (2004) 242–250. [11] B.R. Houghtaling, G. Yang, A. Matov, G. Danuser, T.M. Kapoor, Op18 reveals the contribution of nonkinetochore microtubules to the dynamic organization of the vertebrate meiotic spindle, Proc. Natl. Acad. Sci. U. S. A. 106 (36) (2009) 15338–15343. [12] E. Alli, J. Bash-Babula, J.M. Yang, W.N. Hait, Effect of stathmin on the sensitivity to antimicrotubule drugs in human breast cancer, Cancer Res. 62 (23) (2002) 6864–6869. [13] C.M. Fife, S.M. Sagnella, W.S. Teo, S.T. Po’uha, F.L. Byrne, Y.Y. Yeap, D.C. Ng, T.P. Davis, J.A. McCarroll, M. Kavallaris, Stathmin mediates neuroblastoma metastasis in a tubulin-independent manner via RhoA/ROCK signaling and enhanced transendothelial migration, Oncogene 36 (4) (2016) 501–511. [14] Y. Song, L. Mu, X. Han, X. Liu, S. Fu, siRNA targeting stathmin inhibits invasion and enhances chemotherapy sensitivity of stem cells derived from glioma cell lines, Acta Biochim. Biophys. Sin. (Shanghai) 46 (12) (2014) 1034–1040. [15] S. Koncarevic, S. Urig, K. Steiner, S. Rahlfs, C. Herold-Mende, H. Sueltmann, K. Becker, Differential genomic and proteomic profiling of glioblastoma cells exposed to terpyridineplatinum(II) complexes, Free Radic. Biol. Med. 46 (8) (2009) 1096–1108.

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