Volume 7 Supplement 1
Integrated analysis of microRNA-target interactions with clinical outcomes for cancers
© Joung et al.; licensee BioMed Central Ltd. 2014
Published: 8 May 2014
Clinical statement alone is not enough to predict the progression of disease. Instead, the gene expression profiles have been widely used to forecast clinical outcomes. Many genes related to survival have been identified, and recently miRNA expression signatures predicting patient survival have been also investigated for several cancers. However, miRNAs and their target genes associated with clinical outcomes have remained largely unexplored.
Here, we demonstrate a survival analysis based on the regulatory relationships of miRNAs and their target genes. The patient survivals for the two major cancers, ovarian cancer and glioblastoma multiforme (GBM), are investigated through the integrated analysis of miRNA-mRNA interaction pairs.
We found that there is a larger survival difference between two patient groups with an inversely correlated expression profile of miRNA and mRNA. It supports the idea that signatures of miRNAs and their targets related to cancer progression can be detected via this approach.
This integrated analysis can help to discover coordinated expression signatures of miRNAs and their target mRNAs that can be employed for therapeutics in human cancers.
KeywordsSurvival Analysis miRNA-mRNA interaction miRNA Expression Gene Expression Profile Cancer Genes
As patterns of gene expression correlate with disease phenotype and patient outcome, mRNA expression profiling has been used to classify disease risks as well as prediction of outcome[1–4]. In addition, survival analysis with gene expression profiles is beneficial for identifying new prognostic targets of diverse diseases.
Many miRNAs have been also found to be correlated with clinical outcome in specific cancer types [5–10]. miRNAs are a class of small and endogenous RNA molecules that regulate their target mRNAs through translational repression or mRNA degradation . In tumors, many miRNAs can be aberrantly expressed, leading to potentially abnormal regulation of their target mRNAs. Although over 1,000 human miRNAs may be encoded in human genome, the potential therapeutic markers provided by miRNAs for a diverse spectrum of diseases are still unexplored.
Recently, several investigations have put emphasis on the integrated analysis of miRNAs and mRNAs in clinical outcomes [12–17]. In general, there are different approaches for the joint analysis of miRNA and mRNA data. For example, several miRNAs associated with survival rate can be extracted by survival analysis and then their relationship of inverse correlation can be identified based on analyzing miRNA and mRNA expression profiles. Most approaches do not test the clinical outcome by considering miRNA expression and mRNAs expression simultaneously. At minimum, this analysis requires the size of the cohort to be large enough for statistically significant measurement outcomes and the paired samples with both mRNA and miRNA expressions should be given. The Cancer Genome Atlas (TCGA)  provides different types of genomic datasets and we systematically integrated multi-omics data for cancer clinical outcome prediction .
Here, we present the survival analysis considering the regulatory relationships of miRNAs and their target genes. We tested clinical outcomes with the patient survival information, miRNA expression profiles and gene expression profiles for ovarian cancer and glioblastoma multiforme (GBM) through the integrated analysis of miRNA-mRNA interaction pairs. We found the miRNA-mRNA pairs with an inversely correlated expression profile that have significant survival differences between two patient groups. The results presented here suggest that this analysis can help to discover expression signatures of miRNAs and their target mRNAs that can be employed for therapeutics in human cancers.
miRNA target dataset
We obtained the largest collection of human miRNA-mRNA target relationships from the TarBase 6.0 . All targets in this collection were manually curated and experimentally validated. We extracted a total of 12,879 interactions consisting of 229 miRNAs and 6,699 target genes.
Gene and miRNA expression profile
The miRNA and gene expression data sets in ovarian cancer and GBM are acquired from the Cancer Genome Atlas (TCGA) . Ovarian cancer and GBM data sets consist of mRNA expression profiles and miRNA expression profiles for 496 and 425 patients, respectively. We transformed the expression values into a Z-score for each miRNA or each gene. If the Z-score of miRNA (or mRNA) is greater (less) than zero, its expression level is defined to be high (low). We excluded miRNA-mRNA pairs from the expression datasets that were not listed in the TarBase in order to avoid unnecessary calculations. Expression matrices for ovarian cancer composed of 137 miRNAs × 496 patients and 5,707 target genes × 496 patients. Among all possible 776,016 miRNAs and mRNA pairs, the number of validated interactions presented in TarBase 6.0 is 10,574 composed from 137 miRNAs and 5,707 mRNAs. Expression matrices for GBM consist of 144 miRNAs × 425 patients and 6,700 target genes × 425 patients. Among all possible 964,800 miRNA-mRNA pairs, the number of validated interactions is 9,073 from 144 miRNAs and 6,700 mRNAs.
Survival analysis of miRNA and target mRNA
We carried out the survival analysis of each miRNA-mRNA pair for six possible pairwise sets (HL:LH, HH:LL, HL:LH, HH:HL, LL:LH and LL:HL). Among these sets, our focus was on the survival test between LH and HL (Figure 1(B)) as they are inversely related to the level of expression. The group HL indicates that high expression of miRNA causes low expression of mRNA through the mechanism that a miRNA represses or cleaves its target mRNA, while the group LH states that the low expression of miRNA results in the high expression of mRNA.
Comparison of the number of significant pairs.
Num of Significant Pairs (p-value < 0.01)
Top 20 ranked miRNA-mRNA interaction pairs (OV).
Protein Kinase C And Casein Kinase Substrate In Neurons Protein
Solute carrier family 43, member 3
Eyes Absent (Drosophila) Homolog 4
Growth arrest-specific 1
TAF7 RNA Polymerase II, TATA Box Binding Protein (TBP)-Associated Factor
Stromal Cell Derived Factor 2 Like Protein 1
Solute carrier family 35, member D2
Pleomorphic adenoma gene-like 1
Stress-associated endoplasmic reticulum protein 1
Leucine-Rich Repeat Containing G Protein-Coupled Receptor 4
Amyloid beta (A4) precursor protein-binding, family B, member 2
Fibronectin type III domain containing 3A
Microtubule associated tumor suppressor 1
CDK5 regulatory subunit associated protein 1-like 1
Zinc finger, BED-type containing 4
FCH and double SH3 domains 2
E74-Like Factor 4 (Ets Domain Transcription Factor)
Guanine Nucleotide Binding Protein (G Protein) Alpha 12
Interferon gamma receptor 1
Among top 20 ranked miRNA-mRNA interaction pairs, the roles of four targets including PLAGL1, MTUS1, MEF and IFNGR1 have been reported in ovarian cancer (Table 2). miR-148a was down-regulated in ovarian cancer cell lines and might be involved in the carcinogenesis of ovarian cancer [21, 22]. Previous research reported that overexpression of miR-148b in ovarian cancer tissues was not associated with any of the pathological features of patients with ovarian cancer. It suggested that miR-148b might be involved in the early stage of ovarian carcinogenesis . Although other miRNAs including miR-196a, miR-374b, miR-124, and miR-98 have not been reported for associations with ovarian cancer, they are related with the oncogenic phenotype or their expression of other cancers [24–27].
Several miRNAs are dominated in significant miRNA-mRNA pairs. miR-98 and miR-148b-3p have more than 446 and 187 target genes, respectively. miR-98 or miR-148b-3p itself shows a significant survival difference between high and low expression (pvalue < 0.0077 and pvalue < 0.0014), while miR-124-3p with 979 targets genes shows a borderline significance (pvalue < 0.0051).
LOT1(PLAGL1/ZAC1) is known to possess anti-proliferative effects and is frequently silenced in ovarian cancer and breast cancer . Previous studies suggest that a shortage of the PLAGL1 protein might impair its role in regulating the cell cycle and interfere with apoptosis. Consequently, cells may grow and divide too quickly in an uncontrolled manner. Mitochondrial tumor suppressor 1 (MTUS1) is a newly identified candidate tumor suppressor gene . Previous studies have shown that MTUS1 expression levels are down-regulated in cancers of the colon, ovary, pancreas, head and neck, and breast cancer . MEF (myeloid ELF1-like factor, also known as ELF4) is expressed in a significant proportion of ovarian carcinomas . The oncogenic activity of MEF was shown by the ability of MEF to transform NIH3T3 cells, and induce the formation of tumors in nude mice. The expression level of IFNGR1 in a typical ovarian cancer population varies, with 22% of them displaying a complete loss of the IFNγ receptor . Low levels of receptor expression seem to have a negative effect on survival and are unrelated to other pathologic variables. Therefore, low expression of IFNGR1 could be regarded as an independent prognostic marker in ovarian cancer. Although we have found only several previously reported functional roles related to ovarian cancer, they hint at the possibility that other target genes might be associated with ovarian cancer development and progression.
Top 20 ranked miRNA-mRNA interaction pairs (GBM).
G Patch Domain Containing 8
CAP-GLY domain containing linker protein 1
Ctr9, Paf1/RNA polymerase II complex component, homolog (S. cerevisiae)
Histone cluster 3, H2a
Cyclin-dependent kinase inhibitor 1A (p21, Cip1)
Family with sequence similarity 3, member C
Protein kinase, AMP-activated, alpha 1 catalytic subunit
Cyclin-dependent kinase inhibitor 1A (p21, Cip1)
Transcription factor AP-2 alpha (activating enhancer binding protein 2 alpha)
Ras and Rab interactor 2
C-type lectin domain family 5, member A
Proteasome (prosome, macropain) 26S subunit, non-ATPase, 9
Cyclin-dependent kinase inhibitor 1A (p21, Cip1)
V-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog
Ataxia telangiectasia mutated
Previous quantitative real-time PCR revealed overexpression of CDKN1A in primary GBM. This result suggests that CDKN1A expression is regarded as a putative marker to distinguish primary GBM from secondary GBM. The gene for methylthioadenosine phosphorylase (MTAP) is located closely to the gene CDKN2A. MTAP-deficiency in many tumors that have been most resistant to treatment occurs commonly. Especially 70% of glioblastoma lack MTAP. Amplification of KIT in 17 (4.4%) glioblastomas was reveled from screening of 390 glioblastomas. A borderline positive association (p=0.0579) between KIT amplification and TP53 mutation was also observed. ATM expression is correlated with radioresistance in primary GBM cells in culture. Genes encoding components of the DNA-damage response (DDR) pathway are frequently altered in human GBM patients and the ATM/Chk2/p53 cascade suppresses GBM formation.
We have introduced the integrated analysis of survival test with datasets of miRNA and mRNA expression profiles, as well as clinical information in ovarian cancer and GBM. We have seen that the combined expression patterns between miRNAs and mRNAs can distinguish between risk groups related to co-regulation of both. In addition, we have presented supporting evidence for functional roles of miRNA and their targets in specific cancer from the literature. Our approach can be utilized to detect clinical and therapeutic miRNAs and their targets related to outcome of several cancers.
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2010-0028631); Ministry of Health and Welfare (HI13C2164).
Funding for open access charge: National Research Foundation of Korea (NRF).
This article has been published as part of BMC Medical Genomics Volume 7 Supplement 1, 2014: Selected articles from the 3rd Translational Bioinformatics Conference (TBC/ISCB-Asia 2013). The full contents of the supplement are available online at http://www.biomedcentral.com/bmcmedgenomics/supplements/7/S1.
- Vasselli JR, Shih JH, Iyengar SR, Maranchie J, Riss J, Worrell R, Torres-Cabala C, Tabios R, Mariotti A, Stearman R, et al: Predicting survival in patients with metastatic kidney cancer by gene-expression profiling in the primary tumor. Proc Natl Acad Sci USA. 2003, 100 (12): 6958-6963. 10.1073/pnas.1131754100.PubMed CentralView ArticlePubMedGoogle Scholar
- van de Vijver MJ, He YD, van't Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ, et al: A gene-expression signature as a predictor of survival in breast cancer. The New England journal of medicine. 2002, 347 (25): 1999-2009. 10.1056/NEJMoa021967.View ArticlePubMedGoogle Scholar
- van 't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, et al: Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002, 415 (6871): 530-536. 10.1038/415530a.View ArticlePubMedGoogle Scholar
- West M, Blanchette C, Dressman H, Huang E, Ishida S, Spang R, Zuzan H, Olson JA, Marks JR, Nevins JR: Predicting the clinical status of human breast cancer by using gene expression profiles. Proc Natl Acad Sci USA. 2001, 98 (20): 11462-11467. 10.1073/pnas.201162998.PubMed CentralView ArticlePubMedGoogle Scholar
- Lawrie CH, Chi J, Taylor S, Tramonti D, Ballabio E, Palazzo S, Saunders NJ, Pezzella F, Boultwood J, Wainscoat JS, et al: Expression of microRNAs in diffuse large B cell lymphoma is associated with immunophenotype, survival and transformation from follicular lymphoma. Journal of cellular and molecular medicine. 2009, 13 (7): 1248-1260. 10.1111/j.1582-4934.2008.00628.x.PubMed CentralView ArticlePubMedGoogle Scholar
- Lawrie CH, Gal S, Dunlop HM, Pushkaran B, Liggins AP, Pulford K, Banham AH, Pezzella F, Boultwood J, Wainscoat JS, et al: Detection of elevated levels of tumour-associated microRNAs in serum of patients with diffuse large B-cell lymphoma. British journal of haematology. 2008, 141 (5): 672-675. 10.1111/j.1365-2141.2008.07077.x.View ArticlePubMedGoogle Scholar
- Lawrie CH, Soneji S, Marafioti T, Cooper CD, Palazzo S, Paterson JC, Cattan H, Enver T, Mager R, Boultwood J, et al: MicroRNA expression distinguishes between germinal center B cell-like and activated B cell-like subtypes of diffuse large B cell lymphoma. International journal of cancer Journal international du cancer. 2007, 121 (5): 1156-1161. 10.1002/ijc.22800.View ArticlePubMedGoogle Scholar
- Iorio MV, Ferracin M, Liu CG, Veronese A, Spizzo R, Sabbioni S, Magri E, Pedriali M, Fabbri M, Campiglio M, et al: MicroRNA gene expression deregulation in human breast cancer. Cancer research. 2005, 65 (16): 7065-7070. 10.1158/0008-5472.CAN-05-1783.View ArticlePubMedGoogle Scholar
- Calin GA, Ferracin M, Cimmino A, Di Leva G, Shimizu M, Wojcik SE, Iorio MV, Visone R, Sever NI, Fabbri M, et al: A MicroRNA signature associated with prognosis and progression in chronic lymphocytic leukemia. The New England journal of medicine. 2005, 353 (17): 1793-1801. 10.1056/NEJMoa050995.View ArticlePubMedGoogle Scholar
- Takamizawa J, Konishi H, Yanagisawa K, Tomida S, Osada H, Endoh H, Harano T, Yatabe Y, Nagino M, Nimura Y, et al: Reduced expression of the let-7 microRNAs in human lung cancers in association with shortened postoperative survival. Cancer research. 2004, 64 (11): 3753-3756. 10.1158/0008-5472.CAN-04-0637.View ArticlePubMedGoogle Scholar
- Bartel DP, MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004, 116 (2): 281-297. 10.1016/S0092-8674(04)00045-5.View ArticlePubMedGoogle Scholar
- Volinia S, Croce CM: Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer. Proc Natl Acad Sci USA. 2013, 110 (18): 7413-7417. 10.1073/pnas.1304977110.PubMed CentralView ArticlePubMedGoogle Scholar
- Buffa FM, Camps C, Winchester L, Snell CE, Gee HE, Sheldon H, Taylor M, Harris AL, Ragoussis J: microRNA-associated progression pathways and potential therapeutic targets identified by integrated mRNA and microRNA expression profiling in breast cancer. Cancer research. 2011, 71 (17): 5635-5645. 10.1158/0008-5472.CAN-11-0489.View ArticlePubMedGoogle Scholar
- Gade S, Porzelius C, Falth M, Brase JC, Wuttig D, Kuner R, Binder H, Sultmann H, Beissbarth T: Graph based fusion of miRNA and mRNA expression data improves clinical outcome prediction in prostate cancer. BMC Bioinformatics. 2011, 12: 488-10.1186/1471-2105-12-488.PubMed CentralView ArticlePubMedGoogle Scholar
- Creighton CJ, Hernandez-Herrera A, Jacobsen A, Levine DA, Mankoo P, Schultz N, Du Y, Zhang Y, Larsson E, Sheridan R, et al: Integrated analyses of microRNAs demonstrate their widespread influence on gene expression in high-grade serous ovarian carcinoma. PLoS One. 2012, 7 (3): e34546-10.1371/journal.pone.0034546.PubMed CentralView ArticlePubMedGoogle Scholar
- Enerly E, Steinfeld I, Kleivi K, Leivonen SK, Aure MR, Russnes HG, Ronneberg JA, Johnsen H, Navon R, Rodland E, et al: miRNA-mRNA integrated analysis reveals roles for miRNAs in primary breast tumors. PLoS One. 2011, 6 (2): e16915-10.1371/journal.pone.0016915.PubMed CentralView ArticlePubMedGoogle Scholar
- Bair E, Tibshirani R: Semi-supervised methods to predict patient survival from gene expression data. PLoS biology. 2004, 2 (4): E108-10.1371/journal.pbio.0020108.PubMed CentralView ArticlePubMedGoogle Scholar
- TCGA Network: Integrated genomic analyses of ovarian carcinoma. Nature. 2011, 474 (7353): 609-615. 10.1038/nature10166.View ArticleGoogle Scholar
- Kim D, Shin H, Song YS, Kim JH: Synergistic effect of different levels of genomic data for cancer clinical outcome prediction. J Biomed Inform. 2012, 45 (6): 1191-1198. 10.1016/j.jbi.2012.07.008.View ArticlePubMedGoogle Scholar
- Vergoulis T, Vlachos IS, Alexiou P, Georgakilas G, Maragkakis M, Reczko M, Gerangelos S, Koziris N, Dalamagas T, Hatzigeorgiou AG: TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic acids research. 2012, 40 (Database): D222-229.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhou X, Zhao F, Wang ZN, Song YX, Chang H, Chiang Y, Xu HM: Altered expression of miR-152 and miR-148a in ovarian cancer is related to cell proliferation. Oncology reports. 2012, 27 (2): 447-454.PubMedGoogle Scholar
- Delfino KR, Rodriguez-Zas SL: Transcription factor-microRNA-target gene networks associated with ovarian cancer survival and recurrence. PLoS One. 2013, 8 (3): e58608-10.1371/journal.pone.0058608.PubMed CentralView ArticlePubMedGoogle Scholar
- Chang H, Zhou X, Wang ZN, Song YX, Zhao F, Gao P, Chiang Y, Xu HM: Increased expression of miR-148b in ovarian carcinoma and its clinical significance. Molecular medicine reports. 2012, 5 (5): 1277-1280.PubMedGoogle Scholar
- Furuta M, Kozaki KI, Tanaka S, Arii S, Imoto I, Inazawa J: miR-124 and miR-203 are epigenetically silenced tumor-suppressive microRNAs in hepatocellular carcinoma. Carcinogenesis. 2010, 31 (5): 766-776. 10.1093/carcin/bgp250.View ArticlePubMedGoogle Scholar
- Dalmay T, Edwards DR: MicroRNAs and the hallmarks of cancer. Oncogene. 2006, 25 (46): 6170-6175. 10.1038/sj.onc.1209911.View ArticlePubMedGoogle Scholar
- Lui WO, Pourmand N, Patterson BK, Fire A: Patterns of known and novel small RNAs in human cervical cancer. Cancer research. 2007, 67 (13): 6031-6043. 10.1158/0008-5472.CAN-06-0561.View ArticlePubMedGoogle Scholar
- Hoffman AE, Zheng T, Yi C, Leaderer D, Weidhaas J, Slack F, Zhang Y, Paranjape T, Zhu Y: microRNA miR-196a-2 and breast cancer: a genetic and epigenetic association study and functional analysis. Cancer research. 2009, 69 (14): 5970-5977. 10.1158/0008-5472.CAN-09-0236.PubMed CentralView ArticlePubMedGoogle Scholar
- Abdollahi A, Pisarcik D, Roberts D, Weinstein J, Cairns P, Hamilton TC: LOT1 (PLAGL1/ZAC1), the candidate tumor suppressor gene at chromosome 6q24-25, is epigenetically regulated in cancer. The Journal of biological chemistry. 2003, 278 (8): 6041-6049. 10.1074/jbc.M210361200.View ArticlePubMedGoogle Scholar
- Xiao J, Chen JX, Zhu YP, Zhou LY, Shu QA, Chen LW: Reduced expression of MTUS1 mRNA is correlated with poor prognosis in bladder cancer. Oncology letters. 2012, 4 (1): 113-118.PubMed CentralPubMedGoogle Scholar
- Califano D, Pignata S, Pisano C, Greggi S, Laurelli G, Losito NS, Ottaiano A, Gallipoli A, Pasquinelli R, De Simone V, et al: FEZ1/LZTS1 protein expression in ovarian cancer. Journal of cellular physiology. 2010, 222 (2): 382-386. 10.1002/jcp.21962.View ArticlePubMedGoogle Scholar
- Yao JJ, Liu Y, Lacorazza HD, Soslow RA, Scandura JM, Nimer SD, Hedvat CV: Tumor promoting properties of the ETS protein MEF in ovarian cancer. Oncogene. 2007, 26 (27): 4032-4037. 10.1038/sj.onc.1210170.View ArticlePubMedGoogle Scholar
- Duncan TJ, Rolland P, Deen S, Scott IV, Liu DT, Spendlove I, Durrant LG: Loss of IFN gamma receptor is an independent prognostic factor in ovarian cancer. Clinical cancer research : an official journal of the American Association for Cancer Research. 2007, 13 (14): 4139-4145. 10.1158/1078-0432.CCR-06-2833.View ArticleGoogle Scholar
- Li Y, Guessous F, Zhang Y, Dipierro C, Kefas B, Johnson E, Marcinkiewicz L, Jiang J, Yang Y, Schmittgen TD, et al: MicroRNA-34a inhibits glioblastoma growth by targeting multiple oncogenes. Cancer Res. 2009, 69 (19): 7569-7576. 10.1158/0008-5472.CAN-09-0529.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhao S, Yang G, Mu Y, Han D, Shi C, Chen X, Deng Y, Zhang D, Wang L, Liu Y, et al: MiR-106a is an independent prognostic marker in patients with glioblastoma. Neuro Oncol. 2013, 15 (6): 707-717. 10.1093/neuonc/not001.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang C, Kang C, You Y, Pu P, Yang W, Zhao P, Wang G, Zhang A, Jia Z, Han L, et al: Co-suppression of miR-221/222 cluster suppresses human glioma cell growth by targeting p27kip1 in vitro and in vivo. Int J Oncol. 2009, 34 (6): 1653-1660.PubMedGoogle Scholar
- Wu N, Zhao X, Liu M, Liu H, Yao W, Zhang Y, Cao S, Lin X: Role of microRNA-26b in glioma development and its mediated regulation on EphA2. PLoS One. 2011, 6 (1): e16264-10.1371/journal.pone.0016264.PubMed CentralView ArticlePubMedGoogle Scholar
- Lu S, Wang S, Geng S, Ma S, Liang Z, Jiao B: Increased expression of microRNA-17 predicts poor prognosis in human glioma. J Biomed Biotechnol. 2012, 2012: 970761-PubMed CentralPubMedGoogle Scholar
- Ernst A, Hofmann S, Ahmadi R, Becker N, Korshunov A, Engel F, Hartmann C, Felsberg J, Sabel M, Peterziel H, et al: Genomic and expression profiling of glioblastoma stem cell-like spheroid cultures identifies novel tumor-relevant genes associated with survival. Clin Cancer Res. 2009, 15 (21): 6541-6550. 10.1158/1078-0432.CCR-09-0695.View ArticlePubMedGoogle Scholar
- Lubin M, Lubin A: Selective killing of tumors deficient in methylthioadenosine phosphorylase: a novel strategy. PLoS One. 2009, 4 (5): e5735-10.1371/journal.pone.0005735.PubMed CentralView ArticlePubMedGoogle Scholar
- Nobusawa S, Stawski R, Kim YH, Nakazato Y, Ohgaki H: Amplification of the PDGFRA, KIT and KDR genes in glioblastoma: a population-based study. Neuropathology. 2011, 31 (6): 583-588. 10.1111/j.1440-1789.2011.01204.x.View ArticlePubMedGoogle Scholar
- Tribius S, Pidel A, Casper D: ATM protein expression correlates with radioresistance in primary glioblastoma cells in culture. Int J Radiat Oncol Biol Phys. 2001, 50 (2): 511-523. 10.1016/S0360-3016(01)01489-4.View ArticlePubMedGoogle Scholar
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