Login Register Cart 0
Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Meta-Analysis

Identifying Biomarkers of Cisplatin Sensitivity in Non-Small Cell Lung Cancer via Comprehensive Integrative Analysis

Author(s): Xin-Ping Xie, Wulin Yang, Lei Zhang and Hong-Qiang Wang*

Volume 17, Issue 6, 2022

Published on: 28 June, 2022

Page: [498 - 509] Pages: 12

DOI: 10.2174/1574893617666220407105905

Price: $65

TIMBC 2025
Abstract

Background: Only 30-40% of non-small cell lung cancer (NSCLC) patients are clinically sensitive to cisplatin-based chemotherapy. Thus, it is necessary to identify biomarkers for personalized cisplatin chemotherapy in NSCLC. However, data heterogeneity and low-value density make it challenging to detect reliable cisplatin efficacy biomarkers using traditional analysis methods.

Objective: This paper aims to find reliable cisplatin efficacy biomarkers for NSCLC patients using comprehensive integrative analysis.

Methods: We searched online resources and collected six NSCLC transcriptomics data sets with responses to cisplatin. The six data sets are divided into two groups: the learning group for biomarker identification and the test group for independent validation. We performed comprehensive integrative analysis under two kinds of frameworks, i.e., one-level and two-level, with three integrative models. Pathway analysis was performed to estimate the biological significance of the resulting biomarkers. For independent validation, logrank statistic was employed to test how significant the difference of Kaplan- Meier (KM) curves between two patient groups is, and the Cox proportional-hazards model was used to test how the expression of a gene is associated with patients’ survival time. Especially, a permutation test was performed to verify the predictive power of a biomarker panel on cisplatin efficacy. For comparison, we also analyzed each learning data set individually, in which three popular differential expression models, Limma, SAM, and RankSum, were used.

Results: A total of 318 genes were identified as a core panel of cisplatin efficacy markers for NSCLC patients, exhibiting consistent differential expression between cisplatin-sensitive and –resistant groups across studies. A total of 129 of 344 KEGG pathways were found to be enriched in the core panel, reflecting a picture of the molecular mechanism of cisplatin resistance in NSCLC. By mapping onto the KEGG pathway tree, we found that a KEGG pathway-level I module, genetic information processing, is most active in the core panel with the highest activity ratio in response to cisplatin in NSCLC as expected. Related pathways include mismatch repair, nucleotide excision repair, aminoacyl-tRNA biosynthesis, and basal transcription factors, most of which respond to DNA double-strand damage in patients. Evaluation on two independent data sets demonstrated the predictive power of the core marker panel for cisplatin sensitivity in NSCLC. Also, some single markers, e.g., MST1R, were observed to be remarkably predictive of cisplatin resistance in NSCLC.

Conclusion: Integrative analysis is more powerful in detecting biomarkers for cisplatin efficacy by overcoming data heterogeneity and low-value density in data sets, and the identified core panel (318 genes) can help develop personalized medicine of cisplatin chemotherapy for NSCLC patients.

Keywords: Integrative analysis, gene expression, biomarkers, cisplatin resistance, cancer, personalized medicine.

« Previous Next »
Graphical Abstract

[1]
Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2015; 136(5): E359-86.
[http://dx.doi.org/10.1002/ijc.29210] [PMID: 25220842]
[2]
Jemal A, Siegel R, Xu J, Ward E. Cancer statistics, 2010. CA Cancer J Clin 2010; 60(5): 277-300.
[http://dx.doi.org/10.3322/caac.20073] [PMID: 20610543]
[3]
Tian Y, Wang Z, Liu X, et al. Prediction of chemotherapeutic efficacy in non-small cell lung cancer by serum metabolomic profiling. Clinical Cancer Research 2018; 24(9): 2100-9.
[http://dx.doi.org/10.1158/1078-0432.CCR-17-2855]
[4]
Unger FT, Witte I, David KA. Prediction of individual response to anticancer therapy: Historical and future perspectives. Cell Mol Life Sci 2015; 72(4): 729-57.
[http://dx.doi.org/10.1007/s00018-014-1772-3] [PMID: 25387856]
[5]
Huang CY, Ju DT, Chang CF, Muralidhar Reddy P, Velmurugan BK. A review on the effects of current chemotherapy drugs and natural agents in treating non-small cell lung cancer. Biomedicine (Taipei) 2017; 7(4): 23.
[http://dx.doi.org/10.1051/bmdcn/2017070423] [PMID: 29130448]
[6]
Rose MC, Kostyanovskaya E, Huang RS. Pharmacogenomics of cisplatin sensitivity in non-small cell lung cancer. Genom Proteom Bioinf 2014; 12(5): 198-209.
[http://dx.doi.org/10.1016/j.gpb.2014.10.003] [PMID: 25449594]
[7]
Jiang P, Sellers WR, Liu XS. Big data approaches for modeling response and resistance to cancer drugs. Annu Rev Biomed Data Sci 2018; 1(1): 1-27.
[http://dx.doi.org/10.1146/annurev-biodatasci-080917-013350] [PMID: 31342013]
[8]
Failli M, Paananen J, Fortino V. ThETA: Transcriptome-driven efficacy estimates for gene-based TArget discovery. Bioinformatics 2020; 36(14): 4214-6.
[http://dx.doi.org/10.1093/bioinformatics/btaa518] [PMID: 32437556]
[9]
Guan N-N, Zhao Y, Wang C-C, Li JQ, Chen X, Piao X. Anticancer drug response prediction in cell lines using weighted graph regularized matrix factorization. Mol Ther Nucleic Acids 2019; 17: 164-74.
[http://dx.doi.org/10.1016/j.omtn.2019.05.017] [PMID: 31265947]
[10]
Wei D, Liu C, Zheng X, Li Y. Comprehensive anticancer drug response prediction based on a simple cell line-drug complex network model. BMC Bioinformatics 2019; 20(1): 44.
[http://dx.doi.org/10.1186/s12859-019-2608-9] [PMID: 30670007]
[11]
Buhl IK, Santoni-Rugiu E, Ravn J, et al. Molecular prediction of adjuvant cisplatin efficacy in Non-Small Cell Lung Cancer (NSCLC)-validation in two independent cohorts. PLoS One 2018; 13(3): e0194609.
[http://dx.doi.org/10.1371/journal.pone.0194609] [PMID: 29566065]
[12]
Safikhani Z, Smirnov P, Freeman M, et al. Revisiting inconsistency in large pharmacogenomic studies. F1000 Res 2016; 5: 2333.
[http://dx.doi.org/10.12688/f1000research.9611.1] [PMID: 28928933]
[13]
Cobo M, Isla D, Massuti B, et al. Customizing cisplatin based on quantitative excision repair cross-complementing 1 mRNA expression: A phase III trial in non-small-cell lung cancer. J Clin Oncol 2007; 25(19): 2747-54.
[http://dx.doi.org/10.1200/JCO.2006.09.7915] [PMID: 17602080]
[14]
Bergot E, Levallet G, Campbell K, Dubois F, Lechapt E, Zalcman G. Predictive biomarkers in patients with resected non-small cell lung cancer treated with perioperative chemotherapy. Eur Respir Rev 2013; 22(130): 565-76.
[http://dx.doi.org/10.1183/09059180.00007113] [PMID: 24293473]
[15]
Bepler G, Williams C, Schell MJ, et al. Randomized international phase III trial of ERCC1 and RRM1 expression-based chemotherapy versus gemcitabine/carboplatin in advanced non-small-cell lung cancer. J Clin Oncol 2013; 31(19): 2404-12.
[http://dx.doi.org/10.1200/JCO.2012.46.9783] [PMID: 23690416]
[16]
Cobo M, Domine M, Sanchezronco M, et al. Interim analysis of the Spanish lung cancer group (SLCG) BRCA1-RAP80 expression customization (BREC) randomized phase III trial of customized therapy in advanced non-small cell lung cancer (NSCLC) patients (p) (NCT00617656/GECP-BREC). J Clin Oncol 2013; 31(18): LBA8002.
[17]
Costello JC, Heiser LM, Georgii E, et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol 2014; 32(12): 1202-12.
[http://dx.doi.org/10.1038/nbt.2877] [PMID: 24880487]
[18]
Nguyen T, Tagett R, Donato M, Mitrea C, Draghici S. A novel bi-level meta-analysis approach: Applied to biological pathway analysis. Bioinformatics 2016; 32(3): 409-16.
[http://dx.doi.org/10.1093/bioinformatics/btv588] [PMID: 26471455]
[19]
Shafi A, Nguyen T, Peyvandipour A, Draghici S. GSMA: An approach to identify robust global and test Gene Signatures using Meta-Analysis. Bioinformatics 2020; 36(2): 487-95.
[http://dx.doi.org/10.1093/bioinformatics/btz561] [PMID: 31329248]
[20]
Barretina J, Caponigro G, Stransky N, et al. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 2012; 483(7391): 603-7.
[http://dx.doi.org/10.1038/nature11003] [PMID: 22460905]
[21]
Garnett MJ, Edelman EJ, Heidorn SJ, et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 2012; 483(7391): 570-5.
[http://dx.doi.org/10.1038/nature11005] [PMID: 22460902]
[22]
Yang W, Soares J, Greninger P, et al. Genomics of Drug Sensitivity in Cancer (GDSC): A resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res 2013; 41: D955-61.
[PMID: 23180760]
[23]
Gemma A, Li C, Sugiyama Y, et al. Anticancer drug clustering in lung cancer based on gene expression profiles and sensitivity database. BMC Cancer 2006; 6(1): 174.
[http://dx.doi.org/10.1186/1471-2407-6-174]
[24]
Vera O, Jimenez J, Pernia O, et al. DNA methylation of miR-7 is a mechanism involved in platinum response through MAFG overexpression in cancer cells. Theranostics 2017; 7(17): 4118-34.
[http://dx.doi.org/10.7150/thno.20112] [PMID: 29158814]
[25]
Zhu C-Q, Ding K, Strumpf D, et al. Prognostic and predictive gene signature for adjuvant chemotherapy in resected non-small-cell lung cancer. J Clin Oncol 2010; 28(29): 4417-24.
[http://dx.doi.org/10.1200/JCO.2009.26.4325] [PMID: 20823422]
[26]
Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur Cancer 2009; 45(2): 228-47.
[27]
Toro-Domínguez D, Martorell-Marugán J, López-Domínguez R, et al. ImaGEO: Integrative gene expression meta-analysis from GEO database. Bioinformatics 2019; 35(5): 880-2.
[http://dx.doi.org/10.1093/bioinformatics/bty721] [PMID: 30137226]
[28]
Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004; 3(1): e3.
[http://dx.doi.org/10.2202/1544-6115.1027] [PMID: 16646809]
[29]
Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 2001; 98(9): 5116-21.
[http://dx.doi.org/10.1073/pnas.091062498] [PMID: 11309499]
[30]
Deng L, Ma J, Pei J. Rank sum method for related gene selection and its application to tumor diagnosis. Chin Sci Bull 2004; 49(15): 1652-7.
[http://dx.doi.org/10.1007/BF03184138]
[31]
Siangphoe U, Archer KJ, Mukhopadhyay ND. Classical and Bayesian random-effects meta-analysis models with sample quality weights in gene expression studies. BMC Bioinformatics 2019; 20(1): 18.
[http://dx.doi.org/10.1186/s12859-018-2491-9] [PMID: 30626315]
[32]
Xu M, Chen L. An empirical likelihood ratio test robust to individual heterogeneity for differential expression analysis of RNA-seq. Brief Bioinform 2018; 19(1): 109-17.
[PMID: 27769992]
[33]
Li Y, Ghosh D. Assumption weighting for incorporating heterogeneity into meta-analysis of genomic data. Bioinformatics 2012; 28(6): 807-14.
[http://dx.doi.org/10.1093/bioinformatics/bts037] [PMID: 22285559]
[34]
Seetharaman S, Flemyng E, Shen J, Conte MR, Ridley AJ. The RNA-binding protein LARP4 regulates cancer cell migration and invasion. Cytoskeleton (Hoboken) 2016; 73(11): 680-90.
[http://dx.doi.org/10.1002/cm.21336] [PMID: 27615744]
[35]
Egiz M, Usui T, Ishibashi M, et al. La-related protein 4 as a suppressor for motility of ovarian cancer cells. Tohoku J Exp Med 2019; 247(1): 59-67.
[http://dx.doi.org/10.1620/tjem.247.59] [PMID: 30686809]
[36]
Jia R, Wang C. MiR-29b-3p reverses cisplatin resistance by targeting COL1A1 in non-small-cell lung cancer A549/DDP Cells. Cancer Manag Res 2020; 12(1): 2559-66.
[http://dx.doi.org/10.2147/CMAR.S246625] [PMID: 32368137]
[37]
Sassa T, Suto S, Okayasu Y, Kihara A. A shift in sphingolipid composition from C24 to C16 increases susceptibility to apoptosis in HeLa cells. Biochim Biophys Acta 2012; 1821(7): 1031-7.
[http://dx.doi.org/10.1016/j.bbalip.2012.04.008] [PMID: 22579584]
[38]
Szklarz LS, Scorrano L. The antiapoptotic OPA1/Parl couple participates in mitochondrial adaptation to heat shock. Biochimica et Bio-physica Acta (BBA) -. Biochim Biophys Acta Bioenerg 2012; 1817(10): 1886-93.
[http://dx.doi.org/10.1016/j.bbabio.2012.05.001]
[39]
Leanza L, O’Reilly P, Doyle A, et al. Correlation between potassium channel expression and sensitivity to drug-induced cell death in tu-mor cell lines. Curr Pharm Des 2014; 20(2): 189-200.
[http://dx.doi.org/10.2174/13816128113199990032] [PMID: 23701546]
[40]
Pillozzi S, D'Amico M, Bartoli G, et al. The combined activation of KCa3.1 and inhibition of Kv11.1/hERG1 currents contribute to over-come Cisplatin resistance in colorectal cancer cells. British J Cancer 2018; 118(2): 200-12.
[41]
Rosa P, Catacuzzeno L, Sforna L, et al. BK channels blockage inhibits hypoxia-induced migration and chemoresistance to cisplatin in human glioblastoma cells. J Cell Physiol 2018; 233(9): 6866-77.
[http://dx.doi.org/10.1002/jcp.26448] [PMID: 29319175]
[42]
Gao J, Meng Q, Zhao Y, Chen X, Cai L. EHD1 confers resistance to cisplatin in non-small cell lung cancer by regulating intracellular cis-platin concentrations. BMC Cancer 2016; 16(1): 470.
[http://dx.doi.org/10.1186/s12885-016-2527-3] [PMID: 27411790]
[43]
Keklikoglou I, Cianciaruso C, Güç E, et al. Chemotherapy elicits pro-metastatic extracellular vesicles in breast cancer models. Nature Cell Biol 2019; 21(2): 190-202.
[http://dx.doi.org/10.1038/s41556-018-0256-3]
[44]
Sawant A, Kothandapani A, Zhitkovich A, Sobol RW, Patrick SM. Role of mismatch repair proteins in the processing of cisplatin inter-strand cross-links. DNA Repair (Amst) 2015; 35: 126-36.
[http://dx.doi.org/10.1016/j.dnarep.2015.10.003] [PMID: 26519826]
[45]
Fink D, Aebi S, Howell SB. The role of DNA mismatch repair in drug resistance. Clin Cancer Res 1998; 4(1): 1-6.
[PMID: 9516945]
[46]
Sharma A, Almasan A. USP14 regulates DNA damage response and is a target for radiosensitization in non-small cell lung cancer. Int J Mol Sci 2020; 21(17): 6383.
[http://dx.doi.org/10.3390/ijms21176383] [PMID: 32887472]
[47]
Xu X, Liu J, Shen C, et al. The role of ubiquitin-specific protease 14 (USP14) in cell adhesion-mediated drug resistance (CAM-DR) of multiple myeloma cells. Eur J Haematol 2017; 98(1): 4-12.
[http://dx.doi.org/10.1111/ejh.12729] [PMID: 26710889]
[48]
Fu Y, Ma G, Liu G, et al. USP14 as a novel prognostic marker promotes cisplatin resistance via Akt/ERK signaling pathways in gastric cancer. Cancer Med 2018; 7(11): 5577-88.
[http://dx.doi.org/10.1002/cam4.1770] [PMID: 30296012]
[49]
Nakagawa T, Xiong Y. X-linked mental retardation gene CUL4B targets ubiquitylation of H3K4 methyltransferase component WDR5 and regulates neuronal gene expression. Mol Cell 2011; 43(3): 381-91.
[http://dx.doi.org/10.1016/j.molcel.2011.05.033] [PMID: 21816345]
[50]
Liu HR, Zhao J. Effect and mechanism of miR-217 on drug resistance, invasion and metastasis of ovarian cancer cells through a regulatory axis of CUL4B gene silencing/inhibited Wnt/β-catenin signaling pathway activation. Eur Rev Med Pharmacol Sci 2021; 25(1): 94-107.
[PMID: 33506897]
[51]
Huang Y, Bell LN, Okamura J, et al. Phospho-ΔNp63α/SREBF1 protein interactions: Bridging cell metabolism and cisplatin chemo-resistance. Cell Cycle 2012; 11(20): 3810-27.
[52]
Cavill R, Kamburov A, Ellis JK, et al. Consensus-phenotype integration of transcriptomic and metabolomic data implies a role for metabolism in the chemosensitivity of tumour cells. PLOS Comput Biol 2011; 7(3): e1001113.
[http://dx.doi.org/10.1371/journal.pcbi.1001113] [PMID: 21483477]
[53]
Zhang T, Nie Y, Gu J, et al. Identification of mitochondrial-related prognostic biomarkers associated with primary bile acid biosynthesis and tumor microenvironment of hepatocellular carcinoma. Front Oncol 2021; 11: 587479.
[http://dx.doi.org/10.3389/fonc.2021.587479] [PMID: 33868990]
[54]
You W, Chen B, Liu X, et al. Farnesoid X receptor, a novel proto-oncogene in non-small cell lung cancer, promotes tumor growth via directly transactivating CCND1. Scientific Reports 2017; 7(1): 591.
[55]
Kast HR, Goodwin B, Tarr PT, et al. Regulation of multidrug resistance-associated protein 2 (ABCC2) by the nuclear receptors pregnane X receptor, farnesoid X-activated receptor, and constitutive androstane receptor. J Biol Chem 2002; 277(4): 2908-15.
[http://dx.doi.org/10.1074/jbc.M109326200] [PMID: 11706036]
[56]
Christofk HR, Vander Heiden MG, Harris MH, et al. The M2 splice isoform of pyruvate kinase is important for cancer metabolism and tumour growth. Nature 2008; 452(7184): 230-3.
[http://dx.doi.org/10.1038/nature06734]
[57]
Guo W, Zhang Y, Chen T, et al. Efficacy of RNAi targeting of pyruvate kinase M2 combined with cisplatin in a lung cancer model. J Cancer Res Clin Oncol 2011; 137(1): 65-72.
[http://dx.doi.org/10.1007/s00432-010-0860-5]
[58]
Kim HJ, Hwang JY, Kim HJ, et al. Expression of a peroxisome proliferator-activated receptor gamma 1 splice variant that was identified in human lung cancers suppresses cell death induced by cisplatin and oxidative stress. Clin Cancer Res 2007; 13(9): 2577-83.
[http://dx.doi.org/10.1158/1078-0432.CCR-06-2062] [PMID: 17473186]
[59]
Yokoyama Y, Xin B, Shigeto T, et al. Combination of ciglitazone, a peroxisome proliferator-activated receptor gamma ligand, and cisplatin enhances the inhibition of growth of human ovarian cancers. J Cancer Res Clin Oncol 2011; 137(8): 1219-28.
[http://dx.doi.org/10.1007/s00432-011-0993-1]
[60]
Wu K, Hu Y, Yan K, et al. microRNA-10b confers cisplatin resistance by activating AKT/mTOR/P70S6K signaling via targeting PPARγ in esophageal cancer. J Cell Physiol 2020; 235(2): 1247-58.
[http://dx.doi.org/10.1002/jcp.29040] [PMID: 31267531]
[61]
Mao X. Nanzhang, Xiao J, Wu H, Ding K. Hypoxia-induced autophagy enhances cisplatin resistance in human bladder cancer cells by targeting hypoxia-inducible factor-1α. J Immunol Res 2021; 2021(1): 8887437.
[http://dx.doi.org/10.1155/2021/8887437] [PMID: 33681390]
[62]
Prislei S, Mariani M, Raspaglio G, et al. RON and cisplatin resistance in ovarian cancer cell lines. Oncol Res 2010; 19(1): 13-22.
[http://dx.doi.org/10.3727/096504010X12828372551713] [PMID: 21141737]
[63]
Catenacci DVT, Cervantes G, Yala S, et al. RON (MST1R) is a novel prognostic marker and therapeutic target for gastroesophageal adeno-carcinoma. Cancer Biol Ther 2011; 12(1): 9-46.
[http://dx.doi.org/10.4161/cbt.12.1.15747] [PMID: 21543897]
[64]
Batth I, Yun H, Hussain S, et al. Crosstalk between RON and androgen receptor signaling in the development of castration resistant prostate cancer. Oncotarget 2016; 7(12): 14048-63.
[http://dx.doi.org/10.18632/oncotarget.7287] [PMID: 26872377]
[65]
Chang H-Y, Chang T-C, Huang W-Y, et al. RON nuclear translocation under hypoxia potentiates chemoresistance to DNA double-strand break–inducing anticancer drugs. Mol Cancer Ther 2016; 15(2): 276-86.
[http://dx.doi.org/10.1158/1535-7163.MCT-15-0311] [PMID: 26772202]
[66]
Liu HS, Hsu PY, Lai MD, et al. An unusual function of RON receptor tyrosine kinase as a transcriptional regulator in cooperation with EGFR in human cancer cells. Carcinogenesis 2010; 31(8): 1456-64.
[http://dx.doi.org/10.1093/carcin/bgq100] [PMID: 20498137]
[67]
Smyth GK. Limma: Linear models for microarray data. In: Gentleman R, Carey V, Dudoit S, R Irizarry WH, Eds. bioinformatics and computational biology solutions using R and bioconductor. New York: Springer 2005.
[http://dx.doi.org/10.1007/0-387-29362-0_23]
[68]
Ganjali M, Baghfalaki T, Berridge D. Robust modeling of differential gene expression data using normal/independent distributions: A Bayesian approach. PLoS One 2015; 10(4): e0123791.
[http://dx.doi.org/10.1371/journal.pone.0123791] [PMID: 25910040]
[69]
Nabavi S, Schmolze D, Maitituoheti M, Malladi S, Beck AH. EMDomics: A robust and powerful method for the identification of genes differentially expressed between heterogeneous classes. Bioinformatics 2016; 32(4): 533-41.
[http://dx.doi.org/10.1093/bioinformatics/btv634] [PMID: 26515818]
[70]
Stark R, Grzelak M, Hadfield J. RNA sequencing: The teenage years. Nat Rev Genet 2019; 20(11): 631-56.
[http://dx.doi.org/10.1038/s41576-019-0150-2]
[71]
Winter C, Kristiansen G, Kersting S, et al. Google goes cancer: Improving outcome prediction for cancer patients by network-based ranking of marker genes. PLOS Comput Biol 2012; 8(5): e1002511.
[http://dx.doi.org/10.1371/journal.pcbi.1002511] [PMID: 22615549]
[72]
Hooshangi S, Thiberge S, Weiss R. Ultrasensitivity and noise propagation in a synthetic transcriptional cascade. Proc Natl Acad Sci USA 2005; 102(10): 3581-6.
[http://dx.doi.org/10.1073/pnas.0408507102] [PMID: 15738412]
[73]
Pedraza JM, van Oudenaarden A. Noise propagation in gene networks. Science 2005; 307(5717): 1965-9.
[http://dx.doi.org/10.1126/science.1109090] [PMID: 15790857]

Rights & Permissions Print Cite
Article Metrics
32
2
Wayfinder Image
TIMBC 2025
© 2025 Bentham Science Publishers | Privacy Policy