684
Views
0
CrossRef citations to date
0
Altmetric
Review

Transcriptional Profiling to Identify Biomarkers of Disease and Drug Response

Pages 235-249 | Published online: 18 Feb 2011

Bibliography

  • Mendrick DL , DanielsKK: Biomarkers of drug-induced adverse events.Expert Rev. Clin. Pharmacol.1 , 81–91 (2008).
  • Marchionni L , WilsonRF, WolffAC et al.: Systematic review: gene expression profiling assays in early-stage breast cancer.Ann. Intern. Med.148 , 358–369 (2008).
  • Liew CC , MaJ, TangHC, ZhengR, DempseyAA: The peripheral blood transcriptome dynamically reflects system wide biology: a potential diagnostic tool.J. Lab. Clin. Med.147 , 126–132 (2006).
  • Powell EE , KroonPA: Low density lipoprotein receptor and 3-hydroxy-3-methylglutaryl coenzyme A reductase gene expression in human mononuclear leukocytes is regulated coordinately and parallels gene expression in human liver.J. Clin. Invest.93 , 2168–2174 (1994).
  • Aggarwal D , FreakeHC, SolimanGA, DuttaA, FernandezML: Validation of using gene expression in mononuclear cells as a marker for hepatic cholesterol metabolism.Lipids Health Dis.5 , 22 (2006).
  • Guan JZ , TamasawaN, MurakamiH et al.: HMG-CoA reductase inhibitor, simvastatin improves reverse cholesterol transport in Type 2 diabetic patients with hyperlipidemia.J. Atheroscler. Thromb.15 , 20–25 (2008).
  • Mikael LG , RozenR: Homocysteine modulates the effect of simvastatin on expression of ApoA-I and NF-κB/iNOS.Cardiovasc. Res.80 , 151–158 (2008).
  • Pham MX , TeutebergJJ, KfouryAG et al.: Gene-expression profiling for rejection surveillance after cardiac transplantation.N. Engl. J. Med.362(20) , 1890–1900 (2010).
  • Burczynski ME , DornerAJ: Transcriptional profiling of peripheral blood cells in clinical pharmacogenomic studies.Pharmacogenomics7 , 187–202 (2006).
  • Tang Y , LuA, AronowBJ, SharpFR: Blood genomic responses differ after stroke, seizures, hypoglycemia, and hypoxia: blood genomic fingerprints of disease.Ann. Neurol.50 , 699–707 (2001).
  • Tang Y , NeeAC, LuA, RanR, SharpFR: Blood genomic expression profile for neuronal injury.J. Cereb. Blood Flow Metab.23 , 310–319 (2003).
  • Du X , TangY, XuH et al.: Genomic profiles for human peripheral blood T cells, B cells, natural killer cells, monocytes, and polymorphonuclear cells: comparisons to ischemic stroke, migraine and Tourette syndrome.Genomics87 , 693–703 (2006).
  • Tang Y , XuH, DuX et al.: Gene expression in blood changes rapidly in neutrophils and monocytes after ischemic stroke in humans: a microarray study.J. Cereb. Blood Flow Metab.26 , 1089–1102 (2006).
  • Xu H , TangY, LiuDZ et al.: Gene expression in peripheral blood differs after cardioembolic compared with large-vessel atherosclerotic stroke: biomarkers for the etiology of ischemic stroke.J. Cereb. Blood Flow Metab.28 , 1320–1328 (2008).
  • Lovrecic L , KastrinA, KobalJ, PirtosekZ, KraincD, PeterlinB: Gene expression changes in blood as a putative biomarker for Huntington‘s disease.Mov. Disord.24 , 2277–2281 (2009).
  • Wong B , GilbertDL, WalkerWL et al.: Gene expression in blood of subjects with Duchenne muscular dystrophy.Neurogenetics10 , 117–125 (2009).
  • Borovecki F , LovrecicL, ZhouJ et al.: Genome-wide expression profiling of human blood reveals biomarkers for Huntington‘s disease.Proc. Natl Acad. Sci. USA102 , 11023–11028 (2005).
  • Runne H , KuhnA, WildEJ et al.: Analysis of potential transcriptomic biomarkers for Huntington‘s disease in peripheral blood.Proc. Natl Acad. Sci. USA104 , 14424–14429 (2007).
  • Scherzer CR , EklundAC, MorseLJ et al.: Molecular markers of early Parkinson‘s disease based on gene expression in blood.Proc. Natl Acad. Sci. USA104 , 955–960 (2007).
  • Bowden NA , WeidenhoferJ, ScottRJ et al.: Preliminary investigation of gene expression profiles in peripheral blood lymphocytes in schizophrenia.Schizophr. Res.82 , 175–183 (2006).
  • Takahashi M , HayashiH, WatanabeY et al.: Diagnostic classification of schizophrenia by neural network analysis of blood-based gene expression signatures.Schizophr. Res.119 , 210–218 (2010).
  • Tang Y , GlauserTA, GilbertDL et al.: Valproic acid blood genomic expression patterns in children with epilepsy – a pilot study.Acta Neurol. Scand.109 , 159–168 (2004).
  • Le Niculescu H , KurianSM, YehyawiN et al.: Identifying blood biomarkers for mood disorders using convergent functional genomics.Mol. Psychiatry14(2) , 156–174 (2008).
  • Ogden CA , RichME, SchorkNJ et al.: Candidate genes, pathways and mechanisms for bipolar (manic-depressive) and related disorders: an expanded convergent functional genomics approach.Mol. Psychiatry9 , 1007–1029 (2004).
  • Kurian SM , Le-NiculescuH, PatelSD et al.: Identification of blood biomarkers for psychosis using convergent functional genomics.Mol. Psychiatry16(1) , 37–58 (2011).
  • Burczynski ME , PetersonRL, TwineNC et al.: Molecular classification of Crohn‘s disease and ulcerative colitis patients using transcriptional profiles in peripheral blood mononuclear cells.J. Mol. Diagn.8 , 51–61 (2006).
  • Knowlton N , JiangK, FrankMB et al.: The meaning of clinical remission in polyarticular juvenile idiopathic arthritis: gene expression profiling in peripheral blood mononuclear cells identifies distinct disease states.Arthritis Rheum.60 , 892–900 (2009).
  • Griffin TA , BarnesMG, IlowiteNT et al.: Gene expression signatures in polyarticular juvenile idiopathic arthritis demonstrate disease heterogeneity and offer a molecular classification of disease subsets.Arthritis Rheum.60 , 2113–2123 (2009).
  • Mesko B , PoliskaS, SzegediA et al.: Peripheral blood gene expression patterns discriminate among chronic inflammatory diseases and healthy controls and identify novel targets.BMC Med. Genomics3 , 15 (2010).
  • Sinnaeve PR , DonahueMP, GrassP et al.: Gene expression patterns in peripheral blood correlate with the extent of coronary artery disease.PLoS ONE4 , e7037 (2009).
  • Wingrove JA , DanielsSE, SehnertAJ et al.: Correlation of peripheral-blood gene expression with the extent of coronary artery stenosis.Circ. Cardiovasc. Genet.1 , 31–38 (2008).
  • Camargo A , RuanoJ, FernandezJM et al.: Gene expression changes in mononuclear cells in patients with metabolic syndrome after acute intake of phenol-rich virgin olive oil.BMC Genomics11 , 253 (2010).
  • Hermsdorff HH , ZuletMA, PuchauB, MartinezJA: Fruit and vegetable consumption and proinflammatory gene expression from peripheral blood mononuclear cells in young adults: a translational study.Nutr. Metab. (Lond.)7 , 42 (2010).
  • Radom-Aizik S , ZaldivarF Jr, Leu SY, Galassetti P, Cooper DM: Effects of 30 min of aerobic exercise on gene expression in human neutrophils. J. Appl. Physiol.104 , 236–243 (2008).
  • Radom-Aizik S , ZaldivarF Jr, Leu SY, Cooper DM: Brief bout of exercise alters gene expression in peripheral blood mononuclear cells of early- and late-pubertal males. Pediatr. Res.65 , 447–452 (2009).
  • Radom-Aizik S , ZaldivarF Jr, Leu SY, Cooper DM: A brief bout of exercise alters gene expression and distinct gene pathways in peripheral blood mononuclear cells of early- and late-pubertal females. J. Appl. Physiol.107 , 168–175 (2009).
  • Hayashi Y , KajimotoK, IidaS et al.: DNA microarray analysis of whole blood cells and insulin-sensitive tissues reveals the usefulness of blood RNA profiling as a source of markers for predicting Type 2 diabetes.Biol. Pharm. Bull.33 , 1033–1042 (2010).
  • Takamura T , HondaM, SakaiY et al.: Gene expression profiles in peripheral blood mononuclear cells reflect the pathophysiology of Type 2 diabetes.Biochem. Biophys. Res. Commun.361 , 379–384 (2007).
  • Huang H , DongX, KangMX et al.: Novel blood biomarkers of pancreatic cancer-associated diabetes mellitus identified by peripheral blood-based gene expression profiles.Am. J. Gastroenterol.105 , 1661–1669 (2010).
  • Bushel PR , HeinlothAN, LiJ et al.: Blood gene expression signatures predict exposure levels.Proc. Natl Acad. Sci. USA104 , 18211–18216 (2007).
  • Lobenhofer EK , AumanJT, BlackshearPE et al.: Gene expression response in target organ and whole blood varies as a function of target organ injury phenotype.Genome Biol.9 , R100 (2008).
  • Huang J , ShiW, ZhangJ et al.: Genomic indicators in the blood predict drug-induced liver injury.Pharmacogenomics J.10 , 267–277 (2010).
  • Wetmore BA , BreesDJ, SinghR et al.: Quantitative analyses and transcriptomic profiling of circulating messenger RNAs as biomarkers of rat liver injury.Hepatology51 , 2127–2139 (2010).
  • O‘Toole M , JanszenDB, SlonimDK et al.: Risk factors associated with β-amyloid(1–42) immunotherapy in preimmunization gene expression patterns of blood cells.Arch. Neurol.62 , 1531–1536 (2005).
  • Yun JW , LeeTR, KimCW et al.: Predose blood gene expression profiles might identify the individuals susceptible to carbon tetrachloride-induced hepatotoxicity.Toxicol. Sci.115 , 12–21 (2010).
  • Yun JW , KimCW, BaeIH et al.: Determination of the key innate genes related to individual variation in carbon tetrachloride-induced hepatotoxicity using a pre-biopsy procedure.Toxicol. Appl. Pharmacol.239 , 55–63 (2009).
  • Thum T , CatalucciD, BauersachsJ: MicroRNAs: novel regulators in cardiac development and disease.Cardiovasc. Res.79 , 562–570 (2008).
  • Cortez MA , CalinGA: MicroRNA identification in plasma and serum: a new tool to diagnose and monitor diseases.Expert Opin. Biol. Ther.9 , 703–711 (2009).
  • van Rooij E , QuiatD, JohnsonBA et al.: A family of microRNAs encoded by myosin genes governs myosin expression and muscle performance.Dev. Cell17 , 662–673 (2009).
  • Fichtlscherer S , DeRS, FoxH et al.: Circulating microRNAs in patients with coronary artery disease.Circ. Res.107 , 677–684 (2010).
  • Liang M , LiuY, MladinovD et al.: MicroRNA: a new frontier in kidney and blood pressure research.Am. J. Physiol. Renal Physiol.297 , F553–F558 (2009).
  • Ryan BM , RoblesAI, HarrisCC: Genetic variation in microRNA networks: the implications for cancer research.Nat. Rev. Cancer10 , 389–402 (2010).
  • Linsen SE , de Wit E, de Bruijn E, Cuppen E: Small RNA expression and strain specificity in the rat. BMC Genomics11 , 249 (2010).
  • Olena AF , PattonJG: Genomic organization of microRNAs.J. Cell. Physiol.222 , 540–545 (2010).
  • Wittmann J , Jack HM: Serum microRNAs as powerful cancer biomarkers. Biochim. Biophys. Acta1806(2) , 200–207 (2010).
  • Liu DZ , TianY, AnderBP et al.: Brain and blood microRNA expression profiling of ischemic stroke, intracerebral hemorrhage, and kainate seizures.J. Cereb. Blood Flow Metab.30 , 92–101 (2010).
  • Cox MB , CairnsMJ, GandhiKS et al.: MicroRNAs miR-17 and miR-20a inhibit T cell activation genes and are under-expressed in MS whole blood.PLoS ONE5 , e12132 (2010).
  • Wang K , ZhangS, MarzolfB et al.: Circulating microRNAs, potential biomarkers for drug-induced liver injury.Proc. Natl Acad. Sci. USA106 , 4402–4407 (2009).
  • Ji X , TakahashiR, HiuraY, HirokawaG, FukushimaY, IwaiN: Plasma miR-208 as a biomarker of myocardial injury.Clin. Chem.55 , 1944–1949 (2009).
  • van Rooij E , SutherlandLB, QiX, RichardsonJA, HillJ, OlsonEN: Control of stress-dependent cardiac growth and gene expression by a microRNA.Science316 , 575–579 (2007).
  • Callis TE , PandyaK, SeokHY et al.: MicroRNA-208a is a regulator of cardiac hypertrophy and conduction in mice.J. Clin. Invest.119 , 2772–2786 (2009).
  • Wang GK , ZhuJQ, ZhangJT et al.: Circulating microRNA: a novel potential biomarker for early diagnosis of acute myocardial infarction in humans.Eur. Heart J.31 , 659–666 (2010).
  • Satoh M , MinamiY, TakahashiY, TabuchiT, NakamuraM: Expression of microRNA-208 is associated with adverse clinical outcomes in human dilated cardiomyopathy.J. Card. Fail.16 , 404–410 (2010).
  • Radom-Aizik S , ZaldivarF Jr, Oliver S, Galassetti P, Cooper DM: Evidence for microRNA involvement in exercise-associated neutrophil gene expression changes. J. Appl. Physiol.109 , 252–261 (2010).
  • Michiels S , KoscielnyS, HillC: Prediction of cancer outcome with microarrays: a multiple random validation strategy.Lancet365 , 488–492 (2005).
  • Ioannidis JP : Microarrays and molecular research: noise discovery?Lancet365 , 454–455 (2005).
  • Shi L , CampbellG, JonesWD et al.: The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.Nat. Biotechnol.28 , 827–838 (2010).
  • Shi W , BessarabovaM, DosymbekovD et al.: Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes.Pharmacogenomics J.10 , 310–323 (2010).
  • Parry RM , JonesW, StokesTH et al.: k-nearest neighbor models for microarray gene expression analysis and clinical outcome prediction.Pharmacogenomics J.10 , 292–309 (2010).
  • Luo J , SchumacherM, SchererA et al.: A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data.Pharmacogenomics J.10 , 278–291 (2010).
  • Oberthuer A , JuraevaD, LiL et al.: Comparison of performance of one-color and two-color gene-expression analyses in predicting clinical endpoints of neuroblastoma patients.Pharmacogenomics J.10 , 258–266 (2010).
  • Fan X , LobenhoferEK, ChenM et al.: Consistency of predictive signature genes and classifiers generated using different microarray platforms.Pharmacogenomics J.10 , 247–257 (2010).
  • Miclaus K , ChiericiM, LambertC et al.: Variability in GWAS analysis: the impact of genotype calling algorithm inconsistencies.Pharmacogenomics J.10 , 324–335 (2010).
  • Miclaus K , WolfingerR, VegaS et al.: Batch effects in the BRLMM genotype calling algorithm influence GWAS results for the Affymetrix 500K array.Pharmacogenomics J.10 , 336–346 (2010).
  • Zhang L , YinS, MiclausK et al.: Assessment of variability in GWAS with CRLMM genotyping algorithm on WTCCC coronary artery disease.Pharmacogenomics J.10 , 347–354 (2010).
  • Hong H , ShiL, SuZ et al.: Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples.Pharmacogenomics J.10 , 364–374 (2010).
  • Chierici M , MiclausK, VegaS, FurlanelloC: An interactive effect of batch size and composition contributes to discordant results in GWAS with the CHIAMO genotyping algorithm.Pharmacogenomics J.10 , 355–363 (2010).
  • Fannin RD , RussoM, O‘ConnellTM et al.: Acetaminophen dosing of humans results in blood transcriptome and metabolome changes consistent with impaired oxidative phosphorylation.Hepatology51 , 227–236 (2010).
  • Stamova BS , AppersonM, WalkerWL et al.: Identification and validation of suitable endogenous reference genes for gene expression studies in human peripheral blood.BMC Med. Genomics2 , 49 (2009).
  • Walker WL , LiaoIH, GilbertDL et al.: Empirical Bayes accomodation of batch-effects in microarray data using identical replicate reference samples: application to RNA expression profiling of blood from Duchenne muscular dystrophy patients.BMC Genomics9 , 494 (2008).
  • Tillinghast GW : Microarrays in the clinic.Nat. Biotechnol.28 , 810–812 (2010).
  • Baggerly KA , CoombesKR: Deriving chemosensitivity from cell lines: forensic bioinformatics and reproducible research in high-throughput biology.Ann. Appl. Stat.3 , 1309–1334 (2009).
  • Goodsaid F , PapalucaM: Evolution of biomarker qualification at the health authorities.Nat. Biotechnol.28 , 441–443 (2010).
  • Goodsaid FM , MendrickDL: Translational medicine and the value of biomarker qualification.Sci. Transl. Med.2(47) , ps44 (2010).
  • Dieterle F , SistareF, GoodsaidF et al.: Renal biomarker qualification submission: a dialog between the FDA–EMEA and Predictive Safety Testing Consortium.Nat. Biotechnol.28 , 455–462 (2010).
  • Mendrick DL : Genomic and genetic biomarkers of toxicity.Toxicology245 , 175–181 (2008).
  • Mattes WB : Public consortium efforts in toxicogenomics. In: Essential Concepts in Toxicogenomics. Mendrick DL, Mattes WB (Eds.). Humana, NJ, USA 221–238 (2008).

▪ Websites

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.