Expresión génica: una visión general de los métodos y aplicaciones en la pesquisa de cáncer
Una visión general de los métodos y aplicaciones en la pesquisa de cáncer
Palabras clave:
secuencia génica, expresión génica, técnicas moleculares, genotipo, oncología veterinariaResumen
La expresión génica es el estudio de cómo el genotipo da lugar al fenotipo mediante la
investigación de la cantidad de RNAm transcrito en un sistema biológico. Una gran cantidad
de métodos fueron estandarizados para identificar variaciones en la expresión génica,
incluyendo la hibridación sustractiva, “differential display”, análisis en serie de la expresión
génica, la hibridación de microarrays, y la secuenciación por RNA-seq. La mayoría de las
técnicas se han centrado en la investigación y diagnóstico del cáncer, produciendo una gran
cantidad de datos, lo que permitió a entender la progresión del cáncer y las vías, descubrir y
evaluar nuevas intervenciones de tratamiento, nuevas herramientas moleculares para el
diagnóstico y el pronóstico, y analizar el tiempo de sobrevivencia en pacientes humanos y
animales. De esta manera, las técnicas de expresión génica trajeron nuevas perspectivas
importantes para el campo de la medicina veterinaria, y nuevas investigaciones centradas en
oncología proporcionarán mucho más conocimiento acerca de las vías y la interacción de las
células sanas y tumorales, mejorando las intervenciones diarias por los oncólogos y los
clínicos.
Citas
1. Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P. Control of gene expression.
In: Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P. Molecular biology of
the cell. 5th ed. New York: Garland Science; 2007. p.1112-30.
2. Hoopes L. Introduction to the gene expression and regulation topic room. Nat Educ.
2008;1(1):160.
3. Mantione KJ, Kream RM, Kuzelova H, Ptacek R, Raboch J, Samuel JM, et al. Comparing
bioinformatic gene expression profiling methods: microarray and RNA-Seq. Med Sci
Monit Basic Res. 2014;20:138-42.
4. Watson JD, Baker TA, Bell SP, Gann A, Levine M, Losick R. Expression of the genome.
In: Watson JD, Baker TA, Bell SP, Gann A, Levine M, Losick R. Molecular biology of
the gene. 7th ed. New York: Cold Spring Harbor Laboratory Press; 2014. p.423-608.
5. Moody DE. Genomics techniques: an overview of methods for the study of gene
expression. J Anim Sci. 2001;79(E-suppl):E128-35.
6. McLoughlin KE, Nalpas NC, Rue-Albrecht K, Browne JA, Magee DA, Killick KE, et al.
RNA-seq transcriptional profiling of peripheral blood leukocytes from cattle infected
with Mycobacterium bovis. Front Immunol. 2014;5:396.
7. Arnvig KB, Comas I, Thomson NR, Houghton J, Boshoff HI, Croucher NJ, et al.
Sequence-based analysis uncovers an abundance of non-coding RNA in the total
transcriptome of Mycobacterium tuberculosis. PLoS Pathog. 2011;7(11):e1002342.
8. Qian F, Chung L, Zheng W, Bruno V, Alexander RP, Wang Z, et al. Identification of
genes critical for resistance to infection by West Nile virus using RNA-Seq analysis.
Viruses. 2013;5(7):1664-81.
9. Capomaccio S, Vitulo N, Verini-Supplizi A, Barcaccia G, Albiero A, D'Angelo M, et al.
RNA sequencing of the exercise transcriptome in equine athletes. PLoS One.
2013;8(12):e83504.
10. Davis BW, Ostrander EA. Domestic dogs and cancer research: a breed-based genomics
approach. ILAR J. 2014;55(1):59-68.
11. Patel AK, Bhatt VD, Tripathi AK, Sajnani MR, Jakhesara SJ, Koringa PG, et al.
Identification of novel splice variants in horn cancer by RNA-Seq analysis in Zebu cattle.
Genomics. 2013;101(1):57-63
12. Beane J, Vick J, Schembri F, Anderlind C, Gower A, Campbell J, et al. Characterizing
the impact of smoking and lung cancer on the airway transcriptome using RNA-Seq.
Cancer Prev Res. 2011;4(6):803-17.
13. Sargent TD, Dawid IB. Differential gene expression in the gastrula of Xenopus laevis.
Science. 1983;222(4620):135-9.
14. Davis MM, Cohen DI, Nielsen EA, Steinmetz M, Paul WE, Hood L. Cell-type-specific
cDNA probes and the murine I region: the localization and orientation of Ad alpha. Proc
Natl Acad Sci U S A. 1984;81(7):2194-8.
15. Diatchenko L, Lau YF, Campbell AP, Chenchik A, Moqadam F, Huang B, et al.
Suppression subtractive hybridization: a method for generating differentially regulated or
tissue-specific cDNA probes and libraries. Proc Natl Acad Sci U S A. 1996;93(12):6025-
30.
16. Hufton SE, Moerkerk PT, Brandwijk R, de Bruine AP, Arends JW, Hoogenboom HR. A
profile of differentially expressed genes in primary colorectal cancer using suppression
subtractive hybridization. FEBS Lett. 1999;463(1-2):77-82.
17. Kuang WW, Thompson DA, Hoch RV, Weigel RJ. Differential screening and
suppression subtractive hybridization identified genes differentially expressed in an
estrogen receptor-positive breast carcinoma cell line. Nucleic Acids Res.
1998;26(4):1116-23.
18. Parmigiani G, Garrett ES, Irizarry RA, Zeger SL. The analysis of gene expression data:
an overview of methods and software. In: The analysis of gene expression data: methods
and software. New York: Springer; 2003. p.1-45.
19. Liang P, Pardee AB. Differential display of eukaryotic messenger RNA by means of the
polymerase chain reaction. Science. 1992;257(5072):967-71.
20. Welsh J, Chada K, Dalal SS, Cheng R, Ralph D, McClelland M. Arbitrarily primed PCR
fingerprinting of RNA. Nucleic Acids Res. 1992;20(19):4965-70.
21. Janzen MA, Kuhlers DL, Jungst SB, Louis CF. ARPP-16 mRNA is up-regulated in the
longissimus muscle of pigs possessing an elevated growth rate. J Anim Sci.
2000;78(6):1475-84.
22. Hu E, Wang D, Chen J, Tao X. Novel cyclotides from Hedyotis diffusa induce apoptosis
and inhibit proliferation and migration of prostate cancer cells. Int J Clin Exp Med.
2015;8(3):4059-65.
23. Schalken JA, Hessels D, Verhaegh G. New targets for therapy in prostate cancer:
differential display code 3 (DD3(PCA3)), a highly prostate cancer-specific gene.
Urology. 2003;62(5 Suppl 1):34-43.
24. Tatsumi Y, Nakagawara A, inventors; Hisamitsu Pharmaceutical Co., Inc. ChibaPrefecture, assignee. Cancer marker and therapeutic agent for cancer. United States
patent US 008202690B2. 2012 Jun 19.
25. Murata T, Sato T, Kamoda T, Moriyama H, Kumazawa Y, Hanada N. Differential
susceptibility to hydrogen sulfide-induced apoptosis between PHLDA1-overexpressing
oral cancer cell lines and oral keratinocytes: role of PHLDA1 as an apoptosis suppressor.
Exp Cell Res. 2014;320(2):247-57.
26. Finocchiaro LM, Fondello C, Gil-Cardeza ML, Rossi UA, Villaverde MS, Riveros MD,
et al. Cytokine-enhanced vaccine and interferon-beta plus suicide gene therapy as surgery
adjuvant treatments for spontaneous canine melanoma. Hum Gene Ther. 2015;26(6):367-
76.
27. Adams MD, Kelley JM, Gocayne JD, Dubnick M, Polymeropoulos MH, Xiao H, et al.
Complementary DNA sequencing: expressed sequence tags and human genome project.
Science. 1991;252(5013):1651-6.
28. Soares MB, Bonaldo MF, Jelene P, Su L, Lawton L, Efstratiadis A. Construction and
characterization of a normalized cDNA library. Proc Natl Acad Sci U S A.
1994;91(20):9228-32.
29. Watson MA, Fleming TP. Isolation of differentially expressed sequence tags from human
breast cancer. Cancer Res. 1994;54(17):4598-602.
30. Velculescu VE, Zhang L, Vogelstein B, Kinzler KW. Serial analysis of gene expression.
Science. 1995;270(5235):484-7.
31. Morrissy AS, Morin RD, Delaney A, Zeng T, McDonald H, Jones S, et al. Nextgeneration tag sequencing for cancer gene expression profiling. Genome Res.
2009;19(10):1825-35.
32. Datson NA, van der Perk-de Jong J, van den Berg MP, de Kloet ER, Vreugdenhil E.
MicroSAGE: a modified procedure for serial analysis of gene expression in limited
amounts of tissue. Nucleic Acids Res. 1999;27(5):1300-7.
33. Hanriot L, Keime C, Gay N, Faure C, Dossat C, Wincker P, et al. A combination of
LongSAGE with Solexa sequencing is well suited to explore the depth and the
complexity of transcriptome. BMC Genomics. 2008;9:418.
34. Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression
patterns with a complementary DNA microarray. Science. 1995;270(5235):467-70.
35. Sealfon SC, Chu TT. RNA and DNA microarrays. Methods Mol Biol. 2011;671:3-34.
36. Howard BE, Hu Q, Babaoglu AC, Chandra M, Borghi M, Tan X, et al. High-throughput
RNA sequencing of pseudomonas-infected Arabidopsis reveals hidden transcriptome
complexity and novel splice variants. PLoS One. 2013;8(10):e74183.
37. Churchill GA. Fundamentals of experimental design for cDNA microarrays. Nat Genet.
2002;32 Suppl:490-5.
38. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genomewide expression patterns. Proc Natl Acad Sci U S A. 1998;95(25):14863-8.
39. Baglia ML, Cai Q, Zheng Y, Wu J, Su Y, Ye F, et al. Dual specificity phosphatase 4 gene
expression in association with triple-negative breast cancer outcome. Breast Cancer Res
Treat. 2014;148(1):211-20.
40. Perez-Rogers JF, Gerrein J, Anderlind C, Kusko RL, Campbell JD, Wang TW, et al.,
editors. Leveraging gene expression in the bronchial airway to develop a nasal biomarker
for early detection of lung cancer. Proceedings of the American Thoracic Society 2014
International Conference; 2014; San Diego. San Diego; ATSJournals; 2014.
41. Walter K, Holcomb T, Januario T, Yauch RL, Du P, Bourgon R, et al. Discovery and
development of DNA methylation-based biomarkers for lung cancer. Epigenomics.
2014;6(1):59-72.
42. Klopfleisch R, Lenze D, Hummel M, Gruber AD. Metastatic canine mammary
carcinomas can be identified by a gene expression profile that partly overlaps with human
breast cancer profiles. BMC Cancer. 2010;10:618.
43. Pawlowski KM, Maciejewski H, Dolka I, Mol JA, Motyl T, Krol M. Gene expression
profiles in canine mammary carcinomas of various grades of malignancy. BMC Vet Res.
2013;9:78.
44. Rao NA, van Wolferen ME, van den Ham R, van Leenen D, Groot Koerkamp MJ,
Holstege FC, et al. cDNA microarray profiles of canine mammary tumour cell lines
reveal deregulated pathways pertaining to their phenotype. Anim Genet. 2008;39(4):333-
45.
45. Fowles JS, Brown KC, Hess AM, Duval DL, Gustafson DL. Intra- and interspecies gene
expression models for predicting drug response in canine osteosarcoma. BMC
Bioinformatics. 2016;17(1):93.
46. Morin R, Bainbridge M, Fejes A, Hirst M, Krzywinski M, Pugh T, et al. Profiling the
HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read
sequencing. Biotechniques. 2008;45(1):81-94.
47. Chu Y, Corey DR. RNA sequencing: platform selection, experimental design, and data
interpretation. Nucleic Acid Ther. 2012;22(4):271-4.
48. Nagalakshmi U, Waern K, Snyder M. RNA-Seq: a method for comprehensive
transcriptome analysis. Curr Protoc Mol Biol. 2010; Suppl 89:1-13.
49. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat
Rev Genet. 2009;10(1):57-63.
50. Gnimpieba EZ, Chango A, Lushbough CM. RNA-Seq gene and transcript expression
analysis using the BioExtract Server and iPlant Collaborative. In: Proceedings of the 5th
ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
(ACM-BCB 2014); 2014 Sept 20-23; Newport Beach, CA. Newport Beach, CA:
Association for Computing Machinery; 2014. p.661-9.
51. Rankin KS, Starkey M, Lunec J, Gerrand CH, Murphy S, Biswas S. Of dogs and men:
comparative biology as a tool for the discovery of novel biomarkers and drug
development targets in osteosarcoma. Pediatr Blood Cancer. 2012;58(3):327-33.
52. Mueller F, Fuchs B, Kaser-Hotz B. Comparative biology of human and canine
osteosarcoma. Anticancer Res. 2007;27(1A):155-64.
53. Ingolia NT, Brar GA, Rouskin S, McGeachy AM, Weissman JS. The ribosome profiling
strategy for monitoring translation in vivo by deep sequencing of ribosome-protected
mRNA fragments. Nat Protoc. 2012;7(8):1534-50.
54. Wan M, Wang J, Gao X, Sklar J. RNA sequencing and its applications in cancer
diagnosis and targeted therapy. North Am J Med Sci. 2014;7(4):156-62.
55. Maher CA, Kumar-Sinha C, Cao X, Kalyana-Sundaram S, Han B, Jing X, et al.
Transcriptome sequencing to detect gene fusions in cancer. Nature. 2009;458(7234):97-
101.
56. Roberts A, Schaeffer L, Pachter L. Updating RNA-Seq analyses after re-annotation.
Bioinformatics. 2013;29(13):1631-7.
57. Ravi I, Baunthiyal M, Saxena J. Advances in biotechnology. New Delhi: Springer; 2014.
58. Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y. RNA-seq: an assessment of
technical reproducibility and comparison with gene expression arrays. Genome Res.
2008;18(9):1509-17.
59. Siu H, Zhu Y, Jin L, Xiong M. Implication of next-generation sequencing on association
studies. BMC Genomics. 2011;12:322.
60. Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq:
accounting for selection bias. Genome Biol. 2010;11(2):R14.
61. Fu X, Fu N, Guo S, Yan Z, Xu Y, Hu H, et al. Estimating accuracy of RNA-Seq and
microarrays with proteomics. BMC Genomics. 2009;10:161.
62. Atak ZK, Gianfelici V, Hulselmans G, De Keersmaecker K, Devasia AG, Geerdens E, et
al. Comprehensive analysis of transcriptome variation uncovers known and novel driver
events in T-cell acute lymphoblastic leukemia. PLoS Genet. 2013;9(12):e1003997.
63. Eswaran J, Horvath A, Godbole S, Reddy SD, Mudvari P, Ohshiro K, et al. RNA
sequencing of cancer reveals novel splicing alterations. Sci Rep. 2013;3:1689.
64. Märtson A, Kõks S, Reimann E, Prans E, Erm T, Maasalu K. Transcriptome analysis of
osteosarcoma identifies suppression of wnt pathway and up-regulation of adiponectin as
potential biomarker. Genom Discov. 2013;1(3):1-9.
In: Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P. Molecular biology of
the cell. 5th ed. New York: Garland Science; 2007. p.1112-30.
2. Hoopes L. Introduction to the gene expression and regulation topic room. Nat Educ.
2008;1(1):160.
3. Mantione KJ, Kream RM, Kuzelova H, Ptacek R, Raboch J, Samuel JM, et al. Comparing
bioinformatic gene expression profiling methods: microarray and RNA-Seq. Med Sci
Monit Basic Res. 2014;20:138-42.
4. Watson JD, Baker TA, Bell SP, Gann A, Levine M, Losick R. Expression of the genome.
In: Watson JD, Baker TA, Bell SP, Gann A, Levine M, Losick R. Molecular biology of
the gene. 7th ed. New York: Cold Spring Harbor Laboratory Press; 2014. p.423-608.
5. Moody DE. Genomics techniques: an overview of methods for the study of gene
expression. J Anim Sci. 2001;79(E-suppl):E128-35.
6. McLoughlin KE, Nalpas NC, Rue-Albrecht K, Browne JA, Magee DA, Killick KE, et al.
RNA-seq transcriptional profiling of peripheral blood leukocytes from cattle infected
with Mycobacterium bovis. Front Immunol. 2014;5:396.
7. Arnvig KB, Comas I, Thomson NR, Houghton J, Boshoff HI, Croucher NJ, et al.
Sequence-based analysis uncovers an abundance of non-coding RNA in the total
transcriptome of Mycobacterium tuberculosis. PLoS Pathog. 2011;7(11):e1002342.
8. Qian F, Chung L, Zheng W, Bruno V, Alexander RP, Wang Z, et al. Identification of
genes critical for resistance to infection by West Nile virus using RNA-Seq analysis.
Viruses. 2013;5(7):1664-81.
9. Capomaccio S, Vitulo N, Verini-Supplizi A, Barcaccia G, Albiero A, D'Angelo M, et al.
RNA sequencing of the exercise transcriptome in equine athletes. PLoS One.
2013;8(12):e83504.
10. Davis BW, Ostrander EA. Domestic dogs and cancer research: a breed-based genomics
approach. ILAR J. 2014;55(1):59-68.
11. Patel AK, Bhatt VD, Tripathi AK, Sajnani MR, Jakhesara SJ, Koringa PG, et al.
Identification of novel splice variants in horn cancer by RNA-Seq analysis in Zebu cattle.
Genomics. 2013;101(1):57-63
12. Beane J, Vick J, Schembri F, Anderlind C, Gower A, Campbell J, et al. Characterizing
the impact of smoking and lung cancer on the airway transcriptome using RNA-Seq.
Cancer Prev Res. 2011;4(6):803-17.
13. Sargent TD, Dawid IB. Differential gene expression in the gastrula of Xenopus laevis.
Science. 1983;222(4620):135-9.
14. Davis MM, Cohen DI, Nielsen EA, Steinmetz M, Paul WE, Hood L. Cell-type-specific
cDNA probes and the murine I region: the localization and orientation of Ad alpha. Proc
Natl Acad Sci U S A. 1984;81(7):2194-8.
15. Diatchenko L, Lau YF, Campbell AP, Chenchik A, Moqadam F, Huang B, et al.
Suppression subtractive hybridization: a method for generating differentially regulated or
tissue-specific cDNA probes and libraries. Proc Natl Acad Sci U S A. 1996;93(12):6025-
30.
16. Hufton SE, Moerkerk PT, Brandwijk R, de Bruine AP, Arends JW, Hoogenboom HR. A
profile of differentially expressed genes in primary colorectal cancer using suppression
subtractive hybridization. FEBS Lett. 1999;463(1-2):77-82.
17. Kuang WW, Thompson DA, Hoch RV, Weigel RJ. Differential screening and
suppression subtractive hybridization identified genes differentially expressed in an
estrogen receptor-positive breast carcinoma cell line. Nucleic Acids Res.
1998;26(4):1116-23.
18. Parmigiani G, Garrett ES, Irizarry RA, Zeger SL. The analysis of gene expression data:
an overview of methods and software. In: The analysis of gene expression data: methods
and software. New York: Springer; 2003. p.1-45.
19. Liang P, Pardee AB. Differential display of eukaryotic messenger RNA by means of the
polymerase chain reaction. Science. 1992;257(5072):967-71.
20. Welsh J, Chada K, Dalal SS, Cheng R, Ralph D, McClelland M. Arbitrarily primed PCR
fingerprinting of RNA. Nucleic Acids Res. 1992;20(19):4965-70.
21. Janzen MA, Kuhlers DL, Jungst SB, Louis CF. ARPP-16 mRNA is up-regulated in the
longissimus muscle of pigs possessing an elevated growth rate. J Anim Sci.
2000;78(6):1475-84.
22. Hu E, Wang D, Chen J, Tao X. Novel cyclotides from Hedyotis diffusa induce apoptosis
and inhibit proliferation and migration of prostate cancer cells. Int J Clin Exp Med.
2015;8(3):4059-65.
23. Schalken JA, Hessels D, Verhaegh G. New targets for therapy in prostate cancer:
differential display code 3 (DD3(PCA3)), a highly prostate cancer-specific gene.
Urology. 2003;62(5 Suppl 1):34-43.
24. Tatsumi Y, Nakagawara A, inventors; Hisamitsu Pharmaceutical Co., Inc. ChibaPrefecture, assignee. Cancer marker and therapeutic agent for cancer. United States
patent US 008202690B2. 2012 Jun 19.
25. Murata T, Sato T, Kamoda T, Moriyama H, Kumazawa Y, Hanada N. Differential
susceptibility to hydrogen sulfide-induced apoptosis between PHLDA1-overexpressing
oral cancer cell lines and oral keratinocytes: role of PHLDA1 as an apoptosis suppressor.
Exp Cell Res. 2014;320(2):247-57.
26. Finocchiaro LM, Fondello C, Gil-Cardeza ML, Rossi UA, Villaverde MS, Riveros MD,
et al. Cytokine-enhanced vaccine and interferon-beta plus suicide gene therapy as surgery
adjuvant treatments for spontaneous canine melanoma. Hum Gene Ther. 2015;26(6):367-
76.
27. Adams MD, Kelley JM, Gocayne JD, Dubnick M, Polymeropoulos MH, Xiao H, et al.
Complementary DNA sequencing: expressed sequence tags and human genome project.
Science. 1991;252(5013):1651-6.
28. Soares MB, Bonaldo MF, Jelene P, Su L, Lawton L, Efstratiadis A. Construction and
characterization of a normalized cDNA library. Proc Natl Acad Sci U S A.
1994;91(20):9228-32.
29. Watson MA, Fleming TP. Isolation of differentially expressed sequence tags from human
breast cancer. Cancer Res. 1994;54(17):4598-602.
30. Velculescu VE, Zhang L, Vogelstein B, Kinzler KW. Serial analysis of gene expression.
Science. 1995;270(5235):484-7.
31. Morrissy AS, Morin RD, Delaney A, Zeng T, McDonald H, Jones S, et al. Nextgeneration tag sequencing for cancer gene expression profiling. Genome Res.
2009;19(10):1825-35.
32. Datson NA, van der Perk-de Jong J, van den Berg MP, de Kloet ER, Vreugdenhil E.
MicroSAGE: a modified procedure for serial analysis of gene expression in limited
amounts of tissue. Nucleic Acids Res. 1999;27(5):1300-7.
33. Hanriot L, Keime C, Gay N, Faure C, Dossat C, Wincker P, et al. A combination of
LongSAGE with Solexa sequencing is well suited to explore the depth and the
complexity of transcriptome. BMC Genomics. 2008;9:418.
34. Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression
patterns with a complementary DNA microarray. Science. 1995;270(5235):467-70.
35. Sealfon SC, Chu TT. RNA and DNA microarrays. Methods Mol Biol. 2011;671:3-34.
36. Howard BE, Hu Q, Babaoglu AC, Chandra M, Borghi M, Tan X, et al. High-throughput
RNA sequencing of pseudomonas-infected Arabidopsis reveals hidden transcriptome
complexity and novel splice variants. PLoS One. 2013;8(10):e74183.
37. Churchill GA. Fundamentals of experimental design for cDNA microarrays. Nat Genet.
2002;32 Suppl:490-5.
38. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genomewide expression patterns. Proc Natl Acad Sci U S A. 1998;95(25):14863-8.
39. Baglia ML, Cai Q, Zheng Y, Wu J, Su Y, Ye F, et al. Dual specificity phosphatase 4 gene
expression in association with triple-negative breast cancer outcome. Breast Cancer Res
Treat. 2014;148(1):211-20.
40. Perez-Rogers JF, Gerrein J, Anderlind C, Kusko RL, Campbell JD, Wang TW, et al.,
editors. Leveraging gene expression in the bronchial airway to develop a nasal biomarker
for early detection of lung cancer. Proceedings of the American Thoracic Society 2014
International Conference; 2014; San Diego. San Diego; ATSJournals; 2014.
41. Walter K, Holcomb T, Januario T, Yauch RL, Du P, Bourgon R, et al. Discovery and
development of DNA methylation-based biomarkers for lung cancer. Epigenomics.
2014;6(1):59-72.
42. Klopfleisch R, Lenze D, Hummel M, Gruber AD. Metastatic canine mammary
carcinomas can be identified by a gene expression profile that partly overlaps with human
breast cancer profiles. BMC Cancer. 2010;10:618.
43. Pawlowski KM, Maciejewski H, Dolka I, Mol JA, Motyl T, Krol M. Gene expression
profiles in canine mammary carcinomas of various grades of malignancy. BMC Vet Res.
2013;9:78.
44. Rao NA, van Wolferen ME, van den Ham R, van Leenen D, Groot Koerkamp MJ,
Holstege FC, et al. cDNA microarray profiles of canine mammary tumour cell lines
reveal deregulated pathways pertaining to their phenotype. Anim Genet. 2008;39(4):333-
45.
45. Fowles JS, Brown KC, Hess AM, Duval DL, Gustafson DL. Intra- and interspecies gene
expression models for predicting drug response in canine osteosarcoma. BMC
Bioinformatics. 2016;17(1):93.
46. Morin R, Bainbridge M, Fejes A, Hirst M, Krzywinski M, Pugh T, et al. Profiling the
HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read
sequencing. Biotechniques. 2008;45(1):81-94.
47. Chu Y, Corey DR. RNA sequencing: platform selection, experimental design, and data
interpretation. Nucleic Acid Ther. 2012;22(4):271-4.
48. Nagalakshmi U, Waern K, Snyder M. RNA-Seq: a method for comprehensive
transcriptome analysis. Curr Protoc Mol Biol. 2010; Suppl 89:1-13.
49. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat
Rev Genet. 2009;10(1):57-63.
50. Gnimpieba EZ, Chango A, Lushbough CM. RNA-Seq gene and transcript expression
analysis using the BioExtract Server and iPlant Collaborative. In: Proceedings of the 5th
ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
(ACM-BCB 2014); 2014 Sept 20-23; Newport Beach, CA. Newport Beach, CA:
Association for Computing Machinery; 2014. p.661-9.
51. Rankin KS, Starkey M, Lunec J, Gerrand CH, Murphy S, Biswas S. Of dogs and men:
comparative biology as a tool for the discovery of novel biomarkers and drug
development targets in osteosarcoma. Pediatr Blood Cancer. 2012;58(3):327-33.
52. Mueller F, Fuchs B, Kaser-Hotz B. Comparative biology of human and canine
osteosarcoma. Anticancer Res. 2007;27(1A):155-64.
53. Ingolia NT, Brar GA, Rouskin S, McGeachy AM, Weissman JS. The ribosome profiling
strategy for monitoring translation in vivo by deep sequencing of ribosome-protected
mRNA fragments. Nat Protoc. 2012;7(8):1534-50.
54. Wan M, Wang J, Gao X, Sklar J. RNA sequencing and its applications in cancer
diagnosis and targeted therapy. North Am J Med Sci. 2014;7(4):156-62.
55. Maher CA, Kumar-Sinha C, Cao X, Kalyana-Sundaram S, Han B, Jing X, et al.
Transcriptome sequencing to detect gene fusions in cancer. Nature. 2009;458(7234):97-
101.
56. Roberts A, Schaeffer L, Pachter L. Updating RNA-Seq analyses after re-annotation.
Bioinformatics. 2013;29(13):1631-7.
57. Ravi I, Baunthiyal M, Saxena J. Advances in biotechnology. New Delhi: Springer; 2014.
58. Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y. RNA-seq: an assessment of
technical reproducibility and comparison with gene expression arrays. Genome Res.
2008;18(9):1509-17.
59. Siu H, Zhu Y, Jin L, Xiong M. Implication of next-generation sequencing on association
studies. BMC Genomics. 2011;12:322.
60. Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq:
accounting for selection bias. Genome Biol. 2010;11(2):R14.
61. Fu X, Fu N, Guo S, Yan Z, Xu Y, Hu H, et al. Estimating accuracy of RNA-Seq and
microarrays with proteomics. BMC Genomics. 2009;10:161.
62. Atak ZK, Gianfelici V, Hulselmans G, De Keersmaecker K, Devasia AG, Geerdens E, et
al. Comprehensive analysis of transcriptome variation uncovers known and novel driver
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2016-12-20
Cómo citar
1.
Mitie de Souza Monobe M, Costa da Silva R. Expresión génica: una visión general de los métodos y aplicaciones en la pesquisa de cáncer: Una visión general de los métodos y aplicaciones en la pesquisa de cáncer. RVZ [Internet]. 20 de diciembre de 2016 [citado 22 de julio de 2024];23(4):532-46. Disponible en: https://rvz.emnuvens.com.br/rvz/article/view/406
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Este obra está licenciado com uma Licença Creative Commons Atribuição-NãoComercial 4.0 Internacional.
