Comparative study of microarray and experimental data on Schwann cells in peripheral nerve degeneration and regeneration: big data analysis
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摘要
A Schwann cell has regenerative capabilities and is an important cell in the peripheral nervous system.This microarray study is part of a bioinformatics study that focuses mainly on Schwann cells. Microarray data provide information on differences between microarray-based and experiment-based gene expression analyses. According to microarray data, several genes exhibit increased expression(fold change) but they are weakly expressed in experimental studies(based on morphology, protein and mRNA levels). In contrast, some genes are weakly expressed in microarray data and highly expressed in experimental studies;such genes may represent future target genes in Schwann cell studies. These studies allow us to learn about additional genes that could be used to achieve targeted results from experimental studies. In the current big data study by retrieving more than 5000 scientific articles from PubMed or NCBI, Google Scholar, and Google, 1016(up-and downregulated) genes were determined to be related to Schwann cells. However,no experiment was performed in the laboratory; rather, the present study is part of a big data analysis. Our study will contribute to our understanding of Schwann cell biology by aiding in the identification of genes.Based on a comparative analysis of all microarray data, we conclude that the microarray could be a good tool for predicting the expression and intensity of different genes of interest in actual experiments.
A Schwann cell has regenerative capabilities and is an important cell in the peripheral nervous system.This microarray study is part of a bioinformatics study that focuses mainly on Schwann cells. Microarray data provide information on differences between microarray-based and experiment-based gene expression analyses. According to microarray data, several genes exhibit increased expression(fold change) but they are weakly expressed in experimental studies(based on morphology, protein and mRNA levels). In contrast, some genes are weakly expressed in microarray data and highly expressed in experimental studies;such genes may represent future target genes in Schwann cell studies. These studies allow us to learn about additional genes that could be used to achieve targeted results from experimental studies. In the current big data study by retrieving more than 5000 scientific articles from PubMed or NCBI, Google Scholar, and Google, 1016(up-and downregulated) genes were determined to be related to Schwann cells. However,no experiment was performed in the laboratory; rather, the present study is part of a big data analysis. Our study will contribute to our understanding of Schwann cell biology by aiding in the identification of genes.Based on a comparative analysis of all microarray data, we conclude that the microarray could be a good tool for predicting the expression and intensity of different genes of interest in actual experiments.
A Schwann cell has regenerative capabilities and is an important cell in the peripheral nervous system.This microarray study is part of a bioinformatics study that focuses mainly on Schwann cells. Microarray data provide information on differences between microarray-based and experiment-based gene expression analyses. According to microarray data, several genes exhibit increased expression(fold change) but they are weakly expressed in experimental studies(based on morphology, protein and mRNA levels). In contrast, some genes are weakly expressed in microarray data and highly expressed in experimental studies;such genes may represent future target genes in Schwann cell studies. These studies allow us to learn about additional genes that could be used to achieve targeted results from experimental studies. In the current big data study by retrieving more than 5000 scientific articles from PubMed or NCBI, Google Scholar, and Google, 1016(up-and downregulated) genes were determined to be related to Schwann cells. However,no experiment was performed in the laboratory; rather, the present study is part of a big data analysis. Our study will contribute to our understanding of Schwann cell biology by aiding in the identification of genes.Based on a comparative analysis of all microarray data, we conclude that the microarray could be a good tool for predicting the expression and intensity of different genes of interest in actual experiments.
引文
Alyass A,Turcotte M,Meyre D(2015)From big data analysis to personalized medicine for all:Challenges and opportunities.BMC Med Genomics 8:1-12.
Barrett JC,Kawasaki ES(2003)Microarrays:the use of oligonucleotides and cDNA for the analysis of gene expression.Drug Discov Today8:134-141.
Dudoit S,Gentleman RC,Quackenbush J(2003)Open source software for the analysis of microarray data.Biotechniques 34:45-51.
Fan J,Han F,Liu H(2014)Challenges of big data analysis.Natl Sci Rev1:293-314.
Gliddon HD,Herberg JA,Levin M,Kaforou M(2018)Genome‐wide host RNA signatures of infectious diseases:discovery and clinical translation.Immunology 153:171-178.
Ham S,Kim TK,Hong H,Kim YS,Tang YP,Im HI(2018)Big data analysis of genes associated with neuropsychiatric disorders in an Alzheimer’s disease animal model.Front Neurosci 12:1-12.
Hira ZM,Gillies DF(2015)A review of feature selection and feature extraction methods applied on microarray data.Adv Bioinformatics2015:198363.
Johnson WE,Li C,Rabinovic A(2007)Adjusting batch effects in microarray expression data using empirical Bayes methods.Biostatistics8:118-127.
Long L,Huang Y,Wu H,Luan W,Zhang Q,Wen H,Ding T,Wang Y(2013)Dynamic change of Prohibitin2 expression in rat sciatic nerve after crush.Cell Mol Neurobiol 33:689-698.
Nisenbaum LK(2002)The ultimate chip shot:can microarray technology deliver for neuroscience?Genes,Brain Behav 1:27-34.
Pollack JR,Perou CM,Alizadeh AA,Eisen MB,Pergamenschikov AWilliams CF,Jeffrey SS,Botstein D,Brown PO(1999)Genome-wide analysis of DNA copy-number changes using cDNA microarrays.Nat Genet 23:41.
Scarlato M,Xu T,Bannerman P,Beesley J,Reddy UR,Rostami A,Scherer SS,Pleasure D(2001)Axon‐Schwann cell interactions regulate the expression of fibroblast growth factor‐5(FGF‐5).J Neurosci Res66:16-22.
Sejnowski TJ,Churchland PS,Movshon JA(2014)Putting big data to good use in neuroscience.Nat Neurosci 17:1440-1441.
Shen AG,Shi SX,Chen ML,Qin J,Gao SF,Cheng C(2008)Dynamic changes of p27 kip1 and Skp2 expression in injured rat sciatic nerve Cell Mol Neurobiol 28:713-725.
Sturn A,Quackenbush J,Trajanoski Z(2002)Genesis:cluster analysis of microarray data.Bioinformatics 18:207-208.
Tarca AL,Romero R,Draghici S(2006)Analysis of microarray experiments of gene expression profiling.Am J Obstet Gynecol 195:373-388.
Thorstad R,Wolff P(2018)A big data analysis of the relationship between future thinking and decision-making.Proc Natl Acad Sci U S A115:E1740-1748.
Whitehead MJ,McGonigal R,Willison HJ,Barnett SC(2018)Heparanase attenuates axon degeneration following sciatic nerve transection Sci Rep 8:5219.
Zhang Z,Yu B,Gu Y,Zhou S,Qian T,Wang Y,Ding G,Ding F,Gu X(2016)Fibroblast-derived tenascin-C promotes Schwann cell migration throughβ1-integrin dependent pathway during peripheral nerve regeneration.Glia 64:374-385.
Barrett JC,Kawasaki ES(2003)Microarrays:the use of oligonucleotides and cDNA for the analysis of gene expression.Drug Discov Today8:134-141.
Dudoit S,Gentleman RC,Quackenbush J(2003)Open source software for the analysis of microarray data.Biotechniques 34:45-51.
Fan J,Han F,Liu H(2014)Challenges of big data analysis.Natl Sci Rev1:293-314.
Gliddon HD,Herberg JA,Levin M,Kaforou M(2018)Genome‐wide host RNA signatures of infectious diseases:discovery and clinical translation.Immunology 153:171-178.
Ham S,Kim TK,Hong H,Kim YS,Tang YP,Im HI(2018)Big data analysis of genes associated with neuropsychiatric disorders in an Alzheimer’s disease animal model.Front Neurosci 12:1-12.
Hira ZM,Gillies DF(2015)A review of feature selection and feature extraction methods applied on microarray data.Adv Bioinformatics2015:198363.
Johnson WE,Li C,Rabinovic A(2007)Adjusting batch effects in microarray expression data using empirical Bayes methods.Biostatistics8:118-127.
Long L,Huang Y,Wu H,Luan W,Zhang Q,Wen H,Ding T,Wang Y(2013)Dynamic change of Prohibitin2 expression in rat sciatic nerve after crush.Cell Mol Neurobiol 33:689-698.
Nisenbaum LK(2002)The ultimate chip shot:can microarray technology deliver for neuroscience?Genes,Brain Behav 1:27-34.
Pollack JR,Perou CM,Alizadeh AA,Eisen MB,Pergamenschikov AWilliams CF,Jeffrey SS,Botstein D,Brown PO(1999)Genome-wide analysis of DNA copy-number changes using cDNA microarrays.Nat Genet 23:41.
Scarlato M,Xu T,Bannerman P,Beesley J,Reddy UR,Rostami A,Scherer SS,Pleasure D(2001)Axon‐Schwann cell interactions regulate the expression of fibroblast growth factor‐5(FGF‐5).J Neurosci Res66:16-22.
Sejnowski TJ,Churchland PS,Movshon JA(2014)Putting big data to good use in neuroscience.Nat Neurosci 17:1440-1441.
Shen AG,Shi SX,Chen ML,Qin J,Gao SF,Cheng C(2008)Dynamic changes of p27 kip1 and Skp2 expression in injured rat sciatic nerve Cell Mol Neurobiol 28:713-725.
Sturn A,Quackenbush J,Trajanoski Z(2002)Genesis:cluster analysis of microarray data.Bioinformatics 18:207-208.
Tarca AL,Romero R,Draghici S(2006)Analysis of microarray experiments of gene expression profiling.Am J Obstet Gynecol 195:373-388.
Thorstad R,Wolff P(2018)A big data analysis of the relationship between future thinking and decision-making.Proc Natl Acad Sci U S A115:E1740-1748.
Whitehead MJ,McGonigal R,Willison HJ,Barnett SC(2018)Heparanase attenuates axon degeneration following sciatic nerve transection Sci Rep 8:5219.
Zhang Z,Yu B,Gu Y,Zhou S,Qian T,Wang Y,Ding G,Ding F,Gu X(2016)Fibroblast-derived tenascin-C promotes Schwann cell migration throughβ1-integrin dependent pathway during peripheral nerve regeneration.Glia 64:374-385.