Partial least squares based identification of Duchenne muscular dystrophy specific genes#

Partial least squares based identification of Duchenne muscular dystrophy specific genes # # 1 first-author †E-mail:huiboan1@sina.com †E-mail:huiboan1@sina.com huiboan1@sina.com 2 1 1 1 Department of Pathology, Children’s Hospital, Shijiazhuang 050031, China Department of Pathology, Children’s Hospital, Shijiazhuang 050031, China Department of Neurology, Children’s Hospital, Shijiazhuang 050031, China Department of Neurology, Children’s Hospital, Shijiazhuang 050031, China Large-scale parallel gene expression analysis has provided a greater ease for investigating the underlying mechanisms of Duchenne muscular dystrophy (DMD). Previous studies typically implemented variance/regression analysis, which would be fundamentally flawed when unaccounted sources of variability in the arrays existed. Here we aim to identify genes that contribute to the pathology of DMD using partial least squares (PLS) based analysis. We carried out PLS-based analysis with two datasets downloaded from the Gene Expression Omnibus (GEO) database to identify genes contributing to the pathology of DMD. Except for the genes related to inflammation, muscle regeneration and extracellular matrix (ECM) modeling, we found some genes with high fold change, which have not been identified by previous studies, such as SRPX , GPNMB , SAT1 , and LYZ . In addition, downregulation of the fatty acid metabolism pathway was found, which may be related to the progressive muscle wasting process. Our results provide a better understanding for the downstream mechanisms of DMD. Partial least squares (PLS) Gene expression profile Duchenne muscular dystrophy (DMD) Introduction 1 Duchenne muscular dystrophy (DMD) is a devastating inherited neuromuscular disorder that affects one in 3600–6000 live male births (Bushby et al., 2010 2010 ). DMD is caused by mutations or deletions in the X-linked dystrophin gene. The dystrophin protein locates underneath the sarcolemma and assembles with sarcolemmal proteins to form the dystrophin-associated protein complex (DAPC), which links the cytoskeleton to the extracellular matrix (ECM). Disruption of DAPC may break the mechanical linkage crucial for sarcolemmal integrity, leading to calcium leak channel openings, activation of calcium-dependent protease and fiber necrosis (Straub and Campbell, 1997 1997 ). In addition, DMD is a progressive disease in which DMD patients gradually lose most of their muscle due to increasing rate of fibrosis and fatty tissue infiltration along with the aging process. Although the responsible gene for DMD has been characterized for more than 20 years (Koenig et al., 1987 1987 ; Kunkel et al., 1987 1987 ), a comprehensive understanding of the downstream mechanisms caused by the absence of dystrophin is still lacking. Previous studies on the secondary changes in DMD have revealed possible involvement of calcium homeostasis (Head, 2010 2010 ), nitric oxide synthase (Altamirano et al., 2012 2012 ), inflammation (Monici et al., 2003 2003 ), and mast cell degranulation (Gorospe et al., 1994 1994 ), suggesting that the pathological process of DMD is highly complicated. Current available large-scale parallel gene expression analysis has provided greater ease for investigating the underlying molecular pathophysiological mechanisms of DMD. Several gene expression profiling studies (Chen et al., 2000 2000 ; 2005 2005 ; Haslett et al., 2002 2002 ; Pescatori et al., 2007 2007 ; Wong et al., 2009 2009 ) have been carried out, providing insights into the pathology of DMD. These studies typically implemented standard analysis of variance/regression to identify the differentially expressed genes over two types of samples. However, this analysis procedure becomes fundamentally flawed when there are unaccounted array-specific factors which are not detectable and cannot be removed by any standard normalizing method. For example, some genes are very highly expressed or depressed as a result of certain demographic profiles. Chakraborty and Datta ( 2012 2012 ) proposed that partial least squares (PLS) based analysis was robust in selecting disease specific genes with expression profile data, yielding higher sensitivity while maintaining reasonable specificity, false discovery rate (FDR), and false non-discovery rate (FNR) compared with variance/regression analysis. In this study, to identify genes truly differentially expressed between DMD and normal skeletal muscles, we performed a PLS-based analysis using two datasets downloaded from the Gene Expression Omnibus (GEO) database. A multivariate linear model based on PLS was used to describe the relationship between genes expression and DMD status. Meanwhile, age was also considered as a possible independent variable that may contribute to the status of the samples. Our results provide a better understanding on the downstream mechanisms of DMD and will facilitate further research of new adjuvant treatments. Materials and methods 1 Simulation studies 2 A simulation study was performed to compare the performance of the PLS-based method and a commonly used tool, linear models for microarray data (limma), which is implemented in the R statistical software ( http://www.r-project.org/ http://www.r-project.org/ ). We envisaged an expression profiling study with 5 000 genes, including 300 differentially expressed genes and 4 700 non-differentially expressed genes, in a 2×15 sample design. The simulation study is divided into two settings: (1) assuming the genes to be independent of each other; (2) assuming dependence within different groups of genes. The log transformed gene expression values ( Y ) for all genes are generated using a linear model and expressed as $$Y = X\beta + e$$ , where X denotes the design matrix corresponding to the above linear model and β denotes the corresponding vector of regression coefficients. The error term e is assumed to be independently distributed as N(0, σ 2 ), where the choice of σ 2 is: (1) For each gene, when β =0, σ is the standard deviation from the control group of the real array data, so different genes are identified with different error scales; (2) Define the noise to signal ratio η = σ 2 /Var( Xβ ), where σ 2 is the random error variance and Var( Xβ ) is the variance of the signal Xβ . This quantity measures the relative intensity of the noise coming from the random error and the confounded primary variable signal depicting the expression effect of the genes over the two groups. We consider three different values 0.1, 0.5, and 1.0 for η to incorporate the cases of strong, moderate, and weak primary signal intensity, respectively. From the three choices of noise to signal ratio, we calculated the corresponding values of σ 2 and used them to simulate the values of e in the model, at different β ~U[−1, 0) and U(0, −1] (assuming different genes have different effects). The simulation study was performed 100 times and the average values of the four performance measures were computed. Microarray data 2 Microarray data were downloaded from the Gene Expression Omnibus (GEO) ( http://www.ncbi.nlm.nih.gov/geo/ http://www.ncbi.nlm.nih.gov/geo/ ) database. Two datasets (GSE6011 and GSE3307), which included 27 DMD patients and 14 healthy controls, were used in subsequent analysis. Dystrophin protein was absent in all patients (Chen et al., 2000 2000 ; Pescatori et al., 2007 2007 ). Sample information for all subjects is listed in Table 1 1 . The two datasets were based on the GPL96 platform: [HG-U133A] Affymetrix Human Genome U133A Array. GEO accession Type ID Age (year) Gender GSM139515 DMD D01 0.1 Male GSM139516 DMD D02 0.2 Male GSM139517 DMD D03 0.3 Male GSM139519 DMD D04 0.4 Male GSM139520 DMD D05 0.4 Male GSM139521 DMD D06 0.5 Male GSM139522 DMD D07 0.5 Male GSM139523 DMD D08 0.6 Male GSM139524 DMD D09 0.7 Male GSM139525 DMD D10 0.7 Male GSM139526 DMD D11 0.9 Male GSM139527 DMD D12 1.0 Male GSM139528 DMD D13 1.0 Male GSM139529 DMD D14 1.2 Male GSM139530 DMD D15 1.2 Male GSM139531 DMD D16 1.3 Male GSM139532 DMD D17 1.7 Male GSM139533 DMD D18 1.7 Male GSM139534 DMD D19 1.8 Male GSM121357 DMD D20 5.0 Male GSM121361 DMD D21 8.0 Male GSM121363 DMD D22 7.0 Male GSM121368 DMD D23 8.0 Male GSM121369 DMD D24 7.0 Male GSM139535 DMD D25 2.3 Male GSM139536 DMD D26 3.9 Male GSM139537 DMD D27 5.1 Male GSM139501 Control C28 0.4 Male GSM139502 Control C29 0.5 Female GSM139503 Control C30 0.5 Female GSM139504 Control C31 0.5 Male GSM139505 Control C32 0.5 Male GSM139506 Control C33 0.6 Male GSM139507 Control C34 0.7 Female GSM139508 Control C35 0.9 Male GSM139509 Control C36 1.5 Male GSM139510 Control C37 2.8 Male GSM139511 Control C38 3.0 Male GSM139512 Control C39 4.2 Male GSM139513 Control C40 5.0 Male Detection of differentially expressed genes 2 Entire datasets including CEL and simple omnibus format in text (SOFT) formatted family files for all samples were downloaded. The CEL files were generated by Affymetrix DNA microarray image analysis software and they contained the information about each probe on the chip. For raw intensity value normalization, robust multiarray analysis (RMA) (Irizarry et al., 2003 2003 ) was used as follows: firstly, a model-based background correction was used to neutralize the effects of background noise and the processing artifacts; secondly, quantile normalization was used to align expression values to a common scale; finally, an iterative median polishing procedure was used to generate a single expression value for each probe set. The resulting RMA expression value (log2-transformed) was then used for further analysis. A multivariate linear model was used to describe the relationship between the gene expression and DMD disease status. Age was also introduced as an independent variable to discover the disease-related genes comprehensively. For each sample, the model is expressed as $$y = \sum\limits_{i = 1}^p {{\alpha _i}{x_i} + \beta \cdot {\text{age}} + b}$$ , where y is the binary variable of disease status (0 coded as control and 1 coded as DMD), and p is the total number of genes in the chip array. Obviously, in our dataset, the number of genes ( p =22 283) was much greater than the sample number ( n =41). PLS (Helland, 1988 1988 ; 1990 1990 ), a dimension reduction procedure for modeling without imposing strong assumptions, were then used to estimate the effect for each gene. The main idea of PLS regression was to build orthogonal components, called “latent variables”, and it is expressed as $$COV({t_k},{u_k}) \to \max$$ , $${\text{s}}.{\text{t}}. \parallel {t_k}\parallel = 1, \parallel {u_k}\parallel = 1$$ , where t k is the k th latent variable decomposes from all individuals’ gene expression data X (the matrix of n × p , n is number of individuals, each column was normalized), u k is the k th latent variable decomposed from the target trait data Y ( n ×1). The nonlinear iterative partial least squares (NIPALS) algorithm (Martins et al., 2010 2010 ) was used to calculate the PLS latent variables derived from the expression profile on the target trait, as (1) Randomly initialize u 0 = Y ; (2) w = X T u 0 , w = w /∥ w ∥ ; (3) t = Xw ; (4) c = Y T t , c = c /∥ c ∥ ; (5) u=Yc ; (6) If u − u 0 < 10 −8 , then go to Step (7); else u 0 = u , repeat Steps (2)–(5); (7) X = X − tt T X , Y = Y − tt T Y . Then go back to Step (2) to calculate the next latent variable. To evaluate the importance of the expressed genes on disease, the statistics of variable importance on the projection (VIP) (Gosselin et al., 2010 2010 ) was calculated by $${\text{VI}}{{\text{P}}_j} = \sqrt {{{p\sum\limits_{k = 1}^h {Co{r^2}({\mathbf{Y}},{t_k})} {\mathbf{w}}_{kj}^2} \mathord{\left/{\vphantom {{p\sum\limits_{k = 1}^h {Co{r^2}({\mathbf{Y}},{t_k})} {\mathbf{w}}_{kj}^2} {\sum\limits_{k = 1}^h {Co{r^2}({\mathbf{Y}},{{\mathbf{t}}_k})} }}} \right.\kern-\nulldelimiterspace} {\sum\limits_{k = 1}^h {Co{r^2}({\mathbf{Y}},{{\mathbf{t}}_k})} }}}$$ , where Cor( ) operator is the Pearson correlation coefficient, and for each w k , it should be normalized by dividing ∥ w k ∥, and h is the number of latent variables used in the model. To avoid the model over fitting, the best number of latent variables h was determined by P < 0.05 based on the logistic regression model with maximum likelihood estimation. The VIP for each gene was then calculated with the significant h latent variables to show genes associated with DMD. Additionally, the false discovered rate (FDR) procedures are used to control the expected proportion of incorrectly rejected null hypotheses. In our study, we used permutation tests to do the FDR control. The permutation procedure ( N= 10 000 times) was implemented to obtain the empirical distribution of PLS-based VIP in each replicate. The FDR for each gene could be evaluated according to the obtained empirical distribution as $${\text{FD}}{{\text{R}}_i} = \frac{{\sum\nolimits_j^{10000} {\sum\nolimits_i^p {Bool({\text{VI}}{{\text{P}}_{i,j}} > {\text{VI}}{{\text{P}}_i})} } }}{{10000p}}$$ , where the Bool( ) is the logical value of the expression while “True” is 1 and “False” is 0. The significant genes were selected with a cutoff of FDR < 0.05. Pathway enrichment analysis 2 Selected probes were annotated according to the SOFT files. All genes were mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways ( http://www.genome.jp/kegg/ http://www.genome.jp/kegg/ ). A hyper geometric distribution test was carried out to identify pathways in which differentially expressed genes are significantly enriched. Results 1 Results of the simulation study are listed in Table 2 2 . Compared with the limma method, PLS-based analysis achieved much higher sensitivity under all choices of η . The proportion among genes declared significant that were not differentially expressed was slightly increased, which is an inevitable price to pay to achieve a better performance in terms of detection power. Overall, the PLS-based method achieved higher sensitivity while maintaining reasonable specificity and impressively small FDR and FNR. NSR: noise/signal ratio; Sensitivity: proportion among differentially expressed genes that were declared significant; Specificity: proportion among non-differentially expressed genes that were declared non-significant; FDR: false discovery rate, proportion among genes declared significant that were not differentially expressed; FNR: false non-discovery rate, proportion among genes declared non-significant that were differentially expressed NSR Method Sensitivity Specificity FDR FNR 2 0.1 Limma 0.376 7 0.999 1 0.034 2 0.038 3 PLS 0.993 3 0.995 3 0.068 8 0.000 4 2 0.5 Limma 0.173 3 1.000 0 0.000 0 0.050 1 PLS 0.826 7 0.997 2 0.049 8 0.011 0 2 1.0 Limma 0.126 7 1.000 0 0.000 0 0.052 8 PLS 0.473 3 0.998 7 0.040 5 0.032 6 As shown in Table 3 3 , three latent variables with P < 0.05 were selected. Sample classification according to the three latent variables is illustrated in Fig. 1 1 . Compared with the normal controls, a total of 1 529 probe sets (representing 1 254 genes) were differentially expressed. Among them, 949 probe sets were upregulated while 580 ones were downregulated. Detailed information for these genes is listed in Table S1 S1 . Thirty-nine differentially expressed genes were found with the absolute value of fold change (|FC|) > 1.5 (Fig. 2 2 ). Only the first three latent variables with P < 0.05 were selected P < 0.05 P < 0.01 Latent variable Estimate Std. Error P -value V1 23.928 7.868 0.002 36 ** ** V2 10.783 3.528 0.002 24 ** ** V3 5.743 2.714 0.027 3 * * V4 3.350 2.274 0.140 7 V5 2.771 2.226 0.213 0 V6 2.591 2.247 0.248 9 V7 0.837 2.177 0.700 7 V8 0.332 2.102 0.874 5 V9 0.176 2.109 0.933 4 V10 0.066 2.111 0.974 9 Information for each sample corresponding to Table 1 1 is shown in the X axis (format: ID-age). The 39 genes were combined and hierarchically clustered to represent the expression patterns using average linkage and Euclidean distance as a measurement of similarity. Red represents upregulation and green represents downregulation. Precise color scheme is illustrated in the color key (Note: for interpretation of the references to color in this figure legend, the reader is referred to the web version of this article) For the 1 254 differentially expressed genes, 620 genes could be mapped on KEGG pathways. Meanwhile, for all the well-characterized human genes in the Affymetrix Human Genome U133A Array, 5 157 genes could be mapped on KEGG pathways. As shown in Table 4 4 , aberrantly regulated genes were enriched in 58 pathways. Among them, the phagosome pathway was identified to be with the most significant enrichment. According to the classification in the KEGG database, 25 of these pathways are involved in the inflammatory/immune response, such as the immune system, immune disease, and infectious disease. In addition, two DMD-related pathways, the dilated cardiomyopathy pathway and the viral myocarditis pathway, were included. Other pathways were metabolism, cell motility, signaling molecules and interaction, transport and catabolism, and so on. ID Pathway description Pathway class False discovery rate 04145 Phagosome Transport and catabolism 1.63×10 −12 05150 Staphylococcus aureus infection Infectious diseases 2.74×10 −12 04612 Antigen processing and presentation Immune system 4.82×10 −12 05416 Viral myocarditis Cardiovascular diseases 5.92×10 −10 05152 Tuberculosis Infectious diseases 9.58×10 −10 05330 Allograft rejection Immune diseases 1.18×10 −8 05140 Leishmaniasis Infectious diseases 3.07×10 −8 05332 Graft-versus-host disease Immune diseases 5.83×10 −8 04514 Cell adhesion molecules (CAMs) Signaling molecules and interaction 1.35×10 −7 04940 Type I diabetes mellitus Endocrine and metabolic diseases 1.47×10 −7 05145 Toxoplasmosis Infectious diseases 2.00×10 −7 04510 Focal adhesion Cell communication 4.50×10 −7 05130 Pathogenic Escherichia coli infection Infectious diseases 1.03×10 −6 05133 Pertussis Infectious diseases 2.16×10 −6 04672 Intestinal immune network for IgA production Immune system 2.87×10 −6 04670 Leukocyte transendothelial migration Immune system 5.04×10 −6 05310 Asthma Immune diseases 5.80×10 −6 05320 Autoimmune thyroid disease Immune diseases 6.88×10 −6 04512 ECM-receptor interaction Signaling molecules and interaction 8.31×10 −6 05166 Human T-cell leukemia virus (HTLV)-I infection Infectious diseases 9.21×10 −6 05169 Epstein-Barr virus infection Infectious diseases 9.06×10 −5 04610 Complement and coagulation cascades Immune system 1.02×10 −4 04142 Lysosome Transport and catabolism 1.95×10 −4 04971 Gastric acid secretion Digestive system 2.18×10 −4 00072 Synthesis and degradation of ketone bodies Lipid metabolism 4.60×10 −4 05323 Rheumatoid arthritis Immune diseases 5.11×10 −4 05146 Amoebiasis Infectious diseases 6.60×10 −4 00020 Citrate cycle (TCA cycle) Carbohydrate metabolism 7.71×10 −4 00071 Fatty acid metabolism Lipid metabolism 1.28×10 −3 05012 Parkinson’s disease Neurodegenerative diseases 1.36×10 −3 04976 Bile secretion Digestive dystem 1.38×10 −3 00640 Propanoate metabolism Carbohydrate metabolism 1.86×10 −3 05322 Systemic lupus erythematosus Immune diseases 2.21×10 −3 04380 Osteoclast differentiation Development 2.70×10 −3 04810 Regulation of actin cytoskeleton Cell motility 3.43×10 −3 05414 Dilated cardiomyopathy (DCM) Cardiovascular diseases 5.22×10 −3 05168 Herpes simplex infection Infectious diseases 5.79×10 −3 04974 Protein digestion and absorption Digestive system 8.45×10 −3 05164 Influenza A Infectious diseases 1.03×10 −2 05010 Alzheimer’s disease Neurodegenerative diseases 1.06×10 −2 04540 Gap junction Cell communication 1.34×10 −2 05134 Legionellosis Infectious diseases 1.50×10 −2 00062 Fatty acid elongation Lipid metabolism 1.50×10 −2 00280 Valine, leucine and isoleucine degradation Amino acid metabolism 1.56×10 −2 04650 Natural killer cell mediated cytotoxicity Immune system 1.89×10 −2 05144 Malaria Infectious diseases 1.89×10 −2 00630 Glyoxylate and dicarboxylate metabolism Carbohydrate metabolism 1.97×10 −2 05222 Small cell lung cancer Cancers 2.34×10 −2 00410 β-Alanine metabolism Metabolism of other amino acids 2.51×10 −2 00010 Glycolysis/gluconeogenesis Carbohydrate metabolism 2.83×10 −2 05220 Chronic myeloid leukemia Cancers 2.92×10 −2 00650 Butanoate metabolism Carbohydrate metabolism 3.02×10 −2 04725 Cholinergic synapse Nervous system 3.59×10 −2 04141 Protein processing in endoplasmic reticulum Folding, sorting and degradation 3.67×10 −2 00360 Phenylalanine metabolism Amino acid metabolism 3.84×10 −2 05219 Bladder cancer Cancers 4.39×10 −2 04640 Hematopoietic cell lineage Immune system 4.44×10 −2 05214 Glioma Cancers 4.46×10 −2 Discussion 1 Progressive pathophysiology of DMD is highly complex, involving many secondary changes. Genome-wide expression profiling is a powerful procedure for investigating the downstream pathophysiological cascades in DMD patients. For expression profiling analysis, it is a challenge to create an effective mathematical model to deal with small sample sizes with a large number of genes (Golub et al., 1999 1999 ). Previous gene expression studies mainly used variance/regression analysis to identify the differentially expressed genes from their respective arrays for the two types of samples. However, with this procedure, the true picture of differential expression may be blurred by hidden biological effects that cannot be removed by a routine normalizing method. Chakraborty and Datta ( 2012 2012 ) demonstrated better performance of the PLS-based method compared with variance/regression analysis. Our simulation study also revealed that the PLS-based method achieved higher sensitivity while maintaining reasonable specificity and impressively small FDR and FNR compared with the commonly used limma method (Table 2 2 ). Here, with two combined datasets downloaded from the GEO database, we used a PLS-based multivariate linear model to describe the relationship between the genes expression and DMD disease status and further detected differentially expressed genes related to DMD pathogenesis. The age factor was also included in the model since DMD is a progressive disease. According to the logistical regression analysis, three latent variables were finally selected (Fig. 1 1 ), and they performed well in the classification of the samples. Total of 1 254 genes were identified as significantly differentially expressed. As consistent with previous studies (Chen et al., 2000 2000 ), upregulation of genes which encode proteins belonging to the major histocompatibility complex was found. In addition, most of the pathways in which aberrantly regulated enriched genes were involved resulted in an inflammatory/immune response. This may result from the infiltration of immune cells into the muscles (Mcdouall et al., 1990 1990 ; Spencer et al., 1997 1997 ; Cai et al., 2000 2000 ) and also in elevated levels of various inflammatory cytokines. For muscle regeneration, two genes which encode embryonic and perinatal myosin heavy chains (MYH3 and MYH8) were overexpressed, consistent with Haslett et al. ( 2002 2002 ). MYH8 was the most significantly differentially expressed gene. Their expression is considered as a hallmark of muscle regeneration after birth and muscular dystrophies. Other muscle structure and regeneration genes encoding tubulin ( TUBA1A and TUBB6 ) or myosin ( MYBPH and MYL5 ) were also overexpressed with |FC| > 1.5, implicating the ongoing muscle regeneration process in DMD patients. In addition, increased expressions of genes encoding ECM components, such as fibril-forming collagens (types I and III), were also detected. This is also consistent with previous studies (Haslett et al., 2002 2002 ; Pescatori et al., 2007 2007 ), and increased ECM synthesis may contribute to the progressive fibrosis of muscle in DMD patients. Moreover, ECM-receptor interaction (hsa4512) is one of the pathways that are enriched with deregulated genes and all differentially expressed genes in this pathway are upregulated (Fig. 3 3 ). Protein symbols were marked according to gene expression pattern to reflect gene-centric data. Those with red frames are overexpressed (Note: for interpretation of the references to color in this figure legend, the reader is referred to the web version of this article) Several deregulated genes with |FC| > 1.5 have not been proposed in previous expression studies, such as SRPX , GPNMB , SAT1 , and LYZ . Their relationships with DMD were still unknown. However, it is worth further investigation. Take the SRPX gene for example, it was upregulated with FC=1.73. SRPX is a cytoskeleton associated protein which interacts with Pelota (PELO). Their interaction may facilitate PELO to detect and degrade aberrant mRNAs (Burnicka-Turek et al., 2010 2010 ). Since PELO is associated with actin microfilaments of mammalian cells (Burnicka-Turek et al., 2010 2010 ), overexpression of SRPX may be related to the muscle regeneration process in DMD patients. For the pathways enriched with differentially expressed genes, the fatty acid metabolism pathway (has00071) was downregulated. As shown in Fig. 4 4 , 12 genes in this pathway were differentially expressed and all of them were downregulated. Nishio et al. ( 1990 1990 ) demonstrated that the free fatty acid level in DMD patients was higher than that in the control group no matter whether the serum creatine kinase level was high or low. Downregulation of the fatty acid metabolism pathway may be the underlying cause of the increased level of fatty acids. Increased concentration of fatty acid may serve as energy sources or substrates, sparing muscle protein (Nishio et al., 1990 1990 ). However, this fatty tissue infiltration process may also contribute to the progressive muscle wasting of DMD patients. Protein symbols were marked according to gene expression pattern to reflect gene-centric data. Those with green frames are downregulated (Note: for interpretation of the references to color in this figure legend, the reader is referred to the web version of this article) In summary, using two datasets downloaded from the GEO database, we carried out a PLS-based analysis to identify differentially expressed genes that may contribute to the pathology of DMD. 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This article does not contain any studies with human or animal subjects performed by any of the authors. 10.1631/jzus.B1300060 , 2013-03-03 2013-10-16 2013-08-29 2022-01-05 Copyright © Zhejiang University and Springer-Verlag Berlin Heidelberg 2013 2013 Journal of Zhejiang University-SCIENCE B (Biomedicine and Biotechnology) 11 14