rheumatoid arthritis ( ra ) is an autoimmune disease of unknown etiology , characterized by chronic polyarthritis that induces joint damage and functional disability .
tumor necrosis factor ( tnf ) plays a key role in the ra pathogenesis , and anti - tnf agents have shown good efficacy in treating refractory ra , especially when methotrexate ( mtx ) and disease - modifying anti - rheumatic drugs ( dmards ) proved ineffective . as the first clinically approved tnf blocker , infliximab has been widely used in ra treatment . however , approximately 30% of ra patients achieved inadequate response .
clinical parameters related with ra diagnosis or prognosis , such as c - reactive protein ( crp ) and disease activity score 28 ( das28 ) , failed to predict clinical response to infliximab .
thus , considering the high cost , adverse effects , and prolonged disease severity in non - responders , a search for predictive biomarkers was needed . in recent years , gene expression profiling has been successfully used on tissue samples or blood to identify biomarkers in various disorders .
compared to synovial biopsy in ra , peripheral blood samples were easy to obtain and facilitated dynamic monitoring , which could also reflect the inflammatory condition .
thus , several studies tested the gene expression pattern of peripheral blood mononuclear cells ( pbmcs ) in ra patients to identify genetic markers for predicting subsequent infliximab response . unfortunately
, huge discrepancies between studies restrained the clinical application of those reported biomarkers , and no systematic reviews have concentrated on this .
therefore , we conducted a systematic bioinformatics analysis of microarray data in public databases to identify the most recognized differential genes associated with ra infliximab response , and then test the viability of these genes as predictive biomarkers .
gene expression omnibus ( geo ) ( https://www.ncbi.nlm.nih.gov/geo/ ) and arrayexpress ( http://www.ebi.ac.uk/arrayexpress/ ) are international public repositories that archive and freely distribute microarray data submitted by the research community .
we searched related microarray data in the databases using key words including  infliximab  ,  anti - tnf  or  anti - tumor necrosis factor  in combination with  rheumatoid arthritis  or
all clinical information and published articles of the microarray datasets were reviewed independently by 2 researchers .
microarray datasets were included if they satisfied the following criteria : ( i ) diagnosis of ra was based on the 1987 revised criteria of the american college of rheumatology ( acr ) for the classification of ra or on the 2010 acr / european league against rheumatism ( eular ) classification criteria ; ( ii ) patients responded inadequately to mtx ; ( iii ) nave to anti - tnf treatment or had an interval of at least 3 months after the last treatment ; ( iv ) peripheral blood samples were collected before first administration of infliximab , and total rna was separated from pbmcs to analyze the global expression pattern of numerous well - annotated genes by microarray ; and ( v ) response to infliximab was assessed at least 6 weeks later according to certain criteria . for those eligible datasets
, the normalized data of gene expression profiling were downloaded from the databases of geo or arrayexpress .
for each microarray dataset , all samples were divided into different groups according to the therapeutic outcomes .
differential genes associated with infliximab response were analyzed using the  gcrma  package of r language ( version 3.2.3 , https://www.r-project.org/ ) .
the genes recognized by half of the datasets were regarded as candidate biomarkers for predicting infliximab response .
p<0.05 was considered significant , and p values from these analyses were not adjusted for multiple testing .
acr and eular response criteria are commonly used to measure individual response in clinical trials , dependent on the extent of change and the level of disease activity reached . in the included datasets , gse58795 and gse42296
were randomly or prospectively designed datasets using the acr and eular response criteria , respectively .
thus , we chose these datasets as validation sets to determine the accuracy of the biomarkers in predicting infliximab response .
roc ( receiver operating characteristic ) analysis was conducted to assess the predictive power of each biomarker .
if no single biomarker showed good predictive power , a logistics regression model was established to evaluate the predictive accuracy of multiple gene models . in gse42296 ,
blood samples were also collected for microarray testing at week 2 after first infliximab treatment , and the data were used to assess the predictive power of biomarkers at the early phase of treatment . in gse58795 , another 29 patients were randomly divided into control group and received placebo treatment , and 13 patients were categorized as moderate response at week 14 . through comparing the biomarkers predictive values between infliximab and placebo groups , the specificity and accuracy of these biomarkers could be assessed indirectly . to overview biological aspects of the biomarkers , the online tool toppgene ( https://toppgene.cchmc.org/ ) was used for a gene ontology analysis of the molecular function , biological processes , and cellular components in the biomarkers .
gene expression omnibus ( geo ) ( https://www.ncbi.nlm.nih.gov/geo/ ) and arrayexpress ( http://www.ebi.ac.uk/arrayexpress/ ) are international public repositories that archive and freely distribute microarray data submitted by the research community .
we searched related microarray data in the databases using key words including  infliximab  ,  anti - tnf  or  anti - tumor necrosis factor  in combination with  rheumatoid arthritis  or
all clinical information and published articles of the microarray datasets were reviewed independently by 2 researchers .
microarray datasets were included if they satisfied the following criteria : ( i ) diagnosis of ra was based on the 1987 revised criteria of the american college of rheumatology ( acr ) for the classification of ra or on the 2010 acr / european league against rheumatism ( eular ) classification criteria ; ( ii ) patients responded inadequately to mtx ; ( iii ) nave to anti - tnf treatment or had an interval of at least 3 months after the last treatment ; ( iv ) peripheral blood samples were collected before first administration of infliximab , and total rna was separated from pbmcs to analyze the global expression pattern of numerous well - annotated genes by microarray ; and ( v ) response to infliximab was assessed at least 6 weeks later according to certain criteria .
for those eligible datasets , the normalized data of gene expression profiling were downloaded from the databases of geo or arrayexpress .
for each microarray dataset , all samples were divided into different groups according to the therapeutic outcomes .
differential genes associated with infliximab response were analyzed using the  gcrma  package of r language ( version 3.2.3 , https://www.r-project.org/ ) .
the genes recognized by half of the datasets were regarded as candidate biomarkers for predicting infliximab response .
p<0.05 was considered significant , and p values from these analyses were not adjusted for multiple testing .
acr and eular response criteria are commonly used to measure individual response in clinical trials , dependent on the extent of change and the level of disease activity reached . in the included datasets , gse58795 and gse42296
were randomly or prospectively designed datasets using the acr and eular response criteria , respectively .
thus , we chose these datasets as validation sets to determine the accuracy of the biomarkers in predicting infliximab response .
roc ( receiver operating characteristic ) analysis was conducted to assess the predictive power of each biomarker .
if no single biomarker showed good predictive power , a logistics regression model was established to evaluate the predictive accuracy of multiple gene models . in gse42296 ,
blood samples were also collected for microarray testing at week 2 after first infliximab treatment , and the data were used to assess the predictive power of biomarkers at the early phase of treatment . in gse58795 , another 29 patients were randomly divided into control group and received placebo treatment , and 13 patients were categorized as moderate response at week 14 . through comparing the biomarkers predictive values between infliximab and placebo groups , the specificity and accuracy of these biomarkers could be assessed indirectly .
to overview biological aspects of the biomarkers , the online tool toppgene ( https://toppgene.cchmc.org/ ) was used for a gene ontology analysis of the molecular function , biological processes , and cellular components in the biomarkers .
eight microarray datasets [ 1219 ] were identified with 183 pre - treated blood samples of infliximab responders and 191 non - responders ( geo accession numbers : gse3592 , gse8350 , gse12051 , gse42296 , gse58795 , gse20690 , gse33377 , and gse78068 ) .
eight different platforms were involved ( gpl3064 , gpl5460 , gpl2507 , gpl6244 , gpl10379 , gpl4133 , gpl5175 , and gpl6480 ) . the dataset of gse19821 was excluded for absence of response categories . among the eligible datasets , most ra patients were diagnosed according to the acr ( 1987 ) criteria . before infliximab treatment , all patients suffered from moderate to severe disease activity , and responded inadequately to disease - modifying anti - rheumatic drugs ( dmard ) and/or mtx .
most datasets used the acr or eular response criteria , and categorized the therapeutic outcomes into response and non - response , while the criteria based on clinical disease activity index ( dai ) and serum crp level were used in gse78068 and gse20690 , respectively .
the differential genes in gse33377 were obtained from the supplementary material of published studies due to the poor annotated files in the microarray dataset .
only 5 genes were recognized by at least half of the datasets : fkbp1a ( fk506 binding protein 1a ) , fgf12 ( fibroblast growth factor 12 ) , ano1 ( anoctamin 1 , calcium activated chloride channel ) , lrrc31 ( leucine rich repeat containing 31 ) , and akr1d1 ( aldo - keto reductase family 1 , member d1 ) ( figure 1 ) .
gene ontology analysis indicated that these 5 genes were significantly associated with the regulation of transmembrane transport ( table 2 ) .
the 5 most recognized genes were regarded as candidate biomarkers , while gse58795 and gse42296 were chose as validation sets .
no single gene showed good power in predicting infliximab response ( auc < 0.85 ) .
however , the logistics regression model of these 5 genes showed good accuracy in predicting infliximab response ( auc=0.963 in gse58795 and 1.000 in gse42296 ) ( figure 2a , 2b ) . at week 2 after the first infliximab infusion , the 5-gene model in peripheral blood samples also showed a good accuracy in predicting the infliximab response at week 14 ( auc=1.000 in gse42296 ) ( figure 2d ) . in the placebo treatment group ,
the 5-gene model failed to predict the response to placebo ( auc=0.697 ) ( figure 2c ) , indirectly indicating the specificity of the 5-gene model in predicting infliximab response .
eight microarray datasets [ 1219 ] were identified with 183 pre - treated blood samples of infliximab responders and 191 non - responders ( geo accession numbers : gse3592 , gse8350 , gse12051 , gse42296 , gse58795 , gse20690 , gse33377 , and gse78068 ) .
eight different platforms were involved ( gpl3064 , gpl5460 , gpl2507 , gpl6244 , gpl10379 , gpl4133 , gpl5175 , and gpl6480 ) . the dataset of gse19821 was excluded for absence of response categories . among the eligible datasets , most ra patients were diagnosed according to the acr ( 1987 ) criteria . before infliximab treatment , all patients suffered from moderate to severe disease activity , and responded inadequately to disease - modifying anti - rheumatic drugs ( dmard ) and/or mtx .
most datasets used the acr or eular response criteria , and categorized the therapeutic outcomes into response and non - response , while the criteria based on clinical disease activity index ( dai ) and serum crp level were used in gse78068 and gse20690 , respectively .
the differential genes in gse33377 were obtained from the supplementary material of published studies due to the poor annotated files in the microarray dataset .
only 5 genes were recognized by at least half of the datasets : fkbp1a ( fk506 binding protein 1a ) , fgf12 ( fibroblast growth factor 12 ) , ano1 ( anoctamin 1 , calcium activated chloride channel ) , lrrc31 ( leucine rich repeat containing 31 ) , and akr1d1 ( aldo - keto reductase family 1 , member d1 ) ( figure 1 ) .
gene ontology analysis indicated that these 5 genes were significantly associated with the regulation of transmembrane transport ( table 2 ) .
the 5 most recognized genes were regarded as candidate biomarkers , while gse58795 and gse42296 were chose as validation sets .
no single gene showed good power in predicting infliximab response ( auc < 0.85 ) .
however , the logistics regression model of these 5 genes showed good accuracy in predicting infliximab response ( auc=0.963 in gse58795 and 1.000 in gse42296 ) ( figure 2a , 2b ) . at week 2 after the first infliximab infusion , the 5-gene model in peripheral blood samples also showed a good accuracy in predicting the infliximab response at week 14 ( auc=1.000 in gse42296 ) ( figure 2d ) .
in the placebo treatment group , the 5-gene model failed to predict the response to placebo ( auc=0.697 ) ( figure 2c ) , indirectly indicating the specificity of the 5-gene model in predicting infliximab response .
response prediction has significant health and economic benefits , but the prediction based on clinical parameters and disease activity scores could not satisfy the need for accuracy . with
the development of high - throughput gene detection technology , gene expression profiling has been used in identifying genes associated with disease diagnosis , prognosis , and treatment .
several studies tested the mrna expression profiles in peripheral blood samples from ra patients before infliximab treatment , and detected gene panels discriminating between future responders and non - responders .
although synovial biopsy samples could reflect the degree of inflammation at a specific joint and the response to infliximab more directly , it was difficult to obtain such samples , particularly from multiple sites and at different time points .
the most common approach to searching for biomarkers is to identify individual genes showing statistically significant differences between responders and non - responders . after systematic analyses
, we found that differential genes identified in these studies were not consistent with each other , and different gene sets were reported to distinguish between responders and non - responders .
first , most studies were limited in sample size , and the response criteria varied from studies ( acr , eular , serum crp , or dai ) .
different platforms contained different gene - oriented probes , and not all platforms were well - annotated .
sampling , rna amplification and labeling , and the hybridization conditions might also affect the results .
thus , we analyzed the differential genes associated with the subsequent infliximab efficacy according to the criteria of each study .
considering that there was no overlap of differential genes across all 8 studies , the genes recognized by at least half of the studies were chosen as candidate biomarkers .
then , 2 random - prospective - designed datasets were used to test the predictive power of candidate biomarkers .
five genes were identified for the validation ( fkbp1a , fgf12 , ano1 , lrrc31 , and akr1d1 ) .
gene ontology analysis indicated that the 5 genes were significantly associated with the regulation of transmembrane transport . however , no single gene showed good predictive power , but the 5-gene model of logistics regression had an accurate prediction at baseline , as well as at the early phrase of treatment . currently , most , if not all , therapeutic effect prediction studies focused on the predictive values of single genes rather than multiple genes .
our study indicated an excellent performance of joint prediction by multiple genes , which could also illuminate the low predictive values of differential genes in eligible studies .
first , we believe this is the first systematic study to identify the circulating biomarkers for predicting ra infliximab response through bioinformatics analysis of the microarray data from public databases .
second , a 5-gene model was established and showed good predictive power , and it was also applicable at the early phrase of treatment .
the models of fkbp1a , fgf12 , ano1 , lrrc31 , and akr1d1 in peripheral blood is useful for efficiently predicting the response to infliximab treatment in rheumatoid arthritis patients .
 backgroundinfliximab shows good efficacy in treating refractory rheumatoid arthritis ( ra ) . however , many patients responded poorly and related studies were inconsistent in predictive biomarkers . 
 this study aimed to identify circulating biomarkers for predicting infliximab response in ra.material/methodspublic databases of gene expression omnibus ( geo ) and arrayexpress were searched for related microarray datasets , focused on the response to infliximab in ra . 
 all peripheral blood samples were collected before infliximab treatment and gene expression profiles were measured using microarray . 
 differential genes associated with infliximab efficacy were analyzed . 
 the genes recognized by half of the datasets were regarded as candidate biomarkers and validated by prospective datasets.resultseight microarray datasets were identified with 374 blood samples of ra patients , among which 191 ( 51.1% ) were diagnosed as non - responders in the subsequent infliximab treatment . 
 five genes ( fkbp1a , fgf12 , ano1 , lrrc31 , and akr1d1 ) were associated with the efficacy and recognized by half of the datasets . 
 the 5-gene model showed a good predictive power in random- and prospective - designed studies , with auc ( area under receiver operating characteristic [ roc ] curve)=0.963 and 1.000 , and it was also applicable at the early phase of treatment ( at week 2 ) for predicting the response at week 14 ( auc=1.000 ) . in the placebo group , the model failed to predict the response ( auc=0.697 ) , indicating the model s specificity in infliximab treatment.conclusionsthe model of fkbp1a , fgf12 , ano1 , lrrc31 , and akr1d1 in peripheral blood is useful for efficiently predicting the response to infliximab treatment in rheumatoid arthritis . 