mouse luecs were immortalized with polyoma middle t antigen and cultured in mcdb131 medium ( gibco ; life technologies ) supplemented with 20% fbs ( invitrogen ) , 2  mm  l - glutamine ( lonza ) , 1  mm na - pyruvate ( gibco ; life technologies ) , 100  g / ml heparin ( sigma - aldrich ) , and 50  g / ml ec growth supplement ( ecgs ) obtained from calf brain . ecs were seeded on plates coated with glutaraldehyde - crosslinked gelatin and cultured in complete medium for 4  days to reach confluence . total rna was extracted from ~  5    10 cells using the rneasy mini kit ( qiagen ) following manufacturer 's instruction . quality control of the rna samples was performed using an agilent bioanalyzer 2100 ( agilent technologies ) . the rna from three independent extractions was processed for each of the cell line under analysis . a total of 150  ng of rna from each sample was used for rna quality check , labeling and hybridization on a mouse gene 1.0 st genechip array according to the manufacturer 's instructions ( affymetrix ) . three independent biological replicates were performed for each condition ( l1-transfected vs. mock - transfected luecs ) . we used the robust multi - array average ( rma )   to normalized data . a total of 35,512 probesets were loaded and 819,041  pm probes were used for analysis . next , we uploaded normalized data in brb arraytools ( http://linus.nci.nih.gov/brb-arraytools.html ) to run sam analysis ( http://www-stat.stanford.edu/~tibs/sam/ ) in order to identify differentially expressed genes upon l1 overexpression in luec cells . the sam parameters of analysis were the following : number of classes : 2number of probesets : 35,512target proportion of false discoveries ( q - value ) : 0.05delta value used to identify significant probesets : 1.24701fudge factor for standard deviation computed : 0.04825 . number of probesets : 35,512 target proportion of false discoveries ( q - value ) : 0.05 delta value used to identify significant probesets : 1.24701 fudge factor for standard deviation computed : 0.04825 . under these conditions , we identified a total of 3409 significant probesets corresponding to 2684 unique annotated genes ( fig .  1 ) . we then selected probesets having   1.5 fold change difference ( l1 vs. ctr ) that resulted in a set of 496 upregulated and 743 downregulated probesets annotated , corresponding to 361 upregulated and 580 downregulated unique genes , referred to as the l1-ecs signature. next , we uploaded the l1-ecs signature ( probeset level ) in the ingenuity pathway analysis software ( ipa , http://www.ingenuity.com ) to identify biological functions / pathways putatively regulated by l1 . the mouse gene 1.0 st array reference set present in the ipa database was used for the  ipa core analysis  . only direct relationships in mammals ( i.e. , human , mouse , and rat ) were considered , including endogenous chemicals for gene network analysis . the p - values for biofunction enrichment were corrected for multiple testing using the benjamini - hochberg correction . with these settings we identified 23 bio - functions significantly enriched ( p - value  <  0.05 ; benjiamini - hochberg correction ) in l1-overexpressing cells compared to mock - transfected cells . considering the massive changes in transcriptional activity and the predicted effect on a variety of biological functions upon l1 overexpression , we then asked whether l1 could activate / inhibit transcription factor(s ) ( tfs ) . to address this question such an analysis allows the identification of transcriptional regulators activated / inhibited under specific experimental conditions , thus accounting for the observed gene expression changes . strikingly , we identified a total of 18 and 11 tfs predicted to be activated or inhibited by l1 , respectively . of note , 5 tfs were differentially regulated in l1-overexpressing vs. mock - transfected cells and , in particular , four of the activated tfs were upregulated in l1 overexpressing cells ( stat1 , stat2 , irf7 and atf4 ) , while one of the inhibited tfs was downregulated ( foxm1 ) . we then performed the mechanistic networks analysis to further explore the contribution of the tfs in regulating gene networks . we found that stat1 , stat2 , irf7 , atf4 and stat3 ( that was among the 18 tfs predicted to be activated ) interact with each other ( fig . 2 ) , thus exerting a coordinated control on a directional network of 105 genes regulated by l1 ( i.e. 11% of all the l1-regulated genes ) . most of these genes were consistently up- or downregulated with the expression change of their upstream tfs . these findings were supported by functional studies which implicated the jak / stat signaling pathway in the biological response of luecs to l1 overexpression . mouse luecs were immortalized with polyoma middle t antigen and cultured in mcdb131 medium ( gibco ; life technologies ) supplemented with 20% fbs ( invitrogen ) , 2  mm  l - glutamine ( lonza ) , 1  mm na - pyruvate ( gibco ; life technologies ) , 100  g / ml heparin ( sigma - aldrich ) , and 50  g / ml ec growth supplement ( ecgs ) obtained from calf brain . ecs were seeded on plates coated with glutaraldehyde - crosslinked gelatin and cultured in complete medium for 4  days to reach confluence . total rna was extracted from ~  5    10 cells using the rneasy mini kit ( qiagen ) following manufacturer 's instruction . quality control of the rna samples was performed using an agilent bioanalyzer 2100 ( agilent technologies ) . the rna from three independent extractions was processed for each of the cell line under analysis . a total of 150  ng of rna from each sample was used for rna quality check , labeling and hybridization on a mouse gene 1.0 st genechip array according to the manufacturer 's instructions ( affymetrix ) . three independent biological replicates were performed for each condition ( l1-transfected vs. mock - transfected luecs ) . we used the robust multi - array average ( rma )   to normalized data . a total of 35,512 probesets were loaded and 819,041  pm probes were used for analysis . next , we uploaded normalized data in brb arraytools ( http://linus.nci.nih.gov/brb-arraytools.html ) to run sam analysis ( http://www-stat.stanford.edu/~tibs/sam/ ) in order to identify differentially expressed genes upon l1 overexpression in luec cells . the sam parameters of analysis were the following : number of classes : 2number of probesets : 35,512target proportion of false discoveries ( q - value ) : 0.05delta value used to identify significant probesets : 1.24701fudge factor for standard deviation computed : 0.04825 . number of probesets : 35,512 target proportion of false discoveries ( q - value ) : 0.05 delta value used to identify significant probesets : 1.24701 fudge factor for standard deviation computed : 0.04825 . under these conditions , we identified a total of 3409 significant probesets corresponding to 2684 unique annotated genes ( fig .  1 ) . we then selected probesets having   1.5 fold change difference ( l1 vs. ctr ) that resulted in a set of 496 upregulated and 743 downregulated probesets annotated , corresponding to 361 upregulated and 580 downregulated unique genes , referred to as the l1-ecs signature. next , we uploaded the l1-ecs signature ( probeset level ) in the ingenuity pathway analysis software ( ipa , http://www.ingenuity.com ) to identify biological functions / pathways putatively regulated by l1 . the mouse gene 1.0 st array reference set present in the ipa database was used for the  ipa core analysis  . only direct relationships in mammals ( i.e. , human , mouse , and rat ) the p - values for biofunction enrichment were corrected for multiple testing using the benjamini - hochberg correction . with these settings we identified 23 bio - functions significantly enriched ( p - value  <  0.05 ; benjiamini - hochberg correction ) in l1-overexpressing cells compared to mock - transfected cells . considering the massive changes in transcriptional activity and the predicted effect on a variety of biological functions upon l1 overexpression , we then asked whether l1 could activate / inhibit transcription factor(s ) ( tfs ) . to address this question such an analysis allows the identification of transcriptional regulators activated / inhibited under specific experimental conditions , thus accounting for the observed gene expression changes . strikingly , we identified a total of 18 and 11 tfs predicted to be activated or inhibited by l1 , respectively . of note , 5 tfs were differentially regulated in l1-overexpressing vs. mock - transfected cells and , in particular , four of the activated tfs were upregulated in l1 overexpressing cells ( stat1 , stat2 , irf7 and atf4 ) , while one of the inhibited tfs was downregulated ( foxm1 ) . we then performed the mechanistic networks analysis to further explore the contribution of the tfs in regulating gene networks . we found that stat1 , stat2 , irf7 , atf4 and stat3 ( that was among the 18 tfs predicted to be activated ) interact with each other ( fig . 2 ) , thus exerting a coordinated control on a directional network of 105 genes regulated by l1 ( i.e. 11% of all the l1-regulated genes ) . most of these genes were consistently up- or downregulated with the expression change of their upstream tfs . these findings were supported by functional studies which implicated the jak / stat signaling pathway in the biological response of luecs to l1 overexpression . here we describe the methods of analysis that were used to define and characterize the gene expression profile induced by l1 in murine lung endothelial cells ( luecs ) . we used the affymetrix mouse gene 1.0 st arrays , which enabled us to produce gene expression data of a total of 28,869 genes ( 764,885 distinct probes ) . through the combined use of the significant analysis of microarrays ( sam ) and the ingenuity pathway analysis ( ipa ) , we identified a set of 941 genes and 23 bio - functions significantly regulated in l1-overexpressing vs. control luecs . we reasoned that these massive transcriptional changes could be the result of the l1-mediated regulation of tfs . indeed , by using the  upstream modulator analysis  in ipa we found that 29 tfs were predicted to be activated / inhibited and , among these , 5 tfs were transcriptionally regulated in our profiling experiment . among the 29 tfs identified , many are involved in the inflammatory and/or angiogenic cascades ( stat1 , stat2 , stat3 , irf1 , irf3 , irf7 , rela / p65 , nfkb1 and atf4 ) . of note , while l1 has been causally linked to nf-b signaling in cancer cells , we are the first to report a crosstalk between l1 and stats , irfs and atf4 . our data , therefore , point to l1 as a novel orchestrator of the cancer - associated response that entails the regulation of gene networks and biochemical cascades which , in turn , impact on the pathophysiology of cancer cells as well as tumor microenvironment . importantly , the results of our in silico analysis have been successfully validated experimentally , thus lending further support to their biological relevance .