Zhang et al. J Transl Med (2020) 18:381 https://doi.org/10.1186/s12967-020-02561-z Journal of Translational Medicine

RESEARCH Open Access correlation network analysis to identify regulatory factors in sepsis Zhongheng Zhang1*† , Lin Chen2†, Ping Xu3†, Lifeng Xing1, Yucai Hong1 and Pengpeng Chen1

Abstract Background and objectives: Sepsis is a leading cause of mortality and morbidity in the intensive care unit. Regu- latory mechanisms underlying the disease progression and prognosis are largely unknown. The study aimed to identify master regulators of mortality-related modules, providing potential therapeutic target for further translational experiments. Methods: The dataset GSE65682 from the Omnibus (GEO) database was utilized for bioinformatic analysis. Consensus weighted gene co-expression netwoek analysis (WGCNA) was performed to identify modules of sepsis. The module most signifcantly associated with mortality were further analyzed for the identifcation of master regulators of transcription factors and miRNA. Results: A total number of 682 subjects with various causes of sepsis were included for consensus WGCNA analysis, which identifed 27 modules. The network was well preserved among diferent causes of sepsis. Two modules desig- nated as black and yellow module were found to be associated with mortality outcome. Key regulators of the black and light yellow modules were the transcription factor CEBPB (normalized enrichment score 5.53) and ETV6 (NES 6), respectively. The top 5 miRNA regulated the most number of were hsa-miR-335-5p= (n 59), hsa- miR-26b-5p= (n 57), hsa-miR-16-5p (n 44), hsa-miR-17-5p (n 42), and hsa-miR-124-3p (n 38). Clustering= analysis in 2-dimension= space derived from manifold= learning identifed= two subclasses of sepsis, which= showed signifcant association with survival in Cox proportional hazard model (p 0.018). = Conclusions: The present study showed that the black and light-yellow modules were signifcantly associated with mortality outcome. Master regulators of the module included transcription factor CEBPB and ETV6. miRNA-target interactions identifed signifcantly enriched miRNA. Keywords: Sepsis; intensive care unit, Gene co-expression netwoek, Mortality

Background few agents are proven to be efective for the treatment of Sepsis is defned as organ dysfunction syndrome caused sepsis. Tus, more regulatory factors need to be identi- by uncontrolled infammatory response to infection. Sep- fed to provide potential targets for the design of efective sis is a leading cause of mortality in hospitalized patients therapeutic agents. [1, 2], and accounts for 30% of case fatality in hospitalized Several studies have used transcriptome analysis to patients [3]. Despite the high mortality and morbidity, investigate potential biological pathways regulating the pathogenesis of sepsis [4–8]. Tese studies were per-

*Correspondence: [email protected] formed by diferential gene expression analysis, followed †Zhongheng Zhang, Lin Che and Ping Xu contributed equally to this work by enrichment analyses to established functional path- 1 Department of Emergency Medicine, Sir Run Run Shaw Hospital, ways. In these analyses, genes were tested individually. Zhejiang University School of Medicine, No 3, East Qingchun Road, Hangzhou 310016, Zhejiang Province, China Te sensitivity to identify biologically meaningful genes Full list of author information is available at the end of the article can be low due to multiple testing adjustment. Sepsis is

© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativeco​ ​ mmons.org/licen​ ses/by/4.0/​ . The Creative Commons Public Domain Dedication waiver (http://creativeco​ mmons​ .org/publi​ cdoma​ in/​ zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Zhang et al. J Transl Med (2020) 18:381 Page 2 of 15

a heterogeneous syndrome and its pathogenesis involves Consensus weighted gene co‑expression network analysis hundreds of genes. In this situation, the individual con- Te frst step in constructing a consensus WGCNA was tribution of a single gene is too small to be detected with to choose the soft threshoulding power to which co- univariate test. In most diseases, genes function via net- expression similarity was raised to calculte adjaency. We works of co-expressed genes with similar biological func- chose from a set of values from 4 to 20 based on the cri- tions. Tus, identifcation of co-expression pattern could terion of approximate scale-free topology [10]. Since the provide further insights into sepsis-associated biological topological overlap matrices (TOM) of diferent sepsis pathways. Weighted gene co-expression network analysis may have diferent statistical property, we performed (WGCNA) is a systems biology approach used for fnding quantile normalization over the three types of sepsis (e.g. gene clusters with highly correlated expression levels and pneumonia, abdomianl and other sepsis). Te consen- for relating them to phenotypic traits [9]. Rather than sus TOM was calculated by taking the component-wise relating thousands of genes to the clinical trait, WGCNA (“parallel”) minimum of the TOMs in individual dataset, focuses on the relationship between a few modules and which was then input to hierarchical clustering. Finally, the trait [10, 11]. To the best of our knowledge, WGCNA modules were identifed in the resulting dendrogram has been used to explore coexpression pattern in mouse using the Dynamic Tree Cut algorithm [19]. Tis algo- model of sepsis [12], HIV infection [13], in vitro infam- rithm has several advantages such as capability of identi- matory cells [14], and pediatric sepsis [15]. Regulatory fying nested clusters and fexibility. Modules with similar factors including transcription factors and miRNA were expression profles were merged at the threshold of 0.25. not systematically explored in adult sepsis. Gene signifcance was defned as the Student t-test sta- Te present study aimed to identify gene co-expression tistic for testing diferential expression between sepsis modules in sepsis by using the consensus WGCNA (con- and healthy controls. Te signifcance level was adjusted sensus from diferent causes of sepsis including pneu- for multiple testing with Bonferroni correction. Te monia and abdominal sepsis). Tese consensus modules dataset also contained critically ill patients such as those were related to clinical traits and enriched to functional with major abdominal surgery without infection. Tere biological pathways. Potential regulators of these mod- were common pathways between critical illness and ules were explored by using well curated databases. severe infection, the comparison between sepsis versus non-infectious critical illness would omit some impor- Materials and methods tant genes. Tus, the diferential expression was tested The GEO dataset and data preprocessing between sepsis versus healthy controls. Te study used the publicly avaiable dataset GSE65682 from the Gene Expression Omnibus (GEO) database. Te Relating consensus module to pneumonia‑specifc sepsis dataset contained 802 samples including healthy con- module trols, non-sepsis critically ill patients and sepsis patients. Futhermore, the sepsis patients could be further cat- Modules specifc to pnenomia sepsis were identifed by egorized into pneumonia sepsis (n 192), abdominal the method as described above. Pneumonia sepsis mod- = ules were then related to consensus modules. We calcu- sepsis (n = 51) and others (n = 443) based on infection site. PAXgene blood RNA was isolated at intensive-care lated the overlaps of each pair of pneumonia-consensus unit (ICU) admission and whole-blood leukocyte tran- modules, and used the hypergeometric test to assign scriptome was performed at the platform of Afym- a p-value to each of the pairwise overlaps. Tis is also etrix U219 Array. Key benefts of the known as the cross-tabulation based comparison of mod- gene chip include increased productivity and efciency ules. Tis method is justifed by the idea that if a module through parallel processing, excellent gene expression is well preserved and reproducible in all types of sepsis accuracy and reproducibility and complete coverage (pneumonia, abdominal and other sepsis), this mod- of the annotated genome. Further detials of the dataset ule could represent the common pathways involving the can be found at other publications [16, 17]. Raw inten- pathogenesis of sepsis [20]. sity expression data were preprocessed with the Robust Module preservation across all three datasets were Multi-array Average (RMA) method [18]. An advantage explored by pairwise comparing eigengene networks in of this method is that normalization occurs at the probe penumonia, abdominal and other sepsis. Mortality was level (rather than at the probeset level) across all of the added as an additional “eigengene”. Network preserva- selected hybridizations. Te maximum expression inten- tion is simply the diference between adjacencies in the sity was used when multiple probe sets mapped an indi- two compared sets [20]. A small diference of the adja- vidual gene symbol. Te quality of processed data were cency matrix between two sets indicate the modules are checked by using MA plot (Additional fle 1: Figure S1). well preserved between the two comparing sets. Zhang et al. J Transl Med (2020) 18:381 Page 3 of 15

Relating consensus modules to clinical traits Identifcation of miRNA‑target interactions Module eigengene was calculated for each module as the Te multiMiR package was employed for the retrieval of frst principal component of gene expressions for that miRNA-target interactions from 14 external databases module. Correlation analysis was performed to relate in R. Tese databases are comprehensive collections of module eigengene to external traits including age, gender, predicted and validated miRNA-target interactions and mortality and survival time (e.g. survivors were censored their associations with diseases and drugs [23]. Te mod- at 28 days). We combined three datasets into one and ule of interest was those associated with mortlaity out- performed correlation analysis. come. It was interesting to check whether some, or all, of Gene signifcance for mortality was correlated to the these genes within a module were targeted by the same module membership to investigate whether genes signif- miRNA(s). We restrited our search to the “mirtarbase” cantly associated with mortality outcome was also asso- table because this table included only experimentally val- ciated with module membership. Module membership idated miRNA-target interactions. (eigengene-based connectivity) for each gene was calcu- lated by correlating its gene expression profle with the Survival analysis module eigengene of a given module. For a given module, Te association of each module with the survival out- a module membership value of 0 indicates that a gene is come was determined by using Cox proportional model. not part of the module; whereas a module memberhsip of Te eigengene value of each module was added into the − 1 or 1 is highly connected to the module. Cox regression model for univariate analysis. Genes matched to the module with the strongest associa- tion with survival were used to cluster patients into two Enrichment analysis for biological function groups using reversed graph embedding (DDRTree), and transcription factors which projects data into a reduced dimensional space Modules associated with important clinical trait such as while constructs a principal tree which passes through mortality were further analyzed for their enrichment in the middle of the data simultaneously [24]. Samples were (GO) pathways [21]. Specifcally, the gene clustered into two groups using k-means clustering. Sur- set from a given module were enriched to GO terms to vival probablity of the two groups were comapred using fnd whether some of functional GO terms are over-rep- the log- test. resented using annotations for that gene set. Upset plot was employed to display overlapped genes among difer- Results ent GO terms. Dotplot shows the gene ratio and adjusted Demographic data p values for each enriched GO terms. Enriched terms A total of 802 subjects were initially identifed from the were organized into a network with edges connecting GEO database. 116 subjects were excluded because they overlapping gene sets. In this way, mutually overlapping were either healthy or non-infectious controls, and 4 gene sets are tend to cluster together, making it easy to subjects were excluded because they were outliers (Addi- identify functional modules. Te category netplot depicts tional fle 1: Figure S2). As a result, a number of 682 sub- the linkages of genes and GO terms as a network, which jects were included in our analysis. Tese subjects were is helpful to see which genes are involved in enriched classifed by the infection site into pneumonia sepsis, pathways and genes that may belong to multiple annota- abdominal sepsis and unselected sepsis (other sepsis). tion categories. Demographic data were comparable between the three Modules (gene lists) signifcantly correlated with the groups (Table 1). Te median age was 63 years (IQR: 53 mortality trait were tested for its over-representation to 72), and 58% (394/682) patients were male. Te overall in transcription factor (TF) binding motifs by using 28 day mortality rate was 17% (113/682). RcisTarget [22]. Two types of databases (i.e. Gene-motif rankings and the annotation of motifs to transcription Consensus Network construction and module detection factors) were employed in the analysis: Gene-motif rank- A soft-thresholding power of 6 was used to obtain ings which provides the rankings of all the genes for each approximate scale-free topology for the network (Addi- motif and the annotation of motifs to transcription fac- tional fle 1: Figure S3). Consensus WGCNA identifed tors. Parameter settings for the score of each pair of gene- 27 modules (Fig. 1). Gene diferential expression analysis motif were: species Homo sapiens, Scoring/search = between healthy controls and sepsis showed that genes space 500 bp uptream the transcription start site (TSS), = such as ARG1, CD177, MMP8 and C19orf59 were upreg- Number of orthologous species 10. Te annotation of = ulated. Up-or down-regulation of modules were deter- motifs to transcription factors was performed using the mined by the median of gene signifcance T value and motifAnnotations_hgnc (’mc9nr’, 24,453 motifs). Zhang et al. J Transl Med (2020) 18:381 Page 4 of 15

Table 1 Demographic data on the study population stratifed by the causes of sepsis Variables Total (n 682) Abdominal sepsis Other sepsis (n 442) Pneumonia sepsis p = (n 48) = (n 192) = = Age, median (IQR) 63 (53, 72) 64 (54, 68.75) 63 (53, 73) 63 (53, 72) 0.977 Sex, n (%) 0.538 Female 288 (42) 23 (48) 189 (43) 76 (40) Male 394 (58) 25 (52) 253 (57) 116 (60) Follow up days, Median (IQR) 28 (28, 28) 28 (28, 28) 28 (20.5, 28) 28 (28, 28) 0.176 Mortality, n (%) 113 (17) 7 (15) 66 (15) 40 (21) 0.172

IQR interquartile range;

Consensus gene dendrogram and module colors .0 0. 91 .8 Height 0. 70 .6 0. 50

Module colors consTomDS fastcluster::hclust (*, "average") Fig. 1 Consensus weighted gene coexporession network analysis. Modules were identifed in the resulting dendrogram using the Dynamic Tree Cut algorithm. A total of 27 modules were identifed. Modules were distinguished from each other by assigning diferent colors

fold changes. For instance, the black model was signif- a consensus counterpart (Fig. 4). Te black color module cantly upregulated with median T value > 3 and log2 fold in the consensus analysis corresponds to the red module change > 0.5 (Fig. 2). in the pneumonia specifc set analysis (275 genes overlap Consensus eigengene networks and their diferential with signifcant p value). analysis are shown in Fig. 3. Te results showed that the eigengene networks in the three datasets were well pre- Relating Consensus modules to external clinical traits served. To explore whether modules identifed in the Te correlation between module eigengene and clini- pneumonia sepsis could also be identifed in consensus cal traits were explored by Pearson’s correlation analysis modules, the correspondence of pneumonia set specifc (Fig. 5). Te black module was signifcantly associated and consensus modules was explored (Fig. 4). Te result with mortality with higher module express correlated indicates that most peumonia set-specifc modules have to lower mortality rate (coefcient = -0.16; p < 0.001). Zhang et al. J Transl Med (2020) 18:381 Page 5 of 15

a 40

20 alue)

0 Gene Significance (T v

−20

w a lue ey an ow ange ello k lue wn an r kred y ey y60 ell ple d lb ow lue o kgreen kgr o n y ange re tan ell black b br cy green gr r pin pur ya y dar dar rkturquoise gre lightcy magent o ro salmon dar dark lightgreen light turquoise da gree midnightb Module Color

bcyellow turquoise Volcano plot tan EnhancedVolcano salmon royalblue red NS Log2 FC p−value p − valueand log2 FC purple pink orange midnightblue S100A8 magenta 150 lightyellow S100A12 lightgreen

lightcyan P C19orf59 grey60 100 NMT2

10 ANXA3 P2RY10 ARG1

Module Color C5orf32

grey g CD96 PTCH1 CD160 PAFAH2 TXN greenyellow NELL2 Lo TIGIT ZAP70 MAFG CNIH4 VNN1 green KLRG1 IL11RA MMP9 − CD247 SRPK1 CLEC4D NCALD FCAR darkturquoise FCRL6 LPIN1NONO CCR3PRKCH TAF15 USP32 LIN7A RGL4 CD177 50 LEF1 GMFG S100P ANKRD22 darkred ITK HNRNPA0 BMX SPON2 ITGA4ZFP62 GPR97 GPR84 FGFBP2SGK1 PAICS MGAM ASPH MMP8 darkorange LIME1 EXOC6FOLR3 LCN2 TCL1A C14orf101 BPI XPC XK OLFM4 darkgrey SULF2CLIP4 AZI2 ITGA2B IFIT1 ANKH TTC35CEACAM6 darkgreen cyan 0 brown −2.5 0.02.5 5.0 blue Log fold change black 2 −2 024 Total = 11222 variables log2 Fold Change for Spesis vs. Healthy Controls Fig. 2 Gene diferential expression analysis (sepsis against healthy control) across modules. a Boxplot of the distribution of signifcance T values. The black and darkorange modules showed the most signifcant diference between sepsis versus controls. The red dashed lines indicate statistical signifcance level of two-tailed T test that have been adjusted by Bonferroni method; b fold changes of the diferential expression across modules. The darkorange and black modules were among the most up-regulated modules; and c volcano plot showing diferentially expressed genes between sepsis and healthy controls. Genes such as ARG1, CD177 and MMP8 were most diferentially expressed

However, the light-yellow module was positively associ- mediated immunity, leukocyte degranulation ated with mortality (coefcient = 0.14; p < 0.001). Te and myeloid cell activation involved in immune response black module was positively associated with survival time (Fig. 6). Te light-yellow module was enriched in biologi- (coefcient = 0.16; p < 0.001). Te correlations between cal pathways such as , nucleobase-containing gene signifcance for mortality and module membership compound catabolic process, heterocycle catabolic pro- were explored and the results showed that there was a cess and cellular nitrogen compound catabolic process highly signifcant correlation between module member- (Fig. 7). ship and gene signifcance for mortality in three modules inclusing the light-yellow, ligh cyan and pink modules Enrichment analysis for transcription factors (Additional fle 1: Figure S4). Modules were made up of co-expressed genes, indicat- ing that they were regulated by common mechanisms Module biological function such as the transcription factors. Tus, we performed Te most enriched pathways of the black mod- enrichment analysis for transcription factors. Te enrich- ule included myeloid leukocyte mediated immunity, ment analysis involved three steps: (1) motif enrichment Zhang et al. J Transl Med (2020) 18:381 Page 6 of 15

Pneumonia Sepsis Abdominal Sepsis Other Sepsis .2 .2 1. 2 0. 8 0. 81 0. 81 alit y alit y .4 .4 .4 Mo rt MEorange MEta n alit y Mo rt ue MEta n ue lu e MEorange lu e ue e lu e MEta n lb e 60 lb ll ow e n lb Mo rt n MEmagent a MEmagent a n ll ow ya ya MEmagent a ye MEorange y ME bl an y y n n 0. 00 ME bl an ll ow ya rang kred 0. 00 kred an ME bl n ye 0. 00 ue 60 60 ll ow ll ow an ac k MEred ac k kred rang an pl e ac k ll ow ll ow MEgreen ue ko orang MEgreen ue an bl pl e pl e ll ow kgreen ye turquoise ll ow kgre kturquoise kgre kgre turquoise bl ko MEro bl MEgr ey green MEro green MEred MEro MEcy ye ME ye MEbr ow MEbr ow MEpink ME bl MEgreen ME bl MEcy MEpink MEred MEdar MEdar ME bl MEpink MEsalmon MEbr ow ME ye MEcy MEdar MEsalmon MEgreen MEsalmon MEgr ey MEgr ey MEpur MEdar MElightcy ME ye MEturquoise MEturquoise MEpur MEpur MEgreen MEdar MEda rk MElightcy MEturquoise MEdar MEdar MElightgree MEdar MEdar MEdar MElight ye MEda rk MElightcy MElightgree MEda rk MElight ye MEda rk MEgreen MElightgree MEda rk MElight MEmidnight MEmidnight MEmidnight Pneumonia Sepsis D= 0.94 D= 0.96 .0 1 1. 0

Mortality .8 0.8 0. 81 .6

0.6 0. 60

0.4 .4 0. 40

0.2 .2 0. 20 0 0. 00 0. 0

rtality Mo Mortality

Mortality

Preservation Abdominal Sepsis D= 0.94 .0 1 1 Mortality Mortality 0.9 0.8 0. 81

0.8 0.6 .6

0.7 0.4 0. 40

0.6 0.2 .2

0.5 0 0. 00

tality Mor ality ality rt Mort Mo

Preservation Preservation Other Sepsis 1 1 1 Mortality Mortality Mortality 0.9 0.9 0.8

0.8 0.8 0.6

0.7 0.7 0.4

0.6 0.6 0.2

0.5 0.5 0

ality ality rt rt rtality Mo Mo Mo Fig. 3 Summary plot of consensus eigengene networks and their diferential analysis. The top 3 panels show the dendrograms (clustering trees) of the consensus module eigengenes in the 3 datasets of pneumonia, abdominal and other sepsis. Below, the eigengene networks in the three sets are shown as heatmaps labeled penumonia sepsis, obdominal sepsis and other sepsis. In the heatmaps, red denotes high adjacency (positive correlation) and blue denotes low adjacency. The Preservation heatmap shows the preservation network, defned as one minus the absolute diference of the eigengene networks in the two datasets. The barplot shows the mean preservation of adjacency for each of the eigengenes to all other eigengenes; in other words, the barplot depicts the column means of the preservation heatmap

analysis with cumulative recovery curves (Additional motif dbcorrdb__CEBPB__ENCSR000BQI_1__m1. A fle 1: Figures S5, S6 and S7), (2) motif-TF annotation, total of 93 genes in the black module was enriched in this and (3) selection of signifcant genes. Te results showed motif. Te normalized enrichment score (NES) was 5.53. that the transcription factor CEBPB was the master regu- Tis motif was directly annotated to the transcription lator for the black module, which was annotated to the factor CEBPB which is an important transcription factor Zhang et al. J Transl Med (2020) 18:381 Page 7 of 15

Correspondence of Pneumonia set−specific and Consensus modules

Pneu magenta: 243 214 200100400000000100110 0000010 50 Pneu blue: 1840 4 1636 13 010013 0160 00002113 1632 0560 7111 Pneu midnightblue: 48 0402 020000000000000000011002 Pneu lightcyan: 44 010330 0 020000000000040020002 Pneu turquoise: 2629 19 370 0513 30110 21 23 01220311 38 13 1722 63 21713 132 10 36 40 Pneu grey60: 33 00000000000000026 30112000000 Pneu red: 570 0900100807000103275 12 30 64 28 0 120 00210 Pneu pink: 289 2190 0000951000000000280156 15 020133 30 Pneu yellow: 1341 145441 122195 101050480229911 754 92 88 193 24 Pneu lightgreen: 31 0000700001000000171 000000113 Pneu cyan: 66 0000300611 2440 000000000000000 20 Pneu tan: 173 1000160 0 031500000000020000001 Pneu brown: 1835 2760 2811 16 1131106 57 3012 600695 3 264 0015 10714 Pneu salmon: 126 030024147133 300000003010030016 Pneu black: 350 0181 0217 1245271 00000043000000004 10 Pneu green: 779 242023115 390 293 100800200110 1100 0133 Pneu greenyellow: 226 2200000231 17 090303 0 0600100 0203424 Pneu purple: 230 1000000142 2010109 54 0100312100012 0 Pneu grey: 369 1221 020021 12 7011200100660 1251 02203

9 2 5 9 3

lue: 63 w: 205 ow: 67 y60: 94 an: 136 an: 105 llo ell e grey: y45 ack: 346 w: 1021 rple: 223 wn: 1381 rk c rkred: 54 blue: 108 yalb orange:bl 38 rkgreen: 50 nye rk yello Cons red: 63kturquoise: 48 Cons tan: 17 Cons Cons pink: 251Cons grey: 428 Cons blue: 2247Cons orange: 41 Cons green:Cons 706 gr Cons Cons pu Cons ro Cons bro Cons da Cons da Cons salmon: 15 Cons Cons magenta: 24 Cons Conslightgreen: lighty 77 Cons da Cons lightcy Cons da Cons turquoise: 227 Cons gree Cons dar Cons midnight Fig. 4 Correspondence of pneumonia set-specifc modules and the consensus modules. Each row of the table corresponds to one pneumonia set-specifc module (labeled by color as well as text), and each column corresponds to one consensus module. Numbers in the table indicate gene counts in the intersection of the corresponding modules. Coloring of the table encodes log(p), with p being the Fisher’s exact test p value for − the overlap of the two modules. The stronger the red color, the more signifcant the overlap is. The table indicates that most peumonia set-specifc modules have a consensus counterpart

regulating the expression of genes involved in immune fle 4: Supplemental Digital Content (SDC) 4]. Te top and infammatory responses [25–27]. All enriched motifs 5 miRNA regulated the most number of genes were and corresponding transcription factors for the black hsa-miR-335-5p (n = 59), hsa-miR-26b-5p (n = 57), hsa- module were shown in Additional fle 2: Supplemen- miR-16-5p (n = 44), hsa-miR-17-5p (n = 42), and hsa- tal Digital Content (SDC) 2. Te network of the top 3 miR-124-3p (n = 38). A network connecting miRNA and enriched motifs and corresponding genes are shown in target genes are shown in Additional fle 1: Figure S9. Additional fle 1: Figure S8. For the light-yellow module, we identifed 893 miRNA Te transcription factor ETV6 was the master regula- that were potential regulators of the module [Additional tor for the light-yellow module, which was annotated to fle 5: Supplemental Digital Content (SDC) 5]. Te top 5 the motif taipale__ETV6_full_CCG​GAA​SCGG​AAG​TN_ miRNA regulated the most number of genes were hsa- repr and cisbp__M5425. A total of 12 genes in the light- miR-16-5p (n = 14), hsa-miR-92a-3p (n = 12), hsa-miR- yellow module were enriched in the motifs. Te NES was 26b-5p (n = 9), hsa-miR-615-3p (n = 9), and hsa-let-7b-5p 6 and 5.98 for these two motifs [Additional fle 3: Supple- (n = 8). mental Digital Content (SDC) 3]. Survival analysis Identifcation of miRNA‑target interactions To further validate that the black module was related to From the “mirtarbase” table with validated miRNA-tar- the mortality outcome, the high-dimension space was get interactions, we identifed 1,981 miRNA that were reduced to lower dimension space by using reversed potential regulators of the black module [Additional graph embedding (DDRTree). Te method provided Zhang et al. J Transl Med (2020) 18:381 Page 8 of 15

Consensus Module−trait relationships MEmagenta 0.081 (0.03) −7e−07 (1)−0.019 (0.6) 0.051 (0.2) MEblue 0.038 (0.3) −0.067 (0.08) 0.052 (0.2)0.095 (0.01) 1 MEroyalblue 0.0043 (0.9) −0.086 (0.02) 0.055 (0.2)0.0012 (1) MEorange −0.036 (0.4) −0.082 (0.03) 0.081 (0.03) −0.039 (0.3) MEgreenyellow −0.049 (0.2) −0.072 (0.06) 0.072 (0.06) −0.033 (0.4) MElightgreen −0.033 (0.4)0.034 (0.4)−0.034 (0.4) −0.11 (0.003) MElightyellow −0.086 (0.03) −0.13 (4e−04) 0.14 (2e−04) −0.086 (0.02) MEbrown 0.028 (0.5)0.0028 (0.9)0.033 (0.4)−0.098 (0.01) 0.5 MEred −0.055 (0.1)0.028 (0.5)−0.042 (0.3) −0.13 (5e−04) MEgreen −0.064 (0.1) −0.03 (0.4)0.029 (0.4)−0.086 (0.02) MEdarkturquoise −0.025 (0.5)0.014 (0.7)−0.015 (0.7) −0.053 (0.2) MEgrey60 −0.057 (0.1) −0.059 (0.1)0.061 (0.1)0.046 (0.2) MEdarkgrey −0.031 (0.4) −0.064 (0.1)0.067 (0.08) −0.035 (0.4) MEcyan −0.026 (0.5) −0.071 (0.06) 0.03 (0.4) −0.018 (0.6) 0 MEdarkred −0.017 (0.7)0.015 (0.7)−0.0053 (0.9)−0.018 (0.6) MEdarkorange −0.017 (0.7) −0.015 (0.7)0.014 (0.7)0.1 (0.009) MEblack −0.026 (0.5)0.16 (3e−05)−0.16 (5e−05)−0.015 (0.7) MEsalmon −0.043 (0.3)0.097 (0.01) −0.11 (0.003) −0.052 (0.2) MEdarkgreen 0.06 (0.1)0.09 (0.02) −0.098 (0.01)0.012 (0.8) MEturquoise −0.035 (0.4)0.071 (0.06) −0.086 (0.02)−0.11 (0.003) −0.5 MElightcyan −0.004 (0.9)0.14 (2e−04)−0.15 (1e−04)−0.1 (0.007) MEtan −0.00027 (1)0.15 (5e−05)−0.079 (0.04)−0.074 (0.05) MEyellow 0.0049 (0.9)0.1 (0.007) −0.072 (0.06)−0.0026 (0.9) MEmidnightblue 0.014 (0.7)0.11 (0.004)−0.085 (0.03)0.013 (0.7) MEpink 0.021 (0.6)0.14 (2e−04)−0.12 (0.002) −6e−04 (1) MEpurple 0.013 (0.7)0.14 (2e−04)−0.1 (0.007)0.016 (0.7) −1 MEgrey −0.014 (0.7)0.099 (0.01) −0.13 (5e−04)−0.071 (0.06)

x rt Se Age Time Mo Fig. 5 Consensus module-trait relationship. Each module was represented by its eigengene value and correaltion analyses between each of the eigengene values and clinical traits were performed. The frst number in the matrix cell represents the corelation coefcient and the number in the parenthesis is the p value for the corelation. The black module was negatively correlated to the mortality and the light-yellow module was positively related to the mortality

non-linear dimension reduction that retains the intrin- Discussion sic structure of the sample data. Te silhouette method Te study employed consensus network analysis to iden- showed that 2-cluster would be the optimal number tify gene co-expression modules. Tese modules were (Fig. 8a). In the 2-dimension space (Fig. 8b), the samples involved in distinct biological functions and were associ- were classifed into two clusters. Heatmap of all module ated with clinical traits. Regulatory mechanisms of some genes showed that the two clusters could be well sepa- important modules involving transcription regulators rated (Fig. 8c), which supported that the intrinsic struc- and miRNA were explored by validated databases. Con- ture was well preserved with the dimension reduction. sistent with previously published studies employing high- Te two clusters were signifcantly associated with sur- throughput dataset to examine sepsis, these modules vival outcome (p = 0.018 in the Cox proportional haz- were enriched for pathways related to immune response ard model, Fig. 8d). Cluster 2, as compared with Cluster [28, 29]. Te black module was signifcantly associ- 1, was characrized by activated functions including ated with survival outcome and the transcription factor myeloid cell activation, cell activation, leukocyte activa- CEBPB was the master regulator of this module. Several tion and myeloid leukocyte activation (Fig. 9). miRNAs including hsa-miR-335-5p, hsa-miR-26b-5p, Te light-yellow module was also related to the mor- hsa-miR-16-5p, hsa-miR-17-5p and hsa-miR-124-3p tality outcome. Te silhouette method showed that were identifed to be important regulators of the gene 2-cluster would be the optimal number (Fig. 10a). Te expressions in the module. study population could be classifed into two clusters in Our analysis has several implications for research and a 2-dimension space. Heatmap (Fig. 10b, c) showed that clinical practice. First, sepsis is shown to have a dysregu- the study population could be well separated into two lated immune response highlighted by the upregulation clusters. Cluster 2 showed signifcantly lower survival of the black module involving biological functions such probability than cluster 1 (p = 0.01, Fig. 10d). as myeloid leukocyte mediated immunity, neutrophil Zhang et al. J Transl Med (2020) 18:381 Page 9 of 15

a b 25 GO Biological Pathways myeloid leukocyte mediated immunity 20 neutrophil mediated immunity leukocyte degranulation 15 myeloid cell activation involved in immune response Count neutrophil degranulation 10 10 neutrophil activation involved in immune response 20

5 neutrophil activation 30 granulocyte activation 40 0 regulation of secretion by cell 50 myeloid leukocyte mediated immunity regulation of vesicle-mediated transport leukocyte degranulation positive regulation of secretion p.adjust myeloid cell activation involved in immune response positive regulation of secretion by cell neutrophil mediated immunity negative regulation of cytokine production granulocyte activation 0.005 defense response to bacterium neutrophil activation 0.010 neutrophil activation involved in immune response regulation of endocytosis 0.015 neutrophil degranulation regulation of interleukin-6 production positive regulation of secretion interleukin-6 production 0.020 positive regulation of secretion by cell negative regulation of interleukin-6 production negative regulation of cytokine production defense response to bacterium NAD metabolic process regulation of endocytosis glucose catabolic process regulation of interleukin-6 production 0.05 0.10 0.15 negative regulation of interleukin-6 production GeneRatio

c glucose catabolic process d LTF ARL8A

SIGLEC9 DNASE1L1 STXBP2 NAD metabolic process LRG1 GPR84 HP LAIR1 SERPINB1 S100P CHIT1 STOM GPI p.adjust MMP8 RAB27A FCER1G neutrophil activation 0.005 FGR S100A12 granulocyte activation 0.010 neutrophil activation involved in immune response BPI RETN neutrophil mediated immunity 0.015 WDR1 size OLFM4 myeloid leukocyte mediated immunity neutrophil degranulation 0.020 neutrophil activation involved in immune response RAB10 48 regulation of endocytosis leukocyte degranulation CD63 neutrophil mediated immunity size neutrophil activation S100A8 defense response to bacterium granulocyte activation regulation of vesicle-mediated transport 10 ARG1 neutrophil degranulation CRISP3 49 myeloid cell activation involved in immune response 20 TSPAN14 regulation of secretion by cell NCKAP1L 30 CEACAM1 positive regulation of secretion 40 HK3 OSCAR positive regulation of secretion by cell 50 RAB4B PGLYRP1 QSOX1 GYG1 interleukin-6 production CKAP4 ANXA3 regulation of interleukin-6 production negative regulation of cytokine production PLD1 SVIP CLEC5A CTSD GLA TCN1 PLAC8 negative regulation of interleukin-6 production DBNL LCN2 CD177 Fig. 6 GO enrichment analysis for the black module. a The upset plot showing overlap of genes across enriched biological pathways. Biological pathways such as myeloid leukocyte mediated immunity, leukocyte degranulation and neutrophil mediated immunity shared large number of genes. b Dot plot showing the ordered enrichment biological pathways; the most enriched pathways included myeloid leukocyte mediated immunity and neutrophil mediated immunity. c Enriched terms were organized into a network with edges connecting overlapping gene sets. In this way, mutually overlapping gene sets are tend to cluster together, making it easy to identify functional modules; and d The category netplot depicts the linkages of genes and GO terms as a network, which is helpful to see which genes are involved in enriched pathways and genes that may belong to multiple annotation categories. The most important biological pathways in the module involves the immune response, including the immune response including myeloid leukocyte mediated immunity, neutrophil mediated immunity, leukocyte degranulation and myeloid cell activation

mediated immunity, leukocyte degranulation and mye- cannot be extrapolated to adult sepsis [33]. Te present loid cell activation involved in immune response. Immune study employed WGCNA to identify modules associated dysfunction has long been recognized as an important with mortality by treating co-expressed genes as a mod- mediator of sepsis [4, 30, 31]. However, previous studies ule. Te idea is that genes are working together to take mostly analyzed high-throughput data at individual gene their functions in disease processes. Second, by focusing level involving diferential expression analysis followed by on modules most signifcantly associated with mortality, functional pathway enrichment [4, 15, 31, 32]. In neonate we identifed several important regulators including tran- sepsis, Meng and colleagues identifed 7 hub genes in key scription factors and miRNAs. Te results support the pathways. However, the study did not relate these gene hypothesis that co-expressed genes are very likely to be expression profling to clinical outcomes and the results regulated by common factors. Tese transcription factors Zhang et al. J Transl Med (2020) 18:381 Page 10 of 15

a b GO Biological Pathways 15 translation nucleobase-containing compound catabolic process heterocycle catabolic process cellular nitrogen compound catabolic process 10 aromatic compound catabolic process

organic cyclic compound catabolic process p.adjust mRNA catabolic process 3e-11 5 RNA catabolic process 6e-11 nuclear-transcribed mRNA catabolic process 9e-11 nuclear-transcribed mRNA catabolic process, nonsense-mediated decay viral transcription 0 Count viral gene expression 15 translation nucleobase-containing compound catabolic process SRP-dependent cotranslational targeting to membrane 20 25 mRNA catabolic process cotranslational protein targeting to membrane RNA catabolic process 30 nuclear-transcribed mRNA catabolic process protein targeting to ER nuclear-transcribed mRNA catabolic process, nonsense-mediated decay establishment of protein localization to endoplasmic reticulum viral gene expression viral transcription protein localization to endoplasmic reticulum cotranslational protein targeting to membrane establishment of protein localization to endoplasmic reticulum protein targeting to localization to endoplasmic reticulum translational initiation protein targeting to ER protein targeting to membrane cytoplasmic translation SRP-dependent cotranslational protein targeting to membrane 0.20.3 0.40.5 translational initiation GeneRatio c d SRP-dependent cotranslational protein targeting to membrane MRPL40 MRPS23 RWDD1 MRPL35 nuclear-transcribed mRNA catabolic process, nonsense-mediated decay RPL26L1 RPL22L1 protein targeting to membrane translation

MRPL47 ZC3H15

cotranslational protein targeting to membrane establishment of protein localization to endoplasmic reticulum size RNA catabolic process MRPL32 15 cytoplasmic translation 20 MRPL13 RPS17 size 25 RPS15A RPL31 translational initiation translation 16 30 RPL17 heterocycle catabolic process RPL34 RPL9 HSPA14 21 p.adjust mRNA catabolic process 25 RPL23 3e-11 nucleobase-containing compound catabolic process RPL26 30 6e-11 EEF1B2

protein targeting to ER 9e-11 RPS24 cellular nitrogen compound catabolic process RPL36A nuclear-transcribed mRNA catabolic process, nonsense-mediated decay MAGOH organic cyclic compound catabolic process RPS27L RPL27 aromatic compound catabolic process protein targeting to ER cotranslational protein targeting to membrane RPL7 SRP-dependent cotranslational protein targeting to membrane RPL41 nuclear-transcribed mRNA catabolic process viral gene expression RPL39 protein localization to endoplasmic reticulum RPS7 viral transcription RPS3A Fig. 7 GO enrichment analysis for the light-yellow module. a The upset plot showing overlap of genes across enriched biological pathways; b dot plot showing the ordered enrichment biological pathways; c Enriched terms were organized into a network with edges connecting overlapping gene sets. In this way, mutually overlapping gene sets are tend to cluster together, making it easy to identify functional modules; and d The category netplot depicts the linkages of genes and GO terms as a network, which is helpful to see which genes are involved in enriched pathways and genes that may belong to multiple annotation categories. The most important biological pathways in the module involves the translation and catabolic processes, including nucleobase-containing compound catabolic process, heterocycle catabolic process and cellular nitrogen compound catabolic process

(such as CEBPB and ETV6) and miRNAs are potential various causes of sepsis, indicating common pathways targets for the treatment of sepsis. Since the upregula- associated with the sepsis. Furthermore, the module net- tion of the black module is signifcantly associated with work constructed by TOM is also well preserved across mortality, drugs targeting these sites may improve the various causes of sepsis. Collectively, these fndings sup- survival outcome. Tird, although sepsis is a heteroge- port the notion that sepsis can be considered as a clinical neous syndrome [34–38], diferent types of sepsis share symdrome because various causes of sepsis lead to com- some common important immune regulatory pathways. mon pathways. Drugs designed to target these common Our study employed consensus WGCNA and examined pathways can be helpful in improving clinical outcomes. whether the module identifed in one cause of sepsis For example, our study showed that cluster 2 identifed can also be found in other causes of sepsis. Te results by the black module was associated with lower survival show that most modules are well preserved despite the probability, and this subtypes of sepsis was characterized Zhang et al. J Transl Med (2020) 18:381 Page 11 of 15

abOptimal number of clusters DDRTree reduced dimension 0.6

Cluster

10 1 2

0.4 2

0 Component

erage silhouette width 0.2 Av

−10

0.0

12345678910 −140 −130 −120 −110 −100 Number of clusters k Component1 cd

Clusters Clusters Strata + Cluster 1 + Cluster 2 PLS100AAC88 C19orf5TPMFGRCD63C1orf162PLP23 9 12 1 IL1R2TSPFLCST7SERPINB1LDHAOTO1 MYL12AGNG5PGK1S100PS100A12C5orf32 CD300LBCCR3FCER1GALGAPDACTBOX5AH P 2 RAI1EHMTTRAF3IP2SSBPTTYHSLC37A22 2 1.0 SYT1RBKSFDHCR7ALGALSAM110BTP2C21 2 10 SULOH12CR1C2orf6SGSHSIL1CMTMOX 40 NFKBIL1FBXL19C8orf7ABCC1MPZDLC1 30 MUSTN1MASTLOC17122EMLPNPLA1RPS6KA2 4 2 0 SLC29AZNF697CD4SYNJ2BPHF1TIGD3 3P BTBD11TNMYNTSR1FFZCCHC24ARFAO10IP8L33 8 KLF1FBNTRIOMAP3K1RPGRIPNOV14 14

PTGR1 y IL10HBXIPROMOSPDCYYRTTNM11 3 CHKTMEM169PNPLA6LOC100129827METTL7BFAM20AA + ZNF784CYB5D2CHMP6GPPLD1DPP3AA1 PPM1NITGATLEEPVPS33BTBX19AS171 6 0.9 COL17ACHIT1FCYP19APCOLCECCNAAM89A1 12 ASFXNOSNEILDUSP1TFFTP9Ta31Alpha13 + TMEM110SFT2D2TTC35KCNEHGFENTPD71 PSMETBC1DGYS1AIG1KIF3C17orf6C370 CPNETDRD9C9orf103ANKRD34BDDCINPA1 5 4 + MARCKSLCNNMSLC12ACLEC5ACDST6GALNAADC13 7 1C3 BBS1HYMAP4TBC1D13PKM2MLYCAL2D ASB1GFOD2MAPKAP1 al probabilit STXBPPECRDYCAPNSAPSAGRKXT2L2342 C7orf4RPL13PC9orf167CYTSA9 5 2 iv VSTMTP53IVWPLB1CDKN2CTRNAA53U1A1A P FBACHST2CISHSVIPSUSDAM177ATF 3 1 0.8 NDUGBSIGLECPPGRPELNDST2A2AFAF119 p = 0.018 + C9orf4KIAA1715CORANKRD5TRPV2NAPRO2AT16 5 FIG4 Su rv ACSNX2SUSDSGK269PTPN22CWF19L1OT081 OPLALIN7ATRPS1GOLGAC2orf5GABBR1H61 ARL8ASNX1TBC1D20CSNK2AACMEISS1 2 1 ZDHHC19CRISPBPFEXOCNAAM127AT15I 43 EMILIN2LAIRMS4A4SMPDL3APFKFB2GPR814A PGEXOSC4CHCHD7RAB2EMRGALNT2LY1RP0 1 ZDHHC3MSRRABGEFHIPKGPFAR2I A2 1 DNAJCC22orf9BLOC1SPPM1MGLLRT25D1PAP141 CARDTIMAP2K6ACMACD300LFPAERCF1RP36 0.7 DDHIP1PPP1R3C20orf177C14orf101NLRC4AH2 D PLEKHOSLC25A28CESESNJMJDBMXAC62AM21 1 CDC37YIPFARHGDIQSNADSYNJOSDOX511 A1 0481216202428 TACYB5R1TTC9CNANSRCCDC53UNXDA31 ASH2RAB4PDLIM5OSCADNASE1L1CTSDLBR PACAOLFMSDF2INTSNOL7PSS14 64 Time in days TINFPTPNPCMTCFL1C19orf3DBNL211 8 SDHCMFTSG10GSTPST2RAB1FR 01 DCTN2C9orf8SPINT2CORRAB3FERMTO1C293 PCYT1TPSMCPEF1MAFGLAOR1AG1A Number at risk TBC1DCD177WIPI1SCPEP1NCKAP1COL4A3B8 LP CDKN2DPADHRSANKRD2UGCGCEDIAC4AM19 2 SRUPP1HKMMADHCCNIH4SH3BP5I3 CYP1B1STRGL4PFN1CD82TSOMPAN14 C4orf3SQRDLBAZ1RAB27APGCKAPD A4 LRG1LILRAGYG1ANXAWDR1356 Cluster 1 CAPGGLC11orf5TBL1XR1OAPGS1TPZ2 9 408 392 381 370 360 355 353 351 LCN2GADD45AARG1CCND3BAENO1T5 GSM1691922 GSM1692429 GSM1692264 GSM1692224 GSM1602873 GSM1692185 GSM1602812 GSM1691981 GSM1692016 GSM1692260 GSM1692127 GSM1691887 GSM1692008 GSM1691891 GSM1692194 GSM1692370 GSM1692265 GSM1692378 GSM1692218 GSM1692229 GSM1691995 GSM1691876 GSM1692252 GSM1692422 GSM1602853 GSM1692368 GSM1691886 GSM1602886 GSM1692143 GSM1602820 GSM1692377 GSM1692334 GSM1692216 GSM1692407 GSM1602846 GSM1691989 GSM1692164 GSM1692479 GSM1692489 GSM1691881 GSM1691924 GSM1692460 GSM1692367 GSM1691938 GSM1692324 GSM1602888 GSM1692451 GSM1602881 GSM1602921 GSM1691910 GSM1691955 GSM1692039 GSM1692020 GSM1602952 GSM1692437 GSM1602836 GSM1692389 GSM1692274 GSM1692473 GSM1692042 GSM1692165 GSM1692384 GSM1602827 GSM1692106 GSM1691939 GSM1691999 GSM1692253 GSM1691879 GSM1692469 GSM1602931 GSM1692142 GSM1692234 GSM1692427 GSM1691892 GSM1692371 GSM1692154 GSM1692263 GSM1692269 GSM1602838 GSM1692466 GSM1692233 GSM1692311 GSM1692172 GSM1602848 GSM1691930 GSM1691869 GSM1691870 GSM1692015 GSM1602851 GSM1692205 GSM1692458 GSM1692462 GSM1602906 GSM1692096 GSM1692292 GSM1692344 GSM1692456 GSM1691929 GSM1692092 GSM1692189 GSM1691950 GSM1692010 GSM1692376 GSM1602950 GSM1692402 GSM1691878 GSM1691932 GSM1692266 GSM1692280 GSM1602912 GSM1692037 GSM1692121 GSM1692169 GSM1691885 GSM1692435 GSM1692085 GSM1602816 GSM1692190 GSM1692200 GSM1692093 GSM1691928 GSM1692300 GSM1692237 GSM1692130 GSM1692297 GSM1692046 GSM1692287 GSM1692267 GSM1692296 GSM1692025 GSM1692004 GSM1692144 GSM1692222 GSM1692087 GSM1691896 GSM1692283 GSM1692206 GSM1692228 GSM1691997 GSM1692497 GSM1691946 GSM1692392 GSM1692034 GSM1692396 GSM1692125 GSM1692204 GSM1602841 GSM1692167 GSM1692035 GSM1692501 GSM1692358 GSM1602843 GSM1602935 GSM1692244 GSM1691982 GSM1692336 GSM1602857 GSM1691868 GSM1691978 GSM1692355 GSM1692109 GSM1692498 GSM1692161 GSM1692197 GSM1692005 GSM1692168 GSM1692503 GSM1691899 GSM1692423 GSM1692089 GSM1692282 GSM1691944 GSM1691993 GSM1692417 GSM1692492 GSM1692094 GSM1692163 GSM1602833 GSM1691966 GSM1692064 GSM1692120 GSM1691984 GSM1692081 GSM1692353 GSM1691942 GSM1691867 GSM1692088 GSM1602937 GSM1692271 GSM1692495 GSM1692363 GSM1691943 GSM1692083 GSM1692136 GSM1692254 GSM1691903 GSM1692478 GSM1602880 GSM1692068 GSM1602849 GSM1692133 GSM1691960 GSM1692290 GSM1692472 GSM1692193 GSM1692393 GSM1602856 GSM1692225 GSM1691991 GSM1602825 GSM1692227 GSM1692348 GSM1692175 GSM1692373 GSM1692430 GSM1691900 GSM1692162 GSM1692406 GSM1692365 GSM1692399 GSM1691880 GSM1692091 GSM1692301 GSM1692369 GSM1692111 GSM1691975 GSM1692310 GSM1692326 GSM1692382 GSM1692330 GSM1692028 GSM1692030 GSM1691897 GSM1692291 GSM1692149 GSM1692405 GSM1692410 GSM1692286 GSM1692293 GSM1692049 GSM1691957 GSM1692277 GSM1692395 GSM1602944 GSM1692420 GSM1692394 GSM1692131 GSM1691971 GSM1691976 GSM1692210 GSM1692247 GSM1692000 GSM1692102 GSM1692105 GSM1691921 GSM1691974 GSM1691873 GSM1691905 GSM1692066 GSM1692245 GSM1692262 GSM1691883 GSM1691972 GSM1692183 GSM1692390 GSM1692077 GSM1692209 GSM1691923 GSM1602930 GSM1602945 GSM1692494 GSM1692027 GSM1692320 GSM1602943 GSM1692152 GSM1692455 GSM1692159 GSM1692346 GSM1691906 GSM1692230 GSM1692352 GSM1602907 GSM1692101 GSM1692272 GSM1692418 GSM1692099 GSM1692065 GSM1692123 GSM1692425 GSM1692003 GSM1692090 GSM1692097 GSM1692372 GSM1692307 GSM1692316 GSM1692383 GSM1692070 GSM1692299 GSM1692412 GSM1692115 GSM1692174 GSM1691912 GSM1692349 GSM1602947 GSM1692026 GSM1692480 GSM1602922 GSM1692356 GSM1692470 GSM1602840 GSM1691901 GSM1692018 GSM1692056 GSM1692441 GSM1602847 GSM1691980 GSM1691898 GSM1691954 GSM1602968 GSM1692350 GSM1692036 GSM1692108 GSM1692179 GSM1692207 GSM1692058 GSM1692208 GSM1692032 GSM1692276 GSM1691862 GSM1692273 GSM1692055 GSM1692019 GSM1692153 GSM1692171 GSM1602946 GSM1692268 GSM1692140 GSM1602811 GSM1692129 GSM1691863 GSM1692409 GSM1602923 GSM1692059 GSM1692191 GSM1692364 GSM1692493 GSM1692006 GSM1692060 GSM1692045 GSM1692184 GSM1692484 GSM1602839 GSM1692126 GSM1692138 GSM1692024 GSM1692347 GSM1692119 GSM1692449 GSM1692177 GSM1692477 GSM1602908 GSM1692007 GSM1692022 GSM1692118 GSM1692322 GSM1692459 GSM1692335 GSM1602813 GSM1691916 GSM1691889 GSM1692446 GSM1602815 GSM1692040 GSM1692331 GSM1692421 GSM1692366 GSM1691909 GSM1692482 GSM1692488 GSM1692436 GSM1691904 GSM1692202 GSM1692002 GSM1692481 GSM1602818 GSM1692251 GSM1692201 GSM1692182 GSM1692294 GSM1692504 GSM1692145 GSM1602975 GSM1692304 GSM1602882 GSM1692445 GSM1691895 GSM1692440 GSM1691953 GSM1692404 GSM1692452 GSM1602837 GSM1692199 GSM1692223 GSM1692259 GSM1692156 GSM1692023 GSM1692107 GSM1692241 GSM1692415 GSM1691965 GSM1692187 GSM1692124 GSM1602925 GSM1691990 GSM1692033 GSM1692009 GSM1602823 GSM1692139 GSM1692122 GSM1692012 GSM1692255 GSM1692303 GSM1692343 GSM1602859 GSM1692309 GSM1691911 GSM1602929 GSM1692398 GSM1602802 GSM1602863 GSM1692052 GSM1692135 GSM1692148 GSM1692388 GSM1602842 GSM1602933 GSM1692359 GSM1602868 GSM1602958 GSM1692079 GSM1692305 GSM1692327 GSM1692375 GSM1692178 GSM1692073 GSM1692226 GSM1602942 GSM1691925 GSM1692257 GSM1692485 GSM1602831 GSM1602850 GSM1692318 GSM1602830 GSM1602928 GSM1602976 GSM1692157 GSM1602845 GSM1602879 GSM1692289 GSM1692306 GSM1691947 GSM1691915 GSM1692061 GSM1691882 GSM1692387 GSM1691935 GSM1692431 GSM1692438 GSM1692100 GSM1692114 GSM1692203 GSM1691877 GSM1692038 GSM1602885 GSM1602834 GSM1692084 GSM1691865 GSM1602829 GSM1691987 GSM1691959 GSM1692454 GSM1692132 GSM1692374 GSM1692434 GSM1691927 GSM1692071 GSM1692192 GSM1691918 GSM1692397 GSM1602936 GSM1602861 GSM1692428 GSM1692357 GSM1691994 GSM1692243 GSM1692448 GSM1692047 GSM1692196 GSM1602858 GSM1692086 GSM1692319 GSM1691861 GSM1602869 GSM1602902 GSM1692467 GSM1691933 GSM1692329 GSM1692337 GSM1691941 GSM1602883 GSM1692186 GSM1692308 GSM1692050 GSM1691983 GSM1692284 GSM1692137 GSM1692302 GSM1602924 GSM1602844 GSM1602932 GSM1692146 GSM1692217 GSM1602826 GSM1692443 GSM1691871 GSM1692465 GSM1692332 GSM1692391 GSM1692198 GSM1692408 GSM1691985 GSM1692238 GSM1692362 GSM1692043 GSM1692361 GSM1692240 GSM1692278 GSM1691968 GSM1692074 GSM1692471 GSM1692261 GSM1692001 GSM1692416 GSM1602803 GSM1691986 GSM1692386 GSM1692232 GSM1602934 GSM1692112 GSM1602855 GSM1692017 GSM1692103 GSM1692379 GSM1602889 GSM1692117 GSM1692499 GSM1691949 GSM1692212 GSM1602927 GSM1692166 GSM1692141 GSM1692424 GSM1692288 GSM1602819 GSM1691992 GSM1692338 GSM1692246 GSM1602810 GSM1691907 GSM1692315 GSM1692104 GSM1692433 GSM1692250 GSM1602973 GSM1692021 GSM1692128 GSM1691988 GSM1692188 GSM1691893 GSM1692160 GSM1692235 GSM1692354 GSM1602824 GSM1692151 GSM1692414 GSM1691998 GSM1602852 GSM1692258 GSM1692158 GSM1691919 GSM1691934 GSM1692214 GSM1692221 GSM1691962 GSM1692461 GSM1691937 GSM1692385 GSM1691958 GSM1691967 GSM1692453 GSM1692312 GSM1602953 GSM1602974 GSM1602878 GSM1691979 GSM1602903 GSM1691970 GSM1692044 GSM1692062 GSM1691864 GSM1692491 GSM1692069 GSM1692323 GSM1692051 GSM1692342 GSM1691951 GSM1692220 GSM1691945 GSM1692116 GSM1691914 GSM1692095 GSM1692098 GSM1691948 GSM1691875 GSM1691884 GSM1692211 GSM1692181 GSM1691973 GSM1692317 GSM1692400 GSM1692082 GSM1692048 GSM1692242 GSM1691888 GSM1691894 GSM1692333 GSM1692439 GSM1691996 GSM1692447 GSM1692345 GSM1602832 GSM1692380 GSM1692067 GSM1692041 GSM1692432 GSM1692313 GSM1692279 GSM1692339 GSM1692180 GSM1692072 GSM1692444 GSM1692215 GSM1692256 GSM1692487 GSM1692295 GSM1691940 GSM1692381 GSM1691908 GSM1692057 GSM1691963 GSM1691977 GSM1691874 GSM1692063 GSM1692450 GSM1692413 GSM1691961 GSM1692360 GSM1602866 GSM1691890 GSM1602805 GSM1692341 GSM1691964 GSM1692340 GSM1692213 GSM1692426 GSM1692011 GSM1692029 GSM1691969 GSM1692170 GSM1692173 GSM1692298 GSM1602860 GSM1602940 GSM1691866 TCN1LTMMP8RETNHPF Strata Cluster 2 277 250 241 237 226 223 220 220

04812 16 20 24 28 Time in days Fig. 8 The survival analysis with the black module. a Optimal number of classes derived from DDRTree reduced dimension space with the silhouette width; the two-class model was the best ft model with the highest silhouette width. b Discriminative dimension reduction (DDR) graph of the black module gene list. K-means clustering was used to separate samples into two groups (blue and red). c Heatmap of gene expression sorted by DDR score. The two clusters identifed by the DDR score were consistent with that identifed by black module genes. Genes showing on the right were those from the black module. d Kaplan–Meier curves for groups defned by DDR k-means clustering of black module genes. Cox’s proportional hazard modelling of module groups showed that the cluster membership was signifcant association with survival (p 0.018) = by leukocyte activation. Such over-activation of infam- a linear transformation of the high-dimensional space. matory response may indicate that immunoregulatory It is well recognized that linear transformation cannnot agents such as arachidonic acid and eicosapentaenoic fully recover the intrinsic structure of a high-dimensional acid can help to improve the survival outcome [39]. space [40–42]. Tus, we also emplyed manifold learning Mortality is an important clinical trait and thus we tried to better capture the black module [24]. Te result is con- to identify modules most signifcantly assocated with sistent with the above observation that the black module mortlaity. Two methods were employed to validate that can well separate survivors from non-survivors. Most the black module was associated with mortality. Firstly, clinical trials of sepsis are focusing on how to reduce we simply correlate the module eigengene to the mortal- mortality. Te identifed black module in our study can ity. Module eigengene is computed as the frst component help to design drugs that potentially useful for reduc- in principal component analysis (PCA), which however is ing mortality rate. For example, our analysis shows that Zhang et al. J Transl Med (2020) 18:381 Page 12 of 15

a Volcano plot b GO Biological Pathways neutrophil degranulation EnhancedVolcano neutrophil activation involved in immune response

NS p-value p − value and log2 FC neutrophil activation neutrophil mediated immunity negative regulation of cytokine production p.adjust EXOSC4 small molecule catabolic process OSTalpha LDHA negative regulation of immune system process 0.005 ENTPD7 DDAH2 SMPDL3A MMP8 regulation of inflammatory response 0.010 HK3 RGL4 RETN HGF 0.015 ST6GALNAC3 HP negative regulation of leukocyte activation 100 NDST2 ZDHHC19 0.020 CKAP4 FAR2 CEACAM1 CLEC5A DHRS9 negative regulation of cell activation P TTC35 PECR CA4 LCN2 ANKRD22 10 DHCR7 negative regulation of defense response Count SFT2D2 BMX TCN1 OLFM4 GADD45A 5 PCOLCE2 cytokine secretion

Lo g KCNE1 FAM89A 10 − JMJD6 regulation of interleukin-6 production ARG1 15 RAB20 50 BPI PLB1 TFF3 LTF interleukin-6 production 20 ANKRD34B 25 CCR3 negative regulation of inflammatory response LGALS2 CRISP3 protein lipidation lipoprotein biosynthetic process 0 cellular hormone metabolic process -2 024 negative regulation of interleukin-6 production Log2 fold change negative regulation of cytokine secretion Total = 346 variables 0.050.100.150.20 GeneRatio c Gene Set Enrichment Analysis with GO d activatedsuppressed negative regulation of cell activation lipoprotein biosynthetic process embryo development ending in birth or egg hatching negative regulation of leukocyte activation chordate embryonic development negative regulation of immune system process cell morphogenesis involved in neuron differentiation protein lipidation distal axon negative regulation of inflammatory response anion transmembrane transport negative regulation of interleukin-6 production inorganic anion transmembrane transporter activity p.adjust p.adjust negative regulation of defense response

inorganic anion transmembrane transport 0.005

methylation 0.01 0.010 interleukin-6 production negative regulation of cytokine production peptidyl-lysine methylation 0.015 0.02 neutrophil mediated immunity 0.020 histone lysine methylation regulation of interleukin-6 production leukocyte activation size 5 cell activation Count negative regulation of cytokine secretion 10 10 myeloid leukocyte activation regulation of inflammatory response 15 20 neutrophil degranulation secretory granule 20 30 neutrophil activation involved in immune response secretory vesicle 25 immune effector process

exocytosis cytokine secretion export from cell neutrophil activation secretion by cell secretion cellular hormone metabolic process

0.4 0.5 0.6 0.7 0.8 0.9 0.4 0.5 0.6 0.7 0.8 0.9 small molecule catabolic process GeneRatio Fig. 9 The diferences in biological functions between cluster 2 and cluster 1 as identifed by the black module. a Volcano plot showing diferentially expressed genes in the black module between cluster 2 and cluster 1. b enriched GO biological pathways. c Gene set enrichment analysis with GO term; as compared to cluster 1, cluster 2 was characterized by activated functions such as leukocyte activation, cell activation and myeloid leukocyte activation. d Enrichment Map for enrichment result of over-representation test

hsa-miR-335-5p is the top ranked miRNA in the regula- eigengene of black module is the most signifcant associ- tion of the black module. Since the black module con- ated with mortality, and thus the master regulator CEBPB sists mostly genes involved in infammatory response, is also an important risk factor for mortality via immu- the upregulation of hsa-miR-335-5p is proposed to have nomodulation [47, 48]. Our fnding is also consistent infammatory suppression efects [43–45]. More recently, with a recent study comparing sepsis with and without hsa-miR-335-5p is shown to reduce infammation via shock, in which CEBPB is signifcantly enriched in septic negative regulation of the TPX2-mediated AKT/GSK3β shock versus non-shock patients [49]. Since the presence signaling pathway in a chronic rhinosinusitis mouse of shock is a signifcant risk factor for mortality, the result model [46]. Collectively, these observations strongly sup- supports the notion that CEBPB can be a potential target port our results that hsa-miR-335-5p can be a candidate for improving survival outcome. therapeutic target. Te strength of the study is the large sample size. Te CEBPB was annotated to the most signifcantly To the best of our knowledge, the MARS consor- enriched motifs for genes in the black module. Te tium is the largest sepsis cohort with genome-wide Zhang et al. J Transl Med (2020) 18:381 Page 13 of 15

abOptimal number of clusters DDRTree reduced dimension

5.0

Cluster

1 2 0.4

2.5

0.0 Component2

0.2 Average silhouette width

-2.5

0.0

-5.0 12345678910 40 50 60 Number of clusters k Component1 cd

Clusters Clusters 12 SNRPG 1 Cluster 1 Cluster 2 COMMD3 Strata + + ZC3H15 COX7B 2 COMMD6 10 COX6C RPL7 POLR2K 1.0 RWDD1 8 LSM3 MAGOH RPS27L C14orf156 MRPL47 6 UQCRHL C8orf59 + TPRKB SUPT3H 4 PIN4 RPAP2 0.9 UQCRB + C9orf123 EMG1 2 IDE + HSPB11 PHF5A POLE4 MRPS23 METTL5 TAF9 MRPL40 UHRF1BP1 C16orf61 0.8 p = 0.01 + RPL22L1 Survival probability DCUN1D5 HSPA14 MRPL32 MRPL13 RPL26L1 FKBP3 MRPL35 TPT1 RPL27 RPS3A RPL41 0.7 RPL39 RPS15A COX7C RPS24 0481216202428 RPL31 RPL17 Time in days RPL9 HMGB1 TXNL1 RPL36A Number at risk RPS7 CCDC72 PFDN5 RPL23 RPL34 RPL26 Cluster 1 RPS17 326315 310 300288 284 283 283 PSMA4 SUB1 CWC15 EEF1B2

HINT1 Strata GSM1692090 GSM1692380 GSM169224 GSM169225 GSM169227 GSM169247 GSM160292 GSM169236 GSM169233 GSM160292 GSM160296 GSM169235 GSM169225 GSM169235 GSM169236 GSM169233 GSM169228 GSM169193 GSM169221 GSM169237 GSM169246 GSM160281 GSM169199 GSM160286 GSM169199 GSM169238 GSM169189 GSM169218 GSM160280 GSM160281 GSM169232 GSM169224 GSM169234 GSM169246 GSM160282 GSM169198 GSM160294 GSM169226 GSM169239 GSM160292 GSM169207 GSM169242 GSM169194 GSM169213 GSM169192 GSM169248 GSM160292 GSM169202 GSM169211 GSM160293 GSM169231 GSM160283 GSM169198 GSM169206 GSM169227 GSM169247 GSM169189 GSM169235 GSM160297 GSM169191 GSM169206 GSM169228 GSM169244 GSM169220 GSM169214 GSM169191 GSM169201 GSM169191 GSM169215 GSM169238 GSM169241 GSM169212 GSM160281 GSM169218 GSM160285 GSM160290 GSM169188 GSM169236 GSM169202 GSM169202 GSM160294 GSM169210 GSM169190 GSM169238 GSM169204 GSM169219 GSM169192 GSM169235 GSM160294 GSM169222 GSM160284 GSM169199 GSM169193 GSM160280 GSM169213 GSM169246 GSM169212 GSM169238 GSM169213 GSM169220 GSM169202 GSM160283 GSM169192 GSM169212 GSM169226 GSM169188 GSM169233 GSM160290 GSM169200 GSM160288 GSM169214 GSM160293 GSM160284 GSM160293 GSM160285 GSM160283 GSM160286 GSM169220 GSM169199 GSM169213 GSM160284 GSM169192 GSM169239 GSM169199 GSM169206 GSM169239 GSM169190 GSM169223 GSM169229 GSM160295 GSM169202 GSM169222 GSM169234 GSM169200 GSM169222 GSM169243 GSM169201 GSM169187 GSM160288 GSM169235 GSM169234 GSM169248 GSM169218 GSM169218 GSM169211 GSM169190 GSM169208 GSM169200 GSM160282 GSM169203 GSM169196 GSM169227 GSM169195 GSM169218 GSM169215 GSM169229 GSM169243 GSM160293 GSM169214 GSM169187 GSM169217 GSM169210 GSM169228 GSM169210 GSM169229 GSM169232 GSM169247 GSM169244 GSM169187 GSM160290 GSM169188 GSM169192 GSM169195 GSM169197 GSM169245 GSM160282 GSM160285 GSM169189 GSM160285 GSM169209 GSM169203 GSM169219 GSM169249 GSM169229 GSM169218 GSM169229 GSM160282 GSM160290 GSM169226 GSM169206 GSM169219 GSM169230 GSM169221 GSM169249 GSM169218 GSM169245 GSM160285 GSM169217 GSM169188 GSM169210 GSM169213 GSM169221 GSM169230 GSM169196 GSM169239 GSM169222 GSM169248 GSM169216 GSM169232 GSM160288 GSM169186 GSM169236 GSM169214 GSM169249 GSM169226 GSM169242 GSM160283 GSM169217 GSM160297 GSM169232 GSM169201 GSM169204 GSM169190 GSM169244 GSM169204 GSM169248 GSM169208 GSM169235 GSM169216 GSM169225 GSM169230 GSM169239 GSM160295 GSM160288 GSM169187 GSM169236 GSM169217 GSM169197 GSM169198 GSM169210 GSM169234 GSM169198 GSM169233 GSM169240 GSM160293 GSM169209 GSM169211 GSM160292 GSM169214 GSM169247 GSM169242 GSM169224 GSM169242 GSM169207 GSM169242 GSM169216 GSM169228 GSM160283 GSM169207 GSM169229 GSM169238 GSM169225 GSM169190 GSM169220 GSM169236 GSM169196 GSM169227 GSM169242 GSM169193 GSM160293 GSM169186 GSM169186 GSM169197 GSM169198 GSM169208 GSM169193 GSM169220 GSM169202 GSM169198 GSM169223 GSM169200 GSM169209 GSM160280 GSM169189 GSM160284 GSM169238 GSM169203 GSM160283 GSM169215 GSM169239 GSM169212 GSM169224 GSM169193 GSM169206 GSM169229 GSM169235 GSM169244 GSM169241 GSM169242 GSM169222 GSM160283 GSM169234 GSM169220 GSM169221 GSM169247 GSM169227 GSM169237 GSM169220 GSM169250 GSM160295 GSM169246 GSM169201 GSM169244 GSM169188 GSM169210 GSM169217 GSM169243 GSM169219 GSM160294 GSM169197 GSM169240 GSM169231 GSM169208 GSM169228 GSM169243 GSM169190 GSM169191 GSM169209 GSM169205 GSM169226 GSM169248 GSM169215 GSM169235 GSM160281 GSM169223 GSM169188 GSM169234 GSM169241 GSM169220 GSM169213 GSM169189 GSM169247 GSM169219 GSM169230 GSM169246 GSM169192 GSM169193 GSM169200 GSM169191 GSM169231 GSM169249 GSM169201 GSM169204 GSM169209 GSM160284 GSM169198 GSM169221 GSM169194 GSM169199 GSM169201 GSM169191 GSM169192 GSM169243 GSM169194 GSM169204 GSM169230 GSM160297 GSM169197 GSM169233 GSM169230 GSM169205 GSM169214 GSM169240 GSM169195 GSM169188 GSM169218 GSM160281 GSM169241 GSM169207 GSM169241 GSM169222 GSM169225 GSM169205 GSM169215 GSM169221 GSM169232 GSM169245 GSM169193 GSM169214 GSM169204 GSM169212 GSM169216 GSM160288 GSM169211 GSM169225 GSM169233 GSM169212 GSM169212 GSM169214 GSM169223 GSM160287 GSM160290 GSM169196 GSM160294 GSM169213 GSM169208 GSM169227 GSM169197 GSM169198 GSM160293 GSM169213 GSM169239 GSM169230 GSM169237 GSM169207 GSM169217 GSM169209 GSM169212 GSM169220 GSM169188 GSM169205 GSM160284 GSM169208 GSM169244 GSM169241 GSM169209 GSM169238 GSM169192 GSM169205 GSM169235 GSM169198 GSM169208 GSM160294 GSM169196 GSM169211 GSM169223 GSM169196 GSM169196 GSM169204 GSM169246 GSM169220 GSM169238 GSM169186 GSM169223 GSM169239 GSM169245 GSM169204 GSM160282 GSM169228 GSM169236 GSM169227 GSM169187 GSM169249 GSM169216 GSM169233 GSM169231 GSM169216 GSM169216 GSM169187 GSM169210 GSM169210 GSM169222 GSM160283 GSM169243 GSM169194 GSM169217 GSM169199 GSM169215 GSM169245 GSM160287 GSM169230 GSM160288 GSM169199 GSM169222 GSM169241 GSM169248 GSM169190 GSM169198 GSM169234 GSM169211 GSM169221 GSM169234 GSM169243 GSM169200 GSM169244 GSM169250 GSM160282 GSM169225 GSM169222 GSM169231 GSM169221 GSM169237 GSM169196 GSM169215 GSM160284 GS GSM1691907 GSM1692009 GSM1692308 GSM1692046 GSM1691962 GSM1691911 GSM1692497 GSM1602866 GSM1602810 GSM1692128 GSM1692034 GSM1692268 GSM1602818 GSM1692188 GSM1691943 GSM1692098 GSM1692373 GSM1692056 GSM1691976 GSM1692360 GSM1692118 GSM1602974 GSM1691957 GSM1692398 GSM1691945 GSM1692026 GSM1692365 GSM1692374 GSM1692168 GSM1692019 GSM1691863 GSM1692032 GSM1602944 Cluster 2 359327 312 307298 294 290 288

0481216202428 6 8 2 1 4 4 8 3 8 2 0 4 6 5 8 3 7 9 7 9 2 8 0 9 0 7 2 2 3 0 7 9 9 7 2 1 7 2 1 4 1 8 7 0 8 4 9 3 8 0 3 0 8 7 8 9 3 6 8 7 5 3 8 8 7 9 4 5 4 4 5 6 2 8 5 2 3 9 7 8 9 8 7 1 5 7 6 8 0 8 7 3 5 6 6 6 0 4 2 6 3 0 4 6 2 2 6 5 6 6 4 2 5 9 1 2 1 7 2 1 6 5 4 0 5 7 6 8 1 4 5 2 1 3 5 5 0 0 9 7 2 3 6 6 2 4 4 0 4 7 8 0 3 4 5 1 2 3 5 5 6 2 3 0 0 3 4 7 7 9 5 7 0 3 6 3 7 2 3 8 3 7 5 8 5 6 5 7 0 7 6 1 9 5 9 2 3 1 3 2 3 3 1 6 2 6 9 3 1 1 3 9 9 1 7 8 5 6 1 8 1 9 5 4 1 3 0 4 1 4 3 2 7 9 7 5 0 0 1 1 6 5 4 1 1 5 5 8 9 4 3 9 5 2 4 3 2 5 2 1 3 5 3 9 9 0 2 7 7 8 1 2 8 0 7 8 4 4 3 6 5 2 1 7 6 2 2 5 5 1 8 9 1 8 4 7 2 0 8 8 0 1 3 4 8 9 4 2 2 2 0 0 7 9 0 9 3 4 7 0 7 0 6 0 4 5 8 3 1 9 5 6 3 1 0 0 6 1 7 2 7 0 1 8 9 7 2 6 2 0 1 3 6 9 0 9 9 8 0 4 2 8 4 6 6 0 1 2 9 1 8 3 9 4 3 8 7 5 7 9 2 6 4 2 9 4 4 0 1 7 9 2 5 7 2 7 9 5 8 3 6 0 9 3 1 8 8 5 6 9 5 5 0 4 9 3 8 8 7 5 4 8 2 7 3 2 1 6 5 6 5 1 5 8 5 8 3 5 1 4 4 2 2 4 9 0 3 8 3 6 4 1 0 5 3 5 9 6 9 3 1 1 0 3 4 7 2 3 4 1 7 9 6 5 4 6 3 4 8 2 9 8 1 1 7 3 5 1 5 2 0 1 3 3 6 4 4 2 3 5 9 1 4 7 5 2 7 0 0 0 2 7 9 1 0 6 2 7 2 7 1 0 6 1 0 5 2 4 6 6 9 3 7 7 4 1 8 3 2 5 4 0 8 7 9 1 9 8 5 8 9 1 5 3 2 2 1 2 8 0 3 7 8 7 7 8 6 0 3 0 9 6 8 9 6 0 7 9 2 9 1 6 3 1 9 7 9 3 3 7 0 4 9 2 1 5 5 4 9 6 9 4 0 8 3 6 3 3 2 5 4 6 8 0 5 7 6 3 9 5 0 9 0 6 6 2 4 7 8 4 1 9 6 6 0 9 1 4 7 4 2 3 9 4 6 2 8 5 3 2 Time in days Fig. 10 The survival analysis with the light-yellow module. a Optimal number of classes derived from DDRTree reduced dimension space with the silhouette width; the two-class model was the best ft model with the highest silhouette width. b Discriminative dimension reduction (DDR) graph of the light-yellow module gene list. K-means clustering was used to separate samples into two groups (blue and red). c Heatmap of gene expression sorted by DDR score. The two clusters identifed by the DDR score were consistent with that identifed by light-yellow module genes. Genes showing on the right were those from the light-yellow module. d Kaplan–Meier curves for groups defned by DDR k-means clustering of light-yellow module genes. Cox’s proportional hazard modelling of module groups showed that the cluster membership was signifcant association with survival (p 0.01) = blood transcriptional profling [16]. Furthermore, the surrogate for the severity of illness. Second, the tran- included sepsis subjects were classifed into diferent scription regulators were predicted by bioinformatic causes of sepsis, allowing for the consensus network analysis, which might have high false positive rate. analysis. Te consensus analysis identifed common Te in vivo function of these transcription factors and functional modules involved in sepsis. However, there miRNAs should be validated in experimental studies. are limitations in the study. First, the severity of ill- In this regard, results from the current analysis can be ness is not available in the cohort, prohibiting correla- considered as hypothesis-generating. Tird, there are tion analysis for modules and severity scores. However, more causes of sepsis that have not been categorized in since most severity scores are developed with the mor- the study. For example, urinary tract infection is also an tality as the end point, the mortality can be used as a important cause of sepsis in clinical practice, however, the dataset did not contain this subclass of sepsis. Zhang et al. J Transl Med (2020) 18:381 Page 14 of 15

Conclusion Received: 26 July 2020 Accepted: 3 October 2020 In conclusion, the present study identifed the black and light-yellow modules to be the most signifcantly associ- ated with mortality. Master regulators of the black mod- References ule included transcription factor CEBPB. miRNA-target 1. Abe T, Yamakawa K, Ogura H, Kushimoto S, Saitoh D, Fujishima S, et al. interactions identifed signifcantly enriched miRNA. Epidemiology of sepsis and septic shock in intensive care units between Tese regulators can be potential therapeutic targets for sepsis-2 and sepsis-3 populations: sepsis prognostication in intensive care unit and emergency room (SPICE-ICU). J Intensive Care. 2020;8:44–9. the treatment of sepsis. 2. Markwart R, Saito H, Harder T, Tomczyk S, Cassini A, Fleischmann-Struzek C, et al. Epidemiology and burden of sepsis acquired in hospitals and Supplementary information intensive care units: a systematic review and meta-analysis. Intensive Care Med. 2020;315:801–16. 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Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Author details et al. Exploration, normalization, and summaries of high density oligonu- 1 Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang cleotide array probe level data. Biostatistics. 2003;4:249–64. University School of Medicine, No 3, East Qingchun Road, Hangzhou 310016, 19. Langfelder P, Horvath S. Eigengene networks for studying the relation- Zhejiang Province, China. 2 Department of Critical Care Medicine, Afliated ships between co-expression modules. BMC Syst Biol. 2007;1:54–17. Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China. 20. Langfelder P, Luo R, Oldham MC, Horvath S. Is my network module 3 Emergency Department, Zigong Fourth People’s Hospital, 19 Tanmulin Road, preserved and reproducible? Bourne PE, editor. PLoS Comput Biol. Zigong, Sichuan, China. 2011;7:e1001057. Zhang et al. J Transl Med (2020) 18:381 Page 15 of 15

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