Aggregation of high-throughput biological data using pathway-based approaches is useful to associate molecular results to functional features related to the studied phenomenon. Biological pathways communicate with one another through the crosstalk phenomenon, forming large networks of interacting processes.In this work, we present the pathway crosstalk perturbation network (PXPN) model, a novel model used to analyze and integrate pathway perturbation data based on graph theory. With this model, the changes in activity and communication between pathways observed in transitions between physiological states are represented as networks. The model presented here is agnostic to the type of biological data and pathway definition used and can be implemented to analyze any type of high-throughput perturbation experiments. We present a case study in which we use our proposed model to analyze a gene expression dataset derived from experiments in a BKS-db/db mouse model of type 2 diabetes mellitus–associated neuropathy (DN) and the effects of the drug pioglitazone in this condition. The networks generated describe the profile of pathway perturbation involved in the transitions between the healthy and the pathological state and the pharmacologically treated pathology. We identify changes in the connectivity of perturbed pathways associated to each biological transition, such as rewiring between extracellular matrix, neuronal system, and G-protein coupled receptor signaling pathways.The PXPN model is a novel, flexible method used to integrate high-throughput data derived from perturbation experiments; it is agnostic to the type of data and enrichment function used, and it is applicable to a wide range of biological phenomena of interest.The online version of this article (10.1186/s12918-018-0674-7) contains supplementary material, which is available to authorized users. The systems biology framework is useful for integrating large-scale data, such as those obtained from high-throughput genomic technologies. Pathway-based approaches can aggregate the results of these technologies regarding biological features of interest, which, if done correctly, can help interpret the phenomenological significance of the molecular observations at a functional level .Network models are useful because they provide a generalized mathematical framework to describe biological states . In thisPathway crosstalk describes communication between functional pathways . This coThe insights that can be obtained from network approaches to biology can be used to identify new leads in the study of complex diseases . An examThis article presents a general method for constructing networks describing altered pathways between physiological states and perturbations in communication between these pathways. This method was used to construct networks to identify perturbations observed between the physiological, pathological, and pharmacological states in a murine model of type 2 DM (T2DM) peripheral neuropathy and the effects of treatment with pioglitazone. These networks provide insights into the importance of certain functional pathways in the different transitions between these states, which may in turn be used to drive novel experimental research on alternative pharmacological treatments for diabetic neuropathy.pathway crosstalk perturbation network (PXPN) is proposed as a model for integrating high-throughput perturbation biological data to gain insights into the changes in communication between functional biological processes. Figure In this work, the https://www.github.com/hurlab/pxpn_neuropathy).Basically, the PXPN model consists of four steps: 1) identification of perturbed pathways between two physiological states, 2) identification of crosstalk among the perturbed pathways, 3) identification of perturbations in the crosstalk regions between perturbed pathways, and 4) Network integration. A pseudocode representation of this model is available in Additional file Step 1 involves taking i) an expression dataset with information on two physiological conditions and ii) a list of pathways, defined by a single set of inclusion criteria and curatorial rules, such as those obtained from the same database. These are used as input for an enrichment function to obtain a list of pathways that are considered to be “enriched,” which in this context indicates a perturbation in pathway activity between the physiological states.crosstalk region (or regions) between these pathways. Importantly, our research focused only on the crosstalk regions of pathways that were identified as perturbed in step 1.Step 2 involves looking for crosstalk between the pathways that were identified as perturbed in the previous step. Crosstalk between pathways is found if the pathways share genes, or, in other words, if the lists of genes for two pathways overlap  db/+ and db/db mice (BKS.Cg-m+/+Leprdb/J) were purchased from the Jackson Laboratory . Mice were fed with or without 15 mg/kg pioglitazone (112.5 mg pioglitazone/kg chow for a dose of 15 mg/kg to the mouse) for 11 weeks starting from 5 weeks of age. Pioglitazone treatment normalized renal function and improved small nerve functions but did not improve large fiber functions. Four complication-prone tissues—sciatic nerve (SCN), dorsal root ganglia (DRG), glomeruli, and kidney cortex—were collected at 16 weeks of age and examined for their genome-wide gene expression profiles using RNA-Seq (HiSeq 2000 paired-end read length of 100 bases). The current study focused on the three groups of SCN data, including db/+ , db/db (diabetic denoted as “disease” group), and db/db + Pio (diabetic with pioglitazone treatment denoted as “treatment” group). There were n = 6 samples in each group.RNA-Seq raw data were obtained from a previous study on pioglitazone’s effects on diabetic complications  using leThe raw sequencing reads were first cleaned by removing reads containing the adapter or poly-N and removing low-quality  reads using Trimmomatic . FastQC Graphite R/Bioconductor package [The Reactome  collecti package . The gen package , a cutofCalculations for topological parameters—degree, clustering coefficient (CC), network density, average path length, and number of connected components (islands in the network)—were carried out using Igraph  for R, NHTD). Given therapy, the patient transitions from the pathological state to a pharmacologically modulated state . Finally, with successful therapy the patient transitions back to the physiological state . Three networks, each representing one of these physiological transitions, were constructed. It is proposed that changes in pathway connectivity in different transitions indicate changes in the overall impact of a particular pathway activity in the observable phenotype.For this study of murine diabetic neuropathy, the previously described RNA-Seq expression dataset and pathways from the Reactome database were the inputs of the model. The differences between these groups represent the transitions observed in a patient. First, the patient transitions from a physiologically functional state to a pathological state .To evaluate the significance of the topological parameters of these three PXPNs, an ensemble of 5000 networks was generated for each transition using a null model by randomly rewiring the edges, with a rewiring probability proportional to the size of the intersection between two pathways (measured as the Jaccard index). For each network, each topological parameter was compared against the null model using a HTD, DTT, and TTH), an ensemble of 1500 random expression datasets was generated by shuffling the gene labels of the original RNA-seq data. These data were used to generate PXPNs using the established pipeline and compared against those of the comparisons.A second null model, for evaluating the overall capacity of the method of obtaining non-trivial network structures from gene expression measurements, was employed. For each of the three comparisons . This transition was associated with 78 altered pathways and 149 crosstalk perturbations among them. Finally, the TTH network, as illustrated in Fig. TTH network included 110 altered pathways, with 213 edges representing perturbed crosstalks between them. Additional file q-values for each transition are listed in Additional file Using the proposed approach, each transition between physiological states was represented as a network with characteristic structural features. The generated networks may be found in Additional files DTT and TTH networks ; the most different were the HTD and TTH networks . Additional files Each physiological transition involves a precise combination of perturbed pathways, with specific communication patterns between them. This is evident in terms of similarity among the networks as each network has a portion of exclusive and shared nodes. In all three transitions, 53 pathways were perturbed and therefore represented as nodes in the network. The most similar networks in terms of both nodes and edges were the As each transition was associated with a specific network, each network was associated with specific structural characteristics. The communication between pathways associated with each transition between physiological states was different, resulting in a unique network topology. This in turn was reflected in network properties, such as the average path length, the clustering coefficient, and the distribution of nodes into connected components and communities inside connected components.HTD, DTT, and TTH comparisons, respectively), empty networks were generated, as no significant pathway perturbation was found; 2) networks with more than one edge included were generated in very few  cases. Furthermore, these networks had trivial structures, such as stars and cliques. Therefore, the structural properties of PXPN networks may not be associated with random expression patterns, but may be associated with the underlying biological changes in pathway communication. Each PXPN had an associated degree distribution . Nevertheless, the clustering coefficient values of all networks were comparable (ranging from 0.513 to 0.620). Average path lengths were slightly higher in the experimentally derived networks than in the null model networks in the cases of the HTD and TTH transitions. Interestingly, in the case of the DTT transition, the average path length was considerably lower than the one predicted by the null model . This result suggests that the transition induced by pharmacological treatment on the pathological state involves perturbations with short-range pathway communication, whereas the perturbations from and to the physiological state require longer-range changes in communication.The three experimentally derived networks had clustering coefficient values that were higher than the ones expected from the null model  and communities (modules inside a connected component in which the nodes belonging to the same module have a higher number of edges between them than the nodes outside of the module). Differences in the organization of these networks are indicative of specific communications between biological processes that were altered in each physiological transition.HTD network  was composed of three communities: one comparable to the “lipoprotein metabolism” community in the HTD network, another related to the “metabolism of lipids,” and the other containing three pathways related to “retinoids.” Three other connected components were comparable to the communities found in the HTD network, such as “ECM organization” (8.97%), “GPCR ligand binding” (8.97%), and “SLC-mediated transmembrane transport” (6.41%). Another connected component with four pathways related to the TCA cycle was also found.The ork Fig.  was compTTH network  metabolism pathways. GAGs are known to play critical roles in the development of the central nervous system [Changes to the composition and function of the ECM pathways have been reported to play a significant role in the loss of nerve fibers during the progression of diabetic neuropathy . In eachs system  and are s system .HTD transition, GPCR signaling was directly connected to the largest connected component of the network through crosstalk with the hemostasis pathway. Hemostasis perturbation was lost in the DTT transition, leading to the formation of an isolated component of GPCR signaling. Finally, while hemostasis perturbation was found again in the TTH transition, the crosstalk between this pathway and GPCR was lost, while new connections from the GPCR pathways to the neuronal system community emerged through three pathways: “G alpha (z) signaling events,” “integration of energy metabolism,” and “adenylate cyclase–activating pathway.” Recently, the parabrachial pituitary adenylate cyclase–activating polypeptide (PACAP) was shown to have an increased expression in chronic pain [GPCR signaling is widely known to be an important mechanism of signal transduction and has been highly studied in the biomedical setting as a drug target . In thisnic pain ; our resFigure In this work, we presented a model to represent the alterations in pathway activity and communication between physiological states of clinical importance. This PXPN model represents each physiological transition as a network of perturbed and interacting pathways with a unique nonrandom structure. These networks reflect the changes in the functional biological processes that are observed in the transitions between different physiological states. More importantly, these networks give insights into the importance that the communication between biological functions may have in the progression between physiological states.db/db mice and treatment of pioglitazone) was used as a case study. Our model identified changes in pathway connectivity, such as the rewiring between pathways of extracellular matrix, neuronal system, and GPCR signaling, in different biological transitions of clinical importance.The PXPN model is agnostic to the type of perturbation dataset and can be applied to analyze a variety of experimental settings; when given an expression/perturbation dataset and a suitable enrichment method, it is possible to generate a PXPN. The topological properties of this network, as well as the biological insights that can be unveiled from its analysis, depend on the nature of the data and the assumptions of the enrichment methodology. In this work, a diabetic neuropathy animal model Additional file 2:Text file with the PXPN model pseudocode. (DOCX 16 kb)Additional file 3:Health to Disease Network in GML format. (GML 42 kb)Additional file 4:Disease to Treatment Network in GML format. (GML 30 kb)Additional file 5:Treatment to Health Network in GML format. (GML 42 kb)Additional file 6:Venn diagram of pathways in PXPNs in TIFF format. (TIFF 413 kb)Additional file 7:Pathway enrichment  for each transition, in XLSX format. (XLSX 13 kb)Additional file 8:Jaccard index matrix of similarity for PXPN nodes in XLSX format. (XLSX 9 kb)Additional file 9:Jaccard index matrix of similarity for PXPN edges in XLSX format. (XLSX 9 kb)Additional file 10:HTD) network  and corresponding null model distributions (colored outlines); PDF format. (PDF 10 kb)Degree distribution for the health to disease  network  and corresponding null model distributions (colored outlines); PDF format. (PDF 8 kb)Degree distribution for the disease to treatment  network  and corresponding null model distributions (colored outlines); PDF format. (PDF 9 kb)Degree distribution for the treatment to health


Type 2 diabetes mellitus (T2DM) is a metabolic disease with high incidence, which has seriously affected human life and health. MicroRNA, a short-chain noncoding RNA, plays an important role in T2DM. Identification of meaningful microRNA modules and the role of microRNAs provide a basis for searching potential biomarkers of T2DM.  In this study, three newly diagnosed patients with T2DM and three controls were selected for Whole Peripheral Blood RNA Sequencing to establish a microRNA library. Weighted gene coexpression network analysis (WGCNA) was applied to construct coexpression modules and to detect the trait-related microRNA modules; then, KEGG enrichment analysis was performed to predict the biological function of the interest modules, and candidate hub microRNAs were screened out by the value of module membership (MM) and protein-protein interaction (PPI) network.  Four microRNA modules  were highly associated with the T2DM; the number of miRNAs in these modules ranged from 41 to 469. The Fc gamma R-mediated phagocytosis pathway, Rap1 signaling pathway, MAPK signaling pathway, and Lysosome pathway were common pathways in three of the four modules. RPS27A, UBC, and RAC1 were the top three proteins in our study; their corresponding RNAs were miR-1271-5p, miR-130a-3p, miR-130b-3p, and miR-574-3p.  In summary, this study identified blood miRNAs in human T2DM using RNA sequencing. The findings may be the foundation for understanding the potential role of miRNAs in T2DM. Diabetes mellitus, characterized by hyperglycemia, is one of the most prevalent metabolic disorders. It is estimated by the International Diabetes Federation's Diabetes Atlas eighth edition that there are about 1 in 11 adults worldwide now with diabetes mellitus, 90% of whom have type 2 diabetes mellitus (T2DM) [MicroRNAs are a class of endogenous short noncoding RNAs, which bind to the 3′terminal noncoding region of target gene at the transcriptional level in the form of complete complementation or incomplete complementation to regulate gene expression and exert biological effects in both health and disease . RelevanWGCNA identifies interest gene clusters  using information from thousands of genes with the greatest changes, or all of them, and makes significant association analysis with traits . InsteadThe aim of this study was to utilize the WGCNA method to construct coexpression modules for the expression data of miRNAs in human biological specimens and to obtain the correlation between different modules and the trait of T2DM. It provided an effective way to explore the role of microRNAs from different perspectives and provided a basis for searching potential biomarkers of T2DM.Three patients who were newly diagnosed with T2DM and three controls without T2DM were recruited at the Second Hospital of Jilin University from April to June 2018. All participants were Han Chinese, and all of them were males aged 40-60 years   according to the manual. RNA purity was checked using the NanoPhotometer® spectrophotometer . RNA concentration was measured using the Qubit® RNA Assay Kit in a Qubit® 2.0 Fluorometer . RNA integrity was further assessed using the RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system .For microRNAs, raw data (raw reads) of fastq format were firstly processed through custom perl and python scripts. In this step, clean data (clean reads) were obtained by removing reads containing ploy-N, with 5′ adapter contaminants, without 3′ adapter or the insert tag, containing ploy A or T or G or C and low-quality reads from raw data. At the same time, Q20, Q30, and GC content of the raw data were calculated. Taken together, all the downstream analyses were based on the clean data.Weighted gene coexpression network analysis was a scale-free network analysis method proposed by Peter Langfelder and Steve Horvath in 2008 . In thisThe data needs to be processed before the WGCNA analysis. The gene and sample data were transformed into the gene×sample matrix. Fewer than five genes with nonzero expression levels were cleared .β was automatically selected through the pickSoftThreshold function in the WGCNA package. The function of β parameter was to amplify the correlation between genes [β, and the weighted results were converted into an adjacency matrix [Bioconductor package in the R programming language was used for WGCNA and goodSamplesGenes function to check whether microRNA data met the requirements of network analysis. Pearson correlation coefficient was calculated for all the genes, and an appropriate soft threshold en genes . Pearsony matrix . Subsequy matrix .Above all, the first principal component module eigengenes (MEs) were calculated to express the expression level of the gene module. Then, the strongly correlated modules were determined by Pearson correlation coefficient between the modules and the phenotype of T2DM. The hub gene was determined by calculating the module membership (MM) and gene significance (GS) of the genes in the key modules. Hub microRNAs were selected according to the top 10% MM value in each module.https://david.ncifcrf.gov/summary.jsp). Then, the functional enrichment analysis was performed by KOBAS3.0 (http://kobas.cbi.pku.edu.cn/index.php) to interpret the biological function of each interest module.The hub genes' information of interest modules was mapped to the DAVID  dataset (https://string-db.org/) to find potential functional proteins and important microRNAs in this study.Module membership (MM) was defined as the correlation of expression profile and MEs. The hub microRNAs were determined by calculating the module membership (MM), and the first 10% according to the value of MM in each module was selected. Subsequently, we used the target genes of hub microRNAs to construct protein-protein interaction (PPI) network by STRING database  of the node with K connectivity is negatively correlated with the logarithmic log(p(k)) of the probability of the node's occurrence, and the absolute value of the correlation coefficient is greater than 0.8. In this study, the correlation coefficient was 0.85, and the soft threshold was 7 . The adjThe most representative gene set in each module represented the overall level of gene expression in the module as the first principal component of the module eigengene (ME). P < 0.05) was obtained to further screen coexpression modules. As shown in P value was 0.02. MEgreen module correlation coefficient was -0.18; P value was 0.7. MEmagenta module correlation coefficient was -0.93; P value was 0.0017. MEturquoise module correlation coefficient was -0.96; P value was 0.003. Except for these four modules, there was no statistical significance in other modules.Identifying genes associated with a certain clinical trait is of great value to explore the molecular mechanisms behind that trait. In the present study, the parameters of six samples included BMI, age, smoking, alcohol, and T2DM. Since this study explored the mechanism of T2DM, we mainly focused on modules highly related to it. Pearson correlation coefficients between ME and the clinical trait of each microRNA module were calculated, and significance module  represented each microRNA's Pearson correlation coefficient with corresponding module eigengene. Since RNA of the same module had the same biological function, hub microRNAs according to the top 10% MM value in each module were utilized to represent the biological function of the whole module . For RACP < 0.1 and ∣Log2FoldChange∣ > 1  pathway is sensitive to a variety of inflammatory cytokines. In insulin resistance, there is usually a chronic inflammatory response, which makes the MAPK pathway play an important role in accompanying the inflammatory response of type 2 diabetes, such as diabetic nephropathy and liver lesions. However, the functional role of MAPKs in insulin resistance has not been clarified; the specific mechanism of the MAPK pathway in type 2 diabetes needs to be further studied –21.The Rap1 signaling pathway contains 211 genes. Rap1 is a small GTPase that controls a variety of processes, including cell adhesion and connection formation. Previous studies have shown that the circulating microRNAs of type 1 diabetes mellitus are enriched by the Rap1 signaling pathway . By inhiThe lysosome pathway contains 122 genes. Lysosomes are membrane-delimited organelles in animal cells serving as the cell's main digestive compartment. Their functions include endocytosis, phagocytosis, and autophagy. Many studies had pointed out that lysosomes play a key role in T2DM and its complications through autophagy and phagocytosis. Autophagy dysfunction was an important factor in triggering T2DM. Autophagy can maintain the normal cell structure of islet B cells, while excessive autophagy can lead to a decrease in number of islet B cells –28. Therγ receptors (FcγRs) are receptors for IgG that classically regulate processes in immune response [Fc gamma R-mediated phagocytosis contains 91 genes Fc response . Experimresponse , 30. Theresponse .In addition to KEGG enrichment analysis of key modules, STRING database was used to conduct protein-protein interaction (PPI) network of some hub genes in key modules. By PPI network analysis, RPS27A, UBC, and RAC1 have the highest degree value in network. An exploratory study had demonstrated that RPS27A may be a potential target for mesenchymal stem cells in the treatment of T2DM . Some stIn order to explore the role of RAC1, we tried to find out the correlation between RAC1 and the KEGG enrichment pathway of the module. RAC1 was involved in 15 of these pathways. Among them, the MAPK signaling pathway and Rap1 signaling pathway were also important common pathways in KEGG enrichment analysis of the modules. Notably, many previous studies had attempted to elucidate the association between RAC1 and diabetes and the molecular mechanisms involved –37. RAC1Due to the small sample size used in this study, the results of this study might be limited when extrapolated. At the same time, this study was an exploratory study, comprising of only a small number of T2DM patients and nondiabetic participants; the results need to be verified by experiments for the next step.In this study, four microRNA modules related to T2DM were identified through WGCNA. In-depth analysis of the four modules, four microRNAs: miR-1271-5p, miR-130a-3p, miR-130b-3p, and miR-574-3p and three proteins: RPS27A, UBC, and RAC1, was identified by KEGG enrichment analysis and PPI network. RAC1 and miR-574-3p were most likely to be biomarkers of T2DM by combining KEGG enrichment analysis and PPI network. The findings may be the foundation for understanding the potential role of miRNAs in T2DM.


The analysis of integrated multi-omics data enables the identification of disease-related biomarkers that cannot be identified from a single omics profile. Although protein-level data reflects the cellular status of cancer tissue more directly than gene-level data, past studies have mainly focused on multi-omics integration using gene-level data as opposed to protein-level data. However, the use of protein-level data (such as mass spectrometry) in multi-omics integration has some limitations. For example, the correlation between the characteristics of gene-level data (such as mRNA) and protein-level data is weak, and it is difficult to detect low-abundance signaling proteins that are used to target cancer. The reverse phase protein array (RPPA) is a highly sensitive antibody-based quantification method for signaling proteins. However, the number of protein features in RPPA data is extremely low compared to the number of gene features in gene-level data. In this study, we present a new method for integrating RPPA profiles with RNA-Seq and DNA methylation profiles for survival prediction based on the integrative directed random walk (iDRW) framework proposed in our previous study. In the iDRW framework, each omics profile is merged into a single pathway profile that reflects the topological information of the pathway. In order to address the sparsity of RPPA profiles, we employ the random walk with restart (RWR) approach on the pathway network.Our model was validated using survival prediction analysis for a breast cancer dataset from The Cancer Genome Atlas. Our proposed model exhibited improved performance compared with other methods that utilize pathway information and also out-performed models that did not include the RPPA data utilized in our study. The risk pathways identified for breast cancer in this study were closely related to well-known breast cancer risk pathways.Our results indicated that RPPA data is useful for survival prediction for breast cancer patients under our framework. We also observed that iDRW effectively integrates RNA-Seq, DNA methylation, and RPPA profiles, while variation in the composition of the omics data can affect both prediction performance and risk pathway identification. These results suggest that omics data composition is a critical parameter for iDRW.The online version of this article (10.1186/s12920-019-0511-x) contains supplementary material, which is available to authorized users. Advances in high-throughput sequencing technologies and their integration, including genome, transcriptome, epigenome, and proteome sequencing, has shifted perspectives from the micro-level to the macro-level in biological research. Certain phenomena can be observed in each omics layer, enabling researchers to understand the complex interactions within and between biological mechanisms. Several studies have proposed methods for integrating omics data obtained from high-throughput sequencing in different layers to provide insights into systems biology –4. In caAlthough many previous studies have focused on the integration of multi-omics data, proteomic data is rarely used for integrative multi-omics data analysis. Proteins are a fundamental unit of a biological complex, in which they have a functional role, and protein expression data can be utilized for the diagnostic prognosis of cancer patients –18. HoweThe reverse phase protein array (RPPA) is an antibody-based protein assay platform for high-throughput sequencing that quantifies the expression of a target protein. It is cost-effective and highly sensitive to the target protein, even at low concentrations . HoweverPathway-level analysis is necessary because it is not possible to define biological functions via a single gene or a single molecule. The Kyoto Encyclopedia of Genes and Genomes (KEGG)  providesIn this study, we propose a pathway-based integration method for RPPAs and other omics data. Specifically, we focus on the utilization of RPPA data in which the proteins are sparse with respect to the pathway gene set. In our experiments, we merge RNA-Seq, DNA methylation, and RPPA profiles into a single pathway profile that is employed for survival prediction and risk-pathway identification in breast cancer.We downloaded pathway annotation files from KEGG . Of thest-tests or DESeq2) was conducted to obtain initial gene weights that indicate the extent to which a gene (or protein) differentiates between long-term and short-term survival for breast cancer.Statistical testing  weight vector was assigned to the corresponding pathway network. Given that the RPPA profile contains few protein expressions (approximately 180 proteins) and that there is only a very small overlap between the genes corresponding to proteins in the RPPA profile and the genes in the pathway, many of the initial weights for the RPPA profile were missing or assigned as zero. We conducted random walk with restart (RWR), which represents a network propagation algorithm, on the protein-level pathway network to estimate the initial weight of the missing proteins.iDRW was utilized to integrate the initial weight of each gene (or protein) into the unified pathway network. In the iDRW approach, the initial gene (or protein) weights are mixed to reflect the topological information of the pathway. Thus, we obtained a final weight vector that represented the topological importance of each gene in terms of distinguishing between the long-term and short-term survival groups.A pathway profile using the pathway activity inference method derived from  was consIn order to integrate the RPPA profile with other multi-omics data and merge them into a single pathway profile, we deployed and extended the iDRW framework . The oveA more detailed description of these steps is given in the following sections.hsa, a pathway network was generated containing a total of 7389 nodes and 58,399 directed edges. In this paper, we refer to the generated pathway networks as the transcriptome network, epigenome network, and proteome network for the RNA-Seq, DNA methylation, and RPPA profiles, respectively.The iDRW framework operates on a unified pathway network that consists of a set of pathway networks corresponding to the number of omics types. We first constructed pathway networks for each of the RNA-Seq, DNA methylation, and RPPA profiles and combined them into a unified pathway network. A pathway network for a single omics profile contains relational information for biological molecules  from the KEGG database . We usedAKT1, while the RPPA profile contains an antibody corresponding to the AKT1 protein; thus, a directional edge is assigned to the node for AKT1 in the transcriptome network and the node for AKT1 in the proteome network. In this structure, we assign directional edges to the overlapping genes (with the inter-network edge following the direction of epigenome network → transcriptome network and proteome network → transcriptome network). We selected this structure for the unified pathway network empirically based on the best performance in survival classification for breast cancer . With diffusr, RWR was used for missing value estimation. rit denotes the propagated weight vector at time step t at node i, and is obtained by RWR . At each time step, ri∞ was obtained from a fixed number of iterations. ri∞ was used as W0 for the RPPA profile, and the W0 for each omics profile was assigned to the corresponding pathway network.where j-th pathway asPathway activity inference using a gene profile was conducted using the method suggested by . To infePj, which corresponds to the j-th pathway, contains nj differentially expressed genes p-value (wg) is < 0.05; k is the number of omics profiles employed; Ol represents the l-th omics profile used when inferring the pathway activity; gi in Ol; gi calculated in Ol; and gi in Ol. In addition, in the RNA-Seq profile, which consists of count-based data, log2fold change from the DESeq2 method  to compare the performance of all models with respect to γ  exhibited better performance when compared with the iDRW(GM) model presented in our previous study. Interestingly, the iDRWprop model had a slightly lower performance (though it was more stable) with changes in γ than that of the iDRW model. This indicates that the iDRWprop model is more robust to changes in the restart probability than is iDRW, and that network propagation mitigates the sparsity problem in RPPA data. Based on the optimized models obtained from the experiment presented in Fig. iDRW(GP) exhibited the best performance for survival classification. All models created using the RPPA profile out-performed models without the RPPA profile. As a result, it is clear that RPPA data is useful for the prediction of long-term or short-term patient survival.Based on the aforementioned results, we compared each pathway-based integration model by varying the restart probability γ for the iDRW method Fig. . Unlike iDRW(GP) and iDRW(GMP). The ranked pathway list was obtained from the p-values for two-tailed t-tests of the pathway activity with respect to long-term and short-term survival. The importance score was calculated based on prediction error using the out-of-bag (OOB) estimate method. OOB estimation is the mean prediction error of random forest without training sample xi. The importance of a feature was defined by the degree to which the prediction error increased when xi was excluded. The score was scaled from 0 to 100 with the caret R package [We identified the top 10 risk pathways from the ranked pathway list for  package .iDRW(GP). It can be observed that the list contains several pathways that have been previously reported to have a direct association with cancer. The p53 signaling pathway is a well-known anti-cancer pathway that plays a role in apoptosis and as a tumor suppressor [pathways in cancer was also extracted [iDRW(GP), consists of a series of chemical reactions for cell metabolism, including the anabolic (storing energy) and catabolic (releasing energy) pathways. It is a general pathway that is not cancer-specific. Evidently, the activity of the metabolic pathway in critical-state patients  exhibits a pattern that is distinct from that of non-critical patients  [Table ppressor –45. The xtracted . Cellulaxtracted . It alsoxtracted –50. The urvival) . The metiDRW(GMP). Several of these pathways were directly related to the immune system. For example, mitophagy is a selective autophagy process that maintains cell health via the degradation of damaged mitochondria [Helicobacter pylori infections. These viral inflammation-related pathways are strongly associated with innate immune responses [Table chondria –54. Splichondria –57. Viraesponses –63 and aesponses –70, whicesponses . Mitochoesponses , 71, 72.iDRW(GP) and iDRW(GMP), we created a pathway-pathway interaction network  and iDRW(GMP). The cellular senescence pathway is a cancer suppressor which regulates cell growth and death by ceasing the division of premalignant or aged cells [p-value = 8.233e-05).To investigate the association of the risk pathways identified by ork Fig.  with theed cells . We examiDRW(GP) and has been reported to be closely related to breast cancer, with HPV DNA being found in breast cancer cells [iDRW(GMP). It has been reported that KSAH is associated with breast cancer. However, KSAH is an etiologic factor for non-familiar breast cancer [iDRW(GP) have a relatively strong connection with cancer-related signaling pathways, such as the PI3K-Akt signaling pathway, Pathway in cancer, Proteoglycans in cancer, and the FoxO signaling pathway. The PI3K-Akt signaling pathway is frequently observed in cancer cells and stimulates cell growth and proliferation [iDRW(GMP) contain viral infection-related and immune response-related pathways. Though hepatitis B and hepatitis C are not directly related to breast cancer, they cause inflammation of liver tissue, which makes it difficult for cancer patients to receive chemotherapy [Viral carcinogenesis induces tumor progression via oncogenic virus infection . The humer cells –79. It ier cells . The inft cancer . The risferation –83, whilferation –86. The otherapy . This caiDRW(GP) model mainly reflected the status of cancer cells at the cellular level, while the iDRW(GMP) model tended to reflect immune response-level information. From our observations, we can infer that the iDRW(GP) model reflects protein-level information more accurately when compared with iDRW(GMP). As in [iDRW(GP) out-performed iDRW(GMP) is that the characteristics of DNA methylation hinder the identification of risk pathways which facilitate the prediction of survivability for breast cancer patients that were otherwise discovered by iDRW(GP). It should be noted that the benefits of different combinations of omics types will depend on the type of clinical problem under examination. Clinical predictions for survival are influenced by both genetic and environmental factors. For survival classification, protein-level information  is more important than gene-level information (such as innate immune response information).It is known that protein-level data reflects the status of cancer cells better than gene-level data . In this). As in , it is giDRWprop model did not out-perform iDRW, it was robust to the restart probability. The combinatorial experiments assessed the performance of each omics combination, with the (G + P) combination in iDRW(GMP) producing the highest accuracy overall. Finally, we observed that iDRW(GP) exhibited the best performance for survival prediction for breast cancer patients and highlighted key differences in the major risk pathways identified using iDRW(GP) and iDRW(GMP). These findings highlight that an appropriate combination of omics data is required to properly address the topic under investigation.In this study, we combined RPPA proteomic data with RNA-Seq and DNA methylation data to successfully derive pathway information based on the iDRW framework. This study found that RPPA data is a rich source of information for survival prediction for breast cancer patients; when RPPA data was employed in the iDRW framework, improved performance was observed and feasible risk pathways extracted. The proposed model successfully identified both well-known and previously undiscovered risk pathways for breast cancer. Systemic analysis was also conducted to obtain better macroscopic insights. Furthermore, network propagation analysis and combinatorial experiments were performed in order to fine-tune our model. We employed network propagation on pathway gene members to overcome the sparsity of RPPA proteins using random walk with restart (RWR). Although the Additional file 1:Supplementary Material 1. Performance comparison on varying structure of the unified pathways network. (PNG 167 kb)Additional file 2:Supplementary Material 2. Survival curve of iDRW(GM) and iDRW(GMP). (a) Survival curve for long-term survival and short-term survival in iDRW(GM). (b) Survival curve for long-term survival and short-term survival in iDRW(GMP). (PNG 117 kb)


The structure and function of microbial communities inhabiting the subseafloor near hydrothermal systems are influenced by fluid geochemistry, geologic setting and fluid flux between vent sites, as well as biological interactions. Here, we used genome‐resolved metagenomics and metatranscriptomics to examine patterns of gene abundance and expression and assess potential niche differentiation in microbial communities in venting fluids from hydrothermal vent sites at the Mid‐Cayman Rise. We observed similar patterns in gene and transcript abundance between two geochemically distinct vent fields at the community level but found that each vent site harbours a distinct microbial community with differing transcript abundances for individual microbial populations. Through an analysis of metabolic pathways in 64 metagenome‐assembled genomes (MAGs), we show that MAG transcript abundance can be tied to differences in metabolic pathways and to potential metabolic interactions between microbial populations, allowing for niche‐partitioning and divergence in both population distribution and activity. Our results illustrate that most microbial populations have a restricted distribution within the seafloor, and that the activity of those microbial populations is tied to both genome content and abiotic factors. Diffuse fluids emerging from the seafloor near hydrothermal vents represent a mix of hydrothermal fluid and deep seawater and provide a window into the subseafloor microbial habitat . In addition, the Piccard sites Hot Chimlet and Shrimp Canyon, with few exceptions, had extremely low transcript abundance for all genes compared to the other vent fluids. Overall, we observed that a wider set of genes was expressed at the Von Damm sites Ginger Castle, Hot Cracks and Old Man Tree compared to vent sites at Piccard  were excluded from additional analysis, as they are most likely seawater organisms mixing with vent fluids at the point of sampling and are thus unlikely to be relevant subseafloor and vent microorganisms.Seventy three MAGs with >70% completeness and <10% redundancy from 15 metagenomes were collected at the Piccard and Von Damm vent fields at the Mid‐Cayman Rise by Anderson et al.   were dominated by high‐temperature bacteria and archaea including the Desulfobacteriales, Archaeoglobales, Thermococcales, and Methanococcales , many Sulfurovum MAGs were expressed at moderately high levels across all four vent sites at Piccard vent field . For this analysis, we selected 56 KEGG modules related to microbial metabolic pathways, including carbohydrate metabolism, carbon fixation, methanogenesis and aerobic methane oxidation, sulphur and nitrogen reduction and oxidation, and various membrane proteins such as cytochromes and Mn/Zn/Fe/S/N transporters. Supporting Information Table We assessed the functional potential of each MAG by defining a module completion score (MCS) for each MAG that describes the functional capability of a MAG while allowing for the existence of multiple enzyme combinations for any step of the pathway, each of which may be sufficient to complete the pathway. This is based on  the module completion ratio (MCR) calculated by Takami et al. . An MCS Desulfobacterales MAGs, denitrification had a high MCS score in Aquificales and Sulfurovum MAGs, and dissimilatory nitrate and sulphate reduction had high MCS scores in Thiotrichales MAGs. Cluster III consisted of a set of pathways related to thiosulfate oxidation, assimilatory nitrate reduction, phosphate transport system (PTS) sugar uptake, and cytochrome c oxidase where MAGs mostly show MCS≥0.9 or MCS≤0.3. Many of these pathways had high MCS scores in several Sulfurovum and other Campylobacterial MAGs. Cluster IV contained many pathways that were highly complete in several archaeal MAGs, including methanogens, Archaeoglobi and Thermoprotei. Cluster V consisted of a set of rare pathways that were only present in a few MAGs with MCS > 0, including the fumarate reductase pathway and the oxidative phase of the pentose phosphate pathway. However, a number of pathways from this group, such as the SO42− and Mg/Zn/Fe transport system, were present with high MCS scores in 2–4 MAGs each. The next cluster (VI) contained modules mostly related to methane metabolism. The most prominent lineages with capabilities in this cluster included Methanomicrobia and Methanocci, and Archaeoglobi contained many genes in these pathways as well. We observed three methanogenesis modules in the cluster along with pathways for F420 biosynthesis, coenzyme M biosynthesis, acetyl‐CoA to CO2 and nitrogen fixation.Through module completion analysis, six distinct metabolic clusters emerged Fig. . The MAGNanohaloarchaea and Nanoarchaea MAGs displayed low MCS for all pathways except those involving gluconeogenesis and glycolysis  in these MAGs compared to MAGs that were expressed elsewhere. The associated pathways for assimilatory nitrate reduction (p < 0.01), the cytochrome bc1 complex (p < 0.005) and cytochrome c oxidase cbb3 (p < 0.05) were significantly enriched as well. In addition, there were several pathways such as the non‐oxidative phase of the pentose phosphate pathway and prokaryotic NADH quinone oxidoreductase that were significantly enriched at Piccard with average MCS > 0.85.Given these correlations, we tested whether certain pathways were enriched in groups of MAGs with similar transcript abundance patterns across vent sites  transcripts and certain hydrogenase transcripts at Von Damm. We also observed a higher diversity of transcripts at certain sites at Von Damm, with Ginger Castle, Hot Cracks and Old Man Tree hosting transcripts across a wider set of genes than at vent sites in Piccard. All of these results are consistent with the fluid chemistry of Von Damm, an ultramafic vent site where methanogenesis is more thermodynamically favourable and with more sources of carbon than at Piccard, resulting in more energy for microbial metabolism and thus more diverse communities and metabolisms  appeared to be occurring exclusively at Shrimp Hole in both years, while methanogenesis was occurring elsewhere at Von Damm.In addition to Old Man Tree, another clear pattern that emerged was the distinction between Shrimp Hole and other vent sites. We observed differences in gene abundance at the community level, as well as differences in microbial population abundance and transcript abundance in both years, suggesting that Shrimp Hole was unique among the vent sites and stable over time. Although the et al., et al., et al., et al., Both the abundance and the activity of microbial populations at each vent site are dictated by a combination of local environmental conditions, such as mixing regime and subseafloor plumbing, as well as biotic interactions with other microbes. While geochemistry and degree of mixing may explain some of the differences seen in the distribution and activity of populations in some sites, it does not explain the restricted distributions of many MAGs to only 1 or 2 vent sites. Our results also suggest there are barriers to exchange between vent sites even within a vent field for some subseafloor microbial populations, resulting in the restricted distribution pattern of particular MAGs. While the exact nature of the barriers to exchange of microbes between sites is unknown at the Mid‐Cayman Rise, work from Axial Seamount, for example, suggests that variations in the local geology at individual vent sites can create fluid flow paths that enable the establishment of stable and distinct microbial communities , they cannot further reduce nitrite to ammonia (these MAGs lack the nitrite reductase nirA). Nitrate reduction by nap has been previously observed to be highly conserved among deep‐sea hydrothermal vent Epsilonbacteraeota , or it may have 100% potential for 50% of the steps in the pathway. Furthermore, using pathway completion data in isolation may miss context‐specific details of a certain module that can reveal more details than its simple presence or absence. For example, in the pathway analysis alone, the methanogenesis and ANME pathways appear identical, requiring a phylogenetic tree to actually differentiate the two. Our analysis was also limited to specific metabolic pathways present in the KEGG metabolic modules database; other pathways may have driven differences in abundance or expression but were not included in our analysis. Finally, it was previously suggested that viruses may drive differences between strains at the Mid‐Cayman Rise  and the realized niche (based on transcript abundance). Instead, our results at the genome‐resolved scale suggest that differences in the abundance and expression of specific strains are driven by synergistic differences in the complement of genes within populations, as well as local environmental conditions, including fluid mixing regimes that may impact available energy sources or restrict exchange of organisms between sites. These results highlight the importance of comparing microbial communities at the genome‐resolved scale to better understand microbial community adaptation.et al.     were identified in all metagenomic contigs based on KEGG annotations through the JGI IMG pipeline. The following reference mcrA sequences were identified in the NCBI Protein database: ANME mcrA uncultured archaeon (AAQ63481.1), ANME mcrA uncultured archaeon (AAQ63476.1), ANME mcrA ANME‐2 cluster archaeon HR1 (PPA79495.1), ANME mcrA uncultured archaeon (CBH39484.1), ANME‐1 mcrA (AFS68368.1), and ANME‐1 mcrA (ADW65684.1). mcrA sequences were also identified in the following genomes on the NCBI Genomes database: Methanothermobacter marburgensis str. Marburg (MTBMA_RS07375), Methanosarcina barkeri strain Fusaro (MBAR_RS04805), Methanothermobacter thermautotrophicus strain ATCC 29095 (MTH_RS05360), Methanopyrus kandleri strain AV19 (Q49605.2), Methanococcus voltae AE (MVOL_RS05625), Methanothermus fervidus DSM 2088 (MFER_RS03735), Methanococcus vannielii SB (MEVAN_RS04455), Methanocaldococcus jannaschii strain ATCC 43067 (MJ_RS04540), Methanococcus maripaludis S2 (MMP_RS08025), Methanobrevibacter ruminantium strain ATCC 35063 (WP_012956722.1), Methanocella arvoryzae strain DSM 22066 (RCI_RS05310), Methanococcoides burtonii strain DSM 6242 (MBUR_RS12560), Methanothrix soehngenii strain ATCC 5969 (WP_013718622.1), and Methanoculleus bourgensis strain ATCC 43281 (CCJ36661.1). To make a phylogenetic tree of mcrA genes, amino acid alignments were generated using muscle v3.8.31 , Methanocaldococcus jannaschii DSM 2661 (L77117.1), Methanococcus maripaludis C7 (CP000745.1), Methanococcus voltae A3 (CP002057.1), Methanocorpusculum parvum strain XII (LMVO01000001.1), Methanomicrobia archaeon JdFR‐19 JGI24020J35080_1000033 (MTMG01000017.1), Methanomicrobiaceae archaeon UBA2 (DAQH01000006.1), Methanopyrus kandleri AV19 (AE009439.1), Methanosarcina barkeri MS (CP009528.1), Methanosphaerula palustris E1‐9c (CP001338.1), Methanothermobacter thermautotrophicus str. Delta H (AE000666.1), Methanothermococcus okinawensis IH1 (CP002792.1), and Thermococcus barossii strain SHCK‐94 (CP015101.1). We used PhyloSift to identify single copy universal genes. The concatenated protein alignments generated by PhyloSift were used to create phylogenetic trees using RAxML using the same method as above. Trees were visualized using the Interactive Tree of Life website (http://www.itol.embl.edu)  database. All code used for this project has been deposited to GitHub at Supplementary Table 1. Temperature, cell count, pH, and geochemistry data associated with each of the samples analysed for this study. No MT, no metatranscriptome was sequenced for this sample. B.d., below detection levels.Supplementary Table 2. Information about all MAGs analysed in this study.Supplementary Table 3. KEGG module accession numbers.Supplementary Table 4. Modules that were significantly enriched according to MCS at Piccard relative to other vent fields or sites.Supplementary Table 5. Modules that were significantly enriched according to MCS at Shrimp Hole relative to other vent fields or sites.Supplementary Table 6. Modules that were significantly enriched according to MCS at Von Damm and Shrimp Hole relative to Piccard.Supplementary Table 7. List of single‐copy COGs used for normalization of gene abundances based on metagenomic mappings.Click here for additional data file.


Pathway analysis is a common approach in diverse biomedical studies, yet the currently-available pathway tools do not typically support the increasingly popular personalized analyses. Another weakness of the currently-available pathway methods is their inability to handle challenging data with only modest group-based effects compared to natural individual variation. In an effort to address these issues, this study presents a novel pathway method PASI  and demonstrates its performance on complex diseases with different levels of group-based differences in gene expression. PASI is freely available as an R package. The increasing relevance of personalized medicine makes it essential to have robust bioinformatic tools that enable sample-specific results. Pathway analysis is an example of a common approach in diverse biomedical studies , 2, for Another limitation with the current methods is their inability to handle data where the group effects on gene expression are moderate compared to natural individual variation. Yet the ability to analyze such challenging data is crucial for novel and subtle discoveries. Additionally, most of the available pathway methods require a list of DE genes as an input, making them by default unsuited to analyze data with only few, if any, such genes –10. In oHere we present a novel pathway method PASI  as a way to address these outstanding issues. Therefore, PASI is designed 1) to provide a pathway deregulation score separately for each sample, 2) to utilize pathway structures, and 3) to highlight group-based differences in the expression even when the group effects are modest. To assess the performance of PASI with regard to these goals, we used six publicly available T1D datasets, including both recently diagnosed T1D cases and early state T1D cases before diagnosis, as well as three datasets on asthma, leukemia, and juvenile idiopathic arthritis (JIA) see . Especiahttps://www.btk.fi/research/computational-biomedicine/.We demonstrate the ability of PASI to highlight the differences between the sample groups as compared to two other state-of-the-art pathway methods providing results in similar format, Pathifier  and PerPPASI was tested using nine different datasets, which contain data on T1D, asthma, Juvenile Idiopathic Arthritis (JIA), or leukemia. The nine datasets were based on seven studies: GSE30211, GSE9006, MTAB1724, GSE55098, GSE27011, GSE79970, and GSE22529, which are publicly available in the Gene Expression Omnibus (GEO)  or in ArIn T1D related datasets, the case samples were either recently diagnosed T1D patients or individuals with seroconversion i.e. auto-antibody detection but before diagnosis. Datasets including case samples before diagnosis were expected to be more challenging to analyze than those after diagnosis, with smaller disease-based changes in gene expression. JIA data represented RNA-seq data in contrast to the other microarray datasets and leukemia data represented data with large disease effect. PASI is designed to detect moderate differences between sample groups so cancer is not in its main focus. However, as both Pathifier and PerPAS have been validated with cancer data, we tested also PASI on leukemia.Dataset GSE30211  includedIn the dataset GSE9006  we compaIn the dataset GSE55098  we used Dataset MTAB1724  includedFrom the dataset GSE27011  we compaFrom the dataset GSE79970  we utiliIn the dataset GSE22529  we compaIn this section, we present an overview of the working principles of PASI. More details about file formats are available in the user manual provided with the R package and detailed description of the method is provided in Gene expression data matrix including preferably at least 15 healthy control samplesMapping from gene/probe IDs to Entrez IDsVector of labels indicating case and control samples andKEGG pathways Version 1.0.0 of PASI requires as inputStep 1 Preprocess the gene expression data to produce scaled, noise-filtered gene-level values.Step 2 Preprocess the pathway files from KEGG pathway database to extract information about their structure and composition.Step 3 Transform the gene expression values from Step 1 and pathway information from Step 2 to pathway node values describing their similarity to the activity in normal situation.Step 4 Calculate the sample-specific pathway deregulation scores using pathway node values from Step 3 and importance of the nodes defined using pathway information from Step 2.In the terminology used here, a node is a functional unit of a pathway, typically a gene product, and a relation is a directional interaction between two nodes. The final pathway scores of PASI describe similarity of pathway’s activity to normal activity  rather than actual pathway activity. The steps of the PASI workflow are:To demonstrate the utility of PASI, we tested it from different perspectives as detailed below.data is the real dataset including samples from two groups (case and control) and mock_datai refers to ith randomized mock data. The higher the FDR ratio, the better a method distinguishes real differences from random noise.To evaluate the performance of PASI in comparison to the other pathway approaches, Pathifier, and PerPAS, we used logarithmic ratios of average FDR values between real and mock data (later denoted as FDR ratio). First, we compared pathway scores of case and control samples in each pathway using Wilcoxon test. The resulting p-values were then converted to FDR values and their average  to the well-known MAPK signaling pathway in the dataset T1D_1, where it originally was among top detections with PASI, based on the difference in the median pathway scores between the case and control samples. Uncertainty was simulated by randomly removing 5, 10, 20, 30, 40 or 50 percent of relations or nodes from the pathway, 100 times for each proportion. More specifically, we investigated whether the pathway remained in the top 25 detections (approximately 10% of tested pathways) despite uncertainty in the structure (simulated by removing nodes/relations from the pathway).www.qiagen.com/ingenuity, accessed 13-September-2016). For testing, we manually constructed IFN and IRF pathways into the KEGG pathway format [Two pathways previously suggested to be enriched with biologically-relevant genes in T1D , 20, 25,y format , gave thcomparison of PASI to the closest state-of-the-art pathway methods Pathifier and PerPAS,investigation of the effects of gene-level scaling and adding pathway information in PASI algorithm,assessment of the effect of sample size on the pathway results,assessment of the effect of uncertainty in pathway structures on the results, andbiological relevance of the results.We tested the performance of PASI in nine datasets, each including a control group of healthy individuals and a case group of either recently diagnosed T1D patients (datasets marked with the prefix T1D), measurements after seroconversion, but before T1D diagnosis (datasets marked with the prefix Sero), severe asthma (dataset Asthma), JIA or leukemia. Since it would be misleading to use a single metric to estimate the usefulness of a method, we assessed the performance of PASI in a variety of ways includingWe first compared PASI results to Pathifier  and PerPFor most of the datasets processed, PASI had consistently higher FDR ratios than Pathifier and PerPAS . All metThe FDR ratios in different datasets were also calculated directly based on the scaled gene expression profiles . Different sample sizes between 5 and 30 were tested with ten randomly-generated data at each size.Overall, the reduction of the sample size did not have a large negative impact on the findings  direct relevance to the biological phenomenon (in this case T1D), (b) overlap with (a), or (c) no (current) known relevance to the phenomenon in question . In ordeecretion  and PRL ecretion . Two verecretion –36. Besiecretion , 20. ConWe introduced here a new pathway analysis tool, PASI, and illustrated its competitive performance. The results were good (FDR ratio ≥ 1.26) in all tested datasets with diagnosed T1D, asthma, JIA or leukemia case samples and relatively good (FDR ratio ≥ 0.89) even in the challenging data with case samples before T1D diagnosis. Although, with large datasets including over 60 samples (T1D_4 and T1D_2) Pathifier performed very well, sample size had a notable impact on the Pathifier results and it seemed to require more samples to perform reliably compared to PASI. While PerPAS did not have the top performance in any of the tested datasets, it outperformed Pathifier in most of the small (under 60 samples) datasets. With PASI, we recommend using at least 15 control samples to keep the false positive effect moderate.Importantly, PASI was not sensitive to small uncertainty in pathway structure (e.g. where missing node / relation percentage did not exceed 10%), which is critical as the pathway structures represent only the current knowledge instead of the absolute truth, and are frequently updated as more information becomes available. As pathway topologies are updated with experimental evidence, it is expected that methods such as PASI that use pathway topology only increase in their utility. Our previous observations about group-level pathway methods suggest that currently, methods applying structural information in relatively general level outperform methods that apply it in detail or do not apply it at all . TherefoOverall, our results demonstrated the utility of pathway-level analysis in identifying moderate group effects. Results obtained without using pathway information did not distinguish the sample groups at all (FDR ratio below or close to 0) in most of the tested datasets . Adding Some of the original papers introducing the data utilized in this study included pathway analysis. Our PASI analysis reproduced many, but not all, of the original findings and introduced several new pathway detections as well. Some examples of the reproduced findings were N-Glycan biosynthesis from dataset Asthma, RIG-I-like receptor signaling pathway from datasets T1D_1 and Sero_1, apoptosis pathway from dataset T1D_2 and Wnt signaling pathway from Leukemia. As reported in section Biological interpretation in T1D datasets, pathway Prolactin signaling was an example of a PASI finding detected systematically from different T1D related datasets, but not by the original publications.In PASI, we chose to use pathway deregulation scores describing similarity to normal rather than scores describing pathway activity. Whilst the pathway activity scores can provide more information than deregulation scores, their weakness is unsuitability for pathways with inhibiting relations. Inhibiting relations in a pathway indicate that not all nodes are highly expressed when the pathway is active. Due to structures like inhibitor of an inhibitor node, it is highly nontrivial task to computationally define which pathway nodes should be highly expressed when the pathway is active. While on average the portion of inhibiting relations in KEGG pathways is currently about 12%, in several pathways more than half of the relations are inhibiting, making those pathways unsuited for simple pathway activity scoring methods assuming that high expression of the nodes indicates high pathway activity.Besides developing better pathway tools, pathway-based studies can be improved by upgrading pathway databases. A number of databases exist with their own focus and special characteristics that make them suited for studies of different topics. However, at the moment, very few pathway databases are free, provide directed interactions, and offer the pathways in a computer-friendly format. The possibility to utilize a larger range of databases may have the potential to significantly enhance pathway analysis and all its applications.PASI enables the identification of group effects even if this effect is small in comparison to individual variation. The intended main applications of PASI lie in studies related to personalized medicine and predictive modeling. PASI is less suited for mechanistic studies where the focus is on detecting few pathways which differ in their activity between the test groups or revealing information about mechanisms behind the conditions. Since PASI is designed to be robust to limited uncertainty in pathway structures, it is currently not sensitive to detect whether assumed structures are incomplete, which is a different research topic with its own methods .S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S1 AppendixDetailed description of PASI workflow.(PDF)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.


SPNs can be surgically resected, there are currently no therapies available for patients with advanced stage disease. Given that these tumors frequently carry CTNNB1 hotspot (recurrently mutated loci in a gene) mutations resulting in β‐catenin nuclear accumulation, it has been speculated that the Wnt pathway may be a driver in this disease. Here, we present a comprehensive “multi‐omics” study where the genome, transcriptome, and methylome of SPNs were analyzed. We found that SPNs are characterized by a low‐complexity genome where somatic mutations in CTNNB1, present in 100% of the cases, are the only actionable genomic lesions. Compared to more common subtypes of pancreatic tumors (adenocarcinomas and pancreatic neuroendocrine tumors), SPNs show high expression levels of genes belonging to the Wnt pathway. Their methylome was consistent with an epithelial cell origin and a general upregulation of Wnt pathway genes. Clinical studies to evaluate the exquisite sensitivity of SPNs to inhibitors of the Wnt pathway are warranted.Solid pseudopapillary neoplasms (SPNs) are rare and relatively indolent tumors of the pancreas. While primary  ADCadenocarcinomaENCODEencyclopedia of DNA elementsindelsmall insertion and deletionPNETpancreatic neuroendocrine tumorsSNVsingle nucleotide variantSPNsolid pseudopapillary neoplasms1et al., et al., Solid pseudopapillary neoplasms (SPNs) are rare pancreatic tumors of unknown etiology, accounting for ~ 1% of all pancreatic exocrine neoplasms. SPNs typically present as large, solitary, well‐circumscribed lesions with indolent clinical course and a low propensity to metastasize . Hotspot mutations in CTNNB1 can cause abnormal accumulation of nuclear β‐catenin through stabilization of the protein and eventually constitutive activation of the pathway.Solid pseudopapillary neoplasms are characterized by the presence of somatic Here, we assessed whether SPNs are dependent on the Wnt signaling pathway and performed a comprehensive “multi‐omics” profiling of SPNs collected at our institution during the last two decades to define their repertoire of somatic mutations and copy number alterations, the transcriptional activation of the Wnt pathway and their methylation profiles.22.1Patients with written informed consent for research that had a diagnosis of SPN treated at Memorial Sloan Kettering Cancer Center (MSKCC) were identified from our institutional database. Clinicopathological characteristics of the SPN patients are shown in Table 2.2et al., Immunohistochemical analysis for β‐catenin  was performed on 4‐μm‐thick sections from representative formalin‐fixed, paraffin‐embedded SPN tissue blocks, as previously described         were discarded. Somatic copy number alterations and loss of heterozygosity were obtained using facets   , fibroblast (n = 10), and muscle cell (n = 6) origin were downloaded from encyclopedia of DNA elements (ENCODE) project  or pancreatoblastomas (n = 2)  , which, based on ontology, would have resulted in upregulation of the respective transcript (e.g. WNT5A). Conversely, hypermethylated probe sets corresponded with genes whose transcripts were downregulated in SPNs , receptor components (LRG6 or FZD10), or other key proteins for signal transduction such as APC. Although these are not formally demonstrated to be Wnt/β‐catenin pathway target genes, their higher expression could suggest and enhanced activation of the pathway additional to that promoted by CTNNB1/β‐catenin activating mutations.We also observed that other genes codifying components of the Wnt pathway are highly expressed in SPN than ADC, such as ligands  and ENCODE samples of epithelial origin.Click here for additional data file.


Molecular network changes are the hallmark of the pathogenesis of ovarian cancers (OCs). Network-based biomarkers benefit for the effective treatment of OC.This study sought to identify key pathway–network alterations and network-based biomarkers for clarification of molecular mechanisms and treatment of OCs.Ingenuity Pathway Analysis (IPA) platform was used to mine signaling pathway networks with 1198 human tissue mitochondrial differentially expressed proteins (mtDEPs) and compared those pathway network changes between OCs and controls. The mtDEPs in important cancer-related pathway systems were further validated with qRT-PCR and Western blot in OC cell models. Moreover, integrative analysis of mtDEPs and Cancer Genome Atlas (TCGA) data from 419 patients was used to identify hub molecules with molecular complex detection method. Hub molecule–based survival analysis and multiple multivariate regression analysis were used to identify survival-related hub molecules and hub molecule signature model.Pathway network analysis revealed 25 statistically significant networks, 192 canonical pathways, and 5 significant molecular/cellular function models. A total of 52 canonical pathways were activated or inhibited in cancer pathogenesis, including antigen presentation, mitochondrial dysfunction, GP6 signaling, EIF2 signaling, and glutathione-mediated detoxification. Of them, mtDEPs  in those canonical pathway and molecular/cellular models were validated in OC cell models at the mRNA and protein levels. Moreover, 102 hub molecules were identified, and they were regulated by post-translational modifications and functioned in multiple biological processes. Of them, 62 hub molecules were individually significantly related to OC survival risk. Furthermore, multivariate regression analysis of 102 hub molecules identified significant seven hub molecule signature models  to assess OC survival risks.These findings provided the overall signaling pathway network profiling of human OCs; offered scientific data to discover pathway network-based cancer biomarkers for diagnosis, prognosis, and treatment of OCs; and clarify accurate molecular mechanisms and therapeutic targets. These findings benefit for the discovery of effective and reliable biomarkers based on pathway networks for OC predictive and personalized medicine.The online version of this article (10.1007/s13167-019-00170-5) contains supplementary material, which is available to authorized users. Ovarian cancer (OC) is one of the gynecologic malignancies, with high morbility and mortality, and is often diagnosed at its late stage . It is aHigh-throughput proteomics offers a promising approach to identify effective cancer biomarkers . Especiahttp://www.ingenuity.com) is a popular and extensively used pathway network analysis platform to mine statistically significant canonical pathways, network, and network-based hub molecules from differentially expressed proteomic data [Ingenuity Pathway Analysis (IPA)  and 11 control ovaries with benign gynecologic disease  were obtained from Department of Gynecology, Xiangya Hospital, Central South University, China. This study was approved by the Medical Ethics Committee of Xiangya Hospital, and the written informed consent was obtained from each patient. Both OC and control tissues were verified by histological analysis. Each tissue sample was immediately placed in liquid nitrogen and then stored at − 80 °C.A total of 1198 mtDEPs dataset was obtained with iTRAQ quantitative proteomics analysis of mitochondrial samples that were isolated and purified from human epithelial OC and control ovary tissues , 14. BriIngenuity Pathway Analysis (IPA) software is a well-cited stand-alone bioinformatics analysis tool to analyze signaling pathways and molecular networks. For the mtDEP data, the Swiss-Prot accession numbers and the corresponding fold changes between OC and control ovaries were input to the IPA data upload workflow. The matched genes/molecules can be indexed automatically with IPA, and the output table contains detailed description of mapped gene/molecules. The input IDs (proteins and genes) were grouped into different categories, including the all IDs , unmapped IDs (no match is found in the IPA system), and mapped IDs (match the corresponding molecules and recognize the duplicate IDs). For the duplicate IDs, the identifier with the highest fold change or the first instance was used in the pathway analysis.2 and atmosphere at 37 °C.OC cell lines TOV-21G and SKOV3 cells and control cell line IOSE80 cells were purchased from Keibai Academy of Science . TOV-21G cells were cultured in RPMI-1640 medium, and SKOV3 and IOSE80 cells were cultured in DMEM medium , supplemented with 10% fetal bovine serum . All these cells were maintained with 5% COTotal RNAs from cell lines were extracted with TRizol reagent (Invitrogen) according to the manufacturer’s instructions. Total RNAs were reversely transcribed into cDNAs and then used to perform qRT-PCR with SYBR Premix ExTaq (TaKaRa). Beta-actin was used as an internal control for mRNA quantification.Equal amounts of proteins were separated by 10% SDSPAGE gels and transferred onto PVDF membranes. The proteins on the membrane were incubated with primary antibodies against TPM1, CALR, GSTP1, CPT2, and AKAP12  and LYN and β-actin  at 4 °C overnight and then were incubated for 2 h with horseradish peroxidase-conjugated goat antirat secondary antibody  at room temperature.p < 0.05). GO biological process (BP) of those hub molecules was analyzed with Cytoscape ClueGO . Chromosome location, cell location, post-translational modifications (PTM), and analysis of prognostic values for these hub molecules were performed by R package (https://www.r-project.org/), SysPTM (http://lifecenter.sgst.cn/SysPTM/), and Kaplan–Meier plotter (http://kmplot.com/private/index.php.p=home), respectively. Biomarkers that had been reported were checked by CooLGeN (http://ci.smu.edu.cn/CooLGeN/Home.php).To evaluate their interactive associations, all mtDEPs were mapped to the STRING database. Subsequently, the protein–protein interactions (PPIs) were analyzed by Cytoscape software  to obtain the PPI network. The criteria of hub molecule searching were set as the molecular complex detection (MCODE) score > 6, and a statistically significant difference (http://cancergenome.nih.gov/). Level 3 RNA-seq V2 data were obtained from the TCGA data of 419 OC patients. The expression data of hub molecules were extracted to do co-expression analysis by RStudio.TCGA data portal provides a platform for researchers to search, download, and analyze datasets generated from TCGA database . Statistical analyses were performed using SPSS 13.0 (SPSS Inc.). A Benjamini–Hochberg was used to calculate the adjusted p value to determine the probability of the association between the genes in the experimental dataset and the canonical pathway in the IPA database. The level of statistical significance was set to p < 0.05. Activation z score was calculated to evaluate that the canonical pathway was activated or inhibited. If z score ≥ 2, it means that the canonical pathway was more likely to be activated. If z score ≤ − 2, it means that the canonical pathway was more likely to be inhibited. The hub genes were subjected to SPSS20 to perform multivariate regression analysis to calculate the regression coefficient for each gene with statistical significance of p < 0.05.For the qPCR and Western blot analysis, each experiment was repeated at least three times; data were expressed as the mean ± SD of triplicates. The Student’s p < 0.05) were identified to involve the identified mtDEPs  or inhibited (z score ≤ − 2). Further analysis of the relationships between those 52 activated or inhibited canonical pathways and cancer biology revealed 29 cancer-related pathways . The important tumor-associated pathways were antigen presentation pathway , HLA-A , HLA-C , HLA-DPA1 , HLA-DPB1 , HLA-DQB1 , HLA-DRA , HLA-DRB1 , HLA-DRB5 , HLA-E , PDIA3 , and TAP2 , which may represent novel therapeutic targets for the prediction and eventual improvement of the response to therapy in patients with OC. Some key molecules, such as CALR, HLA-A, HLA-DRB1, PDIA3, HLA-DQB1, HLA-E, and TAP2, have been implicated in various types of cancers and emphasized their important values related to the tumor progression [Along with the advancement in tumor immunology, the immune-checkpoint blockade therapy has been an important part in the mode of combined therapy of tumor. One of the most important immune checkpoint pathways has been applied between the PD-1 receptor expressed on activated T cells and its ligands, programmed death-1 ligand (PD-L1) and PD-L2. With the arrival of PD-1 in the domestic antitumor drug market, the hot topic of tumor immunotherapy appears frequently in people’s view. A large number of studies on immunotherapy combined with other therapies are also in full swing. Treatment approaches that target the PD-L1 have yielded objective responses in a subset of individuals with advanced carcinomas in some clinical trials . The mtDgression . Immunotp = 0.006), AIFM1 , ATPAF1 , ATPAF2 , BCL2 , COX17 , COX4I1 , COX4I2 , COX6C , COX7A2 , COX7A2L , CYCS , GPX7 , HTRA2 , MAOB , TXN2 , and UQCRH . Some key molecules identified with quantitative mitochondrial proteomics, such as BCL2, PRDX5, AIFM1, VDAC1, HTRA2, COX1, and CYTB, were reported in previous studies [During the last decade, a great attention has also been paid to energy metabolic reprogramming of cancer. However, cancer basic studies fail to reach a consistent conclusion on mitochondrial function in cancer energy metabolism . The mtD studies , 14. Meap = 0.0003), COL10A1 , COL16A1 , COL1A1 , COL1A2 , COL2A1 , COL3A1 , COL4A2 , COL5A2 , COL6A1 , COL6A2 , COL6A6 , COL8A1 , FGFR4 , ITPR1 , LAMA2 , LYN , PRKCA , and PTPN11 . Some tumor-related molecules in this pathway are frequently reported in recent years. PTPN11 is a member of the protein tyrosine phosphatase family. PTPs are well known to be signaling molecules that regulate a series of cellular processes, including cell migration, differentiation, metabolic control, cell growth, oncogenic transformation, and mitotic cycle [GP6 is widely recognized as a requisite factor for the formation of platelet aggregation on a collagen surface under blood flow. Currently, platelets play a critical role in cancer development, progression, and spread of malignancy. Platelets activated by cancer cells were always detected in vitro, and the similar results appeared in clinical studies that increased levels of platelet activation in cancer patients. Moreover, platelets are in all probability involved in the development of venous thromboembolism  in cancer patients associated with high mortality . Those dic cycle . ProteinThe translation initiation factor EIF2 catalyzes the first regulated step of protein synthesis initiation, promoting the binding of the initiator tRNA to 40S ribosomal subunits. EIF2 is composed of 3 nonidentical subunits, the 36-kD EIF2-alpha subunit EIF2S1), the 38-kD EIF2-beta subunit (EIF2S2), and the 52-kD EIF2-gamma subunit (EIF2S3). EIF2S1, EIF2S2, and EIF2S3 were all reported to associate tumor progression . The mtD, the 38-Beyond those important tumor-associated pathways, the other significant pathways were also closely related to the tumor occurrence and development, such as mTOR signaling, Gap junction signaling, OC signaling, protein ubiquitination pathway, and PPAR-α/RXRα activation , CALR , GSTP1 , CPT2 , AKAP12 , and LYN , with the expression levels of mRNAs and proteins of those DEPs in the cultured OC cells TOV21G and SKOV3 and control cells IOSE80. The significant changes in the mRNA and protein expression levels of TPM1, CALR, GSTP1, CPT2, AKAP12, and LYN were observed in the cultured cells by qRT-PCR and Western blot, respectively . Among those OC quantitative mitochondrial proteomics data, 25 networks were identified to involve mtDEPs  revealed that 62 of 102 hub molecules resulted in significant OC overall survival. A total of 12 hub molecules, including CXCL12 , HSPA5 , P4HB , HSPE1 , LUM , XRCC6 , ALDH1L1 , RPS19 , GSTM3 , PRKACB , COL1A1 , and COL6A2 , were coincidence with previous studies  + (0.171 × expression level of ALB) + (0.115 × expression level of RRAS2) + (0.101 × expression level of HIBCH) + (− 0.120 × expression level of EIF3E) + (0.228 × expression level of RPS20) + (− 0.184 × expression level of RPL23A). Higher score of this model indicated longer survival time or lower mortality risk for OC patients.Moreover, survival risk score system was constructed based on hub molecule signature. Those 102 hub molecules were subjected to SPSS 20 to construct a survival risk score system by multivariate regression analysis. The regression coefficient for hub molecule was generated Table . The sigMolecular network changes are the hallmark in the pathogenesis of OCs and benefit for the discovery of effective and reliable biomarkers for early diagnosis, prognostic evaluation, or targeted therapy to prolong survival time of the patients . Mitochon = 1198) were identified to offer a direct data for analysis of mitochondrial function abnormalities in OCs [z score > 2  to assess EOC survival risks. Comprehensive analysis of all obtained data, including canonical pathways, networks, TCGA data, and experimental validated data, revealed several signaling pathways and networks were significantly associated with OC through regulating fundamental biologic behavior of tumor, such as immune dysregulation, tumor promotion inflammation, deregulating cellular energetics, cell death, proliferation signaling, and tissue invasion and metastasis. Two important OC-associated pathway network systems are in-depth discussed here, including immune dysregulation and cellular energetics.In our long-term program of mitochondrial proteomics in OCs, we firstly quantitatively mapped 5115 mitochondrial proteins in OC mitochondrial samples and 52 statistically significant KEGG pathways from those mapped 5115 mitochondrial proteins , which is in OCs , 18. Hers in OCs , and twos in OCs  and quans in OCs , with acImmune dysregulation signaling pathway system: An emerging hallmark of tumor evading immune dysregulation was still an unresolved issue as to how immune system plays in resisting formation and progression of tumors or micrometastases . Immune Cellular energetic signaling pathway network: An emerging hallmark of reprogramming energy metabolism made researches refocus efforts to a novel anticancer strategy on cancer cells. The study of tumor cell energy metabolism is multiangle, which focused on glucose metabolism and lipid metabolism in the past 10 years. The metabolism of glucose to lactic acid in the presence of oxygen had been recognized in cancer cells, commonly called the Warburg effect . HoweverMoreover, some other canonical pathways and hub molecules also get ones to pay more attention to, including iron homeostasis signaling pathway, endoplasmic reticulum stress pathway, inhibition of matrix metalloproteases, mTOR signaling, protein ubiquitination pathway, RAS activation, RhoA signaling, role of tissue factor in cancer, and role of JAK family kinase in IL-6 type cytokine signaling. All those pathways are worth further studying to verify their relationship with OCs.In addition to the pathway analysis, the key molecules in those pathway network system were also extensive analyzed with multiple methods, including molecular and cellular functions, network, and hub molecule analyses, which were potential biomarkers for the development of an OC, such that CXCL12, HSPA5, P4HB, HSPE1, LUM, ALDH1L1, RPS19, GSTM3, PRKACB, COL1A1, and COL6A2, as reported were significantly related to overall survival. To some degree, these results indicate that our findings were consistent with previous studies and also make new discoveries. Mitochondrial changes lead to morphology and functions transformation and influence downstream metabolic processes. Mitochondrial proteins can be developed as molecular biomarkers or therapeutic targets in the development of new interventions to selectively kill cancer versus normal cells . For exan = 419) was acceptable for analysis of hub molecules, survival analysis, and survival regression model in the mRNA level. However, protein is the final performer of gene. It would be necessary to further verify those hub molecule biomarkers and seven hub molecule signature models  in the protein level among significantly increased clinical samples.This study focused on mitochondria proteome and its involved molecular network alterations in OCs to reveal mitochondria-related signaling pathways and candidate biomarkers. Seven epithelial OC tissues and 11 control ovaries with benign gynecologic disease were used to prepare OC and control mitochondria for identification of 1198 mtDEPs, followed by construction of 25 statistically significant networks and 192 canonical pathways. Some important molecules, including TPM1, CALR, GSTP1, CPT2, AKAP12, and LYN, in those pathway networks were further verified in OC cells (TOV-21G and SKOV3) compared to normal ovarian cells (IOSE80), which showed the consistent results with the results of tissue mitochondrial proteomics. These findings provided the scientific data to better understand the roles of OC mitochondrial proteome from systems biology angle. Here, one should realize that (i) mitochondria from 7 OC tissues were mixed as the cancer mitochondrial sample, and mitochondria from 11 control ovaries were mixed as the control mitochondrial sample, and then the cancer and control mitochondrial samples were used for quantitative proteomics analysis to decrease between-individual heterogeneity. However, for identification of mtDEPs as biomarkers, it is necessary to verify them in an expanded OC and control ovary tissues in future. (ii) For OC cell lines, there were many types of OC cell lines, including epithelial cells , epithelial-like cells , and endometrioid OC cells . Cell lines SKOV3 and TOV-21G used in this study were from American Type Culture Collection (ATCC), and they were frequently used in OC-related studies. SKOV3 isolated from ovarian adenocarcinoma was one of serous epithelial ovarian carcinoma cell lines and moderately differentiated. TOV-21G from ovarian surface epithelium was isolated from primary malignant adenocarcinoma. SKOV3 and TOV-21G were all tumorigenic in nude mice to indicate malignancy degree of tumor. The control ovarian cell line (IOSE80) was also from normal ovarian surface epithelium, and it is reasonable to be used as control group. In addition, three cell lines SKOV3, TOV-21G, and IOSE80 in this study were consistent in cell origin  with our analyzed OC and control tissues; thus, those three cell lines can be used to verify our tissue proteomics results. However, for identification of mtDEPs as biomarker or therapeutic targets, it is necessary to use more OC cell lines and normal control cells for future validation. (iii) In order to determine the clinical values of 102 hub molecules derived from 1198 mtDEPs, the present survival analysis and multivariate regression analysis of those hub molecules were not based on those 7 OC and 11 control ovary tissues used for proteomic analysis, but based on 419 OC patients in the TCGA database through obtaining TCGA mRNA data corresponding to 102 OC-tissue mtDEPs. Therefore, survival analysis and multivariate regression model analyses were derived from mtDEP-corresponding mRNA data among 419 OC patients and their clinical survival data. Thereby, the sample size  were identified with integrative analysis of 1198 mtDEPs and TCGA transcriptomic data of 419 OC patients, including 62 hub molecules related to survival risk of OCs. Moreover, statistically significant seven hub molecule signature models  were constructed for assessment of survival risk of OC patients. These findings provided pathway network database of OCs. Moreover, in-depth analysis of hub molecules and construction of seven hub molecule signature models could assist in discovery of potential biomarkers and novel mechanisms of ovarian carcinogenesis.We recommend this research article to promote mitochondria-based molecular network studies in OCs from multiparameter systematic opinion and emphasize the importance of OC multiomics such as integrating proteomics and transcriptomics in basic research and translational and application research in the field of personalized medicine in OCs. Especially, molecular network-based biomarkers are important for reliable and effective in personalized diagnosis and prognosis assessment of OCs, and molecular network-based clarification of molecular mechanisms is important for discovery of effective and reliable drug targets for OC personalized treatment. The identified survival-related hub molecules and seven hub molecule signature models are important resource of pattern biomarker for personalized medicine in OCs.ESM 1(PDF 5356 kb)ESM 2(PDF 3550 kb)ESM 3(PDF 3766 kb)ESM 4(PDF 2864 kb)ESM 5(XLS 242 kb)ESM 6(XLS 142 kb)ESM 7(XLS 51 kb)ESM 8(XLS 794 kb)ESM 9(XLS 86 kb)ESM 10(XLS 44 kb)