Epilepsy is a complex neurological disorder and a significant health problem. The pathogenesis of epilepsy remains obscure in a significant number of patients and the current treatment options are not adequate in about a third of individuals which were known as refractory epilepsies (RE). Network medicine provides an effective approach for studying the molecular mechanisms underlying complex diseases. Here we integrated 1876 disease-gene associations of RE and located those genes to human protein-protein interaction (PPI) network to obtain 42 significant RE-associated disease modules. The functional analysis of these disease modules showed novel molecular pathological mechanisms of RE, such as the novel enriched pathways . Further analysis on the relationships between current drug targets and the RE-related disease genes showed the rational mechanisms of most antiepileptic drugs. In addition, we detected ten potential novel drug targets  located in three RE related disease modules, which might provide novel insights into the new drug discovery for RE therapy. Seizures are abnormal firing of brain cells which may cause changes in behavior or attention, which affect over 65 million people in the world. The cauisorders –4. Seizuisorders . Microglisorders . Antiepiisorders ,10, whicisorders –14.The Aisorders . Howeverisorders .Furthermore, to fully address the drug resistance and side effects of AEDs, researchers have tried to investigate the underlying molecular mechanisms of RE to search for novel molecular targets and drugs. Meta-anMolecular and genetic studies of epilepsy over the recent decades have produced an impressive list of disease-gene associations, together with the interactome network database, which can be used to investigate the molecular network mechanisms and potential drug targets related to epilepsy. MoleculThe MeSH classification was defined by experts and provided a comprehensive vocabulary across all disease categories, which were systematically organized in a hierarchical tree,26. BaseDetailed human PPI data was downloaded from the STRING 9.1 database. The STRTopological module refers to the subnetwork of one entire network, which has relatively dense links when compared with the links outside of the subnetwork. BGLL, aMany online analysis platforms, as well as relevant analysis softwares, are available for GO enrichment analysis ,37. In tThe BiNGO 2.44 plug-in can map the genes to GO terms using a hypergeometric distribution relationship. Then, we can obtain the GO terms with related genes. Through Bonferroni correction to control the false positive rate of analysis, this process will return a p-value. All the results of pathway enrichment analysis were obtained through the online analysis tool KOBAS 2.0, accordilog to transform p-values to correlation values, since the smaller of the corrected p-value small the bigger of the correlation between modules and functional entities. The similarity between the vectors, A and B, of the two modules, A and B, is calculated as follows:i and Bi are components of vector A and B respectively. The range of functional similarity is zero (no similarity of two modules) to one (two modules have same function), and a larger value for similarity represents more functional similarity between two modules. The combined score equals 0.5*((BP similarity+CC similarity+MF similarity)/3) +0.5*Pathway similarity.A widely used method in both text mining and biomedical literature to quantify the similarity between two concepts is the cosine similarity of respective vectors . In thisWe searched for relevant drugs by searching for disease keywords in the ‘indications’ field of drug information obtained from the DrugBank database, which combines detailed drug data with comprehensive drug-target information,44. ShorUsing epilepsy as the keyword, MeSH terms related to RE were extracted and evaluated from the MeSH vocabulary. As shown in The MeSH headings which were listed in this manuscript all were according to the consensus of International League Against Epilepsy (ILAE) ,49. In tTwenty-three MeSH headings were integrated in order to make comparisons to understand the reliability of target screening for correlation with disease phenotype.After filtering the relationships with significant correlations , total 3,219 disease-gene relationships were obtained with the corresponding number of occurrences in the CoreMine PubMed search engine system. All litTo obtain the functional descriptions of RE associated genes, we performed the enrichment analysis of the GO and pathways of these genes. GO contIn addition, it also showed the potential novel mechanisms of RE, which included the enriched GO terms, such as “regulation of programmed cell death” (sixth level), “apoptotic process” (sixth level), “response to ethanol” (sixth level) and “response to nicotine” (sixth level). Recent study suggested that neuronal death seems closely linked to epileptogenesis, but the effect of seizures on neuronal death and the role of seizure-induced neuronal death in acquired epileptogenesis need further investigation,56. SimiFurthermore, we identified 55 enriched pathways  in the Reactome database , in whicDisease associated genes are not distributed randomly on the molecular interaction network and they tend to work together in similar biological modules or pathways. To inveWe combined two filtering conditions to obtain the significant associated modules for RE disorders. Firstly, we calculated the correlation between 185 modules and RE disorders by Chi-Square test (with Bonferroni correction) and finally we found that there are five modules with corrected p-value<0.01, in which module M37 is the last top ranked module with corrected p-value ~3.67e-4 . It suggTo distinguish the core modules from those 42 significant modules, we constructed a network with nodes representing the modules and links representing shared PPI interactions between them . It showWe further examined the enriched GO terms of the 42 modules , in whic2+ through voltage-gated channels, which gives rise to a transient increase in Ca2+ concentration within the presynaptic terminal. Module M37, M65 and M155 had significant enrichment pathways (red color) among the 20 modules. For module M37, the highly enriched individual pathway was “Respiratory electron transport, ATP synthesis by chemiosmotic coupling, and heat production by uncoupling proteins”. Module M65 was closely related to the “Activation of NMDA receptor upon glutamate binding and postsynaptic events” “Glutamate Binding, Activation of AMPA Receptors and Synaptic Plasticity” “Trafficking of AMPA receptors” and “Unblocking of NMDA receptor, glutamate binding and activation”. Module M155 was most related to the “activation of voltage-gated potassium channels”, “GABA receptor activation”. NMDA receptor-mediated signal transduction is critical for synaptic plasticity. In acute and chronic seizures, a selective NMDA receptor antagonist has broad clinical application prospects[Most modules (except module M139 and M150) have their enriched pathways using six pathway databases in KOBAS 2.0. However, we only obtained 317 enriched pathways  for 28 modules using the Reactome database. The 317 pathways were classified into two types according to whether it was located in a single module or multiple modules. We call the former as “individual pathway” and the latter as “common pathway” for RE. Statistical analysis yielded 223 individual pathways located on 27 modules, and 94 common pathways distributed across 20 modules. We generated a heat map to display the 94 common pathways corresponding to their enrichments in 20 modules . It showprospects.To further detect the functional similarity between two module pairs, the vectors corresponding to each module were constructed using their GO terms and pathways as features, and the cosine similarity of the module vectors was calculated to quantify the similarity between the two modules pairs. A combined score was then used to comprehensively evaluate the functional similarity between these modules. The combined scores of the top seven module pairs are shown in Only 36 (36/42 = 85.7%) modules that had functional similarity were classified into five clusters using the community detection algorithm in Gephi software. Each cluster corresponds well to the RE MeSH headings related to the PPI topological modules (RR>1.0). These allocations were based on the observation that genes causing similar diseases tend to link to each other in the interactome ,69. The We have further investigated the associated modules corresponded to each MeSH headings and actually we found that there are distinct modules associated to some specific RE MeSH headings . We founMoreover, frontal lobe epilepsy and temporal lobe epilepsy have very different gene distributions within their modules. Frontal lobe epilepsy with 49 associated genes correlates to two modules when RR>1.0, while temporal lobe epilepsy with 185 associated genes correlates to seven modules when RR>1.0. The therapeutic effect of different brain regions affected by disease varies considerably, which can, to some extent, be demonstrated from the difference in module distribution. Although the module distribution is disparate, there were functional similarities between the modules, which might indicate shared similar molecular pathologies between them.Related antiepileptic terms, such as anticonvulsant, antiseizure, and antiepileptic, were used to obtain the names of current AEDs from the DrugBank and Sider databases . FinallyDrugs do not target diseases equally, but are clearly enriched in some modules. To quanThe most important modules that represented the greatest correlation were selected as the modules that included more drugs that are available. To understand the degree of a module in the RE drug discovery, it would be relevant to normalize the number of drugs by the number of proteins of each module. The number of drugs corresponding to each module was determined statistically . M155 haA disease module, a local neighborhood of the interactome whose perturbation is associated with epilepsy, can be mechanistically linked to a particular disease phenotype,72. The In order to be considered as a novel drug target for epilepsy, the protein still needs to fulfill the following two conditions: 1) no known anti-epilepsy drugs target this protein; 2) The target should exist in the most enriched pathways related to epilepsy. Considering these two conditions, we finally identified six proteins  from M155 as the potential drug targets .The aforementioned functional similarity analysis revealed that module M155 and M65 had the largest correlations. Drugs distribution showed that module M65 had the second highest number of drugs when RR > = 2.0. Similarly, we, obtained four proteins  from module M65 as potential drug targets , and oneIn this study, network medicine approaches were used to integrate the data from multiple databases to investigate the molecular mechanisms and possible drug targets of RE. Total 1086 genes associated with RE were curated after validating each disease-gene relationships from medical literature. This gene list might be the most comprehensive phenotype-genotype association data repository for studying RE molecular mechanisms. Forty two primary disease-related gene networks (modules) were constructed and selected based on the interaction information of gene-encoded proteins in which about 35 modules had significant interactions and functional similarity between each other. These architectures of interaction module indicated the complicated molecular mechanisms of RE and the possible pharmacological targets of RE personalized treatment. The detected RE-related modules provided a potential subtyping of RE from molecular network perspectives. For example, three significant core modules  may contain potential drug targets. The protein components of these three modules were consistent with the current understanding of pathological mechanisms and pharmacology of epilepsy. Besides, the significant enriched pathways, for example, “GABA A receptor activation”, “unblocking of NMDA receptor, glutamate binding and activation”, are likely the main biological mechanisms of RE and deliver highly insightful information for its drug discovery.potassium channels; KCNAs (e.g. KCNA1 and KCNA6) and KCNQ2 are associated with peripheral nerve hyperexcitability in humans[The ten potential targets for antiepileptic medications could be classified into two groups including the ion channel  and glutamate receptors. Consistently, the KCNAs, components of potassium channel, were detected as the potential drug target for RE in this study. Ezogabine or retigabine was an antiepileptic drug that reduces neuronal excitability by enhancing the activity of the KCNQ in humans. Based oin humans. Recent in humans.However, we searched the drugs for these ten potential targets from the Drugbank database, and found that most of the drugs were general anesthetics  or potassium channels blockers . Although the ten potential drug targets that were identified in this study have approved drugs but they are not antiepilepsy drugs. This means that these ten drug targets might be the novel targets for epilepsy treatment since they located in the significant associated modules of RE disorders. In addition, these approved drugs for other diseases might have the opportunity to be repurposed for RE treatment as well. As a matter of fact, six of the ten potential targets  have been already used in clinical practice. These proteins are the targets of Dalfampridine, which is a potassium channel blocker used to help multiple sclerosis patients walk . DalfampOur results showed that the curated RE-related 1086 genes were enriched in the nicotine addiction pathway. It reported that nicotine addiction could cause seizures in human subjects and reduction in the activity of the glutamate transporter type 3, leading to a decrease in glutamate uptake,81. GRM1Furthermore, the modules: M37, M80, M114, and M197, which had no available AEDs targets, also played central roles in the molecular network of RE. Functional analysis of these modules showed that M80 and M114 were mainly relevant to mTOR signaling pathway. This is consistent with that mTOR signaling pathway plays a role in RE physiopathology; Module However, our results would also be influenced by incomplete data and may contain bias introduced by available publications. As for the topology of modules, different algorithms may produce different community detection results that might influence some specific results. In addition, this study did not experimentally validate the 10 potential drug targets in vivo or vitro. This study is also not currently involved in clinical practice, but similar bioinformatics analysis has already applied to clinical cases . HoweverIn the further research, we will validate some potential effective targets through molecular biology and animal model. We will also apply computer aided drug design (CADD) related software, such as DOCK, Auto Dock, and MOE, to facilitate potential drug design through docking computation. It is wS1 TableHere we just showed the top 200 BP terms .(DOCX)Click here for additional data file.S2 TableHere we listed all the 214 CC terms.(DOCX)Click here for additional data file.S3 TableHere we listed all the 176 MF terms.(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 TableWe listed the smallest p-value terms of each module. If the smallest p-value >0.05, we leave it blank.(DOCX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.S9 TableHere we listed all the 193 records.(DOCX)Click here for additional data file.S10 Table(DOCX)Click here for additional data file.


CDKN1A), toll-like receptor 4 (TLR4), CD19 molecule (CD19), breast cancer 1, early onset (BRCA1), platelet-derived growth factor subunit A (PDGFA), and matrix metallopeptidase 1 (MMP1) were the DEGs involved in the pathways and the PPIs. The clinical validation of the DEGs in mCRC  revealed that high PDGFA expression levels were associated with poor overall survival, whereas high BRCA1 and MMP1 expression levels were associated with favorable progress free survival(PFS). The identified genes and pathways can be potential targets and predictors of therapeutic resistance and prognosis in bevacizumab-treated patients with mCRC.Bevacizumab combined with cytotoxic chemotherapy is the backbone of metastatic colorectal cancer (mCRC) therapy; however, its treatment efficacy is hampered by therapeutic resistance. Therefore, understanding the mechanisms underlying bevacizumab resistance is crucial to increasing the therapeutic efficacy of bevacizumab. The Gene Expression Omnibus (GEO) database  was used to identify the key genes and pathways involved in bevacizumab-resistant mCRC. The GEO2R web tool was used to identify differentially expressed genes (DEGs). Functional and pathway enrichment analyses of the DEGs were performed using the Database for Annotation, Visualization, and Integrated Discovery(DAVID). Protein–protein interaction (PPI) networks were established using the Search Tool for the Retrieval of Interacting Genes/Proteins database(STRING) and visualized using Cytoscape software. A total of 124 DEGs were obtained, 57 of which upregulated and 67 were downregulated. PPI network analysis showed that seven upregulated genes and nine downregulated genes exhibited high PPI degrees. In the functional enrichment, the DEGs were mainly enriched in negative regulation of phosphate metabolic process and positive regulation of cell cycle process gene ontologies (GOs); the enriched pathways were the phosphoinositide 3-kinase-serine/threonine kinase signaling pathway, bladder cancer, and microRNAs in cancer. Cyclin-dependent kinase inhibitor 1A( Colorectal cancer (CRC) is the third most frequently diagnosed cancer and the second leading cause of cancer deaths worldwide, accounting for 10% of the worldwide cancer incidence and mortality , 3. The Bevacizumab is the first agent to influence OS in patients with metastatic CRC (mCRC); when combined with irinotecan-based chemotherapy, the median OS improved from 15.6 to 20.3 months, median PFS from 6.2 to 10.6 months and RR from 34.8% to 44.8%, while iIn this study, we investigated the predictive biomarkers and pathways of bevacizumab resistance in mCRC by using microarray data from the Genetic Expression Omnibus (GEO) database. The new biomarkers were assessed for their ability to predict OS and PFS. The identification of predictive and prognostic biomarkers can facilitate improving the therapeutic index of bevacizumab.http://www.ncbi.nlm.nih.gov/geo/) database [7) suspended in phosphate-buffered saline were subcutaneously injected into the flanks of BALB/c nude mice, and the tumor-bearing mice were treated with bevacizumab  for 3 weeks to obtain bevacizumab-resistant tumors. MTT colorimetric assays were used to determine the 50% inhibitory concentration for bevacizumab-resistant and untreated xenograft tumors; the tumor sizes were compared between the two groups. The sample tissues were immediately frozen under liquid nitrogen after isolation. Total RNAs were extracted from the samples, evaluated, labeled and hybridized, using a SurePrint G3 Human GE 8 × 60K microarray (Agilent Technologies). Array images were captured using a DNA microarray scanner (Agilent Technologies), and the data were analyzed using Feature Extraction Software (Agilent Technologies) to obtain background-corrected signal intensities. The expression data were further analyzed using GeneSpring GX software , and the differentially expressed genes (DEGs) between the bevacizumab-resistant HT29 tumors vs untreated control were compared using the Fisher exact test, followed by multiple corrections using the Benjamini and Hochberg false discovery rate (FDR) method [The gene expression profile of GSE86525 was obtained from the GEO  method . Gene set test and Benjamini and Hochberg method were used to calculate the P values and FDR, respectively [The data were recalculated using the GEO2R analytical tool to identify the DEGs associated with acquired bevacizumab-resistant CRC , 24. Theectively . The genectively .http://www.string-db.org/), an online database comprising comprehensive known and predicted interactions, to determine the interactive relationships among the DEG-encoded proteins. A combined score of >0.7 (high confidence) was used as the cutoff criterion [http://www.cytoscape.org/), and the CytoNCA tool was used to subcluster the plotted PPI networks [Protein–protein interaction (PPI) networks were plotted using the Search Tool for the Retrieval of Interacting Genes/Proteins  was used to classify the DEGs involved in the PPI networks according to their biological processes, molecular functions, or cellular components by using the Gene Ontology (GO) Consortium Reference (http://www.geneontology.org/) [http://www.genome.jp/kegg/) database website to perform KEGG pathway enrichment analysis for the DEGs involved in the PPI networks, with a P value of <0.05 and FDR value of <0.05 being considered statistically significant [The Database for Annotation, Visualization, and Integrated Discovery  , 32. Gennificant , 34.www-01.ibm.com) for plotting Kaplan–Meier survival curves.The clinical assessment of DEGs associated with bevacizumab resistance was performed using the SurvExpress tool . The colhttps://cancergenome.nih.gov/) was used to obtain CRC data containing gene expression profiles. Level 3 RNASeq data containing gene expression profiles of 635 CRC cases  were obtained. The standard Pearson correlation coefficients (-1 to 1) and the coefficient of variation (the ratio of standard deviation to mean) of the desired gene pairs were calculated using SPSS for Macintosh . A P value of <0.05 was considered statistically significant and was used as the cutoff criterion.The Cancer Genome Atlas , matrix metallopeptidase 1 , pre-B cell leukemia transcription factor 1 (PBX1), platelet-derived growth factor alpha polypeptide (PDGFA), kin of IRRE-like (Drosophila) (KIRREL), insulin-like growth factor binding protein 7 (IGFBP7), and dual specificity phosphatase 5 (DUSP5), exhibited higher PPI degrees and betweenness values . In the The DAVID tool was used to classify the DEGs involved in the PPI networks according to their common biological processes, molecular functions, or cellular components. Of the 1,454 GO gene sets included from the reference database, 111 were significantly enriched . The DAVID tool was applied to classify the DEGs involved in the PPI networks by using the reference pathways from KEGG. KEGG pathway analysis revealed significant results  for three pathways: the phosphoinositide 3-kinase-serine/threonine kinase (PI3K-AKT) signaling pathway , bladder cancer (involving CDKN1A and MMP1), and microRNAs in cancer , and against most B-cell malignancies with successful preclinical experiments and first-generation clinical trials –72. CDKNNotably, five of the six genes that were commonly enriched as well as associated with bevacizumab resistance belonged to the PI3K-AKT signaling pathway. Therefore, we suggest that the PI3K-AKT signaling pathway is responsible for restraining the therapeutic efficacy of bevacizumab in mCRC. This observation is in accordance with the results of previous studies, which have suggested that modifications in the PI3K-AKT signaling pathway increase bevacizumab resistance as an alternative pathway to VEGF inhibition –87. MoreThe present findings provide novel data that could predict bevacizumab treatment response and the emergence of resistance. Furthermore, this approach can predict patient prognosis; however, additional studies are required to validate the study findings and determine their clinical applicability.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


LIM and SH3 domain protein (LASP-1) is responsible for the development of several types of human cancers via the interaction with other proteins; however, the precise biological functions of proteins interacting with LASP-1 are not fully clarified. Although the role of LASP-1 in hepatocarcinogenesis has been reported, the implication of LASP-1 interactors in HBV-related hepatocellular carcinoma (HCC) is not clearly evaluated. We obtained information regarding LASP-1 interactors from public databases and published studies. Via bioinformatics analysis, we found that LASP-1 interactors were related to distinct molecular functions and associated with various biological processes. Through an integrated network analysis of the interaction and pathways of LASP-1 interactors, cross-talk between different proteins and associated pathways was found. In addition, LASP-1 and several its interactors are significantly altered in HBV-related HCC through microarray analysis and could form a complex co-expression network. In the disease, LASP-1 and its interactors were further predicted to be regulated by a complex interaction network composed of different transcription factors. Besides, numerous LASP-1 interactors were associated with various clinical factors and related to the survival and recurrence of HBV-related HCC. Taken together, these results could help enrich our understanding of LASP-1 interactors and their relationships with HBV-related HCC. LIM and SH3 domain protein LASP-1) is a scaffold protein that has been identified to facilitate the development of several types of human cancers is a sca14589et al. reported that LASP-1 interacts with 14–3–3σ in colorectal cancer (CRC) cells and contributes to the progression and metastasis of CRC via the inhibition of 14-3-3σ expressionStructurally, LASP-1 contains an N-terminal LIM domain, two nebulin-like repeats named R1 and R2 domain, and a C-terminal SH3 domain1et al. indicate that the increase of LASP-1 in HCC tissues is related to hepatitis B virus (HBV) infectionet al. identified that vimentin is a new binding partner of LASP-1 in HCC cellsLASP-1 over-expression is also observed in hepatocellular carcinoma (HCC) and associated with poor clinical prognosis of the disease15et al.293031To investigate the proteins that interact with LASP-1, the information of target human proteins was retrieved from different public databases, including IntActAmong these proteins, 79 proteins were obtained from different public databases, and these proteins were mainly identified by different methods, such as co-fractionation, affinity capture-MS, proximity label-MS, and two-hybrid array. Ten proteins, including CXCR1Next, we assessed the protein class of LASP-1 interactors using the PANTHER classification systemIn the study, gene function annotation of LASP-1 interactors was performed with GO analysis using the Database for Annotation, Visualization and Integrated Discovery 39The pathways that LASP-1 interactors were involved in were assessed by KEGG pathway and PANTHER pathway. The two different pathway enrichment systems identified many different pathways associated with LASP-1 interactors  and 6. FTo better understand the interaction of LASP-1 with its interactors and the crosstalk of these proteins with annotated pathways, we constructed the interaction network containing nodes corresponding to LASP-1 with its interacting proteins and further integrated the interaction information of LASP-1 interactors with their associated pathways. The interaction data of LASP-1 and its interactors was not only extracted from STRING toolsSince the over-expression of LASP-1 has been reported in HCC15164344Given that the co-expression network of LASP-1 and its interactors was present in HBV-related HCC tissues, we explored whether the expression of LASP-1 interactors was regulated by the interaction of distinct transcription elements. Via transcription factor enrichment analysis using the Enrichr tool46We investigated the association of LASP-1 and its interactors with clinical parameters, including gender, HBV viral status, ALT, main tumour size, multinodular disease, cirrhosis, BCLC staging and AFP, in HBV-related HCC tissues. As shown in Using the Cox regression model in the multivariate analysis via SPSS 16.0 software, we further investigated the association of LASP-1 interactors with clinical outcomes, such as survival and recurrence, in patients with HBV-related HCC. The results show that among these LASP-1 interactors, SLC25A44 and SNRNP27 are not only related to poor survival but also associated with disease recurrence  and 3. IThe function of LASP-1 is based on the interaction with other proteins. However, the precise molecular functions and physiological processes involved in proteins interacting with LASP-1 are not clear. Current studies suggest that LASP-1 is involved in the progression of HCC with HBV infectionCurrently, the implication of LASP-1 in several types of cancers has been reported149Previous reports show that LASP-1 mainly interacts with cytoskeletal-associated proteins, such as F-actin, Krp1, LPP, VASP and zyxin, and is located in cytoplasm, nucleus, membrane and multiple sites of dynamic actin assembly, such as focal contacts, focal adhesions and pseudopodia1We next investigated the enriched distinct molecular functions and biological pathways through GO, KEGG and PANTHER databases. In the present study, multiple GO terms and KEGG and PANTHER pathways were identified for LASP-1 interactors, suggesting that LASP-1 was involved in a variety of molecular functions and pathways by interacting with its binding partners. In GO biological processes, LASP-1 interactors are associated with signal transduction and regulation of apoptosis, and these biological processes are consistent with previous reports that LASP-1 plays key roles in apoptosis regulation and cell signallinget al.In addition, via KEGG and PANTHER pathway analysis, various pathways are identified to be associated with LASP-1 interacting proteins, and the distinct enriched KEGG pathways of LASP-1 interactors suggests that these proteins are mainly involved in the chemokine signalling pathway, endocytosis, and bacterial invasion of epithelial cells, while the results of the PANTHER pathway analysis indicate that LASP-1 interacting proteins are associated with inflammation mediated by the chemokine and cytokine signalling pathways, CCKR signalling map, and integrin signalling pathway. It is a well-known fact that the pathway information, annotation, nomenclature, and update vary between KEGG and PANTHER pathway databases. In addition, as mentioned by Chowdhury S We further constructed an integrated network containing both the interaction of LASP-1 with its interacting proteins and the association of LASP-1 interactors with their annotated pathways. Based on the integrated network analysis, Ubiquitin C was found to be a significant hub protein that could interact with LASP-1 and many of its interactors. Since Ubiquitin C plays a critical role in protein degradation through ubiquitination pathway to modulate the activity and function of targeted proteins5556et al. indicates that ARFGAP1 is a new factor for HCV replication via the interaction with HCV NS5A in HCC cellsRecently, the aberrant over-expression of LASP-1 has been reported in HCC tissues151643Gene expression is mediated by transcription factors, and several transcription factors are functionally altered in HCC and participate in the development of liver cancer. In this study, we assessed whether LASP-1 and its interactors were controlled by distinct transcription factors in HBV-related HCC. The results suggested that the expression of target genes was mainly controlled by 22 predicted transcription factors. Current studies suggest that cooperativity between transcription factors is vital in the regulation of gene expression6465et al. reported that LEF1 is increased in HBV-associated HCCset al. found that HNF-4α facilitated HBV replication in HCC cellsWe also measured the expression levels of the 22 predicted transcription factors in HBV-related HCC tissues via microarray data from GSE14520. Compared with non-HCC, an at least 1.2-fold change was noted for most transcription factors in HBV-related HCC. Previous reports indicate that a value of 1.2 is considered a low and acceptable cut-off for fold-change with statistical significance in microarrayset al. indicates that LASP-1 is a potential prognostic factor of HCCThe association of LASP-1 and its interactors with clinical factors, survival and the recurrence of HBV-related HCC were further investigated. Our results indicate that many LASP-1 interactors are associated with different clinical factors, including gender, multinodular, cirrhosis, ALT, main tumour size and AFP, implying that LASP-1 interactors might play critical roles in the abnormalities of these clinical factors in HBV-related HCC patients. Furthermore, the results of multivariate analysis with the Cox regression model show that SLC25A44 and SNRNP27 are not only associated with overall survival of HBV-related HCC but also related to recurrence of the disease. In addition, ILK, NFYA and RBPMS are associated with poor survival of HBV-related HCC. These results suggest that LASP-1 interactors could be used as biomarkers of poor clinical prognosis of HBV-related HCC patients. Current research from Wang H In conclusion, based on bioinformatics analysis, we investigated the characteristics, associated molecular functions and pathways of LASP-1 interactors obtained from public databases or published studies, and a variety of different LASP-1 interactors were identified. In addition, LASP-1 interacting proteins are not only associated with distinct functions and pathways but could also form complex networks. In addition, LASP-1 and its interactors are altered in HBV-related HCC tissues and potentially controlled by a complex regulatory network composed of different predicted transcription factors. Furthermore, various LASP-1 interactors are significantly associated with clinical factors and related to the poor progress of HBV-related HCC. Given the distinct functions and clinical significance of LASP-1 and its interactors, both LASP-1 and its interactors could be used as novel biomarkers or therapeutic targets for patients with HBV-related HCC.22, MenthaThe proteins, which have been predicted or experimentally detected to interact with LASP-1, were extracted from the public databases, including IntActThe PANTHER classification system was used to identify the protein class of LASP-1 interactorsGene ontology (GO) analysis was used to identify the enriched molecular functions of clustered genes of LASP-1 interactors. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was used to determine the clusters of genes of LASP-1 interactors with associated biological pathways. In addition, the DAVID online tool was applied to perform the enrichment GO and KEGG pathway analysis39According to the manufacturer’s instructions, Cytoscape 3.2.1 software41The microarray data GSE14520 was downloaded from the GEO database43The microarray data were performed with both Affymetrix Expression Console and Affymetrix Transcriptome Analysis Console v3.0 software, according to the manufacturer’s instructions. Using Affymetrix Expression Console and Affymetrix Transcriptome Analysis Console v3.0 software, probe signal values of microarray were converted into log2 values, and genes annotated by the probes were analysed. Data were normalized using the robust multi-array average (RMA) algorithm. The details of data processing and the identification of differential gene expression were previously describedThe co-expression network analysis of LASP-1 interactors was performed with the DeGNServer online platform in accordance with its operating instructionsThe enrichment analysis of transcription factors of LASP-1 interactors was performed using the “TRANSFAC_and_JASPAR_PWMs” section in the Enrichr online tool46Statistical analysis was performed using SPSS 16.0 Software . Survival analysis was performed with multivariate analysis with the Cox regression model. Data are presented as the mean ± SD and analysed using t tests. P < 0.05 was considered significant.How to cite this article: Kong, F.-Y. et al. Bioinformatics analysis of the proteins interacting with LASP-1 and their association with HBV-related hepatocellular carcinoma. Sci. Rep.7, 44017; doi: 10.1038/srep44017 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Autoinflammatory and autoimmune disorders are characterized by aberrant changes in innate and adaptive immunity that may lead from an initial inflammatory state to an organ specific damage. These disorders possess heterogeneity in terms of affected organs and clinical phenotypes. However, despite the differences in etiology and phenotypic variations, they share genetic associations, treatment responses and clinical manifestations. The mechanisms involved in their initiation and development remain poorly understood, however the existence of some clear similarities between autoimmune and autoinflammatory disorders indicates variable degrees of interaction between immune-related mechanisms.Our study aims at contributing to a holistic, pathway-centered view on the inflammatory condition of autoimmune and autoinflammatory diseases. We have evaluated similarities and specificities of pathway activity changes in twelve autoimmune and autoinflammatory disorders by performing meta-analysis of publicly available gene expression datasets generated from peripheral blood mononuclear cells, using a bioinformatics pipeline that integrates Self Organizing Maps and Pathway Signal Flow algorithms along with KEGG pathway topologies.The results reveal that clinically divergent disease groups share common pathway perturbation profiles. We identified pathways, similarly perturbed in all the studied diseases, such as PI3K-Akt, Toll-like receptor, and NF-kappa B signaling, that serve as integrators of signals guiding immune cell polarization, migration, growth, survival and differentiation. Further, two clusters of diseases were identified based on specifically dysregulated pathways: one gathering mostly autoimmune and the other mainly autoinflammatory diseases. Cluster separation was driven not only by apparent involvement of pathways implicated in adaptive immunity in one case, and inflammation in the other, but also by processes not explicitly related to immune response, but rather representing various events related to the formation of specific pathophysiological environment. Thus, our data suggest that while all of the studied diseases are affected by activation of common inflammatory processes, disease-specific variations in their relative balance are also identified. Epidemiological studies provide increasing evidence for the rise in prevalence of immune-related disorders including autoimmune and allergic diseases in Western countries –3. It isAutoimmune diseases(AI) are characterized by dysfunction of the immune system leading to loss of immune tolerance against self-tissues, by the presence of autoreactive T and B cells, and by a complex pathogenesis of multifactorial etiology, whereas genetics and environmental factors together are responsible for disease onset ,12.ThereMVK gene are responsible for an increase of mevalonic acid concentrations, elevated levels of IgD in the serum and also an increased secretion of IL-1β [NLRP3) are implicated [Autoinflammatory diseases (AIF) are a relatively new and expanding group of self-directed inflammatory disorders, clinically described as periodic fever syndromes but also with episodes of acute inexplicable inflammation involving the innate immune system ,16. Theyof IL-1β ,22. Simiof IL-1β . Pyrin aof IL-1β . Finallyplicated –27.The pplicated ,28, otheplicated ,30. Moreplicated –33 indicLuckily, the advances in high-throughput biological data measurements, explosive growth of data in various public repositories and development of new bioinformatics algorithms for data analysis, already provide an opportunity to address the above mentioned issues from a systems biology viewpoint. Previously, we had developed a bioinformatics pipeline for pathway perturbation based analysis and similarity/specificity assessment of disease groups, and have applied it to a number of lung diseases . The obtWe have combined several publicly available datasets for autoimmune and autoinflammatory diseases in an attempt to uncover their common and specific pathobiological features. Our goal was to include as many conditions as possible, while in the meantime minimizing heterogeneity of microarray platforms and source tissue used for transcriptome measurement. These criteria left us with four monogenic autoinflammatory, four polygenic autoinflammatory disorders sharing characteristics with autoinflammation and autoimmunity, and four polygenic autoimmune conditions . MonogenOur study offers a systems biology approach for comparative analysis of gene expression data from cohorts carrying apparent divergent diseases with common immune related manifestations. Despite the limitations discussed below, such studies may contribute to identification of common pathways in autoimmunity and autoinflammation, and have the potential to facilitate development of new experimental strategies for understanding the molecular mechanisms underlying the interplay between those processes.We have used data deposited at Gene Expression Omnibus (GEO), which is the largest repository for microarray gene expression studies . The seaOur selection resulted in eight microarray datasets containing gene expression profiles of PBMC samples from patients with autoimmune and (or) autoinflammatory diseases, as well as healthy subjects. All of the platforms were Affymetrix microarrays, of which five were Human Genome U133 Plus 2.0, four were Human Genome U133A 2.0, two were Human Genome 133A, while only one was from Human Exome 1.0 ST Array platforms . Data onOnly untreated samples were included in the analyses. Dataset accessions, descriptions and sample counts are presented in Raw Affymetrix CEL files have been downloaded for all the datasets, except for GSE3365 (gene expression in Crohn’s disease and Ulcerative colitis), for which no raw data were available. Probe signal intensity conversions, RMA normalization and chip annotation were performed using the “affy” package for R , since “All the genes with known Entrez IDs were annotated, and expression values for multiple probes of the same gene were averaged. The mean gene expression values of the controls within each dataset were used as reference to calculate the log fold changes (logFC) for each gene. The logFC values were then anti-logged to linear scale FC values.Pathway signal flow analysis was performed as described previously . BrieflyThe Pathway Signal Flow (PSF) algorithm for calculation of the activity values of pathway branches is described in detail in –41. In sWe have evaluated in total 1825 sinks for the 168 pathways analyzed. Thus, each dataset is described with a vector of 1825 PSF values. Each sink is described by a profile of PSF values across all samples.To estimate the possible systematic effect of biological and technical variation, we have substantially extended our analyses based on additional datasets . We haveThe PSF profiles described above were clustered and visualized with the Self Organizing Maps (SOM) algorithm, implemented in the R package “oposSOM” ,43. ThisThe similarity between diseases was assessed based on the number of shared spots between disease pairs. For this, we have constructed a graph object, with nodes representing the individual SOM-portraits of each disease, and edges connecting the disease pairs that share the highest number of spots: in case of ties, more than one edge was introduced. Community search within this graph was performed with the walktrap algorithm implemented in the R “igraph” package .Functional annotation of sink dysregulation was assessed using overrepresentation approach implemented in the WebGestalt web-tool ,46. The The SOM method transforms the multidimensional PSF data into a series of two-dimensional images, called "portraits", which visualize the activities of the pathway sink nodes in each studied disease . These PAt-a-glance examination of the spot distribution across disease portraits revealed considerable similarities, though some differences were observed as well: almost all the diseases were characterized by the presence of upregulated spot areas at the lower left corner, except for MVK. In addition, MS had a characteristic red spot in upper right corner, while MVK, CAPS, MS and to lesser extent PAPA (PSTPIP1) were characterized by the presence of an additional spot near the bottom right corner of corresponding SOM portraits. Finally, it should be noted that three monogenic autoinflammatory diseases , as well as JIA and MS, and to lesser extent UC and CD, contained an additional upregulated spot near the lower left corner of their SOM portraits. In order to proceed with comparative analysis and functional annotation, all the up- and down-regulated spots from the averaged portraits of each disease were transferred into a single summary map for an overview . We haveIt should be noted that oposSOM scales data individually, before drawing the expression portraits, but applies spot detection thresholds on the global summary map, using all of the datasets at once . Hence, To assess similarities between the PSF portraits we have created a graph object where the nodes represent the diseases, and the edges connect the disease portraits sharing the highest number of spots. Based on a walktrap community detection search on this graph structure, we have identified two clusters or communities of highly similar diseases .i.e. T1D, BD, SS and MS, as well as one autoinflammatory syndrome, the PAPA (PSTPIP1). This cluster is characterized by up-regulated spots D and F and collects mostly autoimmune diseases, which have been shown to largely share symptoms and clinical manifestations. Several published case reports documented that primary SS manifestations can mimic those of MS [Idd3 and Idd5) protected the organism from inflammation and also of dysfunction of the salivary glands [Cluster 1 contains four polygenic autoimmune diseases studied se of MS –51. Morese of MS  and in tse of MS . Studiesy glands . This iny glands . MS shary glands . BD in ty glands ,57.Notably, despite the fact that PAPA is an autoinflammatory syndrome and mutations in PSTPIP1 are thought to be linked to inflammasome activation and IL-1β oveproduction, PAPA lacks spot C and clusters with autoimmune diseases. The observed difference between PAPA and other autoinflammatory disorders has also been reported in the literature. So, patients with PAPA syndrome are less responsive to anti-inflammatory treatment targeted against IL-1β and TNF alpha signaling . MoreoveBased on unexpected clustering of PAPA with autoimmune disorders, and the observed difference between autoinflammatory diseases described by inflammasome activation, we have looked into PSF signals of NOD-like receptor signaling pathway in all the studied conditions, since it collects inflammasome related signaling events. The results show expected clustering of MVK, PAPA, TRAPS and CAPS that were separate from other diseases . This inCluster 2 collects CD, CAPS, UC, JIA, TRAPS and MVK and is specifically characterized by up-regulated spots C and D . This clet al [et al [et al [Surprisingly, SLE, being an autoimmune disorder, also fell into this cluster. The systemic inflammatory nature of SLE whereas the immune system attacks healthy tissues in several parts of the body may explain the shared spots with the other autoinflammatory syndromes in the cluster . Recent et al  and Zhanl [et al  demonstrl [et al  has founThe obtained results suggest that regardless of the disease initiation event, the downstream pathway activity profiles share considerable similarities that can be attributed to chronic inflammation, which is an essential pathological event implicated in all the studied diseases. Although this is not surprising, a formal demonstration of shared inflammation-related pathways between these diseases suggests that the observed pathway dysregulations may create conditions for the development of comorbid syndromes.For functional annotation of pathway sinks associated with the spots of the summary map, we have performed GO term enrichment analysis using the WebGestalt and REVIGO programs ,47. AccoTwo other spots, B and E, seem to serve as supporting nodes that are either amplifiers  or provide alternative mechanisms for immune response activation (as in the case of MVK). The complete list of GO terms associated with the pathway sinks and the enrichment p values can be found in the In total, 131 from 168 signaling and metabolic pathways with at least one significantly dysregulated sink in at least one disease were detected in the spots indicating that almost all pathways were dysregulated in the studied diseases  resulting in a cascade of processes that involve pyrin inflammasome activation and enhanced IL-1β secretion [Here we have employed a systematic approach that demonstrates the universality of pathway dysregulations in autoimmune and autoinflammatory disorders, as well as uncovers novel aspects of similarity in the pathomechanisms of these diseases that were not reported previously. The results of this study clearly suggest that there is no single mechanism governing the development of the studied conditions. On one hand, the picture is rather complex and conditioned by a big number of variables. On the other hand, our pathway activity based disease clustering showed that diseases characterized by highly divergent clinical manifestations largely share the underlying pathomechanisms, at least at pathway activation levels. In some cases, disease linkage in the obtained clusters was supported by previous observations, and in others, the obtained results were novel. For example, we have addressed in our study the specifics of pathway dysregulation in PAPA  and HIDSecretion ,87. Thisecretion , providiOur aim in this study was to compare diseases that share self-inflammatory phenotypes without prior restriction for taking in consideration genetic determinants specific to each disease. Therefore the most appropriate approach was the comparative analysis of existing microarray transcriptome data by our PSF-SOM method, allowing to tackle quantitative variations in pathways rather than in single genes. We appreciate that utilization of RNA sequencing data would further improve the analysis results, as it has a number of advantages over microarrays and even more valuable results could be obtained with the use of single cell transcriptome data. Unfortunately, current availability of RNA sequencing data for cell subpopulations is limited, and it is impossible to collect homogeneous data in terms of sequencing platform and sample types for all the studied diseases. Single cell data are even scarcer and still possess technological and algorithmic challenges . Howeverper se, are the key to disease expression, such perturbations may be reflected in the peripheral blood. Previous reports of transcriptome analysis in the peripheral blood have demonstrated that it is a well suited surrogate tissue, reflecting distal organismal perturbations and providing a large sensitive pool of gene transcripts with quantitative fluctuations detectable as gene expression modifications [In our analysis we have considered only PBMC. While this choice offers data homogeneity, together with the facility of a non-invasive and easily obtainable tissue, it may reveal inflammatory-related and/or other unrelated processes, reflecting a steady state of the corresponding organism including influences from a disease-unrelated environmental status. However, taking into consideration the concept of an immunological continuum, whereas tissue perturbations at the target sites of inflammation, rather than the immune system ications .In order to show if the processes identified in PBMC at least to some extent reflect the processes activated in the target tissues, we have performed additional analysis to explore the overlap of pathway activities between PBMCs and target tissues for organ-specific autoimmune disorders, namely, diabetes type 1, multiple sclerosis, Crohn’s disease and ulcerative colitis, where immune responses are directed against antigens present in a particular organ . We haveIt is worth pointing out that the results of this study should be interpreted with some degree of caution. The difference in the number of samples for each condition may lead to uneven variation of pathway activity values across datasets, which may bias the interpretation of the results. Another issue concerns the fact that according to the dataset descriptions available in the GEO, the samples were collected at different stages of disease progression, and not at the stage of initiation, thus making it perhaps more difficult to identify disease-specific determinants. Indeed, the initiating factors of each disease can be easier identified at the early stages, while those can be masked at later stages of the disease. This could lead to clustering of diseases otherwise being of different molecular origin. Nevertheless, the difficulty of collecting human tissues at early disease time points, due to the absence of early phenotypic landmarks and/or known early markers, renders such studies not feasible for the moment. A few studies have addressed this issue in both AI and AIF disorders –92. One The data presented in this report support the notion that diseases could be distinguished by pathways of the adaptive or innate immune responses, with the majority of conditions connecting by variable degrees of interaction between these two systems ,95. Our Overall, our study offers a systems biology approach addressing the complexity and redundancy of the immune system mechanisms implicated in the inflammatory origin of the pathogenesis of the studied diseases. Identification of pathways shared between different diseases, has the potential to generate novel hypotheses and elaborate methods to subvert the immune dysfunction by modulating networks through regulation of one gene or miRNAs. Moreover, it may contribute to our understanding of similarities and differences in responses to similar treatment depending on specific patterns of pathway alterations. The selection of pathway level resolution provides a more aggregated level of information, as compared to single gene level analysis. We believe that both gene- and pathway- level analyses are important, one complementing the other.S1 File(DOCX)Click here for additional data file.S2 File(DOCX)Click here for additional data file.S3 File(XLSX)Click here for additional data file.S4 File(XLSX)Click here for additional data file.S5 File(XLSX)Click here for additional data file.S6 File(DOCX)Click here for additional data file.S7 File(DOCX)Click here for additional data file.


Intratumor heterogeneity (ITH) is observed at different stages of tumor progression, metastasis and reouccurence, which can be important for clinical applications. We used RNA-sequencing data from tumor samples, and measured the level of ITH in terms of biological network states. To model complex relationships among genes, we used a protein interaction network to consider gene-gene dependency. ITH was measured by using an entropy-based distance metric between two networks, nJSD, with Jensen-Shannon Divergence (JSD). With nJSD, we defined transcriptome-based ITH (tITH). The effectiveness of tITH was extensively tested for the issues related with ITH using real biological data sets. Human cancer cell line data and single-cell sequencing data were investigated to verify our approach. Then, we analyzed TCGA pan-cancer 6,320 patients. Our result was in agreement with widely used genome-based ITH inference methods, while showed better performance at survival analysis. Analysis of mouse clonal evolution data further confirmed that our transcriptome-based ITH was consistent with genetic heterogeneity at different clonal evolution stages. Additionally, we found that cell cycle related pathways have significant contribution to increasing heterogeneity on the network during clonal evolution. We believe that the proposed transcriptome-based ITH is useful to characterize heterogeneity of a tumor sample at RNA level. Cancer has a complex system consisting of different cancer clones that interact with each other and also with normal cells, known as intratumor heterogienty (ITH)72911de novo mutational signatures, furthermore copy number alterations (CNA) and loss of heterozygosity (LOH)161319202224Molecular level ITH has been identified with multiregional sequencing11Although the ITH inference based on genomic information were successful, there remain a few more issues that need further investigation. For example, a study reported that patients with a moderate number of subclones (3 or 4 clones) implicates a higher risk than more heterogeneous patients (above 4 clones)13032To investigate the functional differences of heterogeneous clones, we developed a method for ITH inference using RNA-sequencing data. There are two major reasons why RNA-sequencing data was used. First, RNA- sequencing data is ubiquitous as much as genomic data. Second, like mutations, transcriptome is also used in evolutionary studies353839Given a network, an effective metric is needed to quantify differences in the network perturbation to reflect both expression levels of individual genes and their relationships such as network topology and also co-expression of genes. We used an information theoretic approach to measure network state. This approach was successful in measuring network perturbations in terms of gene expressional changes4142444748Our hypothesis is that a heterogeneous tumor will have more ambiguity in network than a homogeneous one . Thus, wJensen-Shannon Divergence is the measure similar to Kullback-Leibler divergence with some modifications to make JSD symmetric and bounded in a finite valueei denotes the expression level of gene-i and a set of neighbor genes of gene-i is Ji. Then, a probability of interaction between two genes is defined asLet pij, we defined a probability distribution of gene-i which has X.With lth-element in the probability distribution PDi(X) be PDi(X)l, then the Kullback-Liebler Divergence of gene-i between normal (N) and tumor (T) is definedLet the i between normal and tumor was defined asThen the JSD of gene-where Finally, nJSD was defined as an average JSD of all genes. Graphical example of calculation of nJSD was described in .tITH), we set a maximally ambiguous network where whole gene-expression values were equal. nJSD was applied as a distance measure between two network states. Here, we defined tITH with two distance values, distance from normal data to cancer data (NT) and distance from cancer data to maximally ambiguous network (TA) .To define transcriptome-based ITH (ork (TA) . This dipathway-tITH was defined using only the gene set in a specific pathway. With this metric, we were able to precisely quantify pathway perturbation value ranging from 0 to 1.To investigate tITH at the pathway level, in silico data to investigate the relationship between sample heterogeneity and network ambiguity using single-cell sequencing data.To calculate tITH, we assumed a maximally ambiguous state (state A). We made Each of cancer cell lines of different molecular characteristics such as drug resistance was cultured in well-controlled environments, so as to consider each cell line as different clone in heterogeneous tumor. With 675 different human cancer cell lines, we created heterogeneous tumor gene-expression data and calculated distance to state A by nJSD. The distance to state A decreased as more cell lines were mixed . This inZ-score = −4.01, p-value = 0.00003) .The relationship between sample heterogeneity and network ambiguity was re-examined with single-cell sequencing data as a real biological data. Diverse mutational patterns among different clones could contribute to differentially activated paths in network across clones, thus we expect that the bulk-cell tumor data will show more ambiguous network status than each of single-cell data. The LUAD data set consists of 3 different experimental sets 0.00003) . Pooled in silico simulated data and single-cell sequencing data, it was possible to identify that the heterogeneous sample had ambiguous network state than homogeneous one. Our tITH measurement would showed how their gene-expression profile changed from normal tissue to maximally ambiguous state using nJSD.With This experiment is to show how well tITH agrees with gITH, genomic information based ITH, using the pan-cancer data set from TCGA. To compare tITH with gITH inferred clonal information was obtained from the study using PyClone and EXPAND55tITH of pan-cancer cohort, 5,630 patients, showed intratumor heterogeneity of each patient. Among the tumor types, there were inter-tumor type differences in the distribution of nJSD . Notablyr = 0.20), KIRC , LUAD  and LUSC , but other 3 cancer types had no relationship , BLCA , PRAD , THCA . Pan-cancer trends were similar between tITH and gITH. However, there were some cancer types has weak correlation between tITH and gITH. This might due to the effect of cancer microenvironments, inter-tumor type difference and also small size of data set. In order to make sure that the pan-cancer result is not dependent on specific topology of PIN, we performed analysis using other PINs such as BioPlex and HINT57When we analyzed the cancer types separately, 4 cancer types showed similar patterns with the pan-cancer result , HNSC . Among the pathways identified by the pathway-tITH analysis, cell cycle and central dogma related pathways were highly correlated with the number of clones obtained from genomic ITH inference . This is22Gene expression data was used to estimate tumor purity and immune cell infiltrations6061r = −0.502, p-value < 2.2e-16, Pearson’s correlation test). The immune score had a weak negative association . The increasing proportion of specific cell types, such as stromal cells and immune cells, led to decreased tITH as expected. The tumor purity score was associated with tITH , which is intuitive that the purer tumor will contain the more diverse clones. When analyzing cancer types separately, stromal scores had consistently negative correlation with tITH  using the Cox regression model, in comparison to that of the gITH information predicted by EXPAND. To test the prognostic power of gITH and tITH for each cancer type, we built two univariate Cox models. One was done with gITH and 5-year survival information. The other was done with tITH and 5-year survival information. The univariate Cox model using gITH was not significant  while the model using tITH was statistically significant . It is reported that the mutation based subclone number has a nonlinear association with survivalThe second experiment was to identify pathways that are relevant to or useful for the patient survival prediction. We analyzed pathway-tITH values measured for each pathway and cox proportional hazard test were done for each of the KEGG pathways. If cox model for a pathway was significant in terms of p-value < 0.001, then the pathway was selected as one that is significant for prognosis. A list of pathways that may have prognostic power is listed . In partThe third experiment was performed using two scores–immune score, and tumor purity score–from ESTIMATE result . Previous results showed a negative correlation between tITH and immune score . BecauseWe have shown that tITH can effectively measure the tumor heterogeneity from cohort data. Now we investigate whether tITH can measure the clonal evolution during tumor progression. Thus, we analyzed a time-series data of xenografted tumor by tITH in the extended time scale of tumor growth and divergence of subclones.HRAS, XT1-XT8, M1 and M2, and compared with reported number of clones from original research  revealed the emergence of different clonal populationsresearch .HRAS was closer to the network of MCF10A than the xenografted tumor samples . However, we observed the abrupt elevation of tITH between MCF10A-HRAS and XT1 (from 0.109 to 0.346). The environmental change from the culture plate to the mouse system was the first big evolutional force which might result in the dramatic network perturbation changeHRAS → XT1, XT2 → XT3, XT5 → XT6, and XT8 → M. tITH values steadily increased as the number of subclones increased (HRAS → XT1 (from 0.109 to 0.346), XT2 → XT3 (from 0.349 to 0.350), XT5 → XT6 (from 0.349 to 0.356) and XT8 → M .The perturbed network status of MCF10A-ncreased . tITH inAgain, we performed the pathway-tITH analysis to further explore the relationship between clonal diversity and tITH. The correlation analysis showed that a number of pathways were either positively or negatively correlated with the number of subclones. Among those pathways, metabolic pathways were highly ranked in the correlation analysis. The metabolic pathway is well known to be important in cancer mechanism67r > 0.3, from XT1 to XT8-M2). Cell cycle and central dogma related pathways such as Parkinson’s disease, Ribosome, mRNA surveillance pathway, Cell cycle, and DNA replication were at the top of the positively correlated pathway list in HRAS mutated cell lines . The tITH values of these pathways were steady or decreased as the number of subclones increased BRCA1, RAD51, PMS2 and FANC proteins–which are higly related to cancers. Like Fanconi anemia pathway, some pathways, such as Hedgehog signaling pathway, NF kappa B signaling pathway, Adherens junction and immune related pathways, were converged to a certain state of cancer during tumor growth (r < −0.6 from XT1 to XT8-M2).There were 60 of 291 pathways negatively correlated with tumor evolution measured by pathway-tITH . We selected cancer types which have more than 10 normal samples: Bladder Urothelial Carcinoma (BLCA), Breast invasive carcinoma (BRCA), Colon adenocarcinoma (COAD), Head and Neck squamous cell carcinoma (HNSC), Kidney renal clear cell carcinoma (KIRC), Kidney renal papillary cell carcinoma (KIRP), Lung adenocarcinoma (LUAD), Lung squamous cell carcinoma (LUSC), Prostate adenocarcinoma (PRAD), Thyroid carcinoma (THCA) and Uterine Corpus Endometrial Carcinoma (UCEC).We used four different datasets to examine the usefulness of nJSD. The lung adenocarcinoma (LUAD) single-cell RNA-sequencing data from GEO under the accession number of GSE69405Protein-protein interaction network was constructed with STRING v9 data57in silico heterogeneous tumor data with gene-expression data of 675 human cancer cell lines. Randomly selected 2, 4, 8, 16, 32, 64, 128, 256, and 512 out of 675 cell line gene-expression data was individually averaged into a single gene-expression profile. Each simulation data had 1,000 gene-expression profiles.We created We calculated tITH and pathway-tITH based on mean expression level of multiple normal samples of each cancer type. The intratumor heterogeneity, number of clones, in TCGA patients data were obtained from a previously published researchHow to cite this article: Park, Y. et al. Measuring intratumor heterogeneity by network entropy using RNA-seq data. Sci. Rep.6, 37767; doi: 10.1038/srep37767 (2016).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Glycine max) is a major source of vegetable oil and protein for both animal and human consumption. The completion of soybean genome sequence led to a number of transcriptomic studies (RNA-seq), which provide a resource for gene discovery and functional analysis. Several data-driven  and knowledge-based  methods have been proposed and implemented. In order to better understand gene relationships and protein interactions, we applied probabilistic graphical methods, based on Bayesian network and knowledgebase constraints using gene expression data to reconstruct soybean metabolic pathways. The results show that this method can predict new relationships between genes, improving on traditional reference pathway maps.Soybean ( Glycine max L. Merr.) is recognized as an important food source for humans and animals because of its relatively high protein and oil ingredients. As one major species of the legume family, soybean contains high-quality protein which is a fundamental requirement, providing complete protein that contains all the essential amino acids that people need. Soybean is also considered “heart healthy” since soybean protein intake can significantly decrease serum (blood) cholesterol and low-density lipoprotein (LDL) levels, contributing to a reduced risk of coronary heart disease [Soybean , which provided the basis for a variety of detailed, genome-wide studies, including completion of a transcriptome atlas based on RNA-seq analysis of different tissues [A remarkable achievement in soybean research was the completion of the genome sequence .In order to visualize and analyze large-scale experimental gene expression data, especially to elucidate gene–gene and protein–protein interactions, gene and protein expression data are commonly mapped to reference metabolic pathways, which provides a context for understanding the functional response of the plant to a given treatment. Metabolic pathways are designed to represent the chemical reactions among a set of small molecules in a cell within one organism. Therefore, reconstruction of metabolic pathways from protein and gene expression data can help researchers discover new, fundamental biological functions for a particular network. Although more and more plant genome sequences are becoming available, there is still need for improved methods for metabolic pathway reconstruction to support functional studies.In order to reconstruct a traditional metabolic pathway for a given species , theThe metabolic pathways were constructed by integrating the available soybean gene expression data, KEGG reference pathways, and the probabilistic network modeling method with knowledgebase constraints. The data- and knowledge-driven methods are shown in Fig. The published RNA-seq gene expression data, representing 14 soybean tissue-specific conditions, were normalized to counts per million reads (CPM) . The CPMAccurate gene translation is essential for developing the initial reference pathway maps. Protein sequences available for the annotated soybean genome were extracted from the Soybean Knowledge Base (SoyKB) , 12. TheMetabolic pathways are graphical representations of cellular processes in the KEGG database. Each reference pathway is composed of a network of enzymes and a set of genes that are functionally related in terms of predicted cellular and molecular functions based on experimental knowledge , 7. One Information on all the existing relationships among KO identifiers (ortholog group) for all the mapped genes in all pathways can be collected from the KEGG database , 7. SuchIn order to generate the relation knowledgebase, based on the gene-to-KO assignments and the list of pathway maps with KGML format generated by the method described above in the “Initial pathway construction” section, we extracted all the sets of relations and reactions for mapped KO numbers from the full set of KEGG reference pathways . The relIn order to improve the space searching efficiency and quality for Bayesian network construction, two knowledgebase sets were generated . A gene The KEGG database , 7 proviHowever, since the complete metabolic pathway is separated into a list of subpathways in terms of different cellular, molecular functions, genes mapped to a specific pathway can only represent a small part of relationships in the whole pathway. The traditional mapping method such as KAAS can only predict a small subset of relationships that exist in each reference pathway. In order to address this weakness, we applied the Bayesian probabilistic network method  to expanAfter the initial pathways for soybean genes were constructed and the associated knowledgebase built, we expanded the pathways by adding more genes with similar gene expression patterns and new relationships and reactions. The new genes were derived from predicted gene clusters and metabolic pathways predicted through the Bayesian network method taking advantage of all existing relationships between KO numbers in the knowledgebase. The score-based heuristic approach was applied to learn all the possible local structures to enlarge the whole metabolic pathway step-by-step .In order to sample genes that are more likely to be involved in the same pathway, the gene cluster containing more genes than in the initial pathway was assigned the highest probability for sampling. The sampling probability for the remaining clusters was assigned automatically based on the Euclidean distance between each cluster and pivot cluster . The proThe Bayesian network approach can be applied to discover casual relationships from gene expression data, which proposes a probabilistic model with joint probability distribution to represent the gene expression patterns for the target genes across the different experimental conditions. Based on the predicted network structure, valuable biological information can be extracted to understand the regulation process among genes. Bayesian network is represented as a directed acyclic graph (DAG), with the gene/protein as nodes and the reactions/relation between genes as directed edges in graphical representation of metabolic pathways. The score function, which is Bayesian Information Criterion (BIC) based, is used to predict networks from gene expression data . The scoSince network learning in a large searching space is time-consuming, we used the knowledge constraints to restrict the network search, resulting in a smaller searching space instead of the overall search space. The concepts of a whitelist and blacklist in Bayesian network were applied. The edges existing in the initial pathways were always present in the graph, serving as a whitelist in the Bayesian network. Based on the sampled genes and existing relationships of orthologous groups (KO numbers) in KEGG, the gene relations that do not exist in the knowledgebase will never be present in the graph, which is served as the blacklist for the Bayesian network.There are several problems that need closer attention during the pathway processing for network construction.Metabolic pathway reflects a series of reactions between enzymes, which is often feed-forward reactions with one direction. However, reversible reaction will also exist in the pathway that leads to feedback loops among sets of enzymes. Before feeding the sampled genes from gene cluster combined with initial mapping pathway into Bayesian network reconstruction, the presence of cycles should be conquered in advance since Bayesian network could not handle loops or cycles in graph. During the whitelist generation step for Bayesian network, the gene–gene reactions with direction from the initial pathway that exists in the KEGG knowledgebase were added into whitelist sets iteratively with checking to see if the cycle exists in the current network at each time. If the gene pairs with directed reaction would cause a loop among the current gene network in the whitelist, the edge was not be added. This step generates the initial gene network as a whitelist with no cycles occurring for Bayesian network prediction. In order to incorporate the reaction information from all species in the KEGG database, if the reaction for a gene pair belongs only to soybean, this edge was also excluded from the whitelist set in order to make the initial mapping network more independent. This also helped to validate the performance of our network prediction. After the new network is predicted, the initial mapping network with cycles was amended to the predicted pathway to complete the existing feedback reaction activity.In the metabolic pathway, multiple proteins may catalyze the same reactions and inhibit or activate the same substrate. Such a set of molecules was grouped together and labeled as one node in the pathway, sharing the same node identifier. In the KGML file for the KEGG pathway, each node is composed of multiple different KO numbers. During the extraction of gene relationships from the initial mapping pathways, the relationships between two nodes in the pathway were assigned to each KO number pair from two nodes to generate the gene–gene relationship. After the gene network was generated by Bayesian network construction, genes belonging to same KO number were grouped together to simplify the network representation.Protein function prediction software MULTICOM-PDCN , 17 was Our initial input data were RNA-seq gene expression data, representing 14 soybean tissue-specific conditions, including 9 different soybean tissues (root hair cells isolated 84 and 120 h after sowing (HAS), root tip, root, mature nodules, leaves, shoot apical meristem (SAM), flower and green pods , as wellSince one gene can have multiple transcripts or protein sequences due to alternative splicing, we extracted all the transcript variant IDs for the 53,175 expressed genes. For example, gene Glyma20g01000 had only one transcript variant Glyma20g01000.1, while Glyma01g00300 had two transcript variants, Glyma01g00300.1 and Glyma01g00300.2. Protein sequence data were extracted from the Soybean Knowledge Base (SoyKB) , 12 provThese 26,873 genes were clustered into 16 clusters using the Expectation-Maximization-based clustering algorithm with an average of 1679 in each cluster. These clusters were then used to enlarge the initial, mapped pathways. The gene size distribution for 16 clusters is shown in Fig. The 90 soybean-related metabolic pathways were downloaded from the KEGG database with the assumption that they represent physiology-relevant pathways in order to validate the pathway prediction results. The statistics of nodes and the edge relationships  for each target pathway are shown in Fig. The KEGG Automatic Annotation Server (KAAS) mapped all 26,873 soybean genes based on the assigned KO number. During KO-gene assignment in the initial mapping process, soybean-related gene information and pathways in the KEGG database were excluded in order to validate our results back to these reference pathways. Among the 26,873 genes, 9272 genes were successfully assigned to KO identifiers in KEGG. The network structure for each pathway can be downloaded and viewed using the KEGG Markup Language (KGML), which is XML format based. KGML represents the pathway as a graph object comprised of the entry nodes labeled as K numbers and edges with the relation and reaction elements. By way of example, the glyoxylate and dicarboxylate metabolism pathway (KO00630) is shown in Fig. In order to evaluate the prediction accuracy for the probabilistic network method using the soybean data, we applied the method on the 81 reference pathways to predict new networks. The 81 new networks were compared to the target pathways downloaded from the KEGG database in terms of the recall rate  and precision rate  . The expAgain, using the glyoxylate and dicarboxylate metabolism pathway (KO00630) as an example, the whole process for the metabolic pathway construction can be viewed through Figs. P value less than 0.05. The predicted edges in each pathway were validated in two ways: (1) the respective percentage of predicted gene pairs belong to the three categories  and (2) the percentage of predicted gene pairs mapped to the same GO terms. Figure l-malate dehydrogenase activity, and malate metabolic process are over-represented in this glyoxylate pathway. Two gluconeogenesis and the peroxisomal glyoxylate cycle-related gene Glyma14g03000.1 (citrate synthase) and Glyma11g04720.1  were predicted to be linked in the reconstructed metabolic pathway, while this edge was not mapped successfully in the initial mapping pathway but clearly represents a true relationship in the target pathway. The gene Glyma17g13730.1  and Glyma06g45950.1/Glyma12g10780.1 (isocitrate lyase) were also paired with a forward direction in the final predicted network.MULTICOM-PDCN , 17 was In this study, we applied probabilistic graphical and knowledge-based methods to reconstruct soybean metabolic pathways based on a comprehensive transcriptome database. Based on the results, the method performed better than the traditional sequence-homology mapping method by predicting more real relationships in the pathways. Functional enrichment analysis on the predicted pathways also revealed that functional related gene pairs were predicted successfully to enlarge the initial mapping network from KEGG. The good performance of the data and knowledge-based probabilistic method provided fundamental, new biological information for soybean research and demonstrates that this method can be generally applicable for other genomes where similar starting data are available.


This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers’ exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes.Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system ( It is generally recognized that using different sources of information and knowledge is better than just using a single source. This is most profound in the post-genomics era. On one hand, the advent of genomic high-throughput technologies realized by DNA microarray and next generation RNAseq technologies enabled a ‘systems level analyses’ by offering the ability to measure the expression status of thousands of genes in parallel. On the other, molecular pathway networks depict the interaction of DNA segments during the transcription of genes into mRNA. The prominent and vital role of pathways in the study of various biology processes is a major sector in contemporary biology research. We introduce MinePath, a pathway analysis methodology that amalgamates information and knowledge from gene expression profiles and molecular pathways. The novelty of MinePath resides in its ability to target not just the genes involved in the pathways, as most of existing methodologies and tools do, but directly their interrelations and interactions. With this approach, the regulatory machinery that putatively governs and guides the expression of disease phenotypes can be explored and revealed. Gene expression profiling via high throughput technology, either in the form of microarrays or in the form of next generation sequencing (NGS) and the subsequent quantitative measurement of RNA abundance in bio-samples, has generated (and continuous to generate) mass gene expression data streams for a number of targeted phenotypes and diseases. After the early years of microarray technology several research efforts were devoted towards the improvement of the quality of gene expression profiling protocols as well as for the standardization of the generated data, with the respective NGS based gene expression quantification to follow a similar progress . On one Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network”. In this sense, a network focus enables a more effective inference of key transcriptional changes, which are related to the specific phenotypes, by examining multiple downstream  effectors of the target. The reached conclusion states that there is progress towards a reliable network-based approach to disease modelling, and at the same time it stresses the fact that progress is currently limited by the incompleteness of the available interactome map and the inadequacies in the existing pathway analysis methodologies and tools , ,  and 18]18].A number of recent pathway analysis methodologies take advantage and exploit the topology and the gene regulatory relations of pathways. Furthermore, some of the relevant tools implement and offer network visualization functionality in order to map and display the underlying regulation machinery of pathways. Based on a literature search we identified relevant pathway analysis methodologies and tools that are presented in th and 8th method/tool in Observing It is generally recognized that in order to efficiently address and to overcome the statistical barriers in traditional gene selection methodologies, the pathway topology and the underlying gene interactions should be taken into account . Even inA key-component in order to indicate the predictive sub-paths and their power to differentiate between the target phenotypes is the efficient visualization of the pathway analysis results, which unfortunately most of these systems do not support. This obstructs the inspection of results and limits the user exploratory potential. Systems such as KEGG Atlas/Mapper , WebGestwww.minepath.org.With the gene-set oriented visualization approach the problem is apparent even for small sub-paths like the single inhibition relation A—| B . The inhibition relation exhibits a ‘dual’ character and could be considered as functional in a specific sample in two cases: when A is up-regulated and B is down-regulated or, when A is down-regulated and B up-regulated. In the first case, up-regulation of the A inhibitor causes the down-regulation of B. In the second case, the down-regulation of the inhibitor in a sense ‘allows’ B to be expressed and up-regulated. So, each of the genes should be visualized with a different color (just to indicate its expression status). The situation becomes even more complicated when one has to visualize the phenotype inclination of an interaction, for example when an inhibition relation is functional just for one phenotype and not for the other. Coloring and visualizing the functional status of pathway relations seems a promising alternative, and this is the approach that MinePath adopts and follows . The MinePath web-application may be accessed by www.ncbi.nlm.nih.gov/geo)) namely, GSE3494 ..in vitroSPIA and GGEA compute the significance of a whole pathway based on the differential power of the involved genes. MinePath utilizes the pathway sub-paths  in order to assess the differential power and significance  of the whole pathway. In this mode MinePath may be directly compared with SPIA and GGEA on the pathway level. Here we should notice that MinePath assesses the significance of pathways not on the basis of the involved genes but on the functional status of sub-paths in the pathways. In this respect we could say that MinePath comprises a sub-path set enrichment analysis methodology, just in analogy with gene set enrichment analysis methodologies . The comparison is performed by targeting a set of known ER-related pathways. Various molecular pathways mediate endocrine resistance and evaluated for BrCa/ER therapeutic targets. The review in  reports www.broadinstitute.org/gsea) follows a gene-set enrichment methodology in order to identify statistically significant phenotype differentiating gene-lists by assessing their functional enrichment in targeted pathways ..Is the gS, and a feature A the method chooses (in an iterative top-down mode) a cutting point TA (selected among the class borderlines) in a way that the joint class information entropy E is minimized. A similar approach is also introduced by Li and Wong . . 129]. TIn addition, MinePath identifies those sub-paths that are almost always functional for both phenotypes . Such sub-paths possess no phenotype differential power but are important in order to fill-in the gap between two discriminant sub-paths, and enable the formation of long sub-paths that reveal biologically relevant routes in pathways.The filter-based assessment of the differential power of sub-paths does not aim to infer causal or even phenotype-associated sub-paths. It just infers a set of highly predictive sub-paths. The causal or associative interpretation of the selected sub-paths requires further investigation and a subsequent downstream analysis.david.ncifcrf.gov/content.jsp?file=functional_annotation.html—fisher) [MinePath assess also the significance and ranks the pathways according to their p-values. The pathway p-value is calculated based on a more strict variation of the Fisher exact test, similar to the EASE score proposed by DAVID (—fisher) . The difBased on the aforementioned contingency matrix, MinePath uses the formula below to compute the p-value of each pathway.ap is the sum of functional relations in either phenotype-1 or phenotype-2 samples. So, it presents an estimate of “how much functional the pathway is” and not “the differential power of the pathway”. In other words, MinePath follows and implements an enrichment pathway-analysis approach, just in analogy with GSEA approaches, with a critical difference that highlights the MinePath paradigm shift in pathway analysis. That is, instead of looking for gene set enrichment features, MinePath assesses the sub-path set enrichment characteristics of a pathway, taking in consideration the functional status of the single relations of the decomposed pathway functional sub-paths. This approach has the following influential effects: (a) the risk to consider equivalent pathways with the same genes is avoided, (b) the underlying  regulatory machinery of the pathways is fully reflected in the assessment of pathway significance, and (c) the significance of a pathway gets a more natural meaning in terms of the number of functional relations and sub-paths used to differentiate between the target phenotypes–an effect that could be further exploited by the MinePath visualization operations and services. The final result of MinePath is a p-value ranked list of pathways from which the user may select a pathway to visualize and explore.The p-value formula takes in consideration all the pathway functional single relations  regardless of their phenotype inclination–js.cytoscape.org). In addition, the system supports active interaction and re-adjustment of the visualized network as it is equipped with special network editing capabilities that enable the reduction of pathways’ complexity. To the best of our knowledge MinePath is the only tool that visualizes differentially expressed pathway relations and sub-paths instead of differential genes, and this is done with a color-coding schema that reflects the phenotype inclination of relations.MinePath identifies and visualizes the differentially expressed sub-paths utilizing and appropriately customizing the Cytoscape graph visualization Web library  but the user may format and upload its own gene expression profile . Optionally, the user may adjust various parameters such as the minimum threshold percentage of sub-paths that are functional for both phenotypes , the minimum polarity rank threshold to decide if a sub-path is discriminant or not , the optional use of p-value or ‘B&H adjusted fdr’ as the threshold , and fold change as a threshold to filter sub-paths (only the sub-paths that exhibit at least a two-fold change between the two target phenotypes are retained).www.pharmgkb.org) relevant gene variants and associated drugs, as a first approach to offer putative clinical utilization of the system. With the ‘Controls’ panel (One of the key features that distinguish MinePath from other pathway analysis methodologies and tools rests in the system’s visualization functionality, and especially in the visualization of functional gene regulatory relations and sub-paths that differentiate between the target phenotypes see . MinePats’ panel  the users’ panel  the whols’ panel , sorted