Volume 13 Supplement 8
Differential combinatorial regulatory network analysis related to venous metastasis of hepatocellular carcinoma
© Zeng et al.; licensee BioMed Central Ltd. 2012
Published: 17 December 2012
Hepatocellular carcinoma (HCC) is one of the most fatal cancers in the world, and metastasis is a significant cause to the high mortality in patients with HCC. However, the molecular mechanism behind HCC metastasis is not fully understood. Study of regulatory networks may help investigate HCC metastasis in the way of systems biology profiling.
By utilizing both sequence information and parallel microRNA(miRNA) and mRNA expression data on the same cohort of HBV related HCC patients without or with venous metastasis, we constructed combinatorial regulatory networks of non-metastatic and metastatic HCC which contain transcription factor(TF) regulation and miRNA regulation. Differential regulation patterns, classifying marker modules, and key regulatory miRNAs were analyzed by comparing non-metastatic and metastatic networks.
Globally TFs accounted for the main part of regulation while miRNAs for the minor part of regulation. However miRNAs displayed a more active role in the metastatic network than in the non-metastatic one. Seventeen differential regulatory modules discriminative of the metastatic status were identified as cumulative-module classifier, which could also distinguish survival time. MiR-16, miR-30a, Let-7e and miR-204 were identified as key miRNA regulators contributed to HCC metastasis.
In this work we demonstrated an integrative approach to conduct differential combinatorial regulatory network analysis in the specific context venous metastasis of HBV-HCC. Our results proposed possible transcriptional regulatory patterns underlying the different metastatic subgroups of HCC. The workflow in this study can be applied in similar context of cancer research and could also be extended to other clinical topics.
Hepatocellular carcinoma (HCC) is one of the most hazardous cancers in the world. Metastasis remains a significant cause to the high mortality in patients with HCC. The molecular mechanism underlying the metastasis of HCC has not been completely unraveled due to the complexity and heterogeneity of this disease.
With the technology advances in genomics and proteomics, many attempts have been made to predict HCC metastasis based on molecular profiling from mRNA or miRNA microarrays and mass spectrometry assays, sampled from tumor or adjacent non-tumor liver tissues [1–3]. These studies were mostly conducted by selecting from a list of genes whose expression level discriminated well between different sample types. However, the signatures or biomarkers from independent studies shared little overlap. Moreover, the signatures or biomarkers brought us insufficient knowledge about mechanism of HCC metastasis, despite the conventional gene set enrichment analysis.
In recent years, systematic approaches have improved the understanding of complex diseases from multiple perspectives. A priori knowledge such as protein interactions, pathways, clinical factors, or other disease-related information from databases, integrated with gene signature analysis have helped marker gene prioritization [4–8]. In addition, gene relationships among different disease statuses were investigated through systematic network analyses [9, 10]. The signature/biomarker identification was also aided by network analysis, which brought advantages over the previous gene-list approaches in prediction accuracy. In 2007, Chuang et.al identified markers for breast cancer metastasis not as individual genes but as subnetworks extracted from protein interaction databases . The subnetwork markers were proved to be more reproducible than individual marker genes and achieved higher accuracy in the classification. In 2010, Li et.al identified breast cancer prognostic modules extracted from GO-term-defined gene sets with both high predictability of metastasis and rational biological senses .
In 2009 Martinez N et.al pointed out the importance about the genome-scale combinatorial regulatory networks involving microRNAs(miRNAs), transcription factors(TFs), and genes . They mapped the first genome-scale TF-miRNA transcription regulatory network in C. elegans and integrated this network with a computationally predicted miRNA-TF post-transcriptional network . They investigated the topology and properties of the network to understand how TFs and miRNAs interact to regulate gene expression. After that, significant progress has been made in studies using gene regulatory network models that capture physical and regulatory interactions between genes and their regulators . In 2009, we also published a preliminary research on the microRNA-driven regulatory mechanisms through the combinatorial regulatory network analysis . In that work we used miRNA perturbed gene expression datasets and developed general miRNA-centered regulatory cascades in human cell lines. Biological context was not of concern then. In recent years, regulatory network analyses were brought into different biological contexts to further understand mechanism of complex diseases such as prostate cancer  and schizophrenia .
As a result, we constructed and compared the TF-miRNA-gene regulatory network in HCC without or with venous metastasis, and thus revealed some molecular characteristics of HCC metastasis. The credibility of the resultant network was estimated by databases and literatures. We identified key regulatory modules that are physically connective and biologically cohesive. The prediction performance for metastasis with our classifying modules was evaluated, which was significantly better than the counterpart gene-list classifiers using leave-one-out cross-validation on the same patient cohort. Some novel key miRNA regulators in HCC metastasis and their mechanisms were implied.
Overview of network statistics and validation of the non-metastatic and metastatic HCC networks
Overall statistics about the nodes and edges of the HCC non-metastatic and metastatic networks
To verify whether our networks are correlated to HCC, we performed one-sided Fisher's exact test respectively on the genes from the two networks and the collected HCC-related genes and HCC-metastasis-related genes resorted from a series of a priori databases and literatures. It turned out that genes from the non-metastatic network were significantly overlapped with HCC-related genes (p = 8.35e-8) but not to HCC-metastasis-related genes (p = 0.094), and that genes from the metastatic network were not only significantly overlapped with HCC-related genes (p = 3.81e-9) but also with HCC-metastasis-related genes (p = 0.031). Such results gave us confidence that our constructed networks reasonably lie in the context of HCC and HCC metastasis.
Comparison of global regulatory patterns between non-metastatic and metastatic HCC networks
Identification of key regulatory modules predictive of HCC metastasis
The basic standards on the defining of our key regulatory modules from the combinatorial networks are as follows: i) the selected module should possess clear biological structure to decipher its regulatory pattern. ii) the selected module should contain nodes and edges discriminative of the metastasis status. With such standards, we obtained 71 ranked differential regulatory modules from the two networks in total, each including one specific regulator and all of its first-layer targets, of which 26 were from the non-metastatic network(NM modules) and 45 from the metastatic network(M modules). Based on these differential regulatory modules a series of classification analyses were performed to further identify predictive modules.
Firstly each single module was tested for classification efficiency. Each of the top 20 modules (involving 5 NM- and 15 M- modules) from the ranked differential list was sequentially taken as the single-module classifier and tested in the recursive partitioning classification model . The performance of these single-modules was evaluated by leave-one out cross validation (LOOCV), the best of which achieved accuracy (ACC) of about 82%, and Matthew Correlation coefficient (MCC)of 64%. And there was no significant difference between the performance of modules from the non-metastatic network and the metastatic one(Additional File 3).
Full list of 17 regulatory modules predictive of HCC metastasis.
ARHGDIA, CEP250, MYO6, TYR, PWP2, RCBTB2, POLR3F
BCLAF1, SUMO1, TMBIM6, FAM168B
NFATC3, ETNK1, BMX, NCOR2, POLR3F
MICAL1, SAMD8, FUBP3, ATXN10, ADAM11, RAB5C, MRPS24, DPAGT1, GPS1, SNRPC, SUMO1, TWF1, SAR1A, PICALM, TXNDC5, HEXIM2, TRIP12, ZDHHC15, SEMA4G, EFHD2
MTERFD2, ARHGDIA, PCSK4, CEP250, PTRF, MYO6, ST6GALNAC6
CHD5, ATF2, POU2F2, TOMM70A, WDR26, SPOP, FAM168B, PLAA, WASF2, SRXN1
SPIB, C20ORF43, SUCNR1, PTRF
NTN4, CACNG5, C12ORF10, TUBA1B, CALB2, RGMA, APOC3, PGD, NDUFV1, CHDH, FBXO24, TCTN2
CREB1, PAWR, NEDD4, RRAS2, VPS26B, TBC1D2B, HTR4, ACAP2, ZFAND5, SPAG9, MICAL1, ATG5
PDCD2, POLR2E, NF2, FAM168B, MEGF9
RUNX1, IFITM2, MARCH5, GPR21, RPL35, TNFRSF10B, CFP, SDHAF2, NUP62CL, YARS, NAGK, GRAMD1A, PLXNB2, BCL2L13, METTL11A, MARK3, ITM2A, HIP1R, BSG
MAFF, LEPROT, MICAL1, PSME1, SAMD8, FUBP3, ATXN10
MYBL1, MAFF, POLA1, EXOG, PGM1, ZDHHC4, WDR24, AMFR, RAD52, TMEM208, MRPL34, GCHFR, ANKRD30A, TRO, LDHAL6A, SERPING1, RNASE4, ARPC5L, SRSF3, CD248
ANKRD52, SLC25A20, PGM1, C1QTNF4, PKDCC
WDR24, RAD52, GCHFR
Comparison of predictive ability of the cumulative modules to gene-list signatures
Some previous reports demonstrated the advantage of subnetwork classification over single gene-lists, probably because of functional relevance in the classifier . In order to check whether our key regulatory modules possess such advantage, we performed gene-list-based classification procedure in a counterpart way to our module-based classification. Signature genes were the selected differentially expressed genes in HCC metastatic vs. non-metastatic samples, with the Student t-test Benjamini-Hochberg adjusted p value < 0.001, and further ranked by the method of minimum redundancy and maximum relevance(MRMR) (Additional File 1), which resulted in a list of 349 ranked candidate genes. Same number of genes as in the cumulative-module classifier were picked with priority from the ranked gene list to compose the single-module classifier or perform metastasis classification and the performance was evaluated by LOOCV. Our results showed, there existed no significant difference of ACC or MCC between the top 20 single-module-classifiers and counterpart gene-list-classifiers (two sided t test p value > 0.5) (Additional File 3). However, when combining modules (even just two) the cumulative-module classifier achieved consistently better performance than the classifying models of corresponding number of signature genes (Additional Figure 2, Additional File 3).
The functional regulatory landscape of the key regulatory modules for HCC metastasis
Enriched KEGG non-metabolic pathways of the 17 key regulatory modules.
05223~Non-small cell lung cancer
04115~p53 signaling pathway
04310~Wnt signaling pathway
04722~Neurotrophin signaling pathway
05014~Amyotrophic lateral sclerosis (ALS)
05217~Basal cell carcinoma
05220~Chronic myeloid leukemia
05222~Small cell lung cancer
05223~Non-small cell lung cancer
04330~Notch signaling pathway
04370~VEGF signaling pathway
04662~B cell receptor signaling pathway
04722~Neurotrophin signaling pathway
04140~Regulation of autophagy
04062~Chemokine signaling pathway
04620~Toll-like receptor signaling pathway
04630~Jak-STAT signaling pathway
Key miRNA regulators from the functional landscape of HCC metastasis
Out of 17 key classifying modules predictive of metastasis, six were enriched in KEGG non-metabolic pathways: FOXO3_NM, TP53_NM, STAT1_M, hsa_miR_16_M, has_let_7e_M, has_miR_30a_M. Based on the hypothesis that miRNAs might actively participate in the tumor progression and metastasis process and act as key roles, we further focused on the regulatory patterns of the three modules headed by miRNAs, which were zoomed in from the regulatory landscape constructed above.
MiR-30a. The module led by miR-30a in the metastatic network shows inextricable links to various cancer-related pathways and some other important regulators (Figure 4B). EP300 and CREBBP, regulated by way of miR-30a and CREB1, are highly related transcriptional co-activators possessing histone acetyltransferase activity and were known to be involved in the survival and invasion pathways of prostate cancer . Functions of TP53 and STAT1 might be modulated through acetylation by CREBBP/EP300. Meanwhile, NEDD4, another target of miR-30a, by further targeting EGFR, might interfere with key cellular signaling pathways. According to a previous report, miR-30a was reported to inhibit epithelial-to-mesenchymal transition in non-small cell lung cancer . The exact role miR-30a might play in HCC metastasis requires more exploration.
MiR-16. MiR-16 targets human nuclear co-repressor 2(NCOR2) in the metastatic network. NCOR2 is a transcriptional co-repressor linked to Notch (Figure 4C). According to previous reports, Notch signaling cascade was regarded as anti-proliferative rather than oncogenic in hepatocellular carcinoma [21, 22], so if miR-16 repressed Notch it might result in more aggressive HCC that would lead to metastasis.
Let-7e and miR-204. In tumor with later metastasis, let-7e targets nerve growth factor (NGF), whose deprivation was supposed to induce apoptosis . Upstream let-7e is ATF2 and miR-204 (Figure 4D). Because we only performed the first-step targets enrichment in KEGG pathways, miR-204 was not among the six key regulators whose targets showed pathway enrichment, yet it was one of the heading regulators of 17 key regulatory modules. Besides, referring to the topology of our HCC metastatic network, miR-204 is a bottleneck with the 7th highest betweenness, and two of miR-204-involved edges rank into the top 10 list of edge betweenness(Additional File 3). Therefore we may hypothesize that the important role of let-7e regulation was driven by its upstream regulator miR-204. MiR-204 represses the expression of its target ATF2, blocking its activation to downstream target let-7e. The lack of let-7e may release NGF deprivation and therefore inhibit apoptosis, leading to tumor aggression and HCC metastasis. Indeed miR-204 was previously reported to regulate mesenchymal progenitor cell differentiation and to be related to head and neck tumor metastasis . Therefore we list both let-7e and its upstream miR-204 to be key regulatory miRNAs that might relate to HCC metastasis.
Prognosis prediction by the HCC metastasis classifying modules
Comparison of clinical characteristics between predicted subgroups of venous metastasis
Patient cohort (n = 198)
n = 137
n = 61
Age(yr, mean ± SD)
49.99 ± 11.22
50.15 ± 9.54
Number of nodule(1/2/3/4)
AFP(log2-transformed, mean ± SD)
6.79 ± 3.96
8.08 ± 4.56
By utilizing both sequence information and parallel miRNA and mRNA expression data on the same cohort of HBV related HCC patients, we constructed gene regulatory networks combining TF and miRNA regulation and specific for HCC without or with metastasis. Based on our combinatorial differential networks, global properties of the gene regulatory patterns in different metastasis subgroups were analyzed. TFs accounted for the main part of regulation, miRNAs for the minor part of regulation; miRNAs played a more active role in the metastatic network. Then differential regulatory modules discriminative of the metastatic status were extracted, and module-based classifier for metastasis prediction was constructed. Module-based classifier achieved better classification performance than the differential gene list-based classifiers. Furthermore, a few novel potential metastasis-related key miRNA regulators were proposed, such as miR-16, miR-30a, and let-7e/miR-204. Biological implications and differential regulatory patterns of key miRNAs were examined through functional regulatory landscape. Survival analysis and clinical characteristics association were conducted to support the importance of the classifying modules and the key regulators.
It is generally conceived that in transcriptional regulation TFs play the controlling roles and miRNAs make auxiliary contributions . In our work, we concordantly got that TFs made up the main part of nodes and edges in HCC networks without or with metastasis, and miRNAs participated in fewer regulations than TFs. However, we also found that miRNAs showed an increased amount of regulations in the metastatic network. Judged by the basic topological properties such as degree, betweenness, and edge betweenness (Additional File 3), most hubs and bottlenecks in both networks were TFs, but one miRNA, hsa-miR-204, was listed as the 7th bottleneck in the metastasis network according to its betweenness. All the top 10 edges with the largest edge betweenness involved only TFs in the non-metastasis network, but 4 out of 10 top edge-betweenness edges in the metastasis network involved miRNAs. Besides, TFs in the metastatic network tend to regulate genes by way of miRNAs (Figure 2C). It might be implied from our results that the process of tumor progression and metastasis is complicated and delicate therefore it takes up more auxiliary regulatory functions performed by miRNAs, in order to facilitate broader regulations by TFs.
The 17 key classifying modules identified in this work were not merely sub-networks, but 'regulatory' modules, each defined as a regulator and its first layer target genes. All the classifying regulatory modules possessed distinct regulatory patterns in either non-metastatic or metastatic subgroup. Since Chuang et.al proposed a pivot method for network-based classification in 2007 , various alternative methods based on network modules have been reported [26, 27]. The 17 key regulatory modules in this work could nicely classify patients into different metastasis sub-groups. Six modules' regulatory targets could be enriched in KEGG non-metabolic pathways, allowing an even clearer elucidation of their functional regulation patterns. It is conceivable that such differential regulatory modules discriminative of metastasis sub-groups might better imply the mechanisms of tumor progression and invasion. The regulatory landscape we drew for these modules could be zoomed in to check in detail the possible roles of each interested module or regulator played in HCC metastasis. We exemplified such analyses by looking at three microRNA modules whose target gene members were enriched in KEGG pathways: miR-30a, miR-16 and let-7e modules. Let-7e module is connected to miR-204, which is another key regulator in metastatic network.
Metastasis is known to be a sign of higher grade of malignance and undermine survival time. The fact that the predicted metastatic group had a significantly worse prognosis in survival analysis justified the classification performance of the selected 17 modules. The predicted metastatic group patients also showed worse BCLC staging, TNM staging, and higher alpha-fetoprotein(AFP) values compared to non-metastatic group. Increased AFP value is a long-established factor of HCC progression. Furthermore, modules headed by the three key miRNAs were associated with both AFP and alanine aminotransferase(ALT), implying that ALT value might also be closely related to venous metastasis in HCC. Module hsa_miR_16_M was simultaneously significantly associated with five cancer staging systems, which further supported the key role of miR-16 in HCC metastasis.
Compared with our preliminary work in 2009 , improvements were achieved not only in biological interpretation but also in network inference algorithm. The linear regression modeling approach we used in 2009 had a shortcoming in that it attempted to determine the regulation structure for each target gene independently, while it is well known that genes that share the same expression pattern are likely to be involved in the same regulatory process, and therefore share the same (or at least a similar) set of regulators. In this work, we used mutual information metric that detects statistical dependence between two variables with no assumption of linearity of the dependence. Among the various gene network inference algorithms based on mutual information developed by different groups such as RN , ARACNE , CLR , MRNET , CLR resulted in the highest true positive rate compared with the others according to a previous report . Therefore CLR algorithm was selected for network inference for our work, as we required all the edges in our network to be also sequence-matched besides expression-correlated, reducing the false positive rate of expression-inferred edges.
The workflow in our study is not restricted in this study alone. According to the schematic illustration (Figure 1), researchers may conduct similar analysis given the data (depicted as rectangle) available for the context. In practical terms, if parallel miRNA and mRNA expression profiles are available on the same cohort of patients with known disease phenotypes, the workflow in this study can be extended to other biomedical problem or cancer context by integrating data from public databases or literatures. The major programs in the workflow were either self-written scripts with little programming complexity or open-source R/Bioconductor packages which were confirmed to be useful and efficient in this study. As to compute runtime and complexity, the most time-consuming step in our workflow was in the network inference, because the CLR algorithm has a complexity in O(n 2p 2) since all pairwise interactions are considered . It computes the mutual information(MI) matrix first, transforms the MI matrix into scores that take into account the empirical distribution of the MI values, and then applies a threshold. When the expression values of genes are treated as continuous random variables and the MI is estimated by kernel methods, computing the pairwise MI can be computationally expensive. In our study, dimension reduction was conducted(filtering of untrustworthy pairs) before the CLR network inference so as to cut down the computational complexity and complete the computation within the limits of system memory.
In summary, in this work we demonstrated an integrative approach to conduct differential combinatorial regulatory network analysis in the specific context of HCC metastasis. Through this systematic analysis, we proposed changes of global regulatory patterns in HCC progression, and identified some key miRNA regulators contributed to HCC metastasis whose regulatory patterns and biological implication were also deduced. Before this, although multi-perspective data have been integrated into HCC-related analyses [6–8], no peer works providing global landscape of combinatorial gene regulatory network or identifying module classifiers for risk prediction has ever been reported in the specific context of venous metastasis of HBV-HCC. Our results proposed possible transcriptional regulatory patterns underlying the different metastatic subgroups of HCC. Meanwhile, miR-30a and miR-16, let-7e/miR-204, which had not been taken as granted to be related with metastasis, especially in HCC, stood out from our results, which may merit further experimental validation. Our results might facilitate the understanding of the molecular regulatory mechanisms and role of miRNAs in HCC metastasis. The workflow in this study can also be applied in similar context of cancer research or extended to other topics.
In this work we demonstrated an integrative approach to conduct differential combinatorial regulatory network analysis in the specific context of HCC metastasis. Through this systematic analysis, we proposed changes of global regulatory patterns in HCC progression, and identified some key miRNA regulators contributed to HCC metastasis whose regulatory patterns and biological implication were also deduced. Our results might facilitate the understanding of the molecular regulatory mechanisms and role of miRNAs in HCC metastasis. The workflow in this study can also be applied in similar context of cancer research or extended to other topics.
Datasets and patients
mRNA and miRNA expression microarray data on the same cohort of HBV-infected HCC patients who underwent radical resection in Zhongshan Hospital were used for integration in this study. Both datasets (GSE5975 and GSE6857) were downloaded from the Gene Expression Omnibus (GEO) database http://www.ncbi.nlm.nih.gov/geo/. The mRNA signal intensities were retrieved from GSE5975, which was generated using the NCI/ATC Hs-OperonV2 array. The miRNA expression levels were obtained from GSE6857, which was generated using OSU-CCC MicroRNA Microarray Version 2.0. Status of venous metastasis of patients were collected from GSE6857. Other clinical pathologic characteristics and survival time of patients were provided by Zhongshan Hospital.
Data preprocessing for combined expression
Microarray data preprocessing was conducted on each dataset separately, and then both mRNA profiles and miRNA profiles were combined to prepare the combined expression profiles among the 198 patients, 150 non-metastatic and 48 metastatic. After quantile normalization across arrays on the combined expression profiles, irrelevant genes and mature-miRs within the 5% smallest standard deviations of tumor/nontumor profiles between metastasis and non-metastasis samples were filtered. Finally, the combined mRNA and miRNA expression profiles of 198 patients included 12434 genes and 132 mature-miRs altogether. More detailed information for data processing is available in Additional File 1.
Candidate sequence-matched relationships between TFs, miRNAs, and genes
In the following data selection, a gene list of 1318 previously defined TFs  from a previous report were regarded as TFs, while others as non-TF genes.
MiRNA-gene. Candidate miRNA-target relationships were downloaded from miRBase Target Version 5.0, TargetScanHuman Version 5.1, and miRDB Version 3.0, each was based on the predicting algorithm miRanda , TargetScan , and miRTarget2 , respectively. The predicted miRNA-gene relationships with accordance in at least two algorithms were retained in our study.
TF-gene. A set of predicted TF-gene relationships were compiled with methods mainly described in our previous work , where TF-TFBS(TF binding sites) relationships and TFBS-gene relationships were first calculated, based on which TF-gene relationships were linked. The difference between the method in this work and our previous work was that the promoter region of each gene in our work was defined as 1k bp up- and down- stream (instead of 1 kb upstream to 0.5 kb downstream of the transcription start site (TSS) according to the ENCODE project .
TF-miRNA. TFBSs mapped to the regions upstream of miRNA primary transcript TSSs were downloaded from miRGen 2.0 . Precursor-miRs were mapped to mature-miRs according to miRBase database. Then the candidate TF-miRNA relationships were generated based on the above TF-TFBS relationships and TFBS-miRNA relationships.
The statistics of the final set of 327711 regulatory relationships based on sequence-matched in human between TFs, miRNAs, and genes were displayed in Additional File 3.
Fisher Exact test to compare constructed networks with HCC-related and HCC-metastasis-related genes
HCC-related genes were collected from HCCdb , EHCO-II , and HCCNet . The union set of 5088 genes from these three databases was taken as the HCC-related genes. HCC-metastasis-related genes were collected using the text-mining tool, SciMiner . ("carcinoma, hepatocellular"[MeSH Terms] OR hepatocellular carcinoma[Text Word]) AND ("liver neoplasms"[MeSH Terms] OR liver cancer[Text Word]) AND ("neoplasm metastasis"[MeSH Terms] OR metastasis[Text Word]) AND metastatic[Text Word]) was set as the query string for full text mining. The resultant 322 genes each cited by at least 2 papers were regarded as the HCC-metastasis-related genes in this study. All the collected HCC-related genes and HCC-metastasis-related genes are listed in the Additional File 4.
One sided Fisher's Exact Test was performed to examine whether genes in our constructed HCC non-metastatic and metastatic networks were significantly overlapped with the collected HCC- or HCC-metastasis- related genes from databases and literatures. All the 12434 genes in the combined expression profiles were used as the set of universe genes in the test.
The combined expression profile was divided into two sub-profiles by sample labels, namely profile of non-metastasis and profile of metastasis, so as to construct gene regulatory network of HCC without and with metastasis respectively.
We assumed that sequence-matched pairs were more possible to be real interaction pairs than sequence-unmatched pairs, and that real interaction pairs were more possible to be correlated in expression than random pairs. In order to construct the network as credible as possible, we filtered out untrustworthy pairs before expression-based network inference. The candidate 327711 sequence-matched relationships genome-wide were first reduced to 78310 non-self-looping pairs whose both nodes were genes and miRNAs with expression in the combined profiles. Then the absolute spearman correlation of the expression was calculated between each of these 78310 sequence-matched pairs, and the mean absolute spearman correlations of the expression were also calculated between randomly sampled 78310 pairs from the combined expression profile for 100 random times. Pairs with the absolute spearman correlation higher than 95% of random pairs were retained as candidate pairs, which were processed to infer the transcriptional interactions.
Based on the two sub-profiles respectively, based on all nodes from the above remaining pairs, Context Likelihood of Relatedness (CLR)  was then applied as the network inference algorithm to identify transcriptional interactions using an R/Bioconductor package minet with default parameters. The CLR algorithm returned a non-negative matrix which was the weighted adjacency matrix of the network whose values represented the edge weights of the network. We set the cutoff for edge weights as 1, and edges whose edge weight below 1 were thus removed, since edges with little weight were considered as marginal relationships and might be noise.
where p(x i , y j ) is the joint probability distribution function of X and Y, and p(x i ) and p(y j ) are the marginal probability distribution functions of X and Y respectively. In the case of continuous random variables, the summations over X and Y are replaced by integrals. For genes, X and Y represent a transcription factor and its potential target gene, and x i and y i represent particular expression levels (Further description in Additional File 1).
Classification of metastasis based on gene regulatory modules
The composition of our 'modules' was defined as one specific regulator and all of its first-layer targets (more than one), and was named as Regulator_Status. Regulator was the name of the regulator, i.e. a TF or a miRNA. Status represented the source network of the module; it could be from the non-metastatic or metastatic network. All the modules in our work included targets only one step down from the regulator such that the regulatory attributes of each module was explicit to read.
Differential modules were first selected before identifying predictive classifying modules of metastasis sub-statuses. As to edges, the non-discriminative edges were excluded from the networks. For all the edges appearing in any of the two networks, we calculated the absolute value of the edge weight difference (The edge weights were directly carried on from the CLR results. The edge weight of a non-existing edge was regarded as zero.) between the two sub-statuses. The edges whose absolute edge weight difference were within the lowest 25% among all the edges were regarded as non-discriminative ones and were filtered out. As to nodes, GlobalAncova  test was performed on each module to measure the discriminance of nodes in that module between the metastasis statuses, which was implemented using R/Bioconductor package GlobalAncova (Additional File 1). Significant differential modules with Benjamini-Hochberg adjusted p < 0.001 were taken as candidate predictive modules, which were sorted by their corresponding p values from smallest to the largest. Finally, these ranked differential modules were proceeded to classification.
A multivariate algorithm, recursive partitioning, was chosen as the classification model . It creates a decision tree that strives to correctly classify members of the patients based on several dichotomous dependent variables, which is simple and intuitive (Further description in Additional File 1). Recursive partitioning has been successfully applied in other cancer biology context to identify multi-gene biomarkers or signatures [46–48]. In this study, the classification procedure was performed using R/Bioconductor package rpart with default setting of parameters. The predicted group and the prediction possibility for each individual were returned at each performance using this program. For cumulative modules as one classifier (a list of modules), the final predicted label for each individual was determined as the label with the larger overall predicted probability by modules in the classifier; for single-module as one classifier (a list of genes), the final predicted label for each individual was determined as the label with the larger predicted probability. Leave-one-out cross-validation (LOOCV) was used to evaluate the classification performance.
Clinical association and survival analysis
The survival analysis was performed to compare patient overall survival. Kaplan-Meier estimation was calculated to plot the survival curve. Log-rank test was used to compare two survival distributions and generate the p value. Comparison between clinical pathological indicators was conducted using chi-square test for discrete variable and Wilcoxon test for continuous variables. The association between clinical pathologic characteristics and classifying modules was examined using GlobalAnova test by R/Bioconductor package GlobalAncova (Additional File 1).
We thank Qiang Zeng for her suggestions on figures and tables. This work was funded by Key Infectious Disease Project 2012ZX10002012-014; National Key Basic Research Program 2010CB912702 and 2011CB910204; National High Technology Project 2012AA020201; and National Natural Science Foundation of China 31070752.
This article has been published as part of BMC Genomics Volume 13 Supplement 8, 2012: Proceedings of The International Conference on Intelligent Biology and Medicine (ICIBM): Genomics. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcgenomics/supplements/13/S8
- Budhu A, Wang XW: Molecular Signatures of Hepatocellular Carcinoma Metastasis. Molecular Genetics of Liver Neoplasia. Edited by: Wang XW, Grisham JW, Thorgeirsson SS. 2010, New York, NY: Springer New York, 241-257.View ArticleGoogle Scholar
- Song P-M, Zhang Y, He Y-F, Bao H-M, Luo J-H, Liu Y-K, Yang P-Y, Chen X: Bioinformatics analysis of metastasis-related proteins in hepatocellular carcinoma. World J Gastroenterol. 2008, 14: 5816-5822. 10.3748/wjg.14.5816.PubMed CentralView ArticlePubMedGoogle Scholar
- Burchard J, Zhang C, Liu AM, Poon RTP, Lee NPY, Wong K-F, Sham PC, Lam BY, Ferguson MD, Tokiwa G, Smith R, Leeson B, Beard R, Lamb JR, Lim L, Mao M, Dai H, Luk JM: microRNA-122 as a regulator of mitochondrial metabolic gene network in hepatocellular carcinoma. Mol Syst Biol. 2010, 6: 402-PubMed CentralView ArticlePubMedGoogle Scholar
- Shi Z, Derow C, Zhang B: Co-expression module analysis reveals biological processes, genomic gain, and regulatory mechanisms associated with breast cancer progression. BMC Systems Biology. 2010, 4: 74-10.1186/1752-0509-4-74.PubMed CentralView ArticlePubMedGoogle Scholar
- Lee Y, Yang X, Huang Y, Fan H, Zhang Q, Wu Y, Li J, Hasina R, Cheng C, Lingen MW, Gerstein MB, Weichselbaum RR, Xing HR, Lussier YA: Network Modeling Identifies Molecular Functions Targeted by miR-204 to Suppress Head and Neck Tumor Metastasis. PLoS Comput Biol. 2010, 6: e1000730-10.1371/journal.pcbi.1000730.PubMed CentralView ArticlePubMedGoogle Scholar
- Villanueva A, Hoshida Y, Battiston C, Tovar V, Sia D, Alsinet C, Cornella H, Liberzon A, Kobayashi M, Kumada H, Thung SN, Bruix J, Newell P, April C, Fan J-B, Roayaie S, Mazzaferro V, Schwartz ME, Llovet JM: Combining Clinical, Pathology, and Gene Expression Data to Predict Recurrence of Hepatocellular Carcinoma. Gastroenterology. 2011, 140: 1501-1512.e2. 10.1053/j.gastro.2011.02.006.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang Y, Wang S, Li D, Zhnag J, Gu D, Zhu Y, He F: A Systems Biology-Based Classifier for Hepatocellular Carcinoma Diagnosis. PLoS ONE. 2011, 6: e22426-10.1371/journal.pone.0022426.PubMed CentralView ArticlePubMedGoogle Scholar
- Zheng S, Tansey WP, Hiebert SW, Zhao Z: Integrative network analysis identifies key genes and pathways in the progression of hepatitis C virus induced hepatocellular carcinoma. BMC Med Genomics. 2011, 4: 62-10.1186/1755-8794-4-62.PubMed CentralView ArticlePubMedGoogle Scholar
- Bonnet E, Michoel T, Van de Peer Y: Prediction of a gene regulatory network linked to prostate cancer from gene expression, microRNA and clinical data. Bioinformatics. 2010, 26: i638-i644. 10.1093/bioinformatics/btq395.PubMed CentralView ArticlePubMedGoogle Scholar
- Guo A-Y, Sun J, Jia P, Zhao Z: A Novel microRNA and transcription factor mediated regulatory network in schizophrenia. BMC Systems Biology. 2010, 4: 10-10.1186/1752-0509-4-10.PubMed CentralView ArticlePubMedGoogle Scholar
- Chuang H-Y, Lee E, Liu Y-T, Lee D, Ideker T: Network-based classification of breast cancer metastasis. Mol Syst Biol. 2007, 3: 140-PubMed CentralView ArticlePubMedGoogle Scholar
- Li J, Lenferink AEG, Deng Y, Collins C, Cui Q, Purisima EO, O'Connor-McCourt MD, Wang E: Identification of high-quality cancer prognostic markers and metastasis network modules. Nat Commun. 2010, 1: 34-PubMedGoogle Scholar
- Martinez N, Walhout A: The interplay between transcription factors and microRNAs in genome-scale regulatory networks. Bioessays. 2009, 31: 435-445. 10.1002/bies.200800212.PubMed CentralView ArticlePubMedGoogle Scholar
- Martinez NJ, Ow MC, Barrasa MI, Hammell M, Sequerra R, Doucette-Stamm L, Roth FP, Ambros VR, Walhout AJM: A C. elegans genome-scale microRNA network contains composite feedback motifs with high flux capacity. Genes Dev. 2008, 22: 2535-2549. 10.1101/gad.1678608.PubMed CentralView ArticlePubMedGoogle Scholar
- Arda HE, Walhout AJM: Gene-centered regulatory networks. Briefings in Functional Genomics. 2010, 9: 4-12. 10.1093/bfgp/elp049.PubMed CentralView ArticlePubMedGoogle Scholar
- Tu K, Yu H, Hua Y-J, Li Y-Y, Liu L, Xie L, Li Y-X: Combinatorial network of primary and secondary microRNA-driven regulatory mechanisms. Nucleic Acids Research. 2009, 37: 5969-5980. 10.1093/nar/gkp638.PubMed CentralView ArticlePubMedGoogle Scholar
- Ventura A, Jacks T: MicroRNAs and Cancer: Short RNAs Go a Long Way. Cell. 2009, 136: 586-591. 10.1016/j.cell.2009.02.005.PubMed CentralView ArticlePubMedGoogle Scholar
- Peng H, Long F, Ding C: Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2005, 27: 1226-1238.View ArticlePubMedGoogle Scholar
- Santer FR, Höschele PPS, Oh SJ, Erb HHH, Bouchal J, Cavarretta IT, Parson W, Meyers DJ, Cole PA, Culig Z: Inhibition of the acetyltransferases p300 and CBP reveals a targetable function for p300 in the survival and invasion pathways of prostate cancer cell lines. Mol Cancer Ther. 2011, 10: 1644-1655. 10.1158/1535-7163.MCT-11-0182.View ArticlePubMedGoogle Scholar
- Kumarswamy R, Mudduluru G, Ceppi P, Muppala S, Kozlowski M, Niklinski J, Papotti M, Allgayer H: MicroRNA-30a inhibits epithelial-to-mesenchymal transition by targeting Snai1 and is downregulated in non-small cell lung cancer. International Journal of Cancer.
- Wang Z, Li Y, Kong D, Ahmad A, Banerjee S, Sarkar FH: Cross-talk between miRNA and Notch signaling pathways in tumor development and progression. Cancer Letters. 2010, 292: 141-148. 10.1016/j.canlet.2009.11.012.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang Z, Li Y, Kong D, Sarkar FH: The Role of Notch Signaling Pathway in Epithelial-Mesenchymal Transition (EMT) During Development and Tumor Aggressiveness. Curr Drug Targets. 2010, 11: 745-751. 10.2174/138945010791170860.PubMed CentralView ArticlePubMedGoogle Scholar
- Yao R, Cooper GM: Requirement for phosphatidylinositol-3 kinase in the prevention of apoptosis by nerve growth factor. Science. 1995, 267: 2003-2006. 10.1126/science.7701324.View ArticlePubMedGoogle Scholar
- Huang J, Zhao L, Xing L, Chen D: MicroRNA-204 Regulates Runx2 Protein Expression and Mesenchymal Progenitor Cell Differentiation. STEM CELLS. 2010, 28: 357-364.PubMed CentralPubMedGoogle Scholar
- Lee Y, Yang X, Huang Y, Fan H, Zhang Q, Wu Y, Li J, Hasina R, Cheng C, Lingen MW, Gerstein MB, Weichselbaum RR, Xing HR, Lussier YA: Network Modeling Identifies Molecular Functions Targeted by miR-204 to Suppress Head and Neck Tumor Metastasis. PLoS Comput Biol. 2010, 6: e1000730-10.1371/journal.pcbi.1000730.PubMed CentralView ArticlePubMedGoogle Scholar
- Lee E, Chuang H-Y, Kim J-W, Ideker T, Lee D: Inferring Pathway Activity toward Precise Disease Classification. PLoS Comput Biol. 2008, 4: e1000217-10.1371/journal.pcbi.1000217.PubMed CentralView ArticlePubMedGoogle Scholar
- Chowdhury SA, Nibbe RK, Chance MR, Koyutürk M: Subnetwork state functions define dysregulated subnetworks in cancer. J Comput Biol. 2011, 18: 263-281. 10.1089/cmb.2010.0269.PubMed CentralView ArticlePubMedGoogle Scholar
- Butte AJ, Kohane IS: Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput. 2000, 418-429.Google Scholar
- Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, Califano A: ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics. 2006, 7 (Suppl 1): S7-10.1186/1471-2105-7-S1-S7.PubMed CentralView ArticlePubMedGoogle Scholar
- Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, Kasif S, Collins JJ, Gardner TS: Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles. PLoS Biol. 2007, 5: e8-10.1371/journal.pbio.0050008.PubMed CentralView ArticlePubMedGoogle Scholar
- Meyer PE, Kontos K, Lafitte F, Bontempi G: Information-theoretic inference of large transcriptional regulatory networks. EURASIP J Bioinform Syst Biol. 2007, 79879-Google Scholar
- Altay G, Emmert-Streib F: Revealing differences in gene network inference algorithms on the network level by ensemble methods. Bioinformatics. 2010, 26: 1738-1744. 10.1093/bioinformatics/btq259.View ArticlePubMedGoogle Scholar
- Narendra V, Lytkin NI, Aliferis CF, Statnikov A: A comprehensive assessment of methods for de-novo reverse-engineering of genome-scale regulatory networks. Genomics. 2011, 97: 7-18. 10.1016/j.ygeno.2010.10.003.PubMed CentralView ArticlePubMedGoogle Scholar
- Vaquerizas JM, Kummerfeld SK, Teichmann SA, Luscombe NM: A census of human transcription factors: function, expression and evolution. Nat Rev Genet. 2009, 10: 252-263. 10.1038/nrg2538.View ArticlePubMedGoogle Scholar
- Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ: miRBase: tools for microRNA genomics. Nucleic Acids Research. 2007, 36: D154-D158. 10.1093/nar/gkm952.PubMed CentralView ArticlePubMedGoogle Scholar
- Lewis BP, Burge CB, Bartel DP: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005, 120: 15-20. 10.1016/j.cell.2004.12.035.View ArticlePubMedGoogle Scholar
- Wang X: miRDB: A microRNA target prediction and functional annotation database with a wiki interface. RNA. 2008, 14: 1012-1017. 10.1261/rna.965408.PubMed CentralView ArticlePubMedGoogle Scholar
- Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature. 2007, 447: 799-816. 10.1038/nature05874.
- Alexiou P, Vergoulis T, Gleditzsch M, Prekas G, Dalamagas T, Megraw M, Grosse I, Sellis T, Hatzigeorgiou AG: miRGen 2.0: a database of microRNA genomic information and regulation. Nucleic Acids Research. 2009, 38: D137-D141.PubMed CentralView ArticlePubMedGoogle Scholar
- Su W-H, Chao C-C, Yeh S-H, Chen D-S, Chen P-J, Jou Y-S: OncoDB.HCC: an integrated oncogenomic database of hepatocellular carcinoma revealed aberrant cancer target genes and loci. Nucleic Acids Research. 2007, 35: D727-D731. 10.1093/nar/gkl845.PubMed CentralView ArticlePubMedGoogle Scholar
- Hsu C-N, Lai J-M, Liu C-H, Tseng H-H, Lin C-Y, Lin K-T, Yeh H-H, Sung T-Y, Hsu W-L, Su L-J, Lee S-A, Chen C-H, Lee G-C, Lee D, Shiue Y-L, Yeh C-W, Chang C-H, Kao C-Y, Huang C-Y: Detection of the inferred interaction network in hepatocellular carcinoma from EHCO (Encyclopedia of Hepatocellular Carcinoma genes Online). BMC Bioinformatics. 2007, 8: 66-10.1186/1471-2105-8-66.PubMed CentralView ArticlePubMedGoogle Scholar
- He B, Qiu X, Li P, Wang L, Lv Q, Shi T: HCCNet: an integrated network database of hepatocellular carcinoma. Cell Res. 2010, 20: 732-734. 10.1038/cr.2010.67.View ArticlePubMedGoogle Scholar
- Hur J, Schuyler AD, States DJ, Feldman EL: SciMiner: web-based literature mining tool for target identification and functional enrichment analysis. Bioinformatics. 2009, 25: 838-840. 10.1093/bioinformatics/btp049.PubMed CentralView ArticlePubMedGoogle Scholar
- Hummel M, Meister R, Mansmann U: GlobalANCOVA: exploration and assessment of gene group effects. Bioinformatics. 2008, 24: 78-85. 10.1093/bioinformatics/btm531.View ArticlePubMedGoogle Scholar
- Zhang H, Yu C-Y, Singer B, Xiong M: Recursive Partitioning for Tumor Classification with Gene Expression Microarray Data. PNAS. 2001, 98: 6730-6735. 10.1073/pnas.111153698.PubMed CentralView ArticlePubMedGoogle Scholar
- Koziol JA, Zhang J-Y, Casiano CA, Peng X-X, Shi F-D, Feng AC, Chan EKL, Tan EM: Recursive partitioning as an approach to selection of immune markers for tumor diagnosis. Clin Cancer Res. 2003, 9: 5120-5126.PubMedGoogle Scholar
- Chen H-Y, Yu S-L, Chen C-H, Chang G-C, Chen C-Y, Yuan A, Cheng C-L, Wang C-H, Terng H-J, Kao S-F, Chan W-K, Li H-N, Liu C-C, Singh S, Chen WJ, Chen JJW, Yang P-C: A five-gene signature and clinical outcome in non-small-cell lung cancer. N Engl J Med. 2007, 356: 11-20. 10.1056/NEJMoa060096.View ArticlePubMedGoogle Scholar
- Jeong Y, Xie Y, Xiao G, Behrens C, Girard L, Wistuba II, Minna JD, Mangelsdorf DJ: Nuclear Receptor Expression Defines a Set of Prognostic Biomarkers for Lung Cancer. PLoS Med. 2010, 7: e1000378-10.1371/journal.pmed.1000378.PubMed CentralView ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.