Next Article in Journal
The Non-Coding RNA Journal Club: Highlights on Recent Papers—5
Previous Article in Journal
Deciphering microRNAs and Their Associated Hairpin Precursors in a Non-Model Plant, Abelmoschus esculentus
Previous Article in Special Issue
Present Scenario of Long Non-Coding RNAs in Plants
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Detecting Disease Specific Pathway Substructures through an Integrated Systems Biology Approach

Salvatore Alaimo
Gioacchino Paolo Marceca
Alfredo Ferro
Alfredo Pulvirenti
Bioinformatics Unit, Department of Clinical and Experimental Medicine, University of Catania, c/o Dipartimento di Matematica e Informatica, Viale A. Doria 6, 95125 Catania, Italy
Author to whom correspondence should be addressed.
Non-Coding RNA 2017, 3(2), 20;
Submission received: 15 January 2017 / Revised: 28 March 2017 / Accepted: 10 April 2017 / Published: 19 April 2017
(This article belongs to the Special Issue Bioinformatics Softwares and Databases for Non-Coding RNA Research)


In the era of network medicine, pathway analysis methods play a central role in the prediction of phenotype from high throughput experiments. In this paper, we present a network-based systems biology approach capable of extracting disease-perturbed subpathways within pathway networks in connection with expression data taken from The Cancer Genome Atlas (TCGA). Our system extends pathways with missing regulatory elements, such as microRNAs, and their interactions with genes. The framework enables the extraction, visualization, and analysis of statistically significant disease-specific subpathways through an easy to use web interface. Our analysis shows that the methodology is able to fill the gap in current techniques, allowing a more comprehensive analysis of the phenomena underlying disease states.

1. Introduction

Knowledge base-driven pathway analysis [1] has become a popular approach in complex disease study in order to improve biological insights for a deeper understanding of the molecular mechanisms underlying specific phenotypes.
“Omics” technologies are capable of identifying differentially expressed genes and metabolites associated with specific diseases. On the other hand, biological pathway databases, such as Kyoto Encyclopedia of Genes and Genomes (KEGG) [2,3,4] or Pathway Commons [5], enable the study and the understanding of the system level effects of differential expressions.
Several applications have been implemented to visualize and analyze “Omics” data in the context of known biological pathways [1]. To this purpose, several statistical tests in connection with known biological databases have been used to detect significant pathways. However, the gap between current analysis techniques and the ability to obtain accurate knowledge is broad. Therefore, using such information to better understand the underlying biological phenomena remains a challenge.
Pathway-based analysis techniques can be grouped into three generations of approaches [1]: (i) Over-Representation Analysis (ORA); (ii) Functional Class Scoring (FCS); and (iii) Pathway Topology-based (PT).
First-generation methods statistically evaluate the number of altered genes in a pathway with respect to the set of all analyzed genes [6,7,8,9,10,11,12] and microRNAs (miRNAs) [13]. This approach, however, presents several limitations mainly due to the type, quality, and structure of the annotations available.
Second-generation methods tried to compensate some disadvantages of ORA approaches [14,15]. They consider the distribution of pathway genes in the entire list of genes and perform a FCS, allowing adjustments for gene correlations. However, they do not take into account the magnitude of the gene deregulation when estimating pathway activity. A further limitation lies in the fact that each functional category is analyzed independently without a unifying analysis at a pathway or system level.
The third generation class, Pathway Topology-based methods, can be divided into two subgroups of tools. The first one examines the effect of a phenotype at the global level. The second, and more recent approach, extracts subpathways specifically related to the phenotype under study.
In Draghici et al. [16], an analytical technique based on an impact factor (IF) calculation was introduced. The impact factor is a pathway-level score that takes into account the magnitude of change in genes expression, the type of interactions between genes, and their location in the pathway. Such impact analysis represents an important attempt to allow a deeper level of statistical analysis, informed by more pathway-specific biology. Authors model each pathway as a graph in which nodes represent genes, while edges are interactions between them. Authors also define a gene-level statistic (called perturbation factor, PF) as a linear function of the change in gene expression and the perturbation of its neighborhood. Such a statistic is then combined for each element in a pathway, and a p-value is computed by means of exponential distribution.
The analysis presented by Draghici et al. [16] has been further improved by Signaling Pathway Impact Analysis (SPIA) [17], which attenuates the dominant effect caused by the change in expression within PF computation, while reducing the high rate of false positives when the input list of genes is small. SPIA uses a bootstrap procedure to evaluate the significance of the observed perturbation in the pathway. This is combined with a p-value computed in ORA style to make a full assessment of the statistical significance of the perturbation of each pathway.
To reduce the number of false positives, Vaske et al. [18] presented PAthway Representation and Analysis by Direct Reference on Graphical Models (PARADIGM), which has been further improved in [19]. PARADIGM is a method to infer patient-specific genetic activity by incorporating information regarding interactions between genes provided in a pathway. The method predicts the degree of alteration in the activity of a pathway by employing a probabilistic inference algorithm. The authors show that their model yields significantly more reliable results than SPIA.
However, both SPIA and PARADIGM completely ignore post-transcriptional regulatory interactions involving miRNAs. To overcome this limitation, Calura et al. [20] developed a new approach, Micrographite, which is able to integrate pathways with predicted and validated miRNA-target interactions. The method, by performing a topological analysis based on expression profiles, is able to identify significant gene circuits specific to a phenotype. The main advantage of the methodology is the ability to accurately describe the cellular context that led to the expression data in input.
Recently, Alaimo et al. [21] proposed Mirna enrIched paTHway Impact anaLysis (MITHrIL), a technique that extends Draghici et al. [16] and Tarca et al. [17], by combining their effectiveness while improving the reliability of the results. The strength of MITHrIL lies in the annotation of pathways with information regarding miRNAs. Starting from expression values of genes and/or microRNAs, MITHrIL returns a list of pathways sorted according to the degree of their deregulation, together with the corresponding statistical significance (p-values), as well as a predicted degree of alteration for each endpoint (a pathway node whose alteration, based on current knowledge, affects the phenotype in some way).
The second subgroup of pathway perturbation methods focus on subpathways (i.e., local areas of the entire biological pathway), which have the potential to represent the underlying biological phenomena more accurately. The central idea in shifting the efforts toward subpathways is that when a subpathway responds to a specific condition (e.g., drugs), its corresponding pathway should also respond. The high redundancy of pathways together with their high degree of crosstalk suggests that subpathways respond more effectively and sensitively than the whole pathway. Indeed, key subpathway regions are more relevant for interpreting the associated biological phenomena than whole-pathway approaches. In addition, the cross-talk among subpathways could allow going beyond existing pathway knowledge. Furthermore, perturbations in subpathway regions of metabolic pathways could allow shedding light on understanding the causes of diseases [22,23].
In Li et al. [24], authors introduced Subpathway-GM, which uses a structural node similarity within pathways to identify metabolic subpathways based on information from genes and metabolites. Subpathway-GM has been implemented as a freely available web-based and R-based tool. Once the user provides genes and metabolites of interest, the system identifies metabolic subpathways by: (i) mapping genes and metabolites of interest to graphs of pathways after graph-based reconstruction of metabolic pathways; (ii) locating subpathways within pathways according to signature nodes; (iii) evaluating the statistical significance of each subpathways using an hypergeometric test. Subpathway-GM improves the identifications of subpathways with respect to previous methodologies, by taking into account that, from a biological perspective, dysfunctional genes should be closely related to dysfunctional metabolites in pathways.
In Judeh et al. [25], authors introduced Topology Enrichment Analysis frameworK (TEAK), a topology enrichment analysis tool based on Bayesian Networks, to extract linear and non-linear subpathways. TEAK fully accounts for the topological information of subpathways and provides an interactive view of the data in the KEGG pathways. Compared with previous approaches, TEAK does not use differential gene expression analysis to determine modules of interest, and, thus, it is not sensitive to threshold values.
In [26], authors propose DE subs to identify differentially expressed subpathways using RNA-seq data. DEsubs is available as an R package. The tool enables users to perform customized analysis on the problem under investigation through several operation modes to uncover local perturbations within networks.
Subpathway-GMir [27] uses a list of condition-specific genes, miRNAs and pathway topologies, and it has been implemented as a freely available R package. It aims to identify abnormal metabolic subpathways mediated by miRNAs. The pipeline contains three main components: (i) creation of reconstructed KEGG metabolic pathway graphs (RMPGs) that integrate miRNA-target interactions (supported by low-throughput experiments), by converting KEGG metabolic pathways into graphs with genes as nodes; (ii) mapping of condition-specific genes and miRNAs into RMPGs and identification of miRNA-mediated metabolic subpathways; (iii) evaluation of the significance of candidate subpathways using the hypergeometric test. Subpathway-GMir provides a platform for identifying abnormal metabolic subpathways mediated by miRNAs, and may help to clarify the roles of miRNAs in diseases. The system has been tested also with The Cancer Genome Atlas (TCGA) data.
More recently, [28] proposed time-vaRying enriCHment integrOmics Subpathway aNalysis tOol (CHRONOS) [28], which for the first time introduces a temporal subpathway search built on top of mRNA and miRNA expression data.
Although the literature is rich with useful tools capable of extracting subpathways from expression data, a simple interface, connected with updated sources, is needed. Moreover, the addition of miRNAs in the subpathway extraction technique is essential since many alterations of these post-trascriptional regulators have been found in several cancer types.
In this paper, we propose SPECifIC (SubPathway ExtraCtor and enrICher), a technique for the extraction, visualization, and enrichment of substructures obtained from a meta-pathway built by combining KEGG pathways. The methodology extends MITHRIL [21] to allow efficient nodes perturbation and statistical significance computation. Therefore, starting from expression data of pathological samples and, optionally, a list of Nodes of Interest (NoIs), the method is able to extract statistically significant disease-specific subpathways and perform functional enrichment, allowing further evaluations. The methodology is implemented as a web application, and is available at [29].

2. Results

Comparing subpathway extraction and enrichment methods is a complex process. Therefore, we focused on a case study based on two cancer types from TCGA: BRCA (Breast invasive carcinoma) and COAD (Colon adenocarcinoma).
Breast cancer is the most common malignancy in women in several countries [30]. It is a heterogeneous disease, classified into several sub-types, each one involving a different gene set controlling for diverse processes. The identification of cancer sub-type relies on the expression of the estrogen receptor (ER), progesterone receptor (PR), epidermal growth factor receptor 2 (ERBB2), and cytokeratin (CK) protein [31].
Accordingly, three basic therapeutic groups [32] have been identified: (i) the ER positive group, also named “luminal group” and, further divided into luminal A and luminal B, is the most numerous and diverse in terms of gene expression and mutation spectrum; (ii) the HER2 (also called erb-b2 receptor tyrosine kinase 2, ERBB2) amplified group; (iii) the triple-negative breast cancers (TNBCs), also known as “basal-like” breast cancers, characterized by lacking expression of ER, PR and HER2, more frequent in patients with germline BRCA1 mutations.
Colon and rectal cancer, which we refer to as colorectal carcinoma (CRC), are major causes of cancer-associated morbidity and mortality all over the world [33]. Although colon (COAD) and rectal (READ) adenocarcinomas present differences from an epidemiological point of view [34], their molecular characterizations in a non-hypermutated state revealed that the overall patterns of changes in copy number, mRNA and miRNA are each indistinguishable from the other [35]. Accordingly, the application of a cluster analysis method termed “cluster-of-cluster assignments” (COCA) joins COAD and READ in a unique group. This revealed a high-grade of convergence between these two carcinomas [36].
To assess the performances of our methodology, we performed a comparison with Subpathway-GM [24], Subpathway-GMir [27], and DEsubs [26]. Therefore, we ran all competitors, using parameters as suggested from their respective manuscripts, on expression data taken from TCGA, and the results of the enrichment, using the KEGG pathways database, were collected, ranked on the basis of the reported p-values, and compared. Since DEsubs returns only the set of significant terms enriching a subpathway, without reporting its p-value, we decided to rank each term by the number of subpathways in which such a term is significant.
We opted to run a comparison using only the KEGG pathways database, since the correct identification of the involved pathophysiological process in a subpathway is crucial for subsequent analysis steps. To this end, pathway representing pathological states were removed from enrichment results. All methodologies have been tested using the same set of pathways, enrichment terms, and expression data, although SubPathway-GMir experienced issues when ranking non-metabolic pathways.
The selection of NoIs for SPECifIC was done by using the semi-automated procedure as described in the “Materials and Methods” section. In order to run SPECifIC, our software implementation was used to compute the substructures present in all stages of the aforementioned cancers (BRCA and COAD). Then, for each statistically significant subpathway, functional enrichment was performed, and all pathway terms were collected (regardless of their p-value in order to analyze non-significant results). If a term enriched more substructures, the minimum p-value among all subpathways was used. The resulting set of terms, ranked by p-value, was used, and the top-20 pathways were further analyzed to determine the performances of our methodology (Table 1).
To assess disease-specificity of the results, we computed metrics on disease genes found in the substructures detected by the methodologies, and compared such values with those obtained by applying the same metrics on KEGG pathways. Disease genes were obtained from [37,38] and Genetic Association Database (GAD) [39], removing all genes not present in at least one pathway. The main metrics are: (i) the average distance between a disease gene and a substructure, and (ii) the average distance between pairs of disease genes within the substructures. The first metric is a specificity index of the results, as subpathways closest to disease genes have a greater likelihood of being involved in some way. The second metric is an indication of the characteristic of the genes associated with the same disease to show close trends and common functions, although this is not always an indication of biological relevance. In Table 2, we present the results of the two metrics along with the total number of nodes present in the substructures, and the number of nodes whose expression appears to be significantly differentially expressed ( p < 0 . 01 ). The same table also reports the number of disease genes contained in at least one subpathway and the number of reachable pairs by a directed path. This latter value is critical to properly weigh the second metric. Given that the introduction of miRNAs topologically changes pathways, we also computed the same metrics removing (Subpathway-GM and DESubs do not integrate any information on miRNA-target interactions when computing subpathways.) such elements (Table S1). Our findings show that SPECifIC detects subpathways significantly closer to disease genes with respect to the other approaches (p-values computed by Wilcoxon rank-sum test available in Tables S11 and S12). Although it is possible to observe an increase in the average distance between internal pairs of disease genes, these latter values are lower than those observed in KEGG pathways (p-values computed by Wilcoxon rank-sum test available in Tables S13 and S14), indicating a potential higher specificity. By removing miRNAs from substructures, we can observe similar results achieved by the compared methodologies. This is an indication of the specificity of the results obtained by the compared methods, although it is not an index of biological reliability.
Finally, due to the high redundancy of biological pathways (different pathways often use the same subpathways in similar roles), we performed an enrichment using disease terms taken from DisGeNET [37,38], in order to evaluate which kind of diseases share the subpathways identified for our two cancer types. The results are available in Table S2.

3. Discussion

Disease-specific subpathways extraction is a fundamental process to accurately study processes at the basis of pathological phenotypes. From these substructures, the identification of significantly involved pathways is necessary to determine if the identified changes are at the origin of the disease or a secondary consequence. Furthermore, determining redundant pathways that lead to the same outcome is of great interest especially for precision medicine to identify and, if necessary, prevent drug resistance phenomena. In the following, we will provide a description of the results obtained from SPECifIC in comparisons with the other methods: Subpathway-GM [24], Subpathway-GMir [27], and DEsubs [26]. Complete results are available in Supplementary Materials. We ran all competitors on expression data taken from TCGA and the results of the enrichment were collected using KEGG pathways database. For each method, we selected and considered only the top-20 statistically significant pathway terms ( p < 0 . 01 ), and compared the four methodologies on the basis of such lists (Tables S3–S10). In Figure 1a,b we report Venn diagrams concerning the overlapping of the selected pathways in BRCA and COAD.

3.1. Analysis of BRCA Results Using SPECifIC

SPECifIC identified 163 potential pathways associated with BRCA, of which 74 are statistically significant (p < 0.01). The list is reported in Table 1 (complete results available in Table S7). The wideness of this subset of pathways is to be considered a reflection of the fact that distinct oncogenic alterations and pathways are associated with diverse breast cancer subtypes [40]. Literature data confirm the involvement of a substantial fraction of this subset of pathways in various BRCA subtypes. First, alterations in gene expression levels within the estrogen and ErbB signaling pathways
play a pivotal role in BRCA development and progression as mentioned above, and dysregulation of various components belonging to these two pathways are themselves the cause of acquired endocrine resistance, typical of many BRCA cases [41]. The involvement of prolactin signaling pathway in breast cancer development is also well documented, and a few works highlight an important cross-talk with the estrogen signaling pathway [42]. Activation of EGFR tyrosine kinase inhibitor resistance characterizes most breast cancer patients also [43], along with the overexpression of CYP19A1, an enzyme with a key role in the steroid hormone biosynthesis, metabolizing testosterone into estradiol and androstenedione to estrone, whose dysregulation is relevant to estrogen-dependent pathologies [44]. Focal adhesion [45,46], platelets activation [47], chemokine signaling pathway [48], Phospholipase D signaling pathway [49], and PPAR signaling pathway [50] are overrepresented in this type of cancer as well as in the other types.
Besides the well-known BRCA-associated pathways, scientific evidence exists for several other ones provided by SPECifIC. For example, considering metabolism of xenobiotics and chemical carcinogenesis, C Y P 1 A 1 and C Y P 1 B 1 genes were found to be highly expressed in a significant number of BRCA individuals [51]. Under physiologic conditions, these genes are expressed at a low level, or are totally suppressed, in a tissue-specific manner [52]. Differently, in breast cancer, C Y P 1 A 1 regulates proliferation and survival of tumor cells, while C Y P 1 B 1 indirectly causes the generation of free radicals. Regarding the involvement of the insulin signaling pathway in BRCA, literature data indicate that insulin receptors ( I N S R ) are usually overexpressed in BRCA patients—more frequently the I N S R isoform A [53]—and a recent work also demonstrated that flotillin expression is positively correlated with that of E R B B 2 in BRCA. In particular, flotillin-2 emerged as a potential predictor of prognosis in H E R 2 -amplified breast cancer [54]. A substantial body of evidence proves that neurotrophin signaling pathway is implicated in the stimulation of breast cancer cell growth [55,56]. A significant dysregulation of oxytocin receptors ( O X T R ) in BRCA subtypes has also been reported [46,57,58].
Finally, regarding the remaining four pathways (drug metabolism–cytochrome P450, drug metabolism–other enzymes, linoleic acid metabolism and phenylalanine metabolism) scarce or no evidence was found in literature.

3.2. Comparison for BRCA-Related Pathways

SubPathway-GM was able to identify 25 potential pathway terms, of which 14 were shared with SPECifIC (Table S8). Cell cycle [59], focal adhesion, jak/stat signaling pathway, tight junction [60,61], adherens junction [62], gap junction [63] and regulation of actin cytoskeleton [64], well known cancer-related pathways, were top-ranked by this method. Of these pathway terms, a few were recognized as statistically non-significant by SPECifIC. In addiction, SubPathway-GM returned some potentially BRCA-related pathways. For example, deregulation of embryonic signalling pathways (notch signaling, Hedgehog, Wnt, and transforming growth factor beta signaling pathways) is recognized as having a strong impact in human cancers, including BRCA [65]. Though our method missed this pathway, it identified the Wnt signaling pathway as statistically significant (but not as top ranked), while SubPathway-GM classified it as statistically non-significant. Another result returned by SubPathway-GM is the Mitogen-activated protein kinase (MAPK) signaling pathway. Indeed, even if it is not a directly a BRCA-correlated pathway, recent works proved a strong synergistic effect with notch signaling pathway [66], and PIK3/Akt signaling pathway [67], enhancing the process of transformation and proliferation of breast cancer cells in certain BRCA subtypes.
DEsubs identified 22 terms, of which 13 were in common with our approach (Table S10). Most of these 22 pathways are well known cancer-associated pathways. It is worth noting that DEsubs could classify the Hedgehog signaling pathway as relevant for BRCA together with the Notch signaling pathway. This method also identified the ECM receptor interaction as BRCA-associated [68]. SPECifIC and SubPathway-GM classified it as statistically non-significant. Finally, DEsubs also returned Gonadotropin-releasing hormone (GnRH) and Vascular endothelial growth factor (VEGF) signaling pathways as top ranked in BRCA, and literature confirms these results [69,70]. SPECifIC also classified them as significantly BRCA-associated (though not included in the top 20 list), whereas SubPathway-GM identified them as statistically non-significant.
SubPathway-GMir was able to identify 53 potential pathways, of which only six were shared with our technique (Table S9). This method showed some shortcomings. First, it has to be noticed that it is able to identify just metabolic pathways. Even though several identified pathways are effectively proved to be altered in BRCA [71,72]. It has to be noticed that results obtained by SubPathway-GMir disagreed with results yielded by the other three methods.

3.3. Analysis of COAD Results Using SPECifIC

SPECifIC identified 165 potential pathways associated with CRC, of which 31 are statistically significant (p < 0.01). The list is reported in Table 1 (complete results available in Supplementary Table S3). Interestingly, half of these top ranked pathways are shared with those found in BRCA. These results are a practical demonstration of the high redundancy of biological pathways. Literature data confirm the reliability of a significant portion of these results. Typical cancer-related pathways, such as Apoptosis pathway [73], MTOR signaling pathway [74], RAS signaling pathway [75], Phospholipase-D signaling pathway [76,77], and HIF-1 signaling pathway [78,79] were returned by SPECifIC. Several works proved that expression of many cytochrome p450 enzymes is altered in CRC [80,81], thus impacting the physiological activity of pathways like metabolism of xenobiotics by cytochrome p450, chemical carcinogenesis and drug metabolism—cytochrome p450. EGFR tyrosine kinase inhibitor resistance in CRC can be developed regardless of the treatment, due to mutations on K R A S , or amplification of M E T [82]. Concerning the linoleic acid metabolism, it is proved that linoleic and α -linolenic acid levels are lower in the cell membranes of colorectal cancer cells [83]. For the other pathways, less evidence exists in literature. For example, nothing is known concerning the role of Rap1 signaling pathway in CRC. However, two recent papers showed that miR-100 and miR-139 strongly decreased colorectal carcinoma cell proliferation by directly targeting R A P 1 B [84,85]. Some data suggest that H S D 17 B 1 , an enzyme catalyzing the conversion from estrone into estradiol, highly expressed in the colonic epithelium, may contribute to sex steroid-mediated effects on CRC development [86]. Two recent works demonstrated that hyper- and hypothyroidism are associated with modestly elevated risk of CRC [87,88]. Furthermore, deregulation of deiodinases (D2–D3) perturb Wnt signaling [89] in CRC patients.
Literature information relative to the involvement in CRC of endocrine resistance, progesterone mediated oocyte maturation, melanogenesis and platinum drug resistance pathways are scarce or absent.

3.4. Comparison for COAD-Related Pathways

SubPathway-GM was able to identify 27 potential terms, of which only three were shared with SPECifIC (Table S4). Alterations in PPAR signaling, tight junction, focal adhesion, apoptosis, MAPK signaling and Wnt signaling are typically associated with CRC cells [90,91,92,93,94,95,96]. Moreover, this method classified nine top ranked metabolic pathways as potentially involved in this disease. Recent scientific works based on gene expression analysis and metabolomics reported results showing a wide metabolic reprogramming in colorectal cancer cells. More precisely, levels in enzymes and metabolites involved in arginine and proline metabolism, alanine aspartate and glutamate metabolism, tyrosine metabolism, pyruvate metabolism, fatty acid degradation and butanoate metabolism were confirmed to be significantly altered [45,97,98,99,100,101]. Concerning the natural killer cell mediated cytotoxicity, it has been proved that in a substantial fraction of cases, natural killer (NK) cells belonging to CRC-affected patients show a significant downregulation of several surface receptors [102,103]. No information was found in literature regarding aldosterone regulated sodium reabsorption and oocyte meiosis.
DEsubs identified 19 terms, of which five were in common with our approach (Table S6). A small fraction of these pathways was discussed above for SPECifIC and SubPathway-GM. A recent scientific work showed that expression of Fc ϵ RI α - and γ -chains were significantly upregulated in 70% of colon cancer patients, suggesting that Fc ϵ RI may contribute to the pathophysiology of the gastrointestinal tract [104]. A growing body of evidence showed that inflammatory cells usually produce large amounts of pro-tumorigenic cytokines, such as TNF, Interleukin-6 and Interleukin-23, which cause cell proliferation and tumor progression in colorectal cells. In addition, it was proved that NF- κ B signaling, activated through TNF- α , strongly induces migration and accumulation of mesenchymal stem cells at CRC-tumor sites [105,106]. No significant information was found about relationships between CRC and B-cell receptor signaling pathway, leukocyte transendothelial migration, long-term depression pathway or GNRH signaling pathway.
SubPathway-GMir was able to identify 35 potential metabolic pathways, of which only three shared with our technique (Table S5). Considering its top 20 pathways, three were identified as statistically significant by SPECifIC, five were classified as statistically significant by SubPathway-GM and only one was shared with DEsubs. Albeit the relation with CRC is validated for a considerable number of these pathways according to the scientific works discussed above for SubPathway-GM, literature information about the remaining fraction of returned metabolic pathways were scarce or not found.

4. Materials and Methods

4.1. Overview of the Methodology

Our algorithm performs the extraction and enrichment of disease-specific subpathways starting from a set of Nodes of Interest (NoIs). A user may specify his own NoIs or employ an automated selection procedure. The methodology performs computation in four main steps: (i) construction of the meta-pathway; (ii) execution of the MITHrIL algorithm to compute nodes perturbations and p-values; (iii) automatic selection of NoIs (if no user-specified ones available), extraction of subpathways, and (iv) enrichment analysis. This is available through a web interface that allows a user to select one disease, identify a set of NoIs, and visualize the results.

4.1.1. Extending MITHrIL

To analyze pathways and determine the impact of differentially expressed genes (miRNAs), we employed the MITHrIL algorithm [21] for its features and performances. The MITHrIL algorithm, in fact, annotates pathways with miRNA-target interactions, therefore obtaining significantly better performances over other competitors, as stated in Alaimo et al. [21].
Meta-pathway construction. In order to extract human-specific subpathways, all KEGG pathways were merged to build a single directed network using a two step procedure. First, all disease-specific pathways were removed to form a reference set of pathways. Therefore, all nodes and edges present in the reference set were put together, removing duplicates, building a meta-pathway, which represents the human organism.
Subsequently, the meta-pathway was annotated with experimentally validated miRNA-target interactions using the procedure described in Alaimo et al. [21], and a perturbation analysis is performed by employing differentially expressed genes and miRNAs for each disease.
Such an analysis is computationally intensive. Therefore, to reduce computational resources, all nodes of the meta-pathway were sorted using a DFS-like (Depth-First Search) algorithm [107] to find an approximation of the topological ordering of the nodes of the graph. The perturbation is therefore computed on each node by following such an ordering, caching the results for each call, allowing a reduction of the number of recursive calls, and improving the performances.
Computing nodes’ p-values. The extraction of subpathway is based on the search of statistically significant substructures within the meta-pathway. This computation is based on the statistical significance of each pathway node, which is the probability of observing a perturbation factor lower than the one computed for the null model.
Therefore, let n be a node in the meta-pathway and P F n its perturbation factor. As in Alaimo et al. [21], we randomly assign Log-Fold-Changes to the nodes of the meta-pathway in order to evaluate a set of random perturbations P F R n = P F 1 R n , , P F k R n . The p-value for n is estimated as:
p n = x P F R n | x P F n k .
p-values are therefore adjusted on multiple hypothesis, and the results are stored for subsequent computations.

4.1.2. Subpathway Extraction

The disease-specific subpathway extraction process starts from the previously built meta-pathway annotated with perturbations and p-values computed by means of the extended MITHrIL algorithm. Our methodology is, therefore, able to extract five types of substructures starting from a set of NoIs: (i) paths; (ii) tree; (iii) neighborhoods; (iv) induced sub-graphs; and (v) communities of induced sub-graphs. The computation of trees, path and neighborhoods is obtained by running a modified version of the Breadth-first search (BFS) algorithm [107] applied to the meta-pathway. The tree and path topologies obtained using our system are then merged into larger connected components to obtain induced sub-graphs and communities.
The BFS algorithm is a search method in a graph, which, starting from a node (source), obtains all the paths to the other reachable nodes of the graph. The algorithm has been modified to take into account the p-value of the nodes: each path from the source is extended with a new node if its p-value is greater than a user-specified threshold. Paths identified by running a BFS search make up a tree whose root is the source node.
For each subpathway identified by the algorithm, a p-value can be computed by aggregating its nodes p-values employing the Empirical Brown’s method [108]. Given a set of k dependent p-values, Brown [109] estimated that:
ψ = 2 i = 1 k l n p i c χ 2 f 2 ,
where p i is the i-th p-value, f = E ψ 2 / v a r ψ , and c = k / f . Therefore, by employing a scaled chi-squared distribution with 2 f degrees of freedom, we can compute a combined p-value for a k-nodes subpathway.
The extraction algorithm also returns subgraphs induced from the BFS visit tree, and the community obtained by merging all induced sub-graphs.
All results are then filtered by removing those subpathways that have a number of nodes below a minimum user-specified threshold, and p-values obtained using Empirical Brown’s method are adjusted on multiple hypotheses using the Holm–Bonferroni [110] correction to control a family-wise error rate.

4.2. NoIs Selection

The selection of NoIs is a fundamental process for the correct extraction of disease-specific sub-pathways. For this purpose, a semi-automated procedure has been devised for the unbiased selection of NoIs. The user specifies a significance threshold for pathways. Then, all significant pathways are selected using such threshold, and all nodes of these pathways are analyzed. The nodes that exhibit a non-significant p-value are discarded. Finally, all duplicates are removed, and a preliminary list of NoIs is generated. The sub-pathway extraction procedure is then applied to such a list, and each resulting path is analyzed to identify overlapping results. The latter are removed in order to keep only unique results, removing duplicates.

Subpathway Enrichment Analysis

All extracted subpathways are examined by means of enrichment analysis in various biological and pharmacological features. We are able to estimate associations with (i) pathways; (ii) gene ontology (GO) terms; (iii) diseases; and (iv) drugs. The enrichment analysis is performed by employing the methodology of Li et al. [24]. Given a sub-graph S of the meta-pathway, we can compute a p-value p S , t for a term t as:
p S , t = 1 i = 1 k M i · N M n i N n ,
where k is the number of nodes in S annotated with t, M is the number of nodes in the meta-pathway annotated with t, N is the number of annotated nodes in the meta-pathway, and n is the number of annotated nodes in S. Finally, all p-values are adjusted on multiple hypotheses using the Benjamini–Hochberg method [111] to control the false discovery rate, and all terms with a p-value lower than an user-specified threshold are kept by the analysis.
Since some terms might annotate few nodes with respect to the total, they could appear significant for a subpathway even if they annotate a single node. Therefore, to avoid this shortcoming, we chose to exclude annotations found in less than a third of a subpathway nodes.

4.3. Web Interface

In order to ensure broad accessibility and usability, our algorithm has been implemented in a web application available at [29]. The service has been mainly developed using PHP, HTML and JavaScript, with the help of the Laravel framework [112]. To increment performances, the component that deals with substructures computation has been developed in Java, and the functional enrichment procedure has implemented using the R statistical environment [113]. The web application also employs Cytoscape.js [114] for displaying subpathways. Furthermore, to minimize user waiting time, all the computations are cached to be reused; therefore no user private data are stored. In Table 3, we report all sources employed for the enrichment analysis.

4.3.1. Expression Data Sources

Our platform has been designed to perform disease-specific analysis on expression data provided by The Cancer Genome Atlas. We collected all patients’ expression profiles of genes (RNASeqV2 obtained through platforms Illumina Genome Analyzer and Illumina HiSeq) and miRNAs (miRNASeq obtained through platforms Illumina Genome Analyzer and Illumina HiSeq) using the TCGA dump provided in Alaimo et al. [21]. Therefore, we removed all tumor samples for which no controls were available, obtaining a dataset of 10 distinct tumor pathologies divided by stage (see Table 4 for more details).
On the aforementioned dataset, we applied the pipeline described in Alaimo et al. [21] in order to pre-compute differentially expressed genes and meta-pathway perturbations using the extended version of the MITHrIL algorithm.

4.3.2. Analysis Workflow

To use the web application, the user selects one of the available pathologies (Figure 2a). After confirming the selection, one or more nodes of interest can be optionally selected (Figure 2b), paying attention to the p-value of each NoI. If the p-value for a node is greater than the maximum threshold, it will be discarded from the analysis. The user can also vary p-value thresholds and the minimum number of nodes in substructures (Figure 2c). From the same screen, the user can also select whether the search should proceed backward from each NoI or forward.
Clicking on the submission button, the job will be put into a computing queue. At the end of computation, a table (Figure 2d) with the list of substructures will be shown. Such table will show the type of structure, the source node, the number of nodes, the p-value adjusted by the Holm–Bonferroni method and a button, which displays the functional enrichment results. The user will be able to download all results as a tab-separated text file.
Functional enrichment results are displayed in a page together (Figure 3) with a Cytoscape.js-based viewer to see substructures. The list of terms is sorted by adjusted p-value. Two buttons will allow the download of the enrichment (tab-separated text file) and the substructure (XGMML format). The list of terms is displayed in a table together with their identifier, number of annotated nodes, p-value, adjusted p-value by applying the Benjamini–Hochberg method [111], and the source database.
All tables in the web interface give the user the ability to filter results in order to facilitate subsequent analysis.

5. Conclusions

In this paper, we proposed a novel methodology, SPECifIC (SubPathway ExtraCtor and enrICher), for extracting, visualizing, and enriching substructures obtained from a meta-pathway built by combining KEGG pathways. The methodology extends MITHrIL [21] to allow efficient nodes’ perturbation and statistical significance computation, and leverages on the increased precision given by the addition of post-trascriptional regulatory elements, such as microRNAs. Starting from expression data of pathological samples, the method is able to extract statistically significant disease-specific subpathways, and perform functional enrichment, allowing further evaluations.
The technique has been compared with three other methodologies, Subpathway-GM [24], Subpathway-GMir [27], and DEsubs [26], on two expression datasets (BRCA and COAD) taken from The Cancer Genome Atlas.
Our findings show that SPECifIC is able to produce results in agreement with the current knowledge of the pathophysiological processes of BRCA and COAD, and with the results produced by the three competitors. In addition, the pathways, which the method discards, do not show any significant correlation with the two pathological phenotypes, according to literature. However, our results are larger than other competitors, leading to the need of a better filters in order to benefit more from them.
On the other hand, our analysis clearly highlights that, although all the compared methods give in general biologically sound results, their consensus is certainly small.
Future developments should also integrate information coming from expressions of other classes of non-coding RNAs, whose alteration has been correlated in different pathological phenotypes, and the introduction of gene mutations and copy number variation in order to detect more disease-specific subpathways based on the pattern of altered genes rather than their expression.

Supplementary Materials

The following are available online at, Tables S1–S14 Detailed comparison of the predictions made by the four algorithms on the COAD and BRCA datasets.


The study was partially supported by grants from the University of Catania (FIR-2014 F14282). The results shown here are in whole or part based upon data generated by The Cancer Genome Atlas (TCGA) Research Network:

Author Contributions

A.P., A.F. and S.A. conceived and designed the experiments; S.A. performed the experiments; S.A. and G.P.M. analyzed the data; S.A., G.P.M., and A.P. wrote the paper. All authors read and approved the final version of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.


The following abbreviations are used in this manuscript:
TCGAThe Cancer Genome Atlas
CRCColorectal Carcinoma
BRCABreast Invasive Carcinoma
COADColon Adenocarcinoma
READRectal Adenocarcinoma
PFPerturbation Factor
MITHrILMirna enrIched paTHway Impact anaLysis
SPECifICSubPathway ExtraCtor and enrICher
SPIASignaling Pathway Impact Analysis
TEAKTopology Enrichment Analysis frameworK
PARADIGMPAthway Representation and Analysis by Direct Reference on Graphical Models
DEGDifferentially Expressed Gene
IFImpact Factor


  1. Khatri, P.; Sirota, M.; Butte, A.J. Ten years of pathway analysis: Current approaches and outstanding challenges. PLoS Comput. Biol. 2012, 8, e1002375. [Google Scholar] [CrossRef] [PubMed]
  2. Kanehisa, M.; Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28, 27–30. [Google Scholar] [CrossRef] [PubMed]
  3. Kanehisa, M.; Sato, Y.; Kawashima, M.; Furumichi, M.; Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 2015, 44, D457–D462. [Google Scholar] [CrossRef] [PubMed]
  4. Kanehisa, M.; Furumichi, M.; Tanabe, M.; Sato, Y.; Morishima, K. KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017, 45, D353–D361. [Google Scholar] [CrossRef] [PubMed]
  5. Cerami, E.G.; Gross, B.E.; Demir, E.; Rodchenkov, I.; Babur, Ö.; Anwar, N.; Schultz, N.; Bader, G.D.; Sander, C. Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res. 2011, 39, D685–D690. [Google Scholar] [CrossRef] [PubMed]
  6. Khatri, P.; Draghici, S.; Ostermeier, G.C.; Krawetz, S.A. Profiling gene expression using onto-express. Genomics 2002, 79, 266–270. [Google Scholar] [CrossRef] [PubMed]
  7. Draghici, S.; Khatri, P.; Martins, R.P.; Ostermeier, G.C.; Krawetz, S.A. Global functional profiling of gene expression. Genomics 2003, 81, 98–104. [Google Scholar] [PubMed]
  8. Berriz, G.F.; King, O.D.; Bryant, B.; Sander, C.; Roth, F.P. Characterizing gene sets with FuncAssociate. Bioinformatics 2003, 19, 2502–2504. [Google Scholar] [CrossRef] [PubMed]
  9. Beißbarth, T.; Speed, T.P. GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics 2004, 20, 1464–1465. [Google Scholar] [CrossRef] [PubMed]
  10. Castillo-Davis, C.I.; Hartl, D.L. GeneMerge—Post-genomic analysis, data mining, and hypothesis testing. Bioinformatics 2003, 19, 891–892. [Google Scholar] [CrossRef] [PubMed]
  11. Martin, D.; Brun, C.; Remy, E.; Mouren, P.; Thieffry, D.; Jacq, B. GOToolBox: Functional analysis of gene datasets based on Gene Ontology. Genome Biol. 2004, 5, 1. [Google Scholar] [CrossRef] [PubMed]
  12. Doniger, S.W.; Salomonis, N.; Dahlquist, K.D.; Vranizan, K.; Lawlor, S.C.; Conklin, B.R. MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol. 2003, 4, 1. [Google Scholar] [CrossRef]
  13. Vlachos, I.S.; Zagganas, K.; Paraskevopoulou, M.D.; Georgakilas, G.; Karagkouni, D.; Vergoulis, T.; Dalamagas, T.; Hatzigeorgiou, A.G. DIANA-miRPath v3.0: Deciphering microRNA function with experimental support. Nucleic Acids Res. 2015, 43, W460–W466. [Google Scholar] [CrossRef] [PubMed]
  14. Tian, L.; Greenberg, S.A.; Kong, S.W.; Altschuler, J.; Kohane, I.S.; Park, P.J. Discovering statistically significant pathways in expression profiling studies. Proc. Natl. Acad. Sci. USA 2005, 102, 13544–13549. [Google Scholar] [CrossRef] [PubMed]
  15. Xiong, H. Non-linear tests for identifying differentially expressed genes or genetic networks. Bioinformatics 2006, 22, 919–923. [Google Scholar] [CrossRef] [PubMed]
  16. Draghici, S.; Khatri, P.; Tarca, A.L.; Amin, K.; Done, A.; Voichita, C.; Georgescu, C.; Romero, R. A systems biology approach for pathway level analysis. Genome Res. 2007, 17, 1537–1545. [Google Scholar] [CrossRef] [PubMed]
  17. Tarca, A.L.; Draghici, S.; Khatri, P.; Hassan, S.S.; Mittal, P.; Kim, J.S.; Kim, C.J.; Kusanovic, J.P.; Romero, R. A novel signaling pathway impact analysis. Bioinformatics 2009, 25, 75–82. [Google Scholar] [CrossRef] [PubMed]
  18. Vaske, C.J.; Benz, S.C.; Sanborn, J.Z.; Earl, D.; Szeto, C.; Zhu, J.; Haussler, D.; Stuart, J.M. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 2010, 26, i237–i245. [Google Scholar] [CrossRef] [PubMed]
  19. Sedgewick, A.J.; Benz, S.C.; Rabizadeh, S.; Soon-Shiong, P.; Vaske, C.J. Learning subgroup-specific regulatory interactions and regulator independence with PARADIGM. Bioinformatics 2013, 29, i62–i70. [Google Scholar] [CrossRef] [PubMed]
  20. Calura, E.; Martini, P.; Sales, G.; Beltrame, L.; Chiorino, G.; D’Incalci, M.; Marchini, S.; Romualdi, C. Wiring miRNAs to pathways: A topological approach to integrate miRNA and mRNA expression profiles. Nucleic Acids Res. 2014, 42, e96. [Google Scholar] [CrossRef] [PubMed]
  21. Alaimo, S.; Giugno, R.; Acunzo, M.; Veneziano, D.; Ferro, A.; Pulvirenti, A. Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification. Oncotarget 2016, 7, 54572–54582. [Google Scholar] [CrossRef] [PubMed]
  22. Li, C.; Shang, D.; Wang, Y.; Li, J.; Han, J.; Wang, S.; Yao, Q.; Wang, Y.; Zhang, Y.; Zhang, C.; et al. Characterizing the network of drugs and their affected metabolic subpathways. PLoS ONE 2012, 7, e47326. [Google Scholar] [CrossRef] [PubMed]
  23. Li, X.; Li, C.; Shang, D.; Li, J.; Han, J.; Miao, Y.; Wang, Y.; Wang, Q.; Li, W.; Wu, C.; et al. The implications of relationships between human diseases and metabolic subpathways. PLoS ONE 2011, 6, e21131. [Google Scholar] [CrossRef] [PubMed]
  24. Li, C.; Han, J.; Yao, Q.; Zou, C.; Xu, Y.; Zhang, C.; Shang, D.; Zhou, L.; Zou, C.; Sun, Z.; et al. Subpathway-GM: Identification of metabolic subpathways via joint power of interesting genes and metabolites and their topologies within pathways. Nucleic Acids Res. 2013, 41, E101. [Google Scholar] [CrossRef] [PubMed]
  25. Judeh, T.; Johnson, C.; Kumar, A.; Zhu, D. TEAK: Topology enrichment analysis framework for detecting activated biological subpathways. Nucleic Acids Res. 2013, 41, 1425–1437. [Google Scholar] [CrossRef] [PubMed]
  26. Vrahatis, A.G.; Balomenos, P.; Tsakalidis, A.K.; Bezerianos, A. DEsubs: An R package for flexible identification of differentially expressed subpathways using RNA-seq experiments. Bioinformatics 2016, 32, 3844–3846. [Google Scholar] [CrossRef] [PubMed]
  27. Feng, L.; Xu, Y.; Zhang, Y.; Sun, Z.; Han, J.; Zhang, C.; Yang, H.; Shang, D.; Su, F.; Shi, X.; et al. Subpathway-GMir: Identifying miRNA-mediated metabolic subpathways by integrating condition-specific genes, microRNAs, and pathway topologies. Oncotarget 2015, 6, 39151. [Google Scholar] [PubMed]
  28. Vrahatis, A.G.; Dimitrakopoulou, K.; Balomenos, P.; Tsakalidis, A.K.; Bezerianos, A. CHRONOS: A time-varying method for microRNA-mediated subpathway enrichment analysis. Bioinformatics 2016, 32, 884–892. [Google Scholar] [CrossRef] [PubMed]
  29. SPECifIC. Sub-Pathway Extractor and Enricher. Available online: (accessed on 28 December 2016).
  30. Béroud, C.; Letovsky, S.I.; Braastad, C.D.; Caputo, S.M.; Beaudoux, O.; Bignon, Y.J.; Paillerets, B.; Bronner, M.; Buell, C.M.; Collod-Béroud, G.; et al. BRCA Share: A Collection of Clinical BRCA Gene Variants. Hum. Mutat. 2016, 37, 1318–1328. [Google Scholar] [CrossRef] [PubMed]
  31. Hollestelle, A.; Nagel, J.H.; Smid, M.; Lam, S.; Elstrodt, F.; Wasielewski, M.; Ng, S.S.; French, P.J.; Peeters, J.K.; Rozendaal, M.J.; et al. Distinct gene mutation profiles among luminal-type and basal-type breast cancer cell lines. Breast Cancer Res. Treat. 2010, 121, 53–64. [Google Scholar] [CrossRef] [PubMed]
  32. The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 2012, 490, 61–70. [Google Scholar]
  33. Torre, L.A.; Bray, F.; Siegel, R.L.; Ferlay, J.; Lortet-Tieulent, J.; Jemal, A. Global Cancer Statistics, 2012. CA: A Cancer J. Clin. 2015, 65, 87–108. [Google Scholar] [CrossRef] [PubMed]
  34. Minsky, B.D. Unique considerations in the patient with rectal cancer. Semin. Oncol. 2011, 4, 542–551. [Google Scholar] [CrossRef] [PubMed]
  35. Cancer Genome Atlas Network; Muzny, D.M.; Bainbridge, M.N.; Chang, K.; Dinh, H.H.; Drummond, J.A.; Fowler, G.; Kovar, C.L.; Lewis, L.R.; Morgan, M.B.; et al. Comprehensive molecular characterization of human colon and rectal cancer. Nature 2012, 487, 330–337. [Google Scholar]
  36. Hoadley, K.A.; Yau, C.; Wolf, D.M.; Cherniack, A.D.; Tamborero, D.; Ng, S.; Leiserson, M.D.; Niu, B.; McLellan, M.D.; Uzunangelov, V.; et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 2014, 158, 929–944. [Google Scholar] [CrossRef] [PubMed]
  37. Piñero, J.; Queralt-Rosinach, N.; Bravo, À.; Deu-Pons, J.; Bauer-Mehren, A.; Baron, M.; Sanz, F.; Furlong, L.I. DisGeNET: A discovery platform for the dynamical exploration of human diseases and their genes. Database 2015, 2015, bav028. [Google Scholar] [CrossRef] [PubMed]
  38. Piñero, J.; Bravo, À.; Queralt-Rosinach, N.; Gutiérrez-Sacristán, A.; Deu-Pons, J.; Centeno, E.; García-García, J.; Sanz, F.; Furlong, L.I. DisGeNET: A comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 2017, 45, D833–D839. [Google Scholar] [CrossRef] [PubMed]
  39. Becker, K.G.; Barnes, K.C.; Bright, T.J.; Wang, S.A. The genetic association database. Nat. Genet. 2004, 36, 431–432. [Google Scholar] [CrossRef] [PubMed]
  40. Hu, X.; Stern, H.M.; Ge, L.; O’Brien, C.; Haydu, L.; Honchell, C.D.; Haverty, P.M.; Peters, B.A.; Wu, T.D.; Amler, L.C.; et al. Genetic Alterations and Oncogenic Pathways Associated with Breast Cancer Subtypes. Mol. Cancer Res. 2009, 7, 511–522. [Google Scholar] [CrossRef] [PubMed]
  41. Osborne, C.K.; Schiff, R. Mechanisms of Endocrine Resistance in Breast Cancer. Annu. Rev. Med. 2011, 62, 233–247. [Google Scholar] [CrossRef] [PubMed]
  42. LaPensee, E.W.; Ben-Jonathan, N. Novel roles of prolactin and estrogens in breast cancer: Resistance to chemotherapy. Endocr.-Relat. Cancer 2010, 17, R91–R107. [Google Scholar] [CrossRef] [PubMed]
  43. Normanno, N.; Campiglio, M.; Maiello, M.R.; De Luca, A.; Mancino, M.; Gallo, M.; D’Alessio, A.; Menard, S. Breast cancer cells with acquired resistance to the EGFR tyrosine kinase inhibitor gefitinib show persistent activation of MAPK signaling. Breast Cancer Res. Treat. 2008, 112, 25–33. [Google Scholar] [CrossRef] [PubMed]
  44. Bulun, S.E.; Chen, D.; Moy, I.; Brooks, D.C.; Zhao, H. Aromatase, breast cancer and obesity: A complex interaction. Trends Endocrinol. Metab. 2012, 23, 83–89. [Google Scholar] [CrossRef] [PubMed]
  45. Chen, L.C.; Tu, S.H.; Huang, C.S.; Chen, C.S.; Ho, C.T.; Lin, H.W.; Lee, C.H.; Chang, H.W.; Chang, C.H.; Wu, C.H.; et al. Human breast cancer cell metastasis is attenuated by lysyl oxidase inhibitors through down-regulation of focal adhesion kinase and the paxillin-signaling pathway. Breast Cancer Res. Treat. 2012, 134, 989–1004. [Google Scholar] [CrossRef] [PubMed]
  46. Emery, L.A.; Tripathi, A.; King, C.; Kavanah, M.; Mendez, J.; Stone, M.D.; de las Morenas, A.; Sebastiani, P.; Rosenberg, C.L. Early Dysregulation of Cell Adhesion and Extracellular Matrix Pathways in Breast Cancer Progression. Am. J. Pathol. 2009, 175, 1292–1302. [Google Scholar] [CrossRef] [PubMed]
  47. Lal, I.; Dittus, K.; Holmes, C.E. Platelets, coagulation and fibrinolysis in breast cancer progression. Breast Cancer Res. 2013, 15, 207. [Google Scholar] [CrossRef] [PubMed]
  48. Fang, W.B.; Jokar, I.; Zou, A.; Lambert, D. CCL2/CCR2 chemokine signaling coordinates survival and motility of breast cancer cells through Smad3 protein-and p42/44 mitogen-activated protein kinase (MAPK)-dependent mechanisms. J. Biol. Chem. 2012, 287, 36593–36608. [Google Scholar] [CrossRef] [PubMed]
  49. Chen, Y.; Rodrik, V.; Foster, D.A. Alternative phospholipase D|[sol]|mTOR survival signal in human breast cancer cells. Oncogene 2005, 24, 672–679. [Google Scholar] [CrossRef] [PubMed]
  50. Krishnan, A.; Nair, S.A.; Pillai, M.R. Biology of PPAR 947; in Cancer: A Critical Review on Existing Lacunae. Curr. Mol. Med. 2007, 7, 532–540. [Google Scholar] [CrossRef]
  51. Do, M.T.; Kim, H.G.; Tran, T.T.P.; Khanal, T.; Choi, J.H.; Chung, Y.C.; Jeong, T.C.; Jeong, H.G. Metformin suppresses CYP1A1 and CYP1B1 expression in breast cancer cells by down-regulating aryl hydrocarbon receptor expression. Toxicol. Appl. Pharmacol. 2014, 280, 138–148. [Google Scholar] [CrossRef] [PubMed]
  52. Hakkola, J.; Pasanen, M.; Pelkonen, O.; Hukkanen, J.; Evisalmi, S.; Anttila, S.; Rane, A.; Mäntylä, M.; Purkunen, R.; Saarikoski, S.; et al. Expression of CYP1B1 in human adult and fetal tissues and differential inducibility of CYP1B1 and CYP1A1 by Ah receptor ligands in human placenta and cultured cells. Carcinogenesis 1997, 18, 391–397. [Google Scholar] [CrossRef] [PubMed]
  53. Sachdev, D.; Yee, D. The IGF system and breast cancer. Endocr.-Relat. Cancer 2001, 8, 197–209. [Google Scholar] [CrossRef] [PubMed]
  54. Pust, S.; Klokk, T.I.; Musa, N.; Jenstad, M.; Risberg, B.; Erikstein, B.; Tcatchoff, L.; Liestøl, K.; Danielsen, H.E.; van Deurs, B.; et al. Flotillins as regulators of ErbB2 levels in breast cancer. Oncogene 2013, 32, 3443–3451. [Google Scholar] [CrossRef] [PubMed]
  55. Patani, N.; Jiang, W.G.; Mokbel, K. Brain-derived neurotrophic factor expression predicts adverse pathological & clinical outcomes in human breast cancer. Cancer Cell Int. 2011, 11, 23. [Google Scholar] [PubMed]
  56. Dolle, L.; Adriaenssens, E.; Yazidi-Belkoura, I.E.; Bourhis, X.L.; Nurcombe, V.; Hondermarck, H. Nerve Growth Factor Receptors and Signaling in Breast Cancer. Curr. Cancer Drug Targets 2004, 4, 463–470. [Google Scholar] [CrossRef] [PubMed]
  57. Ma, X.J.; Dahiya, S.; Richardson, E.; Erlander, M.; Sgroi, D.C. Gene expression profiling of the tumor microenvironment during breast cancer progression. Breast Cancer Res. 2009, 11, R7. [Google Scholar] [CrossRef] [PubMed]
  58. Turton, N.J.; Judah, D.J.; Riley, J.; Davies, R.; Lipson, D. Gene expression and amplification in breast carcinoma cells with intrinsic and acquired doxorubicin resistance. Oncogene 2001, 20, 1300–1306. [Google Scholar] [CrossRef] [PubMed]
  59. Viglietto, G.; Motti, M.L.; Bruni, P.; Melillo, R.M.; D’Alessio, A.; Califano, D.; Vinci, F.; Chiappetta, G.; Tsichlis, P.; Bellacosa, A.; et al. Cytoplasmic relocalization and inhibition of the cyclin-dependent kinase inhibitor p27Kip1 by PKB/Akt-mediated phosphorylation in breast cancer. Nat. Med. 2002, 8, 1136–1144. [Google Scholar] [CrossRef] [PubMed]
  60. Hoover, K.B.; Liao, S.Y.; Bryant, P.J. Loss of the Tight Junction MAGUK ZO-1 in Breast Cancer. Am. J. Pathol. 1998, 153, 1767–1773. [Google Scholar] [CrossRef]
  61. Kominsky, S.L.; Argani, P.; Korz, D.; Evron, E.; Raman, V.; Garrett, E.; Rein, A.; Sauter, G.; Kallioniemi, O.P.; Sukumar, S. Loss of the tight junction protein claudin-7 correlates with histological grade in both ductal carcinoma in situ and invasive ductal carcinoma of the breast. Oncogene 2003, 22, 2021–2033. [Google Scholar] [CrossRef] [PubMed]
  62. Pierceall, W.E.; Woodard, A.S.; Morrow, J.S.; Rimm, D.; Fearon, E.R. Frequent alterations in E-cadherin and alpha- and beta-catenin expression in human breast cancer cell lines. Oncogene 1995, 11, 1319–1326. [Google Scholar] [PubMed]
  63. McLachlan, E.; Shao, Q.; Laird, D.W. Connexins and Gap Junctions in Mammary Gland Development and Breast Cancer Progression. J. Membr. Biol. 2007, 218, 107–121. [Google Scholar] [CrossRef] [PubMed]
  64. Jiang, P.; Enomoto, A.; Takahashi, M. Cell biology of the movement of breast cancer cells: Intracellular signalling and the actin cytoskeleton. Cancer Lett. 2009, 284, 122–130. [Google Scholar] [CrossRef] [PubMed]
  65. Takebe, N.; Warren, R.Q.; Ivy, S.P. Breast cancer growth and metastasis: interplay between cancer stem cells, embryonic signaling pathways and epithelial-to-mesenchymal transition. Breast Cancer Res. 2011, 13, 211. [Google Scholar] [CrossRef] [PubMed]
  66. Mittal, S.; Subramanyam, D.; Dey, D.; Kumar, R.V.; Rangarajan, A. Cooperation of Notch and Ras/MAPK signaling pathways in human breast carcinogenesis. Mol. Cancer 2009, 8, 128. [Google Scholar] [CrossRef] [PubMed]
  67. Ebi, H.; Costa, C.; Faber, A.C.; Nishtala, M.; Kotani, H.; Juric, D.; Della Pelle, P.; Song, Y.; Yano, S.; Mino-Kenudson, M.; et al. PI3K regulates MEK/ERK signaling in breast cancer via the Rac-GEF, P-Rex1. Proc. Natl. Acad. Sci. USA 2013, 110, 21124–21129. [Google Scholar] [CrossRef] [PubMed]
  68. Shekhar, M.P.; Pauley, R.; Heppner, G. Host microenvironment in breast cancer development: Extracellular matrix–stromal cell contribution to neoplastic phenotype of epithelial cells in the breast. Breast Cancer Res. 2003, 5, 130. [Google Scholar] [CrossRef] [PubMed]
  69. Emons, G.; Grndker, C.G.; Grnthert, A.R.G.; Westphalen, S.; Kavanagh, J.; Verschraegen, C. GnRH antagonists in the treatment of gynecological and breast cancers. Endocr.-Relat. Cancer 2003, 10, 291–299. [Google Scholar] [CrossRef] [PubMed]
  70. McMahon, G. VEGF Receptor Signaling in Tumor Angiogenesis. Oncologist 2000, 5, 3–10. [Google Scholar] [CrossRef] [PubMed]
  71. Simpson, E.R.; Brown, K.A. Minireview: Obesity and Breast Cancer: A Tale of Inflammation and Dysregulated Metabolism. Mol. Endocrinol. 2013, 27, 715–725. [Google Scholar] [CrossRef] [PubMed]
  72. Dean, S.J.; Rhodes, A. Triple negative breast cancer: the role of metabolic pathways. Malays J. Pathol. 2014, 6, 155–162. [Google Scholar]
  73. Zeestraten, E.; Benard, A.; Reimers, M.S. The prognostic value of the apoptosis pathway in colorectal cancer: A review of the literature on biomarkers identified by immunohistochemistry. Biomark. Cancer 2013, 5, 13–29. [Google Scholar] [CrossRef] [PubMed]
  74. Johnson, S.M.; Gulhati, P.; Rampy, B.A.; Han, Y.; Rychahou, P.G.; Doan, H.Q.; Weiss, H.L.; Evers, B.M. Novel Expression Patterns of PI3K/Akt/mTOR Signaling Pathway Components in Colorectal Cancer. J. Am. Coll. Surg. 2010, 210, 767–776. [Google Scholar] [CrossRef] [PubMed]
  75. Benvenuti, S.; Sartore-Bianchi, A.; Di Nicolantonio, F.; Zanon, C.; Moroni, M.; Veronese, S.; Siena, S.; Bardelli, A. Oncogenic Activation of the RAS/RAF Signaling Pathway Impairs the Response of Metastatic Colorectal Cancers to Anti–Epidermal Growth Factor Receptor Antibody Therapies. Cancer Res. 2007, 67, 2643–2648. [Google Scholar] [CrossRef] [PubMed]
  76. Saito, M.; Iwadate, M.; Higashimoto, M. Expression of phospholipase D2 in human colorectal carcinoma. Oncol. Rep. 2007, 18, 1329–1334. [Google Scholar] [CrossRef] [PubMed]
  77. Kang, D.W.; Min, D.S. Positive Feedback Regulation between Phospholipase D and Wnt Signaling Promotes Wnt-Driven Anchorage-Independent Growth of Colorectal Cancer Cells. PLoS ONE 2010, 5, e12109. [Google Scholar] [CrossRef] [PubMed]
  78. Zong, H.; Chiles, K.; Feldser, D.; Laughner, E.; Hanrahan, C. Overexpression of Hypoxia inducible Factor 1alpha in common Human Cancer and their Metastasis. Cancer Res. 1999, 59, 5830–5835. [Google Scholar]
  79. Kaidi, A.; Qualtrough, D.; Williams, A.C.; Paraskeva, C. Direct Transcriptional Up-regulation of Cyclooxygenase- 2 by Hypoxia-Inducible Factor (HIF)-1 Promotes Colorectal Tumor Cell Survival and Enhances HIF-1 Transcriptional Activity during Hypoxia. Cancer Res. 2006, 66, 6683–6691. [Google Scholar] [CrossRef] [PubMed]
  80. Kumarakulasingham, M.; Rooney, P.H.; Dundas, S.R.; Telfer, C.; Melvin, W.T.; Curran, S.; Murray, G.I. Cytochrome P450 Profile of Colorectal Cancer: Identification of Markers of Prognosis. Clin. Cancer Res. 2005, 11, 3758–3765. [Google Scholar] [CrossRef] [PubMed]
  81. Tamási, V.; Monostory, K.; Prough, R.A.; Falus, A. Role of xenobiotic metabolism in cancer: Involvement of transcriptional and miRNA regulation of P450s. Cell. Mol. Life Sci. 2011, 68, 1131–1146. [Google Scholar] [CrossRef] [PubMed]
  82. Bardelli, A.; Corso, S.; Bertotti, A.; Hobor, S.; Valtorta, E.; Siravegna, G.; Sartore-Bianchi, A.; Scala, E.; Cassingena, A.; Zecchin, D.; et al. Amplification of the MET Receptor Drives Resistance to Anti-EGFR Therapies in Colorectal Cancer. Cancer Discov. 2013, 3, 658–673. [Google Scholar] [CrossRef] [PubMed]
  83. Szachowicz-Petelska, B.; Sulkowski, S.; Figaszewski, Z.A. Altered membrane free unsaturated fatty acid composition in human colorectal cancer tissue. Mol. Cell. Biochem. 2007, 294, 237–242. [Google Scholar] [CrossRef] [PubMed]
  84. Peng, H.; Luo, J.; Hao, H.; Hu, J.; Xie, S.K.; Ren, D.; Rao, B. MicroRNA-100 regulates SW620 colorectal cancer cell proliferation and invasion by targeting RAP1B. Oncol. Rep. 2014, 31, 2055–2062. [Google Scholar] [CrossRef] [PubMed]
  85. Guo, H.; Hu, X.; Ge, S.; Qian, G.; Zhang, J. Regulation of RAP1B by miR-139 suppresses human colorectal carcinoma cell proliferation. Int. J. Biochem. Cell Biol. 2012, 44, 1465–1472. [Google Scholar] [CrossRef] [PubMed]
  86. Sainz, J.; Rudolph, A.; Hein, R.; Hoffmeister, M.; Buch, S.; von Schönfels, W.; Hampe, J.; Schafmayer, C.; Völzke, H.; Frank, B.; et al. Association of genetic polymorphisms in ESR2, HSD17B1, ABCB1, and SHBG genes with colorectal cancer risk. Endocr.-Relat. Cancer 2011, 18, 265–276. [Google Scholar] [CrossRef] [PubMed]
  87. Boursi, B.; Haynes, K.; Mamtani, R.; Yang, Y.X. Thyroid Dysfunction, Thyroid Hormone Replacement and Colorectal Cancer Risk. JNCI J. Natl. Cancer Inst. 2015, 107. [Google Scholar] [CrossRef] [PubMed]
  88. Tomlinson, I.P.M.; Carvajal-Carmona, L.G.; Dobbins, S.E.; Tenesa, A.; Jones, A.M.; Howarth, K.; Palles, C.; Broderick, P.; Jaeger, E.E.M.; Farrington, S.; et al. Multiple Common Susceptibility Variants near BMP Pathway Loci GREM1, BMP4, and BMP2 Explain Part of the Missing Heritability of Colorectal Cancer. PLoS Genet. 2011, 7, e1002105. [Google Scholar] [CrossRef] [PubMed]
  89. Catalano, V.; Dentice, M.; Ambrosio, R.; Luongo, C.; Carollo, R.; Benfante, A.; Todaro, M.; Stassi, G.; Salvatore, D. Activated thyroid hormone promotes differentiation and chemotherapeutic sensitization of colorectal cancer stem cells by regulating Wnt and BMP4 signaling. Cancer Res. 2016, 76, 1237–1244. [Google Scholar] [CrossRef] [PubMed]
  90. Sarraf, P.; Mueller, E.; Jones, D.; King, F.J.; DeAngelo, D.J.; Partridge, J.B.; Holden, S.A.; Chen, L.B.; Singer, S.; Fletcher, C.; et al. Differentiation and reversal of malignant changes in colon cancer through PPAR|[ggr]|. Nat. Med. 1998, 4, 1046–1052. [Google Scholar] [CrossRef] [PubMed]
  91. Michalik, L.; Wahli, W. PPARs Mediate Lipid Signaling in Inflammation and Cancer. PPAR Res. 2008, 2008, 1–15. [Google Scholar] [CrossRef] [PubMed]
  92. Hollande, F.; Papin, M. Tight Junctions in Colorectal Cancer. In Tight Junctions in Cancer Metastasis; Martin, T.A., Jiang, W.G., Eds.; Springer: Dordrecht, The Netherlands, 2013; pp. 149–167. [Google Scholar]
  93. Albasri, A.; Fadhil, W.; Scholefield, J.H.; Durrant, L.G.; Ilyas, M. Nuclear expression of phosphorylated focal adhesion kinase is associated with poor prognosis in human colorectal cancer. Anticancer Res. 2014, 34, 3969–3974. [Google Scholar] [PubMed]
  94. Watson, A.J.M. Apoptosis and colorectal cancer. Gut 2004, 53, 1701–1709. [Google Scholar] [CrossRef] [PubMed]
  95. Fang, J.Y.; Richardson, B.C. The MAPK signalling pathways and colorectal cancer. Lancet Oncol. 2005, 6, 322–327. [Google Scholar] [CrossRef]
  96. Vermeulen, L.; De Sousa E Melo, F.; van der Heijden, M.; Cameron, K.; de Jong, J.H.; Borovski, T.; Tuynman, J.B.; Todaro, M.; Merz, C.; Rodermond, H.; et al. Wnt activity defines colon cancer stem cells and is regulated by the microenvironment. Nat. Cell Biol. 2010, 12, 468–476. [Google Scholar] [CrossRef] [PubMed]
  97. Manna, S.K.; Tanaka, N.; Krausz, K.W.; Haznadar, M.; Xue, X.; Matsubara, T.; Bowman, E.D.; Fearon, E.R.; Harris, C.C.; Shah, Y.M.; et al. Biomarkers of Coordinate Metabolic Reprogramming in Colorectal Tumors in Mice and Humans. Gastroenterology 2014, 146, 1313–1324. [Google Scholar] [CrossRef] [PubMed]
  98. Hirayama, A.; Kami, K.; Sugimoto, M.; Sugawara, M.; Toki, N.; Onozuka, H.; Kinoshita, T.; Saito, N.; Ochiai, A.; Tomita, M.; et al. Quantitative Metabolome Profiling of Colon and Stomach Cancer Microenvironment by Capillary Electrophoresis Time-of-Flight Mass Spectrometry. Cancer Res. 2009, 69, 4918–4925. [Google Scholar] [CrossRef] [PubMed]
  99. Wang, H.; Tso, V.K.; Slupsky, C.M.; Fedorak, R.N. Metabolomics and detection of colorectal cancer in humans: A systematic review. Future Oncol. 2010, 6, 1395–1406. [Google Scholar] [CrossRef] [PubMed]
  100. Vetvik, K.K.; Sonerud, T.; Lindeberg, M.; Lüders, T.; Størkson, R.H.; Jonsdottir, K.; Frengen, E.; Pietiläinen, K.H.; Bukholm, I. Globular adiponectin and its downstream target genes are up-regulated locally in human colorectal tumors: Ex vivo and in vitro studies. Metabolism 2014, 63, 672–681. [Google Scholar] [CrossRef] [PubMed]
  101. Mazzarelli, P.; Pucci, S.; Bonanno, E.; Sesti, F.; Calvani, M.; Spagnoli, L.G. Carnitine palmitoyltransferase I in human carcinomas: A novel role in histone deacetylation? Cancer Biol. Ther. 2007, 6, 1606–1613. [Google Scholar] [CrossRef] [PubMed]
  102. Peng, Y.P.; Zhu, Y.; Zhang, J.J.; Xu, Z.K.; Qian, Z.Y.; Dai, C.C.; Jiang, K.R.; Wu, J.L.; Gao, W.T.; Li, Q.; et al. Comprehensive analysis of the percentage of surface receptors and cytotoxic granules positive natural killer cells in patients with pancreatic cancer, gastric cancer, and colorectal cancer. J. Transl. Med. 2013, 11, 262. [Google Scholar] [CrossRef] [PubMed]
  103. Rocca, Y.S.; Roberti, M.P.; Arriaga, J.M.; Amat, M.; Bruno, L.; Pampena, M.B.; Huertas, E.; Loria, F.S.; Pairola, A.; Bianchini, M.; et al. Altered phenotype in peripheral blood and tumor-associated NK cells from colorectal cancer patients. Innate Immunity 2012, 19, 76–85. [Google Scholar] [CrossRef] [PubMed]
  104. Untersmayr, E.; Bises, G.; Starkl, P.; Bevins, C.L.; Scheiner, O.; Boltz-Nitulescu, G.; Wrba, F.; Jensen-Jarolim, E. The High Affinity IgE Receptor FcεRI Is Expressed by Human Intestinal Epithelial Cells. PLoS ONE 2010, 5, e9023. [Google Scholar] [CrossRef] [PubMed]
  105. Francescone, R.; Hou, V.; Grivennikov, S.I. Cytokines, IBD and colitis-associated cancer. Inflamm. Bowel Dis. 2015, 21, 409–418. [Google Scholar] [CrossRef] [PubMed]
  106. Uchibori, R.; Tsukahara, T.; Mizuguchi, H.; Saga, Y.; Urabe, M.; Mizukami, H.; Kume, A.; Ozawa, K. NF-κB Activity Regulates Mesenchymal Stem Cell Accumulation at Tumor Sites. Cancer Res. 2013, 73, 364–372. [Google Scholar] [CrossRef] [PubMed]
  107. Cormen, T.H. Introduction to Algorithms; MIT Press: Cambridge, MA, USA, 2009. [Google Scholar]
  108. Poole, W.; Gibbs, D.L.; Shmulevich, I.; Bernard, B.; Knijnenburg, T.A. Combining dependent p-values with an empirical adaptation of Brown’s method. Bioinformatics 2016, 32, i430–i436. [Google Scholar] [CrossRef] [PubMed]
  109. Brown, M.B. 400: A method for combining non-independent, one-sided tests of significance. Biometrics 1975, 31, 987–992. [Google Scholar] [CrossRef]
  110. Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 1979, 6, 65–70. [Google Scholar]
  111. Benjamini, Y.; Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 1995, 57, 289–300. [Google Scholar]
  112. Otwell, T. Laravel. Available online: (accessed on 28 December 2016).
  113. R Development Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2013. [Google Scholar]
  114. Franz, M.; Lopes, C.T.; Huck, G.; Dong, Y.; Sumer, O.; Bader, G.D. Cytoscape. js: A graph theory library for visualisation and analysis. Bioinformatics 2015, 32, 309–311. [Google Scholar] [PubMed]
  115. Hamosh, A.; Scott, A.F.; Amberger, J.S.; Bocchini, C.A.; McKusick, V.A. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005, 33, D514–D517. [Google Scholar] [CrossRef] [PubMed]
  116. Wishart, D.S.; Knox, C.; Guo, A.C.; Shrivastava, S.; Hassanali, M.; Stothard, P.; Chang, Z.; Woolsey, J. DrugBank: A comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006, 34, D668–D672. [Google Scholar] [CrossRef] [PubMed]
  117. Ashburner, M.; Ball, C.A.; Blake, J.A.; Botstein, D.; Butler, H.; Cherry, J.M.; Davis, A.P.; Dolinski, K.; Dwight, S.S.; Eppig, J.T.; et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 2000, 25, 25–29. [Google Scholar] [CrossRef] [PubMed]
  118. Gene Ontology Consortium; Blake, J.A.; Christie, K.R.; Dolan, M.E.; Drabkin, H.J.; Hill, D.P.; Ni, L.; Sitnikov, D.; Burgess, S.; Buza, T.; et al. Gene ontology consortium: Going forward. Nucleic Acids Res. 2015, 43, D1049–D1056. [Google Scholar]
Figure 1. Comparison of the results obtained by SubPathway ExtraCtor and enrICher (SPECifIC), Subpathway- GM [24], Subpathway-GMir [27], and DEsubs [26] for the two datasets: breast invasive carcinoma (BRCA) (a) and colon adenocarcinoma (COAD) (b). The Venn diagrams have been obtained considering only pathways for which the reported p-value was significant ( p < 0 . 01 ).
Figure 1. Comparison of the results obtained by SubPathway ExtraCtor and enrICher (SPECifIC), Subpathway- GM [24], Subpathway-GMir [27], and DEsubs [26] for the two datasets: breast invasive carcinoma (BRCA) (a) and colon adenocarcinoma (COAD) (b). The Venn diagrams have been obtained considering only pathways for which the reported p-value was significant ( p < 0 . 01 ).
Ncrna 03 00020 g001
Figure 2. Subpathway extraction analysis workflow. (a) After selecting a disease and a stage among those available into the drop-down box, (b) the user can optionally choose one or more Nodes of Interest (NoIs); (c) and, after modifying the optional parameters, the job can be submitted to our servers. After processing, (d) a table will be shown with a list of found substructures, giving the ability to perform the functional annotation.
Figure 2. Subpathway extraction analysis workflow. (a) After selecting a disease and a stage among those available into the drop-down box, (b) the user can optionally choose one or more Nodes of Interest (NoIs); (c) and, after modifying the optional parameters, the job can be submitted to our servers. After processing, (d) a table will be shown with a list of found substructures, giving the ability to perform the functional annotation.
Ncrna 03 00020 g002
Figure 3. Results of the functional annotation analysis of a substructure. The results are shown in a table together with the graph representing the structure itself. In the table, we show the term identifier in the source database, the term description, the number of annotated nodes, the p-value, the adjusted p-value, and the source of the term. By clicking on any term identifier, the annotated nodes will be highlighted in the graph. The user is also able to download annotation results in a tab-separated file and the network in XGMML format.
Figure 3. Results of the functional annotation analysis of a substructure. The results are shown in a table together with the graph representing the structure itself. In the table, we show the term identifier in the source database, the term description, the number of annotated nodes, the p-value, the adjusted p-value, and the source of the term. By clicking on any term identifier, the annotated nodes will be highlighted in the graph. The user is also able to download annotation results in a tab-separated file and the network in XGMML format.
Ncrna 03 00020 g003
Table 1. Top-20 pathways obtained for breast invasive carcinoma (BRCA) and colon adenocarcinoma (COAD) after subpathways extraction and enrichment performed by means of SubPathway ExtraCtor and enrICher (SPECifIC). All terms were first ranked by adjusted p-value and the top-20 significant results were taken for further analysis.
Table 1. Top-20 pathways obtained for breast invasive carcinoma (BRCA) and colon adenocarcinoma (COAD) after subpathways extraction and enrichment performed by means of SubPathway ExtraCtor and enrICher (SPECifIC). All terms were first ranked by adjusted p-value and the top-20 significant results were taken for further analysis.
metabolism of xenobiotics by cytochrome p4500metabolism of xenobiotics by cytochrome p4500
steroid hormone biosynthesis0drug metabolism cytochrome p4500
drug metabolism cytochrome p4500chemical carcinogenesis0
chemical carcinogenesis0steroid hormone biosynthesis0
drug metabolism other enzymes0drug metabolism other enzymes0
linoleic acid metabolism0linoleic acid metabolism0
longevity regulating pathway 3 . 27 × 10 8 ppar signaling pathway0
egfr tyrosine kinase inhibitor resistance 2 . 17 × 10 7 phenylalanine metabolism0
endocrine resistance 2 . 35 × 10 7 estrogen signaling pathway 1 . 09 × 10 30
rap1 signaling pathway 5 . 32 × 10 7 chemokine signaling pathway 1 . 28 × 10 30
progesterone mediated oocyte maturation 5 . 39 × 10 7 erbb signaling pathway 8 . 64 × 10 29
hif 1 signaling pathway 5 . 89 × 10 7 phospholipase d signaling pathway 1 . 11 × 10 27
melanogenesis 6 . 15 × 10 7 neurotrophin signaling pathway 4 . 63 × 10 27
apoptosis 1 . 27 × 10 6 insulin signaling pathway 7 . 95 × 10 26
platinum drug resistance 1 . 27 × 10 6 egfr tyrosine kinase inhibitor resistance 2 . 76 × 10 25
phospholipase d signaling pathway 1 . 30 × 10 6 prolactin signaling pathway 1 . 20 × 10 24
mtor signaling pathway 1 . 55 × 10 6 oxytocin signaling pathway 5 . 67 × 10 24
ras signaling pathway 1 . 55 × 10 6 platelet activation 5 . 67 × 10 24
thyroid hormone signaling pathway 3 . 13 × 10 6 endocrine resistance 5 . 87 × 10 24
erbb signaling pathway 3 . 32 × 10 6 focal adhesion 6 . 00 × 10 24
Table 2. Metrics computed for the subpathways disease-specificity assessment of the two datasets in our case study. The table shows the number of substructures nodes, the number of significant nodes ( p < 0 . 01 ), the number of disease genes, the number of significant disease genes ( p < 0 . 01 ), the number of reachable pairs of disease genes within subpathways, the average distance between a disease gene and a substructure , and the average distance between disease genes contained within each substructure . The results are compared with a reference computed directly in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.
Table 2. Metrics computed for the subpathways disease-specificity assessment of the two datasets in our case study. The table shows the number of substructures nodes, the number of significant nodes ( p < 0 . 01 ), the number of disease genes, the number of significant disease genes ( p < 0 . 01 ), the number of reachable pairs of disease genes within subpathways, the average distance between a disease gene and a substructure , and the average distance between disease genes contained within each substructure . The results are compared with a reference computed directly in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.
# Nodes# Disease Genes
DatasetAlgorithm p < 0 . 01 All p < 0 . 01 AllReachable Pairs
KEGG Pathways1009712130104283-7
KEGG Pathways100971211181490-9
Table 3. List of terms sources used for the enrichment phase. All sources are grouped by category. For each source, we report the name, the number of terms, and the number of enriched nodes.
Table 3. List of terms sources used for the enrichment phase. All sources are grouped by category. For each source, we report the name, the number of terms, and the number of enriched nodes.
CategorySource# Terms# Nodes
DisGeNET [37,38]76072978
GAD [39]4031519
KEGG [2,3,4] Diseases12781234
OMIM [115]89518
Drugbank [116] Carriers2477
Drugbank [116] Enzymes797180
Drugbank [116] Targets48151494
Drugbank [116] Transporters56018
KEGG [2,3,4] Drugs3793706
Gene Ontology
GO [117,118] Biological Processes11,3864850
GO [117,118] Cellular Component15454852
GO [117,118] Molecular Function41464832
KEGG [2,3,4] Pathways3104904
Table 4. List of cancer types extracted from TCGA with their codes, number of case and control samples, and subcategories
Table 4. List of cancer types extracted from TCGA with their codes, number of case and control samples, and subcategories
CodeCancer TypeControl SamplesCase SamplesCase Samples Categories
BLCABladder Urothelial Carcinoma19193Stage I, II, III, IV
BRCABreast invasive carcinoma86642Stage I, II, III, IV, X
COADColon adenocarcinoma8389Stage I, II, III, IV
KICHKidney Chromophobe2566Stage I, II, III, IV
KIRCKidney renal clear cell carcinoma71224Stage I, II, III, IV
LUADLung adenocarcinoma19388Stage I, II, III, IV
LUSCLung squamous cell carcinoma37247Stage I, II, III, IV
PRADProstate adenocarcinoma50191Category 6, 7, 8, 9, 10
READRectum adenocarcinoma3150Stage I, II, III, IV
UCECUterine Corpus Endometrial Carcinoma14231Stage I, II, III, IV
   All Samples3322721

Share and Cite

MDPI and ACS Style

Alaimo, S.; Marceca, G.P.; Ferro, A.; Pulvirenti, A. Detecting Disease Specific Pathway Substructures through an Integrated Systems Biology Approach. Non-Coding RNA 2017, 3, 20.

AMA Style

Alaimo S, Marceca GP, Ferro A, Pulvirenti A. Detecting Disease Specific Pathway Substructures through an Integrated Systems Biology Approach. Non-Coding RNA. 2017; 3(2):20.

Chicago/Turabian Style

Alaimo, Salvatore, Gioacchino Paolo Marceca, Alfredo Ferro, and Alfredo Pulvirenti. 2017. "Detecting Disease Specific Pathway Substructures through an Integrated Systems Biology Approach" Non-Coding RNA 3, no. 2: 20.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop