A new framework for exploratory network mediator analysis in omics data.

Omics methods are widely used in basic biology and translational medicine research. More and more omics data are collected to explain the impact of certain risk factors on clinical outcomes. To explain the mechanism of the risk factors, a core question is how to find the genes/proteins/metabolites that mediate their effects on the clinical outcome. Mediation analysis is a modeling framework to study the relationship between risk factors and pathological outcomes, via mediator variables. However, high-dimensional omics data are far more challenging than traditional data: (1) From tens of thousands of genes, can we overcome the curse of dimensionality to reliably select a set of mediators? (2) How do we ensure that the selected mediators are functionally consistent? (3) Many biological mechanisms contain nonlinear effects. How do we include nonlinear effects in the high-dimensional mediation analysis? (4) How do we consider multiple risk factors at the same time? To meet these challenges, we propose a new exploratory mediation analysis framework, medNet, which focuses on finding mediators through predictive modeling. We propose new definitions for predictive exposure, predictive mediator, and predictive network mediator, using a statistical hypothesis testing framework to identify predictive exposures and mediators. Additionally, two heuristic search algorithms are proposed to identify network mediators, essentially subnetworks in the genome-scale biological network that mediate the effects of single or multiple exposures. We applied medNet on a breast cancer data set and a metabolomics data set combined with food intake questionnaire data. It identified functionally consistent network mediators for the exposures' impact on the outcome, facilitating data interpretation.

Long-term abuse of caffeine sodium benzoate induces endothelial cells injury and leads to coagulation dysfunction.

Our hospital admitted a patient who had difficulty in coagulation even after blood replacement, and the patient had abused caffeine sodium benzoate (CSB) for more than 20 years. Hence, we aimed to explore whether CSB may cause dysfunction in vascular endothelial cells and its possible mechanism. Low, medium, and high concentrations of serum of long-term CSB intake patients were used to treat HUVECs, with LPS as the positive control. MTT and CCK8 were performed to verify CSB's damaging effect on HUVECs. The expression of ET-1, ICAM-1, VCAM-1, and E-selectin were measured by ELISA. TUNEL assay and Matrigel tube formation assay were carried out to detect apoptosis and angiogenesis of HUVECs. Flow cytometry was applied to analyze cell cycles and expression of CD11b, PDGF, and ICAM-1. Expression of PDGF-BB and PCNA were examined by western blot. The activation of MAPK signaling pathway was detected by qRT-PCR and western blot. Intracellular Ca2+ density was detected by fluorescent probes. CCK8 assay showed high concentration of CSB inhibited cell viability. Cell proliferation and angiogenesis were inhibited by CSB. ET-1, ICAM-1, VCAM-1, and E-selectin upregulated in CSB groups. CSB enhanced apoptosis of HUVECs. CD11b, ICAM-1 increased and PDGF reduced in CSB groups. The expression level and phosphorylation level of MEK, ERK, JUN, and p38 in MAPK pathway elevated in CSB groups. The expression of PCNA and PDGF-BB was suppressed by CSB. Intracellular Ca2+ intensity was increased by CSB. Abuse of CSB injured HUVECs and caused coagulation disorders.

Intradermal but not intramuscular modified vaccinia Ankara immunizations protect against intravaginal tier2 simian-human immunodeficiency virus challenges in female macaques.

Route of immunization can markedly influence the quality of immune response. Here, we show that intradermal (ID) but not intramuscular (IM) modified vaccinia Ankara (MVA) vaccinations provide protection from acquisition of intravaginal tier2 simian-human immunodeficiency virus (SHIV) challenges in female macaques. Both routes of vaccination induce comparable levels of serum IgG with neutralizing and non-neutralizing activities. The protection in MVA-ID group correlates positively with serum neutralizing and antibody-dependent phagocytic activities, and envelope-specific vaginal IgA; while the limited protection in MVA-IM group correlates only with serum neutralizing activity. MVA-ID immunizations induce greater germinal center Tfh and B cell responses, reduced the ratio of Th1 to Tfh cells in blood and showed lower activation of intermediate monocytes and inflammasome compared to MVA-IM immunizations. This lower innate activation correlates negatively with induction of Tfh responses. These data demonstrate that the MVA-ID vaccinations protect against intravaginal SHIV challenges by modulating the innate and T helper responses.

Graph Random Forest: A Graph Embedded Algorithm for Identifying Highly Connected Important Features.

Random Forest (RF) is a widely used machine learning method with good performance on classification and regression tasks. It works well under low sample size situations, which benefits applications in the field of biology. For example, gene expression data often involve much larger numbers of features (p) compared to the size of samples (n). Though the predictive accuracy using RF is often high, there are some problems when selecting important genes using RF. The important genes selected by RF are usually scattered on the gene network, which conflicts with the biological assumption of functional consistency between effective features. To improve feature selection by incorporating external topological information between genes, we propose the Graph Random Forest (GRF) for identifying highly connected important features by involving the known biological network when constructing the forest. The algorithm can identify effective features that form highly connected sub-graphs and achieve equivalent classification accuracy to RF. To evaluate the capability of our proposed method, we conducted simulation experiments and applied the method to two real datasets-non-small cell lung cancer RNA-seq data from The Cancer Genome Atlas, and human embryonic stem cell RNA-seq dataset (GSE93593). The resulting high classification accuracy, connectivity of selected sub-graphs, and interpretable feature selection results suggest the method is a helpful addition to graph-based classification models and feature selection procedures.

Metabolomic analysis of the sera of patients with the long-term inhalation of caffeine-sodium benzoate using LC-MS.

The present study aimed to systematically assess the potential biomarkers in the serum samples of patients with long-term inhalation of caffeine-sodium benzoate (CSB). LC-MS was applied to analyze the metabolic profiles of serum samples of patients with the long-term intake of CSB (n = 35) and other volunteers with no intake of CSB treated as the control group (n = 35). The raw data of metabolic profiles were analyzed via principal component analysis, partial least squares analysis, and orthogonal partial least squares analysis. MBRole 2.0 online tools were used to analyze the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of different metabolites. The serum metabolic profiles showed several metabolites with large variations, including 2-propyl-2,4-pentadienoic acid, 24-hydroxycholesterol, 3-O-sulfogalactosylceramide (d18:1/24:1(15Z)), 3-O-sulfogalactosylceramide (d18:1/12:0), 3-O-sulfogalactosylceramide (d18:1/14:0), 3a,7a-dihydroxy-5b-cholestan-26-al, 3a,7a-dihydroxy-5b-cholestane, 7a,25-dihydroxycholesterol, bilirubin, and dehydroepiandrosterone sulfate. The Kyoto Encyclopedia of Genes and Genomes pathways involved in metabolism included 'choline metabolism in cancer' and 'glycerophospholipid metabolism'. In conclusion, the present study provides a basis with which to explore the molecular-specific mechanisms concerning the effects of the long-term inhalation of CSB on human physical and mental health.

Sphinganine is associated with 24-h MAP in the non-sleepy with OSA.

INTRODUCTION: Excessive daytime sleepiness is a debilitating symptom of obstructive sleep apnea (OSA) linked to cardiovascular disease, and metabolomic mechanisms underlying this relationship remain unknown. We examine whether metabolites from inflammatory and oxidative stress-related pathways that were identified in our prior work could be involved in connecting the two phenomena. METHODS: This study included 57 sleepy (Epworth Sleepiness Scale (ESS) ≥ 10) and 37 non-sleepy (ESS < 10) participants newly diagnosed and untreated for OSA that completed an overnight in-lab or at home sleep study who were recruited from the Emory Mechanisms of Sleepiness Symptoms Study (EMOSS). Differences in fasting blood samples of metabolites were explored in participants with sleepiness versus those without and multiple linear regression models were utilized to examine the association between metabolites and mean arterial pressure (MAP). RESULTS: The 24-h MAP was higher in sleepy 92.8 mmHg (8.4) as compared to non-sleepy 88.8 mmHg (8.1) individuals (P = 0.03). Although targeted metabolites were not significantly associated with MAP, when we stratified by sleepiness group, we found that sphinganine is significantly associated with MAP (Estimate = 8.7, SE = 3.7, P = 0.045) in non-sleepy patients when controlling for age, BMI, smoking status, and apnea-hypopnea index (AHI). CONCLUSION: This is the first study to evaluate the relationship of inflammation and oxidative stress related metabolites in sleepy versus non-sleepy participants with newly diagnosed OSA and their association with 24-h MAP. Our study suggests that Sphinganine is associated with 24 hour MAP in the non-sleepy participants with OSA.

Feature selection and classification over the network with missing node observations.

Jointly analyzing transcriptomic data and the existing biological networks can yield more robust and informative feature selection results, as well as better understanding of the biological mechanisms. Selecting and classifying node features over genome-scale networks has become increasingly important in genomic biology and genomic medicine. Existing methods have some critical drawbacks. The first is they do not allow flexible modeling of different subtypes of selected nodes. The second is they ignore nodes with missing values, very likely to increase bias in estimation. To address these limitations, we propose a general modeling framework for Bayesian node classification (BNC) with missing values. A new prior model is developed for the class indicators incorporating the network structure. For posterior computation, we resort to the Swendsen-Wang algorithm for efficiently updating class indicators. BNC can naturally handle missing values in the Bayesian modeling framework, which improves the node classification accuracy and reduces the bias in estimating gene effects. We demonstrate the advantages of our methods via extensive simulation studies and the analysis of the cutaneous melanoma dataset from The Cancer Genome Atlas.

HK2 Is a Crucial Downstream Regulator of miR-148a for the Maintenance of Sphere-Forming Property and Cisplatin Resistance in Cervical Cancer Cells.

The acquisition of cancer stem-like properties is believed to be responsible for cancer metastasis and therapeutic resistance in cervical cancer (CC). CC tissues display a high expression level of hexokinase 2 (HK2), which is critical for the proliferation and migration of CC cells. However, little is known about the functional role of HK2 in the maintenance of cancer stem cell-like ability and cisplatin resistance of CC cells. Here, we showed that the expression of HK2 is significantly elevated in CC tissues, and high HK2 expression correlates with poor prognosis. HK2 overexpression (or knockdown) can promote (or inhibit) the sphere-forming ability and cisplatin resistance in CC cells. In addition, HK2-overexpressing CC cells show enhanced expression of cancer stem cell-associated genes (including SOX2 and OCT4) and drug resistance-related gene MDR1. The expression of HK2 is mediated by miR-145, miR-148a, and miR-497 in CC cells. Overexpression of miR-148a is sufficient to reduce sphere formation and cisplatin resistance in CC cells. Our results elucidate a novel mechanism through which miR-148a regulates CC stem cell-like properties and chemoresistance by interfering with the oncogene HK2, providing the first evidence that dysregulation of the miR-148a/HK2 signaling plays a critical role in the maintenance of sphere formation and cisplatin resistance of CC cells. Our findings may guide future studies on therapeutic strategies that reverse cisplatin resistance by targeting this pathway.

Bayesian Network Marker Selection via the Thresholded Graph Laplacian Gaussian Prior.

Selecting informative nodes over large-scale networks becomes increasingly important in many research areas. Most existing methods focus on the local network structure and incur heavy computational costs for the large-scale problem. In this work, we propose a novel prior model for Bayesian network marker selection in the generalized linear model (GLM) framework: the Thresholded Graph Laplacian Gaussian (TGLG) prior, which adopts the graph Laplacian matrix to characterize the conditional dependence between neighboring markers accounting for the global network structure. Under mild conditions, we show the proposed model enjoys the posterior consistency with a diverging number of edges and nodes in the network. We also develop a Metropolis-adjusted Langevin algorithm (MALA) for efficient posterior computation, which is scalable to large-scale networks. We illustrate the superiorities of the proposed method compared with existing alternatives via extensive simulation studies and an analysis of the breast cancer gene expression dataset in the Cancer Genome Atlas (TCGA).

A hypergraph-based method for large-scale dynamic correlation study at the transcriptomic scale.

BACKGROUND: The biological regulatory system is highly dynamic. Correlations between functionally related genes change over different biological conditions, which are often unobserved in the data. At the gene level, the dynamic correlations result in three-way gene interactions involving a pair of genes that change correlation, and a third gene that reflects the underlying cellular conditions. This type of ternary relation can be quantified by the Liquid Association statistic. Studying these three-way interactions at the gene triplet level have revealed important regulatory mechanisms in the biological system. Currently, due to the extremely large amount of possible combinations of triplets within a high-throughput gene expression dataset, no method is available to examine the ternary relationship at the biological system level and formally address the false discovery issue. RESULTS: Here we propose a new method, Hypergraph for Dynamic Correlation (HDC), to construct module-level three-way interaction networks. The method is able to present integrative uniform hypergraphs to reflect the global dynamic correlation pattern in the biological system, providing guidance to down-stream gene triplet-level analyses. To validate the method's ability, we conducted two real data experiments using a melanoma RNA-seq dataset from The Cancer Genome Atlas (TCGA) and a yeast cell cycle dataset. The resulting hypergraphs are clearly biologically plausible, and suggest novel relations relevant to the biological conditions in the data. CONCLUSIONS: We believe the new approach provides a valuable alternative method to analyze omics data that can extract higher order structures. The software is at https://github.com/yunchuankong/HypergraphDynamicCorrelation .

Longitudinal Analysis of the Human B Cell Response to Ebola Virus Infection.

Ebola virus (EBOV) remains a public health threat. We performed a longitudinal study of B cell responses to EBOV in four survivors of the 2014 West African outbreak. Infection induced lasting EBOV-specific immunoglobulin G (IgG) antibodies, but their subclass composition changed over time, with IgG1 persisting, IgG3 rapidly declining, and IgG4 appearing late. Striking changes occurred in the immunoglobulin repertoire, with massive recruitment of naive B cells that subsequently underwent hypermutation. We characterized a large panel of EBOV glycoprotein-specific monoclonal antibodies (mAbs). Only a small subset of mAbs that bound glycoprotein by ELISA recognized cell-surface glycoprotein. However, this subset contained all neutralizing mAbs. Several mAbs protected against EBOV disease in animals, including one mAb that targeted an epitope under evolutionary selection during the 2014 outbreak. Convergent antibody evolution was seen across multiple donors, particularly among VH3-13 neutralizing antibodies specific for the GP1 core. Our study provides a benchmark for assessing EBOV vaccine-induced immunity.

A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data.

Motivation: Gene expression data represents a unique challenge in predictive model building, because of the small number of samples (n) compared with the huge amount of features (p). This 'n≪p' property has hampered application of deep learning techniques for disease outcome classification. Sparse learning by incorporating external gene network information could be a potential solution to this issue. Still, the problem is very challenging because (i) there are tens of thousands of features and only hundreds of training samples, (ii) the scale-free structure of the gene network is unfriendly to the setup of convolutional neural networks. Results: To address these issues and build a robust classification model, we propose the Graph-Embedded Deep Feedforward Networks (GEDFN), to integrate external relational information of features into the deep neural network architecture. The method is able to achieve sparse connection between network layers to prevent overfitting. To validate the method's capability, we conducted both simulation experiments and real data analysis using a breast invasive carcinoma RNA-seq dataset and a kidney renal clear cell carcinoma RNA-seq dataset from The Cancer Genome Atlas. The resulting high classification accuracy and easily interpretable feature selection results suggest the method is a useful addition to the current graph-guided classification models and feature selection procedures. Availability and implementation: The method is available at https://github.com/yunchuankong/GEDFN. Supplementary information: Supplementary data are available at Bioinformatics online.

miR-486-3p, miR-139-5p, and miR-21 as Biomarkers for the Detection of Oral Tongue Squamous Cell Carcinoma.

Oral tongue squamous cell carcinoma (TSCC) is a complex disease with extensive genetic and epigenetic defects, including microRNA deregulation. The aims of the present study were to test the feasibility of performing the microRNA profiling analysis on archived TSCC specimens and to assess the potential diagnostic utility of the identified microRNA biomarkers for the detection of TSCC. TaqMan array-based microRNA profiling analysis was performed on 10 archived TSCC samples and their matching normal tissues. A panel of 12 differentially expressed microRNAs was identified. Eight of these differentially expressed microRNAs were validated in an independent sample set. A random forest (RF) classification model was built with miR-486-3p, miR-139-5p, and miR-21, and it was able to detect TSCC with a sensitivity of 100% and a specificity of 86.7% (overall error rate = 6.7%). As such, this study demonstrated the utility of the archived clinical specimens for microRNA biomarker discovery. The feasibility of using microRNA biomarkers (miR-486-3p, miR-139-5p, and miR-21) for the detection of TSCC was confirmed.

miR-486-3p, miR-139-5p, and miR-21 as Biomarkers for the Detection of Oral Tongue Squamous Cell Carcinoma.

Oral tongue squamous cell carcinoma (TSCC) is a complex disease with extensive genetic and epigenetic defects, including microRNA deregulation. The aims of the present study were to test the feasibility of performing the microRNA profiling analysis on archived TSCC specimens and to assess the potential diagnostic utility of the identified microRNA biomarkers for the detection of TSCC. TaqMan array-based microRNA profiling analysis was performed on 10 archived TSCC samples and their matching normal tissues. A panel of 12 differentially expressed microRNAs was identified. Eight of these differentially expressed microRNAs were validated in an independent sample set. A random forest (RF) classification model was built with miR-486-3p, miR-139-5p, and miR-21, and it was able to detect TSCC with a sensitivity of 100% and a specificity of 86.7% (overall error rate = 6.7%). As such, this study demonstrated the utility of the archived clinical specimens for microRNA biomarker discovery. The feasibility of using microRNA biomarkers (miR-486-3p, miR-139-5p, and miR-21) for the detection of TSCC was confirmed.

microRNA-21 and microRNA-375 from oral cytology as biomarkers for oral tongue cancer detection.

OBJECTIVE: We previously performed a meta-analysis of microRNA profiling studies on head and neck/oral cancer (HNOC), and identified 11 consistently dysregulated microRNAs in HNOC. Here, we evaluate the diagnostic values of these microRNAs in oral tongue squamous cell carcinoma (OTSCC) using oral cytology samples. MATERIALS AND METHODS: The levels of 11 microRNAs were assessed in 39 oral cytology samples (19 OTSCC and 20 normal subjects), and 10 paired OTSCC and adjacent normal tissues. The predictive power of these microRNAs was analyzed by receiver operating characteristic curve (ROC) and random forest (RF) model. A classification and regression trees (CART) model was generated using miR-21 and miR-375, and further validated using both independent oral cytology validation sample set (14 OTSCC and 11 normal subjects) and tissue validation sample set (12 paired OTSCC and adjacent normal tissues). RESULTS: Differential expression of miR-21, miR-100, miR-125b and miR-375 was validated in oral cytology training sample set. Based on the RF model, the combination of miR-21 and miR-375 was selected which provide best prediction of OTSCC. A CART model was constructed using miR-21 and miR-375, and was tested in both oral cytology and tissue validation sample sets. A sensitivity of 100% and specificity of 64% was achieved in distinguishing OTSCC from normal in the oral cytology validation set, and a sensitivity of 83% and specificity of 83% was achieved in the tissue validation set. CONCLUSION: The utility of microRNA from oral cytology samples as biomarkers for OTSCC detection is successfully demonstrated in this study.

MeDiA: Mean Distance Association and Its Applications in Nonlinear Gene Set Analysis.

Probabilistic association discovery aims at identifying the association between random vectors, regardless of number of variables involved or linear/nonlinear functional forms. Recently, applications in high-dimensional data have generated rising interest in probabilistic association discovery. We developed a framework based on functions on the observation graph, named MeDiA (Mean Distance Association). We generalize its property to a group of functions on the observation graph. The group of functions encapsulates major existing methods in association discovery, e.g. mutual information and Brownian Covariance, and can be expanded to more complicated forms. We conducted numerical comparison of the statistical power of related methods under multiple scenarios. We further demonstrated the application of MeDiA as a method of gene set analysis that captures a broader range of responses than traditional gene set analysis methods.

Absorption and chemopreventive targets of sulforaphane in humans following consumption of broccoli sprouts or a myrosinase-treated broccoli sprout extract.

SCOPE: Sulforaphane (SFN), an isothiocyanate derived from crucifers, has numerous health benefits. SFN bioavailability from dietary sources is a critical determinant of its efficacy in humans. A key factor in SFN absorption is the release of SFN from its glucosinolate precursor, glucoraphanin, by myrosinase. Dietary supplements are used in clinical trials to deliver consistent SFN doses, but myrosinase is often inactivated in available supplements. We evaluated SFN absorption from a myrosinase-treated broccoli sprout extract (BSE) and are the first to report effects of twice daily, oral dosing on SFN exposure in healthy adults. METHODS AND RESULTS: Subjects consumed fresh broccoli sprouts or the BSE, each providing 200 µmol SFN daily, as a single dose and as two 100-µmol doses taken 12 h apart. Using HPLC-MS/MS, we detected ∼3 x higher SFN metabolite levels in plasma and urine of sprout consumers, indicating enhanced SFN absorption from sprouts. Twelve-hour dosing retained higher plasma SFN metabolite levels at later time points than 24-hour dosing. No dose responses were observed for molecular targets of SFN (i.e. heme oxygenase-1, histone deacetylase activity, p21). CONCLUSION: We conclude that the dietary form and dosing schedule of SFN may impact SFN absorption and efficacy in human trials.

CD40L-adjuvanted DNA/modified vaccinia virus Ankara simian immunodeficiency virus SIV239 vaccine enhances SIV-specific humoral and cellular immunity and improves protection against a heterologous SIVE660 mucosal challenge.

UNLABELLED: It remains a challenge to develop a successful human immunodeficiency virus (HIV) vaccine that is capable of preventing infection. Here, we utilized the benefits of CD40L, a costimulatory molecule that can stimulate both dendritic cells (DCs) and B cells, as an adjuvant for our simian immunodeficiency virus (SIV) DNA vaccine in rhesus macaques. We coexpressed the CD40L with our DNA/SIV vaccine such that the CD40L is anchored on the membrane of SIV virus-like particle (VLP). These CD40L containing SIV VLPs showed enhanced activation of DCs in vitro. We then tested the potential of DNA/SIV-CD40L vaccine to adjuvant the DNA prime of a DNA/modified vaccinia virus Ankara (MVA) vaccine in rhesus macaques. Our results demonstrated that the CD40L adjuvant enhanced the functional quality of anti-Env antibody response and breadth of anti-SIV CD8 and CD4 T cell responses, significantly delayed the acquisition of heterologous mucosal SIV infection, and improved viral control. Notably, the CD40L adjuvant enhanced the control of viral replication in the gut at the site of challenge that was associated with lower mucosal CD8 immune activation, one of the strong predictors of disease progression. Collectively, our results highlight the benefits of CD40L adjuvant for enhancing antiviral humoral and cellular immunity, leading to enhanced protection against a pathogenic SIV. A single adjuvant that enhances both humoral and cellular immunity is rare and thus underlines the importance and practicality of CD40L as an adjuvant for vaccines against infectious diseases, including HIV-1. IMPORTANCE: Despite many advances in the field of AIDS research, an effective AIDS vaccine that can prevent infection remains elusive. CD40L is a key stimulator of dendritic cells and B cells and can therefore enhance T cell and antibody responses, but its overly potent nature can lead to adverse effects unless used in small doses. In order to modulate local expression of CD40L at relatively lower levels, we expressed CD40L in a membrane-bound form, along with SIV antigens, in a nucleic acid (DNA) vector. We tested the immunogenicity and efficacy of the CD40L-adjuvanted vaccine in macaques using a heterologous mucosal SIV infection. The CD40L-adjuvanted vaccine enhanced the functional quality of anti-Env antibody response and breadth of anti-SIV T cell responses and improved protection. These results demonstrate that VLP-membrane-bound CD40L serves as a novel adjuvant for an HIV vaccine.

EgoNet: identification of human disease ego-network modules.

BACKGROUND: Mining novel biomarkers from gene expression profiles for accurate disease classification is challenging due to small sample size and high noise in gene expression measurements. Several studies have proposed integrated analyses of microarray data and protein-protein interaction (PPI) networks to find diagnostic subnetwork markers. However, the neighborhood relationship among network member genes has not been fully considered by those methods, leaving many potential gene markers unidentified. The main idea of this study is to take full advantage of the biological observation that genes associated with the same or similar diseases commonly reside in the same neighborhood of molecular networks. RESULTS: We present EgoNet, a novel method based on egocentric network-analysis techniques, to exhaustively search and prioritize disease subnetworks and gene markers from a large-scale biological network. When applied to a triple-negative breast cancer (TNBC) microarray dataset, the top selected modules contain both known gene markers in TNBC and novel candidates, such as RAD51 and DOK1, which play a central role in their respective ego-networks by connecting many differentially expressed genes. CONCLUSIONS: Our results suggest that EgoNet, which is based on the ego network concept, allows the identification of novel biomarkers and provides a deeper understanding of their roles in complex diseases.