Integrating Gene Expression Profiles Research Articles

Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium.

Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), often modulated by potential endocrine disrupting chemicals. ERα biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ERα agonists and 3 ERα antagonists in ERα-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ERα as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ERα-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including "very weak" agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ERα signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells.

Molecular mechanisms associated with breast cancer based on integrated gene expression profiling by bioinformatics analysis

In this study, we aimed to gain more insights into the underlying molecular mechanisms responsible for breast cancer (BC) progression. Three gene expression profiles of human BC were integrated and used to screen the differentially expressed genes (DEGs) between healthy breast samples and BC samples. Protein–protein interaction (PPI) network of DEGs was constructed by mapping DEGs into the Search Tool for the Retrieval of Interacting Genes (STRING) database; then the subnetworks of PPI were constructed with plug-in, MCODE and DEGs in Subnetwork 1 were analysed based on Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway database (http://www.genome.jp/kegg/). In addition, co-expression network of DEGs was established using the Cytoscape. Totalally 931 DEGs were selected, including 340 up-regulated genes and 591 down-regulated genes. KEGG pathway analysis for DEGs in Subnetwork 1 showed that the pathogenesis of BC was associated with cell cycle, oocyte meiosis, progesterone-mediated oocyte maturation and p53 signalling pathways. Meanwhile, the most significant-related DEGs were found by co-expression network analysis of DEGs. In conclusion, CCNG1 might be involved in the progression of BC via inhibiting cell proliferation, and ADAMTS1 might play a crucial role in BC development through the regulation of angiogenesis.

Dissection of transcriptional and cis-regulatory control of differentiation in human pancreatic cancer.

The histological grade of carcinomas describes the ability of tumor cells to organize in differentiated epithelial structures and has prognostic and therapeutic impact. Here, we show that differential usage of the genomic repertoire of transcriptional enhancers leads to grade-specific gene expression programs in human pancreatic ductal adenocarcinoma (PDAC). By integrating gene expression profiling, epigenomic footprinting, and loss-of-function experiments in PDAC cell lines of different grade, we identified the repertoires of enhancers specific to high- and low-grade PDACs and the cognate set of transcription factors acting to maintain their activity. Among the candidate regulators of PDAC differentiation, KLF5 was selectively expressed in pre-neoplastic lesions and low-grade primary PDACs and cell lines, where it maintained the acetylation of grade-specific enhancers, the expression of epithelial genes such as keratins and mucins, and the ability to organize glandular epithelia in xenografts. The identification of the transcription factors controlling differentiation in PDACs will help clarify the molecular bases of its heterogeneity and progression.

Ecotoxicity of bisphenol A to Caenorhabditis elegans by multigenerational exposure and variations of stress response in vivo across generations

In order to understand how bisphenol A (BPA) exposure acts on the evolutionary dynamics of populations and changes of stress response across generations, the model animal Caenorhabditis elegans was used to conduct the multigenerational testing. Multiple endpoints at the physiological (growth, reproduction, and locomotion behaviors) and molecular (stress-related gene expressions) levels were examined by multigenerational exposure to low-concentration BPA (0.001–10 μM) across four generations. The results showed that changes of physiological-level effects across four generations varied in magnitude and direction, depending on the exposure concentrations. C. elegans individuals in the first generation grew smaller, moved slower, and produced less offsprings than the controls by BPA exposure. As for each trait tested, the first generation response could be commonly mirrored in the subsequent generations at the highest concentration of 10 μM. However, at lower concentrations, response of parental generation was a relatively poor predictor of the effects on progeny, as acclimation or cumulative damage could occur in the subsequent generations. The integrated gene expression profiles visually illustrated that the tested gene expressions at low concentrations (0.001–0.01 μM) were more obviously changed in both G1 and G4 generations, and the G1 generation showed a much greater degree of increase in stress-related gene expressions than the G4 generation. The multigenerational toxicity data emphasize the need of considering biological effects over multiple generations to conduct accurate assessment of environmental risks of toxicants on population dynamics.

Analysis of differences in the molecular mechanism of rheumatoid arthritis and osteoarthritis based on integration of gene expression profiles

BackgroundWe aimed to elucidate the molecular mechanisms underlying rheumatoid arthritis (RA) and osteoarthritis (OA) and analyze the mechanism differences between them. MethodsThe gene expression profile of GSE1919, GSE12021, GSE21959 and GSE48780 were downloaded from Gene Expression Omnibus. Total 165 samples of synovial fibroblasts (118 RA samples, 15 OA samples and 32 normal controls) were used. The differentially expressed genes (DEGs) in RA samples but no differences in OA samples (RA.DEGs) and genes in OA samples but no differences in RA samples (OA.DEGs) were screened using limma package. Functional enrichment analysis was performed using DAVID. Moreover, transcriptional regulatory network (TRN) and microRNA regulatory network were constructed. ResultsTotal 211 RA.DEGs (96 up- and 115 down-regulate) and 497 OA.DEGs (224 up- and 273 down-regulated) were identified. TRN analysis showed that C-ETS-1 and P53 were important transcription factors. C-ETS-1 could interact with matrix metallopeptidase 1 (MMP1) and CD53 while P53 could interact with epidermal growth factor receptor (EGFR) and dual specificity phosphatase 1 (DUSP1). Besides, v-myc avian myelocytomatosis viral oncogene homolog (MYC) and interleukin 1, beta (IL1B) could be regulated by the most microRNAs in microRNA regulatory network. Our study indicates that ETS-1 may contribute to RA progression by up-regulation of MMP1 and result in OA progression via up-regulating CD53. ConclusionsP53 may be involved in the progression of RA and OA via targeting downstream EGFR and DUSP1 respectively. Besides, MYC and IL1B may play an important role in OA progression via the regulation of microRNAs.

Identifying gene subnetworks associated with clinical outcome in ovarian cancer using Network Based Coalition Game.

The problem of identifying interacting genes that jointly are associated with a phenotype is considered. When the number of features are extremely large compared to the number of samples, there may be several subsets of features that provide acceptable levels of predictability. This is particularly true in cancer genomics, where we are interested in finding functionally related gene sets likely to jointly drive cancer phenotypes. In this paper, a novel game theoretic solution is proposed by modeling genes as players of a Coalition Game. This method discovers and develops informative gene subnetworks by integrating gene expression profiling of cancer tissues with protein-protein interaction (PPI) networks. These subnetworks are gradually developed by selective addition of candidate genes that present maximal Shapely values in coalition with subnetworks of genes. We applied the proposed algorithm to an ovarian cancer dataset (N = 201), in order to identify optimal subnetworks that can predict cancer progression risk in response to platinum-based therapy. We show improved predictive power of the proposed method when compared to state-of-the-art feature selection methods, with the added advantage of identifying potentially functional gene subnetworks that may provide insights into the mechanisms underlying cancer progression.

Integrative miRNA and Gene Expression Profiling Analysis of Human Quiescent Hepatic Stellate Cells.

Unveiling the regulatory pathways maintaining hepatic stellate cells (HSC) in a quiescent (q) phenotype is essential to develop new therapeutic strategies to treat fibrogenic diseases. To uncover the miRNA-mRNA regulatory interactions in qHSCs, HSCs were FACS-sorted from healthy livers and activated HSCs (aHSCs) were generated in vitro. MiRNA Taqman array analysis showed HSCs expressed a low number of miRNAs (n = 259), from which 47 were down-regulated and 212 up-regulated upon activation. Computational integration of miRNA and gene expression profiles revealed that 66% of qHSC-associated miRNAs correlated with more than 6 altered target mRNAs (17,28 ± 10,7 targets/miRNA) whereas aHSC-associated miRNAs had an average of 1,49 targeted genes. Interestingly, interaction networks generated by miRNA-targeted genes in qHSCs were associated with key HSC activation processes. Next, selected miRNAs were validated in healthy and cirrhotic human livers and miR-192 was chosen for functional analysis. Down-regulation of miR-192 in HSCs was found to be an early event during fibrosis progression in mouse models of liver injury. Moreover, mimic assays for miR-192 in HSCs revealed its role in HSC activation, proliferation and migration. Together, these results uncover the importance of miRNAs in the maintenance of the qHSC phenotype and form the basis for understanding the regulatory networks in HSCs.

High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin.

Understanding cell type identity in a multicellular organism requires the integration of gene expression profiles from individual cells with their spatial location in a particular tissue. Current technologies allow whole-transcriptome sequencing of spatially identified cells but lack the throughput needed to characterize complex tissues. Here we present a high-throughput method to identify the spatial origin of cells assayed by single-cell RNA-sequencing within a tissue of interest. Our approach is based on comparing complete, specificity-weighted mRNA profiles of a cell with positional gene expression profiles derived from a gene expression atlas. We show that this method allocates cells to precise locations in the brain of the marine annelid Platynereis dumerilii with a success rate of 81%. Our method is applicable to any system that has a reference gene expression database of sufficiently high resolution.

Effects of lithium on growth, maturation, reproduction and gene expression in the nematode Caenorhabditis elegans.

Lithium (Li) has been widely used to treat bipolar disorder, and industrial use of Li has been increasing; thus, environmental pollution and ecological impacts of Li have become a concern. This study was conducted to clarify the potential biological effects of LiCl and Li(2)CO(3) on a nematode, Caenorhabditis elegans as a model system for evaluating soil contaminated with Li. Exposure of C. elegans to LiCl and Li(2)CO(3) decreased growth/maturation and reproduction. The lowest observed effect concentrations for growth, maturation and reproduction were 1250, 313 and 10 000 µm, respectively, for LiCl and 750, 750 and 3000 µm, respectively, for Li(2)CO(3). We also investigated the physiological function of LiCl and LiCO(3) in C. elegans using DNA microarray analysis as an eco-toxicogenomic approach. Among approximately 300 unique genes, including metabolic genes, the exposure to 78 µm LiCl downregulated the expression of 36 cytochrome P450, 16 ABC transporter, 10 glutathione S-transferase, 16 lipid metabolism and two vitellogenin genes. On the other hand, exposure to 375 µm Li(2)CO(3) downregulated the expression of 11 cytochrome P450, 13 ABC transporter, 13 lipid metabolism and one vitellogenin genes. No gene was upregulated by LiCl or Li(2)CO(3). These results suggest that LiCl and Li(2)CO(3) potentially affect the biological and physiological function in C. elegans associated with alteration of the gene expression such as metabolic genes. Our data also provide experimental support for the utility of toxicogenomics by integrating gene expression profiling into a toxicological study of an environmentally important organism such as C. elegans.

Development and validation of a gene expression tumour classifier for cancer of unknown primary

Accurate identification of the primary tumour in cancer of unknown primary (CUP) is required for effective treatment selection and improved patient outcomes. The aim of this study was to develop and validate a gene expression tumour classifier and integrate it with histopathology to identify the likely site of origin in CUP.RNA was extracted from 450 formalin fixed, paraffin embedded samples of known origin comprising 18 tumour groups. Whole genome expression analysis was performed using a bead-based array. Classification of the tumours made use of a binary support vector machine, together with recursive feature elimination. A hierarchical tumour classifier was developed and incorporated with conventional histopathology to identify the origins of metastatic tumours.The classifier demonstrated an accuracy of 88% for correctly predicting the tumour type on a validation set of known tumours (n = 94). For CUP samples (n = 49) having a final clinical diagnosis, the classifier improved the accuracy of histology alone for both single and multiple predictions. Furthermore, where histology alone could not suggest any specific diagnosis, the classifier was able to correctly predict the primary site of origin.We demonstrate the integration of gene expression profiling with conventional histopathology to aid the investigation of CUP.

Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis.

Integrating gene expression profiles with certain proteins can improve our understanding of the fundamental mechanisms in protein-ligand binding. This paper spotlights the integration of gene expression data and target prediction scores, providing insight into mechanism of action (MoA). Compounds are clustered based upon the similarity of their predicted protein targets and each cluster is linked to gene sets using Linear Models for Microarray Data. MLP analysis is used to generate gene sets based upon their biological processes and a qualitative search is performed on the homogeneous target-based compound clusters to identify pathways. Genes and proteins were linked through pathways for 6 of the 8 MCF7 and 6 of the 11 PC3 clusters. Three compound clusters are studied; (i) the target-driven cluster involving HSP90 inhibitors, geldanamycin and tanespimycin induces differential expression for HSP90-related genes and overlap with pathway response to unfolded protein. Gene expression results are in agreement with target prediction and pathway annotations add information to enable understanding of MoA. (ii) The antipsychotic cluster shows differential expression for genes LDLR and INSIG-1 and is predicted to target CYP2D6. Pathway steroid metabolic process links the protein and respective genes, hypothesizing the MoA for antipsychotics. A sub-cluster (verepamil and dexverepamil), although sharing similar protein targets with the antipsychotic drug cluster, has a lower intensity of expression profile on related genes, indicating that this method distinguishes close sub-clusters and suggests differences in their MoA. Lastly, (iii) the thiazolidinediones drug cluster predicted peroxisome proliferator activated receptor (PPAR) PPAR-alpha, PPAR-gamma, acyl CoA desaturase and significant differential expression of genes ANGPTL4, FABP4 and PRKCD. The targets and genes are linked via PPAR signalling pathway and induction of apoptosis, generating a hypothesis for the MoA of thiazolidinediones. Our analysis show one or more underlying MoA for compounds and were well-substantiated with literature.

Integrative Analysis with Monte Carlo Cross-Validation Reveals miRNAs Regulating Pathways Cross-Talk in Aggressive Breast Cancer.

In this work an integrated approach was used to identify functional miRNAs regulating gene pathway cross-talk in breast cancer (BC). We first integrated gene expression profiles and biological pathway information to explore the underlying associations between genes differently expressed among normal and BC samples and pathways enriched from these genes. For each pair of pathways, a score was derived from the distribution of gene expression levels by quantifying their pathway cross-talk. Random forest classification allowed the identification of pairs of pathways with high cross-talk. We assessed miRNAs regulating the identified gene pathways by a mutual information analysis. A Fisher test was applied to demonstrate their significance in the regulated pathways. Our results suggest interesting networks of pathways that could be key regulatory of target genes in BC, including stem cell pluripotency, coagulation, and hypoxia pathways and miRNAs that control these networks could be potential biomarkers for diagnostic, prognostic, and therapeutic development in BC. This work shows that standard methods of predicting normal and tumor classes such as differentially expressed miRNAs or transcription factors could lose intrinsic features; instead our approach revealed the responsible molecules of the disease.

Multiscale characterization of ageing and cancer progression by a novel network entropy measure.

We characterize different cell states, related to cancer and ageing phenotypes, by a measure of entropy of network ensembles, integrating gene expression profiling values and protein interaction network topology. In our case studies, network entropy, that by definition estimates the number of possible network instances satisfying the given constraints, can be interpreted as a measure of the "parameter space" available to the cell. Network entropy was able to characterize specific pathological conditions: normal versus cancer cells, primary tumours that developed metastasis or relapsed, and extreme longevity samples. Moreover, this approach has been applied at different scales, from whole network to specific subnetworks (biological pathways defined on a priori biological knowledge) and single nodes (genes), allowing a deeper understanding of the cell processes involved.

Prediction of essential proteins based on overlapping essential modules.

Many computational methods have been proposed to identify essential proteins by using the topological features of interactome networks. However, the precision of essential protein discovery still needs to be improved. Researches show that majority of hubs (essential proteins) in the yeast interactome network are essential due to their involvement in essential complex biological modules and hubs can be classified into two categories: date hubs and party hubs. In this study, combining with gene expression profiles, we propose a new method to predict essential proteins based on overlapping essential modules, named POEM. In POEM, the original protein interactome network is partitioned into many overlapping essential modules. The frequencies and weighted degrees of proteins in these modules are employed to decide which categories does a protein belong to? The comparative results show that POEM outperforms the classical centrality measures: Degree Centrality (DC), Information Centrality (IC), Eigenvector Centrality (EC), Subgraph Centrality (SC), Betweenness Centrality (BC), Closeness Centrality (CC), Edge Clustering Coefficient Centrality (NC), and two newly proposed essential proteins prediction methods: PeC and CoEWC. Experimental results indicate that the precision of predicting essential proteins can be improved by considering the modularity of proteins and integrating gene expression profiles with network topological features.

Abstract A53: Biomarkers of chemotherapy resistance in serous epithelial ovarian cancer identified by integrative genomic and transcriptomic analysis

Abstract Resistance to platinum-based chemotherapy remains a major impediment in the treatment of serous epithelial ovarian cancer. The objective of this study was to use gene expression and copy number profiling to delineate major deregulated pathways and biomarker networks associated with the development of intrinsic chemotherapy resistance with exposure to standard first-line therapy for ovarian cancer. The study cohort comprised 28 high grade serous ovarian cancer patients divided into two groups based on their varying sensitivity to first-line chemotherapy using progression free survival (PFS) as a surrogate of response. Twelve patient tumors demonstrating relative resistance to platinum based chemotherapy corresponding to shorter PFS (less than 6 months) were compared to 16 tumors from platinum-sensitive patients (PFS more than 18months). Molecular profiling was performed using Affymetrix high-resolution microarray platforms to permit global comparisons of gene expression levels and copy number profiles between tumors from the resistant group with the sensitive group. Microarray data analysis using statistical methods revealed a set of 204 discriminating genes of which expression levels may be influencing differential chemotherapy response between the two groups. Pathway analysis of the differentiating genes showed IGF1 network to be significantly altered between the two groups in addition to PI3K, NFkB, distinguishing the chemotherapy resistant with the sensitive group. Copy number analysis showed differences in the chromosomal regions, 4q31.22, 5q13.2, 9p24.3, 2p23.2, 16q21, 6q14.1, 7p22.3, 12p13 and Xq. Integrative copy number and gene expression profiling will delineate the drivers of chemotherapy resistance in patients undergoing standard platinum-based treatment of ovarian cancer. Future studies to validate these markers are necessary to apply this knowledge to biomarker-based clinical trials. Citation Format: Madhuri Koti, Robert J. Gooding, Paulo Nuin, Alexandria Haslehurst, Colleen Crane, Johanne Weberpals, Timothy Chids, Peter Bryson, Moyez Dharsee, Kenneth R. Evans, Harriet E. Feilotter, Paul C. Park, Jeremy A. Squire. Biomarkers of chemotherapy resistance in serous epithelial ovarian cancer identified by integrative genomic and transcriptomic analysis. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: From Concept to Clinic; Sep 18-21, 2013; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2013;19(19 Suppl):Abstract nr A53.

Molecular causes of transcriptional response: a Bayesian prior knowledge approach

The abundance of many transcripts changes significantly in response to a variety of molecular and environmental perturbations. A key question in this setting is as follows: what intermediate molecular perturbations gave rise to the observed transcriptional changes? Regulatory programs are not exclusively governed by transcriptional changes but also by protein abundance and post-translational modifications making direct causal inference from data difficult. However, biomedical research over the last decades has uncovered a plethora of causal signaling cascades that can be used to identify good candidates explaining a specific set of transcriptional changes. We take a Bayesian approach to integrate gene expression profiling with a causal graph of molecular interactions constructed from prior biological knowledge. In addition, we define the biological context of a specific interaction by the corresponding Medical Subject Headings terms. The Bayesian network can be queried to suggest upstream regulators that can be causally linked to the altered expression profile. Our approach will treat candidate regulators in the right biological context preferentially, enables hierarchical exploration of resulting hypotheses and takes the complete network of causal relationships into account to arrive at the best set of upstream regulators. We demonstrate the power of our method on distinct biological datasets, namely response to dexamethasone treatment, stem cell differentiation and a neuropathic pain model. In all cases relevant biological insights could be validated. Source code for the method is available upon request.

Unraveling regulatory programs for NF-kappaB, p53 and microRNAs in head and neck squamous cell carcinoma.

In head and neck squamous cell carcinoma (HNSCC), mutations of p53 usually coexist with aberrant activation of NF-kappaB (NF-κB), other transcription factors and microRNAs, which promote tumor pathogenesis. However, how these factors and microRNAs interact to globally modulate gene expression and mediate oncogenesis is not fully understood. We devised a novel bioinformatics method to uncover interactive relationships between transcription factors or microRNAs and genes. This approach is based on matrix decomposition modeling under the joint constraints of sparseness and regulator-target connectivity, and able to integrate gene expression profiling and binding data of regulators. We employed this method to infer the gene regulatory networks in HNSCC. We found that the majority of the predicted p53 targets overlapped with those for NF-κB, suggesting that the two transcription factors exert a concerted modulation on regulatory programs in tumor cells. We further investigated the interrelationships of p53 and NF-κB with five additional transcription factors, AP1, CEBPB, EGR1, SP1 and STAT3, and microRNAs mir21 and mir34ac. The resulting gene networks indicate that interactions among NF-κB, p53, and the two miRNAs likely regulate progression of HNSCC. We experimentally validated our findings by determining expression of the predicted NF-κB and p53 target genes by siRNA knock down, and by examining p53 binding activity on promoters of predicted target genes in the tumor cell lines. Our results elucidating the cross-regulations among NF-κB, p53, and microRNAs provide insights into the complex regulatory mechanisms underlying HNSCC, and shows an efficient approach to inferring gene regulatory programs in biological complex systems.

Integrated analyses of DNA copy number variations and gene expression in inflammatory breast cancer (IBC).

28 Background: Inflammatory breast cancer (IBC) is an aggressive form of BC poorly defined at the molecular level. We aim to perform genome-wide analyses of copy number variation and gene expression to identify genes reproducibly associated with survival in IBC patients. Methods: We performed concurrent genome-wide microarray analyses of copy number variable regions (CNVRs) and gene expression in IBC patients. Fifty-six pairs of breast cancer and normal specimens from IBC patients were analyzed by using Affymetrix SNP 6.0 and Affymetrix U133 plus 2.0 microarrays. To investigate genomic alterations, we used an Affymetrix Genome-Wide Human SNP 6.0 array containing 1.8 million SNP and CNV probes in total. The microarray data were imported into the Partek Genomic Suite to perform CNV analysis. Ingenuity Pathway Analysis was carried out to describe gene-gene interaction networks and canonical pathways. Cox regression model was used to evaluate the association between expression of these CNV-driven genes and survival outcomes. Results: The genomic landscape of frequent copy number variable regions (CNVRs) in at least 35% of samples was revealed. Further statistical analysis for genes located in the CNVRs identified 387 genes differentially expressed between tumor and normal tissues (p < 0.001). We demonstrated the concordance between copy number variations and gene expression changes by elevated Pearson correlation coefficients. Fisher’s exact test identified five canonical pathways that were significantly enriched among the 387 CNV-driven genes. Pathway analysis revealed two major dysregulated functions in IBC: survival regulation via Rac/PAK and PTEN/PI3K/AKT signaling pathway. Further validation using three independent cohorts demonstrated prediction of survival. Conclusions: We identified genes/pathways that may serve as prognostic markers for IBC patients by integrating gene expression profiles and copy number variations.

Integrative approaches for predicting protein function and prioritizing genes for complex phenotypes using protein interaction networks

With the rapid development of biotechnologies, many types of biological data including molecular networks are now available. However, to obtain a more complete understanding of a biological system, the integration of molecular networks with other data, such as molecular sequences, protein domains and gene expression profiles, is needed. A key to the use of networks in biological studies is the definition of similarity among proteins over the networks. Here, we review applications of similarity measures over networks with a special focus on the following four problems: (i) predicting protein functions, (ii) prioritizing genes related to a phenotype given a set of seed genes that have been shown to be related to the phenotype, (iii) prioritizing genes related to a phenotype by integrating gene expression profiles and networks and (iv) identification of false positives and false negatives from RNAi experiments. Diffusion kernels are demonstrated to give superior performance in all these tasks, leading to the suggestion that diffusion kernels should be the primary choice for a network similarity metric over other similarity measures such as direct neighbors and shortest path distance.

Mining of Network Markers for Brain Tumor from Transcriptome and Interactome Data

Glioblastoma multiforme (GBM: grade IV astrocytoma) is the most common but lethal form of brain cancer. The median survival time of GBM patients is only 15 months. Only a few predictive markers have been reported for prognosis and treatment. This study integrates gene expression and protein-protein interaction data to search for pathways that are differentially regulated between long-term and short-term survivors of GBM patients. A novel objective function for greedy search was introduced in search for 47 significantly and differentially expressed sub-networks (SDES) or pathways in a greedy fashion. The resultant putative pathways (involving 156 genes) were tested for enrichment of known GBM cancer genes as well as GO terms related to “biological process.” Integration of gene expression profiles of GBM patients with a PPI network improves the recall rate of known GBM driver genes and shows the better GO enrichment in comparison to the conventional gene-set approach that is based solely on the expression data. Keywords: Brain, cancer, GBM, gene, integration, pathway, PPI, protein, sub-network.