Integrative pathway analysis of genome-wide association studies and gene expression data in prostate cancer

iODA: An integrated tool for analysis of cancer pathway consistency from heterogeneous multi-omics data

The latest advances in the next generation sequencing technology have greatly facilitated the extensive research of genomics and transcriptomics, thereby promoting the decoding of carcinogenesis with unprecedented resolution. Considering the contribution of analyzing high-throughput multi-omics data to the exploration of cancer molecular mechanisms, an integrated tool for heterogeneous multi-omics data analysis (iODA) is proposed for the systems-level interpretation of multi-omics data, i.e., transcriptomic profiles (mRNA or miRNA expression data) and protein-DNA interactions (ChIP-Seq data). Considering the data heterogeneity, iODA can compare six statistical algorithms in differential analysis for the selected sample data and assist users in choosing the globally optimal one for dysfunctional mRNA or miRNA identification. Since molecular signatures are more consistent at the pathway level than at the gene level, the tool is able to enrich the identified dysfunctional molecules onto the KEGG pathways and extracted the consistent items as key components for further pathogenesis investigation. Compared with other tools, iODA is multi-functional for the systematic analysis of different level of omics data, and its analytical power was demonstrated through case studies of single and cross-level prostate cancer omics data. iODA is open source under GNU GPL and can be downloaded from http://www.sysbio.org.cn/iODA.

Comprehensive analysis of the lncRNA‑associated competing endogenous RNA network in breast cancer

Long noncoding RNAs (lncRNAs) have been confirmed to be potential prognostic markers in a variety of cancers and to interact with microRNAs (miRNAs) as competing endogenous RNAs (ceRNAs) to regulate target gene expression. However, the role of lncRNA‑mediated ceRNAs in breast cancer (BC) remains unclear. In the present study, a ceRNA network was generated to explore their role in BC. The expression profiles of mRNAs, miRNAs and lncRNAs in 1,109 BC tissues and 113 normal breast tissues were obtained from The Cancer Genome Atlas database (TCGA). A total of 3,198 differentially expressed (DE) mRNAs, 150 differentially DEmiRNAs and 1,043 DElncRNAs were identified between BC and normal tissues. A lncRNA‑miRNA‑mRNA network associated with BC was successfully constructed based on the combined data obtained from RNA databases, and comprised 97 lncRNA nodes, 24 miRNA nodes and 74 mRNA nodes. The biological functions of the 74 DEmRNAs were further investigated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The results demonstrated that the DEmRNAs were significantly enriched in two GO biological process categories; the main biological process enriched term was 'positive regulation of GTPase activity'. By KEGG analysis, four key enriched pathways were obtained, including the 'MAPK signaling pathway', the 'Ras signaling pathway', 'prostate cancer', and the 'FoxO signaling pathway'. Kaplan‑Meier survival analysis revealed that six DElncRNAs (INC AC112721.1, LINC00536, MIR7‑3HG, ADAMTS9‑AS1, AL356479.1 and LINC00466), nine DEmRNAs (KPNA2, RACGAP1, SHCBP1, ZNF367, NTRK2, ORS1, PTGS2, RASGRP1 and SFRP1) and two DEmiRNAs (hsa‑miR‑301b and hsa‑miR‑204) had significant effects on overall survival in BC. The present results demonstrated the aberrant expression of INC AC112721.1, AL356479.1, LINC00466 and MIR7‑3HG in BC, indicating their potential prognostic role in patients with BC.

Post-GWAS in prostate cancer: from genetic association to biological contribution

Genome-wide association studies (GWAS) have been successful in deciphering the genetic component of predisposition to many human complex diseases including prostate cancer. Germline variants identified by GWAS progressively unravelled the substantial knowledge gap concerning prostate cancer heritability. With the beginning of the post-GWAS era, more and more studies reveal that, in addition to their value as risk markers, germline variants can exert active roles in prostate oncogenesis. Consequently, current research efforts focus on exploring the biological mechanisms underlying specific susceptibility loci known as causal variants by applying novel and precise analytical methods to available GWAS data. Results obtained from these post-GWAS analyses have highlighted the potential of exploiting prostate cancer risk-associated germline variants to identify new gene networks and signalling pathways involved in prostate tumorigenesis. In this Review, we describe the molecular basis of several important prostate cancer-causal variants with an emphasis on using post-GWAS analysis to gain insight into cancer aetiology. In addition to discussing the current status of post-GWAS studies, we also summarize the main molecular mechanisms of potential causal variants at prostate cancer risk loci and explore the major challenges in moving from association to functional studies and their implication in clinical translation.

Integrating proteomic and phosphoproteomic data for pathway analysis in breast cancer

Background

As protein is the basic unit of cell function and biological pathway, shotgun proteomics, the large-scale analysis of proteins, is contributing greatly to our understanding of disease mechanisms. Proteomics study could detect the changes of both protein expression and modification. With the releases of large-scale cancer proteome studies, how to integrate acquired proteomic and phosphoproteomic data in more comprehensive pathway analysis becomes implemented, but remains challenging. Integrative pathway analysis at proteome level provides a systematic insight into the signaling network adaptations in the development of cancer.

Results

Here we integrated proteomic and phosphoproteomic data to perform pathway prioritization in breast cancer. We manually collected and curated breast cancer well-known related pathways from the literature as target pathways (TPs) or positive control in method evaluation. Three different strategies including Hypergeometric test based over-representation analysis, Kolmogorov-Smirnov (K-S) test based gene set analysis and topology-based pathway analysis, were applied and evaluated in integrating protein expression and phosphorylation. In comparison, we also assessed the ranking performance of the strategy using information of protein expression or protein phosphorylation individually. Target pathways were ranked more top with the data integration than using the information from proteomic or phosphoproteomic data individually. In the comparisons of pathway analysis strategies, topology-based method outperformed than the others. The subtypes of breast cancer, which consist of Luminal A, Luminal B, Basal and HER2-enriched, vary greatly in prognosis and require distinct treatment. Therefore we applied topology-based pathway analysis with integrating protein expression and phosphorylation profiles on four subtypes of breast cancer. The results showed that TPs were enriched in all subtypes but their ranks were significantly different among the subtypes. For instance, p53 pathway ranked top in the Basal-like breast cancer subtype, but not in HER2-enriched type. The rank of Focal adhesion pathway was more top in HER2- subtypes than in HER2+ subtypes. The results were consistent with some previous researches.

Conclusions

The results demonstrate that the network topology-based method is more powerful by integrating proteomic and phosphoproteomic in pathway analysis of proteomics study. This integrative strategy can also be used to rank the specific pathways for the disease subtypes.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0646-y) contains supplementary material, which is available to authorized users.

Association study of genetic variation in the autophagy lysosome pathway genes and risk of eight kinds of cancers

The autophagy lysosome pathway is essential to maintain cell viability and homeostasis in response to many stressful environments, which is reported to play a vital role in cancer development and therapy. However, the association of genetic alterations of this pathway with risk of cancer remains unclear. Based on genome-wide association study data of eight kinds of cancers, we used an adaptive rank truncated product approach to perform a pathway-level and gene-level analysis, and used a logistic model to calculate SNP-level associations to examine whether an altered autophagy lysosome pathway contributes to cancer susceptibility. Among eight kinds of cancers, four of them showed significant statistics in the pathway-level analysis, including breast cancer (p = 0.00705), gastric cancer (p = 0.00880), lung cancer (p = 0.000100) and renal cell carcinoma (p = 0.00190). We also found that some autophagy lysosome genes had signals of association with cancer risk. Our results demonstrated that inherited genetic variants in the overall autophagy lysosome pathway and certain associated genes might contribute to cancer susceptibility, which warrant further evaluation in other independent datasets.

A Review of Pathway-Based Analysis Tools That Visualize Genetic Variants

Pathway analysis is a powerful method for data analysis in genomics, most often applied to gene expression analysis. It is also promising for single-nucleotide polymorphism (SNP) data analysis, such as genome-wide association study data, because it allows the interpretation of variants with respect to the biological processes in which the affected genes and proteins are involved. Such analyses support an interactive evaluation of the possible effects of variations on function, regulation or interaction of gene products. Current pathway analysis software often does not support data visualization of variants in pathways as an alternate method to interpret genetic association results, and specific statistical methods for pathway analysis of SNP data are not combined with these visualization features. In this review, we first describe the visualization options of the tools that were identified by a literature review, in order to provide insight for improvements in this developing field. Tool evaluation was performed using a computational epistatic dataset of gene–gene interactions for obesity risk. Next, we report the necessity to include in these tools statistical methods designed for the pathway-based analysis with SNP data, expressly aiming to define features for more comprehensive pathway-based analysis tools. We conclude by recognizing that pathway analysis of genetic variations data requires a sophisticated combination of the most useful and informative visual aspects of the various tools evaluated.

Genomics of human pulmonary tuberculosis: from genes to pathways

Purpose of review

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains a major public health threat globally. Several lines of evidence support a role for host genetic factors in resistance/susceptibility to TB disease and MTB infection. However, results across candidate gene and genome-wide association studies (GWAS) are largely inconsistent, so a cohesive genetic model underlying TB risk has not emerged.

Recent Findings

Despite the difficulties in identifying consistent genetic associations, genetic studies of TB and MTB infection have revealed a few well-documented loci. These well validated genes are presented in this review, but there remains a large gap in how these genes translate into better understanding of TB. To address this, we present a pathway based extension of standard association analyses, seeding the results with the best validated genes from candidate gene and GWAS studies.

Summary

Several pathways were significantly enriched using pathway analyses that may help to explain population patterns of TB risk. In conclusion, we advocate for novel approaches to the study of host genetic analysis of TB that extend traditional association approaches.

AIM1 is an actin-binding protein that suppresses cell migration and micrometastatic dissemination

A defining hallmark of primary and metastatic cancers is the migration and invasion of malignant cells. These invasive properties involve altered dynamics of the cytoskeleton and one of its major structural components β-actin. Here we identify AIM1 (absent in melanoma 1) as an actin-binding protein that suppresses pro-invasive properties in benign prostate epithelium. Depletion of AIM1 in prostate epithelial cells increases cytoskeletal remodeling, intracellular traction forces, cell migration and invasion, and anchorage-independent growth. In addition, decreased AIM1 expression results in increased metastatic dissemination in vivo. AIM1 strongly associates with the actin cytoskeleton in prostate epithelial cells in normal tissues, but not in prostate cancers. In addition to a mislocalization of AIM1 from the actin cytoskeleton in invasive cancers, advanced prostate cancers often harbor AIM1 deletion and reduced expression. These findings implicate AIM1 as a key suppressor of invasive phenotypes that becomes dysregulated in primary and metastatic prostate cancer.

Invasion of malignant cells involves changes in cytoskeleton dynamics. Here the authors identify absent in melanoma 1 as an actin binding protein and show that it regulates cytoskeletal remodeling and cell migration in prostate epithelial cells, acting as a metastatic suppressor in cancer cells.

An Integrative Genomics Approach for Associating Genome-Wide Association Studies Information With Localized and Metastatic Prostate Cancer Phenotypes

High-throughput genotyping has enabled discovery of genetic variants associated with an increased risk of developing prostate cancer using genome-wide association studies (GWAS). The goal of this study was to associate GWAS information of patients with primary organ–confined and metastatic prostate cancer using gene expression data and to identify molecular networks and biological pathways enriched for genetic susceptibility variants involved in the 2 disease states. The analysis revealed gene signatures for the 2 disease states and a gene signature distinguishing the 2 patient groups. In addition, the analysis revealed molecular networks and biological pathways enriched for genetic susceptibility variants. The discovered pathways include the androgen, apoptosis, and insulinlike growth factor signaling pathways. This analysis established putative functional bridges between GWAS discoveries and the biological pathways involved in primary organ–confined and metastatic prostate cancer.

Analysis of the interplay between methylation and expression reveals its potential role in cancer aetiology

With ongoing developments in technology, changes in DNA methylation levels have become prevalent to study cancer biology. Previous studies report that DNA methylation affects gene expression in a direct manner, most probably by blocking gene regulatory regions. In this study, we have studied the interplay between methylation and expression to improve our knowledge of cancer aetiology. For this purpose, we have investigated which genomic regions are of higher importance; hence, first exon, 5'UTR and 200 bp near the transcription start sites are proposed as being more crucial compared to other genomic regions. Furthermore, we have searched for a valid methylation level change threshold, and as a result, 25 % methylation change in previously determined genomic regions showed the highest inverse correlation with expression data. As a final step, we have examined the commonly affected genes and pathways by integrating methylation and expression information. Remarkably, the GPR115 gene and ErbB signalling pathway were found to be significantly altered for all cancer types in our analysis. Overall, combining methylation and expression information and identifying commonly affected genes and pathways in a variety of cancer types revealed new insights of cancer disease mechanisms. Moreover, compared to previous methylation-based studies, we have identified more important genomic regions and have defined a methylation change threshold level in order to obtain more reliable results. In addition to the novel analysis framework that involves the analysis of four different cancer types, our study exposes essential information regarding the contribution of methylation changes and its impact on cancer disease biology, which may facilitate the identification of new drug targets.

Meta-dimensional data integration identifies critical pathways for susceptibility, tumorigenesis and progression of endometrial cancer

Endometrial Cancer (EC) is one of the most common female cancers. Genome-wide association studies (GWAS) have been investigated to identify genetic polymorphisms that are predictive of EC risks. Here we utilized a meta-dimensional integrative approach to seek genetically susceptible pathways that may be associated with tumorigenesis and progression of EC. We analyzed GWAS data obtained from Connecticut Endometrial Cancer Study (CECS) and identified the top 20 EC susceptible pathways. To further verify the significance of top 20 EC susceptible pathways, we conducted pathway-level multi-omics analyses using EC exome-Seq, RNA-Seq and survival data, all based on The Cancer Genome Atlas (TCGA) samples. We measured the overall consistent rankings of these pathways in all four data types. Some well-studied pathways, such as p53 signaling and cell cycle pathways, show consistently high rankings across different analyses. Additionally, other cell signaling pathways (e.g. IGF-1/mTOR, rac-1 and IL-5 pathway), genetic information processing pathway (e.g. homologous recombination) and metabolism pathway (e.g. sphingolipid metabolism) are also highly associated with EC risks, diagnosis and prognosis. In conclusion, the meta-dimensional integration of EC cohorts has suggested some common pathways that may be associated from predisposition, tumorigenesis to progression.

Proteomic Upregulation of Fatty Acid Synthase and Fatty Acid Binding Protein 5 and Identification of Cancer- and Race-Specific Pathway Associations in Human Prostate Cancer Tissues

Protein profiling studies of prostate cancer have been widely used to characterize molecular differences between diseased and non-diseased tissues. When combined with pathway analysis, profiling approaches are able to identify molecular mechanisms of prostate cancer, group patients by cancer subtype, and predict prognosis. This strategy can also be implemented to study prostate cancer in very specific populations, such as African Americans who have higher rates of prostate cancer incidence and mortality than other racial groups in the United States. In this study, age-, stage-, and Gleason score-matched prostate tumor specimen from African American and Caucasian American men, along with non-malignant adjacent prostate tissue from these same patients, were compared. Protein expression changes and altered pathway associations were identified in prostate cancer generally and in African American prostate cancer specifically. In comparing tumor to non-malignant samples, 45 proteins were significantly cancer-associated and 3 proteins were significantly downregulated in tumor samples. Notably, fatty acid synthase (FASN) and epidermal fatty acid-binding protein (FABP5) were upregulated in human prostate cancer tissues, consistent with their known functions in prostate cancer progression. Aldehyde dehydrogenase family 1 member A3 (ALDH1A3) was also upregulated in tumor samples. The Metastasis Associated Protein 3 (MTA3) pathway was significantly enriched in tumor samples compared to non-malignant samples. While the current experiment was unable to detect statistically significant differences in protein expression between African American and Caucasian American samples, differences in overrepresentation and pathway enrichment were found. Structural components (Cytoskeletal Proteins and Extracellular Matrix Protein protein classes, and Biological Adhesion Gene Ontology (GO) annotation) were overrepresented in African American but not Caucasian American tumors. Additionally, 5 pathways were enriched in African American prostate tumors: the Small Cell Lung Cancer, Platelet-Amyloid Precursor Protein, Agrin, Neuroactive Ligand-Receptor Interaction, and Intrinsic pathways. The protein components of these pathways were either basement membrane proteins or coagulation proteins.

Top associated SNPs in prostate cancer are significantly enriched in cis-expression quantitative trait loci and at transcription factor binding sites

While genome-wide association studies (GWAS) have revealed thousands of disease risk single nucleotide polymorphisms (SNPs), their functions remain largely unknown. Recent studies have suggested the regulatory roles of GWAS risk variants in several common diseases; however, the complex regulatory structure in prostate cancer is unclear.

We investigated the potential regulatory roles of risk variants in two prostate cancer GWAS datasets by their interactions with expression quantitative trait loci (eQTL) and/or transcription factor binding sites (TFBSs) in three populations.

Our results indicated that the moderately associated GWAS SNPs were significantly enriched with cis-eQTLs and TFBSs in Caucasians (CEU), but not in African Americans (AA) or Japanese (JPT); this was also observed in an independent pan-cancer related SNPs from the GWAS Catalog. We found that the eQTL enrichment in the CEU population was tissue-specific to eQTLs from CEU lymphoblastoid cell lines. Importantly, we pinpointed two SNPs, rs2861405 and rs4766642, by overlapping results from cis-eQTL and TFBS as applied to the CEU data.

These results suggested that prostate cancer associated SNPs and pan-cancer associated SNPs are likely to play regulatory roles in CEU. However, the negative enrichment results in AA or JPT and the potential mechanisms remain to be elucidated in additional samples.

Identification of shared and unique susceptibility pathways among cancers of the lung, breast, and prostate from genome-wide association studies and tissue-specific protein interactions

Results from genome-wide association studies (GWAS) have indicated that strong single-gene effects are the exception, not the rule, for most diseases. We assessed the joint effects of germline genetic variations through a pathway-based approach that considers the tissue-specific contexts of GWAS findings. From GWAS meta-analyses of lung cancer (12 160 cases/16 838 controls), breast cancer (15 748 cases/18 084 controls) and prostate cancer (14 160 cases/12 724 controls) in individuals of European ancestry, we determined the tissue-specific interaction networks of proteins expressed from genes that are likely to be affected by disease-associated variants. Reactome pathways exhibiting enrichment of proteins from each network were compared across the cancers. Our results show that pathways associated with all three cancers tend to be broad cellular processes required for growth and survival. Significant examples include the nerve growth factor (P = 7.86 × 10(-33)), epidermal growth factor (P = 1.18 × 10(-31)) and fibroblast growth factor (P = 2.47 × 10(-31)) signaling pathways. However, within these shared pathways, the genes that influence risk largely differ by cancer. Pathways found to be unique for a single cancer focus on more specific cellular functions, such as interleukin signaling in lung cancer (P = 1.69 × 10(-15)), apoptosis initiation by Bad in breast cancer (P = 3.14 × 10(-9)) and cellular responses to hypoxia in prostate cancer (P = 2.14 × 10(-9)). We present the largest comparative cross-cancer pathway analysis of GWAS to date. Our approach can also be applied to the study of inherited mechanisms underlying risk across multiple diseases in general.

Differentially Expressed Genes and Signature Pathways of Human Prostate Cancer

Genomic technologies including microarrays and next-generation sequencing have enabled the generation of molecular signatures of prostate cancer. Lists of differentially expressed genes between malignant and non-malignant states are thought to be fertile sources of putative prostate cancer biomarkers. However such lists of differentially expressed genes can be highly variable for multiple reasons. As such, looking at differential expression in the context of gene sets and pathways has been more robust. Using next-generation genome sequencing data from The Cancer Genome Atlas, differential gene expression between age- and stage- matched human prostate tumors and non-malignant samples was assessed and used to craft a pathway signature of prostate cancer. Up- and down-regulated genes were assigned to pathways composed of curated groups of related genes from multiple databases. The significance of these pathways was then evaluated according to the number of differentially expressed genes found in the pathway and their position within the pathway using Gene Set Enrichment Analysis and Signaling Pathway Impact Analysis. The “transforming growth factor-beta signaling” and “Ran regulation of mitotic spindle formation” pathways were strongly associated with prostate cancer. Several other significant pathways confirm reported findings from microarray data that suggest actin cytoskeleton regulation, cell cycle, mitogen-activated protein kinase signaling, and calcium signaling are also altered in prostate cancer. Thus we have demonstrated feasibility of pathway analysis and identified an underexplored area (Ran) for investigation in prostate cancer pathogenesis.

Mutated Pathways as a Guide to Adjuvant Therapy Treatments for Breast Cancer

Adjuvant therapy following breast cancer surgery generally consists of either a course of chemotherapy, if the cancer lacks hormone receptors, or a course of hormonal therapy, otherwise. Here, we report a correlation between adjuvant strategy and mutated pathway patterns. In particular, we find that for breast cancer patients, pathways enriched in nonsynonymous mutations in the chemotherapy group are distinct from those of the hormonal therapy group. We apply a recently developed method that identifies collaborative pathway groups for hormone and chemotherapy patients. A collaborative group of pathways is one in which each member is altered in the same-generally large-number of samples. In particular, we find the following: (i) a chemotherapy group consisting of three pathways and a hormone therapy group consisting of 20, the members of the two groups being mutually exclusive; (ii) each group is highly enriched in breast cancer drivers; and (iii) the pathway groups are correlates of subtype-based therapeutic recommendations. These results suggest that patient profiling using these pathway groups can potentially enable the development of personalized treatment plans that may be more accurate and specific than those currently available.

Analysis of the differential secretome of nasopharyngeal carcinoma cell lines CNE-2R and CNE-2

Radioresistance is the major cause of poor prognosis in nasopharyngeal carcinoma (NPC). To identify and characterize the secretome associated with NPC radioresistance, we compared the conditioned serum-free medium of radioresistant CNE-2R cells with that of the parental radiosensitive CNE-2 cells using isobaric tags for relative and absolute quantitation (iTRAQ) with liquid chromatography-electrospray tandem mass spectrometry (LC-ESI-MS/MS) quantitative proteomics. Before proceeding to quantitative proteomics, we investigated the survival curves of CNE-2R and CNE-2 cells by colony formation assay, and the CNE-2R survival curves were significantly higher than those for CNE-2. In total, 3,581 proteins were identified in the quantitative proteomics experiments, and 40 proteins exhibited significant differences between the CNE-2R and CNE-2 cells. Twenty-six of the 40 proteins were secreted by classical, non-classical, or exosomal secretion pathways. To verify the reliability of iTRAQ quantitative proteomics, we applied western blotting (WB) to study the secretory protein expression of fibrillin-2, CD166, sulfhydryl oxidase 1 and cofilin-2, which are involved in cell adhesion, migration and invasion. The WB results showed that fibrillin-2 (p=0.017) and sulfhydryl oxidase 1 (p=0.000) were highly expressed in the CNE-2 cells, while CD166 (p=0.012) and cofilin-2 (p=0.003) were highly expressed in the CNE-2R cells, which was in accordance with iTRAQ quantitative proteomics. Finally, a phenotypic subset of CD166-positive NPC cells was verified by immunocytochemistry. In summary, we defined a collection of secretory proteins that may be relevant to the radioresistance in NPC cells, and we determined that CD166, which is widely used as a positive marker of cancer stem cells, is expressed in NPC cells.

Transcriptome sequencing and genome-wide association analyses reveal lysosomal function and actin cytoskeleton remodeling in schizophrenia and bipolar disorder

Schizophrenia (SCZ) and bipolar disorder (BPD) are severe mental disorders with high heritability. Clinicians have long noticed the similarities of clinic symptoms between these disorders. In recent years, accumulating evidence indicates some shared genetic liabilities. However, what is shared remains elusive. In this study, we conducted whole transcriptome analysis of post-mortem brain tissues (cingulate cortex) from SCZ, BPD and control subjects, and identified differentially expressed genes in these disorders. We found 105 and 153 genes differentially expressed in SCZ and BPD, respectively. By comparing the t-test scores, we found that many of the genes differentially expressed in SCZ and BPD are concordant in their expression level (q⩽0.01, 53 genes; q⩽0.05, 213 genes; q⩽0.1, 885 genes). Using genome-wide association data from the Psychiatric Genomics Consortium, we found that these differentially and concordantly expressed genes were enriched in association signals for both SCZ (P<10(-7)) and BPD (P=0.029). To our knowledge, this is the first time that a substantially large number of genes show concordant expression and association for both SCZ and BPD. Pathway analyses of these genes indicated that they are involved in the lysosome, Fc gamma receptor-mediated phagocytosis, regulation of actin cytoskeleton pathways, along with several cancer pathways. Functional analyses of these genes revealed an interconnected pathway network centered on lysosomal function and the regulation of actin cytoskeleton. These pathways and their interacting network were principally confirmed by an independent transcriptome sequencing data set of the hippocampus. Dysregulation of lysosomal function and cytoskeleton remodeling has direct impacts on endocytosis, phagocytosis, exocytosis, vesicle trafficking, neuronal maturation and migration, neurite outgrowth and synaptic density and plasticity, and different aspects of these processes have been implicated in SCZ and BPD.

Genetic variants in the inositol phosphate metabolism pathway and risk of different types of cancer

Members of the inositol phosphate metabolism pathway regulate cell proliferation, migration and phosphatidylinositol-3-kinase (PI3K)/Akt signaling, and are frequently dysregulated in cancer. Whether germline genetic variants in inositol phosphate metabolism pathway are associated with cancer risk remains to be clarified. We examined the association between inositol phosphate metabolism pathway genes and risk of eight types of cancer using data from genome-wide association studies. Logistic regression models were applied to evaluate SNP-level associations. Gene- and pathway-based associations were tested using the permutation-based adaptive rank-truncated product method. The overall inositol phosphate metabolism pathway was significantly associated with risk of lung cancer (P = 2.00 × 10⁻⁴), esophageal squamous cell carcinoma (P = 5.70 × 10⁻³), gastric cancer (P = 3.03 × 10⁻²) and renal cell carcinoma (P = 1.26 × 10⁻²), but not with pancreatic cancer (P = 1.40 × 10⁻¹), breast cancer (P = 3.03 × 10⁻¹), prostate cancer (P = 4.51 × 10⁻¹), and bladder cancer (P = 6.30 × 10⁻¹). Our results provide a link between inherited variation in the overall inositol phosphate metabolism pathway and several individual genes and cancer. Further studies will be needed to validate these positive findings, and to explore its mechanisms.

Integrative Pathway Analysis Using Graph-Based Learning with Applications to TCGA Colon and Ovarian Data

Recent method development has included multi-dimensional genomic data algorithms because such methods have more accurately predicted clinical phenotypes related to disease. This study is the first to conduct an integrative genomic pathway-based analysis with a graph-based learning algorithm. The methodology of this analysis, graph-based semi-supervised learning, detects pathways that improve prediction of a dichotomous variable, which in this study is cancer stage. This analysis integrates genome-level gene expression, methylation, and single nucleotide polymorphism (SNP) data in serous cystadenocarcinoma (OV) and colon adenocarcinoma (COAD). The top 10 ranked predictive pathways in COAD and OV were biologically relevant to their respective cancer stages and significantly enhanced prediction accuracy and area under the ROC curve (AUC) when compared to single data-type analyses. This method is an effective way to simultaneously predict binary clinical phenotypes and discover their biological mechanisms.

Integrative genomic analysis for the discovery of biomarkers in prostate cancer

Genome-wide association studies (GWAS) have achieved great success in identifying single nucleotide polymorphisms (SNPs, herein called genetic variants) and genes associated with risk of developing prostate cancer. However, GWAS do not typically link the genetic variants to the disease state or inform the broader context in which the genetic variants operate. Here, we present a novel integrative genomics approach that combines GWAS information with gene expression data to infer the causal association between gene expression and the disease and to identify the network states and biological pathways enriched for genetic variants. We identified gene regulatory networks and biological pathways enriched for genetic variants, including the prostate cancer, IGF-1, JAK2, androgen, and prolactin signaling pathways. The integration of GWAS information with gene expression data provides insights about the broader context in which genetic variants associated with an increased risk of developing prostate cancer operate.

Network.assisted analysis to prioritize GWAS results: principles, methods and perspectives

Genome-wide association studies (GWAS) have rapidly become a powerful tool in genetic studies of complex diseases and traits. Traditionally, single marker-based tests have been used prevalently in GWAS and have uncovered tens of thousands of disease-associated SNPs. Network-assisted analysis (NAA) of GWAS data is an emerging area in which network-related approaches are developed and utilized to perform advanced analyses of GWAS data in order to study various human diseases or traits. Progress has been made in both methodology development and applications of NAA in GWAS data, and it has already been demonstrated that NAA results may enhance our interpretation and prioritization of candidate genes and markers. Inspired by the strong interest in and high demand for advanced GWAS data analysis, in this review article, we discuss the methodologies and strategies that have been reported for the NAA of GWAS data. Many NAA approaches search for subnetworks and assess the combined effects of multiple genes participating in the resultant subnetworks through a gene set analysis. With no restriction to pre-defined canonical pathways, NAA has the advantage of defining subnetworks with the guidance of the GWAS data under investigation. In addition, some NAA methods prioritize genes from GWAS data based on their interconnections in the reference network. Here, we summarize NAA applications to various diseases and discuss the available options and potential caveats related to their practical usage. Additionally, we provide perspectives regarding this rapidly growing research area.

Integrative gene set analysis of multi-platform data with sample heterogeneity

MOTIVATION

Gene set analysis is a popular method for large-scale genomic studies. Because genes that have common biological features are analyzed jointly, gene set analysis often achieves better power and generates more biologically informative results. With the advancement of technologies, genomic studies with multi-platform data have become increasingly common. Several strategies have been proposed that integrate genomic data from multiple platforms to perform gene set analysis. To evaluate the performances of existing integrative gene set methods under various scenarios, we conduct a comparative simulation analysis based on The Cancer Genome Atlas breast cancer dataset.

RESULTS

We find that existing methods for gene set analysis are less effective when sample heterogeneity exists. To address this issue, we develop three methods for multi-platform genomic data with heterogeneity: two non-parametric methods, multi-platform Mann-Whitney statistics and multi-platform outlier robust T-statistics, and a parametric method, multi-platform likelihood ratio statistics. Using simulations, we show that the proposed multi-platform Mann-Whitney statistics method has higher power for heterogeneous samples and comparable performance for homogeneous samples when compared with the existing methods. Our real data applications to two datasets of The Cancer Genome Atlas also suggest that the proposed methods are able to identify novel pathways that are missed by other strategies.

AVAILABILITY AND IMPLEMENTATION

http://www4.stat.ncsu.edu/∼jytzeng/Software/Multiplatform_gene_set_analysis/

Pathway-based analysis of genomic variation data

A holy grail of genetics is to decipher the mapping from genotype to phenotype. Recent advances in sequencing technologies allow the efficient genotyping of thousands of individuals carrying a particular phenotype in an effort to reveal its genetic determinants. However, the interpretation of these data entails tackling significant statistical and computational problems that stem from the complexity of human phenotypes and the huge genotypic search space. Recently, an alternative pathway-level analysis has been employed to combat these problems. In this review we discuss these developments, describe the challenges involved and outline possible solutions and future directions for improvement.

iPEAP: integrating multiple omics and genetic data for pathway enrichment analysis

A challenge in biodata analysis is to understand the underlying phenomena among many interactions in signaling pathways. Such study is formulated as the pathway enrichment analysis, which identifies relevant pathways functional enriched in high-throughput data. The question faced here is how to analyze different data types in a unified and integrative way by characterizing pathways that these data simultaneously reveal. To this end, we developed integrative Pathway Enrichment Analysis Platform, iPEAP, which handles transcriptomics, proteomics, metabolomics and GWAS data under a unified aggregation schema. iPEAP emphasizes on the ability to aggregate various pathway enrichment results generated in different high-throughput experiments, as well as the quantitative measurements of different ranking results, thus providing the first benchmark platform for integration, comparison and evaluation of multiple types of data and enrichment methods.

AVAILABILITY AND IMPLEMENTATION

iPEAP is freely available at http://www.tongji.edu.cn/∼qiliu/ipeap.html.

Genome-wide association studies on prostate cancer: the end or the beginning?

Prostate cancer (PCa) is the second most frequently diagnosed malignancy in men. Genome-wide association studies (GWAS) has been highly successful in discovering susceptibility loci for prostate cancer. Currently, more than twenty GWAS have identified more than fifty common variants associated with susceptibility with PCa. Yet with the increase in loci, voices from the scientific society are calling for more. In this review, we summarize current findings, discuss the common problems troubling current studies and shed light upon possible breakthroughs in the future. GWAS is the beginning of something wonderful. Although we are quite near the end of the beginning, post-GWAS studies are just taking off and future studies are needed extensively. It is believed that in the future GWAS information will be helpful to build a comprehensive system intergraded with PCa prevention, diagnosis, molecular classification, personalized therapy.

Advances in systems biology: computational algorithms and applications

The 2012 International Conference on Intelligent Biology and Medicine (ICIBM 2012) was held on April 22-24, 2012 in Nashville, Tennessee, USA. The conference featured six technical sessions, one tutorial session, one workshop, and 3 keynote presentations that covered state-of-the-art research activities in genomics, systems biology, and intelligent computing. In addition to a major emphasis on the next generation sequencing (NGS)-driven informatics, ICIBM 2012 aligned significant interests in systems biology and its applications in medicine. We highlight in this editorial the selected papers from the meeting that address the developments of novel algorithms and applications in systems biology.