Discovery of pathway biomarkers from coupled proteomics and systems biology methods

PAGER-scFGA: unveiling cell functions and molecular mechanisms in cell trajectories through single-cell functional genomics analysis

Background: Understanding how cells and tissues respond to stress factors and perturbations during disease processes is crucial for developing effective prevention, diagnosis, and treatment strategies. Single-cell RNA sequencing (scRNA-seq) enables high-resolution identification of cells and exploration of cell heterogeneity, shedding light on cell differentiation/maturation and functional differences. Recent advancements in multimodal sequencing technologies have focused on improving access to cell-specific subgroups for functional genomics analysis. To facilitate the functional annotation of cell groups and characterization of molecular mechanisms underlying cell trajectories, we introduce the Pathways, Annotated Gene Lists, and Gene Signatures Electronic Repository for Single-Cell Functional Genomics Analysis (PAGER-scFGA). Results: We have developed PAGER-scFGA, which integrates cell functional annotations and gene-set enrichment analysis into popular single-cell analysis pipelines such as Scanpy. Using differentially expressed genes (DEGs) from pairwise cell clusters, PAGER-scFGA infers cell functions through the enrichment of potential cell-marker genesets. Moreover, PAGER-scFGA provides pathways, annotated gene lists, and gene signatures (PAGs) enriched in specific cell subsets with tissue compositions and continuous transitions along cell trajectories. Additionally, PAGER-scFGA enables the construction of a gene subcellular map based on DEGs and allows examination of the gene functional compartments (GFCs) underlying cell maturation/differentiation. In a real-world case study of mouse natural killer (mNK) cells, PAGER-scFGA revealed two major stages of natural killer (NK) cells and three trajectories from the precursor stage to NK T-like mature stage within blood, spleen, and bone marrow tissues. As the trajectories progress to later stages, the DEGs exhibit greater divergence and variability. However, the DEGs in different trajectories still interact within a network during NK cell maturation. Notably, PAGER-scFGA unveiled cell cytotoxicity, exocytosis, and the response to interleukin (IL) signaling pathways and associated network models during the progression from precursor NK cells to mature NK cells. Conclusion: PAGER-scFGA enables in-depth exploration of functional insights and presents a comprehensive knowledge map of gene networks and GFCs, which can be utilized for future studies and hypothesis generation. It is expected to become an indispensable tool for inferring cell functions and detecting molecular mechanisms within cell trajectories in single-cell studies. The web app (accessible at https://au-singlecell.streamlit.app/) is publicly available.

How cancer hijacks the body's homeostasis through the neuroendocrine system

During oncogenesis, cancer not only escapes the body's regulatory mechanisms, but also gains the ability to affect local and systemic homeostasis. Specifically, tumors produce cytokines, immune mediators, classical neurotransmitters, hypothalamic and pituitary hormones, biogenic amines, melatonin, and glucocorticoids, as demonstrated in human and animal models of cancer. The tumor, through the release of these neurohormonal and immune mediators, can control the main neuroendocrine centers such as the hypothalamus, pituitary, adrenals, and thyroid to modulate body homeostasis through central regulatory axes. We hypothesize that the tumor-derived catecholamines, serotonin, melatonin, neuropeptides, and other neurotransmitters can affect body and brain functions. Bidirectional communication between local autonomic and sensory nerves and the tumor, with putative effects on the brain, is also envisioned. Overall, we propose that cancers can take control of the central neuroendocrine and immune systems to reset the body homeostasis in a mode favoring its expansion at the expense of the host.

WINNER: A network biology tool for biomolecular characterization and prioritization

Background and contribution

In network biology, molecular functions can be characterized by network-based inference, or “guilt-by-associations.” PageRank-like tools have been applied in the study of biomolecular interaction networks to obtain further the relative significance of all molecules in the network. However, there is a great deal of inherent noise in widely accessible data sets for gene-to-gene associations or protein-protein interactions. How to develop robust tests to expand, filter, and rank molecular entities in disease-specific networks remains an ad hoc data analysis process.

Results

We describe a new biomolecular characterization and prioritization tool called Weighted In-Network Node Expansion and Ranking (WINNER). It takes the input of any molecular interaction network data and generates an optionally expanded network with all the nodes ranked according to their relevance to one another in the network. To help users assess the robustness of results, WINNER provides two different types of statistics. The first type is a node-expansion p-value, which helps evaluate the statistical significance of adding “non-seed” molecules to the original biomolecular interaction network consisting of “seed” molecules and molecular interactions. The second type is a node-ranking p-value, which helps evaluate the relative statistical significance of the contribution of each node to the overall network architecture. We validated the robustness of WINNER in ranking top molecules by spiking noises in several network permutation experiments. We have found that node degree–preservation randomization of the gene network produced normally distributed ranking scores, which outperform those made with other gene network randomization techniques. Furthermore, we validated that a more significant proportion of the WINNER-ranked genes was associated with disease biology than existing methods such as PageRank. We demonstrated the performance of WINNER with a few case studies, including Alzheimer's disease, breast cancer, myocardial infarctions, and Triple negative breast cancer (TNBC). In all these case studies, the expanded and top-ranked genes identified by WINNER reveal disease biology more significantly than those identified by other gene prioritizing software tools, including Ingenuity Pathway Analysis (IPA) and DiAMOND.

Conclusion

WINNER ranking strongly correlates to other ranking methods when the network covers sufficient node and edge information, indicating a high network quality. WINNER users can use this new tool to robustly evaluate a list of candidate genes, proteins, or metabolites produced from high-throughput biology experiments, as long as there is available gene/protein/metabolic network information.

DEMA: a distance-bounded energy-field minimization algorithm to model and layout biomolecular networks with quantitative features

SUMMARY

In biology, graph layout algorithms can reveal comprehensive biological contexts by visually positioning graph nodes in their relevant neighborhoods. A layout software algorithm/engine commonly takes a set of nodes and edges and produces layout coordinates of nodes according to edge constraints. However, current layout engines normally do not consider node, edge or node-set properties during layout and only curate these properties after the layout is created. Here, we propose a new layout algorithm, distance-bounded energy-field minimization algorithm (DEMA), to natively consider various biological factors, i.e., the strength of gene-to-gene association, the gene's relative contribution weight and the functional groups of genes, to enhance the interpretation of complex network graphs. In DEMA, we introduce a parameterized energy model where nodes are repelled by the network topology and attracted by a few biological factors, i.e., interaction coefficient, effect coefficient and fold change of gene expression. We generalize these factors as gene weights, protein-protein interaction weights, gene-to-gene correlations and the gene set annotations-four parameterized functional properties used in DEMA. Moreover, DEMA considers further attraction/repulsion/grouping coefficient to enable different preferences in generating network views. Applying DEMA, we performed two case studies using genetic data in autism spectrum disorder and Alzheimer's disease, respectively, for gene candidate discovery. Furthermore, we implement our algorithm as a plugin to Cytoscape, an open-source software platform for visualizing networks; hence, it is convenient. Our software and demo can be freely accessed at http://discovery.informatics.uab.edu/dema.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PAGER Web APP: An Interactive, Online Gene Set and Network Interpretation Tool for Functional Genomics

Functional genomics studies have helped researchers annotate differentially expressed gene lists, extract gene expression signatures, and identify biological pathways from omics profiling experiments conducted on biological samples. The current geneset, network, and pathway analysis (GNPA) web servers, e.g., DAVID, EnrichR, WebGestaltR, or PAGER, do not allow automated integrative functional genomic downstream analysis. In this study, we developed a new web-based interactive application, “PAGER Web APP”, which supports online R scripting of integrative GNPA. In a case study of melanoma drug resistance, we showed that the new PAGER Web APP enabled us to discover highly relevant pathways and network modules, leading to novel biological insights. We also compared PAGER Web APP’s pathway analysis results retrieved among PAGER, EnrichR, and WebGestaltR to show its advantages in integrative GNPA. The interactive online web APP is publicly accessible from the link, https://aimed-lab.shinyapps.io/PAGERwebapp/.

Behavioral and Gene Expression Analysis of Stxbp6-Knockout Mice

Since the first report that Stxbp6, a brain-enriched protein, regulates the assembly of soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complexes, little has been discovered about its functions over the past two decades. To determine the effects of Stxbp6 loss on nervous-system-associated phenotypes and underlying mechanisms, we constructed a global Stxbp6-knockout mouse. We found that Stxbp6-null mice survive normally, with normal behavior, but gained less weight relative to age- and sex-matched wildtype mice. RNA-seq analysis of the cerebral cortex of Stxbp6-null mice relative to wildtype controls identified 126 differentially expressed genes. Of these, 57 were upregulated and 69 were downregulated. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the most significant enriched KEGG term was “complement and coagulation cascades”. Our results suggest some potential regulatory pathways of Stxbp6 in the central nervous system, providing a remarkable new resource for understanding Stxbp6 function at the organism level.

Proteomic Evaluation of the Acute Radiation Syndrome of the Gastrointestinal Tract in a Murine Total-body Irradiation Model

Radiation exposure to the gastrointestinal system contributes to the acute radiation syndrome in a dose- and time-dependent manner. Molecular mechanisms that lead to the gastrointestinal acute radiation syndrome remain incompletely understood. Using a murine model of total-body irradiation, C57BL/6J male mice were irradiated at 8, 10, 12, and 14 Gy and assayed at day 1, 3, and 6 after exposure and compared to nonirradiated (sham) controls. Tryptic digests of gastrointestinal tissues (upper ileum) were analyzed by liquid chromatography-tandem mass spectrometry on a Waters nanoLC coupled to a Thermo Scientific Q Exactive hybrid quadrupole-orbitrap mass spectrometer. Pathway and gene ontology analysis were performed with Qiagen Ingenuity, Panther GO, and DAVID databases. A number of trends were identified in our proteomic data including pronounced protein changes as well as protein changes that were consistently up regulated or down regulated at all time points and dose levels interrogated. Time- and dose-dependent protein changes, canonical pathways affected by irradiation, and changes in proteins that serve as upstream regulators were also identified. Additionally, proteins involved in key processes including inflammation, radiation, and retinoic acid signaling were identified. The proteomic profiling conducted here represents an untargeted systems biology approach to identify acute molecular events that will be useful for a greater understanding of animal models and may be potentially useful toward the development of medical countermeasures and/or biomarkers.

Acute Proteomic Changes in the Lung After WTLI in a Mouse Model: Identification of Potential Initiating Events for Delayed Effects of Acute Radiation Exposure

Radiation-induced lung injury is a delayed effect of acute radiation exposure resulting in pulmonary pneumonitis and fibrosis. Molecular mechanisms that lead to radiation-induced lung injury remain incompletely understood. Using a murine model of whole-thorax lung irradiation, C57BL/6J mice were irradiated at 8, 10, 12, and 14 Gy and assayed at day 1, 3, and 6 postexposure and compared to nonirradiated (sham) controls. Tryptic digests of lung tissues were analyzed by liquid chromatography-tandem mass spectrometry on a Waters nanoLC instrument coupled to a Thermo Scientific Q Exactive hybrid quadrupole-orbitrap mass spectrometer. Pathway and gene ontology analysis were performed with Qiagen Ingenuity, Panther GO, and DAVID databases. A number of trends were identified in the proteomic data, including protein changes greater than 10 fold, protein changes that were consistently up regulated or down regulated at all time points and dose levels interrogated, time and dose dependency of protein changes, canonical pathways affected by irradiation, changes in proteins that serve as upstream regulators, and proteins involved in key processes including inflammation, radiation, and retinoic acid signaling. The proteomic profiling conducted here represents an untargeted systems biology approach to identify acute molecular events that could potentially be initiating events for radiation-induced lung injury.

Identification of long non-coding RNA-related and -coexpressed mRNA biomarkers for hepatocellular carcinoma

BACKGROUND

While changes in mRNA expression during tumorigenesis have been used widely as molecular biomarkers for the diagnosis of a number of cancers, the approach has limitations. For example, traditional methods do not consider the regulatory and positional relationship between mRNA and lncRNA. The latter has been largely shown to possess tumor suppressive or oncogenic properties. The combined analysis of mRNA and lncRNA is likely to facilitate the identification of biomarkers with higher confidence.

RESULTS

Therefore, we have developed an lncRNA-related method to identify traditional mRNA biomarkers. First we identified mRNAs that are differentially expressed in Hepatocellular Carcinoma (HCC) by comparing cancer and matched adjacent non-tumorous liver tissues. Then, we performed mRNA-lncRNA relationship and coexpression analysis and obtained 41 lncRNA-related and -coexpressed mRNA biomarkers. Next, we performed network analysis, gene ontology analysis and pathway analysis to unravel the functional roles and molecular mechanisms of these lncRNA-related and -coexpressed mRNA biomarkers. Finally, we validated the prediction and performance of the 41 lncRNA-related and -coexpressed mRNA biomarkers using Support Vector Machine model with five-fold cross-validation in an independent HCC dataset from RNA-seq.

CONCLUSIONS

Our results suggested that mRNAs expression profiles coexpressed with positionally related lncRNAs can provide important insights into early diagnosis and specific targeted gene therapy of HCC.

Comprehensive tissue-specific gene set enrichment analysis and transcription factor analysis of breast cancer by integrating 14 gene expression datasets

Breast cancer is the most commonly diagnosed malignancy in women. Several key genes and pathways have been proven to correlate with breast cancer pathology. This study sought to explore the differences in key transcription factors (TFs), transcriptional regulation networks and dysregulated pathways in different tissues in breast cancer. We employed 14 breast cancer datasets from NCBI-GEO and performed an integrated analysis in three different tissues including breast, blood and saliva. The results showed that there were eight genes (CEBPD, EGR1, EGR2, EGR3, FOS, FOSB, ID1 and NFIL3) down-regulated in breast tissue but up-regulated in blood tissue. Furthermore, we identified several unreported tissue-specific TFs that may contribute to breast cancer, including ATOH8, DMRT2, TBX15 and ZNF367. The dysregulation of these TFs damaged lipid metabolism, development, cell adhesion, proliferation, differentiation and metastasis processes. Among these pathways, the breast tissue showed the most serious impairment and the blood tissue showed a relatively moderate damage, whereas the saliva tissue was almost unaffected. This study could be helpful for future biomarker discovery, drug design, and therapeutic and predictive applications in breast cancers.

Correlation of pre-operative plasma protein concentrations in cardiac surgery patients with bleeding outcomes using a targeted quantitative proteomics approach

PURPOSE

Despite recent advancements in the use of thrombelastography (TEG) in the surgical setting, adequate technology to accurately predict bleeding phenotypes for patients undergoing cardiopulmonary bypass on the basis of non-mechanical parameters is lacking. While basic science and translational studies have provided key mechanistic insights about the protein components of coagulation cascades and regulatory mediators of hemostasis and fibrinolysis, targeted protein assays are still missing and the association of protein profiles to bleeding phenotypes and TEG readouts have yet to be discovered.

OBJECTIVE

To identify protein biomarkers of bleeding phenotypes of cardiopulmonary bypass patients in pre-operative plasma.

EXPERIMENTAL DESIGN

We applied a targeted proteomics approach to quantify 123 plasma proteins from 23 patients undergoing cardiopulmonary bypass (CPB) and sternotomy. We then correlated these measurements to bleeding outcomes and TEG parameters, associated with speed of clot formation and strength.

RESULTS

In this pilot study, we demonstrate the feasibility of protein quantitation as a viable strategy to predict low versus high bleeding phenotypes (loss of < or > than 20% of estimated blood volume, calculated as 70 mL/kg for BMI<29.9, 60 mL/kg for BMI = 30-39.9, and 50 mL/kg for BMI>40. Statistical elaborations highlighted a core set of proteins showing significant correlations to either total blood loss or TEG R/MA parameters.

CONCLUSION AND CLINICAL RELEVANCE

Though prospective verification and validation in larger cohorts will be necessary, this report suggests a potential for targeted quantitative proteomics of pre-operative plasma protein concentrations in the prediction of estimated blood loss following CPB.

Differential Amino Acid, Carbohydrate and Lipid Metabolism Perpetuations Involved in a Subtype of Rheumatoid Arthritis with Chinese Medicine Cold Pattern

Pattern classification is a key approach in Traditional Chinese Medicine (TCM), and it is used to classify the patients for intervention selection accordingly. TCM cold and heat patterns, two main patterns of rheumatoid arthritis (RA) had been explored with systems biology approaches. Different regulations of apoptosis were found to be involved in cold and heat classification in our previous works. For this study, the metabolic profiling of plasma was explored in RA patients with typical TCM cold or heat patterns by integrating liquid chromatography/mass spectrometry (LC/MS) and gas chromatography/mass spectrometry (GC/MS) platforms in conjunction with the Ingenuity Pathway Analysis (IPA) software. Three main processes of metabolism, including amino acid, carbohydrate and lipid were focused on for function analysis. The results showed that 29 and 19 differential metabolites were found in cold and heat patterns respectively, compared with healthy controls. The perturbation of amino acid metabolism (increased essential amino acids), carbohydrate metabolism (galactose metabolism) and lipid metabolism, were found to be involved in both cold and heat pattern RA. In particular, more metabolic perturbations in protein and collagen breakdown, decreased glycolytic activity and aerobic oxidation, and increased energy utilization associated with RA cold pattern patients. These findings may be useful for obtaining a better understanding of RA pathogenesis and for achieving a better efficacy in RA clinical practice.

On the privacy risks of sharing clinical proteomics data

Although the privacy issues in human genomic studies are well known, the privacy risks in clinical proteomic data have not been thoroughly studied. As a proof of concept, we reported a comprehensive analysis of the privacy risks in clinical proteomic data. It showed that a small number of peptides carrying the minor alleles (referred to as the minor allelic peptides) at non-synonymous single nucleotide polymorphism (nsSNP) sites can be identified in typical clinical proteomic datasets acquired from the blood/serum samples of individual patient, from which the patient can be identified with high confidence. Our results suggested the presence of significant privacy risks in raw clinical proteomic data. However, these risks can be mitigated by a straightforward pre-processing step of the raw data that removing a very small fraction (0.1%, 7.14 out of 7,504 spectra on average) of MS/MS spectra identified as the minor allelic peptides, which has little or no impact on the subsequent analysis (and re-use) of these datasets.

Voltammetric aptasensors for protein disease biomarkers detection: A review

An electrochemical aptasensor is a compact analytical device where the bioreceptor (aptamer) is coupled to a transducer surface to convert a biological interaction into a measurable signal (current) that can be easily processed, recorded and displayed. Since the discovery of the Systematic Evolution of Ligands by Enrichment (SELEX) methodology, the selection of aptamers and their application as bioreceptors has become a promising tool in the design of electrochemical aptasensors. Aptamers present several advantages that highlight their usefulness as bioreceptors such as chemical stability, cost effectiveness and ease of modification towards detection and immobilization at different transducer surfaces. In this review, a special emphasis is given to the potential use of electrochemical aptasensors for the detection of protein disease biomarkers using voltammetry techniques. Methods for the immobilization of aptamers onto electrode surfaces are discussed, as well as different electrochemical strategies that can be used for the design of aptasensors.

DEOD: uncovering dominant effects of cancer-driver genes based on a partial covariance selection method

MOTIVATION

The generation of a large volume of cancer genomes has allowed us to identify disease-related alterations more accurately, which is expected to enhance our understanding regarding the mechanism of cancer development. With genomic alterations detected, one challenge is to pinpoint cancer-driver genes that cause functional abnormalities.

RESULTS

Here, we propose a method for uncovering the dominant effects of cancer-driver genes (DEOD) based on a partial covariance selection approach. Inspired by a convex optimization technique, it estimates the dominant effects of candidate cancer-driver genes on the expression level changes of their target genes. It constructs a gene network as a directed-weighted graph by integrating DNA copy numbers, single nucleotide mutations and gene expressions from matched tumor samples, and estimates partial covariances between driver genes and their target genes. Then, a scoring function to measure the cancer-driver score for each gene is applied. To test the performance of DEOD, a novel scheme is designed for simulating conditional multivariate normal variables (targets and free genes) given a group of variables (driver genes). When we applied the DEOD method to both the simulated data and breast cancer data, DEOD successfully uncovered driver variables in the simulation data, and identified well-known oncogenes in breast cancer. In addition, two highly ranked genes by DEOD were related to survival time. The copy number amplifications of MYC (8q24.21) and TRPS1 (8q23.3) were closely related to the survival time with P-values = 0.00246 and 0.00092, respectively. The results demonstrate that DEOD can efficiently uncover cancer-driver genes.

Pathway and network analysis in proteomics

Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics.

A neural network approach to multi-biomarker panel discovery by high-throughput plasma proteomics profiling of breast cancer

BACKGROUND

In the past several years, there has been increasing interest and enthusiasm in molecular biomarkers as tools for early detection of cancer. Liquid chromatography tandem mass spectrometry (LC/MS/MS) based plasma proteomics profiling technique is a promising technology platform to study candidate protein biomarkers for early detection of cancer. Factors such as inherent variability, protein detectability limitation, and peptide discovery biases among LC/MS/MS platforms have made the classification and prediction of proteomics profiles challenging. Developing proteomics data analysis methods to identify multi-protein biomarker panels for breast cancer diagnosis based on neural networks provides hope for improving both the sensitivity and the specificity of candidate cancer biomarkers for early detection.

RESULTS

In our previous method, we developed a Feed Forward Neural Network-based method to build the classifier for plasma samples of breast cancer and then applied the classifier to predict blind dataset of breast cancer. However, the optimal combination C* in our previous method was actually determined by applying the trained FFNN on the testing set with the combination. Therefore, in this paper, we applied a three way data split to the Feed Forward Neural Network for training, validation and testing based. We found that the prediction performance of the FFNN model based on the three way data split outperforms our previous method and the prediction performance is improved from (AUC = 0.8706, precision = 82.5%, accuracy = 82.5%, sensitivity = 82.5%, specificity = 82.5% for the testing set) to (AUC = 0.895, precision = 86.84%, accuracy = 85%, sensitivity = 82.5%, specificity = 87.5% for the testing set).

CONCLUSIONS

Further pathway analysis showed that the top three five-marker panels are associated with complement and coagulation cascades, signaling, activation, and hemostasis, which are consistent with previous findings. We believe the new approach is a better solution for multi-biomarker panel discovery and it can be applied to other clinical proteomics.

Multiple biomarker panels for early detection of breast cancer in peripheral blood

Detecting breast cancer at early stages can be challenging. Traditional mammography and tissue microarray that have been studied for early breast cancer detection and prediction have many drawbacks. Therefore, there is a need for more reliable diagnostic tools for early detection of breast cancer due to a number of factors and challenges. In the paper, we presented a five-marker panel approach based on SVM for early detection of breast cancer in peripheral blood and show how to use SVM to model the classification and prediction problem of early detection of breast cancer in peripheral blood. We found that the five-marker panel can improve the prediction performance (area under curve) in the testing data set from 0.5826 to 0.7879. Further pathway analysis showed that the top four five-marker panels are associated with signaling, steroid hormones, metabolism, immune system, and hemostasis, which are consistent with previous findings. Our prediction model can serve as a general model for multibiomarker panel discovery in early detection of other cancers.

Proteomics analysis of hepatoprotective effects for scoparone using MALDI-TOF/TOF mass spectrometry with bioinformatics

Abstract Scoparone is an active ingredient of Yinchenhao (Artemisia annua L.), a well-known Chinese medicinal plant, and has been utilized in prevention and therapy of liver damage. However, the molecular drug targets associated with the pharmacological effects of scoparone are largely unknown. In the present article, we extend the previous research on Yinchenhao through a study of its active ingredient and thus the putative targets of scoparone. We employed two-dimensional gel electrophoresis, and all proteins expressed were identified by MALDI-TOF/TOF MS and database research. Protein-interacting networks and pathways were also mapped and evaluated. The possible protein network associated with scoparone was constructed, and contribution of these proteins to the protective effect of scoparone against the carbon tetrachloride-induced acute liver injury in rats are discussed herein. Hepatoprotective effects of scoparone on liver injury in rats were associated with regulated expression of six proteins which were closely related in our protein-protein interaction network, and appear to be involved in antioxidation and signal transduction, energy production, immunity, metabolism, and chaperoning. These observations collectively provide new insights on the molecular mechanisms of scoparone action against hepatic damage in rats.

Breast cancer subtyping from plasma proteins

BACKGROUND

Early detection of breast cancer in blood is both appealing clinically and challenging technically due to the disease's illusive nature and heterogeneity. Today, even though major breast cancer subtypes have been characterized, i.e., luminal A, luminal B, HER2+, and basal-like, little is known about the heterogeneity of breast cancer in blood, which could help to discover minimally invasive protein biomarkers with which clinical researchers can detect, classify, and monitor different breast cancer subtypes.

RESULTS

In this study, we performed an integrative pathway-assisted clustering analysis of breast cancer subtypes from plasma proteome samples collected from 80 patients diagnosed with breast cancer and 80 healthy women. First, four breast cancer subtypes and additionally unknown subtype (according to existing annotation) were determined based on pathology lab test results in primary tumors of enrolled patients. Next, we developed and applied four distance metrics, i.e., Protein Intensity, Q-Value, Pathway Profile, and Distance Score Function, to measure and characterize these cancer subtypes. Then, we developed a permutation test to evaluate the significant protein level changes in each biological pathway for each breast cancer subtype, using q-value. Lastly, we developed a pathway-protein matrix for each of the four distance methods to estimate the distance between breast cancer subtypes, for which further Pathway Association Network analysis were performed.

CONCLUSIONS

We found that 1) the luminal group (luminal A and luminal B) are clustered together, as well as the basal group (basal-like and HER2+) and 2) luminal A and luminal B are more close to each other than basal-like and HER2+ to each other. Our results were consistent with a recent independent breast cancer research from the Cancer Genome Atlas Network using genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, microRNA sequencing and reverse-phase protein arrays. Our results showed that changes of different breast cancer subtypes at the pathway level are more profound and less variable than those at the molecular level. Similar subtypes share distinct yet similar pathway activation networks, while dissimilar subtypes are different also at the level of pathway activation networks. The results also showed that distance or similarity of cancer subtypes based on pathway analysis might be able to provide further insight into the intrinsic relationship of breast cancer subtypes. We believe integrative pathway-assisted proteomics analysis described here can become a model for reliable clustering or classification of other cancer subtypes.

Recursive SVM biomarker selection for early detection of breast cancer in peripheral blood

BACKGROUND

Breast cancer is worldwide the second most common type of cancer after lung cancer. Traditional mammography and Tissue Microarray has been studied for early cancer detection and cancer prediction. However, there is a need for more reliable diagnostic tools for early detection of breast cancer. This can be a challenge due to a number of factors and logistics. First, obtaining tissue biopsies can be difficult. Second, mammography may not detect small tumors, and is often unsatisfactory for younger women who typically have dense breast tissue. Lastly, breast cancer is not a single homogeneous disease but consists of multiple disease states, each arising from a distinct molecular mechanism and having a distinct clinical progression path which makes the disease difficult to detect and predict in early stages.

RESULTS

In the paper, we present a Support Vector Machine based on Recursive Feature Elimination and Cross Validation (SVM-RFE-CV) algorithm for early detection of breast cancer in peripheral blood and show how to use SVM-RFE-CV to model the classification and prediction problem of early detection of breast cancer in peripheral blood.The training set which consists of 32 health and 33 cancer samples and the testing set consisting of 31 health and 34 cancer samples were randomly separated from a dataset of peripheral blood of breast cancer that is downloaded from Gene Express Omnibus. First, we identified the 42 differentially expressed biomarkers between "normal" and "cancer". Then, with the SVM-RFE-CV we extracted 15 biomarkers that yield zero cross validation score. Lastly, we compared the classification and prediction performance of SVM-RFE-CV with that of SVM and SVM Recursive Feature Elimination (SVM-RFE).

CONCLUSIONS

We found that 1) the SVM-RFE-CV is suitable for analyzing noisy high-throughput microarray data, 2) it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features, and 3) it can improve the prediction performance (Area Under Curve) in the testing data set from 0.5826 to 0.7879. Further pathway analysis showed that the biomarkers are associated with Signaling, Hemostasis, Hormones, and Immune System, which are consistent with previous findings. Our prediction model can serve as a general model for biomarker discovery in early detection of other cancers. In the future, Polymerase Chain Reaction (PCR) is planned for validation of the ability of these potential biomarkers for early detection of breast cancer.

Cliques for the identification of gene signatures for colorectal cancer across population

Background

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. Studies have correlated risk of CRC development with dietary habits and environmental conditions. Gene signatures for any disease can identify the key biological processes, which is especially useful in studying cancer development. Such processes can be used to evaluate potential drug targets. Though recognition of CRC gene-signatures across populations is crucial to better understanding potential novel treatment options for CRC, it remains a challenging task.

Results

We developed a topological and biological feature-based network approach for identifying the gene signatures across populations. In this work, we propose a novel approach of using cliques to understand the variability within population. Cliques are more conserved and co-expressed, therefore allowing identification and comparison of cliques across a population which can help researchers study gene variations. Our study was based on four publicly available expression datasets belonging to four different populations across the world. We identified cliques of various sizes (0 to 7) across the four population networks. Cliques of size seven were further analyzed across populations for their commonality and uniqueness. Forty-nine common cliques of size seven were identified. These cliques were further analyzed based on their connectivity profiles. We found associations between the cliques and their connectivity profiles across networks. With these clique connectivity profiles (CCPs), we were able to identify the divergence among the populations, important biological processes (cell cycle, signal transduction, and cell differentiation), and related gene pathways. Therefore the genes identified in these cliques and their connectivity profiles can be defined as the gene-signatures across populations. In this work we demonstrate the power and effectiveness of cliques to study CRC across populations.

Conclusions

We developed a new approach where cliques and their connectivity profiles helped elucidate the variation and similarity in CRC gene profiles across four populations with unique dietary habits.

Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data

INTRODUCTION: The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful molecular signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities for more sophisticated approaches to integrating purely statistical and expert knowledge-based approaches. AREAS COVERED: In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges that have been encountered in deriving valid and useful signatures of disease. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches. EXPERT OPINION: Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to identify predictive signatures of disease are key to future success in the biomarker field. We will describe our recommendations for possible approaches to this problem including metrics for the evaluation of biomarkers.