The Inhibition Activity of Natural Methoxyflavonoid from Inula britannica on Soluble Epoxide Hydrolase and NO Production in RAW264.7 Cells.

Soluble epoxide hydrolase (sEH) is an enzyme targeted for the treatment of inflammation and cardiovascular diseases. Activated inflammatory cells produce nitric oxide (NO), which induces oxidative stress and exacerbates inflammation. We identify an inhibitor able to suppress sEH and thus NO production. Five flavonoids 1-5 isolated from Inula britannica flowers were evaluated for their abilities to inhibit sEH with IC50 values of 12.1 ± 0.1 to 62.8 ± 1.8 µM and for their effects on enzyme kinetics. A simulation study using computational chemistry was conducted as well. Furthermore, five inhibitors (1-5) were confirmed to suppress NO levels at 10 µM. The results showed that flavonoids 1-5 exhibited inhibitory activity in all tests, with compound 3 exhibiting the most significant efficacy. Thus, in the development of anti-inflammatory inhibitors, compound 3 is a promising natural candidate.

DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.

In recent years, major advances have been made in various chromosome conformation capture technologies to further satisfy the needs of researchers for high-quality, high-resolution contact interactions. Discriminating the loops from genome-wide contact interactions is crucial for dissecting three-dimensional(3D) genome structure and function. Here, we present a deep learning method to predict genome-wide chromatin loops, called DLoopCaller, by combining accessible chromatin landscapes and raw Hi-C contact maps. Some available orthogonal data ChIA-PET/HiChIP and Capture Hi-C were used to generate positive samples with a wider contact matrix which provides the possibility to find more potential genome-wide chromatin loops. The experimental results demonstrate that DLoopCaller effectively improves the accuracy of predicting genome-wide chromatin loops compared to the state-of-the-art method Peakachu. Moreover, compared to two of most popular loop callers, such as HiCCUPS and Fit-Hi-C, DLoopCaller identifies some unique interactions. We conclude that a combination of chromatin landscapes on the one-dimensional genome contributes to understanding the 3D genome organization, and the identified chromatin loops reveal cell-type specificity and transcription factor motif co-enrichment across different cell lines and species.

Finding miRNA-RNA Network Biomarkers for Predicting Metastasis and Prognosis in Cancer.

Despite remarkable progress in cancer research and treatment over the past decades, cancer ranks as a leading cause of death worldwide. In particular, metastasis is the major cause of cancer deaths. After an extensive analysis of miRNAs and RNAs in tumor tissue samples, we derived miRNA-RNA pairs with substantially different correlations from those in normal tissue samples. Using the differential miRNA-RNA correlations, we constructed models for predicting metastasis. A comparison of our model to other models with the same data sets of solid cancer showed that our model is much better than the others in both lymph node metastasis and distant metastasis. The miRNA-RNA correlations were also used in finding prognostic network biomarkers in cancer patients. The results of our study showed that miRNA-RNA correlations and networks consisting of miRNA-RNA pairs were more powerful in predicting prognosis as well as metastasis. Our method and the biomarkers obtained using the method will be useful for predicting metastasis and prognosis, which in turn will help select treatment options for cancer patients and targets of anti-cancer drug discovery.

First Report of Verticillium Wilt Caused by Verticillium dahliae Infection on Chinese Cabbage in Korea.

Chinese cabbage (Brassica rapa L.) is one of the most important vegetables in Korea due to its role as the main ingredient for the making of Kimchi. In June 2014, disease symptoms of leaves wilt, dry, and drop off on Chinese cabbage were observed in a Chinese cabbage farm located at Taebeak (37°26'50.7"N 128°95'50.0"E), Gangwon province, Korea. This disease was observed on approximately 35% of the plants in the field, causing an almost 10% decrease in total production. At the early stage of infection, the color at the edge of the plant foliage changed from green to yellow. As the disease progressed, infected leaves wilted, dried off, and detached from the plant. Soft rot that occurred at the base of the leaf stem and root tissues caused the infected leaves to dry and fell off the plant. To identify the causal agent, a small piece of infected leaf tissues was sterilized with 1% sodium hypochlorite solution for 1 min and rinsed with sterile water before it was transferred onto potato dextrose agar (PDA) media. The plates were then incubated at 25°C for 10 days in the dark. Fungal colonies grown on PDA media were of white-creamy in color with an abundance of mycelia and later develop into black color due to the formation of microsclerotia embedded in the media. Microscopic examination showed conidiophores and phialides were both appeared in a verticillate arrangement, whereas conidia were hyaline, smooth-walled, and ellipsoidal to oval with average size 5.4×2.5 µm (n=100). Microsclerotia appeared in elongate to an irregularly spherical shape and greatly variable in size. The morphological attributes of the fungal isolate described above were comparable to the characteristics of Verticillium dahliae Kleb. (V. dahliae) described by Hawksworth and Talboys (1970), and V. dahliae isolated from Chinese cabbage in Japan reported in Kishi (1998). Pathogenicity test was performed by soaking twelve individual Chinese cabbage seedlings for 15 min into fungal pathogen conidial suspension (1x106 conidium/ml) before transferred into soil tray. The same number of non-inoculated seedlings on the soil tray was used as a control. Inoculated and control plants were then covered with a plastic bag for 24 hours to maintain high humidity before transferred into the greenhouse (25°C). Seven days post-inoculation (dpi), treated plant leaves turned yellow, and soft rot was observed. At 10-dpi, plant leaf tissues dried off and severe soft rot occurred. Pathogenicity test was repeated three times and consistent results were obtained. The re-isolated fungal pathogen from the inoculated plants showed identical morphological characteristics to the original isolates, thus fulfilling Koch's postulates. For further identification, PCR amplification targeting Internal Transcribed Spacer (ITS) and RNA polymerase II gene (RPB2) regions were performed (Liu et al., 1999; White et al., 1990). Each PCR product was sequenced and deposited in the GenBank under the accession LC549667 and LC061275, respectively. Sequence analysis using BLAST showed that the nucleotide sequences of ITS and RPB2 DNA fragments are 99-100% identical to the reference strain of V. dahliae available in the NCBI database (MG585719, HE972023, XM_009652520 and DQ522468, respectively). Therefore, based on the results of morphological and molecular analyses, the fungal pathogen isolated from Chinese cabbage in this study was identified as V. dahliae and deposited in the National Institute of Horticultural and Herbal Science germplasm collection (NIHHS 13-252). Recently, due to high demand and a more competitive price, more Chrysanthemum farmers in Korea switch their crops to Chinese cabbage. Interestingly, the occurrence of V. dahliae infection was also reported to occur in Chrysanthemum plants in Korea (Han et al. 2007), which indicates a serious problem ahead to these farmers. Therefore, in this current study, the identification of V. dahliae pathogenic to Chinese cabbage will provide vital knowledge for the development of disease management strategies to minimize the loss of crop production. To our knowledge, this is the first report that V. dahliae causes Verticillium wilt disease on Chinese cabbage in Korea.

A generative model for constructing nucleic acid sequences binding to a protein.

BACKGROUND: Interactions between protein and nucleic acid molecules are essential to a variety of cellular processes. A large amount of interaction data generated by high-throughput technologies have triggered the development of several computational methods either to predict binding sites in a sequence or to determine whether a pair of sequences interacts or not. Most of these methods treat the problem of the interaction of nucleic acids with proteins as a classification problem rather than a generation problem. RESULTS: We developed a generative model for constructing single-stranded nucleic acids binding to a target protein using a long short-term memory (LSTM) neural network. Experimental results of the generative model are promising in the sense that DNA and RNA sequences generated by the model for several target proteins show high specificity and that motifs present in the generated sequences are similar to known protein-binding motifs. CONCLUSIONS: Although these are preliminary results of our ongoing research, our approach can be used to generate nucleic acid sequences binding to a target protein. In particular, it will help design efficient in vitro experiments by constructing an initial pool of potential aptamers that bind to a target protein with high affinity and specificity.

WIG1 is crucial for AGO2-mediated ACOT7 mRNA silencing via miRNA-dependent and -independent mechanisms.

RNA-binding proteins (RBPs) are involved in mRNA splicing, maturation, transport, translation, storage and turnover. Here, we identified ACOT7 mRNA as a novel target of human WIG1. ACOT7 mRNA decay was triggered by the microRNA miR-9 in a WIG1-dependent manner via classic recruitment of Argonaute 2 (AGO2). Interestingly, AGO2 was also recruited to ACOT7 mRNA in a WIG1-dependent manner in the absence of miR-9, which indicates an alternative model whereby WIG1 controls AGO2-mediated gene silencing. The WIG1-AGO2 complex attenuated translation initiation via an interaction with translation initiation factor 5B (eIF5B). These results were confirmed using a WIG1 tethering system based on the MS2 bacteriophage coat protein and a reporter construct containing an MS2-binding site, and by immunoprecipitation of WIG1 and detection of WIG1-associated proteins using liquid chromatography-tandem mass spectrometry. We also identified WIG1-binding motifs using photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation analyses. Altogether, our data indicate that WIG1 governs the miRNA-dependent and the miRNA-independent recruitment of AGO2 to lower the stability of and suppress the translation of ACOT7 mRNA.

A generalized approach to predicting protein-protein interactions between virus and host.

BACKGROUND: Viral infection involves a large number of protein-protein interactions (PPIs) between virus and its host. These interactions range from the initial binding of viral coat proteins to host membrane receptor to the hijacking the host transcription machinery by viral proteins. Therefore, identifying PPIs between virus and its host helps understand the mechanism of viral infections and design antiviral drugs. Many computational methods have been developed to predict PPIs, but most of them are intended for PPIs within a species rather than PPIs across different species such as PPIs between virus and host. RESULTS: In this study, we developed a prediction model of virus-host PPIs, which is applicable to new viruses and hosts. We tested the prediction model on independent datasets of virus-host PPIs, which were not used in training the model. Despite a low sequence similarity between proteins in training datasets and target proteins in test datasets, the prediction model showed a high performance comparable to the best performance of other methods for single virus-host PPIs. CONCLUSIONS: Our method will be particularly useful to find PPIs between host and new viruses for which little information is available. The program and support data are available at http://bclab.inha.ac.kr/VirusHostPPI .

PNImodeler: web server for inferring protein-binding nucleotides from sequence data.

BACKGROUND: Interactions between DNA and proteins are essential to many biological processes such as transcriptional regulation and DNA replication. With the increased availability of structures of protein-DNA complexes, several computational studies have been conducted to predict DNA binding sites in proteins. However, little attempt has been made to predict protein binding sites in DNA. RESULTS: From an extensive analysis of protein-DNA complexes, we identified powerful features of DNA and protein sequences which can be used in predicting protein binding sites in DNA sequences. We developed two support vector machine (SVM) models that predict protein binding nucleotides from DNA and/or protein sequences. One SVM model that used DNA sequence data alone achieved a sensitivity of 73.4%, a specificity of 64.8%, an accuracy of 68.9% and a correlation coefficient of 0.382 with a test dataset that was not used in training. Another SVM model that used both DNA and protein sequences achieved a sensitivity of 67.6%, a specificity of 74.3%, an accuracy of 71.4% and a correlation coefficient of 0.418. CONCLUSIONS: Predicting binding sites in double-stranded DNAs is a more difficult task than predicting binding sites in single-stranded molecules. Our study showed that protein binding sites in double-stranded DNA molecules can be predicted with a comparable accuracy as those in single-stranded molecules. Our study also demonstrated that using both DNA and protein sequences resulted in a better prediction performance than using DNA sequence data alone. The SVM models and datasets constructed in this study are available at http://bclab.inha.ac.kr/pnimodeler.

DBBP: database of binding pairs in protein-nucleic acid interactions.

BACKGROUND: Interaction of proteins with other molecules plays an important role in many biological activities. As many structures of protein-DNA complexes and protein-RNA complexes have been determined in the past years, several databases have been constructed to provide structure data of the complexes. However, the information on the binding sites between proteins and nucleic acids is not readily available from the structure data since the data consists mostly of the three-dimensional coordinates of the atoms in the complexes. RESULTS: We analyzed the huge amount of structure data for the hydrogen bonding interactions between proteins and nucleic acids and developed a database called DBBP (DataBase of Binding Pairs in protein-nucleic acid interactions, http://bclab.inha.ac.kr/dbbp). DBBP contains 44,955 hydrogen bonds (H-bonds) of protein-DNA interactions and 77,947 H-bonds of protein-RNA interactions. CONCLUSIONS: Analysis of the huge amount of structure data of protein-nucleic acid complexes is labor-intensive, yet provides useful information for studying protein-nucleic acid interactions. DBBP provides the detailed information of hydrogen-bonding interactions between proteins and nucleic acids at various levels from the atomic level to the residue level. The binding information can be used as a valuable resource for developing a computational method aiming at predicting new binding sites in proteins or nucleic acids.

KGVDB: a population-based genomic map of CNVs tagged by SNPs in Koreans.

SUMMARY: Despite a growing interest in a correlation between copy number variations (CNVs) and flanking single nucleotide polymorphisms, few databases provide such information. In particular, most information on CNV available so far was obtained in Caucasian and Yoruba populations, and little is known about CNV in Asian populations. This article presents a database that provides CNV regions tagged by single nucleotide polymorphisms in about 4700 Koreans, which were detected under strict quality control, manually curated and experimentally validated. AVAILABILITY: KGVDB is freely available for non-commercial use at http://biomi.cdc.go.kr/KGVDB. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Constructing a Cancer Patient-Specific Network Based on Second-Order Partial Correlations of Gene Expression and DNA Methylation.

Typically patient-specific gene networks are constructed with gene expression data only. Such networks cannot distinguish direct gene interactions from indirect interactions via others such as the effect of epigenetic events to gene activity. There is an increasing evidence of inter-individual variations not only in gene expression but also in epigenetic events such as DNA methylation. In this paper we propose a new method for constructing a cancer patient-specific gene correlation network using both gene expression and DNA methylation data. We derive a patient-specific network from differential second-order partial correlations of gene expression and DNA methylation between normal samples and the patient sample. The network represents direct interactions between genes by controlling the effect of DNA methylation. Using this method, we constructed 4,000 patient-specific networks for 10 types of cancer. The networks are highly effective in classifying different types of cancer and in deriving potential prognostic gene pairs. In particular, potential prognostic gene pairs derived from the networks were powerful in predicting the survival time of cancer patients. This approach will help identify patient-specific gene correlations and predict prognosis of cancer patients.

Prediction of Cancer Metastasis Using Correlations Between miRNAs and Competing Endogenous RNAs.

Cancer metastasis is a complex process which involves the spread of tumor cells from the primary site to other parts of the body. Metastasis is the major cause of cancer mortality, accounting for about 90% of cancer deaths. Metastasis is primarily diagnosed by clinical examinations and imaging techniques, but such a diagnosis is made after metastasis has occurred. Prediction or early detection of metastasis is important for treatment planning since it has an impact on the survival of patients. Recently a few methods have been developed to predict lymph node metastasis, but few methods are available for predicting distant metastasis. Motivated by a gene regulation mechanism involving miRNAs, we have developed a new method for predicting both lymph node metastasis and distant metastasis. We have derived differential correlations of miRNAs and their target RNAs in cancer, and built prediction models using the differential correlations. Testing the method on several types of cancer showed that differential correlations of miRNAs and target RNAs are much more powerful and stable than expressions of known metastasis predictive genes in predicting distant metastasis as well as lymph node metastasis. The method developed in this study will be useful in predicting metastasis and thereby in determining treatment options for cancer patients.

Constructing Integrative ceRNA Networks and Finding Prognostic Biomarkers in Renal Cell Carcinoma.

Inspired by a newly discovered gene regulation mechanism known as competing endogenous RNA (ceRNA) interactions, several computational methods have been proposed to generate ceRNA networks. However, most of these methods have focused on deriving restricted types of ceRNA interactions such as lncRNA-miRNA-mRNA interactions. Competition for miRNA-binding occurs not only between lncRNAs and mRNAs but also between lncRNAs or between mRNAs. Furthermore, a large number of pseudogenes also act as ceRNAs, thereby regulate other genes. In this study, we developed a general method for constructing integrative networks of all possible interactions of ceRNAs in renal cell carcinoma (RCC). From the ceRNA networks we derived potential prognostic biomarkers, each of which is a triplet of two ceRNAs and miRNA (i.e., ceRNA-miRNA-ceRNA). Interestingly, some prognostic ceRNA triplets do not include mRNA at all, and consist of two non-coding RNAs and miRNA, which have been rarely known so far. Comparison of the prognostic ceRNA triplets to known prognostic genes in RCC showed that the triplets have a better predictive power of survival rates than the known prognostic genes. Our approach will help us construct integrative networks of ceRNAs of all types and find new potential prognostic biomarkers in cancer.

Comparison of Quality, Antioxidant Capacity, and Anti-Inflammatory Activity of Adlay [Coix lacryma-jobi L. var. ma-yuen (Rom. Caill.) Stapf.] Sprout at Several Harvest Time.

Recently, there has been a growing interest in the consumption of plant-based foods such as vegetables and grains for the purpose of disease prevention and treatment. Adlay seeds contain physiologically active substances, including coixol, coixenolide, and lactams. In this study, adlay sprouts were cultivated and harvested at various time points, specifically at 3, 5, 7, 9, and 11 days after sowing. The antioxidant activity of the extracts was evaluated using assays such as DPPH radical scavenging, ABTS radical scavenging, reducing power, and total polyphenol contents. The toxicity of the extracts was assessed using cell culture and the WST-1 assay. The aboveground components of the sprouts demonstrated a significant increase in length, ranging from 2.75 cm to 21.87 cm, weight, ranging from 0.05 g to 0.32 g, and biomass, ranging from 161.4 g to 1319.1 g, as the number of days after sowing advanced, reaching its peak coixol content of 39.38 mg/g on the third day after sowing. Notably, the antioxidant enzyme activity was highest between the third and fifth days after sowing. Regarding anti-inflammatory activity, the inhibition of cyclooxygenase 2 (COX-2) expression was most prominent in samples harvested from the ninth to eleventh days after sowing, corresponding to the later stage of growth. While the overall production mass increased with the number of days after sowing, considering factors such as yield increase index per unit area, turnover rate, and antioxidant activity, harvesting at the early growth stage, specifically between the fifth and seventh days after sowing, was found to be economically advantageous. Thus, the quality, antioxidant capacity, and anti-inflammatory activity of adlay sprouts varied depending on the harvest time, highlighting the importance of determining the appropriate harvest time based on the production objectives. This study demonstrates the changes in the growth and quality of adlay sprouts in relation to the harvest time, emphasizing the potential for developing a market for adlay sprouts as a new food product.

Modeling the interactions of Alzheimer-related genes from the whole brain microarray data and diffusion tensor images of human brain.

BACKGROUND: In recent years the genome-wide microarray-based gene expression profiles and diffusion tensor images (DTI) in human brain have been made available with accompanying anatomic and histology data. The challenge is to integrate various types of data to investigate the interactions of genes that are associated with specific neurological disorder. RESULTS: In this study, we analyzed the whole brain microarray data and the physical connectivity of the hippocampus with other brain regions to identify the genes related to Alzheimer's disease and their interactions with proteins. We generated a physical connectivity map of the left and right hippocampuses with 12 other brain regions and identified 33 Alzheimer-related genes that interact with many proteins. These genes are highly linked to the development of Alzheimer's disease. CONCLUSIONS: In Alzheimer's brain both brain regions and inter-regional communications through the white matter are often hampered. So far the connectivity of regions in Alzheimer's brain has been studied mostly at the functional level using functional MRI (fMRI). Analyzing the inter-regional fiber connectivity without tracking crossing-fiber regions often provides coarse and inaccurate results. A few deep brain fibers were analyzed but the inter-regional fiber connectivity was not analyzed in their studies. The inter-regional fiber connectivity analysis can provide comprehensive and measurable degradation of fiber tracts in AD patients' brains, but is not easy to perform. We tracked crossing-fiber regions and identified genes with high expression levels in the fiber pathways of the hippocampus. The interactions of the genes with other proteins can provide comprehensive and measurable degradation of fiber tracts in Alzheimer brains. To the best of our knowledge, this is the first attempt to integrate the whole brain microarray data with DTI data to identify specific genes and their interactions.

Prediction of protein-protein interactions between viruses and human by an SVM model.

BACKGROUND: Several computational methods have been developed to predict protein-protein interactions from amino acid sequences, but most of those methods are intended for the interactions within a species rather than for interactions across different species. Methods for predicting interactions between homogeneous proteins are not appropriate for finding those between heterogeneous proteins since they do not distinguish the interactions between proteins of the same species from those of different species. RESULTS: We developed a new method for representing a protein sequence of variable length in a frequency vector of fixed length, which encodes the relative frequency of three consecutive amino acids of a sequence. We built a support vector machine (SVM) model to predict human proteins that interact with virus proteins. In two types of viruses, human papillomaviruses (HPV) and hepatitis C virus (HCV), our SVM model achieved an average accuracy above 80%, which is higher than that of another SVM model with a different representation scheme. Using the SVM model and Gene Ontology (GO) annotations of proteins, we predicted new interactions between virus proteins and human proteins. CONCLUSIONS: Encoding the relative frequency of amino acid triplets of a protein sequence is a simple yet powerful representation method for predicting protein-protein interactions across different species. The representation method has several advantages: (1) it enables a prediction model to achieve a better performance than other representations, (2) it generates feature vectors of fixed length regardless of the sequence length, and (3) the same representation is applicable to different types of proteins.

Predicting lymph node metastasis and prognosis of individual cancer patients based on miRNA-mediated RNA interactions.

BACKGROUND: Lymph node metastasis is usually detected based on the images obtained from clinical examinations. Detecting lymph node metastasis from clinical examinations is a direct way of diagnosing metastasis, but the diagnosis is done after lymph node metastasis occurs. RESULTS: We developed a new method for predicting lymph node metastasis based on differential correlations of miRNA-mediated RNA interactions in cancer. The types of RNAs considered in this study include mRNAs, lncRNAs, miRNAs, and pseudogenes. We constructed cancer patient-specific networks of miRNA mediated RNA interactions and identified key miRNA-RNA pairs from the network. A prediction model using differential correlations of the miRNA-RNA pairs of a patient as features showed a much higher performance than other methods which use gene expression data. The key miRNA-RNA pairs were also powerful in predicting prognosis of an individual patient in several types of cancer. CONCLUSIONS: Differential correlations of miRNA-RNA pairs identified from patient-specific networks of miRNA mediated RNA interactions are powerful in predicting lymph node metastasis in cancer patients. The key miRNA-RNA pairs were also powerful in predicting prognosis of an individual patient of solid cancer.

A Deep Learning Model for RNA-Protein Binding Preference Prediction Based on Hierarchical LSTM and Attention Network.

Attention mechanism has the ability to find important information in the sequence. The regions of the RNA sequence that can bind to proteins are more important than those that cannot bind to proteins. Neither conventional methods nor deep learning-based methods, they are not good at learning this information. In this study, LSTM is used to extract the correlation features between different sites in RNA sequence. We also use attention mechanism to evaluate the importance of different sites in RNA sequence. We get the optimal combination of k-mer length, k-mer stride window, k-mer sentence length, k-mer sentence stride window, and optimization function through hyper-parm experiments. The results show that the performance of our method is better than other methods. We tested the effects of changes in k-mer vector length on model performance. We show model performance changes under various k-mer related parameter settings. Furthermore, we investigate the effect of attention mechanism and RNA structure data on model performance.

A New Approach to Deriving Prognostic Gene Pairs From Cancer Patient-Specific Gene Correlation Networks.

Many of the known prognostic gene signatures for cancer are individual genes or combination of genes, found by the analysis of microarray data. However, many of the known cancer signatures are less predictive than random gene expression signatures, and such random signatures are significantly associated with proliferation genes. With the availability of RNA-seq gene expression data for thousands of human cancer patients, we have analyzed RNA-seq and clinical data of cancer patients and constructed gene correlation networks specific to individual cancer patients. From the patient-specific gene correlation networks, we derived prognostic gene pairs for three types of cancer. In this paper, we propose a new method for inferring prognostic gene pairs from patient-specific gene correlation networks. The main difference of our method from previous ones includes (1) it is focused on finding prognostic gene pairs rather than prognostic genes, (2) it can identify prognostic gene pairs from RNA-seq data even when no significant prognostic genes exist, and (3) prognostic gene pairs can serve as robust prognostic biomarkers in the sense that most prognostic gene pairs show little association with proliferation genes, the major boosting factor of the predictive power of random gene signatures. Evaluation of our method with extensive data of three types of cancer (liver cancer, pancreatic cancer, and stomach cancer) showed that our approach is general and that gene pairs can serve as more reliable prognostic signatures for cancer than genes. Analysis of patient-specific gene networks suggests that prognosis of individual cancer patients is affected by the existence of prognostic gene pairs in the patient-specific network and by the size of the patient-specific network. Although preliminary, our approach will be useful for finding gene pairs to predict survival time of patients and to tailor treatments to individual characteristics. The program for dynamically constructing patient-specific gene networks and for finding prognostic gene pairs is available at http://bclab.inha.ac.kr/LPS.

Comparative Analysis of Gene Correlation Networks of Breast Cancer Patients Based on Mutations in TP53.

Breast cancer is one of the most prevalent cancers in females, with more than 450,000 deaths each year worldwide. Among the subtypes of breast cancer, basal-like breast cancer, also known as triple-negative breast cancer, shows the lowest survival rate and does not have effective treatments yet. Somatic mutations in the TP53 gene frequently occur across all breast cancer subtypes, but comparative analysis of gene correlations with respect to mutations in TP53 has not been done so far. The primary goal of this study is to identify gene correlations in two groups of breast cancer patients and to derive potential prognostic gene pairs for breast cancer. We partitioned breast cancer patients into two groups: one group with a mutated TP53 gene (mTP53) and the other with a wild-type TP53 gene (wtTP53). For every gene pair, we computed the hazard ratio using the Cox proportional hazard model and constructed gene correlation networks (GCNs) enriched with prognostic information. Our GCN is more informative than typical GCNs in the sense that it indicates the type of correlation between genes, the concordance index, and the prognostic type of a gene. Comparative analysis of correlation patterns and survival time of the two groups revealed several interesting findings. First, we found several new gene pairs with opposite correlations in the two GCNs and the difference in their correlation patterns was the most prominent in the basal-like subtype of breast cancer. Second, we obtained potential prognostic genes for breast cancer patients with a wild-type TP53 gene. From a comparative analysis of GCNs of mTP53 and wtTP53, we found several gene pairs that show significantly different correlation patterns in the basal-like breast cancer subtype and obtained prognostic genes for patients with a wild-type TP53 gene. The GCNs and prognostic genes identified in this study will be informative for the prognosis of survival and for selecting a drug target for breast cancer, in particular for basal-like breast cancer. To the best of our knowledge, this is the first attempt to construct GCNs for breast cancer patients with or without mutations in the TP53 gene and to find prognostic genes accordingly.