gutMGene: a comprehensive database for target genes of gut microbes and microbial metabolites.

gutMGene (http://bio-annotation.cn/gutmgene), a manually curated database, aims at providing a comprehensive resource of target genes of gut microbes and microbial metabolites in humans and mice. Metagenomic sequencing of fecal samples has identified 3.3 × 106 non-redundant microbial genes from up to 1500 different species. One of the contributions of gut microbiota to host biology is the circulating pool of bacterially derived small-molecule metabolites. It has been estimated that 10% of metabolites found in mammalian blood are derived from the gut microbiota, where they can produce systemic effects on the host through activating or inhibiting gene expression. The current version of gutMGene documents 1331 curated relationships between 332 gut microbes, 207 microbial metabolites and 223 genes in humans, and 2349 curated relationships between 209 gut microbes, 149 microbial metabolites and 544 genes in mice. Each entry in the gutMGene contains detailed information on a relationship between gut microbe, microbial metabolite and target gene, a brief description of the relationship, experiment technology and platform, literature reference and so on. gutMGene provides a user-friendly interface to browse and retrieve each entry using gut microbes, disorders and intervention measures. It also offers the option to download all the entries and submit new experimentally validated associations.

RNAactDrug: a comprehensive database of RNAs associated with drug sensitivity from multi-omics data.

Drug sensitivity has always been at the core of individualized cancer chemotherapy. However, we have been overwhelmed by large-scale pharmacogenomic data in the era of next-generation sequencing technology, which makes it increasingly challenging for researchers, especially those without bioinformatic experience, to perform data integration, exploration and analysis. To bridge this gap, we developed RNAactDrug, a comprehensive database of RNAs associated with drug sensitivity from multi-omics data, which allows users to explore drug sensitivity and RNA molecule associations directly. It provides association data between drug sensitivity and RNA molecules including mRNAs, long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) at four molecular levels (expression, copy number variation, mutation and methylation) from integrated analysis of three large-scale pharmacogenomic databases (GDSC, CellMiner and CCLE). RNAactDrug currently stores more than 4 924 200 associations of RNA molecules and drug sensitivity at four molecular levels covering more than 19 770 mRNAs, 11 119 lncRNAs, 438 miRNAs and 4155 drugs. A user-friendly interface enriched with various browsing sections augmented with advance search facility for querying the database is offered for users retrieving. RNAactDrug provides a comprehensive resource for RNA molecules acting in drug sensitivity, and it could be used to prioritize drug sensitivity-related RNA molecules, further promoting the identification of clinically actionable biomarkers in drug sensitivity and drug development more cost-efficiently by making this knowledge accessible to both basic researchers and clinical practitioners. Database URL: http://bio-bigdata.hrbmu.edu.cn/RNAactDrug.

Identification and comprehensive characterization of lncRNAs with copy number variations and their driving transcriptional perturbed subpathways reveal functional significance for cancer.

Numerous studies have shown that copy number variation (CNV) in lncRNA regions play critical roles in the initiation and progression of cancer. However, our knowledge about their functionalities is still limited. Here, we firstly provided a computational method to identify lncRNAs with copy number variation (lncRNAs-CNV) and their driving transcriptional perturbed subpathways by integrating multidimensional omics data of cancer. The high reliability and accuracy of our method have been demonstrated. Then, the method was applied to 14 cancer types, and a comprehensive characterization and analysis was performed. LncRNAs-CNV had high specificity in cancers, and those with high CNV level may perturb broad biological functions. Some core subpathways and cancer hallmarks widely perturbed by lncRNAs-CNV were revealed. Moreover, subpathways highlighted the functional diversity of lncRNAs-CNV in various cancers. Survival analysis indicated that functional lncRNAs-CNV could be candidate prognostic biomarkers for clinical applications, such as ST7-AS1, CDKN2B-AS1 and EGFR-AS1. In addition, cascade responses and a functional crosstalk model among lncRNAs-CNV, impacted genes, driving subpathways and cancer hallmarks were proposed for understanding the driving mechanism of lncRNAs-CNV. Finally, we developed a user-friendly web interface-LncCASE (http://bio-bigdata.hrbmu.edu.cn/LncCASE/) for exploring lncRNAs-CNV and their driving subpathways in various cancer types. Our study identified and systematically characterized lncRNAs-CNV and their driving subpathways and presented valuable resources for investigating the functionalities of non-coding variations and the mechanisms of tumorigenesis.

MetSigDis: a manually curated resource for the metabolic signatures of diseases.

Complex diseases cannot be understood only on the basis of single gene, single mRNA transcript or single protein but the effect of their collaborations. The combination consequence in molecular level can be captured by the alterations of metabolites. With the rapidly developing of biomedical instruments and analytical platforms, a large number of metabolite signatures of complex diseases were identified and documented in the literature. Biologists' hardship in the face of this large amount of papers recorded metabolic signatures of experiments' results calls for an automated data repository. Therefore, we developed MetSigDis aiming to provide a comprehensive resource of metabolite alterations in various diseases. MetSigDis is freely available at http://www.bio-annotation.cn/MetSigDis/. By reviewing hundreds of publications, we collected 6849 curated relationships between 2420 metabolites and 129 diseases across eight species involving Homo sapiens and model organisms. All of these relationships were used in constructing a metabolite disease network (MDN). This network displayed scale-free characteristics according to the degree distribution (power-law distribution with R2 = 0.909), and the subnetwork of MDN for interesting diseases and their related metabolites can be visualized in the Web. The common alterations of metabolites reflect the metabolic similarity of diseases, which is measured using Jaccard index. We observed that metabolite-based similar diseases are inclined to share semantic associations of Disease Ontology. A human disease network was then built, where a node represents a disease, and an edge indicates similarity of pair-wise diseases. The network validated the observation that linked diseases based on metabolites should have more overlapped genes.

Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data.

BACKGROUND: Individualized drug response prediction is vital for achieving personalized treatment of cancer and moving precision medicine forward. Large-scale multi-omics profiles provide unprecedented opportunities for precision cancer therapy. METHODS: In this study, we propose a pipeline to identify subpathway signatures for anticancer drug response of individuals by integrating the comprehensive contributions of multiple genetic and epigenetic (gene expression, copy number variation and DNA methylation) alterations. RESULTS: Totally, 46 subpathway signatures associated with individual responses to different anticancer drugs were identified based on five cancer-drug response datasets. We have validated the reliability of subpathway signatures in two independent datasets. Furthermore, we also demonstrated these multi-omics subpathway signatures could significantly improve the performance of anticancer drug response prediction. In-depth analysis of these 46 subpathway signatures uncovered the essential roles of three omics types and the functional associations underlying different anticancer drug responses. Patient stratification based on subpathway signatures involved in anticancer drug response identified subtypes with different clinical outcomes, implying their potential roles as prognostic biomarkers. In addition, a landscape of subpathways associated with cellular responses to 191 anticancer drugs from CellMiner was provided and the mechanism similarity of drug action was accurately unclosed based on these subpathways. Finally, we constructed a user-friendly web interface-CancerDAP ( http://bio-bigdata.hrbmu.edu.cn/CancerDAP/ ) available to explore 2751 subpathways relevant with 191 anticancer drugs response. CONCLUSIONS: Taken together, our study identified and systematically characterized subpathway signatures for individualized anticancer drug response prediction, which may promote the precise treatment of cancer and the study for molecular mechanisms of drug actions.

Inference of patient-specific subpathway activities reveals a functional signature associated with the prognosis of patients with breast cancer.

Breast cancer is one of the most deadly forms of cancer in women worldwide. Better prediction of breast cancer prognosis is essential for more personalized treatment. In this study, we aimed to infer patient-specific subpathway activities to reveal a functional signature associated with the prognosis of patients with breast cancer. We integrated pathway structure with gene expression data to construct patient-specific subpathway activity profiles using a greedy search algorithm. A four-subpathway prognostic signature was developed in the training set using a random forest supervised classification algorithm and a prognostic score model with the activity profiles. According to the signature, patients were classified into high-risk and low-risk groups with significantly different overall survival in the training set (median survival of 65 vs 106 months, P = 1.82e-13) and test set (median survival of 75 vs 101 months, P = 4.17e-5). Our signature was then applied to five independent breast cancer data sets and showed similar prognostic values, confirming the accuracy and robustness of the subpathway signature. Stratified analysis suggested that the four-subpathway signature had prognostic value within subtypes of breast cancer. Our results suggest that the four-subpathway signature may be a useful biomarker for breast cancer prognosis.

Integrative Pathway Analysis of Genes and Metabolites Reveals Metabolism Abnormal Subpathway Regions and Modules in Esophageal Squamous Cell Carcinoma.

Aberrant metabolism is one of the main driving forces in the initiation and development of ESCC. Both genes and metabolites play important roles in metabolic pathways. Integrative pathway analysis of both genes and metabolites will thus help to interpret the underlying biological phenomena. Here, we performed integrative pathway analysis of gene and metabolite profiles by analyzing six gene expression profiles and seven metabolite profiles of ESCC. Multiple known and novel subpathways associated with ESCC, such as 'beta-Alanine metabolism', were identified via the cooperative use of differential genes, differential metabolites, and their positional importance information in pathways. Furthermore, a global ESCC-Related Metabolic (ERM) network was constructed and 31 modules were identified on the basis of clustering analysis in the ERM network. We found that the three modules located just to the center regions of the ERM network-especially the core region of Module_1-primarily consisted of aldehyde dehydrogenase (ALDH) superfamily members, which contributes to the development of ESCC. For Module_4, pyruvate and the genes and metabolites in its adjacent region were clustered together, and formed a core region within the module. Several prognostic genes, including GPT, ALDH1B1, ABAT, WBSCR22 and MDH1, appeared in the three center modules of the network, suggesting that they can become potentially prognostic markers in ESCC.

A potential signature of eight long non-coding RNAs predicts survival in patients with non-small cell lung cancer.

BACKGROUND: Accumulated evidence suggests that dysregulated expression of long non-coding RNAs (lncRNAs) may play a critical role in tumorigenesis and prognosis of cancer, indicating the potential utility of lncRNAs as cancer prognostic or diagnostic markers. However, the power of lncRNA signatures in predicting the survival of patients with non-small cell lung cancer (NSCLC) has not yet been investigated. METHODS: We performed an array-based transcriptional analysis of lncRNAs in large patient cohorts with NSCLC by repurposing microarray probes from the gene expression omnibus database. A risk score model was constructed based on the expression data of these eight lncRNAs in the training dataset of NSCLC patients and was subsequently validated in other two independent NSCLC datasets. The biological implications of prognostic lncRNAs were also analyzed using the functional enrichment analysis. RESULTS: An expression pattern of eight lncRNAs was found to be significantly associated with overall survival (OS) of NSCLC patients in the training dataset. With the eight-lncRNA signature, patients of the training dataset could be classified into high- and low-risk groups with significantly different OS (median survival 1.67 vs. 6.06 years, log-rank test p = 4.33E-09). The prognostic power of eight-lncRNA signature was further validated in other two non-overlapping independent NSCLC cohorts, demonstrating good reproducibility and robustness of this eight-lncRNA signature in predicting OS of NSCLC patients. Multivariate regression and stratified analysis suggested that the prognostic power of the eight-lncRNA signature was independent of clinical and pathological factors. Functional enrichment analyses revealed potential functional roles of the eight prognostic lncRNAs in tumorigenesis. CONCLUSIONS: These findings indicate that the eight-lncRNA signature may be an effective independent prognostic molecular biomarker in the prediction of NSCLC patient survival.

Subpathway-GM: identification of metabolic subpathways via joint power of interesting genes and metabolites and their topologies within pathways.

Various 'omics' technologies, including microarrays and gas chromatography mass spectrometry, can be used to identify hundreds of interesting genes, proteins and metabolites, such as differential genes, proteins and metabolites associated with diseases. Identifying metabolic pathways has become an invaluable aid to understanding the genes and metabolites associated with studying conditions. However, the classical methods used to identify pathways fail to accurately consider joint power of interesting gene/metabolite and the key regions impacted by them within metabolic pathways. In this study, we propose a powerful analytical method referred to as Subpathway-GM for the identification of metabolic subpathways. This provides a more accurate level of pathway analysis by integrating information from genes and metabolites, and their positions and cascade regions within the given pathway. We analyzed two colorectal cancer and one metastatic prostate cancer data sets and demonstrated that Subpathway-GM was able to identify disease-relevant subpathways whose corresponding entire pathways might be ignored using classical entire pathway identification methods. Further analysis indicated that the power of a joint genes/metabolites and subpathway strategy based on their topologies may play a key role in reliably recalling disease-relevant subpathways and finding novel subpathways.

Topologically inferring risk-active pathways toward precise cancer classification by directed random walk.

MOTIVATION: The accurate prediction of disease status is a central challenge in clinical cancer research. Microarray-based gene biomarkers have been identified to predict outcome and outperform traditional clinical parameters. However, the robustness of the individual gene biomarkers is questioned because of their little reproducibility between different cohorts of patients. Substantial progress in treatment requires advances in methods to identify robust biomarkers. Several methods incorporating pathway information have been proposed to identify robust pathway markers and build classifiers at the level of functional categories rather than of individual genes. However, current methods consider the pathways as simple gene sets but ignore the pathway topological information, which is essential to infer a more robust pathway activity. RESULTS: Here, we propose a directed random walk (DRW)-based method to infer the pathway activity. DRW evaluates the topological importance of each gene by capturing the structure information embedded in the directed pathway network. The strategy of weighting genes by their topological importance greatly improved the reproducibility of pathway activities. Experiments on 18 cancer datasets showed that the proposed method yielded a more accurate and robust overall performance compared with several existing gene-based and pathway-based classification methods. The resulting risk-active pathways are more reliable in guiding therapeutic selection and the development of pathway-specific therapeutic strategies. AVAILABILITY: DRW is freely available at http://210.46.85.180:8080/DRWPClass/

The detection of risk pathways, regulated by miRNAs, via the integration of sample-matched miRNA-mRNA profiles and pathway structure.

The use of genome-wide, sample-matched miRNA (miRNAs)-mRNA expression data provides a powerful tool for the investigation of miRNAs and genes involved in diseases. The identification of miRNA-regulated pathways has been crucial for analysis of the role of miRNAs. However, the classical identification method fails to consider the structural information of pathways and the regulation of miRNAs simultaneously. We proposed a method that simultaneously integrated the change in gene expression and structural information in order to identify pathways. Our method used fold changes in miRNAs and gene products, along with the quantification of the regulatory effect on target genes, to measure the change in gene expression. Topological characteristics were investigated to measure the influence of gene products on entire pathways. Through the analysis of multiple myeloma and prostate cancer expression data, our method was proven to be effective and reliable in identifying disease risk pathways that are regulated by miRNAs. Further analysis showed that the structure of a pathway plays a crucial role in the recognition of the pathway as a factor in disease risk.

A novel network-based method for measuring the functional relationship between gene sets.

MOTIVATION: In the functional genomic era, a large number of gene sets have been identified via high-throughput genomic and proteomic technologies. These gene sets of interest are often related to the same or similar disorders or phenotypes, and are commonly presented as differentially expressed gene lists, co-expressed gene modules, protein complexes or signaling pathways. However, biologists are still faced by the challenge of comparing gene sets and interpreting the functional relationships between gene sets into an understanding of the underlying biological mechanisms. RESULTS: We introduce a novel network-based method, designated corrected cumulative rank score (CCRS), which analyzes the functional communication and physical interaction between genes, and presents an easy-to-use web-based toolkit called GsNetCom to quantify the functional relationship between two gene sets. To evaluate the performance of our method in assessing the functional similarity between two gene sets, we analyzed the functional coherence of complexes in functional catalog and identified protein complexes in the same functional catalog. The results suggested that CCRS can offer a significant advance in addressing the functional relationship between different gene sets compared with several other available tools or algorithms with similar functionality. We also conducted the case study based on our method, and succeeded in prioritizing candidate leukemia-associated protein complexes and expanding the prioritization and analysis of cancer-related complexes to other cancer types. In addition, GsNetCom provides a new insight into the communication between gene modules, such as exploring gene sets from the perspective of well-annotated protein complexes. AVAILABILITY AND IMPLEMENTATION: GsNetCom is a freely available web accessible toolkit at http://bioinfo.hrbmu.edu.cn/GsNetCom.

Non-coding RNAs as Novel Biomarkers in Cancer Drug Resistance.

Chemotherapy is often the primary and most effective anticancer treatment; however, drug resistance remains a major obstacle to it being curative. Recent studies have demonstrated that non-coding RNAs (ncRNAs), especially microRNAs and long non-coding RNAs, are involved in drug resistance of tumor cells in many ways, such as modulation of apoptosis, drug efflux and metabolism, epithelial-to-mesenchymal transition, DNA repair, and cell cycle progression. Exploring the relationships between ncRNAs and drug resistance will not only contribute to our understanding of the mechanisms of drug resistance and provide ncRNA biomarkers of chemoresistance, but will also help realize personalized anticancer treatment regimens. Due to the high cost and low efficiency of biological experimentation, many researchers have opted to use computational methods to identify ncRNA biomarkers associated with drug resistance. In this review, we summarize recent discoveries related to ncRNA-mediated drug resistance and highlight the computational methods and resources available for ncRNA biomarkers involved in chemoresistance.

Rapid analysis of forchlorfenuron in fruits using molecular complex-based dispersive liquid-liquid microextraction.

The main sample preparation method for analysis of pesticide residues in fruits is QuEChERS. In this study, a novel sample preparation method using molecular complex-based dispersive liquid-liquid microextraction is introduced with detection of forchlorfenuron by high-performance liquid chromatography coupled with diode array and mass spectrometric detection. Sample treatment involves initial extraction of a 5 g sample with 3 mL acetonitrile, and then the selective concentration of the analyte is performed using 150 µL tributyl phosphate by forming intermolecular hydrogen bonds with the analyte. The extraction mechanism was proved using ATR-FTIR. Under the optimised conditions, recovery rates varied between 88% and 107% for various sample matrices spiked at three levels in the range 0.01-0.1 mg kg-1. Intra-day and inter-day repeatabilities were in the ranges of 2.2-8.0% and 1.6-9.5%, respectively. Detection limit and quantitation limit were 0.33 µg kg-1 and 1.09 µg kg-1 for diode-array detection; 0.01 µg kg-1 and 0.04 µg kg-1 for tandem mass spectrometry detection. This method was successfully applied for the analysis of 149 various fruits. The analyte was found in 4 of the 149 samples and the contents were not over the specific maximum residue limit established by domestic and international regulations.

Prioritizing Disease-Related Microbes Based on the Topological Properties of a Comprehensive Network.

Many microbes are parasitic within the human body, engaging in various physiological processes and playing an important role in human diseases. The discovery of new microbe-disease associations aids our understanding of disease pathogenesis. Computational methods can be applied in such investigations, thereby avoiding the time-consuming and laborious nature of experimental methods. In this study, we constructed a comprehensive microbe-disease network by integrating known microbe-disease associations from three large-scale databases (Peryton, Disbiome, and gutMDisorder), and extended the random walk with restart to the network for prioritizing unknown microbe-disease associations. The area under the curve values of the leave-one-out cross-validation and the fivefold cross-validation exceeded 0.9370 and 0.9366, respectively, indicating the high performance of this method. Despite being widely studied diseases, in case studies of inflammatory bowel disease, asthma, and obesity, some prioritized disease-related microbes were validated by recent literature. This suggested that our method is effective at prioritizing novel disease-related microbes and may offer further insight into disease pathogenesis.

Time-ordered dysregulated ceRNA networks reveal disease progression and diagnostic biomarkers in ischemic and dilated cardiomyopathy.

Ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM) are the two main causes of heart failure (HF). Despite similar clinical characteristics and common "HF pathways", ICM and DCM are expected to have different personalized treatment strategies. The underlying mechanisms of ICM and DCM have yet to be fully elucidated. The present study developed a novel computational method for identifying dysregulated long noncoding RNA (lncRNA)-microRNA (miRNA)-mRNA competing endogenous RNA (ceRNA) triplets. Time-ordered dysregulated ceRNA networks were subsequently constructed to reveal the possible disease progression of ICM and DCM based on the method. Biological functional analysis indicated that ICM and DCM had similar features during myocardial remodeling, whereas their characteristics differed during progression. Specifically, disturbance of myocardial energy metabolism may be the main characteristic during DCM progression, whereas early inflammation and response to oxygen are the characteristics that may be specific to ICM. In addition, several panels of diagnostic biomarkers for differentiating non-heart failure (NF) and ICM (NF-ICM), NF-DCM, and ICM-DCM were identified. Our study reveals biological differences during ICM and DCM progression and provides potential diagnostic biomarkers for ICM and DCM.

In silico detection and characteristics of novel microRNA genes in the Equus caballus genome using an integrated ab initio and comparative genomic approach.

The importance of microRNAs at the post-transcriptional regulation level has recently been recognized in both animals and plants. We used the simple but effective sequential method of first Blasting known animal miRNAs against the horse genome and then using the located candidates to search for novel miRNAs by RNA folding method in the vicinity (+ -500 bp) of the candidates. Here, a total of 407 novel horse miRNA genes including 354 mature miRNAs were identified, of these, 75 miRNAs were grouped into 32 families based on seed sequence identity. MiRNA genes tend to be present as clusters in some chromosomes, and 146 miRNA genes accounted for 36% of the total were observed as part of polycistronic transcripts. Detailed analysis of sequence characteristics in novel horse and all previous known animal miRNAs were carried out. Our study will provide a reference point for further study on miRNAs identification in animals and improve the understanding of genome in horse.

ncDRMarker: a computational method for identifying non-coding RNA signatures of drug resistance based on heterogeneous network.

BACKGROUND: Drug resistance is the primary cause of failure in the treatment of cancer. Identifying signatures of chemoresistance will help to overcome this problem. Current drug resistance studies focus on protein-coding genes and ignore non-coding RNAs (ncRNAs), rendering it a challenging task to systematically identify ncRNAs involved in drug resistance. METHODS: In this study, protein-protein, miRNA-target gene, miRNA-lncRNA interactions were integrated to construct a mRNA-miRNA-lncRNA network. Then, the random walk with restart (RWR) method was extended to the network for identifying ncRNA signatures of drug resistance. The leave-one-out cross validation (LOOCV) and receiver operating characteristic curve (ROC) were used to estimate the performance of ncDRMarker. Wilcoxon rank-sum test was used to validate the identified ncRNAs in NCI-60 cancer cell lines. KEGG pathway enrichment analysis was implemented to characterize the biological function of some identified ncRNAs. RESULTS: We performed this method on ten common clinical chemotherapy drugs and analyzed the results in detail. The region beneath the ROC was up to 0.881-0.951, which did not change significantly in the incomplete network, indicating the high performance and robustness of the method. Further, we confirmed the role of the identified ncRNAs in drug resistance, i.e., miR-92a-3p, a candidate chemoresistance ncRNA of tamoxifen and paclitaxel, can significantly classify cancer cell lines into sensitive or resistant to tamoxifen (or paclitaxel). We also dissected the mRNA-miRNA-lncRNA composite network and found that some hub ncRNAs, such as miR-124-3p, were involved in resistance of multiple drugs and engaged in many significant cancer-related pathways. Lastly, we have provided a ncDRMarker platform for users to identify candidate ncRNAs of drug resistance, which is available at http://bio-bigdata.hrbmu.edu.cn/ncDRMarker/index. CONCLUSIONS: Our findings suggest that ncDRMarker is an effective computational technique for prioritizing candidate ncRNAs of drug resistance. Additionally, the identified ncRNAs could be targeted to overcome drug resistance and help realize individualized treatment.

System level characterization of small molecule drugs and their affected long noncoding RNAs.

Long noncoding RNAs (lncRNAs) have multiple regulatory roles and are involved in many human diseases. A potential therapeutic strategy based on targeting lncRNAs was recently developed. To gain insight into the global relationship between small molecule drugs and their affected lncRNAs, we constructed a small molecule lncRNA network consisting of 1206 nodes (1033 drugs and 173 lncRNAs) and 4770 drug-lncRNA associations using LNCmap, which reannotated the microarray data from the Connectivity Map (CMap) database. Based on network biology, we found that the connected drug pairs tended to share the same targets, indications, and side effects. In addition, the connected drug pairs tended to have a similar structure. By inferring the functions of lncRNAs through their co-expressing mRNAs, we found that lncRNA functions related to the modular interface were associated with the mode of action or side effects of the corresponding connected drugs, suggesting that lncRNAs may directly/indirectly participate in specific biological processes after drug administration. Finally, we investigated the tissue-specificity of drug-affected lncRNAs and found that some kinds of drugs tended to have a broader influence (e.g. antineoplastic and immunomodulating drugs), whereas some tissue-specific lncRNAs (nervous system) tended to be affected by multiple types of drugs.

Global view of a drug-sensitivity gene network.

An important challenge in drug development is to gain insight into the mechanism of drug sensitivity. Looking for insights into the global relationships between drugs and their sensitivity genes would be expected to reveal mechanism of drug sensitivity. Here we constructed a drug-sensitivity gene network (DSGN) based on the relationships between drugs and their sensitivity genes, using drug screened genomic data from the NCI-60 cell line panel, including 181 drugs and 1057 sensitivity genes, and 1646 associations between them. Through network analysis, we found that two drugs that share the same sensitivity genes tend to share the same Anatomical Therapeutic Chemical classification and side effects. We then found that the sensitivity genes of same drugs tend to cluster together in the human interactome and participate in the same biological function modules (pathways). Finally, we noticed that the sensitivity genes and target genes of the same drug have a significant dense distance in the human interactome network and they were functionally related. For example, target genes such as epidermal growth factor receptor gene can activate downstream sensitivity genes of the same drug in the PI3K/Akt pathway. Thus, the DSGN would provide great insights into the mechanism of drug sensitivity.