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Liu Y, Pang Z, Zhao X, Zeng Y, Shen H, Du J. Prognostic model of AU-rich genes predicting the prognosis of lung adenocarcinoma. PeerJ 2021; 9:e12275. [PMID: 34707942 PMCID: PMC8504460 DOI: 10.7717/peerj.12275] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 09/19/2021] [Indexed: 12/15/2022] Open
Abstract
Background AU-rich elements (ARE) are vital cis-acting short sequences in the 3’UTR affecting mRNA stability and translation. The deregulation of ARE-mediated pathways can contribute to tumorigenesis and development. Consequently, ARE-genes are promising to predict prognosis of lung adenocarcinoma (LUAD) patients. Methods Differentially expressed ARE-genes between LUAD and adjacent tissues in TCGA were investigated by Wilcoxon test. LASSO and Cox regression analyses were performed to identify a prognostic genetic signature. The genetic signature was combined with clinicopathological features to establish a prognostic model. LUAD patients were divided into high- and low-risk groups by the model. Kaplan–Meier curve, Harrell’s concordance index (C-index), calibration curves and decision curve analyses (DCA) were used to assess the model. Function enrichment analysis, immunity and tumor mutation analyses were performed to further explore the underlying molecular mechanisms. GEO data were used for external validation. Results Twelve prognostic genes were identified. The gene riskScore, age and stage were independent prognostic factors. The high-risk group had worse overall survival and was less sensitive to chemotherapy and radiotherapy (P < 0.01). C-index and calibration curves showed good performance on survival prediction in both TCGA (1, 3, 5-year ROC: 0.788, 0.776, 0.766) and the GSE13213 validation cohort (1, 3, 5-year ROC: 0.781, 0.811, 0.734). DCA showed the model had notable clinical net benefit. Furthermore, the high-risk group were enriched in cell cycle, DNA damage response, multiple oncological pathways and associated with higher PD-L1 expression, M1 macrophage infiltration. There was no significant difference in tumor mutation burden (TMB) between high- and low-risk groups. Conclusion ARE-genes can reliably predict prognosis of LUAD and may become new therapeutic targets for LUAD.
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Affiliation(s)
- Yong Liu
- Institute of Oncology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China
| | - Zhaofei Pang
- Institute of Oncology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China.,Institute of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, China.,Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, China
| | - Xiaogang Zhao
- Department of Thoracic Surgery, The Second Hospital of Shandong University, Jinan, Shandong Province, China
| | - Yukai Zeng
- Institute of Oncology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China
| | - Hongchang Shen
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, China
| | - Jiajun Du
- Institute of Oncology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China.,Institute of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, China.,Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China
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2
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Musa A, Ghoraie LS, Zhang SD, Glazko G, Yli-Harja O, Dehmer M, Haibe-Kains B, Emmert-Streib F. A review of connectivity map and computational approaches in pharmacogenomics. Brief Bioinform 2018; 19:506-523. [PMID: 28069634 PMCID: PMC5952941 DOI: 10.1093/bib/bbw112] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.
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Affiliation(s)
- Aliyu Musa
- Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Laleh Soltan Ghoraie
- Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Shu-Dong Zhang
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, University of Ulster, C-TRIC Building, Altnagelvin Area Hospital, Glenshane Road, Derry/Londonderry, Northern Ireland, UK
| | - Galina Glazko
- University of Rochester Department of Biostatistics and Computational Biology, Rochester, New York, USA
| | - Olli Yli-Harja
- Computational Systems Biology, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Matthias Dehmer
- Institute for Bioinformatics and Translational Research, UMIT- The Health and Life Sciences University, Eduard Wallnoefer Zentrum 1, Hall in Tyrol, Austria
| | - Benjamin Haibe-Kains
- Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Ontario Institute of Cancer Research, Toronto, ON, Canada
| | - Frank Emmert-Streib
- Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
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3
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Musa A, Ghoraie LS, Zhang SD, Glazko G, Yli-Harja O, Dehmer M, Haibe-Kains B, Emmert-Streib F. A review of connectivity map and computational approaches in pharmacogenomics. Brief Bioinform 2018. [PMID: 28069634 DOI: 10.1093/bib] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023] Open
Abstract
Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.
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Affiliation(s)
- Aliyu Musa
- Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Laleh Soltan Ghoraie
- Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Shu-Dong Zhang
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, University of Ulster, C-TRIC Building, Altnagelvin Area Hospital, Glenshane Road, Derry/Londonderry BT47 6SB, Northern Ireland, UK
| | - Galina Glazko
- University of Rochester Department of Biostatistics and Computational Biology, Rochester, New York 14642, USA
| | - Olli Yli-Harja
- Computational Systems Biology, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Matthias Dehmer
- Institute for Bioinformatics and Translational Research, UMIT- The Health and Life Sciences University, Eduard Wallnoefer Zentrum 1, 6060 Hall in Tyrol, Austria
| | - Benjamin Haibe-Kains
- Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Ontario Institute of Cancer Research, Toronto, ON, Canada
| | - Frank Emmert-Streib
- Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
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Lv C, Wang YC, Liu RH, Zhang WD. Application of Connectivity Map Database to Research on Chinese Materia Medica. CHINESE HERBAL MEDICINES 2016. [DOI: 10.1016/s1674-6384(16)60019-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
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Mohanta TK, Mohanta N, Mohanta YK, Bae H. Genome-Wide Identification of Calcium Dependent Protein Kinase Gene Family in Plant Lineage Shows Presence of Novel D-x-D and D-E-L Motifs in EF-Hand Domain. FRONTIERS IN PLANT SCIENCE 2015; 6:1146. [PMID: 26734045 PMCID: PMC4690006 DOI: 10.3389/fpls.2015.01146] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Accepted: 12/02/2015] [Indexed: 05/04/2023]
Abstract
Calcium ions are considered ubiquitous second messengers in eukaryotic signal transduction pathways. Intracellular Ca(2+) concentration are modulated by various signals such as hormones and biotic and abiotic stresses. Modulation of Ca(2+) ion leads to stimulation of calcium dependent protein kinase genes (CPKs), which results in regulation of gene expression and therefore mediates plant growth and development as well as biotic and abiotic stresses. Here, we reported the CPK gene family of 40 different plant species (950 CPK genes) and provided a unified nomenclature system for all of them. In addition, we analyzed their genomic, biochemical and structural conserved features. Multiple sequence alignment revealed that the kinase domain, auto-inhibitory domain and EF-hands regions of regulatory domains are highly conserved in nature. Additionally, the EF-hand domains of higher plants were found to contain four D-x-D and two D-E-L motifs, while lower eukaryotic plants had two D-x-D and one D-x-E motifs in their EF-hands. Phylogenetic analysis showed that CPK genes are clustered into four different groups. By studying the CPK gene family across the plant lineage, we provide the first evidence of the presence of D-x-D motif in the calcium binding EF-hand domain of CPK proteins.
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Affiliation(s)
- Tapan K. Mohanta
- School of Biotechnology, Yeungnam UniversityGyeongsan, South Korea
| | - Nibedita Mohanta
- Department Of Biotechnology, North Orissa UniversityBaripada, India
| | | | - Hanhong Bae
- School of Biotechnology, Yeungnam UniversityGyeongsan, South Korea
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Integration of a prognostic gene module with a drug sensitivity module to identify drugs that could be repurposed for breast cancer therapy. Comput Biol Med 2015; 61:163-71. [DOI: 10.1016/j.compbiomed.2014.12.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2014] [Revised: 12/20/2014] [Accepted: 12/22/2014] [Indexed: 11/22/2022]
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7
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Zhu L, Zhu F. Identification association of drug-disease by using functional gene module for breast cancer. BMC Med Genomics 2015; 8 Suppl 2:S3. [PMID: 26045063 PMCID: PMC4460962 DOI: 10.1186/1755-8794-8-s2-s3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
In oncology drug development, it is important to develop low risk drugs efficiently. Meanwhile, computational methods have been paid more and more attention in drug discovery. However, few studies attempt to discover the mutual gene modules shared by the drug and disease association. Here we introduce a novel method to identify repositioned drug for breast cancer by integrating the breast cancer survival data with the drug sensitivity information. Among the 140 drug candidates, we are able to filter 4 FDA approved drugs and identify 2 breast cancer drugs among 4 known breast cancer therapeutic drug in total.
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Integrated analysis identifies interaction patterns between small molecules and pathways. BIOMED RESEARCH INTERNATIONAL 2014; 2014:931825. [PMID: 25114931 PMCID: PMC4121214 DOI: 10.1155/2014/931825] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2014] [Revised: 05/13/2014] [Accepted: 05/22/2014] [Indexed: 01/21/2023]
Abstract
Previous studies have indicated that the downstream proteins in a key pathway can be potential drug targets and that the pathway can play an important role in the action of drugs. So pathways could be considered as targets of small molecules. A link map between small molecules and pathways was constructed using gene expression profile, pathways, and gene expression of cancer cell line intervened by small molecules and then we analysed the topological characteristics of the link map. Three link patterns were identified based on different drug discovery implications for breast, liver, and lung cancer. Furthermore, molecules that significantly targeted the same pathways tended to treat the same diseases. These results can provide a valuable reference for identifying drug candidates and targets in molecularly targeted therapy.
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Zhu L, Liu J, Liang F, Rayner S, Xiong J. Predicting response to preoperative chemotherapy agents by identifying drug action on modeled microRNA regulation networks. PLoS One 2014; 9:e98140. [PMID: 24848634 PMCID: PMC4029965 DOI: 10.1371/journal.pone.0098140] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2013] [Accepted: 04/29/2014] [Indexed: 01/05/2023] Open
Abstract
Identifying patients most responsive to specific chemotherapy agents in neoadjuvant settings can help to maximize the benefits of treatment and minimize unnecessary side effects. Metagene approaches that predict response based on gene expression signatures derived from an associative analysis of clinical data can identify chance associations caused by the heterogeneity of a tumor, leading to reproducibility issues in independent validations. In this study, to incorporate information from drug mechanisms of action, we explore the potential of microRNA regulation networks as a new feature space for identifying predictive markers. We introduce a measure we term the CoMi (Context-specific-miRNA-regulation) pattern to represent a descriptive feature of the miRNA regulation network in the transcriptome. We examine whether the modifications to the CoMi pattern on specific biological processes are a useful representation of drug action by predicting the response to neoadjuvant Paclitaxel treatment in breast cancer and show that the drug counteracts the CoMi network dysregulation induced by tumorigenesis. We then generate a quantitative testbed to investigate the ability of the CoMi pattern to distinguish FDA approved breast cancer drugs from other FDA approved drugs not related to breast cancer. We also compare the ability of the CoMi and metagene methods to predict response to neoadjuvant Paclitaxel treatment in clinical cohorts. We find the CoMi method outperforms the metagene method, achieving area under curve (AUC) values of 0.78 and 0.66 respectively. Furthermore, several of the predicted CoMi features highlight the network-based mechanism of drug resistance. Thus, our study suggests that explicitly modeling the drug action using network biology provides a promising approach for predictive marker discovery.
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Affiliation(s)
- Lida Zhu
- School of Computer Science, Wuhan University, Wuhan, P. R. China
| | - Juan Liu
- School of Computer Science, Wuhan University, Wuhan, P. R. China
- * E-mail: (JL); (JX); (SR)
| | - Fengji Liang
- State Key Lab of Space Medicine Fundamentals and Application (SMFA), China Astronaut Research and Training Center (ACC), Beijing, P. R. China
| | - Simon Rayner
- Key Laboratory of Agricultural and Environmental Microbiology, Wuhan Institute of Virology, Wuhan, China
- * E-mail: (JL); (JX); (SR)
| | - Jianghui Xiong
- State Key Lab of Space Medicine Fundamentals and Application (SMFA), China Astronaut Research and Training Center (ACC), Beijing, P. R. China
- The CUHK-ACC Space Medicine Centre on Health Maintenance of Musculoskeletal System, The Chinese University of Hong Kong Shenzhen Research Institute, Shenzhen, China
- * E-mail: (JL); (JX); (SR)
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10
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Yu S, Zheng L, Li Y, Li C, Ma C, Li Y, Li X, Hao P. A cross-species analysis method to analyze animal models' similarity to human's disease state. BMC SYSTEMS BIOLOGY 2012; 6 Suppl 3:S18. [PMID: 23282076 PMCID: PMC3524072 DOI: 10.1186/1752-0509-6-s3-s18] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background Animal models are indispensable tools in studying the cause of human diseases and searching for the treatments. The scientific value of an animal model depends on the accurate mimicry of human diseases. The primary goal of the current study was to develop a cross-species method by using the animal models' expression data to evaluate the similarity to human diseases' and assess drug molecules' efficiency in drug research. Therefore, we hoped to reveal that it is feasible and useful to compare gene expression profiles across species in the studies of pathology, toxicology, drug repositioning, and drug action mechanism. Results We developed a cross-species analysis method to analyze animal models' similarity to human diseases and effectiveness in drug research by utilizing the existing animal gene expression data in the public database, and mined some meaningful information to help drug research, such as potential drug candidates, possible drug repositioning, side effects and analysis in pharmacology. New animal models could be evaluated by our method before they are used in drug discovery. We applied the method to several cases of known animal model expression profiles and obtained some useful information to help drug research. We found that trichostatin A and some other HDACs could have very similar response across cell lines and species at gene expression level. Mouse hypoxia model could accurately mimic the human hypoxia, while mouse diabetes drug model might have some limitation. The transgenic mouse of Alzheimer was a useful model and we deeply analyzed the biological mechanisms of some drugs in this case. In addition, all the cases could provide some ideas for drug discovery and drug repositioning. Conclusions We developed a new cross-species gene expression module comparison method to use animal models' expression data to analyse the effectiveness of animal models in drug research. Moreover, through data integration, our method could be applied for drug research, such as potential drug candidates, possible drug repositioning, side effects and information about pharmacology.
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Affiliation(s)
- Shuhao Yu
- Key Lab of Systems Biology/Key Laboratory of Synthetic Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, PR China
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Combining ZHENG Theory and High-Throughput Expression Data to Predict New Effects of Chinese Herbal Formulae. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2012; 2012:986427. [PMID: 22666299 PMCID: PMC3359832 DOI: 10.1155/2012/986427] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2012] [Accepted: 03/09/2012] [Indexed: 11/18/2022]
Abstract
ZHENG is the key theory in traditional Chinese medicine (TCM) and it is very important to find the molecular pharmacology of traditional Chinese herbal formulae. One ZHENG is related to many diseases and the herbal formulae are aiming to ZHENG. Therefore, many herbal formulae whose effects on a certain disease have been confirmed might also treat other diseases with the same ZHENG. In this study, the microarrays collected from patients with QiXuXueYu ZHENG (Qi-deficiency and Blood-stasis syndrome) before treatment and after being treated with Fuzheng Huayu Capsule were analyzed by a high-throughput gene microarrays-based drug similarity comparison method, which could find the small molecules which had similar effects with Fuzheng Huayu Capsule. Besides getting the results of anti-inflammatory and anti-fibrosis drugs which embody the known effect of Fuzheng Huayu Capsule, many other small molecules were screened out and could reflect other types of effects of this formula in treating QiXuXueYu ZHENG, including anti-hyperglycemic, anti-hyperlipidemic, hyposenstive effect. Then we integrated this information to display the effect of Fuzheng Huayu Capsule and its potential multiple-target molecular pharmacology. Moreover, through using clinical blood-tested data to verify our prediction, Fuzheng Huayu Capsule was proved to have effects on diabetes and dyslipidemia.
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12
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Azad MA, Wright GD. Determining the mode of action of bioactive compounds. Bioorg Med Chem 2012; 20:1929-39. [DOI: 10.1016/j.bmc.2011.10.088] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2011] [Revised: 10/14/2011] [Accepted: 10/30/2011] [Indexed: 10/14/2022]
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Identification of links between small molecules and miRNAs in human cancers based on transcriptional responses. Sci Rep 2012; 2:282. [PMID: 22355792 PMCID: PMC3282946 DOI: 10.1038/srep00282] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2011] [Accepted: 01/31/2012] [Indexed: 12/31/2022] Open
Abstract
The use of small molecules to target miRNAs is a new type of therapy for human diseases, particularly cancers. We proposed a novel high-throughput approach to identify the biological links between small molecules and miRNAs in 23 different cancers and constructed the Small Molecule-MiRNA Network (SMirN) for each cancer to systematically analyze the properties of their associations. In each SMirN, we partitioned small molecules (miRNAs) into modules, in which small molecules (miRNAs) were connected with one miRNA (small molecule). Almost all of the miRNA modules comprised miRNAs that had similar target genes and functions or were members of the same miRNA family. Most of the small molecule modules involved compounds with similar chemical structures, modes of action, or drug interactions. These modules can be used to identify drug candidates and new indications for existing drugs. Therefore, our approach is valuable to drug discovery and cancer therapy.
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LI YUN, TU KANG, ZHENG SIYUAN, WANG JINGFANG, LI YIXUE, HAO PEI, LI XUAN. ASSOCIATION OF FEATURE GENE EXPRESSION WITH STRUCTURAL FINGERPRINTS OF CHEMICAL COMPOUNDS. J Bioinform Comput Biol 2011; 9:503-19. [DOI: 10.1142/s0219720011005446] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2010] [Revised: 02/13/2011] [Accepted: 03/04/2011] [Indexed: 11/18/2022]
Abstract
Exploring the relationship between a chemical structure and its biological function is of great importance for drug discovery. For understanding the mechanisms of drug action, researchers traditionally focused on the molecular structures in the context of interactions with targets. The newly emerged high-throughput "omics" technology opened a new dimension to study the structure–function relationship of chemicals. Previous studies made attempts to introduce transcriptomics data into chemical function investigation. But little effort has been made to link structural fingerprints of compounds with defined intracellular functions, i.e. expression of particular genes and altered pathways. By integrating the chemical structural information with the gene expression profiles of chemical-treated cells, we developed a novel method to associate the structural difference between compounds with the expression of a definite set of genes, which were called feature genes. A subtraction protocol was designed to extract a minimum gene set related to chemical structural features, which can be utilized in practice as markers for drug screening. Case studies demonstrated that our approach is capable of finding feature genes associated with chemical structural fingerprints.
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Affiliation(s)
- YUN LI
- Key Laboratory of Systems Biology/Key Laboratory of Synthetic Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China
- Graduate School of the Chinese Academy of Sciences, Shanghai 200031, PR China
| | - KANG TU
- Key Laboratory of Systems Biology/Key Laboratory of Synthetic Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China
| | - SIYUAN ZHENG
- Key Laboratory of Systems Biology/Key Laboratory of Synthetic Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China
| | - JINGFANG WANG
- Shanghai Centre for Systems Biomedicine, Shanghai Jiaotong University, Shanghai 200240, PR China
| | - YIXUE LI
- Key Laboratory of Systems Biology/Key Laboratory of Synthetic Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China
- Shanghai Centre for Systems Biomedicine, Shanghai Jiaotong University, Shanghai 200240, PR China
- Shanghai Center for Bioinformation Technology, 100 Qinzhou Road, Shanghai 200235, PR China
| | - PEI HAO
- Key Laboratory of Systems Biology/Key Laboratory of Synthetic Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China
- Shanghai Center for Bioinformation Technology, 100 Qinzhou Road, Shanghai 200235, PR China
| | - XUAN LI
- Key Laboratory of Systems Biology/Key Laboratory of Synthetic Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China
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Wang G, Ye Y, Yang X, Liao H, Zhao C, Liang S. Expression-based in silico screening of candidate therapeutic compounds for lung adenocarcinoma. PLoS One 2011; 6:e14573. [PMID: 21283735 PMCID: PMC3024967 DOI: 10.1371/journal.pone.0014573] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2010] [Accepted: 12/21/2010] [Indexed: 01/26/2023] Open
Abstract
Background Lung adenocarcinom (AC) is the most common form of lung cancer. Currently, the number of medical options to deal with lung cancer is very limited. In this study, we aimed to investigate potential therapeutic compounds for lung adenocarcinoma based on integrative analysis. Methodology/Principal Findings The candidate therapeutic compounds were identified in a two-step process. First, a meta-analysis of two published microarray data was conducted to obtain a list of 343 differentially expressed genes specific to lung AC. In the next step, expression profiles of these genes were used to query the Connectivity-Map (C-MAP) database to identify a list of compounds whose treatment reverse expression direction in various cancer cells. Several compounds in the categories of HSP90 inhibitor, HDAC inhibitor, PPAR agonist, PI3K inhibitor, passed our screening to be the leading candidates. On top of the list, three HSP90 inhibitors, i.e. 17-AAG (also known as tanespimycin), monorden, and alvespimycin, showed significant negative enrichment scores. Cytotoxicity as well as effects on cell cycle regulation and apoptosis were evaluated experimentally in lung adenocarcinoma cell line (A549 or GLC-82) with or without treatment with 17-AAG. In vitro study demonstrated that 17-AAG alone or in combination with cisplatin (DDP) can significantly inhibit lung adenocarcinoma cell growth by inducing cell cycle arrest and apoptosis. Conclusions/Significance We have used an in silico screening to identify compounds for treating lung cancer. One such compound 17-AAG demonstrated its anti-lung AC activity by inhibiting cell growth and promoting apoptosis and cell cycle arrest.
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Affiliation(s)
- Guiping Wang
- Bioinformatics Group, Institute of Genetic Engineering, Southern Medical University, Guangzhou, People's Republic of China
- Guangzhou Medical College, Guangzhou, People's Republic of China
| | - Yun Ye
- Bioinformatics Group, Institute of Genetic Engineering, Southern Medical University, Guangzhou, People's Republic of China
- Department of Biological and Chemical Engineering, Guangxi University of Technology, Liuzhou, People's Republic of China
| | - Xiaoqin Yang
- Bioinformatics Group, Institute of Genetic Engineering, Southern Medical University, Guangzhou, People's Republic of China
| | - Hongying Liao
- Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Canguo Zhao
- Guangzhou Medical College, Guangzhou, People's Republic of China
| | - Shuang Liang
- Bioinformatics Group, Institute of Genetic Engineering, Southern Medical University, Guangzhou, People's Republic of China
- * E-mail:
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Yu Y, Xu T, Yu Y, Hao P, Li X. Association of tissue lineage and gene expression: conservatively and differentially expressed genes define common and special functions of tissues. BMC Bioinformatics 2010; 11 Suppl 11:S1. [PMID: 21172044 PMCID: PMC3024865 DOI: 10.1186/1471-2105-11-s11-s1] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Embryogenesis is the process by which the embryo is formed, develops, and establishes developmental hierarchies of tissues. The recent advance in microarray technology made it possible to investigate the tissue specific patterns of gene expression and their relationship with tissue lineages. This study is focused on how tissue specific functions, tissue lineage, and cell differentiation are correlated, which is essential to understand embryonic development and organism complexity. RESULTS We performed individual gene and gene set based analysis on multiple tissue expression data, in association with the classic topology of mammalian fate maps of embryogenesis. For each sub-group of tissues on the fate map, conservatively, differentially and correlatively expressed genes or gene sets were identified. Tissue distance was found to correlate with gene expression divergence. Tissues of the ectoderm or mesoderm origins from the same segments on the fate map shared more similar expression pattern than those from different origins. Conservatively expressed genes or gene sets define common functions in a tissue group and are related to tissue specific diseases, which is supported by results from Gene Ontology and KEGG pathway analysis. Gene expression divergence is larger in certain human tissues than in the mouse homologous tissues. CONCLUSION The results from tissue lineage and gene expression analysis indicate that common function features of neighbor tissue groups were defined by the conservatively expressed genes and were related to tissue specific diseases, and differentially expressed genes contribute to the functional divergence of tissues. The difference of gene expression divergence in human and mouse homologous tissues reflected the organism complexity, i.e. distinct neural development levels and different body sizes.
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Affiliation(s)
- Yao Yu
- Key Lab of Systems Biology/Key Laboratory of Synthetic Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
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Gruber AR, Fallmann J, Kratochvill F, Kovarik P, Hofacker IL. AREsite: a database for the comprehensive investigation of AU-rich elements. Nucleic Acids Res 2010; 39:D66-9. [PMID: 21071424 PMCID: PMC3013810 DOI: 10.1093/nar/gkq990] [Citation(s) in RCA: 129] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
AREsite is an online resource for the detailed investigation of AU-rich elements (ARE) in vertebrate mRNA 3'-untranslated regions (UTRs). AREs are one of the most prominent cis-acting regulatory elements found in 3'-UTRs of mRNAs. Various ARE-binding proteins that possess RNA stabilizing or destabilizing functions are recruited by sequence-specific motifs. Recent findings suggest an essential role of the structural mRNA context in which these sequence motifs are embedded. AREsite is the first database that allows to quantify the structuredness of ARE motif sites in terms of opening energies and accessibility probabilities. Moreover, we also provide a detailed phylogenetic analysis of ARE motifs and incorporate information about experimentally validated targets of the ARE-binding proteins TTP, HuR and Auf1. The database is publicly available at: http://rna.tbi.univie.ac.at/AREsite.
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Affiliation(s)
- Andreas R Gruber
- Department of Microbiology and Immunobiology, Institute for Theoretical Chemistry, Max F Perutz Laboratories, University of Vienna, Vienna, Austria.
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Freudenberg JM, Sivaganesan S, Phatak M, Shinde K, Medvedovic M. Generalized random set framework for functional enrichment analysis using primary genomics datasets. ACTA ACUST UNITED AC 2010; 27:70-7. [PMID: 20971985 DOI: 10.1093/bioinformatics/btq593] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
MOTIVATION Functional enrichment analysis using primary genomics datasets is an emerging approach to complement established methods for functional enrichment based on predefined lists of functionally related genes. Currently used methods depend on creating lists of 'significant' and 'non-significant' genes based on ad hoc significance cutoffs. This can lead to loss of statistical power and can introduce biases affecting the interpretation of experimental results. RESULTS We developed and validated a new statistical framework, generalized random set (GRS) analysis, for comparing the genomic signatures in two datasets without the need for gene categorization. In our tests, GRS produced correct measures of statistical significance, and it showed dramatic improvement in the statistical power over other methods currently used in this setting. We also developed a procedure for identifying genes driving the concordance of the genomics profiles and demonstrated a dramatic improvement in functional coherence of genes identified in such analysis. AVAILABILITY GRS can be downloaded as part of the R package CLEAN from http://ClusterAnalysis.org/. An online implementation is available at http://GenomicsPortals.org/.
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Affiliation(s)
- Johannes M Freudenberg
- Department of Environmental Health, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
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Rahman SA, Bashton M, Holliday GL, Schrader R, Thornton JM. Small Molecule Subgraph Detector (SMSD) toolkit. J Cheminform 2009; 1:12. [PMID: 20298518 PMCID: PMC2820491 DOI: 10.1186/1758-2946-1-12] [Citation(s) in RCA: 86] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2009] [Accepted: 08/10/2009] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Finding one small molecule (query) in a large target library is a challenging task in computational chemistry. Although several heuristic approaches are available using fragment-based chemical similarity searches, they fail to identify exact atom-bond equivalence between the query and target molecules and thus cannot be applied to complex chemical similarity searches, such as searching a complete or partial metabolic pathway.In this paper we present a new Maximum Common Subgraph (MCS) tool: SMSD (Small Molecule Subgraph Detector) to overcome the issues with current heuristic approaches to small molecule similarity searches. The MCS search implemented in SMSD incorporates chemical knowledge (atom type match with bond sensitive and insensitive information) while searching molecular similarity. We also propose a novel method by which solutions obtained by each MCS run can be ranked using chemical filters such as stereochemistry, bond energy, etc. RESULTS In order to benchmark and test the tool, we performed a 50,000 pair-wise comparison between KEGG ligands and PDB HET Group atoms. In both cases the SMSD was shown to be more efficient than the widely used MCS module implemented in the Chemistry Development Kit (CDK) in generating MCS solutions from our test cases. CONCLUSION Presently this tool can be applied to various areas of bioinformatics and chemo-informatics for finding exhaustive MCS matches. For example, it can be used to analyse metabolic networks by mapping the atoms between reactants and products involved in reactions. It can also be used to detect the MCS/substructure searches in small molecules reported by metabolome experiments, as well as in the screening of drug-like compounds with similar substructures.Thus, we present a robust tool that can be used for multiple applications, including the discovery of new drug molecules. This tool is freely available on http://www.ebi.ac.uk/thornton-srv/software/SMSD/
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Affiliation(s)
- Syed Asad Rahman
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
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