1
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Banik SK, Baishya S, Das Talukdar A, Choudhury MD. Network analysis of atherosclerotic genes elucidates druggable targets. BMC Med Genomics 2022; 15:42. [PMID: 35241081 PMCID: PMC8893053 DOI: 10.1186/s12920-022-01195-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 08/18/2021] [Indexed: 11/22/2022] Open
Abstract
Background Atherosclerosis is one of the major causes of cardiovascular disease. It is characterized by the accumulation of atherosclerotic plaque in arteries under the influence of inflammatory responses, proliferation of smooth muscle cell, accumulation of modified low density lipoprotein. The pathophysiology of atherosclerosis involves the interplay of a number of genes and metabolic pathways. In traditional translation method, only a limited number of genes and pathways can be studied at once. However, the new paradigm of network medicine can be explored to study the interaction of a large array of genes and their functional partners and their connections with the concerned disease pathogenesis. Thus, in our study we employed a branch of network medicine, gene network analysis as a tool to identify the most crucial genes and the miRNAs that regulate these genes at the post transcriptional level responsible for pathogenesis of atherosclerosis. Result From NCBI database 988 atherosclerotic genes were retrieved. The protein–protein interaction using STRING database resulted in 22,693 PPI interactions among 872 nodes (genes) at different confidence score. The cluster analysis of the 872 genes using MCODE, a plug-in of Cytoscape software revealed a total of 18 clusters, the topological parameter and gene ontology analysis facilitated in the selection of four influential genes viz., AGT, LPL, ITGB2, IRS1 from cluster 3. Further, the miRNAs (miR-26, miR-27, and miR-29 families) targeting these genes were obtained by employing MIENTURNET webtool. Conclusion Gene network analysis assisted in filtering out the 4 probable influential genes and 3 miRNA families in the pathogenesis of atherosclerosis. These genes, miRNAs can be targeted to restrict the occurrence of atherosclerosis. Given the importance of atherosclerosis, any approach in the understanding the genes involved in its pathogenesis can substantially enhance the health care system. Supplementary Information The online version contains supplementary material available at 10.1186/s12920-022-01195-y.
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Affiliation(s)
- Sheuli Kangsa Banik
- Department of Life Science and Bioinformatics, Assam University, Silchar, India
| | - Somorita Baishya
- Department of Life Science and Bioinformatics, Assam University, Silchar, India
| | - Anupam Das Talukdar
- Department of Life Science and Bioinformatics, Assam University, Silchar, India
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2
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Sheng M, Cai H, Yang Q, Li J, Zhang J, Liu L. A Random Walk-Based Method to Identify Candidate Genes Associated With Lymphoma. Front Genet 2021; 12:792754. [PMID: 34899868 PMCID: PMC8655984 DOI: 10.3389/fgene.2021.792754] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 11/02/2021] [Indexed: 11/16/2022] Open
Abstract
Lymphoma is a serious type of cancer, especially for adolescents and elder adults, although this malignancy is quite rare compared with other types of cancer. The cause of this malignancy remains ambiguous. Genetic factor is deemed to be highly associated with the initiation and progression of lymphoma, and several genes have been related to this disease. Determining the pathogeny of lymphoma by identifying the related genes is important. In this study, we presented a random walk-based method to infer the novel lymphoma-associated genes. From the reported 1,458 lymphoma-associated genes and protein–protein interaction network, raw candidate genes were mined by using the random walk with restart algorithm. The determined raw genes were further filtered by using three screening tests (i.e., permutation, linkage, and enrichment tests). These tests could control false-positive genes and screen out essential candidate genes with strong linkages to validate the lymphoma-associated genes. A total of 108 inferred genes were obtained. Analytical results indicated that some inferred genes, such as RAC3, TEC, IRAK2/3/4, PRKCE, SMAD3, BLK, TXK, PRKCQ, were associated with the initiation and progression of lymphoma.
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Affiliation(s)
- Minjie Sheng
- Department of Ophthalmology, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Haiying Cai
- Department of Ophthalmology, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Qin Yang
- Department of Ophthalmology, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jing Li
- Department of Ophthalmology, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jian Zhang
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai, China.,Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China.,National Clinical Research Center for Eye Diseases, Shanghai, China.,Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Lihua Liu
- Department of Ophthalmology, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, China
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3
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Pourreza Shahri M, Kahanda I. Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes. BMC Bioinformatics 2021; 22:500. [PMID: 34656098 PMCID: PMC8520253 DOI: 10.1186/s12859-021-04421-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 10/04/2021] [Indexed: 11/13/2022] Open
Abstract
Background Identifying human protein-phenotype relationships has attracted researchers in bioinformatics and biomedical natural language processing due to its importance in uncovering rare and complex diseases. Since experimental validation of protein-phenotype associations is prohibitive, automated tools capable of accurately extracting these associations from the biomedical text are in high demand. However, while the manual annotation of protein-phenotype co-mentions required for training such models is highly resource-consuming, extracting millions of unlabeled co-mentions is straightforward. Results In this study, we propose a novel deep semi-supervised ensemble framework that combines deep neural networks, semi-supervised, and ensemble learning for classifying human protein-phenotype co-mentions with the help of unlabeled data. This framework allows the ability to incorporate an extensive collection of unlabeled sentence-level co-mentions of human proteins and phenotypes with a small labeled dataset to enhance overall performance. We develop PPPredSS, a prototype of our proposed semi-supervised framework that combines sophisticated language models, convolutional networks, and recurrent networks. Our experimental results demonstrate that the proposed approach provides a new state-of-the-art performance in classifying human protein-phenotype co-mentions by outperforming other supervised and semi-supervised counterparts. Furthermore, we highlight the utility of PPPredSS in powering a curation assistant system through case studies involving a group of biologists. Conclusions This article presents a novel approach for human protein-phenotype co-mention classification based on deep, semi-supervised, and ensemble learning. The insights and findings from this work have implications for biomedical researchers, biocurators, and the text mining community working on biomedical relationship extraction.
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Affiliation(s)
| | - Indika Kahanda
- School of Computing, University of North Florida, Jacksonville, USA.
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4
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Li W, Zhang Y, Wang Y, Rong Z, Liu C, Miao H, Chen H, He Y, He W, Chen L. Candidate gene prioritization for chronic obstructive pulmonary disease using expression information in protein-protein interaction networks. BMC Pulm Med 2021; 21:280. [PMID: 34481483 PMCID: PMC8418003 DOI: 10.1186/s12890-021-01646-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 08/23/2021] [Indexed: 11/30/2022] Open
Abstract
Background Identifying or prioritizing genes for chronic obstructive pulmonary disease (COPD), one type of complex disease, is particularly important for its prevention and treatment. Methods In this paper, a novel method was proposed to Prioritize genes using Expression information in Protein–protein interaction networks with disease risks transferred between genes (abbreviated as PEP). A weighted COPD PPI network was constructed using expression information and then COPD candidate genes were prioritized based on their corresponding disease risk scores in descending order. Results Further analysis demonstrated that the PEP method was robust in prioritizing disease candidate genes, and superior to other existing prioritization methods exploiting either topological or functional information. Top-ranked COPD candidate genes and their significantly enriched functions were verified to be related to COPD. The top 200 candidate genes might be potential disease genes in the diagnosis and treatment of COPD. Conclusions The proposed method could provide new insights to the research of prioritizing candidate genes of COPD or other complex diseases with expression information from sequencing or microarray data. Supplementary Information The online version contains supplementary material available at 10.1186/s12890-021-01646-9.
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Affiliation(s)
- Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, Heilongjiang, China
| | - Yihua Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, Heilongjiang, China
| | - Yahui Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, Heilongjiang, China
| | - Zherou Rong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, Heilongjiang, China
| | - Chenyu Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, Heilongjiang, China
| | - Hui Miao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, Heilongjiang, China
| | - Hongwei Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, Heilongjiang, China
| | - Yuehan He
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, Heilongjiang, China
| | - Weiming He
- Institute of Opto-Electronics, Harbin Institute of Technology, Harbin, 150000, Heilongjiang, China.
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, Heilongjiang, China.
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5
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Harikumar H, Quinn TP, Rana S, Gupta S, Venkatesh S. Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient. BioData Min 2021; 14:37. [PMID: 34353329 PMCID: PMC8340371 DOI: 10.1186/s13040-021-00263-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 05/10/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND The last decade has seen a major increase in the availability of genomic data. This includes expert-curated databases that describe the biological activity of genes, as well as high-throughput assays that measure gene expression in bulk tissue and single cells. Integrating these heterogeneous data sources can generate new hypotheses about biological systems. Our primary objective is to combine population-level drug-response data with patient-level single-cell expression data to predict how any gene will respond to any drug for any patient. METHODS We take 2 approaches to benchmarking a "dual-channel" random walk with restart (RWR) for data integration. First, we evaluate how well RWR can predict known gene functions from single-cell gene co-expression networks. Second, we evaluate how well RWR can predict known drug responses from individual cell networks. We then present two exploratory applications. In the first application, we combine the Gene Ontology database with glioblastoma single cells from 5 individual patients to identify genes whose functions differ between cancers. In the second application, we combine the LINCS drug-response database with the same glioblastoma data to identify genes that may exhibit patient-specific drug responses. CONCLUSIONS Our manuscript introduces two innovations to the integration of heterogeneous biological data. First, we use a "dual-channel" method to predict up-regulation and down-regulation separately. Second, we use individualized single-cell gene co-expression networks to make personalized predictions. These innovations let us predict gene function and drug response for individual patients. Taken together, our work shows promise that single-cell co-expression data could be combined in heterogeneous information networks to facilitate precision medicine.
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Affiliation(s)
- Haripriya Harikumar
- Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia.
- Institute for Health Transformation, Deakin University, Geelong, Australia.
| | - Thomas P Quinn
- Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia.
| | - Santu Rana
- Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia
| | - Sunil Gupta
- Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia
| | - Svetha Venkatesh
- Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia
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6
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Zhang J, Zhang M, Zhao H, Xu X. Identification of proliferative diabetic retinopathy-associated genes on the protein–protein interaction network by using heat diffusion algorithm. Biochim Biophys Acta Mol Basis Dis 2020; 1866:165794. [DOI: 10.1016/j.bbadis.2020.165794] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 03/25/2020] [Accepted: 04/04/2020] [Indexed: 12/11/2022]
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7
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Wekesa JS, Meng J, Luan Y. A deep learning model for plant lncRNA-protein interaction prediction with graph attention. Mol Genet Genomics 2020; 295:1091-1102. [DOI: 10.1007/s00438-020-01682-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Accepted: 05/01/2020] [Indexed: 02/06/2023]
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8
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Liu F, Dong H, Mei Z, Huang T. Investigation of miRNA and mRNA Co-expression Network in Ependymoma. Front Bioeng Biotechnol 2020; 8:177. [PMID: 32266223 PMCID: PMC7096354 DOI: 10.3389/fbioe.2020.00177] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 02/20/2020] [Indexed: 12/18/2022] Open
Abstract
Ependymoma (EPN) is a rare primary tumor of the central nervous system (CNS) that affects both children and adults. Despite the definition and classification of distinct molecular subgroups, there remains a group of EPNs with a balanced genome, which makes it difficult to predict a prognosis of patients with EPN. The role of miRNA-mRNA network on EPN is still poorly understood. We assessed the involvement of miRNA-mRNA pairs in EPN by applying a weighted co-expression network analysis (WGCNA) approach. Using whole genome expression profile analysis followed by functional enrichment, we detected hub genes involved in active proliferation and DNA replication of nerve cells. Key genes including CYP11B1, KRT33B, RUNX1T1, SIK1, MAP3K4, MLANA, and SFRP5 identified in co-expression networks were regulated by miR-15a and miR-24-1. These seven miRNA-mRNA pairs were considered to influence not only pathways in cancer and tumor suppression process, but also MAPK, NF-kappaB, and WNT signaling pathways which were associated with tumorigenesis and development. This study provides a novel insight into potential diagnostic biomarkers of EPN and may have value in choosing therapeutic targets with clinical utility.
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Affiliation(s)
- Feili Liu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Hang Dong
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Zi Mei
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Tao Huang
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
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9
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Inferring novel genes related to oral cancer with a network embedding method and one-class learning algorithms. Gene Ther 2019; 26:465-478. [PMID: 31455874 DOI: 10.1038/s41434-019-0099-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 06/18/2019] [Accepted: 07/15/2019] [Indexed: 12/14/2022]
Abstract
Oral cancer (OC) is one of the most common cancers threatening human lives. However, OC pathogenesis has yet to be fully uncovered, and thus designing effective treatments remains difficult. Identifying genes related to OC is an important way for achieving this purpose. In this study, we proposed three computational models for inferring novel OC-related genes. In contrast to previously proposed computational methods, which lacked the learning procedures, each proposed model adopted a one-class learning algorithm, which can provide a deep insight into features of validated OC-related genes. A network embedding algorithm (i.e., node2vec) was applied to the protein-protein interaction network to produce the representation of genes. The features of the OC-related genes were used in the training of the one-class algorithm, and the performance of the final inferring model was improved through a feature selection procedure. Then, candidate genes were produced by applying the trained inferring model to other genes. Three tests were performed to screen out the important candidate genes. Accordingly, we obtained three inferred gene sets, any two of which were different. The inferred genes were also different from previous reported genes and some of them have been included in the public Oral Cancer Gene Database. Finally, we analyzed several inferred genes to confirm whether they are novel OC-related genes.
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10
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Kwon D, Lee D, Kim J, Lee J, Sim M, Kim J. INTERSPIA: a web application for exploring the dynamics of protein-protein interactions among multiple species. Nucleic Acids Res 2019; 46:W89-W94. [PMID: 29746660 PMCID: PMC6031021 DOI: 10.1093/nar/gky378] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 04/27/2018] [Indexed: 02/06/2023] Open
Abstract
Proteins perform biological functions through cascading interactions with each other by forming protein complexes. As a result, interactions among proteins, called protein-protein interactions (PPIs) are not completely free from selection constraint during evolution. Therefore, the identification and analysis of PPI changes during evolution can give us new insight into the evolution of functions. Although many algorithms, databases and websites have been developed to help the study of PPIs, most of them are limited to visualize the structure and features of PPIs in a chosen single species with limited functions in the visualization perspective. This leads to difficulties in the identification of different patterns of PPIs in different species and their functional consequences. To resolve these issues, we developed a web application, called INTER-Species Protein Interaction Analysis (INTERSPIA). Given a set of proteins of user's interest, INTERSPIA first discovers additional proteins that are functionally associated with the input proteins and searches for different patterns of PPIs in multiple species through a server-side pipeline, and second visualizes the dynamics of PPIs in multiple species using an easy-to-use web interface. INTERSPIA is freely available at http://bioinfo.konkuk.ac.kr/INTERSPIA/.
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Affiliation(s)
- Daehong Kwon
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Daehwan Lee
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Juyeon Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Jongin Lee
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Mikang Sim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Jaebum Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
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11
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Lu S, Zhu ZG, Lu WC. Inferring novel genes related to colorectal cancer via random walk with restart algorithm. Gene Ther 2019; 26:373-385. [PMID: 31308477 DOI: 10.1038/s41434-019-0090-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2018] [Revised: 05/20/2019] [Accepted: 06/11/2019] [Indexed: 12/12/2022]
Abstract
Colorectal cancer (CRC) is the third most common type of cancer. In recent decades, genomic analysis has played an increasingly important role in understanding the molecular mechanisms of CRC. However, its pathogenesis has not been fully uncovered. Identification of genes related to CRC as complete as possible is an important way to investigate its pathogenesis. Therefore, we proposed a new computational method for the identification of novel CRC-associated genes. The proposed method is based on existing proven CRC-associated genes, human protein-protein interaction networks, and random walk with restart algorithm. The utility of the method is indicated by comparing it to the methods based on Guilt-by-association or shortest path algorithm. Using the proposed method, we successfully identified 298 novel CRC-associated genes. Previous studies have validated the involvement of the majority of these 298 novel genes in CRC-associated biological processes, thus suggesting the efficacy and accuracy of our method.
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Affiliation(s)
- Sheng Lu
- Department of General Surgery, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Digestive Surgery, Shanghai, 200025, China
| | - Zheng-Gang Zhu
- Department of General Surgery, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Digestive Surgery, Shanghai, 200025, China
| | - Wen-Cong Lu
- Department of Chemistry, College of Sciences, Shanghai University, Shanghai, 200444, China.
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12
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Wang T, Chen L, Zhao X. Prediction of Drug Combinations with a Network Embedding Method. Comb Chem High Throughput Screen 2019; 21:789-797. [DOI: 10.2174/1386207322666181226170140] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 11/02/2018] [Accepted: 11/28/2018] [Indexed: 01/10/2023]
Abstract
Aim and Objective:
There are several diseases having a complicated mechanism. For such
complicated diseases, a single drug cannot treat them very well because these diseases always
involve several targets and single targeted drugs cannot modulate these targets simultaneously. Drug
combination is an effective way to treat such diseases. However, determination of effective drug
combinations is time- and cost-consuming via traditional methods. It is urgent to build quick and
cheap methods in this regard. Designing effective computational methods incorporating advanced
computational techniques to predict drug combinations is an alternative and feasible way.
Method:
In this study, we proposed a novel network embedding method, which can extract
topological features of each drug combination from a drug network that was constructed using
chemical-chemical interaction information retrieved from STITCH. These topological features were
combined with individual features of drug combination reported in one previous study. Several
advanced computational methods were employed to construct an effective prediction model, such as
synthetic minority oversampling technique (SMOTE) that was used to tackle imbalanced dataset,
minimum redundancy maximum relevance (mRMR) and incremental feature selection (IFS)
methods that were adopted to analyze features and extract optimal features for building an optimal
support machine vector (SVM) classifier.
Results and Conclusion:
The constructed optimal SVM classifier yielded an MCC of 0.806, which
is superior to the classifier only using individual features with or without SMOTE. The performance
of the classifier can be improved by combining the topological features and essential features of a
drug combination.
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Affiliation(s)
- Tianyun Wang
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Xian Zhao
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
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13
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Jiang W, Zhan H, Jiao Y, Li S, Gao W. A novel lncRNA-miRNA-mRNA network analysis identified the hub lncRNA RP11-159F24.1 in the pathogenesis of papillary thyroid cancer. Cancer Med 2018; 7:6290-6298. [PMID: 30474931 PMCID: PMC6308055 DOI: 10.1002/cam4.1900] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Revised: 08/28/2018] [Accepted: 10/01/2018] [Indexed: 12/13/2022] Open
Abstract
Papillary thyroid cancer (PTC) is one of the most common cancers worldwide, and its carcinogenesis is influenced by a complex network of gene interactions. In this study, the microarray expression profile was re-annotated into a lncRNA-mRNA biphasic profile. LncRNA-mRNA interactions were confirmed by established miRNA-RNA data and hypergeometric test. Then, a PTC-related lncRNA-miRNA-mRNA network (PTCRN) was constructed by integrating differentially expressed genes with the RNA-RNA networks. The new network consisted of 21 lncRNAs, 241 mRNAs and 803 edges. To prioritize PTC-related genes, we performed topological analysis and random walk with restart (PWR) algorithm analysis of PTCRN. Both analyses identified lncRNA RP11-159F24.1 as a hub node in the network, which could interact with 47 mRNAs by sponging miR-485. In functional enrichment analysis, these interacting mRNAs were associated with the pathways in cancer. In validation, RP11-159F24.1 (up-regulated; P = 0.0013) showed an opposite expression pattern with its target miR-485 (down-regulated; P = 0.0013) in PTC, indicating that the RP11-159F24.1/miR-485/mRNAs axis might play an important role in the development of PTC. In conclusion, this study has constructed a PTC-related lncRNA-miRNA-mRNA network and identified the hub lncRNA RP11-159F24.1 in the tumorigenesis, which provided novel insights to explore the underlying mechanism of PTC.
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Affiliation(s)
- Wei Jiang
- Department of Endocrinologythe First Affiliated Hospital of Harbin Medical UniversityHarbinChina
| | - Hua Zhan
- Department of Neurosurgerythe First Affiliated Hospital of Harbin Medical UniversityHarbinChina
| | - Yanyan Jiao
- Department of Endocrinologythe First Affiliated Hospital of Harbin Medical UniversityHarbinChina
| | - Sha Li
- Department of Endocrinologythe First Affiliated Hospital of Harbin Medical UniversityHarbinChina
| | - Weixu Gao
- Department of Endocrinologythe First Affiliated Hospital of Harbin Medical UniversityHarbinChina
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14
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Sheng M, Dong Z, Xie Y. Identification of tumor-educated platelet biomarkers of non-small-cell lung cancer. Onco Targets Ther 2018; 11:8143-8151. [PMID: 30532555 PMCID: PMC6241732 DOI: 10.2147/ott.s177384] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Lung cancer is a severe cancer with a high death rate. The 5-year survival rate for stage III lung cancer is much lower than stage I. Early detection and intervention of lung cancer patients can significantly increase their survival time. However, conventional lung cancer-screening methods, such as chest X-rays, sputum cytology, positron-emission tomography (PET), low-dose computed tomography (CT), magnetic resonance imaging, and gene-mutation, -methylation, and -expression biomarkers of lung tissue, are invasive, radiational, or expensive. Liquid biopsy is non-invasive and does little harm to the body. It can reflect early-stage dysfunctions of tumorigenesis and enable early detection and intervention. METHODS In this study, we analyzed RNA-sequencing data of tumor-educated platelets (TEPs) in 402 non-small-cell lung cancer (NSCLC) patients and 231 healthy controls. A total of 48 biomarker genes were selected with advanced minimal-redundancy, maximal-relevance, and incremental feature-selection (IFS) methods. RESULTS A support vector-machine (SVM) classifier based on the 48 biomarker genes accurately predicted NSCLC with leave-one-out cross-validation (LOOCV) sensitivity, specificity, accuracy, and Matthews correlation coefficients of 0.925, 0.827, 0.889, and 0.760, respectively. Network analysis of the 48 genes revealed that the WASF1 actin cytoskeleton module, PRKAB2 kinase module, RSRC1 ribosomal protein module, PDHB carbohydrate-metabolism module, and three intermodule hubs (TPM2, MYL9, and PPP1R12C) may play important roles in NSCLC tumorigenesis and progression. CONCLUSION The 48-gene TEP liquid-biopsy biomarkers will facilitate early screening of NSCLC and prolong the survival of cancer patients.
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Affiliation(s)
- Meiling Sheng
- Department of Respiration, Jinhua People's Hospital, Jinhua, Zhejiang 321000, China
| | - Zhaohui Dong
- Department of Intensive Care Unit, First Hospital of Huzhou, First Affiliated Hospital of Huzhou University, Huzhou, Zhejiang 313000, China
| | - Yanping Xie
- Department of Respiratory Medicine, First Hospital of Huzhou, First Affiliated Hospital of Huzhou University, Huzhou, Zhejiang 313000, China,
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15
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Lu S, Zhao K, Wang X, Liu H, Ainiwaer X, Xu Y, Ye M. Use of Laplacian Heat Diffusion Algorithm to Infer Novel Genes With Functions Related to Uveitis. Front Genet 2018; 9:425. [PMID: 30349554 PMCID: PMC6186792 DOI: 10.3389/fgene.2018.00425] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 09/10/2018] [Indexed: 12/17/2022] Open
Abstract
Uveitis is the inflammation of the uvea and is a serious eye disease that can cause blindness for middle-aged and young people. However, the pathogenesis of this disease has not been fully uncovered and thus renders difficulties in designing effective treatments. Completely identifying the genes related to this disease can help improve and accelerate the comprehension of uveitis. In this study, a new computational method was developed to infer potential related genes based on validated ones. We employed a large protein–protein interaction network reported in STRING, in which Laplacian heat diffusion algorithm was applied using validated genes as seed nodes. Except for the validated ones, all genes in the network were filtered by three tests, namely, permutation, association, and function tests, which evaluated the genes based on their specialties and associations to uveitis. Results indicated that 59 inferred genes were accessed, several of which were confirmed to be highly related to uveitis by literature review. In addition, the inferred genes were compared with those reported in a previous study, indicating that our reported genes are necessary supplements.
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Affiliation(s)
- Shiheng Lu
- Department of Ophthalmology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Pudong, China
| | - Ke Zhao
- Department of Ophthalmology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Pudong, China
| | - Xuefei Wang
- Department of Ophthalmology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Pudong, China
| | - Hui Liu
- Department of Ophthalmology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Pudong, China
| | - Xiamuxiya Ainiwaer
- Department of Ophthalmology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Pudong, China
| | - Yan Xu
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Min Ye
- Department of Ophthalmology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Pudong, China
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Chen L, Zhang YH, Zhang Z, Huang T, Cai YD. Inferring Novel Tumor Suppressor Genes with a Protein-Protein Interaction Network and Network Diffusion Algorithms. MOLECULAR THERAPY-METHODS & CLINICAL DEVELOPMENT 2018; 10:57-67. [PMID: 30069494 PMCID: PMC6068090 DOI: 10.1016/j.omtm.2018.06.007] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2018] [Accepted: 06/19/2018] [Indexed: 02/07/2023]
Abstract
Extensive studies on tumor suppressor genes (TSGs) are helpful to understand the pathogenesis of cancer and design effective treatments. However, identifying TSGs using traditional experiments is quite difficult and time consuming. Developing computational methods to identify possible TSGs is an alternative way. In this study, we proposed two computational methods that integrated two network diffusion algorithms, including Laplacian heat diffusion (LHD) and random walk with restart (RWR), to search possible genes in the whole network. These two computational methods were LHD-based and RWR-based methods. To increase the reliability of the putative genes, three strict screening tests followed to filter genes obtained by these two algorithms. After comparing the putative genes obtained by the two methods, we designated twelve genes (e.g., MAP3K10, RND1, and OTX2) as common genes, 29 genes (e.g., RFC2 and GUCY2F) as genes that were identified only by the LHD-based method, and 128 genes (e.g., SNAI2 and FGF4) as genes that were inferred only by the RWR-based method. Some obtained genes can be confirmed as novel TSGs according to recent publications, suggesting the utility of our two proposed methods. In addition, the reported genes in this study were quite different from those reported in a previous one.
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Affiliation(s)
- Lei Chen
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People’s Republic of China
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, People’s Republic of China
| | - Yu-Hang Zhang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People’s Republic of China
| | - Zhenghua Zhang
- Department of Clinical Oncology, Jing’an District Centre Hospital of Shanghai (Huashan Hospital Fudan University Jing’An Branch), Shanghai 200040, People’s Republic of China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People’s Republic of China
- Corresponding author: Tao Huang, Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People’s Republic of China.
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai 200444, People’s Republic of China
- Corresponding author: Yu-Dong Cai, School of Life Sciences, Shanghai University, Shanghai 200444, People’s Republic of China.
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Zhang TM, Huang T, Wang RF. Cross talk of chromosome instability, CpG island methylator phenotype and mismatch repair in colorectal cancer. Oncol Lett 2018; 16:1736-1746. [PMID: 30008861 PMCID: PMC6036478 DOI: 10.3892/ol.2018.8860] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Accepted: 05/22/2018] [Indexed: 12/20/2022] Open
Abstract
Colorectal cancer is a severe cancer associated with a high prevalence and fatality rate. There are three major mechanisms for colorectal cancer: (1) Chromosome instability (CIN), (2) CpG island methylator phenotype (CIMP) and (3) mismatch repair (MMR), of which CIN is the most common type. However, these subtypes are not exclusive and overlap. To investigate their biological mechanisms and cross talk, the gene expression profiles of 585 colorectal cancer patients with CIN, CIMP and MMR status records were collected. By comparing the CIN+ and CIN-samples, CIMP+ and CIMP-samples, MMR+ and MMR-samples with minimal redundancy maximal relevance (mRMR) and incremental feature selection (IFS) methods, the CIN, CIMP and MMR associated genes were selected. Unfortunately, there was little direct overlap among them. To investigate their indirect interactions, downstream genes of CIN, CIMP and MMR were identified using the random walk with restart (RWR) method and a greater overlap of downstream genes was indicated. The common downstream genes were involved in biosynthetic and metabolic pathways. These findings were consistent with the clinical observation of wide range metabolite aberrations in colorectal cancer. To conclude, the present study gave a gene level explanation of CIN, CIMP and MMR, but also showed the network level cross talk of CIN, CIMP and MMR. The common genes of CIN, CIMP and MMR may be useful for cross-subtype general colorectal cancer drug development.
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Affiliation(s)
- Tian-Ming Zhang
- Department of Colorectal and Anal Surgery, Jinhua Hospital of Zhejiang University, Jinhua, Zhejiang 321000, P.R. China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, P.R. China
| | - Rong-Fei Wang
- Department of Colorectal and Anal Surgery, Jinhua People's Hospital, Jinhua, Zhejiang 321000, P.R. China
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Computational Approach to Investigating Key GO Terms and KEGG Pathways Associated with CNV. BIOMED RESEARCH INTERNATIONAL 2018; 2018:8406857. [PMID: 29850576 PMCID: PMC5925134 DOI: 10.1155/2018/8406857] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2017] [Revised: 02/28/2018] [Accepted: 03/06/2018] [Indexed: 12/25/2022]
Abstract
Choroidal neovascularization (CNV) is a severe eye disease that leads to blindness, especially in the elderly population. Various endogenous and exogenous regulatory factors promote its pathogenesis. However, the detailed molecular biological mechanisms of CNV have not been fully revealed. In this study, by using advanced computational tools, a number of key gene ontology (GO) terms and KEGG pathways were selected for CNV. A total of 29 validated genes associated with CNV and 17,639 nonvalidated genes were encoded based on the features derived from the GO terms and KEGG pathways by using the enrichment theory. The widely accepted feature selection method-maximum relevance and minimum redundancy (mRMR)-was applied to analyze and rank the features. An extensive literature review for the top 45 ranking features was conducted to confirm their close associations with CNV. Identifying the molecular biological mechanisms of CNV as described by the GO terms and KEGG pathways may contribute to improving the understanding of the pathogenesis of CNV.
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