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Tao Q, Wu L, An J, Liu Z, Zhang K, Zhou L, Zhang X. Proteomic analysis of human aqueous humor from fuchs uveitis syndrome. Exp Eye Res 2024; 239:109752. [PMID: 38123010 DOI: 10.1016/j.exer.2023.109752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 11/25/2023] [Accepted: 12/11/2023] [Indexed: 12/23/2023]
Abstract
Fuchs uveitis syndrome (FUS) is a commonly misdiagnosed uveitis syndrome often presenting as an asymptomatic mild inflammatory condition until complications arise. The diagnosis of this disease remains clinical because of the lack of specific laboratory tests. The aqueous humor (AH) is a complex fluid containing nutrients and metabolic wastes from the eye. Changes in the AH protein provide important information for diagnosing intraocular diseases. This study aimed to analyze the proteomic profile of AH in individuals diagnosed with FUS and to identify potential biomarkers of the disease. We used liquid chromatography-tandem mass spectrometry-based proteomic methods to evaluate the AH protein profiles of all 37 samples, comprising 15 patients with FUS, six patients with Posner-Schlossman syndrome (PSS), and 16 patients with age-related cataract. A total of 538 proteins were identified from a comprehensive spectral library of 634 proteins. Subsequent differential expression analysis, enrichment analysis, and construction of key sub-networks revealed that the inflammatory response, complement activation and hypoxia might be crucial in mediating the process of FUS. The hypoxia inducible factor-1 may serve as a key regulator and therapeutic target. Additionally, the innate and adaptive immune responses are considered dominant in the patients with FUS. A diagnostic model was constructed using machine-learning algorithm to classify FUS, PSS, and normal controls. Two proteins, complement C1q subcomponent subunit B and secretogranin-1, were found to have the highest scores by the Extreme Gradient Boosting, suggesting their potential utility as a biomarker panel. Furthermore, these two proteins as biomarkers were validated in a cohort of 18 patients using high resolution multiple reaction monitoring assays. Therefore, this study contributes to advancing of the current knowledge of FUS pathogenesis and promotes the development of effective diagnostic strategies.
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Affiliation(s)
- Qingqin Tao
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
| | - Lingzi Wu
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China; Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Jinying An
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
| | | | - Kai Zhang
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), Tianjin Key Laboratory of Medical Epigenetics, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Lei Zhou
- School of Optometry, Department of Applied Biology and Chemical Technology, Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University, Hong Kong, China; Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park, Hong Kong, China
| | - Xiaomin Zhang
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China.
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Han S, Hong J, Yun SJ, Koo HJ, Kim TY. PWN: enhanced random walk on a warped network for disease target prioritization. BMC Bioinformatics 2023; 24:105. [PMID: 36944912 PMCID: PMC10031933 DOI: 10.1186/s12859-023-05227-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 03/13/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Extracting meaningful information from unbiased high-throughput data has been a challenge in diverse areas. Specifically, in the early stages of drug discovery, a considerable amount of data was generated to understand disease biology when identifying disease targets. Several random walk-based approaches have been applied to solve this problem, but they still have limitations. Therefore, we suggest a new method that enhances the effectiveness of high-throughput data analysis with random walks. RESULTS We developed a new random walk-based algorithm named prioritization with a warped network (PWN), which employs a warped network to achieve enhanced performance. Network warping is based on both internal and external features: graph curvature and prior knowledge. CONCLUSIONS We showed that these compositive features synergistically increased the resulting performance when applied to random walk algorithms, which led to PWN consistently achieving the best performance among several other known methods. Furthermore, we performed subsequent experiments to analyze the characteristics of PWN.
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Affiliation(s)
- Seokjin Han
- Standigm Inc., 70, Nonhyeon-ro 85-gil, Gangnam-gu, Seoul, 06234, Republic of Korea
| | - Jinhee Hong
- Standigm Inc., 70, Nonhyeon-ro 85-gil, Gangnam-gu, Seoul, 06234, Republic of Korea
| | - So Jeong Yun
- Standigm Inc., 70, Nonhyeon-ro 85-gil, Gangnam-gu, Seoul, 06234, Republic of Korea
| | - Hee Jung Koo
- Standigm UK Co., Ltd, 50-60 Station Road, Cambridge, CB1 2JH, UK.
| | - Tae Yong Kim
- Standigm Inc., 70, Nonhyeon-ro 85-gil, Gangnam-gu, Seoul, 06234, Republic of Korea.
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3
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Lu S, Wang H, Zhang J. Identification of uveitis-associated functions based on the feature selection analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment scores. Front Mol Neurosci 2022; 15:1007352. [PMID: 36157069 PMCID: PMC9493498 DOI: 10.3389/fnmol.2022.1007352] [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: 07/30/2022] [Accepted: 08/22/2022] [Indexed: 11/13/2022] Open
Abstract
Uveitis is a typical type of eye inflammation affecting the middle layer of eye (i.e., uvea layer) and can lead to blindness in middle-aged and young people. Therefore, a comprehensive study determining the disease susceptibility and the underlying mechanisms for uveitis initiation and progression is urgently needed for the development of effective treatments. In the present study, 108 uveitis-related genes are collected on the basis of literature mining, and 17,560 other human genes are collected from the Ensembl database, which are treated as non-uveitis genes. Uveitis- and non-uveitis-related genes are then encoded by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment scores based on the genes and their neighbors in STRING, resulting in 20,681 GO term features and 297 KEGG pathway features. Subsequently, we identify functions and biological processes that can distinguish uveitis-related genes from other human genes by using an integrated feature selection method, which incorporate feature filtering method (Boruta) and four feature importance assessment methods (i.e., LASSO, LightGBM, MCFS, and mRMR). Some essential GO terms and KEGG pathways related to uveitis, such as GO:0001841 (neural tube formation), has04612 (antigen processing and presentation in human beings), and GO:0043379 (memory T cell differentiation), are identified. The plausibility of the association of mined functional features with uveitis is verified on the basis of the literature. Overall, several advanced machine learning methods are used in the current study to uncover specific functions of uveitis and provide a theoretical foundation for the clinical treatment of uveitis.
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Affiliation(s)
- Shiheng Lu
- Department of Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, China
- 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 Research Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
- *Correspondence: Shiheng Lu,
| | - Hui Wang
- Department of Orthopedics, Shanghai Yangpu Hospital of Traditional Chinese Medicine, 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 Research Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
- Jian Zhang,
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Pang Y, Liu B. SelfAT-Fold: Protein Fold Recognition Based on Residue-Based and Motif-Based Self-Attention Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1861-1869. [PMID: 33090951 DOI: 10.1109/tcbb.2020.3031888] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The protein fold recognition is a fundamental and crucial step of tertiary structure determination. In this regard, several computational predictors have been proposed. Recently, the predictive performance has been obviously improved by the fold-specific features generated by deep learning techniques. However, these methods failed to measure the global associations among residues or motifs along the protein sequences. Furthermore, these deep learning techniques are often treated as black boxes without interpretability. Inspired by the similarities between protein sequences and natural language sentences, we applied the self-attention mechanism derived from natural language processing (NLP) field to protein fold recognition. The motif-based self-attention network (MSAN) and the residue-based self-attention network (RSAN) were constructed based on a training set to capture the global associations among the structure motifs and residues along the protein sequences, respectively. The fold-specific attention features trained and generated from the training set were then combined with Support Vector Machines (SVMs) to predict the samples in the widely used LE benchmark dataset, which is fully independent from the training set. Experimental results showed that the proposed two SelfAT-Fold predictors outperformed 34 existing state-of-the-art computational predictors. The two SelfAT-Fold predictors were further tested on an independent dataset SCOP_TEST, and they can achieve stable performance. Furthermore, the fold-specific attention features can be used to analyse the characteristics of protein folds. The trained models and data of SelfAT-Fold can be downloaded from http://bliulab.net/selfAT_fold/.
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Ben Abdelghani K, Gzam Y, Fazaa A, Miladi S, Sellami M, Souabni L, Kassab S, Chekili S, Zakraoui L, Laater A. Non-radiographic axial spondyloarthritis in Tunisia: main characteristics and detailed comparison with ankylosing spondylitis. Clin Rheumatol 2020; 40:1361-1367. [PMID: 32974836 DOI: 10.1007/s10067-020-05415-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 08/25/2020] [Accepted: 09/16/2020] [Indexed: 11/28/2022]
Abstract
OBJECTIVES The aim of the present study is to compare the clinical features, disease activity, and physical impairment between non-radiographic axial spondyloarthritis and ankylosing spondylitis in Tunisian patients. METHODS This is a retrospective study conducted in a single rheumatology center in Tunisia. Patients with axial spondyloarthritis fulfilling the 2009 ASAS criteria were included. The various spondyloarthritis-related variables were compared between non-radiographic axial spondyloarthritis and ankylosing spondylitis. p Values below 0.05 were considered statistically significant. RESULTS Among 200 patients with axial spondyloarthritis, 40 had non-radiographic axial spondyloarthritis and 160 had ankylosing spondylitis. The non-radiographic axial spondyloarthritis patients were more frequently female, were younger, and had shorter disease duration. Patients with non-radiographic axial spondyloarthritis experienced enthesitis more frequently compared with ankylosing spondylitis patients. Psoriasis was more frequent in non-radiographic axial spondyloarthritis group, while inflammatory bowel disease was more frequent in ankylosing spondylitis group. The C-reactive protein level and functional score were significantly higher in patients with ankylosing spondylitis compared with non-radiographic axial spondyloarthritis. Tumor necrosis factor inhibitors were offered significantly more often to the ankylosing spondylitis group. There was no statistically significant difference between the 2 groups in other spondyloarthritis parameters. CONCLUSION The non-radiographic axial spondyloarthritis is characterized mainly by a marked female prevalence, a higher enthesitis prevalence, and a better physical function. KEY POINTS • Patients with nr-axSpA in Tunisia are more frequently female and have shorter disease duration compared with those with AS. • Peripheral manifestations were similar between nr-axSpA and AS patients except for enthesitis which were more frequent within nr-axSpA patients. • The disease activity is similar between the 2 groups of axSpA but the physical function is better within nr-axSpA patients.
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Affiliation(s)
- Kawther Ben Abdelghani
- Rheumatology Department, Mongi Slim Hospital, La Marsa, Tunisia.,Faculty of Medecine, University of Tunis el Manar, Tunis, Tunisia
| | - Yosra Gzam
- Rheumatology Department, Mongi Slim Hospital, La Marsa, Tunisia. .,Faculty of Medecine, University of Tunis el Manar, Tunis, Tunisia.
| | - Alia Fazaa
- Rheumatology Department, Mongi Slim Hospital, La Marsa, Tunisia.,Faculty of Medecine, University of Tunis el Manar, Tunis, Tunisia
| | - Saoussen Miladi
- Rheumatology Department, Mongi Slim Hospital, La Marsa, Tunisia.,Faculty of Medecine, University of Tunis el Manar, Tunis, Tunisia
| | - Meriem Sellami
- Rheumatology Department, Mongi Slim Hospital, La Marsa, Tunisia.,Faculty of Medecine, University of Tunis el Manar, Tunis, Tunisia
| | - Leila Souabni
- Rheumatology Department, Mongi Slim Hospital, La Marsa, Tunisia.,Faculty of Medecine, University of Tunis el Manar, Tunis, Tunisia
| | - Selma Kassab
- Rheumatology Department, Mongi Slim Hospital, La Marsa, Tunisia.,Faculty of Medecine, University of Tunis el Manar, Tunis, Tunisia
| | - Selma Chekili
- Rheumatology Department, Mongi Slim Hospital, La Marsa, Tunisia.,Faculty of Medecine, University of Tunis el Manar, Tunis, Tunisia
| | - Leith Zakraoui
- Rheumatology Department, Mongi Slim Hospital, La Marsa, Tunisia.,Faculty of Medecine, University of Tunis el Manar, Tunis, Tunisia
| | - Ahmed Laater
- Rheumatology Department, Mongi Slim Hospital, La Marsa, Tunisia.,Faculty of Medecine, University of Tunis el Manar, Tunis, Tunisia
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Tang J, Mou M, Wang Y, Luo Y, Zhu F. MetaFS: Performance assessment of biomarker discovery in metaproteomics. Brief Bioinform 2020; 22:5854399. [PMID: 32510556 DOI: 10.1093/bib/bbaa105] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 04/17/2020] [Accepted: 05/05/2020] [Indexed: 12/19/2022] Open
Abstract
Metaproteomics suffers from the issues of dimensionality and sparsity. Data reduction methods can maximally identify the relevant subset of significant differential features and reduce data redundancy. Feature selection (FS) methods were applied to obtain the significant differential subset. So far, a variety of feature selection methods have been developed for metaproteomic study. However, due to FS's performance depended heavily on the data characteristics of a given research, the well-suitable feature selection method must be carefully selected to obtain the reproducible differential proteins. Moreover, it is critical to evaluate the performance of each FS method according to comprehensive criteria, because the single criterion is not sufficient to reflect the overall performance of the FS method. Therefore, we developed an online tool named MetaFS, which provided 13 types of FS methods and conducted the comprehensive evaluation on the complex FS methods using four widely accepted and independent criteria. Furthermore, the function and reliability of MetaFS were systematically tested and validated via two case studies. In sum, MetaFS could be a distinguished tool for discovering the overall well-performed FS method for selecting the potential biomarkers in microbiome studies. The online tool is freely available at https://idrblab.org/metafs/.
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Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:1573543. [PMID: 32454877 PMCID: PMC7232712 DOI: 10.1155/2020/1573543] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 04/14/2020] [Accepted: 04/23/2020] [Indexed: 01/07/2023]
Abstract
Drugs are an important way to treat various diseases. However, they inevitably produce side effects, bringing great risks to human bodies and pharmaceutical companies. How to predict the side effects of drugs has become one of the essential problems in drug research. Designing efficient computational methods is an alternative way. Some studies paired the drug and side effect as a sample, thereby modeling the problem as a binary classification problem. However, the selection of negative samples is a key problem in this case. In this study, a novel negative sample selection strategy was designed for accessing high-quality negative samples. Such strategy applied the random walk with restart (RWR) algorithm on a chemical-chemical interaction network to select pairs of drugs and side effects, such that drugs were less likely to have corresponding side effects, as negative samples. Through several tests with a fixed feature extraction scheme and different machine-learning algorithms, models with selected negative samples produced high performance. The best model even yielded nearly perfect performance. These models had much higher performance than those without such strategy or with another selection strategy. Furthermore, it is not necessary to consider the balance of positive and negative samples under such a strategy.
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8
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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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9
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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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10
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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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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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Abstract
Computational identification of special protein molecules is a key issue in understanding protein function. It can guide molecular experiments and help to save costs. I assessed 18 papers published in the special issue of Int. J. Mol. Sci., and also discussed the related works. The computational methods employed in this special issue focused on machine learning, network analysis, and molecular docking. New methods and new topics were also proposed. There were in addition several wet experiments, with proven results showing promise. I hope our special issue will help in protein molecules identification researches.
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Zou Q, He W. Special Protein Molecules Computational Identification. Int J Mol Sci 2018; 19:ijms19020536. [PMID: 29439426 PMCID: PMC5855758 DOI: 10.3390/ijms19020536] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Revised: 02/02/2018] [Accepted: 02/10/2018] [Indexed: 01/29/2023] Open
Abstract
Computational identification of special protein molecules is a key issue in understanding protein function. It can guide molecular experiments and help to save costs. I assessed 18 papers published in the special issue of Int. J. Mol. Sci., and also discussed the related works. The computational methods employed in this special issue focused on machine learning, network analysis, and molecular docking. New methods and new topics were also proposed. There were in addition several wet experiments, with proven results showing promise. I hope our special issue will help in protein molecules identification researches.
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Affiliation(s)
- Quan Zou
- School of Computer Science and Technology, Tianjin University, Tianjin 300354, China.
| | - Wenying He
- School of Computer Science and Technology, Tianjin University, Tianjin 300354, China.
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Davoudi S, Chang VS, Navarro-Gomez D, Stanwyck LK, Sevgi DD, Papavasileiou E, Ren A, Uchiyama E, Sullivan L, Lobo AM, Papaliodis GN, Sobrin L. Association of genetic variants in RAB23 and ANXA11 with uveitis in sarcoidosis. Mol Vis 2018; 24:59-74. [PMID: 29416296 PMCID: PMC5783744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Accepted: 01/19/2018] [Indexed: 11/24/2022] Open
Abstract
PURPOSE Uveitis occurs in a subset of patients with sarcoidosis. The purpose of this study was to determine whether genetic variants that have been associated previously with overall sarcoidosis are associated with increased risk of developing uveitis. METHODS Seventy-seven subjects were enrolled, including 45 patients diagnosed with sarcoidosis-related uveitis as cases and 32 patients with systemic sarcoidosis without ocular involvement as controls. Thirty-eight single nucleotide polymorphisms (SNPs) previously associated with sarcoidosis, sarcoidosis severity, or other organ-specific sarcoidosis involvement were identified. Allele frequencies in ocular sarcoidosis cases versus controls were compared using the chi-square test, and p values were corrected for multiple hypotheses testing using permutation. All analyses were conducted with PLINK. RESULTS SNPs rs1040461 and rs61860052, in ras-related protein RAS23 (RAB23) and annexin A11 (ANXA11) genes, respectively, were associated with sarcoidosis-associated uveitis. The T allele of rs1040461 and the A allele of rs61860052 were found to be more prevalent in ocular sarcoidosis cases. These associations remained after correction for the multiple hypotheses tested (p=0.01 and p=0.02). In a subanalysis of Caucasian Americans only, two additional variants within the major histocompatibility complex (MHC) genes on chromosome 6, in HLA-DRB5 and HLA-DRB1, were associated with uveitis as well (p=0.009 and p=0.04). CONCLUSIONS Genetic variants in RAB23 and ANXA11 genes were associated with an increased risk of sarcoidosis-associated uveitis. These loci have previously been associated with overall sarcoidosis risk.
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Affiliation(s)
- Samaneh Davoudi
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA
| | - Victoria S. Chang
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA
| | - Daniel Navarro-Gomez
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA
| | - Lynn K. Stanwyck
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA
| | - Damla Duriye Sevgi
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA
| | - Evangelia Papavasileiou
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA
| | - Aiai Ren
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA
| | - Eduardo Uchiyama
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA
| | - Lynn Sullivan
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA
| | - Ann-Marie Lobo
- Department of Ophthalmology, University of Illinois-Chicago, Chicago, IL
| | - George N. Papaliodis
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA
| | - Lucia Sobrin
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA
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Chen L, Liu T, Zhao X. Inferring anatomical therapeutic chemical (ATC) class of drugs using shortest path and random walk with restart algorithms. Biochim Biophys Acta Mol Basis Dis 2017; 1864:2228-2240. [PMID: 29247833 DOI: 10.1016/j.bbadis.2017.12.019] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2017] [Revised: 12/01/2017] [Accepted: 12/12/2017] [Indexed: 01/02/2023]
Abstract
The anatomical therapeutic chemical (ATC) classification system is a widely accepted drug classification scheme. This system comprises five levels and includes several classes in each level. Drugs are classified into classes according to their therapeutic effects and characteristics. The first level includes 14 main classes. In this study, we proposed two network-based models to infer novel potential chemicals deemed to belong in the first level of ATC classification. To build these models, two large chemical networks were constructed using the chemical-chemical interaction information retrieved from the Search Tool for Interactions of Chemicals (STITCH). Two classic network algorithms, shortest path (SP) and random walk with restart (RWR) algorithms, were executed on the corresponding network to mine novel chemicals for each ATC class using the validated drugs in a class as seed nodes. Then, the obtained chemicals yielded by these two algorithms were further evaluated by a permutation test and an association test. The former can exclude chemicals produced by the structure of the network, i.e., false positive discoveries. By contrast, the latter identifies the most important chemicals that have strong associations with the ATC class. Comparisons indicated that the two models can provide quite dissimilar results, suggesting that the results yielded by one model can be essential supplements for those obtained by the other model. In addition, several representative inferred chemicals were analyzed to confirm the reliability of the results generated by the two models. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.
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Affiliation(s)
- Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, People's Republic of China.
| | - Tao Liu
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, People's Republic of China.
| | - Xian Zhao
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, People's Republic of China
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17
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Zhang J, Suo Y, Liu M, Xu X. Identification of genes related to proliferative diabetic retinopathy through RWR algorithm based on protein-protein interaction network. Biochim Biophys Acta Mol Basis Dis 2017; 1864:2369-2375. [PMID: 29237571 DOI: 10.1016/j.bbadis.2017.11.017] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Revised: 11/15/2017] [Accepted: 11/25/2017] [Indexed: 12/14/2022]
Abstract
Proliferative diabetic retinopathy (PDR) is one of the most common complications of diabetes and can lead to blindness. Proteomic studies have provided insight into the pathogenesis of PDR and a series of PDR-related genes has been identified but are far from fully characterized because the experimental methods are expensive and time consuming. In our previous study, we successfully identified 35 candidate PDR-related genes through the shortest-path algorithm. In the current study, we developed a computational method using the random walk with restart (RWR) algorithm and the protein-protein interaction (PPI) network to identify potential PDR-related genes. After some possible genes were obtained by the RWR algorithm, a three-stage filtration strategy, which includes the permutation test, interaction test and enrichment test, was applied to exclude potential false positives caused by the structure of PPI network, the poor interaction strength, and the limited similarity on gene ontology (GO) terms and biological pathways. As a result, 36 candidate genes were discovered by the method which was different from the 35 genes reported in our previous study. A literature review showed that 21 of these 36 genes are supported by previous experiments. These findings suggest the robustness and complementary effects of both our efforts using different computational methods, thus providing an alternative method to study PDR pathogenesis.
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Affiliation(s)
- Jian Zhang
- Department of Ophthalmology, Shanghai General Hospital, School of Medicine, Shanghai JiaoTong University, Shanghai, China; Shanghai Key Laboratory of Fundus Disease, Shanghai, China; Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China
| | - Yan Suo
- Department of Ophthalmology, Shanghai General Hospital, School of Medicine, Shanghai JiaoTong University, Shanghai, China; Shanghai Key Laboratory of Fundus Disease, Shanghai, China; Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China
| | - Min Liu
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Xun Xu
- Department of Ophthalmology, Shanghai General Hospital, School of Medicine, Shanghai JiaoTong University, Shanghai, China; Shanghai Key Laboratory of Fundus Disease, Shanghai, China; Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China.
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18
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Yuan F, Lu W. Prediction of potential drivers connecting different dysfunctional levels in lung adenocarcinoma via a protein-protein interaction network. Biochim Biophys Acta Mol Basis Dis 2017; 1864:2284-2293. [PMID: 29197663 DOI: 10.1016/j.bbadis.2017.11.018] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Revised: 11/13/2017] [Accepted: 11/23/2017] [Indexed: 12/14/2022]
Abstract
Lung cancer is a serious disease that threatens an affected individual's life. Its pathogenesis has not yet to be fully described, thereby impeding the development of effective treatments and preventive measures. "Cancer driver" theory considers that tumor initiation can be associated with a number of specific mutations in genes called cancer driver genes. Four omics levels, namely, (1) methylation, (2) microRNA, (3) mutation, and (4) mRNA levels, are utilized to cluster cancer driver genes. In this study, the known dysfunctional genes of these four levels were used to identify novel driver genes of lung adenocarcinoma, a subtype of lung cancer. These genes could contribute to the initiation and progression of lung adenocarcinoma in at least two levels. First, random walk with restart algorithm was performed on a protein-protein interaction (PPI) network constructed with PPI information in STRING by using known dysfunctional genes as seed nodes for each level, thereby yielding four groups of possible genes. Second, these genes were further evaluated in a test strategy to exclude false positives and select the most important ones. Finally, after conducting an intersection operation in any two groups of genes, we obtained several inferred driver genes that contributed to the initiation of lung adenocarcinoma in at least two omics levels. Several genes from these groups could be confirmed according to recently published studies. The inferred genes reported in this study were also different from those described in a previous study, suggesting that they can be used as essential supplementary data for investigations on the initiation of lung adenocarcinoma. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.
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Affiliation(s)
- Fei Yuan
- Department of Science & Technology, Binzhou Medical University Hospital, Binzhou 256603, Shandong, China.
| | - WenCong Lu
- Department of Chemistry, Shanghai University, Shanghai 200072, China.
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Zhu L, Su F, Xu Y, Zou Q. Network-based method for mining novel HPV infection related genes using random walk with restart algorithm. Biochim Biophys Acta Mol Basis Dis 2017; 1864:2376-2383. [PMID: 29197659 DOI: 10.1016/j.bbadis.2017.11.021] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Revised: 11/03/2017] [Accepted: 11/26/2017] [Indexed: 12/27/2022]
Abstract
The human papillomavirus (HPV), a common virus that infects the reproductive tract, may lead to malignant changes within the infection area in certain cases and is directly associated with such cancers as cervical cancer, anal cancer, and vaginal cancer. Identification of novel HPV infection related genes can lead to a better understanding of the specific signal pathways and cellular processes related to HPV infection, providing information for the development of more efficient therapies. In this study, several novel HPV infection related genes were predicted by a computation method based on the known genes involved in HPV infection from HPVbase. This method applied the algorithm of random walk with restart (RWR) to a protein-protein interaction (PPI) network. The candidate genes were further filtered by the permutation and association tests. These steps eliminated genes occupying special positions in the PPI network and selected key genes with strong associations to known HPV infection related genes based on the interaction confidence and functional similarity obtained from published databases, such as STRING, gene ontology (GO) terms and KEGG pathways. Our study identified 104 novel HPV infection related genes, a number of which were confirmed to relate to the infection processes and complications of HPV infection, as reported in the literature. These results demonstrate the reliability of our method in identifying HPV infection related genes. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.
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Affiliation(s)
- Liucun Zhu
- School of Life Sciences, Shanghai University, Shanghai 200444, China.
| | - Fangchu Su
- School of Life Sciences, Shanghai University, Shanghai 200444, China.
| | - YaoChen Xu
- Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
| | - Quan Zou
- School of Computer Science and Technology, TianJin University, Tianjin 300350, China.
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