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Schweickart A, Chetnik K, Batra R, Kaddurah-Daouk R, Suhre K, Halama A, Krumsiek J. AutoFocus: a hierarchical framework to explore multi-omic disease associations spanning multiple scales of biomolecular interaction. Commun Biol 2024; 7:1094. [PMID: 39237774 PMCID: PMC11377741 DOI: 10.1038/s42003-024-06724-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 08/13/2024] [Indexed: 09/07/2024] Open
Abstract
Recent advances in high-throughput measurement technologies have enabled the analysis of molecular perturbations associated with disease phenotypes at the multi-omic level. Such perturbations can range in scale from fluctuations of individual molecules to entire biological pathways. Data-driven clustering algorithms have long been used to group interactions into interpretable functional modules; however, these modules are typically constrained to a fixed size or statistical cutoff. Furthermore, modules are often analyzed independently of their broader biological context. Consequently, such clustering approaches limit the ability to explore functional module associations with disease phenotypes across multiple scales. Here, we introduce AutoFocus, a data-driven method that hierarchically organizes biomolecules and tests for phenotype enrichment at every level within the hierarchy. As a result, the method allows disease-associated modules to emerge at any scale. We evaluated this approach using two datasets: First, we explored associations of biomolecules from the multi-omic QMDiab dataset (n = 388) with the well-characterized type 2 diabetes phenotype. Secondly, we utilized the ROS/MAP Alzheimer's disease dataset (n = 500), consisting of high-throughput measurements of brain tissue to explore modules associated with multiple Alzheimer's Disease-related phenotypes. Our method identifies modules that are multi-omic, span multiple pathways, and vary in size. We provide an interactive tool to explore this hierarchy at different levels and probe enriched modules, empowering users to examine the full hierarchy, delve into biomolecular drivers of disease phenotype within a module, and incorporate functional annotations.
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Affiliation(s)
- Annalise Schweickart
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Kelsey Chetnik
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Richa Batra
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Bioinformatics Core, Weill Cornell Medical College-Qatar Education City, Doha, Qatar
| | - Anna Halama
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Bioinformatics Core, Weill Cornell Medical College-Qatar Education City, Doha, Qatar
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
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2
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Lan Y, Yang X, Wei Y, Tian Z, Zhang L, Zhou J. Explore Key Genes and Mechanisms Involved in Colon Cancer Progression Based on Bioinformatics Analysis. Appl Biochem Biotechnol 2024:10.1007/s12010-023-04812-3. [PMID: 38294732 DOI: 10.1007/s12010-023-04812-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/09/2023] [Indexed: 02/01/2024]
Abstract
To explore underlying mechanisms related to the progression of colon cancer and identify hub genes associated with the prognosis of patients with colon cancer. GSE10950 and GSE62932 were downloaded from the Gene Expression Omnibus (GEO) database. GEO2R was utilized to screen out the differentially expressed genes (DEGs). Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted on DEGs. Moreover, STRING and Cytoscape software were utilized for establishing the network of protein-protein interaction (PPI) and identifying hub genes. Afterward, data from The Cancer Genome Atlas (TCGA) was utilized for identifying prognosis-related hub genes by Kaplan-Meier survival analysis. Colon cancer cell line LOVO and human normal intestinal epithelial cell line NCM-460 were exploited to demonstrate the differential expression of selected hub genes through RT-qPCR and western blot. The LOVO cells were transfected to regulate expressions of prognosis-associated genes, followed by exploring the effects of those genes on prognosis by Cell Counting Kit-8 assay and colony-forming assay for cancer cell proliferation, cell scratch test and transwell migration assay for cancer cell migration and Annexin V-PE/7-AAD double staining as well as flow cytometry for cancer cell apoptosis. In this study, 266 common DEGs were obtained from the intersection of two datasets. The GO analysis suggested the common DEGs mainly participated in the one-carbon metabolic process, cell cycle G2/M phase transition, organelle fission, cell cycle phase transition regulation, and regulation of mitotic cell cycle phase transition. The KEGG analysis demonstrated the common DEGs were related to the p53 signaling pathway, nitrogen metabolism, mineral absorption, and cell cycle. 10 hub genes including CCNB1, KIF4A, TPX2, MT1F, PRC1, PLK4, CALD1, MMP9, CLCA1, and MMP1 were identified and CCNB1, CLCA1, and PLK4 were prognosis-related. Increased expression of CCNB1, CLCA1, and PLK4 restrained proliferation as well as migration of cancer cells and induced apoptosis of cancer cells. CCNB1, KIF4A, TPX2, MT1F, PRC1, PLK4, CALD1, MMP9, CLCA1, and MMP1 were identified as hub genes and CCNB1, CLCA1, and PLK4 could inhibit the progression of colon cancer through inhibiting proliferation as well as migration of the cancer cell and promoting apoptosis of cancer cell.
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Affiliation(s)
- Yongting Lan
- Department of Gastroenterology, Zibo Central Hospital, Zibo, 255036, Shandong, China
| | - Xiuzhen Yang
- Department of Clinical Laboratory, Zibo Central Hospital, Zibo, 255036, Shandong, China
| | - Yulian Wei
- Department of Nursing, Zibo Central Hospital, Zibo, 255036, Shandong, China
| | - Zhaobing Tian
- Department of Clinical Laboratory, Zibo Cancer Hospital, Zibo, 255036, Shandong, China
| | - Lina Zhang
- Department of Nursing, Zibo Central Hospital, Zibo, 255036, Shandong, China.
| | - Jian Zhou
- Center of Translational Medicine, Zibo Central Hospital, 54 Gongqingtuan Xi Road, Zibo, 255036, Shandong, China.
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3
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Hasan MAM, Maniruzzaman M, Shin J. Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning. Sci Rep 2023; 13:3771. [PMID: 36882493 PMCID: PMC9992474 DOI: 10.1038/s41598-023-30851-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 03/02/2023] [Indexed: 03/09/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common lethal malignancy of the liver worldwide. Thus, it is important to dig the key genes for uncovering the molecular mechanisms and to improve diagnostic and therapeutic options for HCC. This study aimed to encompass a set of statistical and machine learning computational approaches for identifying the key candidate genes for HCC. Three microarray datasets were used in this work, which were downloaded from the Gene Expression Omnibus Database. At first, normalization and differentially expressed genes (DEGs) identification were performed using limma for each dataset. Then, support vector machine (SVM) was implemented to determine the differentially expressed discriminative genes (DEDGs) from DEGs of each dataset and select overlapping DEDGs genes among identified three sets of DEDGs. Enrichment analysis was performed on common DEDGs using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and the central hub genes were identified depending on the degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness criteria using CytoHubba. Simultaneously, significant modules were selected using MCODE scores and identified their associated genes from the PPI networks. Moreover, metadata were created by listing all hub genes from previous studies and identified significant meta-hub genes whose occurrence frequency was greater than 3 among previous studies. Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes. Two independent test datasets (GSE76427 and TCGA-LIHC) were utilized to validate these key candidate genes using the area under the curve. Moreover, the prognostic potential of these six key candidate genes was also evaluated on the TCGA-LIHC cohort using survival analysis.
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Affiliation(s)
- Md Al Mehedi Hasan
- School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, 965-8580, Japan.,Department of Computer Science and Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh
| | - Md Maniruzzaman
- School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, 965-8580, Japan.,Statistics Discipline, Khulna University, Khulna, 9208, Bangladesh
| | - Jungpil Shin
- School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, 965-8580, Japan.
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4
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Zhang Y, Shi W, Sun Y. A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology. BMC Genomics 2023; 24:76. [PMID: 36797662 PMCID: PMC9936134 DOI: 10.1186/s12864-023-09157-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 01/31/2023] [Indexed: 02/18/2023] Open
Abstract
Since genes do not function individually, the gene module is considered an important tool for interpreting gene expression profiles. In order to consider both functional similarity and expression similarity in module identification, GMIGAGO, a functional Gene Module Identification algorithm based on Genetic Algorithm and Gene Ontology, was proposed in this work. GMIGAGO is an overlapping gene module identification algorithm, which mainly includes two stages: In the first stage (initial identification of gene modules), Improved Partitioning Around Medoids Based on Genetic Algorithm (PAM-GA) is used for the initial clustering on gene expression profiling, and traditional gene co-expression modules can be obtained. Only similarity of expression levels is considered at this stage. In the second stage (optimization of functional similarity within gene modules), Genetic Algorithm for Functional Similarity Optimization (FSO-GA) is used to optimize gene modules based on gene ontology, and functional similarity within gene modules can be improved. Without loss of generality, we compared GMIGAGO with state-of-the-art gene module identification methods on six gene expression datasets, and GMIGAGO identified the gene modules with the highest functional similarity (much higher than state-of-the-art algorithms). GMIGAGO was applied in BRCA, THCA, HNSC, COVID-19, Stem, and Radiation datasets, and it identified some interesting modules which performed important biological functions. The hub genes in these modules could be used as potential targets for diseases or radiation protection. In summary, GMIGAGO has excellent performance in mining molecular mechanisms, and it can also identify potential biomarkers for individual precision therapy.
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Affiliation(s)
- Yan Zhang
- grid.440686.80000 0001 0543 8253College of Environmental Science and Engineering, Dalian Maritime University, 116026 Dalian, Liaoning China
| | - Weiyu Shi
- grid.440686.80000 0001 0543 8253College of Maritime Economics & Management, Dalian Maritime University, 116026 Dalian, Liaoning China
| | - Yeqing Sun
- College of Environmental Science and Engineering, Dalian Maritime University, 116026, Dalian, Liaoning, China.
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5
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Zhao S, Zhang D, Liu S, Huang J. The roles of NOP56 in cancer and SCA36. Pathol Oncol Res 2023; 29:1610884. [PMID: 36741964 PMCID: PMC9892063 DOI: 10.3389/pore.2023.1610884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 01/06/2023] [Indexed: 01/20/2023]
Abstract
NOP56 is a highly conserved nucleolar protein. Amplification of the intron GGCCTG hexanucleotide repeat sequence of the NOP56 gene results in spinal cerebellar ataxia type 36 (SCA36). NOP56 contains an N-terminal domain, a coiled-coil domain, and a C-terminal domain. Nucleolar protein NOP56 is significantly abnormally expressed in a number of malignant tumors, and its mechanism is different in different tumors, but its regulatory mechanism in most tumors has not been fully explored. NOP56 promotes tumorigenesis in some cancers and inhibits tumorigenesis in others. In addition, NOP56 is associated with methylation in some tumors, suggesting that NOP56 has the potential to become a tumor-specific marker. This review focuses on the structure, function, related signaling pathways, and role of NOP56 in the progression of various malignancies, and discusses the progression of NOP56 in neurodegenerative and other diseases.
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Affiliation(s)
- Shimin Zhao
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China,Jiangxi Province Key Laboratory of Molecular Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Dongdong Zhang
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China,Jiangxi Province Key Laboratory of Molecular Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Sicheng Liu
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China,Jiangxi Province Key Laboratory of Molecular Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jun Huang
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China,*Correspondence: Jun Huang,
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6
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You Y, Lai X, Pan Y, Zheng H, Vera J, Liu S, Deng S, Zhang L. Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther 2022; 7:156. [PMID: 35538061 PMCID: PMC9090746 DOI: 10.1038/s41392-022-00994-0] [Citation(s) in RCA: 74] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 03/14/2022] [Accepted: 04/05/2022] [Indexed: 02/08/2023] Open
Abstract
Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.
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Affiliation(s)
- Yujie You
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Xin Lai
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, 91052, Germany
| | - Yi Pan
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Room D513, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, 518055, China
| | - Huiru Zheng
- School of Computing, Ulster University, Belfast, BT15 1ED, UK
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, 91052, Germany
| | - Suran Liu
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Senyi Deng
- Institute of Thoracic Oncology, Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610065, China.
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, China.
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, 310024, China.
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China.
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7
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Short-Term Load Forecasting of Distributed Energy System Based on Kernel Principal Component Analysis and KELM Optimized by Fireworks Algorithm. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112412014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Accurate and stable load forecasting has great significance to ensure the safe operation of distributed energy system. For the purpose of improving the accuracy and stability of distributed energy system load forecasting, a forecasting model in view of kernel principal component analysis (KPCA), kernel extreme learning machine (KELM) and fireworks algorithm (FWA) is proposed. First, KPCA modal is used to reduce the dimension of the feature, thus redundant input samples are merged. Next, FWA is employed to optimize the parameters C and σ of KELM. Lastly, the load forecasting modal of KPCA-FWA-KELM is established. The relevant data of a distributed energy system in Beijing, China, is selected for training test to verify the effectiveness of the proposed method. The results show that the new hybrid KPCA-FWA-KELM method has superior performance, robustness and versatility in load prediction of distributed energy systems.
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8
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Rong MH, Li JD, Zhong LY, Huang YZ, Chen J, Xie LY, Qin RX, He XL, Zhu ZH, Huang SN, Zhou XG. CCNB1 promotes the development of hepatocellular carcinoma by mediating DNA replication in the cell cycle. Exp Biol Med (Maywood) 2021; 247:395-408. [PMID: 34743578 DOI: 10.1177/15353702211049149] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
In our studies, cyclin B1 (CCNB1) mRNA and protein were overexpressed in hepatocellular carcinoma (HCC) tissues compared with non-HCC tissues. Moreover, CCNB1 was overexpressed in the serum of HCC patients. The expression of CCNB1 was associated with several crucial clinicopathologic characteristics, and the HCC patients with overexpressed CCNB1 had worse overall survival outcomes. In the screening of interactional genes, a total of 266 upregulated co-expression genes, which were positively associated with CCNB1, were selected from the datasets, and 67 downregulated co-expression genes, which were negatively associated with CCNB1, were identified. The key genes might be functionally enriched in DNA replication and the cell cycle pathways. CDC20, CCNA2, PLK1, and FTCD were selected for further research because they were highly connected in the protein-protein interaction networks. Upregulated CDC20, CCNA2, and PLK1 and downregulated FTCD might result in undesirable overall survival outcomes for HCC patients. The univariate Cox analysis results showed that CDC20 and PLK1 might be two independent risk factors, while FTCD might be protective in HCC. Therefore, CCNB1 may participate in the cell cycle of HCC by regulating DNA replication, and CCNB1 may provide a direction for the diagnosis of early-stage HCC and targeted HCC therapy.
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Affiliation(s)
- Min-Hua Rong
- Research Department, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China
| | - Jian-Di Li
- Research Department, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China
| | - Lu-Yang Zhong
- Research Department, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China
| | - Yu-Zhen Huang
- Research Department, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China
| | - Juan Chen
- Research Department, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China
| | - Li-Yuan Xie
- Research Department, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China
| | - Rong-Xing Qin
- Research Department, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China
| | - Xiao-Lian He
- Research Department, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China
| | - Zhan-Hui Zhu
- Research Department, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China
| | - Su-Ning Huang
- Department of Radiotherapy, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China
| | - Xian-Guo Zhou
- Research Department, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China
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9
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Zhang Z, Wu H, Peng Q, Xie Z, Chen F, Ma Y, Zhang Y, Zhou Y, Yang J, Chen C, Li S, Zhang Y, Tian W, Wang Y, Xu Y, Luo H, Zhu M, Kuang YQ, Yu J, Wang K. Integration of Molecular Inflammatory Interactome Analyses Reveals Dynamics of Circulating Cytokines and Extracellular Vesicle Long Non-Coding RNAs and mRNAs in Heroin Addicts During Acute and Protracted Withdrawal. Front Immunol 2021; 12:730300. [PMID: 34489980 PMCID: PMC8416766 DOI: 10.3389/fimmu.2021.730300] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 08/04/2021] [Indexed: 01/01/2023] Open
Abstract
Heroin addiction and withdrawal influence multiple physiological functions, including immune responses, but the mechanism remains largely elusive. The objective of this study was to investigate the molecular inflammatory interactome, particularly the cytokines and transcriptome regulatory network in heroin addicts undergoing withdrawal, compared to healthy controls (HCs). Twenty-seven cytokines were simultaneously assessed in 41 heroin addicts, including 20 at the acute withdrawal (AW) stage and 21 at the protracted withdrawal (PW) stage, and 38 age- and gender-matched HCs. Disturbed T-helper(Th)1/Th2, Th1/Th17, and Th2/Th17 balances, characterized by reduced interleukin (IL)-2, elevated IL-4, IL-10, and IL-17A, but normal TNF-α, were present in the AW subjects. These imbalances were mostly restored to the baseline at the PW stage. However, the cytokines TNF-α, IL-2, IL-7, IL-10, and IL-17A remained dysregulated. This study also profiled exosomal long non-coding RNA (lncRNA) and mRNA in the plasma of heroin addicts, constructed co-expression gene regulation networks, and identified lncRNA-mRNA-pathway pairs specifically associated with alterations in cytokine profiles and Th1/Th2/Th17 imbalances. Altogether, a large amount of cytokine and exosomal lncRNA/mRNA expression profiling data relating to heroin withdrawal was obtained, providing a useful experimental and theoretical basis for further understanding of the pathogenic mechanisms of withdrawal symptoms in heroin addicts.
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Affiliation(s)
- Zunyue Zhang
- National Health Commission (NHC) Key Laboratory of Drug Addiction Medicine (Kunming Medical University), The First Affiliated Hospital of Kunming Medical University, Kunming, China.,Centre for Experimental Studies and Research, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Hongjin Wu
- National Health Commission (NHC) Key Laboratory of Drug Addiction Medicine (Kunming Medical University), The First Affiliated Hospital of Kunming Medical University, Kunming, China.,Centre for Experimental Studies and Research, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Qingyan Peng
- National Health Commission (NHC) Key Laboratory of Drug Addiction Medicine (Kunming Medical University), The First Affiliated Hospital of Kunming Medical University, Kunming, China.,Centre for Experimental Studies and Research, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zhenrong Xie
- National Health Commission (NHC) Key Laboratory of Drug Addiction Medicine (Kunming Medical University), The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Fengrong Chen
- National Health Commission (NHC) Key Laboratory of Drug Addiction Medicine (Kunming Medical University), The First Affiliated Hospital of Kunming Medical University, Kunming, China.,Centre for Experimental Studies and Research, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuru Ma
- National Health Commission (NHC) Key Laboratory of Drug Addiction Medicine (Kunming Medical University), The First Affiliated Hospital of Kunming Medical University, Kunming, China.,Centre for Experimental Studies and Research, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yizhi Zhang
- National Health Commission (NHC) Key Laboratory of Drug Addiction Medicine (Kunming Medical University), The First Affiliated Hospital of Kunming Medical University, Kunming, China.,Centre for Experimental Studies and Research, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yong Zhou
- National Health Commission (NHC) Key Laboratory of Drug Addiction Medicine (Kunming Medical University), The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jiqing Yang
- National Health Commission (NHC) Key Laboratory of Drug Addiction Medicine (Kunming Medical University), The First Affiliated Hospital of Kunming Medical University, Kunming, China.,Centre for Experimental Studies and Research, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Cheng Chen
- National Health Commission (NHC) Key Laboratory of Drug Addiction Medicine (Kunming Medical University), The First Affiliated Hospital of Kunming Medical University, Kunming, China.,Yunnan Institute of Digestive Disease, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Shaoyou Li
- National Health Commission (NHC) Key Laboratory of Drug Addiction Medicine (Kunming Medical University), The First Affiliated Hospital of Kunming Medical University, Kunming, China.,Centre for Experimental Studies and Research, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yongjin Zhang
- National Health Commission (NHC) Key Laboratory of Drug Addiction Medicine (Kunming Medical University), The First Affiliated Hospital of Kunming Medical University, Kunming, China.,Centre for Experimental Studies and Research, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Weiwei Tian
- National Health Commission (NHC) Key Laboratory of Drug Addiction Medicine (Kunming Medical University), The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuan Wang
- Department of Research and Development, Echo Biotech Co., Ltd, Beijing, China
| | - Yu Xu
- National Health Commission (NHC) Key Laboratory of Drug Addiction Medicine (Kunming Medical University), The First Affiliated Hospital of Kunming Medical University, Kunming, China.,Yunnan Institute of Digestive Disease, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Huayou Luo
- National Health Commission (NHC) Key Laboratory of Drug Addiction Medicine (Kunming Medical University), The First Affiliated Hospital of Kunming Medical University, Kunming, China.,Yunnan Institute of Digestive Disease, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Mei Zhu
- National Health Commission (NHC) Key Laboratory of Drug Addiction Medicine (Kunming Medical University), The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yi-Qun Kuang
- National Health Commission (NHC) Key Laboratory of Drug Addiction Medicine (Kunming Medical University), The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Juehua Yu
- National Health Commission (NHC) Key Laboratory of Drug Addiction Medicine (Kunming Medical University), The First Affiliated Hospital of Kunming Medical University, Kunming, China.,Centre for Experimental Studies and Research, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Kunhua Wang
- National Health Commission (NHC) Key Laboratory of Drug Addiction Medicine (Kunming Medical University), The First Affiliated Hospital of Kunming Medical University, Kunming, China.,Centre for Experimental Studies and Research, The First Affiliated Hospital of Kunming Medical University, Kunming, China.,Yunnan Institute of Digestive Disease, The First Affiliated Hospital of Kunming Medical University, Kunming, China.,Yunnan University, Kunming, China
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