1
|
Guo X, Feng X, Yang Y, An W, Bai L. Machine learning-based identification and immune characterization of ferroptosis-related molecular clusters in osteoarthritis and validation. Aging (Albany NY) 2024; 16:9437-9459. [PMID: 38814177 PMCID: PMC11210262 DOI: 10.18632/aging.205875] [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: 01/15/2024] [Accepted: 04/18/2024] [Indexed: 05/31/2024]
Abstract
Osteoarthritis (OA), a degenerative joint disease, involves synovial inflammation, subchondral bone erosion, and cartilage degeneration. Ferroptosis, a regulated non-apoptotic programmed cell death, is associated with various diseases. This study investigates ferroptosis-related molecular subtypes in OA to comprehend underlying mechanisms. The Gene Expression Omnibus datasets GSE206848, GSE55457, GSE55235, GSE77298 and GSE82107 were used utilized. Unsupervised clustering identified the ferroptosis-related gene (FRG) subtypes, and their immune characteristics were assessed. FRG signatures were derived using LASSO and SVM-RFE algorithms, forming models to evaluate OA's ferroptosis-related immune features. Three FRG clusters were found to be immunologically heterogeneous, with cluster 1 displaying robust immune response. Models identified nine key signature genes via algorithms, demonstrating strong diagnostic and prognostic performance. Finally, qRT-PCR and Western blot validated these genes, offering consistent results. In addition, some of these genes may have implications as new therapeutic targets and can be used to guide clinical applications.
Collapse
Affiliation(s)
- Xiaocheng Guo
- Department of Orthopedics, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xinyuan Feng
- Department of Orthopedics, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yue Yang
- Department of Orthopedics, Shengjing Hospital of China Medical University, Shenyang, China
| | - Wenying An
- Department of Cadre Wards, Liaoning University of Traditional Chinese Medicine Affiliated Orthopedic Hospital, Shenyang, China
| | - Lunhao Bai
- Department of Orthopedics, Shengjing Hospital of China Medical University, Shenyang, China
| |
Collapse
|
2
|
Wu F, Zhang J, Wang Q, Liu W, Zhang X, Ning F, Cui M, Qin L, Zhao G, Liu D, Lv S, Xu Y. Identification of immune-associated genes in vascular dementia by integrated bioinformatics and inflammatory infiltrates. Heliyon 2024; 10:e26304. [PMID: 38384571 PMCID: PMC10879030 DOI: 10.1016/j.heliyon.2024.e26304] [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: 12/11/2023] [Revised: 02/04/2024] [Accepted: 02/09/2024] [Indexed: 02/23/2024] Open
Abstract
Objective Dysregulation of the immune system plays a vital role in the pathological process of vascular dementia, and this study aims to spot critical biomarkers and immune infiltrations in vascular dementia employing a bioinformatics approach. Methods We acquired gene expression profiles from the Gene Expression Database. The gene expression data were analyzed using the bioinformatics method to identify candidate immune-related central genes for the diagnosis of vascular dementia. and the diagnostic value of nomograms and Receiver Operating Characteristic (ROC) curves were evaluated. We also examined the role of the VaD hub genes. Using the database and potential therapeutic drugs, we predicted the miRNA and lncRNA controlling the Hub genes. Immune cell infiltration was initiated to examine immune cell dysregulation in vascular dementia. Results 1321 immune genes were included in the combined immune dataset, and 2816 DEGs were examined in GSE122063. Twenty potential genes were found using differential gene analysis and co-expression network analysis. PPI network design and functional enrichment analysis were also done using the immune system as the main subject. To create the nomogram for evaluating the diagnostic value, four potential core genes were chosen by machine learning. All four putative center genes and nomograms have a solid diagnostic value (AUC ranged from 0.81 to 0.92). Their high confidence level became unquestionable by validating each of the four biomarkers using a different dataset. According to GeneMANIA and GSEA enrichment investigations, the pathophysiology of VaD is strongly related to inflammatory responses, drug reactions, and central nervous system degeneration. The data and Hub genes were used to construct a ceRNA network that includes three miRNAs, 90 lncRNA, and potential VaD therapeutics. Immune cells with varying dysregulation were also found. Conclusion Using bioinformatic techniques, our research identified four immune-related candidate core genes (HMOX1, EBI3, CYBB, and CCR5). Our study confirms the role of these Hub genes in the onset and progression of VaD at the level of immune infiltration. It predicts potential RNA regulatory pathways control VaD progression, which may provide ideas for treating clinical disease.
Collapse
Affiliation(s)
- Fangchao Wu
- Department of Rehabilitation Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Junling Zhang
- Shandong Medicine Technician College, Taian 271000, China
| | - Qian Wang
- Department of Central Laboratory, The Affiliated Taian City Central Hospital of Qingdao University, Taian, 271000, China
| | - Wenxin Liu
- Department of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, China
| | - Xinlei Zhang
- Department of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, China
| | - Fangli Ning
- Department of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, China
| | - Mengmeng Cui
- Department of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, China
| | - Lei Qin
- Department of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, China
| | - Guohua Zhao
- Department of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, China
| | - Di Liu
- Department of Neurology, Dongping County People's Hospital, Taian, 271000, China
| | - Shi Lv
- Department of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, China
| | - Yuzhen Xu
- Department of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, China
| |
Collapse
|
3
|
Zhao D, Zeng LF, Liang GH, Luo MH, Pan JK, Dou YX, Lin FZ, Huang HT, Yang WY, Liu J. Transcriptomic analyses and machine-learning methods reveal dysregulated key genes and potential pathogenesis in human osteoarthritic cartilage. Bone Joint Res 2024; 13:66-82. [PMID: 38310924 PMCID: PMC10838620 DOI: 10.1302/2046-3758.132.bjr-2023-0074.r1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2024] Open
Abstract
Aims This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA. Methods Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning algorithms were used to screen crucial genes in osteoarthritic cartilage, and genome enrichment and functional annotation analyses were used to decipher the related categories of gene function. Single-sample gene set enrichment analysis was performed to analyze immune cell infiltration. Correlation analysis was used to explore the relationship among the hub genes and immune cells, as well as markers related to articular cartilage degradation and bone mineralization. Results A total of 46 genes were obtained from the intersection of significantly upregulated genes in osteoarthritic cartilage and the key module genes screened by WGCNA. Functional annotation analysis revealed that these genes were closely related to pathological responses associated with OA, such as inflammation and immunity. Four key dysregulated genes (cartilage acidic protein 1 (CRTAC1), iodothyronine deiodinase 2 (DIO2), angiopoietin-related protein 2 (ANGPTL2), and MAGE family member D1 (MAGED1)) were identified after using machine-learning algorithms. These genes had high diagnostic value in both the training cohort and external validation cohort (receiver operating characteristic > 0.8). The upregulated expression of these hub genes in osteoarthritic cartilage signified higher levels of immune infiltration as well as the expression of metalloproteinases and mineralization markers, suggesting harmful biological alterations and indicating that these hub genes play an important role in the pathogenesis of OA. A competing endogenous RNA network was constructed to reveal the underlying post-transcriptional regulatory mechanisms. Conclusion The current study explores and validates a dysregulated key gene set in osteoarthritic cartilage that is capable of accurately diagnosing OA and characterizing the biological alterations in osteoarthritic cartilage; this may become a promising indicator in clinical decision-making. This study indicates that dysregulated key genes play an important role in the development and progression of OA, and may be potential therapeutic targets.
Collapse
Affiliation(s)
- Di Zhao
- Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
- Bone and Joint Research Team of Degeneration and Injury, Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou, China
| | - Ling-feng Zeng
- Bone and Joint Research Team of Degeneration and Injury, Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou, China
- Department of Orthopedics, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Gui-hong Liang
- Bone and Joint Research Team of Degeneration and Injury, Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou, China
- Department of Orthopedics, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ming-hui Luo
- Bone and Joint Research Team of Degeneration and Injury, Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou, China
- Department of Orthopedics, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jian-ke Pan
- Bone and Joint Research Team of Degeneration and Injury, Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou, China
- Department of Orthopedics, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yao-xing Dou
- Bone and Joint Research Team of Degeneration and Injury, Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou, China
- Department of Orthopedics, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Fang-zheng Lin
- Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
- Bone and Joint Research Team of Degeneration and Injury, Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou, China
| | - He-tao Huang
- Bone and Joint Research Team of Degeneration and Injury, Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou, China
- Department of Orthopedics, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Wei-yi Yang
- Bone and Joint Research Team of Degeneration and Injury, Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou, China
- Department of Orthopedics, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jun Liu
- Bone and Joint Research Team of Degeneration and Injury, Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou, China
- Guangdong Second Traditional Chinese Medicine Hospital, Guangdong Province Engineering Technology Research Institute of Traditional Chinese Medicine, Guangzhou, China
- Fifth Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| |
Collapse
|
4
|
Huang J, Zhou J, Xue X, Dai T, Zhu W, Jiao S, Wu H, Meng Q. Identification of aging-related genes in diagnosing osteoarthritis via integrating bioinformatics analysis and machine learning. Aging (Albany NY) 2024; 16:153-168. [PMID: 38175691 PMCID: PMC10817387 DOI: 10.18632/aging.205357] [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: 07/26/2023] [Accepted: 11/13/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND Osteoarthritis (OA) is one of the main causes of pain and disability in the world, it may be caused by many factors. Aging plays a significant role in the onset and progression of OA. However, the mechanisms underlying it remain unknown. Our research aimed to uncover the role of aging-related genes in the progression of OA. METHODS In Human OA datasets and aging-related genes were obtained from the GEO database and the HAGR website, respectively. Bioinformatics methods including Gene Ontology (GO), Kyoto Encyclopedia of Genes Genomes (KEGG) pathway enrichment, and Protein-protein interaction (PPI) network analysis were used to analyze differentially expressed aging-related genes (DEARGs) in the normal control group and the OA group. And then weighted gene coexpression network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO) regression, and the Random Forest (RF) machine learning algorithms were used to find the hub genes. RESULTS Four overlapping hub genes: HMGB2, CDKN1A, JUN, and DDIT3 were identified. According to the nomogram model and receiver operating characteristic (ROC) curve analysis, four hub DEARGs had good diagnostic value in distinguishing normal from OA. Furthermore, the qRT-PCR test demonstrated that HMGB2, CDKN1A, JUN, and DDIT3 mRNA expression levels were lower in OA group than in normal group. CONCLUSION Finally, these four-hub aging-related genes may help us understand the underlying mechanism of aging in osteoarthritis and could be used as possible diagnostic and therapeutic targets.
Collapse
Affiliation(s)
- Jian Huang
- Guangzhou Institute of Traumatic Surgery, Guangzhou Red Cross Hospital of Jinan University, Guangzhou 510220, China
- Department of Traumatic Orthopedics, The Central Hospital of Xiaogan, Hubei 432100, China
| | - Jiangfei Zhou
- Department of Orthopedics, Guangzhou Red Cross Hospital of Jinan University, Guangzhou 510220, China
| | - Xiang Xue
- Department of Orthopedics, Guangzhou Red Cross Hospital of Jinan University, Guangzhou 510220, China
| | - Tianming Dai
- Guangzhou Institute of Traumatic Surgery, Guangzhou Red Cross Hospital of Jinan University, Guangzhou 510220, China
| | - Weicong Zhu
- Guangzhou Institute of Traumatic Surgery, Guangzhou Red Cross Hospital of Jinan University, Guangzhou 510220, China
| | - Songsong Jiao
- Department of Orthopedics, Guangzhou Red Cross Hospital of Jinan University, Guangzhou 510220, China
| | - Hang Wu
- Department of Traumatic Orthopedics, The Central Hospital of Xiaogan, Hubei 432100, China
| | - Qingqi Meng
- Guangzhou Institute of Traumatic Surgery, Guangzhou Red Cross Hospital of Jinan University, Guangzhou 510220, China
| |
Collapse
|
5
|
Cai X, Li M, Zhong Y, Yang W, Liang Z. COMP Improves Ang-II-Induced Atrial Fibrillation via TGF-β Signaling Pathway. Cardiovasc Toxicol 2023; 23:305-316. [PMID: 37584842 DOI: 10.1007/s12012-023-09799-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 06/27/2023] [Indexed: 08/17/2023]
Abstract
Cartilage oligomeric matrix protein (COMP) regulates transforming growth factor-β (TGF-β) signaling pathway, which has been proved to be associated with skin fibrosis and pulmonary fibrosis. Atrial fibrosis is a major factor of atrial fibrillation (AF). Nevertheless, the interaction between COMP and TGF-β as well as their role in AF remains undefined. The purpose of this study is to clarify the role of COMP in AF and explore its potential mechanism. The hub gene of AF was identified from two datasets using bioinformatics. Furthermore, it was verified by the downregulation of COMP in angiotensin-II (Ang-II)-induced AF in mice. Moreover, the effect on AF was examined using CCK8 assay, ELISA, and western blot. The involvement of TGF-β pathway was further discussed. The expression of COMP was the most significant among all these hub genes. Our experimental results revealed that the protein levels of TGF-β1, phosphorylated Smad2 (P-Smad2), and phosphorylated Smad3 (P-Smad3) were decreased after silencing COMP, which indicated that COMP knockdown could inhibit the activation of TGF-β pathway in AF cells. However, the phenomenon was reversed when the activator SRI was added. COMP acts as a major factor and can improve Ang-II-induced AF via TGF-β signaling pathway. Thus, our research enriches the understanding of the interaction between COMP and TGF-β in AF, and provides reference for the pathogenesis and diagnosis of AF.
Collapse
Affiliation(s)
- XiaoBi Cai
- Department of Cardiovascular Surgery, The Affiliated Hospital of Guangdong Medical University, No. 57, Renmin Avenue South, Xiashan District, Zhangjian City, 524001, Guangdong Province, China
| | - Mingliang Li
- Department of Cardiovascular Surgery, The Affiliated Hospital of Guangdong Medical University, No. 57, Renmin Avenue South, Xiashan District, Zhangjian City, 524001, Guangdong Province, China
| | - Ying Zhong
- Department of Cardiovascular Surgery, The Affiliated Hospital of Guangdong Medical University, No. 57, Renmin Avenue South, Xiashan District, Zhangjian City, 524001, Guangdong Province, China
| | - Wenkun Yang
- Department of Cardiovascular Surgery, The Affiliated Hospital of Guangdong Medical University, No. 57, Renmin Avenue South, Xiashan District, Zhangjian City, 524001, Guangdong Province, China
| | - Zhu Liang
- Department of Cardiovascular and Thoracic Surgery, The Affiliated Hospital of Guangdong Medical University, No. 57, Renmin Avenue South, Xiashan District, Zhangjian City, 524001, Guangdong Province, China.
| |
Collapse
|
6
|
Das R, Savina EA, Tatarinova TV, Orlov YL. Editorial: Population and ancestry specific variation in disease susceptibility. Front Genet 2023; 14:1267719. [PMID: 37799142 PMCID: PMC10548457 DOI: 10.3389/fgene.2023.1267719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 09/12/2023] [Indexed: 10/07/2023] Open
Affiliation(s)
- Ranajit Das
- Yenepoya Research Centre, Yenepoya University, Mangalore, India
| | - Ekaterina A. Savina
- The Digital Health Institute, I.M.Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
- Engelhardt Institute of Molecular Biology RAS, Moscow, Russia
| | | | - Yuriy L. Orlov
- The Digital Health Institute, I.M.Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia (RUDN University), Moscow, Russia
| |
Collapse
|
7
|
Klimontov VV, Koshechkin KA, Orlova NG, Sekacheva MI, Orlov YL. Medical Genetics, Genomics and Bioinformatics-2022. Int J Mol Sci 2023; 24:ijms24108968. [PMID: 37240312 DOI: 10.3390/ijms24108968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
The analysis of molecular mechanisms of disease progression challenges the development of bioinformatics tools and omics data integration [...].
Collapse
Affiliation(s)
- Vadim V Klimontov
- Research Institute of Clinical and Experimental Lymphology-Branch of the Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (RICEL-Branch of IC&G SB RAS), 630060 Novosibirsk, Russia
| | - Konstantin A Koshechkin
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia
| | - Nina G Orlova
- Department of Mathematics, Financial University under the Government of the Russian Federation, 125167 Moscow, Russia
| | - Marina I Sekacheva
- Institute of Personalized Oncology, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia
| | - Yuriy L Orlov
- Research Institute of Clinical and Experimental Lymphology-Branch of the Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (RICEL-Branch of IC&G SB RAS), 630060 Novosibirsk, Russia
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia
- Institute of Life Sciences and Biomedicine, Far Eastern Federal University, 690922 Vladivostok, Russia
- Agrarian and Technological Institute, Peoples' Friendship University of Russia, 117198 Moscow, Russia
| |
Collapse
|
8
|
Anashkina AA, Orlova NG, Ignatov AN, Chen M, Orlov YL. Editorial: Bioinformatics of genome regulation and systems biology, Volume III. Front Genet 2023; 14:1215987. [PMID: 37274783 PMCID: PMC10233740 DOI: 10.3389/fgene.2023.1215987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 05/10/2023] [Indexed: 06/07/2023] Open
Affiliation(s)
- Anastasia A. Anashkina
- The Digital Health Institute, I.M.Sechenov First Moscow State Medical University of the Russian Ministry of Health (Sechenov University), Moscow, Russia
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | - Nina G. Orlova
- Department of Mathematics, Financial University Under the Government of the Russian Federation, Moscow, Russia
| | - Alexander N. Ignatov
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia (RUDN University), Moscow, Russia
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China
| | - Yuriy L. Orlov
- The Digital Health Institute, I.M.Sechenov First Moscow State Medical University of the Russian Ministry of Health (Sechenov University), Moscow, Russia
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia (RUDN University), Moscow, Russia
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
- Institute of Life Sciences and Biomedicine, Far East Federal University, Vladivostok, Russia
| |
Collapse
|
9
|
Orlov YL, Anashkina AA, Kumeiko VV, Chen M, Kolchanov NA. Research Topics of the Bioinformatics of Gene Regulation. Int J Mol Sci 2023; 24:ijms24108774. [PMID: 37240120 DOI: 10.3390/ijms24108774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 05/09/2023] [Indexed: 05/28/2023] Open
Abstract
The study of gene expression regulation raises the challenge of developing bioinformatics tools and algorithms, demanding data integration [...].
Collapse
Affiliation(s)
- Yuriy L Orlov
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Life Sciences Department, Novosibirsk State University, 630090 Novosibirsk, Russia
- Agrarian and Technological Institute, Peoples' Friendship University of Russia, 117198 Moscow, Russia
| | - Anastasia A Anashkina
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Vadim V Kumeiko
- Institute of Life Sciences and Biomedicine, Far Eastern Federal University, 690922 Vladivostok, Russia
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Nikolay A Kolchanov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Life Sciences Department, Novosibirsk State University, 630090 Novosibirsk, Russia
| |
Collapse
|
10
|
Orlov YL, Chen WL, Sekacheva MI, Cai G, Li H. Editorial: High-Throughput Sequencing-Based Investigation of Chronic Disease Markers and Mechanisms. Front Genet 2022; 13:922206. [PMID: 35801080 PMCID: PMC9253685 DOI: 10.3389/fgene.2022.922206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 05/12/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Yuriy L. Orlov
- I. M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
- Novosibirsk State University, Novosibirsk, Russia
- Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia, Moscow, Russia
| | - Wen-Lian Chen
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Marina I. Sekacheva
- I. M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Guoshuai Cai
- Environmental Health Sciences, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States
| | - Hua Li
- Bio-ID Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Hua Li,
| |
Collapse
|
11
|
Hu X, Ni S, Zhao K, Qian J, Duan Y. Bioinformatics-Led Discovery of Osteoarthritis Biomarkers and Inflammatory Infiltrates. Front Immunol 2022; 13:871008. [PMID: 35734177 PMCID: PMC9207185 DOI: 10.3389/fimmu.2022.871008] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 05/12/2022] [Indexed: 12/27/2022] Open
Abstract
The molecular mechanisms of osteoarthritis, the most common chronic disease, remain unexplained. This study aimed to use bioinformatic methods to identify the key biomarkers and immune infiltration in osteoarthritis. Gene expression profiles (GSE55235, GSE55457, GSE77298, and GSE82107) were selected from the Gene Expression Omnibus database. A protein-protein interaction network was created, and functional enrichment analysis and genomic enrichment analysis were performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) databases. Immune cell infiltration between osteoarthritic tissues and control tissues was analyzed using the CIBERSORT method. Identify immune patterns using the ConsensusClusterPlus package in R software using a consistent clustering approach. Molecular biological investigations were performed to discover the important genes in cartilage cells. A total of 105 differentially expressed genes were identified. Differentially expressed genes were enriched in immunological response, chemokine-mediated signaling pathway, and inflammatory response revealed by the analysis of GO and KEGG databases. Two distinct immune patterns (ClusterA and ClusterB) were identified using the ConsensusClusterPlus. Cluster A patients had significantly lower resting dendritic cells, M2 macrophages, resting mast cells, activated natural killer cells and regulatory T cells than Cluster B patients. The expression levels of TCA1, TLR7, MMP9, CXCL10, CXCL13, HLA-DRA, and ADIPOQSPP1 were significantly higher in the IL-1β-induced group than in the osteoarthritis group in an in vitro qPCR experiment. Explaining the differences in immune infiltration between osteoarthritic tissues and normal tissues will contribute to the understanding of the development of osteoarthritis.
Collapse
Affiliation(s)
- Xinyue Hu
- Department of Clinical Laboratory, Kunming First People’s Hospital, Kunming Medical University, Kunming, China
| | - Songjia Ni
- Department of Orthopedic Trauma, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Kai Zhao
- Neurosurgery Department, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jing Qian
- Department of Clinical Laboratory, Kunming First People’s Hospital, Kunming Medical University, Kunming, China
| | - Yang Duan
- Department of Spine Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- *Correspondence: Yang Duan,
| |
Collapse
|
12
|
Das R, Tatarinova TV, Galieva ER, Orlov YL. Editorial: Association Between Individuals' Genomic Ancestry and Variation in Disease Susceptibility. Front Genet 2022; 13:831320. [PMID: 35186044 PMCID: PMC8847436 DOI: 10.3389/fgene.2022.831320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 01/14/2022] [Indexed: 12/18/2022] Open
Affiliation(s)
- Ranajit Das
- Yenepoya Research Centre, Yenepoya University, Mangalore, India
| | - Tatiana V Tatarinova
- Natural Science Division, La Verne University, La Verne, CA, United States.,Department of Fundamental Biology and Biotechnology, Siberian Federal University, Krasnoyarsk, Russia
| | - Elvira R Galieva
- Life Sciences Department, Novosibirsk State University, Novosibirsk, Russia.,Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
| | - Yuriy L Orlov
- Life Sciences Department, Novosibirsk State University, Novosibirsk, Russia.,Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia.,Agrarian and Technological Institute, Peoples' Friendship University of Russia (RUDN University), Moscow, Russia.,Institute of Digital Medicine, I.M.Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| |
Collapse
|
13
|
Zhang X, Xiao H, Fu S, Yu J, Cheng Y, Jiang Y. Investigate the genetic mechanisms of diabetic kidney disease complicated with inflammatory bowel disease through data mining and bioinformatic analysis. Front Endocrinol (Lausanne) 2022; 13:1081747. [PMID: 36726458 PMCID: PMC9884696 DOI: 10.3389/fendo.2022.1081747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 12/21/2022] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Patients with diabetic kidney disease (DKD) often have gastrointestinal dysfunction such as inflammatory bowel disease (IBD). This study aims to investigate the genetic mechanism leading to IBD in DKD patients through data mining and bioinformatics analysis. METHODS The disease-related genes of DKD and IBD were searched from the five databases of OMIM, GeneCards, PharmGkb, TTD, and DrugBank, and the intersection part of the two diseases were taken to obtain the risk genes of DKD complicated with IBD. A protein-protein interaction (PPI) network analysis was performed on risk genes, and three topological parameters of degree, betweenness, and closeness of nodes in the network were used to identify key risk genes. Finally, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed on the risk genes to explore the related mechanism of DKD merging IBD. RESULTS This study identified 495 risk genes for DKD complicated with IBD. After constructing a protein-protein interaction network and screening for three times, six key risk genes were obtained, including matrix metalloproteinase 2 (MMP2), hepatocyte growth factor (HGF), fibroblast growth factor 2 (FGF2), interleukin (IL)-18, IL-13, and C-C motif chemokine ligand 5 (CCL5). Based on GO enrichment analysis, we found that DKD genes complicated with IBD were associated with 3,646 biological processes such as inflammatory response regulation, 121 cellular components such as cytoplasmic vesicles, and 276 molecular functions such as G-protein-coupled receptor binding. Based on KEGG enrichment analysis, we found that the risk genes of DKD combined with IBD were associated with 181 pathways, such as the PI3K-Akt signaling pathway, advanced glycation end product-receptor for AGE (AGE-RAGE) signaling pathway and hypoxia-inducible factor (HIF)-1 signaling pathway. CONCLUSION There is a genetic mechanism for the complication of IBD in patients with CKD. Oxidative stress, chronic inflammatory response, and immune dysfunction were possible mechanisms for DKD complicated with IBD.
Collapse
Affiliation(s)
- Xiaoyu Zhang
- Department of Gastrointestinal and Colorectal Surgery, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Huijie Xiao
- Department of Gastrointestinal and Colorectal Surgery, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Shaojie Fu
- Department of Nephrology, The First Hospital of Jilin University, Changchun, China
| | - Jinyu Yu
- Department of Urology, The First Hospital of Jilin University, Changchun, China
| | - Yanli Cheng
- Department of Nephrology, The First Hospital of Jilin University, Changchun, China
- *Correspondence: Yanli Cheng, ; Yang Jiang,
| | - Yang Jiang
- Department of Gastrointestinal and Colorectal Surgery, China-Japan Union Hospital of Jilin University, Changchun, China
- *Correspondence: Yanli Cheng, ; Yang Jiang,
| |
Collapse
|
14
|
Orlov YL, Tatarinova TV, Oparina NY, Galieva ER, Baranova AV. Editorial: Bioinformatics of Genome Regulation, Volume I. Front Genet 2021; 12:803273. [PMID: 34938326 PMCID: PMC8687738 DOI: 10.3389/fgene.2021.803273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 11/08/2021] [Indexed: 11/23/2022] Open
Affiliation(s)
- Yuriy L Orlov
- Institute of Digital Medicine, I.M.Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.,Agrarian and Technological Institute, Peoples' Friendship University of Russia (RUDN University), Moscow, Russia.,Life Sciences Department, Novosibirsk State University, Novosibirsk, Russia.,Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
| | | | - Nina Y Oparina
- Institute of Medicine, University of Gothenburg, Göteborg, Sweden
| | - Elvira R Galieva
- Life Sciences Department, Novosibirsk State University, Novosibirsk, Russia
| | - Ancha V Baranova
- School of Systems Biology, George Mason University, Fairfax, VA, United States.,Research Centre for Medical Genetics, Moscow, Russia
| |
Collapse
|
15
|
Orlov YL, Anashkina AA, Tatarinova TV, Baranova AV. Editorial: Bioinformatics of Genome Regulation, Volume II. Front Genet 2021; 12:795257. [PMID: 34819949 PMCID: PMC8606529 DOI: 10.3389/fgene.2021.795257] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 10/25/2021] [Indexed: 12/17/2022] Open
Affiliation(s)
- Yuriy L Orlov
- The Digital Health Institute, I.M.Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.,Agrobiotechnology Department, Agrarian and Technological Institute, Peoples' Friendship University of Russia (RUDN University), Moscow, Russia
| | - Anastasia A Anashkina
- The Digital Health Institute, I.M.Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.,Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | | | - Ancha V Baranova
- School of Systems Biology, George Mason University, Fairfax, VA, United States.,Research Centre for Medical Genetics, Moscow, Russia
| |
Collapse
|
16
|
Gubanova NV, Orlova NG, Dergilev AI, Oparina NY, Orlov YL. Glioblastoma gene network reconstruction and ontology analysis by online bioinformatics tools. J Integr Bioinform 2021; 18:jib-2021-0031. [PMID: 34783229 PMCID: PMC8709738 DOI: 10.1515/jib-2021-0031] [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: 08/31/2021] [Accepted: 10/18/2021] [Indexed: 12/13/2022] Open
Abstract
Glioblastoma is the most aggressive type of brain tumors resistant to a number of antitumor drugs. The problem of therapy and drug treatment course is complicated by extremely high heterogeneity in the benign cell populations, the random arrangement of tumor cells, and polymorphism of their nuclei. The pathogenesis of gliomas needs to be studied using modern cellular technologies, genome- and transcriptome-wide technologies of high-throughput sequencing, analysis of gene expression on microarrays, and methods of modern bioinformatics to find new therapy targets. Functional annotation of genes related to the disease could be retrieved based on genetic databases and cross-validated by integrating complementary experimental data. Gene network reconstruction for a set of genes (proteins) proved to be effective approach to study mechanisms underlying disease progression. We used online bioinformatics tools for annotation of gene list for glioma, reconstruction of gene network and comparative analysis of gene ontology categories. The available tools and the databases for glioblastoma gene analysis are discussed together with the recent progress in this field.
Collapse
Affiliation(s)
- Natalya V Gubanova
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Nina G Orlova
- Financial University under the Government of the Russian Federation, 119991 Moscow, Russia.,Moscow State Technical University of Civil Aviation, 125993 Moscow, Russia
| | | | | | - Yuriy L Orlov
- Novosibirsk State University, 630090 Novosibirsk, Russia.,The Digital Health Institute, I.M.Sechenov First Moscow State Medical University of the Russian Ministry of Health, 119991 Moscow, Russia
| |
Collapse
|
17
|
Biophysical Reviews' "Meet the Councilor"-a profile of Anastasia A. Anashkina. Biophys Rev 2021; 13:817-820. [PMID: 34786027 PMCID: PMC8587497 DOI: 10.1007/s12551-021-00873-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 10/26/2021] [Indexed: 12/29/2022] Open
Abstract
As one of the twelve Councilors of the International Union of Pure and Applied Biophysics elected in summer 2021, I have been asked to provide this short biographical sketch for the journal readers. I am a new member of the IUPAB Council. I hold a specialist degree in Applied Physics and Mathematics from the Moscow Institute of Physics and Technology and PhD in Biophysics from Moscow State University. I have spent my entire professional career at Engelhardt Institute of Molecular Biology of the Russian Academy of Sciences in Moscow, where I am currently a senior researcher. I am Associate Professor at the Digital Health Institute of the I.M. Sechenov First Moscow State Medical University since 2018, and have trained undergraduate students in structural biology, biophysics, and bioinformatics. In addition, I serve as the Guest Editor of special journal issues of International Journal of Molecular Sciences and Frontiers in Genetics BMC genomics. Now I joined Biophysical Reviews Editorial Board as IUPAB Councilor. I am a Secretary of National Committee of Russian Biophysicists, and have helped to organize scientific conferences and workshops, such as the VI Congress of Russian Biophysicists.
Collapse
|
18
|
Anashkina AA, Leberfarb EY, Orlov YL. Recent Trends in Cancer Genomics and Bioinformatics Tools Development. Int J Mol Sci 2021; 22:ijms222212146. [PMID: 34830028 PMCID: PMC8618360 DOI: 10.3390/ijms222212146] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 11/08/2021] [Indexed: 02/07/2023] Open
Affiliation(s)
- Anastasia A. Anashkina
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia;
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Elena Y. Leberfarb
- Department of Medicinal Chemistry, Novosibirsk State Medical University, 630091 Novosibirsk, Russia;
| | - Yuriy L. Orlov
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia;
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Life Sciences Department, Novosibirsk State University, 630090 Novosibirsk, Russia
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia, 117198 Moscow, Russia
- Correspondence: or
| |
Collapse
|
19
|
Orlov YL, Anashkina AA. Life: Computational Genomics Applications in Life Sciences. Life (Basel) 2021; 11:life11111211. [PMID: 34833087 PMCID: PMC8622464 DOI: 10.3390/life11111211] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 11/06/2021] [Indexed: 01/19/2023] Open
Affiliation(s)
- Yuriy L. Orlov
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia;
- Life Sciences Department, Novosibirsk State University, 630090 Novosibirsk, Russia
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia, 117198 Moscow, Russia
- Correspondence:
| | - Anastasia A. Anashkina
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia;
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| |
Collapse
|
20
|
Bioinformatics Applications to Reveal Molecular Mechanisms of Gene Expression Regulation in Model Organisms. Int J Mol Sci 2021; 22:ijms222111973. [PMID: 34769403 PMCID: PMC8584893 DOI: 10.3390/ijms222111973] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/01/2021] [Accepted: 11/03/2021] [Indexed: 02/06/2023] Open
|