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Peng L, Chen H, Wang Z, He Y, Zhang X. Identification and validation of a classifier based on hub aging-related genes and aging subtypes correlation with immune microenvironment for periodontitis. Front Immunol 2022; 13:1042484. [PMID: 36389665 PMCID: PMC9663931 DOI: 10.3389/fimmu.2022.1042484] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 10/18/2022] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Periodontitis (PD), an age-related disease, is characterized by inflammatory periodontal tissue loss, and with the general aging of the global population, the burden of PD is becoming a major health concern. Nevertheless, the mechanism underlying this phenomenon remains indistinct. We aimed to develop a classification model for PD and explore the relationship between aging subtypes and the immune microenvironment for PD based on bioinformatics analysis. MATERIALS AND METHODS The PD-related datasets were acquired from the Gene Expression Omnibus (GEO) database, and aging-related genes (ARGs) were obtained from the Human Aging Genomic Resources (HAGR). Four machine learning algorithms were applied to screen out the hub ARGs. Then, an artificial neural network (ANN) model was constructed and the accuracy of the model was validated by receiver operating characteristic (ROC) curve analysis. The clinical effect of the model was evaluated by decision curve analysis (DCA). Consensus clustering was employed to determine the aging expression subtypes. A series of bioinformatics analyses were performed to explore the PD immune microenvironment and its subtypes. The hub aging-related modules were defined using weighted correlation network analysis (WGCNA). RESULTS Twenty-seven differentially expressed ARGs were dysregulated and a classifier based on four hub ARGs (BLM, FOS, IGFBP3, and PDGFRB) was constructed to diagnose PD with excellent accuracy. Subsequently, the mRNA levels of the hub ARGs were validated by quantitative real-time PCR (qRT-PCR). Based on differentially expressed ARGs, two aging-related subtypes were identified. Distinct biological functions and immune characteristics including infiltrating immunocytes, immunological reaction gene sets, the human leukocyte antigen (HLA) gene, and immune checkpoints were revealed between the subtypes. Additionally, the black module correlated with subtype-1 was manifested as the hub aging-related module and its latent functions were identified. CONCLUSION Our findings highlight the critical implications of aging-related genes in modulating the immune microenvironment. Four hub ARGs (BLM, FOS, IGFBP3, and PDGFRB) formed a classification model, and accompanied findings revealed the essential role of aging in the immune microenvironment for PD, providing fresh inspiration for PD etiopathogenesis and potential immunotherapy.
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Affiliation(s)
- Limin Peng
- College of Stomatology, Chongqing Medical University, Chongqing, China,Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China,Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, China
| | - Hang Chen
- College of Stomatology, Chongqing Medical University, Chongqing, China,Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China,Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, China
| | - Zhenxiang Wang
- College of Stomatology, Chongqing Medical University, Chongqing, China,Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China,Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, China
| | - Yujuan He
- Department of Laboratory Medicine, Key Laboratory of Diagnostic Medicine (Ministry of Education), Chongqing Medical University, Chongqing, China
| | - Xiaonan Zhang
- College of Stomatology, Chongqing Medical University, Chongqing, China,Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China,Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, China,*Correspondence: Xiaonan Zhang,
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Ren H, Bazhin AV, Pretzsch E, Jacob S, Yu H, Zhu J, Albertsmeier M, Lindner LH, Knösel T, Werner J, Angele MK, Bösch F. A novel immune-related gene signature predicting survival in sarcoma patients. Mol Ther Oncolytics 2022; 24:114-126. [PMID: 35024438 PMCID: PMC8718575 DOI: 10.1016/j.omto.2021.12.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 12/07/2021] [Indexed: 02/08/2023] Open
Abstract
Sarcomas are a heterogeneous group of rare mesenchymal tumors. The migration of immune cells into these tumors and the prognostic impact of tumor-specific factors determining their interaction with these tumors remain poorly understood. The current risk stratification system is insufficient to provide a precise survival prediction and treatment response. Thus, valid prognostic models are needed to guide treatment. This study analyzed the gene expression and outcome of 980 sarcoma patients from seven public datasets. The abundance of immune cells and the response to immunotherapy was calculated. Immune-related genes (IRGs) were screened through a weighted gene co-expression network analysis (WGCNA). A least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish a powerful IRG signature predicting prognosis. The identified IRG signature incorporated 14 genes and identified high-risk patients in sarcoma cohorts. The 14-IRG signature was identified as an independent risk factor for overall and disease-free survival. Moreover, the IRG signature acted as a potential indicator for immunotherapy. The nomogram based on the risk score was built to provide a more accurate survival prediction. The decision tree with IRG risk score discriminated risk subgroups powerfully. This proposed IRG signature is a robust biomarker to predict outcomes and treatment responses in sarcoma patients.
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Affiliation(s)
- Haoyu Ren
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, 81377 Munich, Germany
| | - Alexandr V Bazhin
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, 81377 Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Elise Pretzsch
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, 81377 Munich, Germany
| | - Sven Jacob
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, 81377 Munich, Germany
| | - Haochen Yu
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, 81377 Munich, Germany
| | - Jiang Zhu
- Department of Liver Surgery and Liver Transplantation Centre, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Markus Albertsmeier
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, 81377 Munich, Germany
| | - Lars H Lindner
- Department of Medicine III, SarKUM, University Hospital, LMU Munich, Munich, Germany
| | - Thomas Knösel
- Institute of Pathology, University Hospital, LMU Munich, Munich, Germany
| | - Jens Werner
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, 81377 Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Martin K Angele
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, 81377 Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Florian Bösch
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, 81377 Munich, Germany
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Li M, Cao W, Huang B, Zhu Z, Chen Y, Zhang J, Cao G, Chen B. Establishment and Analysis of an Individualized Immune-Related Gene Signature for the Prognosis of Gastric Cancer. Front Surg 2022; 9:829237. [PMID: 35174205 PMCID: PMC8841693 DOI: 10.3389/fsurg.2022.829237] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 01/05/2022] [Indexed: 12/20/2022] Open
Abstract
A growing number of studies have shown that immunity plays an important clinical role in the process of gastric cancer (GC). The purpose of this study was to explore the function of differentially expressed immune-related genes (DEIRGs) of GC, and construct a gene signature to predict the overall survival (OS) of patients. Gene expression profiles and clinical data of GC patients were downloaded from TCGA and GEO databases. Combined with immune-related genes (IRGs) downloaded from the ImmPort database, 357 DEIRGs in GC tissues and adjacent tissues were identified. Based on the analysis of Lasso and Cox in the training set, a prognostic risk scoring model consisting of 9 (RBP7, DES, CCR1, PNOC, SPP1, VIP, TNFRSF12A, TUBB3, PRKCG) DEIRGs was obtained. Functional analysis revealed that model genes may participate in the formation and development of tumor cells by affecting the function of cell gap junction intercellular communication (GJJC). According to the model score, the samples were divided into high-risk and low-risk groups. In multivariate Cox regression analysis, the risk score was an independent prognostic factor (HR = 1.674, 95% CI = 1.470–1.907, P < 0.001). Survival analysis showed that the OS of high-risk GC patients was significantly lower than that of low-risk GC patients (P < 0.001). The area under the receiver operating characteristic curve (ROC) of the model was greater than other clinical indicators when verified in various data sets, confirming that the prediction model has a reliable accuracy. In conclusion, this study has explored the biological functions of DEIRGs in GC and discovered novel gene targets for the treatment of GC. The constructed prognostic gene signature is helpful for clinicians to determine the prognosis of GC patients and formulate personalized treatment plans.
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Affiliation(s)
- Mengying Li
- Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Clinical Medicine, Anhui Medical University, Hefei, China
| | - Wei Cao
- Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Bingqian Huang
- Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Clinical Medicine, Anhui Medical University, Hefei, China
| | - Zhipeng Zhu
- Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Clinical Medicine, Anhui Medical University, Hefei, China
| | - Yaxin Chen
- Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Clinical Medicine, Anhui Medical University, Hefei, China
| | - Jiawei Zhang
- Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, China
- *Correspondence: Jiawei Zhang
| | - Guodong Cao
- Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, China
- Guodong Cao
| | - Bo Chen
- Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, China
- Bo Chen
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Yu Z, Du M, Lu L. A Novel 16-Genes Signature Scoring System as Prognostic Model to Evaluate Survival Risk in Patients with Glioblastoma. Biomedicines 2022; 10:biomedicines10020317. [PMID: 35203526 PMCID: PMC8869708 DOI: 10.3390/biomedicines10020317] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 01/20/2022] [Accepted: 01/21/2022] [Indexed: 12/15/2022] Open
Abstract
Previous studies have found that gene expression levels are associated with prognosis and some genes can be used to predict the survival risk of glioblastoma (GBM) patients. However, most of them just built the survival-related gene signature, and personal survival risk can be evaluated only in group. This study aimed to find the prognostic survival related genes of GBM, and construct survival risk prediction model, which can be used to evaluate survival risk by individual. We collected gene expression data and clinical information from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Cox regression analysis and LASSO-cox regression analysis were performed to get survival-related genes and establish the overall survival prediction model. The ROC curve and Kaplan Meier analysis were used to evaluate the prediction ability of the model in training set and two independent cohorts. We also analyzed the biological functions of survival-related genes by GO and KEGG enrichment analysis. We identified 99 genes associated with overall survival and selected 16 genes (IGFBP2, GPRASP1, C1R, CHRM3, CLSTN2, NELL1, SEZ6L2, NMB, ICAM5, HPCAL4, SNAP91, PCSK1N, PGBD5, INA, UCHL1 and LHX6) to establish the survival risk prediction model. Multivariate Cox regression analysis indicted that the risk score could predict overall survival independent of age and gender. ROC analyses showed that our model was more robust than four existing signatures. The sixteen genes can also be potential transcriptional biomarkers and the model can assist doctors on clinical decision-making and personalized treatment of GBM patients.
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Bertucci F, Niziers V, de Nonneville A, Finetti P, Mescam L, Mir O, Italiano A, Le Cesne A, Blay JY, Ceccarelli M, Bedognetti D, Birnbaum D, Mamessier E. Immunologic constant of rejection signature is prognostic in soft-tissue sarcoma and refines the CINSARC signature. J Immunother Cancer 2022; 10:jitc-2021-003687. [PMID: 35017155 PMCID: PMC8753443 DOI: 10.1136/jitc-2021-003687] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/01/2021] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Soft-tissue sarcomas (STSs) are heterogeneous and aggressive tumors, with high metastatic risk. The immunologic constant of rejection (ICR) 20-gene signature is a signature of cytotoxic immune response. We hypothesized that ICR might improve the prognostic assessment of early-stage STS. METHODS We retrospectively applied ICR to 1455 non-metastatic STS and searched for correlations between ICR classes and clinicopathological and biological variables, including metastasis-free survival (MFS). RESULTS Thirty-four per cent of tumors were classified as ICR1, 27% ICR2, 24% ICR3, and 15% ICR4. These classes were associated with patients' age, pathological type, and tumor depth, and an enrichment from ICR1 to ICR4 of quantitative/qualitative scores of immune response. ICR1 class was associated with a 59% increased risk of metastatic relapse when compared with ICR2-4 class. In multivariate analysis, ICR classification remained associated with MFS, as well as pathological type and Complexity Index in Sarcomas (CINSARC) classification, suggesting independent prognostic value. A prognostic clinicogenomic model, including the three variables, was built in a learning set (n=339) and validated in an independent set (n=339), showing greater prognostic precision than each variable alone or in doublet. Finally, connectivity mapping analysis identified drug classes potentially able to reverse the expression profile of poor-prognosis tumors, such as chemotherapy and targeted therapies. CONCLUSION ICR signature is independently associated with postoperative MFS in early-stage STS, independently from other prognostic features, including CINSARC. We built a robust prognostic clinicogenomic model integrating ICR, CINSARC, and pathological type, and suggested differential vulnerability of each prognostic group to different systemic therapies.
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Affiliation(s)
- Francois Bertucci
- Laboratory of Predictive Oncology, Centre de Recherche en Cancérologie de Marseille (CRCM), Institut Paoli-Calmettes, Aix-Marseille Université, INSERM UMR1068, CNRS UMR725, Marseille, France .,Department of Medical Oncology, Institut Paoli-Calmettes, Marseille, France.,French Sarcoma Group, Lyon, France
| | - Vincent Niziers
- Laboratory of Predictive Oncology, Centre de Recherche en Cancérologie de Marseille (CRCM), Institut Paoli-Calmettes, Aix-Marseille Université, INSERM UMR1068, CNRS UMR725, Marseille, France.,Department of Surgery, Institut Paoli-Calmettes, Marseille, France
| | - Alexandre de Nonneville
- Laboratory of Predictive Oncology, Centre de Recherche en Cancérologie de Marseille (CRCM), Institut Paoli-Calmettes, Aix-Marseille Université, INSERM UMR1068, CNRS UMR725, Marseille, France.,Department of Medical Oncology, Institut Paoli-Calmettes, Marseille, France
| | - Pascal Finetti
- Laboratory of Predictive Oncology, Centre de Recherche en Cancérologie de Marseille (CRCM), Institut Paoli-Calmettes, Aix-Marseille Université, INSERM UMR1068, CNRS UMR725, Marseille, France
| | - Léna Mescam
- French Sarcoma Group, Lyon, France.,Department of Pathology, Institut Paoli-Calmettes, Marseille, France
| | - Olivier Mir
- French Sarcoma Group, Lyon, France.,Department of Medical Oncology, Gustave Roussy, Villejuif, France
| | - Antoine Italiano
- French Sarcoma Group, Lyon, France.,Department of Medical Oncology, Institut Bergonie, Bordeaux, France
| | - Axel Le Cesne
- French Sarcoma Group, Lyon, France.,Department of Medical Oncology, Gustave Roussy, Villejuif, France
| | - Jean-Yves Blay
- French Sarcoma Group, Lyon, France.,Department of Medical Oncology, Centre Leon Berard, Lyon, France
| | - Michele Ceccarelli
- DIETI, University of Naples Federico II Faculty of Engineering, Naples, Italy
| | - Davide Bedognetti
- Cancer Research, Sidra Medicine, Doha, Qatar.,Department of Internal Medicine and Medical Specialties, University of Genova, Genova, Italy
| | - Daniel Birnbaum
- Laboratory of Predictive Oncology, Centre de Recherche en Cancérologie de Marseille (CRCM), Institut Paoli-Calmettes, Aix-Marseille Université, INSERM UMR1068, CNRS UMR725, Marseille, France
| | - Emilie Mamessier
- Laboratory of Predictive Oncology, Centre de Recherche en Cancérologie de Marseille (CRCM), Institut Paoli-Calmettes, Aix-Marseille Université, INSERM UMR1068, CNRS UMR725, Marseille, France
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A Novel Four-Gene Prognostic Signature for Prediction of Survival in Patients with Soft Tissue Sarcoma. Cancers (Basel) 2021; 13:cancers13225837. [PMID: 34830998 PMCID: PMC8616347 DOI: 10.3390/cancers13225837] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/16/2021] [Accepted: 11/20/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Soft tissue sarcomas (STS) still lack effective clinical stratification and prognostic models. The aim of this study is to establish a reliable prognostic gene signature in STS. Using 189 STS samples from the TCGA database, a four-gene signature (including DHRS3, JRK, TARDBP and TTC3) and nomograms that can be used to predict the overall survival and relapse free survival of STS patients was developed. The predictive ability for metastasis free survival was externally verified in the GEO cohort. We demonstrated that the novel gene signature provides an attractive platform for risk stratification and prognosis prediction of STS patients, which is of great importance for individualized clinical treatment and long-term management of patients with this rare and severe disease. Abstract Soft tissue sarcomas (STS), a group of rare malignant tumours with high tissue heterogeneity, still lack effective clinical stratification and prognostic models. Therefore, we conducted this study to establish a reliable prognostic gene signature. Using 189 STS patients’ data from The Cancer Genome Atlas database, a four-gene signature including DHRS3, JRK, TARDBP and TTC3 was established. A risk score based on this gene signature was able to divide STS patients into a low-risk and a high-risk group. The latter had significantly worse overall survival (OS) and relapse free survival (RFS), and Cox regression analyses showed that the risk score is an independent prognostic factor. Nomograms containing the four-gene signature have also been established and have been verified through calibration curves. In addition, the predictive ability of this four-gene signature for STS metastasis free survival was verified in an independent cohort (309 STS patients from the Gene Expression Omnibus database). Finally, Gene Set Enrichment Analysis indicated that the four-gene signature may be related to some pathways associated with tumorigenesis, growth, and metastasis. In conclusion, our study establishes a novel four-gene signature and clinically feasible nomograms to predict the OS and RFS. This can help personalized treatment decisions, long-term patient management, and possible future development of targeted therapy.
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Wu X, Yang T, Qian L, Zhang D, Yang H. Construction of a New Tumor Immunity-Related Signature to Assess and Classify the Prognostic Risk of Colorectal Cancer. Int J Gen Med 2021; 14:6661-6676. [PMID: 34675628 PMCID: PMC8520451 DOI: 10.2147/ijgm.s325511] [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: 06/29/2021] [Accepted: 09/28/2021] [Indexed: 12/16/2022] Open
Abstract
Purpose Although immunotherapy and checkpoint inhibitors contribute to the treatment of colorectal cancer (CRC), few patients can benefit from these treatments. Therefore, our goal was to develop a marker based on immune-related genes to predict the prognosis of patients with CRC to guide treatment strategies. Methods Gene expression data from colorectal cancer patients in the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas were analyzed systematically. We used Cox regression to identify immune-related genes with potential prognostic value. The expression of immune genes, infiltration level of immune cells, and several immune-related molecules were further compared between the high-risk and low-risk groups. Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway analyses were used for functional analysis. Results Five GEO datasets were integrated into a merged GEO dataset, which showed obvious survival in StromalScore and ESTIMATEScore. WGCNA showed that 749 genes of the pink module are related to immunity, 95 of which are related to prognosis, correlating with cytokine–cytokine receptor interaction and natural killer cell-mediated cytotoxicity. Among these genes, an 11-gene signature was developed through stability selection and LASSO Cox regression. Univariate and multifactorial Cox regression analyses demonstrated that gene signature was an independent prognostic factor for predicting survival in patients with colorectal cancer. Samples from the low-risk group may be more sensitive to immunotherapy. In addition, the nomogram risk prediction model effectively predicted the prognosis of CRC patients by appropriately stratifying the risk scores. Conclusion In conclusion, we developed a novel immune-related gene signature that may be useful in predicting cancer progression and prognosis, thus contributing to the individualized management of colorectal cancer patients.
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Affiliation(s)
- Xiaocheng Wu
- Zhejiang Chinese Medical University, Hangzhou City, People's Republic of China.,Pathology Laboratory, Hangzhou Dian Medical Laboratories, Hangzhou City, People's Republic of China
| | - Tianxing Yang
- Department of Medical Oncology, Sanmen People's Hospital, Taizhou City, People's Republic of China
| | - Liping Qian
- Hang Zhou Cancer Hospital, Hangzhou City, People's Republic of China
| | - Desheng Zhang
- Department of Radiology, Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, People's Republic of China
| | - Hui Yang
- Department of Gastroenterology, Changxing People's Hospital, Huzhou City, People's Republic of China
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Zou M, Su X, Wang L, Yi X, Qiu Y, Yin X, Zhou X, Niu X, Wang L, Su M. The Molecular Mechanism of Multiple Organ Dysfunction and Targeted Intervention of COVID-19 Based on Time-Order Transcriptomic Analysis. Front Immunol 2021; 12:729776. [PMID: 34504502 PMCID: PMC8421734 DOI: 10.3389/fimmu.2021.729776] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 08/04/2021] [Indexed: 12/22/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19) pandemic is caused by the novel coronavirus that has spread rapidly around the world, leading to high mortality because of multiple organ dysfunction; however, its underlying molecular mechanism is unknown. To determine the molecular mechanism of multiple organ dysfunction, a bioinformatics analysis method based on a time-order gene co-expression network (TO-GCN) was performed. First, gene expression profiles were downloaded from the gene expression omnibus database (GSE161200), and a TO-GCN was constructed using the breadth-first search (BFS) algorithm to infer the pattern of changes in the different organs over time. Second, Gene Ontology enrichment analysis was used to analyze the main biological processes related to COVID-19. The initial gene modules for the immune response of different organs were defined as the research object. The STRING database was used to construct a protein-protein interaction network of immune genes in different organs. The PageRank algorithm was used to identify five hub genes in each organ. Finally, the Comparative Toxicogenomics Database played an important role in exploring the potential compounds that target the hub genes. The results showed that there were two types of biological processes: the body's stress response and cell-mediated immune response involving the lung, trachea, and olfactory bulb (olf) after being infected by COVID-19. However, a unique biological process related to the stress response is the regulation of neuronal signals in the brain. The stress response was heterogeneous among different organs. In the lung, the regulation of DNA morphology, angiogenesis, and mitochondrial-related energy metabolism are specific biological processes related to the stress response. In particular, an effect on tracheal stress response was made by the regulation of protein metabolism and rRNA metabolism-related biological processes, as biological processes. In the olf, the distinctive stress responses consist of neural signal transmission and brain behavior. In addition, myeloid leukocyte activation and myeloid leukocyte-mediated immunity in response to COVID-19 can lead to a cytokine storm. Immune genes such as SRC, RHOA, CD40LG, CSF1, TNFRSF1A, FCER1G, ICAM1, LAT, LCN2, PLAU, CXCL10, ICAM1, CD40, IRF7, and B2M were predicted to be the hub genes in the cytokine storm. Furthermore, we inferred that resveratrol, acetaminophen, dexamethasone, estradiol, statins, curcumin, and other compounds are potential target drugs in the treatment of COVID-19.
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Affiliation(s)
- Miao Zou
- Department of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, ChangChun, China
| | - Xiaoyun Su
- Department of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, ChangChun, China
| | - Luoying Wang
- Department of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, ChangChun, China
| | - Xingcheng Yi
- Department of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, ChangChun, China
| | - Yue Qiu
- Department of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, ChangChun, China
| | - Xirui Yin
- Department of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, ChangChun, China
| | - Xuan Zhou
- Department of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, ChangChun, China
| | - Xinhui Niu
- Department of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, ChangChun, China
| | - Liuli Wang
- Department of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, ChangChun, China
| | - Manman Su
- Department of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, ChangChun, China
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