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Li Z, Zhou B, Zhu X, Yang F, Jin K, Dai J, Zhu Y, Song X, Jiang G. Differentiation-related genes in tumor-associated macrophages as potential prognostic biomarkers in non-small cell lung cancer. Front Immunol 2023; 14:1123840. [PMID: 36969247 PMCID: PMC10033599 DOI: 10.3389/fimmu.2023.1123840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 02/23/2023] [Indexed: 03/12/2023] Open
Abstract
BackgroundThe purpose of this study was to evaluate the role of differentiation-related genes (DRGs) in tumor-associated macrophages (TAMs) in non-small cell lung cancer (NSCLC).MethodsSingle cell RNA-seq (scRNA-seq) data from GEO and bulk RNA-seq data from TCGA were analyzed to identify DRGs using trajectory method. Functional gene analysis was carried out by GO/KEGG enrichment analysis. The mRNA and protein expression in human tissue were analyzed by HPA and GEPIA databases. To investigate the prognostic value of these genes, three risk score (RS) models in different pathological types of NSCLC were generated and predicted NSCLC prognosis in datasets from TCGA, UCSC and GEO databases.Results1,738 DRGs were identified through trajectory analysis. GO/KEGG analysis showed that these genes were predominantly related to myeloid leukocyte activation and leukocyte migration. 13 DRGs (C1QB, CCL4, CD14, CD84, FGL2, MS4A6A, NLRP3, PLEK, RNASE6, SAMSN1, SPN, TMEM176B, ZEB2) related to prognosis were obtained through univariate Cox analysis and Lasso regression. C1QB, CD84, FGL2, MS4A6A, NLRP3, PLEK, SAMSN1, SPN, and ZEB2 were downregulated in NSCLC compared to non-cancer tissue. The mRNA of 13 genes were significantly expressed in pulmonary macrophages with strong cell specificity. Meanwhile, immunohistochemical staining showed that C1QB, CCL4, SPN, CD14, NLRP3, SAMSN1, MS4A6A, TMEM176B were expressed in different degrees in lung cancer tissues. ZEB2 (HR=1.4, P<0.05) and CD14 (HR=1.6, P<0.05) expression were associated with a worse prognosis in lung squamous cell carcinoma; ZEB2 (HR=0.64, P<0.05), CD84 (HR=0.65, P<0.05), PLEK (HR=0.71, P<0.05) and FGL2 (HR=0.61, P<0.05) expression were associated with a better prognosis in lung adenocarcinoma. Three RS models based on 13 DRGs both showed that the high RS was significantly associated with poor prognosis in different pathological types of NSCLC.ConclusionsThis study highlights the prognostic value of DRGs in TAMs in NSCLC patients, providing novel insights for the development of therapeutic and prognostic targets based on TAM functional differences.
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Affiliation(s)
| | | | | | | | | | | | | | - Xiao Song
- *Correspondence: Xiao Song, ; Gening Jiang,
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Randhawa V, Kumar M. An integrated network analysis approach to identify potential key genes, transcription factors, and microRNAs regulating human hematopoietic stem cell aging. Mol Omics 2021; 17:967-984. [PMID: 34605522 DOI: 10.1039/d1mo00199j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Hematopoietic stem cells (HSCs) undergo functional deterioration with increasing age that causes loss of their self-renewal and regenerative potential. Despite various efforts, significant success in identifying molecular regulators of HSC aging has not been achieved, one prime reason being the non-availability of appropriate human HSC samples. To demonstrate the scope of integrating and re-analyzing the HSC transcriptomics data available, we used existing tools and databases to structure a sequential data analysis pipeline to predict potential candidate genes, transcription factors, and microRNAs simultaneously. This sequential approach comprises (i) collecting matched young and aged mice HSC sample datasets, (ii) identifying differentially expressed genes, (iii) identifying human homologs of differentially expressed genes, (iv) inferring gene co-expression network modules, and (v) inferring the microRNA-transcription factor-gene regulatory network. Systems-level analyses of HSC interaction networks provided various insights based on which several candidates were predicted. For example, 16 HSC aging-related candidate genes were predicted (e.g., CD38, BRCA1, AGTR1, GSTM1, etc.) from GCN analysis. Following this, the shortest path distance-based analyses of the regulatory network predicted several novel candidate miRNAs and TFs. Among these, miR-124-3p was a common regulator in candidate gene modules, while TFs MYC and SP1 were identified to regulate various candidate genes. Based on the regulatory interactions among candidate genes, TFs, and miRNAs, a potential regulation model of biological processes in each of the candidate modules was predicted, which provided systems-level insights into the molecular complexity of each module to regulate HSC aging.
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Affiliation(s)
- Vinay Randhawa
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific & Industrial Research, Chandigarh-160036, India.
| | - Manoj Kumar
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific & Industrial Research, Chandigarh-160036, India. .,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India
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Yang Y, Yuan L, Yang M, Du X, Qin L, Wang L, Zhou K, Wu M, He R, Feng J, Xiang Y, Qu X, Liu H, Qin X, Liu C. Aberrant Methylation of Aging-Related Genes in Asthma. Front Mol Biosci 2021; 8:655285. [PMID: 34136532 PMCID: PMC8203316 DOI: 10.3389/fmolb.2021.655285] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 05/04/2021] [Indexed: 12/18/2022] Open
Abstract
Background: Asthma is a complex pulmonary inflammatory disease which is common among older adults. Aging-related alterations have also been found in structural cells and immune cells of asthma patients. Nonetheless, the underlying mechanism by which differenced aging-related gene contributes to asthma pathology remains unclear. Of note, DNA methylation (DNAm) has been proven to play a critical mechanism for age-related gene expression changes. However, the methylation changes of aging-related genes in asthma patients are still obscure. Methods: First, changes in DNAm and gene expression were detected with multiple targeted bisulfite enrichment sequencing (MethTarget) and qPCR in peripheral blood of 51 healthy controls (HCs) and 55 asthmatic patients. Second, the correlation between the DNAm levels of specific altered CpG sites and the pulmonary function indicators of asthma patients was evaluated. Last, the receiver operator characteristic (ROC) curve and principal component analysis (PCA) were used to identify the feasibility of the candidate CpG sites as biomarkers for asthma. Results: Compared with HCs, there was a differential mRNA expression for nine aging-related genes in peripheral blood of asthma patients. Besides, the methylation levels of the nine aging-related genes were also altered in asthma patients, and a total of 68 CpG sites were associated with the severity of asthma. Notably, 9 of the 68 CpG sites were significantly associated with pulmonary function parameters. Moreover, ROC curve and PCA analysis showed that the candidate differential methylation sites (DMSs) can be used as potential biomarkers for asthma. Conclusions: In summary, this study confirmed the differentially expressed mRNA and aberrant DNAm level of aging-related genes in asthma patients. DMSs are associated with the clinical evaluation indicators of asthma, which indicate the involvement of aging-related genes in the pathogenesis of asthma and provide some new possible biomarkers for asthma.
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Affiliation(s)
- Yu Yang
- Department of Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, Xiangya Hospital, Central South University, Changsha, China.,Department of Physiology, School of Basic Medicine Science, Central South University, Changsha, China.,Basic and Clinical Research Laboratory of Major Respiratory Diseases, Central South University, Changsha, China
| | - Lin Yuan
- Department of Physiology, School of Basic Medicine Science, Central South University, Changsha, China
| | - Ming Yang
- Centre for Asthma and Respiratory Disease, School of Biomedical Sciences and Pharmacy, Faculty of Health and Medicine, University of Newcastle and Hunter Medical Research Institute, Callaghan, NSW, Australia
| | - Xizi Du
- Department of Physiology, School of Basic Medicine Science, Central South University, Changsha, China
| | - Ling Qin
- Department of Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, Xiangya Hospital, Central South University, Changsha, China.,Basic and Clinical Research Laboratory of Major Respiratory Diseases, Central South University, Changsha, China
| | - Leyuan Wang
- Department of Physiology, School of Basic Medicine Science, Central South University, Changsha, China
| | - Kai Zhou
- Department of Physiology, School of Basic Medicine Science, Central South University, Changsha, China
| | - Mengping Wu
- Department of Physiology, School of Basic Medicine Science, Central South University, Changsha, China
| | - Ruoxi He
- Department of Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, Xiangya Hospital, Central South University, Changsha, China.,Basic and Clinical Research Laboratory of Major Respiratory Diseases, Central South University, Changsha, China
| | - Juntao Feng
- Department of Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, Xiangya Hospital, Central South University, Changsha, China.,Basic and Clinical Research Laboratory of Major Respiratory Diseases, Central South University, Changsha, China
| | - Yang Xiang
- Department of Physiology, School of Basic Medicine Science, Central South University, Changsha, China
| | - Xiangping Qu
- Department of Physiology, School of Basic Medicine Science, Central South University, Changsha, China
| | - Huijun Liu
- Department of Physiology, School of Basic Medicine Science, Central South University, Changsha, China
| | - Xiaoqun Qin
- Department of Physiology, School of Basic Medicine Science, Central South University, Changsha, China
| | - Chi Liu
- Department of Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, Xiangya Hospital, Central South University, Changsha, China.,Department of Physiology, School of Basic Medicine Science, Central South University, Changsha, China.,Research Center of China-Africa Infectious Diseases, Xiangya School of Medicine Central South University, Changsha, China
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Du X, Yuan L, Wu M, Men M, He R, Wang L, Wu S, Xiang Y, Qu X, Liu H, Qin X, Hu C, Qin L, Liu C. Variable DNA methylation of aging-related genes is associated with male COPD. Respir Res 2019; 20:243. [PMID: 31684967 PMCID: PMC6829949 DOI: 10.1186/s12931-019-1215-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 10/15/2019] [Indexed: 12/12/2022] Open
Abstract
Background Chronic obstructive pulmonary disease (COPD) is a chronic lung inflammatory disease which has a close relationship with aging. Genome-wide analysis reveals that DNA methylation markers vary obviously with age. DNA methylation variations in peripheral blood have the potential to be biomarkers for COPD. However, the specific DNA methylation of aging-related genes in the peripheral blood of COPD patients remains largely unknown. Methods Firstly, 9 aging-related differentially expressed genes (DEGs) in COPD patients were screened out from the 25 aging-related genes profile through a comprehensive screening strategy. Secondly, qPCR and multiple targeted bisulfite enrichment sequencing (MethTarget) were used to detect the mRNA level and DNA methylation level of the 9 differentially expressed genes in the peripheral blood of 60 control subjects and 45 COPD patients. The candidate functional CpG sites were selected on the basis of the regulation ability of the target gene expression. Thirdly, the correlation was evaluated between the DNA methylation level of the key CpG sites and the clinical parameters of COPD patients, including forced expiratory volume in one second (FEV1), forced expiratory volume in one second as percentage of predicted volume (FEV1%), forced expiratory volume/ forced vital capacity (FEV/FVC), modified British medical research council (mMRC) score, acute exacerbation frequency and the situation of frequent of acute aggravation (CAT) score. Lastly, differentially methylated CpG sites unrelated to smoking were also determined in COPD patients. Results Of the 9 differentially expressed aging-related genes, the mRNA expression of 8 genes were detected to be significantly down-regulated in COPD group, compared with control group. Meanwhile, the methylated level of all aging-related genes was changed in COPD group containing 219 COPD-related CpG sites in total. Notably, 27 CpG sites of FOXO3 gene showed a lower False Discovery Rate (FDR) and higher methylation difference values. Also, some variable DNA methylation is associated with the severity of COPD. Additionally, of the 219 COPD-related CpG sites, 147 CpG sites were not related to smoking. Conclusion These results identified that the mRNA expression and DNA methylation level of aging-related genes were changed in male COPD patients, which provides a molecular link between aging and COPD. The identified CpG markers are associated with the severity of COPD and provide new insights into the prediction and identification of COPD.
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Affiliation(s)
- Xizi Du
- Department of Physiology; China-Africa Infection Diseases Research Center, Xiangya School of Medicine, Central South University, Changsha, 410078, Hunan, China.,Department of Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Lin Yuan
- Department of Physiology; China-Africa Infection Diseases Research Center, Xiangya School of Medicine, Central South University, Changsha, 410078, Hunan, China
| | - Mengping Wu
- Department of Physiology; China-Africa Infection Diseases Research Center, Xiangya School of Medicine, Central South University, Changsha, 410078, Hunan, China
| | - Meichao Men
- Health Management Center, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Ruoxi He
- Department of Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Leyuan Wang
- Department of Physiology; China-Africa Infection Diseases Research Center, Xiangya School of Medicine, Central South University, Changsha, 410078, Hunan, China
| | - Shuangyan Wu
- Department of Physiology; China-Africa Infection Diseases Research Center, Xiangya School of Medicine, Central South University, Changsha, 410078, Hunan, China
| | - Yang Xiang
- Department of Physiology; China-Africa Infection Diseases Research Center, Xiangya School of Medicine, Central South University, Changsha, 410078, Hunan, China
| | - Xiangping Qu
- Department of Physiology; China-Africa Infection Diseases Research Center, Xiangya School of Medicine, Central South University, Changsha, 410078, Hunan, China
| | - Huijun Liu
- Department of Physiology; China-Africa Infection Diseases Research Center, Xiangya School of Medicine, Central South University, Changsha, 410078, Hunan, China
| | - Xiaoqun Qin
- Department of Physiology; China-Africa Infection Diseases Research Center, Xiangya School of Medicine, Central South University, Changsha, 410078, Hunan, China
| | - Chengping Hu
- Department of Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Ling Qin
- Department of Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, Xiangya Hospital, Central South University, Changsha, Hunan, China.
| | - Chi Liu
- Department of Physiology; China-Africa Infection Diseases Research Center, Xiangya School of Medicine, Central South University, Changsha, 410078, Hunan, China.
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Zhai Y, Chen Y, Li Q, Zhang L. Exploration of the hub genes and miRNAs in lung adenocarcinoma. Oncol Lett 2019; 18:1713-1722. [PMID: 31423238 PMCID: PMC6607253 DOI: 10.3892/ol.2019.10478] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 05/14/2019] [Indexed: 01/01/2023] Open
Abstract
In order to investigate the oncogenic mechanisms of lung adenocarcinoma (LUAD), hub genes can be identified by constructing co-expression networks, and the potential linkages between hub genes, transcription factors (TFs) and microRNAs (miRNAs/miRs) can be visualized and identified. In the present study, a total of 12 co-expressed modules were constructed, and 9 of these were significantly correlated with clinical traits in LUAD. The differentially expressed genes and differentially expressed miRNAs were determined, and the targets of differentially expressed miRNA were identified from the hub genes or TFs. The results of the present study demonstrated that 10 hub genes and 12 TFs are the predicted targets for the 5 and 8 differentially expressed miRNAs, respectively. Genes in pink and red modules, which have a high correlation with the clinical trait of days to death, are significantly enriched in 'nucleosome assembly' and 'microtubule-based process', respectively. These results indicated that miR-206, miR-137, miR-153, hub genes and enriched TFs in the pink and red modules exert a potentially pivotal function in the development of LUAD.
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Affiliation(s)
- Yuanyuan Zhai
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, Inner Mongolia 010021, P.R. China
| | - Yingli Chen
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, Inner Mongolia 010021, P.R. China
| | - Qianzhong Li
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, Inner Mongolia 010021, P.R. China.,The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University, Hohhot, Inner Mongolia 010070, P.R. China
| | - Luqiang Zhang
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, Inner Mongolia 010021, P.R. China
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Wang W, Jiang W, Hou L, Duan H, Wu Y, Xu C, Tan Q, Li S, Zhang D. Weighted gene co-expression network analysis of expression data of monozygotic twins identifies specific modules and hub genes related to BMI. BMC Genomics 2017; 18:872. [PMID: 29132311 PMCID: PMC5683603 DOI: 10.1186/s12864-017-4257-6] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Accepted: 11/01/2017] [Indexed: 02/08/2023] Open
Abstract
Background The therapeutic management of obesity is challenging, hence further elucidating the underlying mechanisms of obesity development and identifying new diagnostic biomarkers and therapeutic targets are urgent and necessary. Here, we performed differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) to identify significant genes and specific modules related to BMI based on gene expression profile data of 7 discordant monozygotic twins. Results In the differential gene expression analysis, it appeared that 32 differentially expressed genes (DEGs) were with a trend of up-regulation in twins with higher BMI when compared to their siblings. Categories of positive regulation of nitric-oxide synthase biosynthetic process, positive regulation of NF-kappa B import into nucleus, and peroxidase activity were significantly enriched within GO database and NF-kappa B signaling pathway within KEGG database. DEGs of NAMPT, TLR9, PTGS2, HBD, and PCSK1N might be associated with obesity. In the WGCNA, among the total 20 distinct co-expression modules identified, coral1 module (68 genes) had the strongest positive correlation with BMI (r = 0.56, P = 0.04) and disease status (r = 0.56, P = 0.04). Categories of positive regulation of phospholipase activity, high-density lipoprotein particle clearance, chylomicron remnant clearance, reverse cholesterol transport, intermediate-density lipoprotein particle, chylomicron, low-density lipoprotein particle, very-low-density lipoprotein particle, voltage-gated potassium channel complex, cholesterol transporter activity, and neuropeptide hormone activity were significantly enriched within GO database for this module. And alcoholism and cell adhesion molecules pathways were significantly enriched within KEGG database. Several hub genes, such as GAL, ASB9, NPPB, TBX2, IL17C, APOE, ABCG4, and APOC2 were also identified. The module eigengene of saddlebrown module (212 genes) was also significantly correlated with BMI (r = 0.56, P = 0.04), and hub genes of KCNN1 and AQP10 were differentially expressed. Conclusion We identified significant genes and specific modules potentially related to BMI based on the gene expression profile data of monozygotic twins. The findings may help further elucidate the underlying mechanisms of obesity development and provide novel insights to research potential gene biomarkers and signaling pathways for obesity treatment. Further analysis and validation of the findings reported here are important and necessary when more sample size is acquired. Electronic supplementary material The online version of this article (10.1186/s12864-017-4257-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Weijing Wang
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, No. 38 Dengzhou Road, Shibei District, Qingdao, 266021, Shandong Province, People's Republic of China
| | - Wenjie Jiang
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, No. 38 Dengzhou Road, Shibei District, Qingdao, 266021, Shandong Province, People's Republic of China
| | - Lin Hou
- Department of Biochemistry, Medical College, Qingdao University, No. 38 Dengzhou Road, Shibei District, Qingdao, 266021, Shandong Province, People's Republic of China
| | - Haiping Duan
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, No. 38 Dengzhou Road, Shibei District, Qingdao, 266021, Shandong Province, People's Republic of China.,Qingdao Municipal Center for Disease Control and Prevention, No. 175 Shandong Road, Shibei District, Qingdao, 266033, Shandong Province, People's Republic of China
| | - Yili Wu
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, No. 38 Dengzhou Road, Shibei District, Qingdao, 266021, Shandong Province, People's Republic of China
| | - Chunsheng Xu
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, No. 38 Dengzhou Road, Shibei District, Qingdao, 266021, Shandong Province, People's Republic of China.,Qingdao Municipal Center for Disease Control and Prevention, No. 175 Shandong Road, Shibei District, Qingdao, 266033, Shandong Province, People's Republic of China.,Qingdao Institute of Preventive Medicine, No. 175 Shandong Road, Shibei District, Qingdao, 266033, Shandong Province, People's Republic of China
| | - Qihua Tan
- Epidemiology, Biostatistics and Bio-demography, Institute of Public Health, University of Southern Denmark, DK-5000, Odense C, Denmark.,Human Genetics, Institute of Clinical Research, University of Southern Denmark, DK-5000, Odense C, Denmark
| | - Shuxia Li
- Human Genetics, Institute of Clinical Research, University of Southern Denmark, DK-5000, Odense C, Denmark
| | - Dongfeng Zhang
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, No. 38 Dengzhou Road, Shibei District, Qingdao, 266021, Shandong Province, People's Republic of China.
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