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Lin J, Cai B, Lin Q, Lin X, Wang B, Chen X. TLE4 downregulation identified by WGCNA and machine learning algorithm promotes papillary thyroid carcinoma progression via activating JAK/STAT pathway. J Cancer 2024; 15:4759-4776. [PMID: 39006072 PMCID: PMC11242334 DOI: 10.7150/jca.95501] [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: 02/20/2024] [Accepted: 05/02/2024] [Indexed: 07/16/2024] Open
Abstract
Background: Papillary Thyroid Carcinoma (PTC), a common type of thyroid cancer, has a pathogenesis that is not fully understood. This study utilizes a range of public databases, sophisticated bioinformatics tools, and empirical approaches to explore the key genetic components and pathways implicated in PTC, particularly concentrating on the Transducin-Like Enhancer of Split 4 (TLE4) gene. Methods: Public databases such as TCGA and GEO were utilized to conduct differential gene expression analysis in PTC. Hub genes were identified using Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning techniques, including Random Forest, LASSO regression, and SVM-RFE, were employed for biomarker identification. The clinical impact of the TLE4 gene was assessed in terms of diagnostic accuracy, prognostic value, and its functional enrichment analysis in PTC. Additionally, the study focused on understanding the role of TLE4 in the dynamics of immune cell infiltration, gene function enhancement, and behaviors of PTC cells like growth, migration, and invasion. To complement these analyses, in vivo studies were performed using a xenograft mouse model. Results: 244 genes with significant differential expression across various databases were identified. WGCNA indicated a strong link between specific gene modules and PTC. Machine learning analysis brought the TLE4 gene into focus as a key biomarker. Bioinformatics studies verified that TLE4 expression is lower in PTC, linking it to immune cell infiltration and the JAK-STAT signaling pathways. Experimental data revealed that decreased TLE4 expression in PTC cell lines leads to enhanced cell growth, migration, invasion, and activates the JAK/STAT pathway. In contrast, TLE4 overexpression in these cells inhibited tumor growth and metastasis. Conclusions: This study sheds light on TLE4's crucial role in PTC pathogenesis, positioning it as a potential biomarker and target for therapy. The integration of multi-omics data and advanced analytical methods provides a robust framework for understanding PTC at a molecular level, potentially guiding personalized treatment strategies.
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Affiliation(s)
- Junyu Lin
- Department of Thyroid and Breast Surgery, the First Affiliated Hospital, Fujian Medical University, 350005, Fuzhou, Fujian, China
- Department of Thyroid and Breast Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 350212, Fuzhou, Fujian, China
| | - Beichen Cai
- Department of Plastic Surgery, the First Affiliated Hospital of Fujian Medical University, 350005, Fuzhou, Fujian, China
- Department of Plastic Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 350212, Fuzhou, Fujian, China
| | - Qian Lin
- Department of Plastic Surgery, the First Affiliated Hospital of Fujian Medical University, 350005, Fuzhou, Fujian, China
- Department of Plastic Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 350212, Fuzhou, Fujian, China
| | - Xinjian Lin
- Key Laboratory of Gastrointestinal Cancer, Fujian Medical University, Ministry of Education, 350108, Fuzhou, Fujian, China
| | - Biao Wang
- Department of Plastic Surgery, the First Affiliated Hospital of Fujian Medical University, 350005, Fuzhou, Fujian, China
- Department of Plastic Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 350212, Fuzhou, Fujian, China
| | - Xiangjin Chen
- Department of Thyroid and Breast Surgery, the First Affiliated Hospital, Fujian Medical University, 350005, Fuzhou, Fujian, China
- Department of Thyroid and Breast Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 350212, Fuzhou, Fujian, China
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Hu J, Yang X, Ren J, Zhong S, Fan Q, Li W. Identification and verification of characteristic differentially expressed ferroptosis-related genes in osteosarcoma using bioinformatics analysis. Toxicol Mech Methods 2023; 33:781-795. [PMID: 37488941 DOI: 10.1080/15376516.2023.2240879] [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: 05/17/2023] [Revised: 07/17/2023] [Accepted: 07/20/2023] [Indexed: 07/26/2023]
Abstract
BACKGROUND This study identified and verified the characteristic differentially expressed ferroptosis-related genes (CDEFRGs) in osteosarcoma (OS). METHODS We extracted ferroptosis-related genes (FRGs), identified differentially expressed FRGs (DEFRGs) in OS, and conducted correlation analysis between DEFRGs. Next, we conducted GO and KEGG analyses to explore the biological functions and pathways of DEFRGs. Furthermore, we used LASSO and SVM-RFE algorithms to screen CDEFRGs, and evaluated its accuracy in diagnosing OS through ROC curves. Then, we demonstrated the molecular function and pathway enrichment of CDEFRGs through GSEA analysis. In addition, we evaluated the differences in immune cell infiltration between OS and NC groups, as well as the correlation between CDEFRGs expressions and immune cell infiltrations. Finally, the expression of CDEFRGs was verified through qRT-PCR, western blotting, and immunohistochemistry experiments. RESULTS We identified 51 DEFRGs and the expression relationship between them. GO and KEGG analysis revealed their key functions and important pathways. Based on four CDEFRGs (PEX3, CPEB1, NOX1, and ALOX5), we built the OS diagnostic model, and verified its accuracy. GSEA analysis further revealed the important functions and pathways of CDEFRGs. In addition, there were differences in immune cell infiltration between OS group and NC group, and CDEFRGs showed significant correlation with certain infiltrating immune cells. Finally, we validated the differential expression levels of four CDEFRGs through external experiments. CONCLUSIONS This study has shed light on the molecular pathological mechanism of OS and has offered novel perspectives for the early diagnosis and immune-targeted therapy of OS patients.
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Affiliation(s)
- Jianhua Hu
- Department of Orthopedic Surgery, The First People's Hospital of Yunnan Province, Affiliated Hospital of Kunming University of Science and Technology, Kunming, P. R. China
- Faculty of Medical Science, Kunming University of Science and Technology, Kunming, P. R. China
| | - Xi Yang
- Department of Orthopedic Surgery, The First People's Hospital of Yunnan Province, Affiliated Hospital of Kunming University of Science and Technology, Kunming, P. R. China
- Yunnan Key Laboratory of Digital Orthopaedics, Kunming, P. R. China
| | - Jing Ren
- Department of Spinal Surgery, Qujing No. 1 Hospital, Affiliated Qujing Hospital of Kunming Medical University, Qujing, P. R. China
| | - Shixiao Zhong
- Faculty of Medical Science, Kunming University of Science and Technology, Kunming, P. R. China
- Yunnan Key Laboratory of Digital Orthopaedics, Kunming, P. R. China
| | - Qianbo Fan
- Faculty of Medical Science, Kunming University of Science and Technology, Kunming, P. R. China
- Yunnan Key Laboratory of Digital Orthopaedics, Kunming, P. R. China
| | - Weichao Li
- Department of Orthopedic Surgery, The First People's Hospital of Yunnan Province, Affiliated Hospital of Kunming University of Science and Technology, Kunming, P. R. China
- Faculty of Medical Science, Kunming University of Science and Technology, Kunming, P. R. China
- Yunnan Key Laboratory of Digital Orthopaedics, Kunming, P. R. China
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Current trends in natural products for the treatment and management of dementia: Computational to clinical studies. Neurosci Biobehav Rev 2023; 147:105106. [PMID: 36828163 DOI: 10.1016/j.neubiorev.2023.105106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 02/17/2023] [Accepted: 02/18/2023] [Indexed: 02/24/2023]
Abstract
The number of preclinical and clinical studies evaluating natural products-based management of dementia has gradually increased, with an exponential rise in 2020 and 2021. Keeping this in mind, we examined current trends from 2016 to 2021 in order to assess the growth potential of natural products in the treatment of dementia. Publicly available literature was collected from various databases like PubMed and Google Scholar. Oxidative stress-related targets, NF-κB pathway, anti-tau aggregation, anti-AChE, and A-β aggregation were found to be common targets and pathways. A retrospective analysis of 33 antidementia natural compounds identified 125 sustainable resources distributed among 65 families, 39 orders, and 7 classes. We found that families such as Berberidaceae, Zingiberaceae, and Fabaceae, as well as orders such as Lamiales, Sapindales, and Myrtales, appear to be important and should be researched further for antidementia compounds. Moreover, some natural products, such as quercetin, curcumin, icariside II, berberine, and resveratrol, have a wide range of applications. Clinical studies and patents support the importance of dietary supplements and natural products, which we will also discuss. Finally, we conclude with the broad scope, future challenges, and opportunities for field researchers.
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An Eight-mRNA Prognostic Model to Predict Survival in Hepatic Cellular Cancer. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2023. [DOI: 10.1155/2023/7278231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Background. Transcriptional dysregulation plays a critical role in the onset and development of malignant tumors. Employing gene dysregulation to forecast the change of tumors is valuable for cancer diagnosis. However, the prognostic prediction for HCC using combined gene models remains insufficient. Methods. The expression profiles of GSE103512 and TCGA-LIHC were downloaded. Gene Ontology (Go) was used to evaluate the overlapping differential genes (DEG) in TCGA and GSE103512. The core genes in the critical module most significantly related to HCC were obtained by WGCNA. Eight genes most significantly related to HCC and OS were identified by reweighted coexpression network analysis and Cox regression. Results. We selected eight genes, FZEB1, CDK1, RAD54L, COL1A2, ATP1B3, CASP8, USP39, and HOXB7. Moreover, we constructed an eight-gene model and forecasted the prognosis of HCC. ROC curve of the eight-mRNA prognostic model was screened out (
), suggesting that this model exhibited a good prediction performance. Survival analysis showed that the survival rate of patients in the high-risk group was significantly lower than that in the low-risk group. Conclusion. The eight-mRNAs model might forecast the OS of HCC patients and advance remedial decision-making.
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Beck JS, Madaj Z, Cheema CT, Kara B, Bennett DA, Schneider JA, Gordon MN, Ginsberg SD, Mufson EJ, Counts SE. Co-expression network analysis of frontal cortex during the progression of Alzheimer's disease. Cereb Cortex 2022; 32:5108-5120. [PMID: 35076713 PMCID: PMC9667180 DOI: 10.1093/cercor/bhac001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/20/2021] [Accepted: 12/21/2021] [Indexed: 01/29/2023] Open
Abstract
Mechanisms of Alzheimer's disease (AD) and its putative prodromal stage, amnestic mild cognitive impairment (aMCI), involve the dysregulation of multiple candidate molecular pathways that drive selective cellular vulnerability in cognitive brain regions. However, the spatiotemporal overlap of markers for pathway dysregulation in different brain regions and cell types presents a challenge for pinpointing causal versus epiphenomenal changes characterizing disease progression. To approach this problem, we performed Weighted Gene Co-expression Network Analysis and STRING interactome analysis of gene expression patterns quantified in frontal cortex samples (Brodmann area 10) from subjects who died with a clinical diagnosis of no cognitive impairment, aMCI, or mild/moderate AD. Frontal cortex was chosen due to the relatively protracted involvement of this region in AD, which might reveal pathways associated with disease onset. A co-expressed network correlating with clinical diagnosis was functionally associated with insulin signaling, with insulin (INS) being the most highly connected gene within the network. Co-expressed networks correlating with neuropathological diagnostic criteria (e.g., NIA-Reagan Likelihood of AD) were associated with platelet-endothelium-leucocyte cell adhesion pathways and hypoxia-oxidative stress. Dysregulation of these functional pathways may represent incipient alterations impacting disease progression and the clinical presentation of aMCI and AD.
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Affiliation(s)
- John S Beck
- Department of Translational Neuroscience, Michigan State University, Grand Rapids, MI 49503, USA
| | - Zachary Madaj
- Bioinformatics and Biostatistics Core, Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | - Calvin T Cheema
- Department of Mathematics and Computer Science, Kalamazoo College, Kalamazoo, MI 49006, USA
| | - Betul Kara
- Department of Translational Neuroscience, Michigan State University, Grand Rapids, MI 49503, USA
- Cell and Molecular Biology Program, Michigan State University, East Lansing, MI 48824, USA
| | - David A Bennett
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
- Rush Alzheimer’s Disease Research Center, Chicago, IL 60612, USA
| | - Julie A Schneider
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
- Rush Alzheimer’s Disease Research Center, Chicago, IL 60612, USA
| | - Marcia N Gordon
- Department of Translational Neuroscience, Michigan State University, Grand Rapids, MI 49503, USA
| | - Stephen D Ginsberg
- Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY 10962, USA
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- NYU Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Elliott J Mufson
- Department of Neurobiology, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Scott E Counts
- Department of Translational Neuroscience, Michigan State University, Grand Rapids, MI 49503, USA
- Cell and Molecular Biology Program, Michigan State University, East Lansing, MI 48824, USA
- Department of Family Medicine, Michigan State University, Grand Rapids, MI 49503, USA
- Hauenstein Neurosciences Center, Mercy Health Saint Mary’s Hospital, Grand Rapids, MI 49503, USA
- Michigan Alzheimer’s Disease Research Center, Ann Arbor, MI 48109, USA
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Li H, Wei M, Ye T, Liu Y, Qi D, Cheng X. Identification of the molecular subgroups in Alzheimer's disease by transcriptomic data. Front Neurol 2022; 13:901179. [PMID: 36204002 PMCID: PMC9530954 DOI: 10.3389/fneur.2022.901179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundAlzheimer's disease (AD) is a heterogeneous pathological disease with genetic background accompanied by aging. This inconsistency is present among molecular subtypes, which has led to diagnostic ambiguity and failure in drug development. We precisely distinguished patients of AD at the transcriptome level.MethodsWe collected 1,240 AD brain tissue samples collected from the GEO dataset. Consensus clustering was used to identify molecular subtypes, and the clinical characteristics were focused on. To reveal transcriptome differences among subgroups, we certificated specific upregulated genes and annotated the biological function. According to RANK METRIC SCORE in GSEA, TOP10 was defined as the hub gene. In addition, the systematic correlation between the hub gene and “A/T/N” was analyzed. Finally, we used external data sets to verify the diagnostic value of hub genes.ResultsWe identified three molecular subtypes of AD from 743 AD samples, among which subtypes I and III had high-risk factors, and subtype II had protective factors. All three subgroups had higher neuritis plaque density, and subgroups I and III had higher clinical dementia scores and neurofibrillary tangles than subgroup II. Our results confirmed a positive association between neurofibrillary tangles and dementia, but not neuritis plaques. Subgroup I genes clustered in viral infection, hypoxia injury, and angiogenesis. Subgroup II showed heterogeneity in synaptic pathology, and we found several essential beneficial synaptic proteins. Due to presenilin one amplification, Subgroup III was a risk subgroup suspected of familial AD, involving abnormal neurogenic signals, glial cell differentiation, and proliferation. Among the three subgroups, the highest combined diagnostic value of the hub genes were 0.95, 0.92, and 0.83, respectively, indicating that the hub genes had sound typing and diagnostic ability.ConclusionThe transcriptome classification of AD cases played out the pathological heterogeneity of different subgroups. It throws daylight on the personalized diagnosis and treatment of AD.
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Affiliation(s)
- He Li
- Innovative Institute of Chinese Medicine and Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Meiqi Wei
- Institute of Chinese Medical Literature and Culture, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Tianyuan Ye
- Innovative Institute of Chinese Medicine and Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yiduan Liu
- School of Rehabilitation Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Dongmei Qi
- Experimental Center, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xiaorui Cheng
- Innovative Institute of Chinese Medicine and Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, China
- *Correspondence: Xiaorui Cheng
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Lin D, Li W, Zhang N, Cai M. Identification of TNFAIP6 as a hub gene associated with the progression of glioblastoma by weighted gene co-expression network analysis. IET Syst Biol 2022; 16:145-156. [PMID: 35766985 PMCID: PMC9469790 DOI: 10.1049/syb2.12046] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 05/29/2022] [Accepted: 06/08/2022] [Indexed: 11/19/2022] Open
Abstract
This study aims to discover the genetic modules that distinguish glioblastoma multiforme (GBM) from low‐grade glioma (LGG) and identify hub genes. A co‐expression network is constructed using the expression profiles of 28 GBM and LGG patients from the Gene Expression Omnibus database. The authors performed gene ontology (GO) and Kyoto encyclopaedia of genes and genomes (KEGG) analysis on these genes. The maximal clique centrality method was used to identify hub genes. Online tools were employed to confirm the link between hub gene expression and overall patient survival rate. The top 5000 genes with major variance were classified into 18 co‐expression gene modules. GO analysis indicated that abnormal changes in ‘cell migration’ and ‘collagen metabolic process’ were involved in the development of GBM. KEGG analysis suggested that ‘focal adhesion’ and ‘p53 signalling pathway’ regulate the tumour progression. TNFAIP6 was identified as a hub gene, and the expression of TNFAIP6 was increased with the elevation of pathological grade. Survival analysis indicated that the higher the expression of TNFAIP6, the shorter the survival time of patients. The authors identified TNFAIP6 as the hub gene in the progression of GBM, and its high expression indicates the poor prognosis of the patients.
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Affiliation(s)
- Dongdong Lin
- Department of Neurosurgery, The Second Affiliated Hospital-Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.,The Second School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Wei Li
- Department of Neurosurgery, The Second Affiliated Hospital-Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.,The Second School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Nu Zhang
- Department of Neurosurgery, The Second Affiliated Hospital-Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Ming Cai
- Department of Neurosurgery, The Second Affiliated Hospital-Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
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Fels JA, Casalena G, Konrad C, Holmes HE, Dellinger RW, Manfredi G. Gene expression profiles in sporadic ALS fibroblasts define disease subtypes and the metabolic effects of the investigational drug EH301. Hum Mol Genet 2022; 31:3458-3477. [PMID: 35652455 DOI: 10.1093/hmg/ddac118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 04/04/2022] [Accepted: 05/17/2022] [Indexed: 01/18/2023] Open
Abstract
Metabolic alterations shared between the nervous system and skin fibroblasts have emerged in ALS. Recently, we found that a subgroup of sporadic ALS (sALS) fibroblasts (sALS1) is characterized by metabolic profiles distinct from other sALS cases (sALS2) and controls, suggesting that metabolic therapies could be effective in sALS. The metabolic modulators nicotinamide riboside and pterostilbene (EH301) are under clinical development for the treatment of ALS. Here, we studied the transcriptome and metabolome of sALS cells to understand the molecular bases of sALS metabotypes and the impact of EH301. Metabolomics and transcriptomics were investigated at baseline and after EH301 treatment. Moreover, weighted gene co-expression network analysis (WGCNA) was used to investigate the association of metabolic and clinical features. We found that the sALS1 transcriptome is distinct from sALS2 and that EH301 modifies gene expression differently in sALS1, sALS2, and controls. Furthermore, EH301 had strong protective effects against metabolic stress, an effect linked to anti-inflammatory and antioxidant pathways. WGCNA revealed that ALS functional rating scale and metabotypes are associated with gene modules enriched for cell cycle, immunity, autophagy, and metabolism genes, which are modified by EH301. Meta-analysis of publicly available transcriptomics data from induced motor neurons by Answer ALS confirmed functional associations of genes correlated with disease traits. A subset of genes differentially expressed in sALS fibroblasts was used in a machine learning model to predict disease progression. In conclusion, multi-omics analyses highlighted differential metabolic and transcriptomic profiles in patient-derived fibroblast sALS, which translate into differential responses to the investigational drug EH301.
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Affiliation(s)
- Jasmine A Fels
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, 407 East 61st Street, New York, NY 10065.,Neuroscience Graduate Program, Weill Cornell Graduate School of Medical Sciences, 1300 York Ave, New York, NY 10065
| | - Gabriella Casalena
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, 407 East 61st Street, New York, NY 10065
| | - Csaba Konrad
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, 407 East 61st Street, New York, NY 10065
| | | | | | - Giovanni Manfredi
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, 407 East 61st Street, New York, NY 10065
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Bi XA, Li L, Wang Z, Wang Y, Luo X, Xu L. IHGC-GAN: influence hypergraph convolutional generative adversarial network for risk prediction of late mild cognitive impairment based on imaging genetic data. Brief Bioinform 2022; 23:6554128. [PMID: 35348583 DOI: 10.1093/bib/bbac093] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 01/28/2022] [Accepted: 02/23/2022] [Indexed: 11/13/2022] Open
Abstract
Predicting disease progression in the initial stage to implement early intervention and treatment can effectively prevent the further deterioration of the condition. Traditional methods for medical data analysis usually fail to perform well because of their incapability for mining the correlation pattern of pathogenies. Therefore, many calculation methods have been excavated from the field of deep learning. In this study, we propose a novel method of influence hypergraph convolutional generative adversarial network (IHGC-GAN) for disease risk prediction. First, a hypergraph is constructed with genes and brain regions as nodes. Then, an influence transmission model is built to portray the associations between nodes and the transmission rule of disease information. Third, an IHGC-GAN method is constructed based on this model. This method innovatively combines the graph convolutional network (GCN) and GAN. The GCN is used as the generator in GAN to spread and update the lesion information of nodes in the brain region-gene hypergraph. Finally, the prediction accuracy of the method is improved by the mutual competition and repeated iteration between generator and discriminator. This method can not only capture the evolutionary pattern from early mild cognitive impairment (EMCI) to late MCI (LMCI) but also extract the pathogenic factors and predict the deterioration risk from EMCI to LMCI. The results on the two datasets indicate that the IHGC-GAN method has better prediction performance than the advanced methods in a variety of indicators.
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Affiliation(s)
- Xia-An Bi
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, and the College of Information Science and Engineering in Hunan Normal University, Changsha 410081, P.R. China
| | - Lou Li
- Department of Computing, School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Zizheng Wang
- Department of Computing, School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Yu Wang
- Department of Computing, School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Xun Luo
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, and the College of Information Science and Engineering in Hunan Normal University, Changsha 410081, P.R. China
| | - Luyun Xu
- College of Business, Hunan Normal University, Changsha 410081, P.R. China
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Singla RK, Joon S, Shen L, Shen B. Translational Informatics for Natural Products as Antidepressant Agents. Front Cell Dev Biol 2022; 9:738838. [PMID: 35127696 PMCID: PMC8811306 DOI: 10.3389/fcell.2021.738838] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 12/13/2021] [Indexed: 12/18/2022] Open
Abstract
Depression, a neurological disorder, is a universally common and debilitating illness where social and economic issues could also become one of its etiologic factors. From a global perspective, it is the fourth leading cause of long-term disability in human beings. For centuries, natural products have proven their true potential to combat various diseases and disorders, including depression and its associated ailments. Translational informatics applies informatics models at molecular, imaging, individual, and population levels to promote the translation of basic research to clinical applications. The present review summarizes natural-antidepressant-based translational informatics studies and addresses challenges and opportunities for future research in the field.
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Affiliation(s)
- Rajeev K. Singla
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- iGlobal Research and Publishing Foundation, New Delhi, India
| | - Shikha Joon
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- iGlobal Research and Publishing Foundation, New Delhi, India
| | - Li Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Bairong Shen,
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Tan MS, Cheah PL, Chin AV, Looi LM, Chang SW. A review on omics-based biomarkers discovery for Alzheimer's disease from the bioinformatics perspectives: Statistical approach vs machine learning approach. Comput Biol Med 2021; 139:104947. [PMID: 34678481 DOI: 10.1016/j.compbiomed.2021.104947] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 10/12/2021] [Accepted: 10/12/2021] [Indexed: 12/26/2022]
Abstract
Alzheimer's Disease (AD) is a neurodegenerative disease that affects cognition and is the most common cause of dementia in the elderly. As the number of elderly individuals increases globally, the incidence and prevalence of AD are expected to increase. At present, AD is diagnosed clinically, according to accepted criteria. The essential elements in the diagnosis of AD include a patients history, a physical examination and neuropsychological testing, in addition to appropriate investigations such as neuroimaging. The omics-based approach is an emerging field of study that may not only aid in the diagnosis of AD but also facilitate the exploration of factors that influence the development of the disease. Omics techniques, including genomics, transcriptomics, proteomics and metabolomics, may reveal the pathways that lead to neuronal death and identify biomolecular markers associated with AD. This will further facilitate an understanding of AD neuropathology. In this review, omics-based approaches that were implemented in studies on AD were assessed from a bioinformatics perspective. Current state-of-the-art statistical and machine learning approaches used in the single omics analysis of AD were compared based on correlations of variants, differential expression, functional analysis and network analysis. This was followed by a review of the approaches used in the integration and analysis of multi-omics of AD. The strengths and limitations of multi-omics analysis methods were explored and the issues and challenges associated with omics studies of AD were highlighted. Lastly, future studies in this area of research were justified.
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Affiliation(s)
- Mei Sze Tan
- Bioinformatics Programme, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Phaik-Leng Cheah
- Department of Pathology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Ai-Vyrn Chin
- Division of Geriatric Medicine, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Lai-Meng Looi
- Department of Pathology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Siow-Wee Chang
- Bioinformatics Programme, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia.
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Wang H, Han X, Gao S. Identification of potential biomarkers for pathogenesis of Alzheimer's disease. Hereditas 2021; 158:23. [PMID: 34225819 PMCID: PMC8259215 DOI: 10.1186/s41065-021-00187-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 05/31/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is an extremely complicated neurodegenerative disorder, which accounts for almost 80 % of all dementia diagnoses. Due to the limited treatment efficacy, it is imperative for AD patients to take reliable prevention and diagnosis measures. This study aimed to explore potential biomarkers for AD. METHODS GSE63060 and GSE140829 datasets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEG) between AD and control groups in GSE63060 were analyzed using the limma software package. The mRNA expression data in GSE140829 was analyzed using weighted gene co-expression network analysis (WGCNA) function package. Protein functional connections and interactions were analyzed using STRING and key genes were screened based on the degree and Maximal Clique Centrality (MCC) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the key genes. RESULTS There were 65 DEGs in GSE63060 dataset between AD patients and healthy controls. In GSE140829 dataset, the turquoise module was related to the pathogenesis of AD, among which, 42 genes were also differentially expressed in GSE63060 dataset. Then 8 genes, RPS17, RPL26, RPS3A, RPS25, EEF1B2, COX7C, HINT1 and SNRPG, were finally screened. Additionally, these 42 genes were significantly enriched in 12 KEGG pathways and 119 GO terms. CONCLUSIONS In conclusion, RPS17, RPL26, RPS3A, RPS25, EEF1B2, COX7C, HINT1 and SNRPG, were potential biomarkers for pathogenesis of AD, which should be further explored in AD in the future.
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Affiliation(s)
- Huimin Wang
- Department of Neurology, Tianjin Hospital of ITCWM Nankai Hospital, 300100, Tianjin, China
| | - Xiujiang Han
- Department of Geriatrics, Tianjin Hospital of ITCWM Nankai Hospital, No.6 Changjiang Road, Nankai, 300100, Tianjin, China
| | - Sheng Gao
- Department of Geriatrics, Tianjin Hospital of ITCWM Nankai Hospital, No.6 Changjiang Road, Nankai, 300100, Tianjin, China.
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Wang X, Huang K, Yang F, Chen D, Cai S, Huang L. Association between structural brain features and gene expression by weighted gene co-expression network analysis in conversion from MCI to AD. Behav Brain Res 2021; 410:113330. [PMID: 33940051 DOI: 10.1016/j.bbr.2021.113330] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 04/16/2021] [Accepted: 04/26/2021] [Indexed: 12/24/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease. Mild cognitive impairment (MCI) represents a state of cognitive function between normal cognition and dementia. Longitudinal studies showed that some MCI patients remained in a state of MCI, and some developed AD. The reason for these different conversions from MCI remains to be investigated. 180 MCI participants were followed for eight years. 143 MCI patients maintained the MCI state (MCI_S), and the remaining thirty-seven MCI patients were re-evaluated as having AD (MCI_AD). We obtained 1,036 structural brain characteristics and 15,481 gene expression values from the 180 MCI participants and applied weighted gene co-expression network analysis (WGCNA) to explore the relationship between structural brain features and gene expression. Regulating mediator effect analysis was employed to explore the relationships among gene expression, brain region measurements and clinical phenotypes. We found that 60 genes from the MCI_S group and 18 genes from the MCI_AD group respectively had the most significant correlations with left paracentral lobule and sulcus (L.PTS) and right subparietal sulcus (R.SubPS) thickness; CTCF, UQCR11 and WDR5B were the mutual genes between the two groups. The expression of CTCF gene and clinical score are completely mediated by L.PTS thickness, and the UQCR11 and WDR5B gene expression levels significantly regulate the mediating effect pathway. In conclusion, the factors affecting the different conversions from MCI are closely related to L.PTS thickness and the CTCF, UQCR11 and WDR5B gene expression levels. Our results add a theoretical foundation of imaging genetics for conversion from MCI to AD.
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Affiliation(s)
- Xuwen Wang
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, 710071, PR China
| | - Kexin Huang
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, 710071, PR China
| | - Fan Yang
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, 710071, PR China
| | - Dihun Chen
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, 710071, PR China
| | - Suping Cai
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, 710071, PR China.
| | - Liyu Huang
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, 710071, PR China.
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Nambou K, Nie X, Tong Y, Anakpa M. Weighted gene co-expression network analysis and drug-gene interaction bioinformatics uncover key genes associated with various presentations of malaria infection in African children and major drug candidates. INFECTION GENETICS AND EVOLUTION 2021; 89:104723. [PMID: 33444859 DOI: 10.1016/j.meegid.2021.104723] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 01/04/2021] [Accepted: 01/08/2021] [Indexed: 01/06/2023]
Abstract
Malaria is a fatal parasitic disease with unelucidated pathogenetic mechanism. Herein, we aimed to uncover genes associated with different clinical aspects of malaria based on the GSE1124 dataset that is publicly accessible by using WGCNA. We obtained 16 co-expression modules and their correlations with clinical features. Using the MCODE tool, we identified THEM4, STYX, VPS36, LCOR, KIAA1143, EEA1, RAPGEF6, LOC439994, ZBTB33, PTPN22, ESCO1, and KLF3 as hub genes positively associated with Plasmodium falciparum infection (ASPF). These hub genes were involved in the biological processes of endosomal transport, regulation of natural killer cell proliferation, and KEGG pathways of endocytosis and fatty acid elongation. For the purple module negatively correlated with ASPF, we identified 19 hub genes that were involved in the biological processes of positive regulation of cellular protein catabolic process and KEGG pathways of other glycan degradation. For the salmon module positively correlated with severe malaria anemia (SMA), we identified 17 hub genes that were among those driving the biological processes of positive regulation of erythrocyte differentiation. For the brown module positively correlated with cerebral malaria (CM), we identified eight hub genes and these genes participated in phagolysosome assembly and positive regulation of exosomal secretion, and animal mitophagy pathway. For the tan module negatively correlated with CM, we identified four hub genes that were involved in CD8-positive, alpha-beta T cell differentiation and notching signaling pathway. These findings may provide new insights into the pathogenesis of malaria and help define new diagnostic and therapeutic approaches for malaria patients.
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Affiliation(s)
- Komi Nambou
- Shenzhen Nambou1 Biotech Company Limited, West Silicon Valley, No. 5010 Bao'an Avenue, Shenzhen 518000, Guangdong Province, China.
| | - Xiaoling Nie
- Shenzhen Nambou1 Biotech Company Limited, West Silicon Valley, No. 5010 Bao'an Avenue, Shenzhen 518000, Guangdong Province, China
| | - Yin Tong
- Shenzhen Nambou1 Biotech Company Limited, West Silicon Valley, No. 5010 Bao'an Avenue, Shenzhen 518000, Guangdong Province, China
| | - Manawa Anakpa
- Key Laboratory of Trustworthy Distributed Computing and Service, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Ministry of Education, Beijing 100876, China
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15
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Mao Y, Liao Z, Liu X, Li T, Hu J, Le D, Pei Y, Sun W, Lin J, Qiu Y, Zhu J, Chen Y, Qi C, Su H, Yu E. Disrupted balance of long and short-range functional connectivity density in Alzheimer's disease (AD) and mild cognitive impairment (MCI) patients: a resting-state fMRI study. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:65. [PMID: 33553358 PMCID: PMC7859805 DOI: 10.21037/atm-20-7019] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Background Alzheimer’s disease (AD) is an age-progressive neurodegenerative disorder that affects cognitive function. There have been several functional connectivity (FC) strengths; however, FC density needs more development in AD. Therefore, this study wanted to determine the alternations in resting-state functional connectivity density (FCD) induced by Alzheimer’s and mild cognitive impairment (MCI). Methods One hundred and eleven AD patients, 29 MCI patients, and 73 healthy controls (age- and sex-matched) were recruited and assessed using resting-state functional magnetic resonance imaging (MRI) scanning. The ultra-fast graph theory called FCD mapping was used to calculate the voxel-wise short- and long-range FCD values of the brain. We performed voxel-based between-group comparisons of FCD values to show the cerebral regions with significant FCD alterations. We performed Pearson’s correlation analyses between aberrant functional connectivity densities and several clinical variables with adjustment for age and sex. Results Patients with cognition decline showed significantly abnormal long-range FCD in the cerebellum crus I, right insula, left inferior frontal gyrus, left superior frontal gyrus, left inferior frontal gyrus, and right middle frontal gyrus. The short-range FCD changed in the cerebellum crus I, left inferior frontal gyrus, left superior occipital gyrus, and right middle frontal gyrus. The long- and short-range functional connectivity in the left inferior frontal gyrus was positively correlated with Mini-mental State Examination (MMSE) scores. Conclusions FCD in the identified regions reflects mechanism and compensation for loss of cognitive function. These findings could improve the pathology of AD and MCI and supply a neuroimaging marker for AD and MCI.
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Affiliation(s)
- Yanping Mao
- Department of Clinical Psychology, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
| | - Zhengluan Liao
- Department of Psychiatry, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Xiaozheng Liu
- Department of Radiology of the Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China
| | - Ting Li
- Medical Department, Qingdao University, Qingdao, China
| | - Jiaojiao Hu
- Department of Clinical Psychology, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
| | - Dansheng Le
- The Second school of Medical, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yangliu Pei
- Graduate Department, Bengbu Medical College, Bengbu, China
| | - Wangdi Sun
- The Second school of Medical, Zhejiang Chinese Medical University, Hangzhou, China
| | - Jixin Lin
- Department of Internal Medicine, Shengsi County People's Hospital, Zhoushan, China
| | - Yaju Qiu
- Department of Psychiatry, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Junpeng Zhu
- Department of Psychiatry, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Yan Chen
- Department of Psychiatry, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Chang Qi
- Department of Psychiatry, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Heng Su
- Department of Psychiatry, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Enyan Yu
- Department of Clinical Psychology, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
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Hu RT, Yu Q, Zhou SD, Yin YX, Hu RG, Lu HP, Hu BL. Co-expression Network Analysis Reveals Novel Genes Underlying Alzheimer's Disease Pathogenesis. Front Aging Neurosci 2020; 12:605961. [PMID: 33324198 PMCID: PMC7725685 DOI: 10.3389/fnagi.2020.605961] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 11/04/2020] [Indexed: 12/14/2022] Open
Abstract
Background: The pathogenesis of Alzheimer’s disease (AD) remains to be elucidated. This study aimed to identify the hub genes in AD pathogenesis and determine their functions and pathways. Methods: A co-expression network for an AD gene dataset with 401 samples was constructed, and the AD status-related genes were screened. The hub genes of the network were identified and validated by an independent cohort. The functional pathways of hub genes were analyzed. Results: The co-expression network revealed a module that related to the AD status, and 101 status-related genes were screened from the trait-related module. Gene enrichment analysis indicated that these status-related genes are involved in synaptic processes and pathways. Four hub genes (ENO2, ELAVL4, SNAP91, and NEFM) were identified from the module, and these hub genes all participated in AD-related pathways, but the associations of each gene with clinical features were variable. An independent dataset confirmed the different expression of hub genes between AD and controls. Conclusions: Four novel genes associated with AD pathogenesis were identified and validated, which provided novel therapeutic targets for AD.
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Affiliation(s)
- Rui-Ting Hu
- Department of Neurology, Minzu Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Qian Yu
- Department of Pharmacy, Affiliated Hospital of Guilin Medical University, Guilin, China
| | - Shao-Dan Zhou
- Department of Neurology, Minzu Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Yi-Xin Yin
- Department of Research, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Rui-Guang Hu
- Department of Neurology, Minzu Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Hai-Peng Lu
- Department of Pharmacy, Minzu Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Bang-Li Hu
- Department of Research, Guangxi Medical University Cancer Hospital, Nanning, China
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Hu VW, Bi C. Phenotypic Subtyping and Re-analyses of Existing Transcriptomic Data from Autistic Probands in Simplex Families Reveal Differentially Expressed and ASD Trait-Associated Genes. Front Neurol 2020; 11:578972. [PMID: 33281715 PMCID: PMC7689346 DOI: 10.3389/fneur.2020.578972] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 10/21/2020] [Indexed: 12/25/2022] Open
Abstract
Autism spectrum disorder (ASD) describes a collection of neurodevelopmental disorders characterized by core symptoms that include social communication deficits and repetitive, stereotyped behaviors often coupled with restricted interests. Primary challenges to understanding and treating ASD are the genetic and phenotypic heterogeneity of cases that complicates all omics analyses as well as a lack of information on relationships among genes, pathways, and autistic traits. In this study, we re-analyze existing transcriptomic data from simplex families by subtyping individuals with ASD according to multivariate cluster analyses of clinical ADI-R scores that encompass a broad range of behavioral symptoms. We also correlate multiple ASD traits, such as deficits in verbal and non-verbal communication, play and social skills, ritualistic behaviors, and savant skills, with expression profiles using Weighted Gene Correlation Network Analyses (WGCNA). Our results show that subtyping greatly enhances the ability to identify differentially expressed genes involved in specific canonical pathways and biological functions associated with ASD within each phenotypic subgroup. Moreover, using WGCNA, we identify gene modules that correlate significantly with specific ASD traits. Network prediction analyses of the genes in these modules reveal canonical pathways as well as neurological functions and disorders relevant to the pathobiology of ASD. Finally, we compare the WGCNA-derived data on autistic traits in simplex families with analogous data from multiplex families using transcriptomic data from our previous studies. The comparison reveals overlapping trait-associated pathways as well as upstream regulators of the module-associated genes that may serve as useful targets for a precision medicine approach to ASD.
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Affiliation(s)
- Valerie W Hu
- Department of Biochemistry and Molecular Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, United States
| | - Chongfeng Bi
- Department of Biochemistry and Molecular Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, United States
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Diagnostic and prognostic value of plasma heat shock protein 90alpha in gastric cancer. Int Immunopharmacol 2020; 90:107145. [PMID: 33162344 DOI: 10.1016/j.intimp.2020.107145] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 10/21/2020] [Accepted: 10/23/2020] [Indexed: 01/11/2023]
Abstract
BACKGROUND The role of plasma heat shock protein 90alpha (Hsp90α) in gastric cancers remains unclear. This study aimed to clarify the diagnostic and prognostic value of plasma Hsp90α in gastric cancer. METHODS Data regarding 976 gastric cancer, 50 gastric inflammatory diseases, and 100 healthy controls were collected. Plasma Hsp90α levels in gastric cancer were compared to those in controls. Its correlation with tumor biomarkers and immune cells was examined. The association of plasma Hsp90α with clinical features and the diagnostic and prognostic value in gastric cancer were also determined. RESULTS Plasma Hsp90α levels were remarkably increased in gastric cancer, compared to those in gastric inflammatory diseases and healthy controls. Moreover, plasma Hsp90α was correlated with CEA, CA125, CA153, CA199, T cells, Th/Ts ratio, and B cells. Plasma Hsp90α was also associated with the metastasis stage. Multivariate logistic regression analysis revealed that Hsp90α, B cells, and T cells were significantly associated with gastric cancer. Plasma Hsp90α has a moderate diagnostic value, which increased when combined with B cell, T cells. Finally, plasma Hsp90α was not associated with the survival of gastric cancer patients. CONCLUSION Plasma Hsp90α was elevated in gastric cancer and correlated with tumor biomarkers and immune cells. Plasma Hsp90α was associated with the metastasis stage and had moderate diagnostic performance but little prognostic value in gastric cancer.
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Dang H, Ye Y, Zhao X, Zeng Y. Identification of candidate genes in ischemic cardiomyopathy by gene expression omnibus database. BMC Cardiovasc Disord 2020; 20:320. [PMID: 32631246 PMCID: PMC7336680 DOI: 10.1186/s12872-020-01596-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Accepted: 06/24/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Ischemic cardiomyopathy (ICM) is one of the most usual causes of death worldwide. This study aimed to find the candidate gene for ICM. METHODS We studied differentially expressed genes (DEGs) in ICM compared to healthy control. According to these DEGs, we carried out the functional annotation, protein-protein interaction (PPI) network and transcriptional regulatory network constructions. The expression of selected candidate genes were confirmed using a published dataset and Quantitative real time polymerase chain reaction (qRT-PCR). RESULTS From three Gene Expression Omnibus (GEO) datasets, we acquired 1081 DEGs (578 up-regulated and 503 down-regulated genes) between ICM and healthy control. The functional annotation analysis revealed that cardiac muscle contraction, hypertrophic cardiomyopathy, arrhythmogenic right ventricular cardiomyopathy and dilated cardiomyopathy were significantly enriched pathways in ICM. SNRPB, BLM, RRS1, CDK2, BCL6, BCL2L1, FKBP5, IPO7, TUBB4B and ATP1A1 were considered the hub proteins. PALLD, THBS4, ATP1A1, NFASC, FKBP5, ECM2 and BCL2L1 were top six transcription factors (TFs) with the most downstream genes. The expression of 6 DEGs (MYH6, THBS4, BCL6, BLM, IPO7 and SERPINA3) were consistent with our integration analysis and GSE116250 validation results. CONCLUSIONS The candidate DEGs and TFs may be related to the ICM process. This study provided novel perspective for understanding mechanism and exploiting new therapeutic means for ICM.
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Affiliation(s)
- Haiming Dang
- Department of cardiac surgery, Capital medical university, Beijing Anzhen hospital, Beijing, China
| | - Yicong Ye
- Department of cardiology, Capital medical university, Beijing Anzhen hospital, No.2, Anzhen Road, Chaoyan District, Beijing, 100029, China
| | - Xiliang Zhao
- Department of cardiology, Capital medical university, Beijing Anzhen hospital, No.2, Anzhen Road, Chaoyan District, Beijing, 100029, China
| | - Yong Zeng
- Department of cardiology, Capital medical university, Beijing Anzhen hospital, No.2, Anzhen Road, Chaoyan District, Beijing, 100029, China.
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