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Gómez-Pascual A, Rocamora-Pérez G, Ibanez L, Botía JA. Targeted co-expression networks for the study of traits. Sci Rep 2024; 14:16675. [PMID: 39030261 PMCID: PMC11271532 DOI: 10.1038/s41598-024-67329-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 07/10/2024] [Indexed: 07/21/2024] Open
Abstract
Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used approach for the generation of gene co-expression networks. However, networks generated with this tool usually create large modules with a large set of functional annotations hard to decipher. We have developed TGCN, a new method to create Targeted Gene Co-expression Networks. This method identifies the transcripts that best predict the trait of interest based on gene expression using a refinement of the LASSO regression. Then, it builds the co-expression modules around those transcripts. Algorithm properties were characterized using the expression of 13 brain regions from the Genotype-Tissue Expression project. When comparing our method with WGCNA, TGCN networks lead to more precise modules that have more specific and yet rich biological meaning. Then, we illustrate its applicability by creating an APP-TGCN on The Religious Orders Study and Memory and Aging Project dataset, aiming to identify the molecular pathways specifically associated with APP role in Alzheimer's disease. Main biological findings were further validated in two independent cohorts. In conclusion, we provide a new framework that serves to create targeted networks that are smaller, biologically relevant and useful in high throughput hypothesis driven research. The TGCN R package is available on Github: https://github.com/aliciagp/TGCN .
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Affiliation(s)
- A Gómez-Pascual
- Communications Engineering and Information Department, University of Murcia, 30100, Murcia, Spain
| | - G Rocamora-Pérez
- Department of Genetics and Genomic Medicine Research and Teaching, UCL GOS Institute of Child Health, London, WC1N 1EH, UK
| | - L Ibanez
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, 63110, USA
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - J A Botía
- Communications Engineering and Information Department, University of Murcia, 30100, Murcia, Spain.
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2
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Zhuang X, Xia Y, Liu Y, Guo T, Xia Z, Wang Z, Zhang G. SCG5 and MITF may be novel markers of copper metabolism immunorelevance in Alzheimer's disease. Sci Rep 2024; 14:13619. [PMID: 38871989 DOI: 10.1038/s41598-024-64599-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 06/11/2024] [Indexed: 06/15/2024] Open
Abstract
The slow-developing neurological disorder Alzheimer's disease (AD) has no recognized etiology. A bioinformatics investigation verified copper metabolism indicators for AD development. GEO contributed AD-related datasets GSE1297 and GSE5281. Differential expression analysis and WGCNA confirmed biomarker candidate genes. Each immune cell type in AD and control samples was scored using single sample gene set enrichment analysis. Receiver Operating Characteristic (ROC) analysis, short Time-series Expression Miner (STEM) grouping, and expression analysis between control and AD samples discovered copper metabolism indicators that impacted AD progression. We test clinical samples and cellular function to ensure study correctness. Biomarker-targeting miRNAs and lncRNAs were predicted by starBase. Trust website anticipated biomarker-targeting transcription factors. In the end, Cytoscape constructed the TF/miRNA-mRNA and lncRNA-miRNA networks. The DGIdb database predicted biomarker-targeted drugs. We identified 57 differentially expressed copper metabolism-related genes (DE-CMRGs). Next, fourteen copper metabolism indicators impacting AD progression were identified: CCK, ATP6V1E1, SYT1, LDHA, PAM, HPRT1, SCG5, ATP6V1D, GOT1, NFKBIA, SPHK1, MITF, BRCA1, and CD38. A TF/miRNA-mRNA regulation network was then established with two miRNAs (hsa-miR-34a-5p and 34c-5p), six TFs (NFKB1, RELA, MYC, HIF1A, JUN, and SP1), and four biomarkers. The DGIdb database contained 171 drugs targeting ten copper metabolism-relevant biomarkers (BRCA1, MITF, NFKBIA, CD38, CCK2, HPRT1, SPHK1, LDHA, SCG5, and SYT1). Copper metabolism biomarkers CCK, ATP6V1E1, SYT1, LDHA, PAM, HPRT1, SCG5, ATP6V1D, GOT1, NFKBIA, SPHK1, MITF, BRCA1, and CD38 alter AD progression, laying the groundwork for disease pathophysiology and novel AD diagnostic and treatment.
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Affiliation(s)
- Xianbo Zhuang
- Department of Neurology, Liaocheng People's Hospital and Liaocheng Hospital affiliated to Shandong First Medical University, Liaocheng, China
| | - Yitong Xia
- School of Rehabilitation Medicine, Jining Medical University, Jining, China
| | - Yingli Liu
- Department of Neurology, Liaocheng People's Hospital and Liaocheng Hospital affiliated to Shandong First Medical University, Liaocheng, China
| | - Tingting Guo
- Department of Neurology, Liaocheng People's Hospital and Liaocheng Hospital affiliated to Shandong First Medical University, Liaocheng, China
| | - Zhangyong Xia
- Department of Neurology, Liaocheng People's Hospital and Liaocheng Hospital affiliated to Shandong First Medical University, Liaocheng, China
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, Shandong Sub-Centre, Liaocheng, China
- Department of Neurology, the Second People's Hospital of Liaocheng, Liaocheng, China
| | - Zheng Wang
- Department of Neurosurgery, Liaocheng Traditional Chinese Medicine Hospital, Liaocheng, China.
| | - Guifeng Zhang
- Department of Neurology, Liaocheng People's Hospital and Liaocheng Hospital affiliated to Shandong First Medical University, Liaocheng, China.
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3
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Zhang Z, Liu X, Zhang S, Song Z, Lu K, Yang W. A review and analysis of key biomarkers in Alzheimer's disease. Front Neurosci 2024; 18:1358998. [PMID: 38445255 PMCID: PMC10912539 DOI: 10.3389/fnins.2024.1358998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 02/02/2024] [Indexed: 03/07/2024] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.
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Affiliation(s)
- Zhihao Zhang
- School of Computer Science and Technology, Xinjiang University, Ürümqi, China
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China
| | - Xiangtao Liu
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China
| | - Suixia Zhang
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- State Key Laboratory of Pathogenesis, Prevention, Treatment of Central Asian High Incidence Diseases, First Affiliated Hospital of Xinjiang Medical University, Ürümqi, China
| | - Zhixin Song
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China
| | - Ke Lu
- School of Computer Science and Technology, Xinjiang University, Ürümqi, China
| | - Wenzhong Yang
- School of Computer Science and Technology, Xinjiang University, Ürümqi, China
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Yu W, Li Y, Zhong F, Deng Z, Wu J, Yu W, Lü Y. Disease-Associated Neurotoxic Astrocyte Markers in Alzheimer Disease Based on Integrative Single-Nucleus RNA Sequencing. Cell Mol Neurobiol 2024; 44:20. [PMID: 38345650 PMCID: PMC10861702 DOI: 10.1007/s10571-024-01453-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 01/11/2024] [Indexed: 02/15/2024]
Abstract
Alzheimer disease (AD) is an irreversible neurodegenerative disease, and astrocytes play a key role in its onset and progression. The aim of this study is to analyze the characteristics of neurotoxic astrocytes and identify novel molecular targets for slowing down the progression of AD. Single-nucleus RNA sequencing (snRNA-seq) data were analyzed from various AD cohorts comprising about 210,654 cells from 53 brain tissue. By integrating snRNA-seq data with bulk RNA-seq data, crucial astrocyte types and genes associated with the prognosis of patients with AD were identified. The expression of neurotoxic astrocyte markers was validated using 5 × FAD and wild-type (WT) mouse models, combined with experiments such as western blot, quantitative real-time PCR (qRT-PCR), and immunofluorescence. A group of neurotoxic astrocytes closely related to AD pathology was identified, which were involved in inflammatory responses and pathways related to neuron survival. Combining snRNA and bulk tissue data, ZEP36L, AEBP1, WWTR1, PHYHD1, DST and RASL12 were identified as toxic astrocyte markers closely related to disease severity, significantly elevated in brain tissues of 5 × FAD mice and primary astrocytes treated with Aβ. Among them, WWTR1 was significantly increased in astrocytes of 5 × FAD mice, driving astrocyte inflammatory responses, and has been identified as an important marker of neurotoxic astrocytes. snRNA-seq analysis reveals the biological functions of neurotoxic astrocytes. Six genes related to AD pathology were identified and validated, among which WWTR1 may be a novel marker of neurotoxic astrocytes.
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Affiliation(s)
- Wuhan Yu
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong, Chongqing, 400016, China
| | - Yin Li
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Fuxin Zhong
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong, Chongqing, 400016, China
| | - Zhangjing Deng
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong, Chongqing, 400016, China
| | - Jiani Wu
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong, Chongqing, 400016, China
| | - Weihua Yu
- Institutes of Neuroscience, Chongqing Medical University, Chongqing, 400016, China
| | - Yang Lü
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong, Chongqing, 400016, China.
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Zhuang H, Cao X, Tang X, Zou Y, Yang H, Liang Z, Yan X, Chen X, Feng X, Shen L. Investigating metabolic dysregulation in serum of triple transgenic Alzheimer's disease male mice: implications for pathogenesis and potential biomarkers. Amino Acids 2024; 56:10. [PMID: 38315232 PMCID: PMC10844422 DOI: 10.1007/s00726-023-03375-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 11/11/2023] [Indexed: 02/07/2024]
Abstract
Alzheimer's disease (AD) is a multifactorial neurodegenerative disease that lacks convenient and accessible peripheral blood diagnostic markers and effective drugs. Metabolic dysfunction is one of AD risk factors, which leaded to alterations of various metabolites in the body. Pathological changes of the brain can be reflected in blood metabolites that are expected to explain the disease mechanisms or be candidate biomarkers. The aim of this study was to investigate the changes of targeted metabolites within peripheral blood of AD mouse model, with the purpose of exploring the disease mechanism and potential biomarkers. Targeted metabolomics was used to quantify 256 metabolites in serum of triple transgenic AD (3 × Tg-AD) male mice. Compared with controls, 49 differential metabolites represented dysregulation in purine, pyrimidine, tryptophan, cysteine and methionine and glycerophospholipid metabolism. Among them, adenosine, serotonin, N-acetyl-5-hydroxytryptamine, and acetylcholine play a key role in regulating neural transmitter network. The alteration of S-adenosine-L-homocysteine, S-adenosine-L-methionine, and trimethylamine-N-oxide in AD mice serum can served as indicator of AD risk. The results revealed the changes of metabolites in serum, suggesting that metabolic dysregulation in periphery in AD mice may be related to the disturbances in neuroinhibition, the serotonergic system, sleep function, the cholinergic system, and the gut microbiota. This study provides novel insights into the dysregulation of several key metabolites and metabolic pathways in AD, presenting potential avenues for future research and the development of peripheral biomarkers.
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Affiliation(s)
- Hongbin Zhuang
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Xueshan Cao
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Xiaoxiao Tang
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Yongdong Zou
- Center for Instrumental Analysis, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Hongbo Yang
- Center for Instrumental Analysis, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Zhiyuan Liang
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Xi Yan
- The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang, 550025, People's Republic of China
| | - Xiaolu Chen
- The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang, 550025, People's Republic of China
| | - Xingui Feng
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Liming Shen
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China.
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, People's Republic of China.
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Li K, Wang Y, Ni H. Hederagenin Upregulates PTPN1 Expression in Aβ-Stimulated Neuronal Cells, Exerting Anti-Oxidative Stress and Anti-Apoptotic Activities. J Mol Neurosci 2023; 73:932-945. [PMID: 37882913 DOI: 10.1007/s12031-023-02160-9] [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: 09/04/2023] [Accepted: 09/26/2023] [Indexed: 10/27/2023]
Abstract
Alzheimer's disease (AD) is a prevalently neurodegenerative disease characterized by neuronal damage which is associated with amyloid-β (Aβ) accumulation. Hederagenin is a triterpenoid saponin, exerting anti-apoptotic, anti-oxidative, anti-inflammatory, anti-tumoral, and neuroprotective activities. However, its role in AD progression is still obscure. The aim of this study was to explore the influences of hederagenin on Aβ-caused neuronal injury in vitro. Neuronal cells were treated with Aβ25-35 (Aβ) to establish a cellular model of AD. Cell viability was assessed using cell counting kit-8 (CCK-8). Oxidative stress was evaluated by detecting reactive oxygen species (ROS) generation and superoxide dismutase (SOD) activity. Apoptosis was investigated using TUNEL staining and caspase-3 activity assays. Protein tyrosine phosphatase nonreceptor type 1 (PTPN1) was screened by bioinformatics analysis. Protein levels of PTPN1 and protein kinase B (Akt) were measured by western blotting. Hederagenin (2.5, 5, and 10 μM) alone did not affect viability of neuronal cells, but relieved Aβ-induced viability reduction. Hederagenin mitigated Aβ-induced increase in ROS accumulation and decrease in SOD activity. Hederagenin attenuated Aβ-induced increase in apoptotic rate and caspase-3 activity. PTPN1 was screened as a target of hederagenin against AD by bioinformatics analysis. Hederagenin treatment resisted Aβ-induced decrease in PTPN1 mRNA and protein levels in neuronal cells. PTPN1 silencing attenuated the suppressive functions of hederagenin in Aβ-stimulated oxidative stress and apoptosis. Hederagenin mitigated Aβ-induced Akt signaling inactivation by upregulating PTPN1 expression. In conclusion, hederagenin attenuates oxidative stress and apoptosis in neuronal cells stimulated with Aβ by promoting PTPN1/Akt signaling activation.
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Affiliation(s)
- Ke Li
- Department of Neurology, Nanyang First People's Hospital, Nanyang, 473004, China
| | - Yu Wang
- Department of Critical Care Medicine, Nanshi Hospital of Nanyang, Nanyang, 473010, China
| | - Hongzao Ni
- Department of Neurosurgery, the Affiliated Huai'an Hospital of Xuzhou Medical University, Huai'an Second People's Hospital, #62 Huaihai South Road, Huai'an, 223300, China.
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O’Connor LM, O’Connor BA, Zeng J, Lo CH. Data Mining of Microarray Datasets in Translational Neuroscience. Brain Sci 2023; 13:1318. [PMID: 37759919 PMCID: PMC10527016 DOI: 10.3390/brainsci13091318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/04/2023] [Accepted: 09/10/2023] [Indexed: 09/29/2023] Open
Abstract
Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases.
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Affiliation(s)
- Lance M. O’Connor
- College of Biological Sciences, University of Minnesota, Minneapolis, MN 55455, USA;
| | - Blake A. O’Connor
- School of Pharmacy, University of Wisconsin, Madison, WI 53705, USA;
| | - Jialiu Zeng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore;
| | - Chih Hung Lo
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore;
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Yang E, Ding Q, Fan X, Ye H, Xuan C, Zhao S, Ji Q, Yu W, Liu Y, Cao J, Fang M, Ding X. Machine learning modeling and prognostic value analysis of invasion-related genes in cutaneous melanoma. Comput Biol Med 2023; 162:107089. [PMID: 37267825 DOI: 10.1016/j.compbiomed.2023.107089] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/06/2023] [Accepted: 05/27/2023] [Indexed: 06/04/2023]
Abstract
In this study, we aimed to develop an invasion-related risk signature and prognostic model for personalized treatment and prognosis prediction in skin cutaneous melanoma (SKCM), as invasion plays a crucial role in this disease. We identified 124 differentially expressed invasion-associated genes (DE-IAGs) and selected 20 prognostic genes (TTYH3, NME1, ORC1, PLK1, MYO10, SPINT1, NUPR1, SERPINE2, HLA-DQB2, METTL7B, TIMP1, NOX4, DBI, ARL15, APOBEC3G, ARRB2, DRAM1, RNF213, C14orf28, and CPEB3) using Cox and LASSO regression to establish a risk score. Gene expression was validated through single-cell sequencing, protein expression, and transcriptome analysis. Negative correlations were discovered between risk score, immune score, and stromal score using ESTIMATE and CIBERSORT algorithms. High- and low-risk groups exhibited significant differences in immune cell infiltration and checkpoint molecule expression. The 20 prognostic genes effectively differentiated between SKCM and normal samples (AUCs >0.7). We identified 234 drugs targeting 6 genes from the DGIdb database. Our study provides potential biomarkers and a risk signature for personalized treatment and prognosis prediction in SKCM patients. We developed a nomogram and machine-learning prognostic model to predict 1-, 3-, and 5-year overall survival (OS) using risk signature and clinical factors. The best model, Extra Trees Classifier (AUC = 0.88), was derived from pycaret's comparison of 15 classifiers. The pipeline and app are accessible at https://github.com/EnyuY/IAGs-in-SKCM.
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Affiliation(s)
- Enyu Yang
- College of Life Sciences and Medicine, Zhejiang Sci-Tech University, 310018, Hangzhou, China.
| | - Qianyun Ding
- Department of 'A', The Children's Hospital, National Clinical Research Center for Child Health, Zhejiang University School of Medicine, 310003, Hangzhou, China.
| | - Xiaowei Fan
- College of Life Sciences and Medicine, Zhejiang Sci-Tech University, 310018, Hangzhou, China.
| | - Haihan Ye
- College of Life Sciences and Medicine, Zhejiang Sci-Tech University, 310018, Hangzhou, China.
| | - Cheng Xuan
- College of Life Sciences and Medicine, Zhejiang Sci-Tech University, 310018, Hangzhou, China.
| | - Shuo Zhao
- College of Life Sciences and Medicine, Zhejiang Sci-Tech University, 310018, Hangzhou, China.
| | - Qing Ji
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Department of Head and Neck and Rare Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, 310022, Hangzhou, China.
| | - Weihua Yu
- Department of Gastroenterology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, 322000, Yiwu, China.
| | - Yongfu Liu
- Department of Emergency, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, China.
| | - Jun Cao
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Department of Head and Neck and Rare Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, 310022, Hangzhou, China.
| | - Meiyu Fang
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Department of Head and Neck and Rare Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, 310022, Hangzhou, China.
| | - Xianfeng Ding
- College of Life Sciences and Medicine, Zhejiang Sci-Tech University, 310018, Hangzhou, China.
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Mei T, Li Y, Orduña Dolado A, Li Z, Andersson R, Berliocchi L, Rasmussen LJ. Pooled analysis of frontal lobe transcriptomic data identifies key mitophagy gene changes in Alzheimer's disease brain. Front Aging Neurosci 2023; 15:1101216. [PMID: 37358952 PMCID: PMC10288858 DOI: 10.3389/fnagi.2023.1101216] [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: 11/17/2022] [Accepted: 05/18/2023] [Indexed: 06/28/2023] Open
Abstract
Background The growing prevalence of Alzheimer's disease (AD) is becoming a global health challenge without effective treatments. Defective mitochondrial function and mitophagy have recently been suggested as etiological factors in AD, in association with abnormalities in components of the autophagic machinery like lysosomes and phagosomes. Several large transcriptomic studies have been performed on different brain regions from AD and healthy patients, and their data represent a vast source of important information that can be utilized to understand this condition. However, large integration analyses of these publicly available data, such as AD RNA-Seq data, are still missing. In addition, large-scale focused analysis on mitophagy, which seems to be relevant for the aetiology of the disease, has not yet been performed. Methods In this study, publicly available raw RNA-Seq data generated from healthy control and sporadic AD post-mortem human samples of the brain frontal lobe were collected and integrated. Sex-specific differential expression analysis was performed on the combined data set after batch effect correction. From the resulting set of differentially expressed genes, candidate mitophagy-related genes were identified based on their known functional roles in mitophagy, the lysosome, or the phagosome, followed by Protein-Protein Interaction (PPI) and microRNA-mRNA network analysis. The expression changes of candidate genes were further validated in human skin fibroblast and induced pluripotent stem cells (iPSCs)-derived cortical neurons from AD patients and matching healthy controls. Results From a large dataset (AD: 589; control: 246) based on three different datasets (i.e., ROSMAP, MSBB, & GSE110731), we identified 299 candidate mitophagy-related differentially expressed genes (DEG) in sporadic AD patients (male: 195, female: 188). Among these, the AAA ATPase VCP, the GTPase ARF1, the autophagic vesicle forming protein GABARAPL1 and the cytoskeleton protein actin beta ACTB were selected based on network degrees and existing literature. Changes in their expression were further validated in AD-relevant human in vitro models, which confirmed their down-regulation in AD conditions. Conclusion Through the joint analysis of multiple publicly available data sets, we identify four differentially expressed key mitophagy-related genes potentially relevant for the pathogenesis of sporadic AD. Changes in expression of these four genes were validated using two AD-relevant human in vitro models, primary human fibroblasts and iPSC-derived neurons. Our results provide foundation for further investigation of these genes as potential biomarkers or disease-modifying pharmacological targets.
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Affiliation(s)
- Taoyu Mei
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
- Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Yuan Li
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Anna Orduña Dolado
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Zhiquan Li
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Robin Andersson
- Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Laura Berliocchi
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Health Sciences, University Magna Græcia of Catanzaro, Catanzaro, Italy
| | - Lene Juel Rasmussen
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
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10
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Alamro H, Thafar MA, Albaradei S, Gojobori T, Essack M, Gao X. Exploiting machine learning models to identify novel Alzheimer’s disease biomarkers and potential targets. Sci Rep 2023; 13:4979. [PMID: 36973386 PMCID: PMC10043000 DOI: 10.1038/s41598-023-30904-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 03/03/2023] [Indexed: 03/29/2023] Open
Abstract
AbstractWe still do not have an effective treatment for Alzheimer's disease (AD) despite it being the most common cause of dementia and impaired cognitive function. Thus, research endeavors are directed toward identifying AD biomarkers and targets. In this regard, we designed a computational method that exploits multiple hub gene ranking methods and feature selection methods with machine learning and deep learning to identify biomarkers and targets. First, we used three AD gene expression datasets to identify 1/ hub genes based on six ranking algorithms (Degree, Maximum Neighborhood Component (MNC), Maximal Clique Centrality (MCC), Betweenness Centrality (BC), Closeness Centrality, and Stress Centrality), 2/ gene subsets based on two feature selection methods (LASSO and Ridge). Then, we developed machine learning and deep learning models to determine the gene subset that best distinguishes AD samples from the healthy controls. This work shows that feature selection methods achieve better prediction performances than the hub gene sets. Beyond this, the five genes identified by both feature selection methods (LASSO and Ridge algorithms) achieved an AUC = 0.979. We further show that 70% of the upregulated hub genes (among the 28 overlapping hub genes) are AD targets based on a literature review and six miRNA (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, hsa-mir-26a-5p, hsa-mir-93-5p, hsa-mir-155-5p) and one transcription factor, JUN, are associated with the upregulated hub genes. Furthermore, since 2020, four of the six microRNA were also shown to be potential AD targets. To our knowledge, this is the first work showing that such a small number of genes can distinguish AD samples from healthy controls with high accuracy and that overlapping upregulated hub genes can narrow the search space for potential novel targets.
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Cheng J, Chen F, Cheng Y. Construction and Evaluation of a Risk Score Model for Lymph Node Metastasis-Associated Circadian Clock Genes in Esophageal Squamous Carcinoma. Cells 2022; 11:cells11213432. [PMID: 36359828 PMCID: PMC9655457 DOI: 10.3390/cells11213432] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/19/2022] [Accepted: 10/25/2022] [Indexed: 11/06/2022] Open
Abstract
Background: Studies suggested that circadian clock genes (CCGs) in human esophageal squamous carcinoma (ESCC) samples are dysregulated. However, the relevance of CCGs to lymph node metastasis (LNM) and prognosis of ESCC remains unclear. Methods: The differentially expressed genes (DEGs) between normal and ESCC samples in The Cancer Genome Atlas database (TCGA) database were intersected with the genes associated with LNM (LNMGs) in ESCC samples and 300 CCGs to obtain the differentially expressed LNM-associated CCGs (DE-LNM-CCGs). The risk model was constructed by Cox regression analysis in the TCGA-ESCC training set, and the accuracy of the risk model was verified by risk profile and overall survival profile. Furthermore, differences of 23 immune cells, 13 immune functions, and immune checkpoint molecules between the high- and low-risk groups were assessed using the single-sample gene set enrichment analysis (ssGSEA) algorithm. Gene set enrichment analysis (GSEA) was conducted to investigate the functional differences between low- and high-risk groups. Finally, we validated the mRNA expression levels of prognostic model genes by quantitative real-time polymerase chain reaction (qRT-PCR). Results: A total of six DE-LNM-CCGs were identified in TCGA-ESCC. TP53 and NAGLU were selected by Cox regression analysis to construct the risk model. Risk profile plots, overall survival plots, and validation results of the risk model in the validation set indicated that the constructed risk model was reliable. The result of ssGSEA showed that the percentages of activated B cells, activated dendritic cells, effector memory CD8 T cells, immune function in neutrophils, plasmacytoid dendritic cells, T cell co-inhibition, and Type 17 T helper cells were different between the high- and low-risk groups. In addition, the expression of CD274, PDCD1, TNFRSF18, and TNFRSF9 was dysregulated between the high- and low-risk groups. GSEA revealed that the high-risk group was associated with cell differentiation, oxidative phosphorylation, and steroid biosynthesis pathways, while the low-risk group was associated with chromosome, ECM–receptor interaction, and other pathways. Finally, qRT-PCR results showed that the mRNA expression levels of two prognostic genes were consistent with TCGA. Conclusion: In conclusion, the risk model constructed based on TP53 and NAGLU could accurately predict the prognosis.
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Affiliation(s)
- Jian Cheng
- Department of Cancer Center, The Second Hospital of Shandong University, 247 Beiyuan Street, Jinan 250033, China
| | - Fang Chen
- Department of Pharmacy, The Second Hospital of Shandong University, 247 Beiyuan Street, Jinan 250033, China
| | - Yufeng Cheng
- Department of Radiation Oncology, Qilu Hospital of Shandong University, No. 107 West Wenhua Road, Jinan 250012, China
- Correspondence:
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Zhang H, Liu X, Zhou L, Deng Z, Wang Y. Identification of RPS7 as the Biomarker of Ferroptosis in Acute Kidney Injury. BIOMED RESEARCH INTERNATIONAL 2022; 2022:3667339. [PMID: 36277893 PMCID: PMC9584673 DOI: 10.1155/2022/3667339] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 09/17/2022] [Indexed: 11/17/2022]
Abstract
Objective This paper aims to explore novel ferroptosis-related biomarkers for acute kidney injury (AKI). Methods Various bioinformatic methods, such as differential expression analysis, functional annotation analysis, machine learning, and chemical-gene network analysis, were used in this study. Furthermore, the expression and proferroptotic role of RPS7 were validated with further bioinformatics analysis and biochemical experiments. Results GSE30718 dataset and GSE139061 dataset were used, and the differentially expressed genes (DEGs) were screened. The DEGs were overlapped with ferroptosis-related genes and genes associated with AKI, which led to the identification of four candidate genes. Machine learning and ROC curve analysis were conducted, and RPS7 and TRIB3 were selected for diagnostic model analysis and functional analysis. Finally, the upregulation of RSP7 in cisplatin-induced AKI was validated in cisplatin-induced AKI, and its proferroptotic role was confirmed in cisplatin-treated proximal tubular cells. Conclusion Our results indicated that RPS7 might present as a novel ferroptosis-related biomarker for AKI, and it derived ferroptosis to accentuate cisplatin-induced AKI.
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Affiliation(s)
- Hao Zhang
- Department of Urology, The Second Xiangya Hospital at Central South University, Changsha, Hunan 410011, China
| | - Xuemei Liu
- Department of Functional Medicine, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, Hunan, China
| | - Lizhi Zhou
- Department of Urology, The Second Xiangya Hospital at Central South University, Changsha, Hunan 410011, China
| | - Zebin Deng
- Department of Urology, The Second Xiangya Hospital at Central South University, Changsha, Hunan 410011, China
| | - Yinhuai Wang
- Department of Urology, The Second Xiangya Hospital at Central South University, Changsha, Hunan 410011, China
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Ji W, An K, Wang C, Wang S. Bioinformatics analysis of diagnostic biomarkers for Alzheimer's disease in peripheral blood based on sex differences and support vector machine algorithm. Hereditas 2022; 159:38. [PMID: 36195955 PMCID: PMC9531459 DOI: 10.1186/s41065-022-00252-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 09/22/2022] [Indexed: 11/28/2022] Open
Abstract
Background The prevalence of Alzheimer's disease (AD) varies based on gender. Due to the lack of early stage biomarkers, most of them are diagnosed at the terminal stage. This study aimed to explore sex-specific signaling pathways and identify diagnostic biomarkers of AD. Methods Microarray dataset for blood was obtained from the Gene Expression Omnibus (GEO) database of GSE63060 to conduct differentially expressed genes (DEGs) analysis by R software limma. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene set enrichment analysis (GSEA) were conducted. Immune checkpoint gene expression was compared between females and males. Using CytoHubba, we identified hub genes in a protein–protein interaction network (PPI). Then, we evaluated their distinct effectiveness using unsupervised hierarchical clustering. Support vector machine (SVM) and ten-fold cross-validation were used to further verify these biomarkers. Lastly, we confirmed our findings by using another independent dataset. Results A total of 37 female-specific DEGs and 27 male-specific DEGs were identified from GSE63060 datasets. Analyses of enrichment showed that female-specific DEGs primarily focused on energy metabolism, while male-specific DEGs mostly involved in immune regulation. Three immune-checkpoint-relevant genes dysregulated in males. In females, however, these eight genes were not differentially expressed. SNRPG, RPS27A, COX7A2, ATP5PO, LSM3, COX7C, PFDN5, HINT1, PSMA6, RPS3A and RPL31 were regarded as hub genes for females, while SNRPG, RPL31, COX7C, RPS27A, RPL35A, RPS3A, RPS20 and PFDN5 were regarded as hub genes for males. Thirteen hub genes mentioned above was significantly lower in both AD and mild cognitive impairment (MCI). The diagnostic model of 15-marker panel (13 hub genes with sex and age) was developed. Both the training dataset and the independent validation dataset have area under the curve (AUC) with a high value (0.919, 95%CI 0.901–0.929 and 0.803, 95%CI 0.789–0.826). Based on GSEA for hub genes, they were associated with some aspects of AD pathogenesis. Conclusion DEGs in males and females contribute differently to AD pathogenesis. Algorithms combining blood-based biomarkers may improve AD diagnostic accuracy, but large validation studies are needed. Supplementary Information The online version contains supplementary material available at 10.1186/s41065-022-00252-x.
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Affiliation(s)
- Wencan Ji
- Nanjing Medical University, Nanjing, 211166, Jiangsu, China.,Department of Endocrinology, Affiliated Zhongda Hospital of Southeast University, Nanjing, 210009, Jiangsu,, China
| | - Ke An
- Department of Endocrinology, Affiliated Zhongda Hospital of Southeast University, Nanjing, 210009, Jiangsu,, China.,School of Medicine, Southeast University, Nanjing, 210009, Jiangsu, China
| | - Canjun Wang
- Department of Laboratory Medicine, Affiliated Zhongda Hospital of Southeast University, Nanjing, 210009, Jiangsu, China
| | - Shaohua Wang
- Nanjing Medical University, Nanjing, 211166, Jiangsu, China. .,Department of Endocrinology, Affiliated Zhongda Hospital of Southeast University, Nanjing, 210009, Jiangsu,, China. .,School of Medicine, Southeast University, Nanjing, 210009, Jiangsu, China.
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New Drug Design Avenues Targeting Alzheimer's Disease by Pharmacoinformatics-Aided Tools. Pharmaceutics 2022; 14:pharmaceutics14091914. [PMID: 36145662 PMCID: PMC9503559 DOI: 10.3390/pharmaceutics14091914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/03/2022] [Accepted: 09/06/2022] [Indexed: 11/30/2022] Open
Abstract
Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer’s disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes.
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Yu H, Yu M, Li Z, Zhang E, Ma H. Identification and analysis of mitochondria-related key genes of heart failure. Lab Invest 2022; 20:410. [PMID: 36071497 PMCID: PMC9450345 DOI: 10.1186/s12967-022-03605-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 08/19/2022] [Indexed: 11/10/2022]
Abstract
Mitochondria-induced cell death is a vital mechanism of heart failure (HF). Thus, identification of mitochondria-related genes (Mito-RGs) based on transcriptome sequencing data of HF might provide novel diagnostic markers and therapeutic targets for HF. First, bioinformatics analysis was conducted on the GSE57338, GSE76701, GSE136547, and GSE77399 datasets in the Gene Expression Omnibus. Next, we analyzed HF-Mito differentially expressed genes (DEGs) using the protein-protein interaction (PPI) network for obtaining critical genes and exploring their functions. Subsequently, immune cell scores of the HF and normal groups were compared. The potential alteration mechanisms of the key genes were investigated by constructing a competing endogenous RNA network. Finally, we predicted potential therapeutic agents and validated the expression levels of the key genes. Twenty-three HF-Mito DEGs were acquired in the GSE57338 dataset, and the PPI network obtained four key genes, including IFIT3, XAF1, RSAD2, and MX1. According to gene set enrichment analysis, the key genes showed high enrichment in myogenesis and hypoxia. Immune cell analysis demonstrated that aDCs, B cells, and 20 other immune cell types varied between the HF and normal groups. Moreover, we observed that H19 might affect the expression of IFIT3, AXF1, and RSAD2. PCGEM1 might regulate RSAD2 expression. A total of 515 potential therapeutic drugs targeting the key genes, such as tretinoin, silicon dioxide, and bisphenol A, were acquired. Finally, IFIT3, RSAD2, and MX1 expression increased in HF samples compared with normal samples in the GSE76701 dataset, conforming to the GSE57338 dataset analysis. This work screened four key genes, namely, IFIT3, XAF1, RSAD2, and MX1, which can be further explored in subsequent studies for their specific molecular mechanisms in HF.
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Affiliation(s)
- Haozhen Yu
- School of Basic Medical Sciences, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Mujun Yu
- School of Life Sciences, Yan'an University, Yan'an, China
| | - Zhuang Li
- School of Basic Medical Sciences, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Enhu Zhang
- School of Basic Medical Sciences, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China.
| | - Heng Ma
- Department of Physiology and Pathophysiology, School of Basic Medicine, Fourth Military Medical University, Xi'an, China.
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Wang X, Tian Y, Li C, Chen M. Exploring the key ferroptosis-related gene in the peripheral blood of patients with Alzheimer’s disease and its clinical significance. Front Aging Neurosci 2022; 14:970796. [PMID: 36118694 PMCID: PMC9475071 DOI: 10.3389/fnagi.2022.970796] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 08/09/2022] [Indexed: 11/29/2022] Open
Abstract
Introduction Alzheimer’s disease (AD) is the most common type of dementia, and there is growing evidence suggesting that ferroptosis is involved in its pathogenesis. In this study, we aimed to investigate the key ferroptosis-related genes in AD and identify a novel ferroptosis-related gene diagnosis model for patients with AD. Materials and methods We extracted the human blood and hippocampus gene expression data of five datasets (GSE63060, GSE63061, GSE97760, GSE48350, and GSE5281) in the Gene Expression Omnibus database as well as the ferroptosis-related genes from FerrDb. Differentially expressed ferroptosis-related genes were screened by random forest classifier, and were further used to construct a diagnostic model of AD using an artificial neural network. The patterns of immune infiltration in the peripheral immune system of AD were also investigated using the CIBERSORT algorithm. Results We first screened and identified 12 ferroptosis-related genes (ATG3, BNIP3, DDIT3, FH, GABARAPL1, MAPK14, SOCS1, SP1, STAT3, TNFAIP3, UBC, and ULK) via a random forest classifier, which was differentially expressed between the AD and normal control groups. Based on the 12 hub genes, we successfully constructed a satisfactory diagnostic model for differentiating AD patients from normal controls using an artificial neural network and validated its diagnostic efficacy in several external datasets. Further, the key ferroptosis-related genes were found to be strongly correlated to immune cells infiltration in AD. Conclusion We successfully identified 12 ferroptosis-related genes and established a novel diagnostic model of significant predictive value for AD. These results may help understand the role of ferroptosis in AD pathogenesis and provide promising therapeutic strategies for patients with AD.
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Affiliation(s)
- Xiaonan Wang
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yaotian Tian
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Chunmei Li
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Min Chen
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
- *Correspondence: Min Chen,
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Cao S, Wang X, Liu X, Li J, Duan L, Gao Z, Lun S, Zhu Y, Yang H, Zhang H, Zhou F. Integrative Analysis of Angiogenesis-Related Long Non-Coding RNA and Identification of a Six-DEARlncRNA Signature Associated with Prognosis and Therapeutic Response in Esophageal Squamous Cell Carcinoma. Cancers (Basel) 2022; 14:cancers14174195. [PMID: 36077731 PMCID: PMC9454540 DOI: 10.3390/cancers14174195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 08/22/2022] [Accepted: 08/23/2022] [Indexed: 11/16/2022] Open
Abstract
Esophageal squamous cell carcinoma (ESCC) is a lethal gastrointestinal malignancy worldwide. We aimed to identify an angiogenesis-related lncRNAs (ARlncRNAs) signature that could predict the prognosis in ESCC. The GSE53624 and GSE53622 datasets were derived from the GEO database. The differently expressed ARlncRNAs (DEARlncRNAs) were retrieved by the weighted gene co-expression network analysis (WGCNA), differential expression analysis, and correlation analysis. Optimal lncRNA biomarkers were screened from the training set and the six-DEARlncRNA signature comprising AP000696.2, LINC01711, RP11-70C1.3, AP000487.5, AC011997.1, and RP11-225N10.1 could separate patients into high- and low-risk groups with markedly different survival. The validation of the reliability of the risk model was performed by the Kaplan-Meier test, ROC curves, and risk curves in the test set and validation set. Predictive independence analysis indicated that risk score is an independent prognostic biomarker for predicting the prognosis of ESCC patients. Subsequently, a ceRNA regulatory network and functional enrichment analysis were performed. The IC50 test revealed that patients in the high-risk group were resistant to Gefitinib and Lapatinib. Finally, the six DEARlncRNAs were detected by qRT-PCR. In conclusion, we demonstrated a novel ARlncRNA signature as an independent prognostic factor to distinguish the risk of ESCC patients and benefit the personalized clinical applications.
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Affiliation(s)
- Shasha Cao
- Henan Medical Key Laboratory, Precise Prevention and Treatment of Esophageal Cancer, Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Anyang 455000, China
| | - Xiaomin Wang
- Henan Medical Key Laboratory, Precise Prevention and Treatment of Esophageal Cancer, Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Anyang 455000, China
| | - Xiaohui Liu
- Henan Medical Key Laboratory, Precise Prevention and Treatment of Esophageal Cancer, Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Anyang 455000, China
| | - Junkuo Li
- Henan Medical Key Laboratory, Precise Prevention and Treatment of Esophageal Cancer, Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Anyang 455000, China
| | - Lijuan Duan
- Henan Medical Key Laboratory, Precise Prevention and Treatment of Esophageal Cancer, Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Anyang 455000, China
| | - Zhaowei Gao
- Henan Medical Key Laboratory, Precise Prevention and Treatment of Esophageal Cancer, Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Anyang 455000, China
| | - Shumin Lun
- Henan Medical Key Laboratory, Precise Prevention and Treatment of Esophageal Cancer, Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Anyang 455000, China
| | - Yanju Zhu
- Henan Medical Key Laboratory, Precise Prevention and Treatment of Esophageal Cancer, Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Anyang 455000, China
| | - Haijun Yang
- Henan Medical Key Laboratory, Precise Prevention and Treatment of Esophageal Cancer, Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Anyang 455000, China
| | - Hao Zhang
- Department of General Surgery, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou 510630, China
- Correspondence: (H.Z.); (F.Z.)
| | - Fuyou Zhou
- Henan Medical Key Laboratory, Precise Prevention and Treatment of Esophageal Cancer, Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Anyang 455000, China
- Correspondence: (H.Z.); (F.Z.)
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Transcriptional Profiling of Hippocampus Identifies Network Alterations in Alzheimer’s Disease. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12105035] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease characterized by rapid brain cell degeneration affecting different areas of the brain. Hippocampus is one of the earliest involved brain regions in the disease. Modern technologies based on high-throughput data have identified transcriptional profiling of several neurological diseases, including AD, for a better comprehension of genetic mechanisms of the disease. In this study, we investigated differentially expressed genes (DEGs) from six Gene Expression Omnibus (GEO) datasets of hippocampus of AD patients. The identified DEGs were submitted to Weighted correlation network analysis (WGCNA) and ClueGo to explore genes with a higher degree centrality and to comprehend their biological role. Subsequently, MCODE was used to identify subnetworks of interconnected DEGs. Our study found 40 down-regulated genes and 36 up-regulated genes as consensus DEGs. Analysis of the co-expression network revealed ACOT7, ATP8A2, CDC42, GAD1, GOT1, INA, NCALD, and WWTR1 to be genes with a higher degree centrality. ClueGO revealed the pathways that were mainly enriched, such as clathrin coat assembly, synaptic vesicle endocytosis, and DNA damage response signal transduction by p53 class mediator. In addition, we found a subnetwork of 12 interconnected genes (AMPH, CA10, CALY, NEFL, SNAP25, SNAP91, SNCB, STMN2, SV2B, SYN2, SYT1, and SYT13). Only CA10 and CALY are targets of known drugs while the others could be potential novel drug targets.
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Biomarker Candidates for Alzheimer’s Disease Unraveled through In Silico Differential Gene Expression Analysis. Diagnostics (Basel) 2022; 12:diagnostics12051165. [PMID: 35626321 PMCID: PMC9139748 DOI: 10.3390/diagnostics12051165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 04/25/2022] [Accepted: 04/29/2022] [Indexed: 01/27/2023] Open
Abstract
Alzheimer’s disease (AD) is neurodegeneration that accounts for 60–70% of dementia cases. Symptoms begin with mild memory difficulties and evolve towards cognitive impairment. The underlying risk factors remain primarily unclear for this heterogeneous disorder. Bioinformatics is a relevant research tool that allows for identifying several pathways related to AD. Open-access databases of RNA microarrays from the peripheral blood and brain of AD patients were analyzed after background correction and data normalization; the Limma package was used for differential expression analysis (DEA) through statistical R programming language. Data were corrected with the Benjamini and Hochberg approach, and genes with p-values equal to or less than 0.05 were considered to be significant. The direction of the change in gene expression was determined by its variation in the log2-fold change between healthy controls and patients. We performed the functional enrichment analysis of GO using goana and topGO-Limma. The functional enrichment analysis of DEGs showed upregulated (UR) pathways: behavior, nervous systems process, postsynapses, enzyme binding; downregulated (DR) were cellular component organization, RNA metabolic process, and signal transduction. Lastly, the intersection of DEGs in the three databases showed eight shared genes between brain and blood, with potential use as AD biomarkers for blood tests.
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Molecular Signatures of Mitochondrial Complexes Involved in Alzheimer’s Disease via Oxidative Phosphorylation and Retrograde Endocannabinoid Signaling Pathways. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:9565545. [PMID: 35432724 PMCID: PMC9006080 DOI: 10.1155/2022/9565545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 03/22/2022] [Indexed: 11/17/2022]
Abstract
Objective The inability to intervene in Alzheimer's disease (AD) forces the search for promising gene-targeted therapies. This study was aimed at exploring molecular signatures and mechanistic pathways to improve the diagnosis and treatment of AD. Methods Microarray datasets were collected to filter differentially expressed genes (DEGs) between AD and nondementia controls. Weight gene correlation network analysis (WGCNA) was employed to analyze the correlation of coexpression modules with AD phenotype. A global regulatory network was established and then visualized using Cytoscape software to determine hub genes and their mechanistic pathways. Receiver operating characteristic (ROC) analysis was conducted to estimate the diagnostic performance of hub genes in AD prediction. Results A total of 2,163 DEGs from 13,049 background genes were screened in AD relative to nondementia controls. Among the six coexpression modules constructed by WGCNA, DEGs of the key modules with the strongest correlation with AD were extracted to build a global regulatory network. According to the Maximal Clique Centrality (MCC) method, five hub genes associated with mitochondrial complexes were chosen. Further pathway enrichment analysis of hub genes, such as oxidative phosphorylation and retrograde endocannabinoid signaling, was identified. According to the area under the curve (AUC) of about 70%, each hub gene exhibited a good diagnostic performance in predicting AD. Conclusions Our findings highlight the perturbation of mitochondrial complexes underlying AD onset, which is mediated by molecular signatures involved in oxidative phosphorylation (COX5A, NDUFAB1, SDHB, UQCRC2, and UQCRFS1) and retrograde endocannabinoid signaling (NDUFAB1) pathways.
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Liu L, Lai Y, Zhan Z, Fu Q, Jiang Y. Downregulation of Three Immune-Specific Core Genes and the Regulatory Pathways in Children and Adult Friedreich's Ataxia: A Comprehensive Analysis Based on Microarray. Front Neurol 2022; 12:816393. [PMID: 35237223 PMCID: PMC8884172 DOI: 10.3389/fneur.2021.816393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 12/29/2021] [Indexed: 11/29/2022] Open
Abstract
Background Friedreich's ataxia (FRDA) is a familial hereditary disorder that lacks available therapy. Therefore, the identification of novel biomarkers and key mechanisms related to FRDA progression is urgently required. Methods We identified the up-regulated and down-regulated differentially expressed genes (DEGs) in children and adult FRDA from the GSE11204 dataset and intersected them to determine the co-expressed DEGs (co-DEGs). Enrichment analysis was conducted and a protein-protein interaction (PPI) network was constructed to identify key pathways and hub genes. The potential diagnostic biomarkers were validated using the GSE30933 dataset. Cytoscape was applied to construct interaction and competitive endogenous RNA (ceRNA) networks. Results Gene Set Enrichment Analysis (GSEA) indicated that the genes in both the child and adult samples were primarily enriched in their immune-related functions. We identified 88 co-DEGs between child and adult FRDA samples. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome enrichment analysis suggested that these co-DEGs were primarily enriched in immune response, inflammatory reaction, and necroptosis. Immune infiltration analysis showed remarkable differences in the proportions of immune cell subtype between FRDA and healthy samples. In addition, ten core genes and one gene cluster module were screened out based on the PPI network. We verified eight immune-specific core genes using a validation dataset and found CD28, FAS, and ITIF5 have high diagnostic significance in FRDA. Finally, NEAT1-hsa-miR-24-3p-CD28 was identified as a key regulatory pathway of child and adult FRDA. Conclusions Downregulation of three immune-specific hub genes, CD28, FAS, and IFIT5, may be associated with the progression of child and adult FRDA. Furthermore, NEAT1-hsa-miR-24-3p-CD28 may be the potential RNA regulatory pathway related to the pathogenesis of child and adult FRDA.
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Affiliation(s)
- Lichun Liu
- Department of Pharmacy, Fujian Children's Hospital, Fuzhou, China
| | - Yongxing Lai
- Department of Geriatric Medicine, Fujian Provincial Hospital, Fuzhou, China
| | - Zhidong Zhan
- Department of Pediatric Intensive Care Unit, Fujian Children's Hospital, Fuzhou, China
| | - Qingxian Fu
- Department of Pediatric Endocrinology, Fujian Children's Hospital, Fuzhou, China
| | - Yuelian Jiang
- Department of Pharmacy, Fujian Children's Hospital, Fuzhou, China
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Meldolesi J. News about Therapies of Alzheimer’s Disease: Extracellular Vesicles from Stem Cells Exhibit Advantages Compared to Other Treatments. Biomedicines 2022; 10:biomedicines10010105. [PMID: 35052785 PMCID: PMC8773509 DOI: 10.3390/biomedicines10010105] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 12/24/2021] [Accepted: 12/27/2021] [Indexed: 02/04/2023] Open
Abstract
Upon its discovery, Alzheimer’s, the neurodegenerative disease that affects many millions of patients in the world, remained without an effective therapy. The first drugs, made available near the end of last century, induced some effects, which remained only marginal. More promising effects are now present, induced by two approaches. Blockers of the enzyme BACE-1 induce, in neurons and glial cells, decreased levels of Aβ, the key peptide of the Alzheimer’s disease. If administered at early AD steps, the BACE-1 blockers preclude further development of the disease. However, they have no effect on established, irreversible lesions. The extracellular vesicles secreted by mesenchymal stem cells induce therapy effects analogous, but more convenient, than the effects of their original cells. After their specific fusion to target cells, the action of these vesicles depends on their ensuing release of cargo molecules, such as proteins and many miRNAs, active primarily on the cell cytoplasm. Operationally, these vesicles exhibit numerous advantages: they exclude, by their accurate selection, the heterogeneity of the original cells; exhibit molecular specificity due to their engineering and drug accumulation; and induce effective actions, mediated by variable concentrations of factors and molecules and by activation of signaling cascades. Their strength is reinforced by their combination with various factors and processes. The recent molecular and operations changes, induced especially by the stem cell target cells, result in encouraging and important improvement of the disease. Their further development is expected in the near future.
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Affiliation(s)
- Jacopo Meldolesi
- San Raffaele Institute, Vita-Salute San Raffaele University, 20132 Milan, Italy;
- Faculty of Medicine, CNR Institute of Neuroscience, University Milano-Bicocca, 20132 Milan, Italy
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