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Wang R, Gao C, Yu M, Song J, Feng Z, Wang R, Pan H, Liu H, Li W, Fan X. Mechanistic prediction and validation of Brevilin A Therapeutic effects in Lung Cancer. BMC Complement Med Ther 2024; 24:214. [PMID: 38840248 PMCID: PMC11151568 DOI: 10.1186/s12906-024-04516-z] [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: 02/25/2024] [Accepted: 05/22/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND Traditional Chinese medicine (TCM) has been found widespread application in neoplasm treatment, yielding promising therapeutic candidates. Previous studies have revealed the anti-cancer properties of Brevilin A, a naturally occurring sesquiterpene lactone derived from Centipeda minima (L.) A.Br. (C. minima), a TCM herb, specifically against lung cancer. However, the underlying mechanisms of its effects remain elusive. This study employs network pharmacology and experimental analyses to unravel the molecular mechanisms of Brevilin A in lung cancer. METHODS The Batman-TCM, Swiss Target Prediction, Pharmmapper, SuperPred, and BindingDB databases were screened to identify Brevilin A targets. Lung cancer-related targets were sourced from GEO, Genecards, OMIM, TTD, and Drugbank databases. Utilizing Cytoscape software, a protein-protein interaction (PPI) network was established. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene set enrichment analysis (GSEA), and gene-pathway correlation analysis were conducted using R software. To validate network pharmacology results, molecular docking, molecular dynamics simulations, and in vitro experiments were performed. RESULTS We identified 599 Brevilin A-associated targets and 3864 lung cancer-related targets, with 155 overlapping genes considered as candidate targets for Brevilin A against lung cancer. The PPI network highlighted STAT3, TNF, HIF1A, PTEN, ESR1, and MTOR as potential therapeutic targets. GO and KEGG analyses revealed 2893 enriched GO terms and 157 enriched KEGG pathways, including the PI3K-Akt signaling pathway, FoxO signaling pathway, and HIF-1 signaling pathway. GSEA demonstrated a close association between hub genes and lung cancer. Gene-pathway correlation analysis indicated significant associations between hub genes and the cellular response to hypoxia pathway. Molecular docking and dynamics simulations confirmed Brevilin A's interaction with PTEN and HIF1A, respectively. In vitro experiments demonstrated Brevilin A-induced dose- and time-dependent cell death in A549 cells. Notably, Brevilin A treatment significantly reduced HIF-1α mRNA expression while increasing PTEN mRNA levels. CONCLUSIONS This study demonstrates that Brevilin A exerts anti-cancer effects in treating lung cancer through a multi-target and multi-pathway manner, with the HIF pathway potentially being involved. These results lay a theoretical foundation for the prospective clinical application of Brevilin A.
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Affiliation(s)
- Ruixue Wang
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Cuiyun Gao
- Department of Rehabilitation Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China
- School of Rehabilitation Medicine, Binzhou Medical University, Yantai, Shandong, China
| | - Meng Yu
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Jialing Song
- Department of Rehabilitation Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China
- School of Rehabilitation Medicine, Binzhou Medical University, Yantai, Shandong, China
| | - Zhenzhen Feng
- Department of Rehabilitation Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Ruyu Wang
- School of clinical medicine, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China
| | - Huafeng Pan
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Haimeng Liu
- Department of Rehabilitation Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China.
| | - Wei Li
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
- Department of Rehabilitation Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China.
| | - Xiangzhen Fan
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
- Department of Rehabilitation Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China.
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Yundung Y, Mohammed S, Paneni F, Reutersberg B, Rössler F, Zimmermann A, Pelisek J. Transcriptomics analysis of long non-coding RNAs in smooth muscle cells from patients with peripheral artery disease and diabetes mellitus. Sci Rep 2024; 14:8615. [PMID: 38616192 PMCID: PMC11016542 DOI: 10.1038/s41598-024-59164-7] [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: 01/23/2024] [Accepted: 04/08/2024] [Indexed: 04/16/2024] Open
Abstract
Diabetes mellitus (DM) is a significant risk factor for peripheral arterial disease (PAD), and PAD is an independent predictor of cardiovascular disorders (CVDs). Growing evidence suggests that long non-coding RNAs (lncRNAs) significantly contribute to disease development and underlying complications, particularly affecting smooth muscle cells (SMCs). So far, no study has focused on transcriptome analysis of lncRNAs in PAD patients with and without DM. Tissue samples were obtained from our Vascular Biobank. Due to the sample's heterogeneity, expression analysis of lncRNAs in whole tissue detected only ACTA2-AS1 with a 4.9-fold increase in PAD patients with DM. In contrast, transcriptomics of SMCs revealed 28 lncRNAs significantly differentially expressed between PAD with and without DM (FDR < 0.1). Sixteen lncRNAs were of unknown function, six were described in cancer, one connected with macrophages polarisation, and four were associated with CVDs, mainly with SMC function and phenotypic switch (NEAT1, MIR100HG, HIF1A-AS3, and MRI29B2CHG). The enrichment analysis detected additional lncRNAs H19, CARMN, FTX, and MEG3 linked with DM. Our study revealed several lncRNAs in diabetic PAD patients associated with the physiological function of SMCs. These lncRNAs might serve as potential therapeutic targets to improve the function of SMCs within the diseased tissue and, thus, the clinical outcome.
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Affiliation(s)
- Yankey Yundung
- Experimental Vascular Surgery/Department of Vascular Surgery, University Hospital Zurich/University of Zurich, Schlieren, Switzerland
| | - Shafeeq Mohammed
- Department of Cardiology/Center for Translational and Experimental Cardiology (CTEC), University Hospital Zurich/University of Zurich, Schlieren, Switzerland
| | - Francesco Paneni
- Department of Cardiology/Center for Translational and Experimental Cardiology (CTEC), University Hospital Zurich/University of Zurich, Schlieren, Switzerland
| | - Benedikt Reutersberg
- Experimental Vascular Surgery/Department of Vascular Surgery, University Hospital Zurich/University of Zurich, Schlieren, Switzerland
| | - Fabian Rössler
- Department of Surgery and Transplantation, University Hospital Zurich, Zürich, Switzerland
| | - Alexander Zimmermann
- Experimental Vascular Surgery/Department of Vascular Surgery, University Hospital Zurich/University of Zurich, Schlieren, Switzerland
| | - Jaroslav Pelisek
- Experimental Vascular Surgery/Department of Vascular Surgery, University Hospital Zurich/University of Zurich, Schlieren, Switzerland.
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Barakat A, Munro G, Heegaard AM. Finding new analgesics: Computational pharmacology faces drug discovery challenges. Biochem Pharmacol 2024; 222:116091. [PMID: 38412924 DOI: 10.1016/j.bcp.2024.116091] [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: 10/02/2023] [Revised: 01/10/2024] [Accepted: 02/23/2024] [Indexed: 02/29/2024]
Abstract
Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.
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Affiliation(s)
- Ahmed Barakat
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Pharmacology and Toxicology, Faculty of Pharmacy, Assiut University, Assiut, Egypt.
| | | | - Anne-Marie Heegaard
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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da Silva Rosa SC, Barzegar Behrooz A, Guedes S, Vitorino R, Ghavami S. Prioritization of genes for translation: a computational approach. Expert Rev Proteomics 2024; 21:125-147. [PMID: 38563427 DOI: 10.1080/14789450.2024.2337004] [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: 05/26/2023] [Accepted: 02/21/2024] [Indexed: 04/04/2024]
Abstract
INTRODUCTION Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation. AREAS COVERED In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes. EXPERT OPINION Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.
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Affiliation(s)
- Simone C da Silva Rosa
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Science, University of Manitoba, Winnipeg, Canada
| | - Amir Barzegar Behrooz
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Science, University of Manitoba, Winnipeg, Canada
- Electrophysiology Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Sofia Guedes
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro, Portugal
| | - Rui Vitorino
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro, Portugal
- Department of Medical Sciences, Institute of Biomedicine-iBiMED, University of Aveiro, Aveiro, Portugal
- UnIC@RISE, Department of Surgery and Physiology, Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Saeid Ghavami
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Science, University of Manitoba, Winnipeg, Canada
- Faculty of Medicine in Zabrze, Academia of Silesia, Katowice, Poland
- Research Institute of Oncology and Hematology, Cancer Care Manitoba, University of Manitoba, Winnipeg, Canada
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Deritei D, Inuzuka H, Castaldi PJ, Yun JH, Xu Z, Anamika WJ, Asara JM, Guo F, Zhou X, Glass K, Wei W, Silverman EK. HHIP protein interactions in lung cells provide insight into COPD pathogenesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.01.586839. [PMID: 38617310 PMCID: PMC11014494 DOI: 10.1101/2024.04.01.586839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. The primary causes of COPD are environmental, including cigarette smoking; however, genetic susceptibility also contributes to COPD risk. Genome-Wide Association Studies (GWASes) have revealed more than 80 genetic loci associated with COPD, leading to the identification of multiple COPD GWAS genes. However, the biological relationships between the identified COPD susceptibility genes are largely unknown. Genes associated with a complex disease are often in close network proximity, i.e. their protein products often interact directly with each other and/or similar proteins. In this study, we use affinity purification mass spectrometry (AP-MS) to identify protein interactions with HHIP , a well-established COPD GWAS gene which is part of the sonic hedgehog pathway, in two disease-relevant lung cell lines (IMR90 and 16HBE). To better understand the network neighborhood of HHIP , its proximity to the protein products of other COPD GWAS genes, and its functional role in COPD pathogenesis, we create HUBRIS, a protein-protein interaction network compiled from 8 publicly available databases. We identified both common and cell type-specific protein-protein interactors of HHIP. We find that our newly identified interactions shorten the network distance between HHIP and the protein products of several COPD GWAS genes, including DSP, MFAP2, TET2 , and FBLN5 . These new shorter paths include proteins that are encoded by genes involved in extracellular matrix and tissue organization. We found and validated interactions to proteins that provide new insights into COPD pathobiology, including CAVIN1 (IMR90) and TP53 (16HBE). The newly discovered HHIP interactions with CAVIN1 and TP53 implicate HHIP in response to oxidative stress.
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Benincasa G, Napoli C, DeMeo DL. Transgenerational Epigenetic Inheritance of Cardiovascular Diseases: A Network Medicine Perspective. Matern Child Health J 2024; 28:617-630. [PMID: 38409452 DOI: 10.1007/s10995-023-03886-z] [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] [Accepted: 12/19/2023] [Indexed: 02/28/2024]
Abstract
INTRODUCTION The ability to identify early epigenetic signatures underlying the inheritance of cardiovascular risk, including trans- and intergenerational effects, may help to stratify people before cardiac symptoms occur. METHODS Prospective and retrospective cohorts and case-control studies focusing on DNA methylation and maternal/paternal effects were searched in Pubmed from 1997 to 2023 by using the following keywords: DNA methylation, genomic imprinting, and network analysis in combination with transgenerational/intergenerational effects. RESULTS Maternal and paternal exposures to traditional cardiovascular risk factors during critical temporal windows, including the preconceptional period or early pregnancy, may perturb the plasticity of the epigenome (mainly DNA methylation) of the developing fetus especially at imprinted loci, such as the insulin-like growth factor type 2 (IGF2) gene. Thus, the epigenome is akin to a "molecular archive" able to memorize parental environmental insults and predispose an individual to cardiovascular diseases onset in later life. Direct evidence for human transgenerational epigenetic inheritance (at least three generations) of cardiovascular risk is lacking but it is supported by epidemiological studies. Several blood-based association studies showed potential intergenerational epigenetic effects (single-generation studies) which may mediate the transmittance of cardiovascular risk from parents to offspring. DISCUSSION In this narrative review, we discuss some relevant examples of trans- and intergenerational epigenetic associations with cardiovascular risk. In our perspective, we propose three network-oriented approaches which may help to clarify the unsolved issues regarding transgenerational epigenetic inheritance of cardiovascular risk and provide potential early biomarkers for primary prevention.
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Affiliation(s)
- Giuditta Benincasa
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", 80138, Naples, Italy
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", 80138, Naples, Italy.
| | - Dawn L DeMeo
- Channing Division of Network Medicine and the Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
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Pei FL, Jia JJ, Lin SH, Chen XX, Wu LZ, Lin ZX, Sun BW, Zeng C. Construction and evaluation of endometriosis diagnostic prediction model and immune infiltration based on efferocytosis-related genes. Front Mol Biosci 2024; 10:1298457. [PMID: 38370978 PMCID: PMC10870152 DOI: 10.3389/fmolb.2023.1298457] [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: 09/21/2023] [Accepted: 12/07/2023] [Indexed: 02/20/2024] Open
Abstract
Background: Endometriosis (EM) is a long-lasting inflammatory disease that is difficult to treat and prevent. Existing research indicates the significance of immune infiltration in the progression of EM. Efferocytosis has an important immunomodulatory function. However, research on the identification and clinical significance of efferocytosis-related genes (EFRGs) in EM is sparse. Methods: The EFRDEGs (differentially expressed efferocytosis-related genes) linked to datasets associated with endometriosis were thoroughly examined utilizing the Gene Expression Omnibus (GEO) and GeneCards databases. The construction of the protein-protein interaction (PPI) and transcription factor (TF) regulatory network of EFRDEGs ensued. Subsequently, machine learning techniques including Univariate logistic regression, LASSO, and SVM classification were applied to filter and pinpoint diagnostic biomarkers. To establish and assess the diagnostic model, ROC analysis, multivariate regression analysis, nomogram, and calibration curve were employed. The CIBERSORT algorithm and single-cell RNA sequencing (scRNA-seq) were employed to explore immune cell infiltration, while the Comparative Toxicogenomics Database (CTD) was utilized for the identification of potential therapeutic drugs for endometriosis. Finally, immunohistochemistry (IHC) and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were utilized to quantify the expression levels of biomarkers in clinical samples of endometriosis. Results: Our findings revealed 13 EFRDEGs associated with EM, and the LASSO and SVM regression model identified six hub genes (ARG2, GAS6, C3, PROS1, CLU, and FGL2). Among these, ARG2, GAS6, and C3 were confirmed as diagnostic biomarkers through multivariate logistic regression analysis. The ROC curve analysis of GSE37837 (AUC = 0.627) and GSE6374 (AUC = 0.635), along with calibration and DCA curve assessments, demonstrated that the nomogram built on these three biomarkers exhibited a commendable predictive capacity for the disease. Notably, the ratio of nine immune cell types exhibited significant differences between eutopic and ectopic endometrial samples, with scRNA-seq highlighting M0 Macrophages, Fibroblasts, and CD8 Tex cells as the cell populations undergoing the most substantial changes in the three biomarkers. Additionally, our study predicted seven potential medications for EM. Finally, the expression levels of the three biomarkers in clinical samples were validated through RT-qPCR and IHC, consistently aligning with the results obtained from the public database. Conclusion: we identified three biomarkers and constructed a diagnostic model for EM in this study, these findings provide valuable insights for subsequent mechanistic research and clinical applications in the field of endometriosis.
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Affiliation(s)
- Fang-Li Pei
- The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jin-Jin Jia
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Shu-Hong Lin
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xiao-Xin Chen
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Li-Zheng Wu
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zeng-Xian Lin
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Bo-Wen Sun
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Cheng Zeng
- The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
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Somers J, Fenner M, Kong G, Thirumalaisamy D, Yashar WM, Thapa K, Kinali M, Nikolova O, Babur Ö, Demir E. A framework for considering prior information in network-based approaches to omics data analysis. Proteomics 2023; 23:e2200402. [PMID: 37986684 DOI: 10.1002/pmic.202200402] [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: 07/19/2023] [Revised: 09/20/2023] [Accepted: 09/21/2023] [Indexed: 11/22/2023]
Abstract
For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks (PKNs) in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of PKNs is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.
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Affiliation(s)
- Julia Somers
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
| | - Madeleine Fenner
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
| | - Garth Kong
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
- Division of Oncological Sciences, Oregon Health and Science University, Portland, Oregon, USA
| | - Dharani Thirumalaisamy
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
| | - William M Yashar
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
- Division of Oncological Sciences, Oregon Health and Science University, Portland, Oregon, USA
| | - Kisan Thapa
- Computer Science Department, University of Massachusetts Boston, College of Science and Mathematics, Boston, Massachusetts, USA
| | - Meric Kinali
- Computer Science Department, University of Massachusetts Boston, College of Science and Mathematics, Boston, Massachusetts, USA
| | - Olga Nikolova
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
- Division of Oncological Sciences, Oregon Health and Science University, Portland, Oregon, USA
| | - Özgün Babur
- Computer Science Department, University of Massachusetts Boston, College of Science and Mathematics, Boston, Massachusetts, USA
| | - Emek Demir
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
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He Y, Chen X, Liu M, Zuo L, Zhai Z, Zhou L, Li G, Chen L, Qi G, Jing C, Hao G. The potential DNA methylation markers of cardiovascular disease in patients with type 2 diabetes. BMC Med Genomics 2023; 16:242. [PMID: 37828521 PMCID: PMC10568935 DOI: 10.1186/s12920-023-01689-3] [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: 01/07/2023] [Accepted: 10/04/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND DNA methylation is associated with cardiovascular (CV) disease. However, in type 2 diabetes (T2D) patients, the role of gene methylation in the development of CV disease is under-studied. We aimed to identify the CV disease-related DNA methylation loci in patients with T2D and to explore the potential pathways underlying the development of CV disease using a two-stage design. METHODS The participants were from the Jinan Diabetes Cohort Study (JNDCS), an ongoing longitudinal study designed to evaluate the development of CV risk in patients with T2D. In the discovery cohort, 10 diabetic patients with CV events at baseline were randomly selected as the case group, and another 10 diabetic patients without CV events were matched for sex, age, smoking status, and body mass index as the control group. In 1438 T2D patients without CV disease at baseline, 210 patients with CV events were identified after a mean 6.5-year follow-up. Of whom, 100 patients who experienced CV events during the follow-up were randomly selected as cases, and 100 patients who did not have CV events were randomly selected as the control group in the validation cohort. Reduced representation bisulfite sequencing and Targeted Bisulfite Sequencing were used to measure the methylation profiles in the discovery and validation cohort, respectively. RESULTS In the discover cohort, 127 DMRs related to CV disease were identified in T2D patients. Further, we validated 23 DMRs mapped to 25 genes, of them, 4 genes (ARSG, PNPLA6, NEFL, and CRYGEP) for the first time were reported. There was evidence that the addition of DNA methylation data improved the prediction performance of CV disease in T2D patients. Pathway analysis identified some significant signaling pathways involved in CV comorbidities, T2D, and inflammation. CONCLUSIONS In this study, we identified 23 DMRs mapped to 25 genes associated with CV disease in T2D patients, of them, 4 DMRs for the first time were reported. DNA methylation testing may help identify a high CV-risk population in T2D patients.
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Affiliation(s)
- Yunbiao He
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, 601 West Huangpu Road, Guangzhou, 510632, Guangdong, China
| | - Xia Chen
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, 601 West Huangpu Road, Guangzhou, 510632, Guangdong, China
| | - Mingliang Liu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, 601 West Huangpu Road, Guangzhou, 510632, Guangdong, China
| | - Lei Zuo
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, 601 West Huangpu Road, Guangzhou, 510632, Guangdong, China
| | - Zhiyu Zhai
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, 601 West Huangpu Road, Guangzhou, 510632, Guangdong, China
| | - Long Zhou
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, 601 West Huangpu Road, Guangzhou, 510632, Guangdong, China
| | - Guangzhen Li
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, 601 West Huangpu Road, Guangzhou, 510632, Guangdong, China
| | - Li Chen
- Department of Medicine, Medical College of Georgia, Georgia Prevention Institute, Augusta University, Augusta, GA, USA
| | - Guolong Qi
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, 601 West Huangpu Road, Guangzhou, 510632, Guangdong, China.
- Department of Epidemiology, School of Medicine, Jinan University, 601 West Huangpu Road, Guangzhou, 510632, Guangdong, China.
| | - Chunxia Jing
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, 601 West Huangpu Road, Guangzhou, 510632, Guangdong, China.
- Guangdong Key Laboratory of Environmental Exposure and Health, Jinan University, Guangzhou, China.
- Department of Epidemiology, School of Medicine, Jinan University, 601 West Huangpu Road, Guangzhou, 510632, Guangdong, China.
| | - Guang Hao
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, 601 West Huangpu Road, Guangzhou, 510632, Guangdong, China.
- Guangdong Key Laboratory of Environmental Exposure and Health, Jinan University, Guangzhou, China.
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Hsieh PH, Lopes-Ramos CM, Zucknick M, Sandve GK, Glass K, Kuijjer ML. Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data. Bioinformatics 2023; 39:btad610. [PMID: 37802917 PMCID: PMC10598588 DOI: 10.1093/bioinformatics/btad610] [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: 11/01/2022] [Revised: 08/05/2023] [Accepted: 10/05/2023] [Indexed: 10/08/2023] Open
Abstract
MOTIVATION Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns across a group of samples. Coordinated expression of genes may indicate that they are controlled by the same transcriptional regulatory program, or involved in common biological processes. Gene co-expression is generally estimated from RNA-Sequencing data, which are commonly normalized to remove technical variability. Here, we demonstrate that certain normalization methods, in particular quantile-based methods, can introduce false-positive associations between genes. These false-positive associations can consequently hamper downstream co-expression network analysis. Quantile-based normalization can, however, be extremely powerful. In particular, when preprocessing large-scale heterogeneous data, quantile-based normalization methods such as smooth quantile normalization can be applied to remove technical variability while maintaining global differences in expression for samples with different biological attributes. RESULTS We developed SNAIL (Smooth-quantile Normalization Adaptation for the Inference of co-expression Links), a normalization method based on smooth quantile normalization specifically designed for modeling of co-expression measurements. We show that SNAIL avoids formation of false-positive associations in co-expression as well as in downstream network analyses. Using SNAIL, one can avoid arbitrary gene filtering and retain associations to genes that only express in small subgroups of samples. This highlights the method's potential future impact on network modeling and other association-based approaches in large-scale heterogeneous data. AVAILABILITY AND IMPLEMENTATION The implementation of the SNAIL algorithm and code to reproduce the analyses described in this work can be found in the GitHub repository https://github.com/kuijjerlab/PySNAIL.
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Affiliation(s)
- Ping-Han Hsieh
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, Oslo 0318, Norway
- Department of Informatics, University of Oslo, Oslo 0316, Norway
| | - Camila Miranda Lopes-Ramos
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
| | - Manuela Zucknick
- Oslo Centre for Biostatistics and Epidemiology, Institute of Basic Medical Sciences, University of Oslo, Oslo 0317, Norway
| | | | - Kimberly Glass
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
| | - Marieke Lydia Kuijjer
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, Oslo 0318, Norway
- Department of Pathology, Leiden University Medical Center, Leiden 2300RC, The Netherlands
- Leiden Center of Computational Oncology, Leiden University Medical Center,Leiden 2300RC, The Netherlands
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11
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Zhang YS, Tu B, Song K, Lin LC, Liu ZY, Lu D, Chen Q, Tao H. Epigenetic hallmarks in pulmonary fibrosis: New advances and perspectives. Cell Signal 2023; 110:110842. [PMID: 37544633 DOI: 10.1016/j.cellsig.2023.110842] [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: 06/07/2023] [Revised: 07/25/2023] [Accepted: 08/03/2023] [Indexed: 08/08/2023]
Abstract
Epigenetics indicates that certain phenotypes of an organism can undergo heritable changes in the absence of changes in the genetic DNA sequence. Many studies have shown that epigenetic patterns play an important role in the lung and lung diseases. Pulmonary fibrosis (PF) is also a type of lung disease. PF is an end-stage change of a large group of lung diseases, characterized by fibroblast proliferation and massive accumulation of extracellular matrix, accompanied by inflammatory injury and histological destruction, that is, structural abnormalities caused by abnormal repair of normal alveolar tissue. It causes loss of lung function in patients with multiple complex diseases, leading to respiratory failure and subsequent death. However, current treatment options for IPF are very limited and no drugs have been shown to significantly prolong the survival of patients. Therefore, based on a systematic understanding of the disease mechanisms of PF, this review integrates the role of epigenetics in the development and course of PF, describes preventive and potential therapeutic targets for PF, and provides a theoretical basis for further exploration of the mechanisms of PF.
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Affiliation(s)
- Yun-Sen Zhang
- Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China
| | - Bin Tu
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China
| | - Kai Song
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China
| | - Li-Chan Lin
- Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China
| | - Zhi-Yan Liu
- Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China
| | - Dong Lu
- Department of Interventional Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, PR China.
| | - Qi Chen
- Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China.
| | - Hui Tao
- Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China; Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China.
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12
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Shi W, Feng H, Li J, Liu T, Liu Z. DapBCH: a disease association prediction model Based on Cross-species and Heterogeneous graph embedding. Front Genet 2023; 14:1222346. [PMID: 37811150 PMCID: PMC10556742 DOI: 10.3389/fgene.2023.1222346] [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: 05/14/2023] [Accepted: 09/11/2023] [Indexed: 10/10/2023] Open
Abstract
The study of comorbidity can provide new insights into the pathogenesis of the disease and has important economic significance in the clinical evaluation of treatment difficulty, medical expenses, length of stay, and prognosis of the disease. In this paper, we propose a disease association prediction model DapBCH, which constructs a cross-species biological network and applies heterogeneous graph embedding to predict disease association. First, we combine the human disease-gene network, mouse gene-phenotype network, human-mouse homologous gene network, and human protein-protein interaction network to reconstruct a heterogeneous biological network. Second, we apply heterogeneous graph embedding based on meta-path aggregation to generate the feature vector of disease nodes. Finally, we employ link prediction to obtain the similarity of disease pairs. The experimental results indicate that our model is highly competitive in predicting the disease association and is promising for finding potential disease associations.
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Affiliation(s)
- Wanqi Shi
- School of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou, Zhejiang, China
| | - Hailin Feng
- School of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou, Zhejiang, China
| | - Jian Li
- School of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou, Zhejiang, China
| | - Tongcun Liu
- School of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou, Zhejiang, China
| | - Zhe Liu
- College of Media Engineering, Zhejiang University of Media and Communications, Hangzhou, Zhejiang, China
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13
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Benincasa G, Napoli C. Unexplored horizons on sex bias and progression of heart failure with preserved ejection fraction. EUROPEAN HEART JOURNAL. CARDIOVASCULAR PHARMACOTHERAPY 2023; 9:502-504. [PMID: 37486244 DOI: 10.1093/ehjcvp/pvad050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 07/13/2023] [Indexed: 07/25/2023]
Affiliation(s)
- Giuditta Benincasa
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Pz. Miraglia, 2, 80138 Naples, Italy
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Pz. Miraglia, 2, 80138 Naples, Italy
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14
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Schiano C, Luongo L, Maione S, Napoli C. Mediator complex in neurological disease. Life Sci 2023; 329:121986. [PMID: 37516429 DOI: 10.1016/j.lfs.2023.121986] [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: 02/13/2023] [Revised: 07/18/2023] [Accepted: 07/26/2023] [Indexed: 07/31/2023]
Abstract
Neurological diseases, including traumatic brain injuries, stroke (haemorrhagic and ischemic), and inherent neurodegenerative diseases cause acquired disability in humans, representing a leading cause of death worldwide. The Mediator complex (MED) is a large, evolutionarily conserved multiprotein that facilities the interaction between transcription factors and RNA Polymerase II in eukaryotes. Some MED subunits have been found altered in the brain, although their specific functions in neurodegenerative diseases are not fully understood. Mutations in MED subunits were associated with a wide range of genetic diseases for MED12, MED13, MED13L, MED20, MED23, MED25, and CDK8 genes. In addition, MED12 and MED23 were deregulated in the Alzheimer's Disease. Interestingly, most of the genomic mutations have been found in the subunits of the kinase module. To date, there is only one evidence on MED1 involvement in post-stroke cognitive deficits. Although the underlying neurodegenerative disorders may be different, we are confident that the signal cascades of the biological-cognitive mechanisms of brain adaptation, which begin after brain deterioration, may also differ. Here, we analysed relevant studies in English published up to June 2023. They were identified through a search of electronic databases including PubMed, Medline, EMBASE and Scopus, including search terms such as "Mediator complex", "neurological disease", "brains". Thematic content analysis was conducted to collect and summarize all studies demonstrating MED alteration to understand the role of this central transcriptional regulatory complex in the brain. Improved and deeper knowledge of the regulatory mechanisms in neurological diseases can increase the ability of physicians to predict onset and progression, thereby improving diagnostic care and providing appropriate treatment decisions.
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Affiliation(s)
- Concetta Schiano
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Italy.
| | - Livio Luongo
- Department of Experimental Medicine, University of Campania "Luigi Vanvitelli", Italy; IRCSS, Neuromed, Pozzilli, Italy
| | - Sabatino Maione
- Department of Experimental Medicine, University of Campania "Luigi Vanvitelli", Italy; IRCSS, Neuromed, Pozzilli, Italy
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Italy; Clinical Department of Internal Medicine and Specialistic Units, Division of Clinical Immunology and Immunohematology, Transfusion Medicine, and Transplant Immunology (SIMT), Regional Reference Laboratory of Transplant Immunology (LIT), Azienda Universitaria Policlinico (AOU), Italy
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15
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Xu J, Li J, Li Y, Shi X, Zhu H, Chen L. Multidimensional Landscape of SA-AKI Revealed by Integrated Proteomics and Metabolomics Analysis. Biomolecules 2023; 13:1329. [PMID: 37759729 PMCID: PMC10526551 DOI: 10.3390/biom13091329] [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: 04/29/2023] [Revised: 08/27/2023] [Accepted: 08/28/2023] [Indexed: 09/29/2023] Open
Abstract
Sepsis-associated acute kidney injury (SA-AKI) is a severe and life-threatening condition with high morbidity and mortality among emergency patients, and it poses a significant risk of chronic renal failure. Clinical treatments for SA-AKI remain reactive and non-specific, lacking effective diagnostic biomarkers or treatment targets. In this study, we established an SA-AKI mouse model using lipopolysaccharide (LPS) and performed proteomics and metabolomics analyses. A variety of bioinformatic analyses, including gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), protein and protein interactions (PPI), and MetaboAnalyst analysis, were conducted to investigate the key molecules of SA-AKI. Integrated proteomics and metabolomics analysis revealed that sepsis led to impaired renal mitochondrial function and metabolic disorders. Immune-related pathways were found to be activated in kidneys upon septic infection. The catabolic products of polyamines accumulated in septic kidneys. Overall, our integrated analysis provides a multidimensional understanding of SA-AKI and identifies potential pathways for this condition.
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Affiliation(s)
- Jiatong Xu
- Emergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China; (J.X.); (Y.L.)
- Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Jiaying Li
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China; (J.L.); (X.S.)
| | - Yan Li
- Emergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China; (J.X.); (Y.L.)
| | - Xiaoxiao Shi
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China; (J.L.); (X.S.)
| | - Huadong Zhu
- Emergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China; (J.X.); (Y.L.)
| | - Limeng Chen
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China; (J.L.); (X.S.)
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16
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Marku M, Pancaldi V. From time-series transcriptomics to gene regulatory networks: A review on inference methods. PLoS Comput Biol 2023; 19:e1011254. [PMID: 37561790 PMCID: PMC10414591 DOI: 10.1371/journal.pcbi.1011254] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023] Open
Abstract
Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data.
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Affiliation(s)
- Malvina Marku
- CRCT, Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Centre de Recherches en Cancérologie de Toulouse, Toulouse, France
| | - Vera Pancaldi
- CRCT, Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Centre de Recherches en Cancérologie de Toulouse, Toulouse, France
- Barcelona Supercomputing Center, Barcelona, Spain
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17
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Wekesa JS, Kimwele M. A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment. Front Genet 2023; 14:1199087. [PMID: 37547471 PMCID: PMC10398577 DOI: 10.3389/fgene.2023.1199087] [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: 04/02/2023] [Accepted: 07/11/2023] [Indexed: 08/08/2023] Open
Abstract
Accurate diagnosis is the key to providing prompt and explicit treatment and disease management. The recognized biological method for the molecular diagnosis of infectious pathogens is polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identifying disease-related genes for diagnosis, prognosis, and treatment. The models reduce the time and cost used by wet-lab experimental procedures. Consequently, sophisticated computational approaches have been developed to facilitate the detection of cancer, a leading cause of death globally, and other complex diseases. In this review, we systematically evaluate the recent trends in multi-omics data analysis based on deep learning techniques and their application in disease prediction. We highlight the current challenges in the field and discuss how advances in deep learning methods and their optimization for application is vital in overcoming them. Ultimately, this review promotes the development of novel deep-learning methodologies for data integration, which is essential for disease detection and treatment.
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18
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Mezi S, Pomati G, Fiscon G, Amirhassankhani S, Zizzari IG, Napoletano C, Rughetti A, Rossi E, Schinzari G, Tortora G, Lanzetta G, D’Amati G, Nuti M, Santini D, Botticelli A. A network approach to define the predictive role of immune profile on tumor response and toxicity of anti PD-1 single agent immunotherapy in patients with solid tumors. Front Immunol 2023; 14:1199089. [PMID: 37483633 PMCID: PMC10361061 DOI: 10.3389/fimmu.2023.1199089] [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: 04/02/2023] [Accepted: 06/14/2023] [Indexed: 07/25/2023] Open
Abstract
Background The immune profile of each patient could be considered as a portrait of the fitness of his/her own immune system. The predictive role of the immune profile in immune-related toxicities (irAEs) development and tumour response to treatment was investigated. Methods A prospective, multicenter study evaluating, through a multiplex assay, the soluble immune profile at the baseline of 53 patients with advanced cancer, treated with immunotherapy as single agent was performed. Four connectivity heat maps and networks were obtained by calculating the Spearman correlation coefficients for each group: responder patients who developed cumulative toxicity (R-T), responders who did not develop cumulative toxicity (R-NT), non-responders who developed cumulative toxicity (NR-T), non-responders who did not develop cumulative toxicity (NR-NT). Results A statistically significant up-regulation of IL-17A, sCTLA4, sCD80, I-CAM-1, sP-Selectin and sEselectin in NR-T was detected. A clear loss of connectivity of most of the soluble immune checkpoints and cytokines characterized the immune profile of patients with toxicity, while an inversion of the correlation for ICAM-1 and sP-selectin was observed in NR-T. Four connectivity networks were built for each group. The highest number of connections characterized the NR-T. Conclusions A connectivity network of immune dysregulation was defined for each subgroup of patients, regardless of tumor type. In patients with the worst prognosis (NR-T) the peculiar connectivity model could facilitate their early and timely identification, as well as the design of a personalized treatment approach to improve outcomes or prevent irAEs.
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Affiliation(s)
- Silvia Mezi
- Department of Radiological, Oncological and Pathological Science, Sapienza University of Rome, Rome, Italy
| | - Giulia Pomati
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Giulia Fiscon
- Department of Computer, Control, and Management Engineering “Antonio Ruberti”, “Sapienza” University of Rome, Rome, Italy
| | - Sasan Amirhassankhani
- Department of Urology, S. Orsola-Malpighi Hospital University of Bologna, Bologna, Italy
| | - Ilaria Grazia Zizzari
- Laboratory of Tumor Immunology and Cell Therapy, Department of Experimental Medicine, Policlinico Umberto I, University of Rome “Sapienza”, Rome, Italy
| | - Chiara Napoletano
- Laboratory of Tumor Immunology and Cell Therapy, Department of Experimental Medicine, Policlinico Umberto I, University of Rome “Sapienza”, Rome, Italy
| | - Aurelia Rughetti
- Laboratory of Tumor Immunology and Cell Therapy, Department of Experimental Medicine, Policlinico Umberto I, University of Rome “Sapienza”, Rome, Italy
| | - Ernesto Rossi
- Medical Oncology, Fondazione Policlinico Universitario Agostino Gemelli Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Giovanni Schinzari
- Medical Oncology, Fondazione Policlinico Universitario Agostino Gemelli Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
- Medical Oncology, Universitá Cattolica del Sacro Cuore, Rome, Italy
| | - Giampaolo Tortora
- Medical Oncology, Fondazione Policlinico Universitario Agostino Gemelli Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
- Medical Oncology, Universitá Cattolica del Sacro Cuore, Rome, Italy
| | - Gaetano Lanzetta
- Clinical Oncology Unit, Istituto Neurotraumatologico Italiano (I.N.I.) Grottaferrata, via di S.Anna snc, Grottaferrata, Italy
| | - Giulia D’Amati
- Department of Radiological, Oncological and Pathological Science, Sapienza University of Rome, Rome, Italy
| | - Marianna Nuti
- Laboratory of Tumor Immunology and Cell Therapy, Department of Experimental Medicine, Policlinico Umberto I, University of Rome “Sapienza”, Rome, Italy
| | - Daniele Santini
- Department of Medico-Surgical Sciences and Biotechnology, Polo Pontino, Sapienza University of Rome, Rome, Italy
| | - Andrea Botticelli
- Department of Radiological, Oncological and Pathological Science, Sapienza University of Rome, Rome, Italy
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Pistollato F, Campia I, Daskalopoulos EP, Bernasconi C, Desaintes C, Di Virgilio S, Kyriakopoulou C, Whelan M, Deceuninck P. Gauging innovation and health impact from biomedical research: survey results and interviews with recipients of EU-funding in the fields of Alzheimer's disease, breast cancer and prostate cancer. Health Res Policy Syst 2023; 21:66. [PMID: 37386455 DOI: 10.1186/s12961-023-00981-z] [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: 10/03/2022] [Accepted: 04/05/2023] [Indexed: 07/01/2023] Open
Abstract
Biomedical research on Alzheimer's disease (AD), breast cancer (BC) and prostate cancer (PC) has globally improved our understanding of the etiopathological mechanisms underlying the onset of these diseases, often with the goal to identify associated genetic and environmental risk factors and develop new medicines. However, the prevalence of these diseases and failure rate in drug development remain high. Being able to retrospectively monitor the major scientific breakthroughs and impact of such investment endeavors is important to re-address funding strategies if and when needed. The EU has supported research into those diseases via its successive framework programmes for research, technological development and innovation. The European Commission (EC) has already undertaken several activities to monitor research impact. As an additional contribution, the EC Joint Research Centre (JRC) launched in 2020 a survey addressed to former and current participants of EU-funded research projects in the fields of AD, BC and PC, with the aim to understand how EU-funded research has contributed to scientific innovation and societal impact, and how the selection of the experimental models may have underpinned the advances made. Further feedback was also gathered through in-depth interviews with some selected survey participants representative of the diverse pre-clinical models used in the EU-funded projects. A comprehensive analysis of survey replies, complemented with the information derived from the interviews, has recently been published in a Synopsis report. Here we discuss the main findings of this analysis and propose a set of priority actions that could be considered to help improving the translation of scientific innovation of biomedical research into societal impact.
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Affiliation(s)
- Francesca Pistollato
- European Commission, Joint Research Centre (JRC), Directorate F-Health, Consumers and Reference Materials, Via E. Fermi 2749, 21027, Ispra, VA, Italy
| | - Ivana Campia
- European Commission, Joint Research Centre (JRC), Directorate F-Health, Consumers and Reference Materials, Via E. Fermi 2749, 21027, Ispra, VA, Italy
| | - Evangelos P Daskalopoulos
- European Commission, Joint Research Centre (JRC), Directorate F-Health, Consumers and Reference Materials, Via E. Fermi 2749, 21027, Ispra, VA, Italy
| | - Camilla Bernasconi
- European Commission, Joint Research Centre (JRC), Directorate F-Health, Consumers and Reference Materials, Via E. Fermi 2749, 21027, Ispra, VA, Italy
| | | | - Sergio Di Virgilio
- European Commission, DG Research & Innovation (DG RTD), Brussels, Belgium
| | | | - Maurice Whelan
- European Commission, Joint Research Centre (JRC), Directorate F-Health, Consumers and Reference Materials, Via E. Fermi 2749, 21027, Ispra, VA, Italy
| | - Pierre Deceuninck
- European Commission, Joint Research Centre (JRC), Directorate F-Health, Consumers and Reference Materials, Via E. Fermi 2749, 21027, Ispra, VA, Italy.
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20
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Thessen AE, Cooper L, Swetnam TL, Hegde H, Reese J, Elser J, Jaiswal P. Using knowledge graphs to infer gene expression in plants. Front Artif Intell 2023; 6:1201002. [PMID: 37384147 PMCID: PMC10298150 DOI: 10.3389/frai.2023.1201002] [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: 04/05/2023] [Accepted: 05/23/2023] [Indexed: 06/30/2023] Open
Abstract
Introduction Climate change is already affecting ecosystems around the world and forcing us to adapt to meet societal needs. The speed with which climate change is progressing necessitates a massive scaling up of the number of species with understood genotype-environment-phenotype (G×E×P) dynamics in order to increase ecosystem and agriculture resilience. An important part of predicting phenotype is understanding the complex gene regulatory networks present in organisms. Previous work has demonstrated that knowledge about one species can be applied to another using ontologically-supported knowledge bases that exploit homologous structures and homologous genes. These types of structures that can apply knowledge about one species to another have the potential to enable the massive scaling up that is needed through in silico experimentation. Methods We developed one such structure, a knowledge graph (KG) using information from Planteome and the EMBL-EBI Expression Atlas that connects gene expression, molecular interactions, functions, and pathways to homology-based gene annotations. Our preliminary analysis uses data from gene expression studies in Arabidopsis thaliana and Populus trichocarpa plants exposed to drought conditions. Results A graph query identified 16 pairs of homologous genes in these two taxa, some of which show opposite patterns of gene expression in response to drought. As expected, analysis of the upstream cis-regulatory region of these genes revealed that homologs with similar expression behavior had conserved cis-regulatory regions and potential interaction with similar trans-elements, unlike homologs that changed their expression in opposite ways. Discussion This suggests that even though the homologous pairs share common ancestry and functional roles, predicting expression and phenotype through homology inference needs careful consideration of integrating cis and trans-regulatory components in the curated and inferred knowledge graph.
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Affiliation(s)
- Anne E. Thessen
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Laurel Cooper
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United States
| | - Tyson L. Swetnam
- BIO5 Institute, University of Arizona, Tucson, AZ, United States
| | - Harshad Hegde
- Environmental Genomics and Systems Biology Division, Berkeley Lab (DOE), Berkeley, CA, United States
| | - Justin Reese
- Environmental Genomics and Systems Biology Division, Berkeley Lab (DOE), Berkeley, CA, United States
| | - Justin Elser
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United States
| | - Pankaj Jaiswal
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United States
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Lin LC, Tu B, Song K, Liu ZY, Sun H, Zhou Y, Sha JM, Yang JJ, Zhang Y, Zhao JY, Tao H. Mitochondrial quality control in cardiac fibrosis: Epigenetic mechanisms and therapeutic strategies. Metabolism 2023:155626. [PMID: 37302693 DOI: 10.1016/j.metabol.2023.155626] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 06/05/2023] [Accepted: 06/05/2023] [Indexed: 06/13/2023]
Abstract
Cardiac fibrosis (CF) is considered an ultimate common pathway of a wide variety of heart diseases in response to diverse pathological and pathophysiological stimuli. Mitochondria are characterized as isolated organelles with a double-membrane structure, and they primarily contribute to and maintain highly dynamic energy and metabolic networks whose distribution and structure exert potent support for cellular properties and performance. Because the myocardium is a highly oxidative tissue with high energy demands to continuously pump blood, mitochondria are the most abundant organelles within mature cardiomyocytes, accounting for up to one-third of the total cell volume, and play an essential role in maintaining optimal performance of the heart. Mitochondrial quality control (MQC), including mitochondrial fusion, fission, mitophagy, mitochondrial biogenesis, and mitochondrial metabolism and biosynthesis, is crucial machinery that modulates cardiac cells and heart function by maintaining and regulating the morphological structure, function and lifespan of mitochondria. Certain investigations have focused on mitochondrial dynamics, including manipulating and maintaining the dynamic balance of energy demand and nutrient supply, and the resultant findings suggest that changes in mitochondrial morphology and function may contribute to bioenergetic adaptation during cardiac fibrosis and pathological remodeling. In this review, we discuss the function of epigenetic regulation and molecular mechanisms of MQC in the pathogenesis of CF and provide evidence for targeting MQC for CF. Finally, we discuss how these findings can be applied to improve the treatment and prevention of CF.
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Affiliation(s)
- Li-Chan Lin
- Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China
| | - Bin Tu
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China
| | - Kai Song
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China
| | - Zhi-Yan Liu
- Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China
| | - He Sun
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China
| | - Yang Zhou
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China
| | - Ji-Ming Sha
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China
| | - Jing-Jing Yang
- Department of Clinical Pharmacy, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China.
| | - Ye Zhang
- Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China.
| | - Jian-Yuan Zhao
- Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China; Institute for Developmental and Regenerative Cardiovascular Medicine, MOE-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, PR China.
| | - Hui Tao
- Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China; Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, PR China; Institute for Developmental and Regenerative Cardiovascular Medicine, MOE-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, PR China.
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22
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Ebbels TMD, van der Hooft JJJ, Chatelaine H, Broeckling C, Zamboni N, Hassoun S, Mathé EA. Recent advances in mass spectrometry-based computational metabolomics. Curr Opin Chem Biol 2023; 74:102288. [PMID: 36966702 PMCID: PMC11075003 DOI: 10.1016/j.cbpa.2023.102288] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 02/16/2023] [Accepted: 02/21/2023] [Indexed: 04/03/2023]
Abstract
The computational metabolomics field brings together computer scientists, bioinformaticians, chemists, clinicians, and biologists to maximize the impact of metabolomics across a wide array of scientific and medical disciplines. The field continues to expand as modern instrumentation produces datasets with increasing complexity, resolution, and sensitivity. These datasets must be processed, annotated, modeled, and interpreted to enable biological insight. Techniques for visualization, integration (within or between omics), and interpretation of metabolomics data have evolved along with innovation in the databases and knowledge resources required to aid understanding. In this review, we highlight recent advances in the field and reflect on opportunities and innovations in response to the most pressing challenges. This review was compiled from discussions from the 2022 Dagstuhl seminar entitled "Computational Metabolomics: From Spectra to Knowledge".
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Affiliation(s)
- Timothy M D Ebbels
- Section of Bioinformatics, Department of Metabolism, Digestion & Reproduction, Imperial College London, Burlington Danes Building, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.
| | - Justin J J van der Hooft
- Bioinformatics Group, Wageningen University & Research, Wageningen 6708 PB, the Netherlands; Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
| | - Haley Chatelaine
- Informatics Core, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Corey Broeckling
- Bioanalysis and Omics Center, Analytical Resources Core, Colorado State University, Fort Collins, CO, USA
| | - Nicola Zamboni
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Soha Hassoun
- Department of Computer Science, Tufts University, Medford, MA, USA; Department of Chemical and Biological Engineering, Tufts University, Medford, MA, USA
| | - Ewy A Mathé
- Informatics Core, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, USA.
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23
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Yang Y, Wang Q. Three genes expressed in relation to lipid metabolism considered as potential biomarkers for the diagnosis and treatment of diabetic peripheral neuropathy. Sci Rep 2023; 13:8679. [PMID: 37248406 PMCID: PMC10227002 DOI: 10.1038/s41598-023-35908-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 05/25/2023] [Indexed: 05/31/2023] Open
Abstract
Diabetic neuropathy is one of the most common chronic complications and is present in approximately 50% of diabetic patients. A bioinformatic approach was used to analyze candidate genes involved in diabetic distal symmetric polyneuropathy and their potential mechanisms. GSE95849 was downloaded from the Gene Expression Omnibus database for differential analysis, together with the identified diabetic peripheral neuropathy-associated genes and the three major metabolism-associated genes in the CTD database to obtain overlapping Differentially Expressed Genes (DEGs). Gene Set Enrichment Analysis and Functional Enrichment Analysis were performed. Protein-Protein Interaction and hub gene networks were constructed using the STRING database and Cytoscape software. The expression levels of target genes were evaluated using GSE24290 samples, followed by Receiver operating characteristic, curve analysis. And Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the target genes. Finally, mRNA-miRNA networks were constructed. A total of 442 co-expressed DEGs were obtained through differential analysis, of which 353 expressed up-regulated genes and 89 expressed down-regulated genes. The up-regulated DEGs were involved in 742 GOs and 10 KEGG enrichment results, mainly associated with lipid metabolism-related pathways, TGF-β receptor signaling pathway, lipid transport, and PPAR signaling pathway. A total of 4 target genes (CREBBP, EP300, ME1, CD36) were identified. Analysis of subject operating characteristic curves indicated that CREBBP (AUC = 1), EP300 (AUC = 0.917), ME1 (AUC = 0.944) and CD36 (AUC = 1) may be candidate serum biomarkers for DPN. Conclusion: Diabetic peripheral neuropathy pathogenesis and progression is caused by multiple pathways, which also provides clinicians with potential therapeutic tools.
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Affiliation(s)
- Ye Yang
- Department of Geriatrics and Cadre Ward, Second Affiliated Hospital of Xinjiang Medical University, Ürümqi, 830063, Xinjiang, China
| | - Qin Wang
- Department of Geriatrics and Cadre Ward, Second Affiliated Hospital of Xinjiang Medical University, Ürümqi, 830063, Xinjiang, China.
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24
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Recanatini M, Menestrina L. Network modeling helps to tackle the complexity of drug-disease systems. WIREs Mech Dis 2023:e1607. [PMID: 36958762 DOI: 10.1002/wsbm.1607] [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: 09/20/2022] [Revised: 02/03/2023] [Accepted: 03/03/2023] [Indexed: 03/25/2023]
Abstract
From the (patho)physiological point of view, diseases can be considered as emergent properties of living systems stemming from the complexity of these systems. Complex systems display some typical features, including the presence of emergent behavior and the organization in successive hierarchic levels. Drug treatments increase this complexity scenario, and from some years the use of network models has been introduced to describe drug-disease systems and to make predictions about them with regard to several aspects related to drug discovery. Here, we review some recent examples thereof with the aim to illustrate how network science tools can be very effective in addressing both tasks. We will examine the use of bipartite networks that lead to the important concept of "disease module", as well as the introduction of more articulated models, like multi-scale and multiplex networks, able to describe disease systems at increasing levels of organization. Examples of predictive models will then be discussed, considering both those that exploit approaches purely based on graph theory and those that integrate machine learning methods. A short account of both kinds of methodological applications will be provided. Finally, the point will be made on the present situation of modeling complex drug-disease systems highlighting some open issues. This article is categorized under: Neurological Diseases > Computational Models Infectious Diseases > Computational Models Cardiovascular Diseases > Computational Models.
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Affiliation(s)
- Maurizio Recanatini
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum-University of Bologna, Via Belmeloro 6, Bologna, 40126, Italy
| | - Luca Menestrina
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum-University of Bologna, Via Belmeloro 6, Bologna, 40126, Italy
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25
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Zhao M, Liu J, Xin M, Yang K, Huang H, Zhang W, Zhang J, He S. Pulmonary arterial hypertension associated with congenital heart disease: An omics study. Front Cardiovasc Med 2023; 10:1037357. [PMID: 36970344 PMCID: PMC10036813 DOI: 10.3389/fcvm.2023.1037357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 02/24/2023] [Indexed: 03/12/2023] Open
Abstract
Pulmonary arterial hypertension associated with congenital heart disease (PAH-CHD) is a severely progressive condition with uncertain physiological course. Hence, it has become increasingly relevant to clarify the specific mechanisms of molecular modification, which is crucial to identify more treatment strategies. With the rapid development of high-throughput sequencing, omics technology gives access to massive experimental data and advanced techniques for systems biology, permitting comprehensive assessment of disease occurrence and progression. In recent years, significant progress has been made in the study of PAH-CHD and omics. To provide a comprehensive description and promote further in-depth investigation of PAH-CHD, this review attempts to summarize the latest developments in genomics, transcriptomics, epigenomics, proteomics, metabolomics, and multi-omics integration.
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Affiliation(s)
- Maolin Zhao
- Department of Cardiovascular Surgery, Affiliated Hospital of Southwest Jiaotong University, General Hospital of Western Theater Command, Chengdu, China
| | - Jian Liu
- Department of Cardiovascular Surgery, Affiliated Hospital of Southwest Jiaotong University, General Hospital of Western Theater Command, Chengdu, China
| | - Mei Xin
- Department of Cardiovascular Surgery, Affiliated Hospital of Southwest Jiaotong University, General Hospital of Western Theater Command, Chengdu, China
| | - Ke Yang
- Department of Cardiovascular Surgery, Affiliated Hospital of Southwest Jiaotong University, General Hospital of Western Theater Command, Chengdu, China
| | - Honghao Huang
- Department of Cardiovascular Surgery, Affiliated Hospital of Southwest Jiaotong University, General Hospital of Western Theater Command, Chengdu, China
| | - Wenxin Zhang
- Department of Cardiovascular Surgery, Affiliated Hospital of Southwest Jiaotong University, General Hospital of Western Theater Command, Chengdu, China
| | - Jinbao Zhang
- Department of Cardiovascular Surgery, Affiliated Hospital of Southwest Jiaotong University, General Hospital of Western Theater Command, Chengdu, China
| | - Siyi He
- Department of Cardiovascular Surgery, Affiliated Hospital of Southwest Jiaotong University, General Hospital of Western Theater Command, Chengdu, China
- Correspondence: Siyi He
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26
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Immune-related toxicity and soluble profile in patients affected by solid tumors: a network approach. Cancer Immunol Immunother 2023:10.1007/s00262-023-03384-9. [PMID: 36869232 DOI: 10.1007/s00262-023-03384-9] [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: 07/16/2022] [Accepted: 01/22/2023] [Indexed: 03/05/2023]
Abstract
BACKGROUND Immune checkpoint inhibitors (ICIs) have particular, immune-related adverse events (irAEs), as a consequence of interfering with self-tolerance mechanisms. The incidence of irAEs varies depending on ICI class, administered dose and treatment schedule. The aim of this study was to define a baseline (T0) immune profile (IP) predictive of irAE development. METHODS A prospective, multicenter study evaluating the immune profile (IP) of 79 patients with advanced cancer and treated with anti-programmed cell death protein 1 (anti-PD-1) drugs as a first- or second-line setting was performed. The results were then correlated with irAEs onset. The IP was studied by means of multiplex assay, evaluating circulating concentration of 12 cytokines, 5 chemokines, 13 soluble immune checkpoints and 3 adhesion molecules. Indoleamine 2, 3-dioxygenase (IDO) activity was measured through a modified liquid chromatography-tandem mass spectrometry using the high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS) method. A connectivity heatmap was obtained by calculating Spearman correlation coefficients. Two different networks of connectivity were constructed, based on the toxicity profile. RESULTS Toxicity was predominantly of low/moderate grade. High-grade irAEs were relatively rare, while cumulative toxicity was high (35%). Positive and statistically significant correlations between the cumulative toxicity and IP10 and IL8, sLAG3, sPD-L2, sHVEM, sCD137, sCD27 and sICAM-1 serum concentration were found. Moreover, patients who experienced irAEs had a markedly different connectivity pattern, characterized by disruption of most of the paired connections between cytokines, chemokines and connections of sCD137, sCD27 and sCD28, while sPDL-2 pair-wise connectivity values seemed to be intensified. Network connectivity analysis identified a total of 187 statistically significant interactions in patients without toxicity and a total of 126 statistically significant interactions in patients with toxicity. Ninety-eight interactions were common to both networks, while 29 were specifically observed in patients who experienced toxicity. CONCLUSIONS A particular, common pattern of immune dysregulation was defined in patients developing irAEs. This immune serological profile, if confirmed in a larger patient population, could lead to the design of a personalized therapeutic strategy in order to prevent, monitor and treat irAEs at an early stage.
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27
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Yuen HY, Jansson J. Normalized L3-based link prediction in protein-protein interaction networks. BMC Bioinformatics 2023; 24:59. [PMID: 36814208 PMCID: PMC9945744 DOI: 10.1186/s12859-023-05178-3] [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: 09/26/2021] [Accepted: 02/08/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND Protein-protein interaction (PPI) data is an important type of data used in functional genomics. However, high-throughput experiments are often insufficient to complete the PPI interactome of different organisms. Computational techniques are thus used to infer missing data, with link prediction being one such approach that uses the structure of the network of PPIs known so far to identify non-edges whose addition to the network would make it more sound, according to some underlying assumptions. Recently, a new idea called the L3 principle introduced biological motivation into PPI link predictions, yielding predictors that are superior to general-purpose link predictors for complex networks. Interestingly, the L3 principle can be interpreted in another way, so that other signatures of PPI networks can also be characterized for PPI predictions. This alternative interpretation uncovers candidate PPIs that the current L3-based link predictors may not be able to fully capture, underutilizing the L3 principle. RESULTS In this article, we propose a formulation of link predictors that we call NormalizedL3 (L3N) which addresses certain missing elements within L3 predictors in the perspective of network modeling. Our computational validations show that the L3N predictors are able to find missing PPIs more accurately (in terms of true positives among the predicted PPIs) than the previously proposed methods on several datasets from the literature, including BioGRID, STRING, MINT, and HuRI, at the cost of using more computation time in some of the cases. In addition, we found that L3-based link predictors (including L3N) ranked a different pool of PPIs higher than the general-purpose link predictors did. This suggests that different types of PPIs can be predicted based on different topological assumptions, and that even better PPI link predictors may be obtained in the future by improved network modeling.
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Affiliation(s)
- Ho Yin Yuen
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China.
| | - Jesper Jansson
- Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan.
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28
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Jimenez-Coll V, Llorente S, Boix F, Alfaro R, Galián JA, Martinez-Banaclocha H, Botella C, Moya-Quiles MR, Muro-Pérez M, Minguela A, Legaz I, Muro M. Monitoring of Serological, Cellular and Genomic Biomarkers in Transplantation, Computational Prediction Models and Role of Cell-Free DNA in Transplant Outcome. Int J Mol Sci 2023; 24:ijms24043908. [PMID: 36835314 PMCID: PMC9963702 DOI: 10.3390/ijms24043908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/03/2023] [Accepted: 02/05/2023] [Indexed: 02/17/2023] Open
Abstract
The process and evolution of an organ transplant procedure has evolved in terms of the prevention of immunological rejection with the improvement in the determination of immune response genes. These techniques include considering more important genes, more polymorphism detection, more refinement of the response motifs, as well as the analysis of epitopes and eplets, its capacity to fix complement, the PIRCHE algorithm and post-transplant monitoring with promising new biomarkers that surpass the classic serum markers such as creatine and other similar parameters of renal function. Among these new biomarkers, we analyze new serological, urine, cellular, genomic and transcriptomic biomarkers and computational prediction, with particular attention to the analysis of donor free circulating DNA as an optimal marker of kidney damage.
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Affiliation(s)
- Víctor Jimenez-Coll
- Immunology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
| | - Santiago Llorente
- Nephrology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
| | - Francisco Boix
- Immunology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
| | - Rafael Alfaro
- Immunology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
| | - José Antonio Galián
- Immunology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
| | - Helios Martinez-Banaclocha
- Immunology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
| | - Carmen Botella
- Immunology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
| | - María R. Moya-Quiles
- Immunology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
| | - Manuel Muro-Pérez
- Immunology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
| | - Alfredo Minguela
- Immunology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
| | - Isabel Legaz
- Department of Legal and Forensic Medicine, Biomedical Research Institute (IMIB), Regional Campus of International Excellence “Campus Mare Nostrum”, Faculty of Medicine, University of Murcia, 30100 Murcia, Spain
- Correspondence: (I.L.); (M.M.); Tel.: +34-699986674 (M.M.); Fax: +34-868834307 (M.M.)
| | - Manuel Muro
- Immunology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
- Correspondence: (I.L.); (M.M.); Tel.: +34-699986674 (M.M.); Fax: +34-868834307 (M.M.)
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29
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Stolfi P, Mastropietro A, Pasculli G, Tieri P, Vergni D. NIAPU: network-informed adaptive positive-unlabeled learning for disease gene identification. Bioinformatics 2023; 39:7023926. [PMID: 36727493 PMCID: PMC9933847 DOI: 10.1093/bioinformatics/btac848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 12/23/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Gene-disease associations are fundamental for understanding disease etiology and developing effective interventions and treatments. Identifying genes not yet associated with a disease due to a lack of studies is a challenging task in which prioritization based on prior knowledge is an important element. The computational search for new candidate disease genes may be eased by positive-unlabeled learning, the machine learning (ML) setting in which only a subset of instances are labeled as positive while the rest of the dataset is unlabeled. In this work, we propose a set of effective network-based features to be used in a novel Markov diffusion-based multi-class labeling strategy for putative disease gene discovery. RESULTS The performances of the new labeling algorithm and the effectiveness of the proposed features have been tested on 10 different disease datasets using three ML algorithms. The new features have been compared against classical topological and functional/ontological features and a set of network- and biological-derived features already used in gene discovery tasks. The predictive power of the integrated methodology in searching for new disease genes has been found to be competitive against state-of-the-art algorithms. AVAILABILITY AND IMPLEMENTATION The source code of NIAPU can be accessed at https://github.com/AndMastro/NIAPU. The source data used in this study are available online on the respective websites. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Paola Stolfi
- Institute for Applied Computing (IAC) 'Mauro Picone', National Research Council of Italy (CNR), Rome 00185, Italy
| | - Andrea Mastropietro
- Department of Computer, Control and Management Engineering (DIAG) 'Antonio Ruberti', Sapienza University of Rome, Rome 00185, Italy
| | - Giuseppe Pasculli
- Department of Computer, Control and Management Engineering (DIAG) 'Antonio Ruberti', Sapienza University of Rome, Rome 00185, Italy
| | - Paolo Tieri
- Institute for Applied Computing (IAC) 'Mauro Picone', National Research Council of Italy (CNR), Rome 00185, Italy
| | - Davide Vergni
- Institute for Applied Computing (IAC) 'Mauro Picone', National Research Council of Italy (CNR), Rome 00185, Italy
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30
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Benincasa G, Viglietti M, Coscioni E, Napoli C. "Transplantomics" for predicting allograft rejection: real-life applications and new strategies from Network Medicine. Hum Immunol 2023; 84:89-97. [PMID: 36424231 DOI: 10.1016/j.humimm.2022.11.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/08/2022] [Accepted: 11/09/2022] [Indexed: 11/23/2022]
Abstract
Although decades of the reductionist approach achieved great milestones in optimizing the immunosuppression therapy, traditional clinical parameters still fail in predicting both acute and chronic (mainly) rejection events leading to higher rates across all solid organ transplants. To clarify the underlying immune-related cellular and molecular mechanisms, current biomedical research is increasingly focusing on "transplantomics" which relies on a huge quantity of big data deriving from genomics, transcriptomics, epigenomics, proteomics, and metabolomics platforms. The AlloMap (gene expression) and the AlloSure (donor-derived cell-free DNA) tests represent two successful examples of how omics and liquid biopsy can really improve the precision medicine of heart and kidney transplantation. One of the major challenges in translating big data in clinically useful biomarkers is the integration and interpretation of the different layers of omics datasets. Network Medicine offers advanced bioinformatic-molecular strategies which were widely used to integrate large omics datasets and clinical information in end-stage patients to prioritize potential biomarkers and drug targets. The application of network-oriented approaches to clarify the complex nature of graft rejection is still in its infancy. Here, we briefly discuss the real-life clinical applications derived from omics datasets as well as novel opportunities for establishing predictive tests in solid organ transplantation. Also, we provide an original "graft rejection interactome" and propose network-oriented strategies which can be useful to improve precision medicine of solid organ transplantation.
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Affiliation(s)
- Giuditta Benincasa
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", 80138, Naples, Italy.
| | - Mario Viglietti
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", 80138, Naples, Italy
| | - Enrico Coscioni
- Division of Cardiac Surgery, AOU San Giovanni di Dio e Ruggi d'Aragona, 84131, Salerno, Italy
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", 80138, Naples, Italy; U.O.C. Division of Clinical Immunology, Immunohematology, Transfusion Medicine and Transplant Immunology, Department of Internal Medicine and Specialistics, University of Campania "Luigi Vanvitelli", Naples, Italy
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31
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Kharb S, Joshi A. Multi-omics and machine learning for the prevention and management of female reproductive health. Front Endocrinol (Lausanne) 2023; 14:1081667. [PMID: 36909346 PMCID: PMC9996332 DOI: 10.3389/fendo.2023.1081667] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Females typically carry most of the burden of reproduction in mammals. In humans, this burden is exacerbated further, as the evolutionary advantage of a large and complex human brain came at a great cost of women's reproductive health. Pregnancy thus became a highly demanding phase in a woman's life cycle both physically and emotionally and therefore needs monitoring to assure an optimal outcome. Moreover, an increasing societal trend towards reproductive complications partly due to the increasing maternal age and global obesity pandemic demands closer monitoring of female reproductive health. This review first provides an overview of female reproductive biology and further explores utilization of large-scale data analysis and -omics techniques (genomics, transcriptomics, proteomics, and metabolomics) towards diagnosis, prognosis, and management of female reproductive disorders. In addition, we explore machine learning approaches for predictive models towards prevention and management. Furthermore, mobile apps and wearable devices provide a promise of continuous monitoring of health. These complementary technologies can be combined towards monitoring female (fertility-related) health and detection of any early complications to provide intervention solutions. In summary, technological advances (e.g., omics and wearables) have shown a promise towards diagnosis, prognosis, and management of female reproductive disorders. Systematic integration of these technologies is needed urgently in female reproductive healthcare to be further implemented in the national healthcare systems for societal benefit.
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Affiliation(s)
- Simmi Kharb
- Department of Biochemistry, Postgraduate Institute of Medical Sciences, Rohtak, Haryana, India
- *Correspondence: Simmi Kharb, ; Anagha Joshi,
| | - Anagha Joshi
- Computational Biology Unit (CBU), Department of Clinical Science, University of Bergen, Bergen, Norway
- *Correspondence: Simmi Kharb, ; Anagha Joshi,
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32
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Benincasa G, Maron BA, Affinito O, D’Alto M, Franzese M, Argiento P, Schiano C, Romeo E, Bontempo P, Golino P, Berrino L, Loscalzo J, Napoli C. Association Between Circulating CD4 + T Cell Methylation Signatures of Network-Oriented SOCS3 Gene and Hemodynamics in Patients Suffering Pulmonary Arterial Hypertension. J Cardiovasc Transl Res 2023; 16:17-30. [PMID: 35960497 PMCID: PMC9944731 DOI: 10.1007/s12265-022-10294-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 07/19/2022] [Indexed: 02/06/2023]
Abstract
Pathogenic DNA methylation changes may be involved in pulmonary arterial hypertension (PAH) onset and its progression, but there is no data on potential associations with patient-derived hemodynamic parameters. The reduced representation bisulfite sequencing (RRBS) platform identified N = 631 differentially methylated CpG sites which annotated to N = 408 genes (DMGs) in circulating CD4+ T cells isolated from PAH patients vs. healthy controls (CTRLs). A promoter-restricted network analysis established the PAH subnetwork that included 5 hub DMGs (SOCS3, GNAS, ITGAL, NCOR2, NFIC) and 5 non-hub DMGs (NR4A2, GRM2, PGK1, STMN1, LIMS2). The functional analysis revealed that the SOCS3 gene was the most recurrent among the top ten significant pathways enriching the PAH subnetwork, including the growth hormone receptor and the interleukin-6 signaling. Correlation analysis showed that the promoter methylation levels of each network-oriented DMG were associated individually with hemodynamic parameters. In particular, SOCS3 hypomethylation was negatively associated with right atrial pressure (RAP) and positively associated with cardiac index (CI) (|r|≥ 0.6). A significant upregulation of the SOCS3, ITGAL, NFIC, NCOR2, and PGK1 mRNA levels (qRT-PCR) in peripheral blood mononuclear cells from PAH patients vs. CTRLs was found (P ≤ 0.05). By immunoblotting, a significant upregulation of the SOCS3 protein was confirmed in PAH patients vs. CTRLs (P < 0.01). This is the first network-oriented study which integrates circulating CD4+ T cell DNA methylation signatures, hemodynamic parameters, and validation experiments in PAH patients at first diagnosis or early follow-up. Our data suggests that SOCS3 gene might be involved in PAH pathogenesis and serve as potential prognostic biomarker.
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Affiliation(s)
- Giuditta Benincasa
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", 80138, Naples, Italy.
| | - Bradley A. Maron
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, MB Boston, USA ,Harvard Medical School, Boston, MA USA
| | | | - Michele D’Alto
- Department of Cardiology, Monaldi Hospital, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | | | - Paola Argiento
- Department of Cardiology, Monaldi Hospital, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Concetta Schiano
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy
| | - Emanuele Romeo
- Department of Cardiology, Monaldi Hospital, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Paola Bontempo
- Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Paolo Golino
- Department of Cardiology, Monaldi Hospital, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Liberato Berrino
- Department of Experimental Medicine, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Joseph Loscalzo
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, MB Boston, USA
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy ,IRCCS SDN, Naples, Italy
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Nardini C, Candelise L, Turrini M, Addimanda O. Semi-automated socio-anthropologic analysis of the medical discourse on rheumatoid arthritis: Potential impact on public health. PLoS One 2022; 17:e0279632. [PMID: 36580470 PMCID: PMC9799325 DOI: 10.1371/journal.pone.0279632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 12/12/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND The debilitating effects of noncommunicable diseases (NCDs) and the accompanying chronic inflammation represent a significant obstacle for the sustainability of our development, with efforts spreading worldwide to counteract the diffusion of NCDs, as per the United Nations Sustainable Development Goals (SDG 3). In fact, despite efforts of varied intensity in numerous directions (from innovations in biotechnology to lifestyle modifications), the incidence of NCDs remains pandemic. The present work wants to contribute to addressing this major concern, with a specific focus on the fragmentation of medical approaches, via an interdisciplinary analysis of the medical discourse, i.e. the heterogenous reporting that biomedical scientific literature uses to describe the anti-inflammatory therapeutic landscape in NCDs. The aim is to better capture the roots of this compartmentalization and the power relations existing among three segregated pharmacological, experimental and unstandardized biomedical approaches to ultimately empower collaboration beyond medical specialties and possibly tap into a more ample and effective reservoir of integrated therapeutic opportunities. METHOD Using rheumatoid arthritis (RA) as an exemplar disease, twenty-eight articles were manually translated into a nine-dimensional categorical variable of medical socio-anthropological relevance, relating in particular (but not only) to legitimacy, temporality and spatialization. This digitalized picture (9 x 28 table) of the medical discourse was further analyzed by simple automated learning approaches to identify differences and highlight commonalities among the biomedical categories. RESULTS Interpretation of these results provides original insights, including suggestions to: empower scientific communication between unstandardized approaches and basic biology; promote the repurposing of non-pharmacological therapies to enhance robustness of experimental approaches; and align the spatial representation of diseases and therapies in pharmacology to effectively embrace the systemic approach promoted by modern personalized and preventive medicines. We hope this original work can expand and foster interdisciplinarity among public health stakeholders, ultimately contributing to the achievement of SDG3.
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Affiliation(s)
- Christine Nardini
- Consiglio Nazionale delle Ricerche, Istituto per le Applicazioni del Calcolo "Mauro Picone", Roma, Italy
- * E-mail: (CN); (LC); (MT)
| | - Lucia Candelise
- ISS, Istitut Sciences Sociales, Université de Lausanne, Lausanne, Switzerland
- CEPED, Centre Population et Développement, Université de Paris, Paris, France
- * E-mail: (CN); (LC); (MT)
| | - Mauro Turrini
- Institute of Public Goods and Policies (IPP), Spanish National Research Council (CSIC), Madrid, Spain
- * E-mail: (CN); (LC); (MT)
| | - Olga Addimanda
- UOC Medicina Interna ad Indirizzo Reumatologico, Ospedale Maggiore, AUSL Bologna, Bologna, Italy
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Galindez G, Sadegh S, Baumbach J, Kacprowski T, List M. Network-based approaches for modeling disease regulation and progression. Comput Struct Biotechnol J 2022; 21:780-795. [PMID: 36698974 PMCID: PMC9841310 DOI: 10.1016/j.csbj.2022.12.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/14/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression.
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Affiliation(s)
- Gihanna Galindez
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany.,Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Sepideh Sadegh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.,Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany.,Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany.,Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
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Punzi C, Petti M, Tieri P. Network-based methods for psychometric data of eating disorders: A systematic review. PLoS One 2022; 17:e0276341. [PMID: 36315522 PMCID: PMC9621460 DOI: 10.1371/journal.pone.0276341] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 10/04/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Network science represents a powerful and increasingly promising method for studying complex real-world problems. In the last decade, it has been applied to psychometric data in the attempt to explain psychopathologies as complex systems of causally interconnected symptoms. One category of mental disorders, relevant for their severity, incidence and multifaceted structure, is that of eating disorders (EDs), serious disturbances that negatively affect a person's eating behavior. AIMS We aimed to review the corpus of psychometric network analysis methods by scrutinizing a large sample of network-based studies that exploit psychometric data related to EDs. A particular focus is given to the description of the methodologies for network estimation, network description and network stability analysis providing also a review of the statistical software packages currently used to carry out each phase of the network estimation and analysis workflow. Moreover, we try to highlight aspects with potential clinical impact such as core symptoms, influences of external factors, comorbidities, and related changes in network structure and connectivity across both time and subpopulations. METHODS A systematic search was conducted (February 2022) on three different literature databases to identify 57 relevant research articles. The exclusion criteria comprehended studies not based on psychometric data, studies not using network analysis, studies with different aims or not focused on ED, and review articles. RESULTS Almost all the selected 57 papers employed the same analytical procedures implemented in a collection of R packages specifically designed for psychometric network analysis and are mostly based on cross-sectional data retrieved from structured psychometric questionnaires, with just few exemptions of panel data. Most of them used the same techniques for all phases of their analysis. In particular, a pervasive use of the Gaussian Graphical Model with LASSO regularization was registered for in network estimation step. Among the clinically relevant results, we can include the fact that all papers found strong symptom interconnections between specific and nonspecific ED symptoms, suggesting that both types should therefore be addressed by clinical treatment. CONCLUSIONS We here presented the largest and most comprehensive review to date about psychometric network analysis methods. Although these methods still need solid validation in the clinical setting, they have already been able to show many strengths and important results, as well as great potentials and perspectives, which have been analyzed here to provide suggestions on their use and their possible improvement.
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Affiliation(s)
- Clara Punzi
- Data Science MSc Program, Sapienza University of Rome, Rome, Italy
| | - Manuela Petti
- DIAG Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
- * E-mail:
| | - Paolo Tieri
- Data Science MSc Program, Sapienza University of Rome, Rome, Italy
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
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Gentili M, Martini L, Sponziello M, Becchetti L. Biological Random Walks: multi-omics integration for disease gene prioritization. Bioinformatics 2022; 38:4145-4152. [PMID: 35792834 DOI: 10.1093/bioinformatics/btac446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 06/22/2022] [Accepted: 07/05/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Over the past decade, network-based approaches have proven useful in identifying disease modules within the human interactome, often providing insights into key mechanisms and guiding the quest for therapeutic targets. This is all the more important, since experimental investigation of potential gene candidates is an expensive task, thus not always a feasible option. On the other hand, many sources of biological information exist beyond the interactome and an important research direction is the design of effective techniques for their integration. RESULTS In this work, we introduce the Biological Random Walks (BRW) approach for disease gene prioritization in the human interactome. The proposed framework leverages multiple biological sources within an integrated framework. We perform an extensive, comparative study of BRW's performance against well-established baselines. AVAILABILITY AND IMPLEMENTATION All codes are publicly available and can be downloaded at https://github.com/LeoM93/BiologicalRandomWalks. We used publicly available datasets, details on their retrieval and preprocessing are provided in the Supplementary Material. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Michele Gentili
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Leonardo Martini
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Marialuisa Sponziello
- Translational and Precision Medicine Department, Sapienza University of Rome, Rome, Italy
| | - Luca Becchetti
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
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Yang Y, Zhang M, Wang Y. The roles of histone modifications in tumorigenesis and associated inhibitors in cancer therapy. JOURNAL OF THE NATIONAL CANCER CENTER 2022. [DOI: 10.1016/j.jncc.2022.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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38
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Network Pharmacology of Adaptogens in the Assessment of Their Pleiotropic Therapeutic Activity. Pharmaceuticals (Basel) 2022; 15:ph15091051. [PMID: 36145272 PMCID: PMC9504187 DOI: 10.3390/ph15091051] [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: 06/24/2022] [Revised: 08/11/2022] [Accepted: 08/19/2022] [Indexed: 02/07/2023] Open
Abstract
The reductionist concept, based on the ligand–receptor interaction, is not a suitable model for adaptogens, and herbal preparations affect multiple physiological functions, revealing polyvalent pharmacological activities, and are traditionally used in many conditions. This review, for the first time, provides a rationale for the pleiotropic therapeutic efficacy of adaptogens based on evidence from recent gene expression studies in target cells and where the network pharmacology and systems biology approaches were applied. The specific molecular targets and adaptive stress response signaling mechanisms involved in nonspecific modes of action of adaptogens are identified.
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Tulsyan S, Aftab M, Sisodiya S, Khan A, Chikara A, Tanwar P, Hussain S. Molecular basis of epigenetic regulation in cancer diagnosis and treatment. Front Genet 2022; 13:885635. [PMID: 36092905 PMCID: PMC9449878 DOI: 10.3389/fgene.2022.885635] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 07/19/2022] [Indexed: 02/01/2023] Open
Abstract
The global cancer cases and mortality rates are increasing and demand efficient biomarkers for accurate screening, detection, diagnosis, and prognosis. Recent studies have demonstrated that variations in epigenetic mechanisms like aberrant promoter methylation, altered histone modification and mutations in ATP-dependent chromatin remodelling complexes play an important role in the development of carcinogenic events. However, the influence of other epigenetic alterations in various cancers was confirmed with evolving research and the emergence of high throughput technologies. Therefore, alterations in epigenetic marks may have clinical utility as potential biomarkers for early cancer detection and diagnosis. In this review, an outline of the key epigenetic mechanism(s), and their deregulation in cancer etiology have been discussed to decipher the future prospects in cancer therapeutics including precision medicine. Also, this review attempts to highlight the gaps in epigenetic drug development with emphasis on integrative analysis of epigenetic biomarkers to establish minimally non-invasive biomarkers with clinical applications.
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Affiliation(s)
- Sonam Tulsyan
- Division of Cellular and Molecular Diagnostics (Molecular Biology Group), ICMR- National Institute of Cancer Prevention and Research, Noida, India
| | - Mehreen Aftab
- Division of Cellular and Molecular Diagnostics (Molecular Biology Group), ICMR- National Institute of Cancer Prevention and Research, Noida, India
| | - Sandeep Sisodiya
- Division of Cellular and Molecular Diagnostics (Molecular Biology Group), ICMR- National Institute of Cancer Prevention and Research, Noida, India
- Symbiosis School of Biological Sciences, Symbiosis International (Deemed University), Pune, India
| | - Asiya Khan
- Laboratory Oncology Unit, Dr. B. R. A. Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, India
| | - Atul Chikara
- Division of Cellular and Molecular Diagnostics (Molecular Biology Group), ICMR- National Institute of Cancer Prevention and Research, Noida, India
- Symbiosis School of Biological Sciences, Symbiosis International (Deemed University), Pune, India
| | - Pranay Tanwar
- Laboratory Oncology Unit, Dr. B. R. A. Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, India
- *Correspondence: Showket Hussain, ; Pranay Tanwar,
| | - Showket Hussain
- Division of Cellular and Molecular Diagnostics (Molecular Biology Group), ICMR- National Institute of Cancer Prevention and Research, Noida, India
- *Correspondence: Showket Hussain, ; Pranay Tanwar,
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Ahmed MM, Shafat Z, Tazyeen S, Ali R, Almashjary MN, Al-Raddadi R, Harakeh S, Alam A, Haque S, Ishrat R. Identification of pathogenic genes associated with CKD: An integrated bioinformatics approach. Front Genet 2022; 13:891055. [PMID: 36035163 PMCID: PMC9403320 DOI: 10.3389/fgene.2022.891055] [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: 03/16/2022] [Accepted: 06/28/2022] [Indexed: 11/23/2022] Open
Abstract
Chronic kidney disease (CKD) is defined as a persistent abnormality in the structure and function of kidneys and leads to high morbidity and mortality in individuals across the world. Globally, approximately 8%–16% of the population is affected by CKD. Proper screening, staging, diagnosis, and the appropriate management of CKD by primary care clinicians are essential in preventing the adverse outcomes associated with CKD worldwide. In light of this, the identification of biomarkers for the appropriate management of CKD is urgently required. Growing evidence has suggested the role of mRNAs and microRNAs in CKD, however, the gene expression profile of CKD is presently uncertain. The present study aimed to identify diagnostic biomarkers and therapeutic targets for patients with CKD. The human microarray profile datasets, consisting of normal samples and treated samples were analyzed thoroughly to unveil the differentially expressed genes (DEGs). After selection, the interrelationship among DEGs was carried out to identify the overlapping DEGs, which were visualized using the Cytoscape program. Furthermore, the PPI network was constructed from the String database using the selected DEGs. Then, from the PPI network, significant modules and sub-networks were extracted by applying the different centralities methods (closeness, betweenness, stress, etc.) using MCODE, Cytohubba, and Centiserver. After sub-network analysis we identified six overlapped hub genes (RPS5, RPL37A, RPLP0, CXCL8, HLA-A, and ANXA1). Additionally, the enrichment analysis was undertaken on hub genes to determine their significant functions. Furthermore, these six genes were used to find their associated miRNAs and targeted drugs. Finally, two genes CXCL8 and HLA-A were common for Ribavirin drug (the gene-drug interaction), after docking studies HLA-A was selected for further investigation. To conclude our findings, we can say that the identified hub genes and their related miRNAs can serve as potential diagnostic biomarkers and therapeutic targets for CKD treatment strategies.
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Affiliation(s)
- Mohd Murshad Ahmed
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Zoya Shafat
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Safia Tazyeen
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Rafat Ali
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
- Department of Biosciences, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India
| | - Majed N. Almashjary
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Rajaa Al-Raddadi
- Community Medicine Department, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Steve Harakeh
- King Fahd Medical Research Center, and Yousef Abdullatif Jameel Chair of Prophetic Medicine Application, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Aftab Alam
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Shafiul Haque
- Research and Scientific Studies Unit, College of Nursing and Allied Health Sciences, Jazan University, Jazan, Saudi Arabia
| | - Romana Ishrat
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
- *Correspondence: Romana Ishrat,
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Shahini E, Pasculli G, Mastropietro A, Stolfi P, Tieri P, Vergni D, Cozzolongo R, Pesce F, Giannelli G. Network Proximity-Based Drug Repurposing Strategy for Early and Late Stages of Primary Biliary Cholangitis. Biomedicines 2022; 10:1694. [PMID: 35884999 PMCID: PMC9312896 DOI: 10.3390/biomedicines10071694] [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: 04/23/2022] [Revised: 06/03/2022] [Accepted: 07/11/2022] [Indexed: 11/30/2022] Open
Abstract
Primary biliary cholangitis (PBC) is a chronic, cholestatic, immune-mediated, and progressive liver disorder. Treatment to preventing the disease from advancing into later and irreversible stages is still an unmet clinical need. Accordingly, we set up a drug repurposing framework to find potential therapeutic agents targeting relevant pathways derived from an expanded pool of genes involved in different stages of PBC. Starting with updated human protein-protein interaction data and genes specifically involved in the early and late stages of PBC, a network medicine approach was used to provide a PBC "proximity" or "involvement" gene ranking using network diffusion algorithms and machine learning models. The top genes in the proximity ranking, when combined with the original PBC-related genes, resulted in a final dataset of the genes most involved in PBC disease. Finally, a drug repurposing strategy was implemented by mining and utilizing dedicated drug-gene interaction and druggable genome information knowledge bases (e.g., the DrugBank repository). We identified several potential drug candidates interacting with PBC pathways after performing an over-representation analysis on our initial 1121-seed gene list and the resulting disease-associated (algorithm-obtained) genes. The mechanism and potential therapeutic applications of such drugs were then thoroughly discussed, with a particular emphasis on different stages of PBC disease. We found that interleukin/EGFR/TNF-alpha inhibitors, branched-chain amino acids, geldanamycin, tauroursodeoxycholic acid, genistein, antioestrogens, curcumin, antineovascularisation agents, enzyme/protease inhibitors, and antirheumatic agents are promising drugs targeting distinct stages of PBC. We developed robust and transparent selection mechanisms for prioritizing already approved medicinal products or investigational products for repurposing based on recognized unmet medical needs in PBC, as well as solid preliminary data to achieve this goal.
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Affiliation(s)
- Endrit Shahini
- National Institute of Research IRCCS “Saverio De Bellis”, Castellana Grotte, 70013 Bari, Italy; (R.C.); (G.G.)
| | - Giuseppe Pasculli
- Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG), Sapienza University of Rome, 00185 Rome, Italy; (G.P.); (A.M.)
| | - Andrea Mastropietro
- Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG), Sapienza University of Rome, 00185 Rome, Italy; (G.P.); (A.M.)
| | - Paola Stolfi
- National Research Council (CNR), Institute for Applied Computing (IAC), 00185 Rome, Italy; (P.S.); (P.T.); (D.V.)
| | - Paolo Tieri
- National Research Council (CNR), Institute for Applied Computing (IAC), 00185 Rome, Italy; (P.S.); (P.T.); (D.V.)
| | - Davide Vergni
- National Research Council (CNR), Institute for Applied Computing (IAC), 00185 Rome, Italy; (P.S.); (P.T.); (D.V.)
| | - Raffaele Cozzolongo
- National Institute of Research IRCCS “Saverio De Bellis”, Castellana Grotte, 70013 Bari, Italy; (R.C.); (G.G.)
| | - Francesco Pesce
- Department of Emergency and Organ Transplantation, Nephrology, Dialysis and Transplantation Unit, University of Bari “A. Moro”, 70121 Bari, Italy;
| | - Gianluigi Giannelli
- National Institute of Research IRCCS “Saverio De Bellis”, Castellana Grotte, 70013 Bari, Italy; (R.C.); (G.G.)
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You X, Wu Y, Li Q, Sheng W, Zhou Q, Fu W. Astragalus–Scorpion Drug Pair Inhibits the Development of Prostate Cancer by Regulating GDPD4-2/PI3K/AKT/mTOR Pathway and Autophagy. Front Pharmacol 2022; 13:895696. [PMID: 35847007 PMCID: PMC9277392 DOI: 10.3389/fphar.2022.895696] [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: 03/14/2022] [Accepted: 05/16/2022] [Indexed: 11/13/2022] Open
Abstract
Objective: Prostate cancer (PCa) is an epithelial malignancy of the prostate that currently lacks effective treatment. Traditional Chinese medicine (TCM) can play an anticancer role through regulating the immune system, anti-tumor angiogenesis, regulating tumor cell apoptosis, autophagy dysfunction, and other mechanisms. This study attempted to explore the active ingredients and potential mechanism of action of the Astragalus–Scorpion (A–S) drug pair in PCa, in order to provide new insights into the treatment of PCa. Methods: Network pharmacology was used to analyze the A–S drug pair and PCa targets. Bioinformatics analysis was used to analyze the LncRNAs with significant differences in PCa. The expression of LC3 protein was detected by immunofluorescence. CCK8 was used to detect cell proliferation. The expressions of GDPD4-2, AC144450.1, LINC01513, AC004009.2, AL096869.1, AP005210.1, and BX119924.1 were detected by RT-qPCR. The expression of the PI3K/AKT/mTOR pathway and autophagy-related proteins were detected by western blot. LC-MS/MS was used to identify the active components of Astragalus and Scorpion. Results: A–S drug pair and PCa have a total of 163 targets, which were mainly related to the prostate cancer and PI3K/AKT pathways. A–S drug pair inhibited the formation of PCa, promoted the expression of LC3Ⅱ and Beclin1 proteins, and inhibited the expression of P62 and PI3K–AKT pathway proteins in PCa mice. Astragaloside IV and polypeptide extract from scorpion venom (PESV) were identified as the main active components of the A–S drug pair. GDPD4-2 was involved in the treatment of PCa by Astragaloside IV-PESV. Silencing GDPD4-2 reversed the therapeutic effects of Astragaloside IV-PESV by regulating the PI3K/AKT/mTOR pathway. Conclusion: Astragaloside IV-PESV is the main active components of A–S drug pair treated PCa by regulating the GDPD4-2/PI3K–AKT/mTOR pathway and autophagy.
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Affiliation(s)
- Xujun You
- Graduate School of Hunan University of Chinese Medicine, Changsha, China
- Department of Andrology, Shenzhen Bao’an Traditional Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Yongrong Wu
- College of Integrated Traditional Chinese and Western Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Qixin Li
- Department of Andrology, Shenzhen Bao’an Traditional Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Wen Sheng
- Andrology Laboratory, Hunan University of Chinese Medicine, Changsha, China
| | - Qing Zhou
- Department of Andrology, The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, China
- *Correspondence: Qing Zhou, ; Wei Fu,
| | - Wei Fu
- Department of Andrology, Shenzhen Bao’an Traditional Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen, China
- *Correspondence: Qing Zhou, ; Wei Fu,
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Song Z, Gao P, Zhong X, Li M, Wang M, Song X. Identification of Five Hub Genes Based on Single-Cell RNA Sequencing Data and Network Pharmacology in Patients With Acute Myocardial Infarction. Front Public Health 2022; 10:894129. [PMID: 35757636 PMCID: PMC9219909 DOI: 10.3389/fpubh.2022.894129] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Acute myocardial infarction (AMI) has a high mortality. The single-cell RNA sequencing (scRNA-seq) method was used to analyze disease heterogeneity at the single-cell level. From the Gene Expression Omnibus (GEO) database (GSE180678), AMI scRNA-seq were downloaded and preprocessed by the Seurat package. Gene expression data came from GSE182923. Cell cluster analysis was conducted. Cell types were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were performed on hub genes. Drugs were predicted by protein–protein interaction (PPI) and molecular docking. In total, 7 cell clusters were defined based on the scRNA-seq dataset, and the clusters were labeled as 5 cell types by marker genes. Hematopoietic stem cell types as a differential subgroups were higher in AMI than in healthy tissues. From available databases and PPI analysis, 52 common genets were identified. Based on 52 genes, 5 clusters were obtained using the MCODE algorithm, and genes in these 5 clusters involved in immune and inflammatory pathways were determined. Correlation analysis showed that hematopoietic stem cell types were negatively correlated with ATM, CARM1, and CASP8 but positively correlated with CASP3 and PPARG. This was reversed with immune cells. Molecular docking analysis showed that DB05490 had the lowest docking score with PPARG. We identified 5 hub genes (ATM, CARM1, CASP8, CASP3, and PPARG) involved in AMI progression. Compound DB05490 was a potential inhibitor of PPAG.
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Affiliation(s)
- Ziguang Song
- Department of Cardiovascular Center, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.,Department of Clinical Medicine, Harbin Medical University, Harbin, China
| | - Pingping Gao
- Department of Cardiovascular Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
| | - Xiao Zhong
- Department of Cardiovascular Center, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.,Department of Clinical Medicine, Harbin Medical University, Harbin, China
| | - Mingyang Li
- Department of Cardiovascular Center, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.,Department of Clinical Medicine, Harbin Medical University, Harbin, China
| | - Mengmeng Wang
- Fourth Department of Clinical Medicine, GI Medicine, Cancer Hospital Affiliated to Harbin Medical University, Harbin, China
| | - Xiang Song
- Department of Cardiovascular Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
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44
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Mechanisms of Quercetin against atrial fibrillation explored by network pharmacology combined with molecular docking and experimental validation. Sci Rep 2022; 12:9777. [PMID: 35697725 PMCID: PMC9192746 DOI: 10.1038/s41598-022-13911-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 05/30/2022] [Indexed: 01/19/2023] Open
Abstract
Atrial fibrillation (AF) is a common atrial arrhythmia for which there is no specific therapeutic drug. Quercetin (Que) has been used to treat cardiovascular diseases such as arrhythmias. In this study, we explored the mechanism of action of Que in AF using network pharmacology and molecular docking. The chemical structure of Que was obtained from Pubchem. TCMSP, Swiss Target Prediction, Drugbank, STITCH, Pharmmapper, CTD, GeneCards, DISGENET and TTD were used to obtain drug component targets and AF-related genes, and extract AF and normal tissue by GEO database differentially expressed genes by GEO database. The top targets were IL6, VEGFA, JUN, MMP9 and EGFR, and Que for AF treatment might involve the role of AGE-RAGE signaling pathway in diabetic complications, MAPK signaling pathway and IL-17 signaling pathway. Molecular docking showed that Que binds strongly to key targets and is differentially expressed in AF. In vivo results showed that Que significantly reduced the duration of AF fibrillation and improved atrial remodeling, reduced p-MAPK protein expression, and inhibited the progression of AF. Combining network pharmacology and molecular docking approaches with in vivo studies advance our understanding of the intensive mechanisms of Quercetin, and provide the targeted basis for clinical Atrial fibrillation treatment.
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45
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A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19. Biomed Pharmacother 2022; 153:113350. [PMID: 35777222 PMCID: PMC9236981 DOI: 10.1016/j.biopha.2022.113350] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/22/2022] [Accepted: 06/24/2022] [Indexed: 11/26/2022] Open
Abstract
Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.
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46
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Sonawane AR, Aikawa E, Aikawa M. Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases. Front Cardiovasc Med 2022; 9:873582. [PMID: 35665246 PMCID: PMC9160390 DOI: 10.3389/fcvm.2022.873582] [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: 02/11/2022] [Accepted: 04/19/2022] [Indexed: 01/18/2023] Open
Abstract
Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.
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Affiliation(s)
- Abhijeet Rajendra Sonawane
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Masanori Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
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47
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Network Approaches for Charting the Transcriptomic and Epigenetic Landscape of the Developmental Origins of Health and Disease. Genes (Basel) 2022; 13:genes13050764. [PMID: 35627149 PMCID: PMC9141211 DOI: 10.3390/genes13050764] [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: 03/04/2022] [Revised: 04/04/2022] [Accepted: 04/13/2022] [Indexed: 02/04/2023] Open
Abstract
The early developmental phase is of critical importance for human health and disease later in life. To decipher the molecular mechanisms at play, current biomedical research is increasingly relying on large quantities of diverse omics data. The integration and interpretation of the different datasets pose a critical challenge towards the holistic understanding of the complex biological processes that are involved in early development. In this review, we outline the major transcriptomic and epigenetic processes and the respective datasets that are most relevant for studying the periconceptional period. We cover both basic data processing and analysis steps, as well as more advanced data integration methods. A particular focus is given to network-based methods. Finally, we review the medical applications of such integrative analyses.
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48
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Mitra S, Munni YA, Dash R, Sultana A, Moon IS. Unveiling the effect of Withania somnifera on neuronal cytoarchitecture and synaptogenesis: A combined in vitro and network pharmacology approach. Phytother Res 2022; 36:2524-2541. [PMID: 35443091 DOI: 10.1002/ptr.7466] [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: 10/02/2021] [Revised: 02/17/2022] [Accepted: 03/29/2022] [Indexed: 11/10/2022]
Abstract
Withania somnifera (WS), is known for its remarkable contribution in herbal medicine and Ayurveda, which is therapeutically applied to improve memory and anxiety in patients. However, the pharmacological details of this plant on memory boosting yet remained undefined. This study provides mechanistic insights on the effect of ethanol solution extract of the whole plant of WS (WSEE) on neuritogenesis by combining in vitro and in silico network pharmacology approaches. WSEE promoted significant neuronal growth through early differentiation, axodendritic arborization, and synaptogenesis on primary hippocampal neurons. The network pharmacological study confirmed that the neuritogenic activity is potentially mediated by modulating the neurotrophin signaling pathway, where NRTK1 (TrkA) was revealed as the primary target of WS secondary metabolites. This neurotrophic activity of WSEE was significantly stifled by the presence of TrkA inhibitor, which further confirms the TrkA-dependent activity of WSEE. In addition, a molecular docking study suggested steroidal lactones present in the WS might act as nerve growth factor (NGF)-mimetics, activating TrkA by binding to the NGF-binding domain. As a whole, the findings of the study suggest a significant role of WSEE on neuritogenesis and its potential to function as a therapeutic agent and in drug designing for the prevention and treatment of memory-related neurological disorders.
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Affiliation(s)
- Sarmistha Mitra
- Department of Anatomy, Dongguk University College of Medicine, Gyeongju, Republic of Korea
| | - Yeasmin Akter Munni
- Department of Anatomy, Dongguk University College of Medicine, Gyeongju, Republic of Korea
| | - Raju Dash
- Department of Anatomy, Dongguk University College of Medicine, Gyeongju, Republic of Korea
| | - Armin Sultana
- Department of Pharmacy, BGC Trust University Bangladesh, Chittagong, Bangladesh
| | - Il Soo Moon
- Department of Anatomy, Dongguk University College of Medicine, Gyeongju, Republic of Korea
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49
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A Comparison of Network-Based Methods for Drug Repurposing along with an Application to Human Complex Diseases. Int J Mol Sci 2022; 23:ijms23073703. [PMID: 35409062 PMCID: PMC8999012 DOI: 10.3390/ijms23073703] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/19/2022] [Accepted: 03/25/2022] [Indexed: 12/10/2022] Open
Abstract
Drug repurposing strategy, proposing a therapeutic switching of already approved drugs with known medical indications to new therapeutic purposes, has been considered as an efficient approach to unveil novel drug candidates with new pharmacological activities, significantly reducing the cost and shortening the time of de novo drug discovery. Meaningful computational approaches for drug repurposing exploit the principles of the emerging field of Network Medicine, according to which human diseases can be interpreted as local perturbations of the human interactome network, where the molecular determinants of each disease (disease genes) are not randomly scattered, but co-localized in highly interconnected subnetworks (disease modules), whose perturbation is linked to the pathophenotype manifestation. By interpreting drug effects as local perturbations of the interactome, for a drug to be on-target effective against a specific disease or to cause off-target adverse effects, its targets should be in the nearby of disease-associated genes. Here, we used the network-based proximity measure to compute the distance between the drug module and the disease module in the human interactome by exploiting five different metrics (minimum, maximum, mean, median, mode), with the aim to compare different frameworks for highlighting putative repurposable drugs to treat complex human diseases, including malignant breast and prostate neoplasms, schizophrenia, and liver cirrhosis. Whilst the standard metric (that is the minimum) for the network-based proximity remained a valid tool for efficiently screening off-label drugs, we observed that the other implemented metrics specifically predicted further interesting drug candidates worthy of investigation for yielding a potentially significant clinical benefit.
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50
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Liu R, Gao Z, Li Q, Fu Q, Han D, Wang J, Li J, Guo Y, Shi Y. Integrated Analysis of ceRNA Network to Reveal Potential Prognostic Biomarkers for Glioblastoma. Front Genet 2022; 12:803257. [PMID: 35237295 PMCID: PMC8882732 DOI: 10.3389/fgene.2021.803257] [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: 10/27/2021] [Accepted: 12/17/2021] [Indexed: 12/27/2022] Open
Abstract
Glioblastoma (GBM), originating in the brain, is a universally aggressive malignant tumor with a particularly poor prognosis. Therefore, insight into the critical role of underlying genetic mechanisms is essential to developing new therapeutic approaches. This study aims to identify potential markers with clinical and prognostic significance in GBM. To this end, increasing numbers of differentially expressed RNA have been identified used to construct competitive endogenous RNA networks for prognostic analysis via comparison and analysis of RNA expression levels of tumor and normal tissues in glioblastoma. This analysis demonstrated that the RNA expression patterns of normal and tumor samples were significantly different. Thus, the resulting differentially expressed RNAs were used to construct competitive endogenous RNA (competing endogenous RNA, ceRNA) networks. The functional enrichment indicated mRNAs in the network are critically involved in a variety of biological functions. Additionally, the prognostic analysis suggested 27 lncRNAs, including LOXL1-AS1, AL356414.1, etc., were significantly associated with patient survival. Given the prognostic significance of these 27 lncRNAs in GBM, we sought to classify the samples. Importantly, Kaplan-Meier analysis revealed that survival times varied significantly among the different categories. Overall, these results identify that the candidate lncRNAs are potential prognostic markers of GBM and its corresponding mRNAs may be a potential target for therapy.
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Affiliation(s)
- Ruifei Liu
- Second Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Zhengzheng Gao
- College of Basic Medicine, Inner Mongolia Medical University, Hohhot, China
| | - Qiwei Li
- Heilongjiang University of Chinese Medicine, Harbin, China
| | - Qiang Fu
- Heilongjiang University of Chinese Medicine, Harbin, China
| | - Dongwei Han
- Heilongjiang University of Chinese Medicine, Harbin, China
| | | | - Ji Li
- Jiaxing University, Jiaxing, China
- *Correspondence: Yuchen Shi, ; Ying Guo, ; Ji Li,
| | - Ying Guo
- First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
- *Correspondence: Yuchen Shi, ; Ying Guo, ; Ji Li,
| | - Yuchen Shi
- Heilongjiang University of Chinese Medicine, Harbin, China
- *Correspondence: Yuchen Shi, ; Ying Guo, ; Ji Li,
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