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Analyzing integrated network of methylation and gene expression profiles in lung squamous cell carcinoma. Sci Rep 2022; 12:15799. [PMID: 36138066 PMCID: PMC9500023 DOI: 10.1038/s41598-022-20232-5] [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: 02/17/2022] [Accepted: 09/09/2022] [Indexed: 11/24/2022] Open
Abstract
Gene expression, DNA methylation, and their organizational relationships are commonly altered in lung squamous cell carcinoma (LUSC). To elucidate these complex interactions, we reconstructed a differentially expressed gene network and a differentially methylated cytosine (DMC) network by partial information decomposition and an inverse correlation algorithm, respectively. Then, we performed graph union to integrate the networks. Community detection and enrichment analysis of the integrated network revealed close interactions between the cell cycle, keratinization, immune system, and xenobiotic metabolism gene sets in LUSC. DMC analysis showed that hypomethylation targeted the gene sets responsible for cell cycle, keratinization, and NRF2 pathways. On the other hand, hypermethylated genes affected circulatory system development, the immune system, extracellular matrix organization, and cilium organization. By centrality measurement, we identified NCAPG2, PSMG3, and FADD as hub genes that were highly connected to other nodes and might play important roles in LUSC gene dysregulation. We also found that the genes with high betweenness centrality are more likely to affect patients’ survival than those with low betweenness centrality. These results showed that the integrated network analysis enabled us to obtain a global view of the interactions and regulations in LUSC.
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Conserved Control Path in Multilayer Networks. ENTROPY 2022; 24:e24070979. [PMID: 35885201 PMCID: PMC9324794 DOI: 10.3390/e24070979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/09/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023]
Abstract
The determination of directed control paths in complex networks is important because control paths indicate the structure of the propagation of control signals through edges. A challenging problem is to identify them in complex networked systems characterized by different types of interactions that form multilayer networks. In this study, we describe a graph pattern called the conserved control path, which allows us to model a common control structure among different types of relations. We present a practical conserved control path detection method (CoPath), which is based on a maximum-weighted matching, to determine the paths that play the most consistent roles in controlling signal transmission in multilayer networks. As a pragmatic application, we demonstrate that the control paths detected in a multilayered pan-cancer network are statistically more consistent. Additionally, they lead to the effective identification of drug targets, thereby demonstrating their power in predicting key pathways that influence multiple cancers.
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Sarno F, Benincasa G, List M, Barabasi AL, Baumbach J, Ciardiello F, Filetti S, Glass K, Loscalzo J, Marchese C, Maron BA, Paci P, Parini P, Petrillo E, Silverman EK, Verrienti A, Altucci L, Napoli C. Clinical epigenetics settings for cancer and cardiovascular diseases: real-life applications of network medicine at the bedside. Clin Epigenetics 2021; 13:66. [PMID: 33785068 PMCID: PMC8010949 DOI: 10.1186/s13148-021-01047-z] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 03/01/2021] [Indexed: 02/07/2023] Open
Abstract
Despite impressive efforts invested in epigenetic research in the last 50 years, clinical applications are still lacking. Only a few university hospital centers currently use epigenetic biomarkers at the bedside. Moreover, the overall concept of precision medicine is not widely recognized in routine medical practice and the reductionist approach remains predominant in treating patients affected by major diseases such as cancer and cardiovascular diseases. By its' very nature, epigenetics is integrative of genetic networks. The study of epigenetic biomarkers has led to the identification of numerous drugs with an increasingly significant role in clinical therapy especially of cancer patients. Here, we provide an overview of clinical epigenetics within the context of network analysis. We illustrate achievements to date and discuss how we can move from traditional medicine into the era of network medicine (NM), where pathway-informed molecular diagnostics will allow treatment selection following the paradigm of precision medicine.
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Affiliation(s)
- Federica Sarno
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | - Giuditta Benincasa
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Albert-Lazlo Barabasi
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Network and Data Science, Central European University, Budapest, Hungary
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
- Chair of Computational Systems Biology, University of Hamburg, Notkestrasse 9, Hamburg, Germany
| | - Fortunato Ciardiello
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | | | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Cinzia Marchese
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Bradley A Maron
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Paola Paci
- Department of Computer, Control, and Management Engineering, Sapienza University, Rome, Italy
| | - Paolo Parini
- Department of Laboratory Medicine and Department of Medicine, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
| | - Enrico Petrillo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Antonella Verrienti
- Department of Translational and Precision Medicine, Sapienza University, Rome, Italy
| | - Lucia Altucci
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Napoli, Italy.
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy
- Clinical Department of Internal Medicine and Specialistic Units, AOU, University of Campania "Luigi Vanvitelli", Naples, Italy
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Target Identification Using Homopharma and Network-Based Methods for Predicting Compounds Against Dengue Virus-Infected Cells. Molecules 2020; 25:molecules25081883. [PMID: 32325755 PMCID: PMC7221756 DOI: 10.3390/molecules25081883] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 04/10/2020] [Accepted: 04/14/2020] [Indexed: 12/28/2022] Open
Abstract
Drug target prediction is an important method for drug discovery and design, can disclose the potential inhibitory effect of active compounds, and is particularly relevant to many diseases that have the potential to kill, such as dengue, but lack any healing agent. An antiviral drug is urgently required for dengue treatment. Some potential antiviral agents are still in the process of drug discovery, but the development of more effective active molecules is in critical demand. Herein, we aimed to provide an efficient technique for target prediction using homopharma and network-based methods, which is reliable and expeditious to hunt for the possible human targets of three phenolic lipids (anarcardic acid, cardol, and cardanol) related to dengue viral (DENV) infection as a case study. Using several databases, the similarity search and network-based analyses were applied on the three phenolic lipids resulting in the identification of seven possible targets as follows. Based on protein annotation, three phenolic lipids may interrupt or disturb the human proteins, namely KAT5, GAPDH, ACTB, and HSP90AA1, whose biological functions have been previously reported to be involved with viruses in the family Flaviviridae. In addition, these phenolic lipids might inhibit the mechanism of the viral proteins: NS3, NS5, and E proteins. The DENV and human proteins obtained from this study could be potential targets for further molecular optimization on compounds with a phenolic lipid core structure in anti-dengue drug discovery. As such, this pipeline could be a valuable tool to identify possible targets of active compounds.
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Benoodt L, Thakar J. Network Analysis of Large-Scale Data and Its Application to Immunology. Methods Mol Biol 2020; 2131:199-211. [PMID: 32162255 DOI: 10.1007/978-1-0716-0389-5_9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Diseases and infections elicit a multilayered immune response which consists of molecular and cellular interaction cascades. Recent advances in high-throughput technologies have facilitated multiparameter investigation of immune cells involved in human immune responses. These multiparameter investigations generate large-scale datasets and advanced computational techniques are required to gain useful information from them. Networks or graphs offer a practical way to represent complex information and develop advanced algorithms to unveil the underlying mechanisms. Here we discuss ways to assemble and analyze networks using genome-wide transcriptional profiles. Additionally, we discuss ways to integrate information available in primary literature and databases with the networks assembled using large-scale datasets. Finally, we describe ways in which network analysis offers insights into human immune responses.
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Affiliation(s)
- Lauren Benoodt
- Department of Biochemistry and Biophysics, University of Rochester Medical Center, Rochester, NY, USA
| | - Juilee Thakar
- Department of Microbiology and Immunology, University of Rochester Medical Center, Rochester, NY, USA. .,Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA.
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