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Scholer AJ, Marcus RK, Garland-Kledzik M, Ghosh D, Ensenyat-Mendez M, Germany J, Santamaria-Barria JA, Khader A, Orozco JIJ, Goldfarb M. Exploring the Genomic Landscape of Hepatobiliary Cancers to Establish a Novel Molecular Classification System. Cancers (Basel) 2024; 16:325. [PMID: 38254814 PMCID: PMC10814719 DOI: 10.3390/cancers16020325] [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: 11/17/2023] [Revised: 12/15/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024] Open
Abstract
Taxonomy of hepatobiliary cancer (HBC) categorizes tumors by location or histopathology (tissue of origin, TO). Tumors originating from different TOs can also be grouped by overlapping genomic alterations (GA) into molecular subtypes (MS). The aim of this study was to create novel HBC MSs. Next-generation sequencing (NGS) data from the AACR-GENIE database were used to examine the genomic landscape of HBCs. Machine learning and gene enrichment analysis identified MSs and their oncogenomic pathways. Descriptive statistics were used to compare subtypes and their associations with clinical and molecular variables. Integrative analyses generated three MSs with different oncogenomic pathways independent of TO (n = 324; p < 0.05). HC-1 "hyper-mutated-proliferative state" MS had rapidly dividing cells susceptible to chemotherapy; HC-2 "adaptive stem cell-cellular senescence" MS had epigenomic alterations to evade immune system and treatment-resistant mechanisms; HC-3 "metabolic-stress pathway" MS had metabolic alterations. The discovery of HBC MSs is the initial step in cancer taxonomy evolution and the incorporation of genomic profiling into the TNM system. The goal is the development of a precision oncology machine learning algorithm to guide treatment planning and improve HBC outcomes. Future studies should validate findings of this study, incorporate clinical outcomes, and compare the MS classification to the AJCC 8th staging system.
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Affiliation(s)
- Anthony J. Scholer
- Division of Surgical Oncology, University of South Carolina School of Medicine, Greenville, SC 29605, USA;
| | - Rebecca K. Marcus
- Department of Surgery, Saint John’s Cancer Institute at Providence St. John’s Health Center, Santa Monica, CA 90404, USA; (R.K.M.); (J.I.J.O.); (M.G.)
| | - Mary Garland-Kledzik
- Department of Surgery, Division of Surgical Oncology, West Virginia University, Morgantown, WV 26506, USA;
| | - Debopriya Ghosh
- Janssen Research and Development LLC, Early Development and Oncology, Biostatistics, Raritan, NJ 08869, USA;
| | - Miquel Ensenyat-Mendez
- Cancer Epigenetics Laboratory, Health Research Institute of the Balearic Islands, 07120 Palma, Spain;
| | - Joshua Germany
- Division of Surgical Oncology, University of South Carolina School of Medicine, Greenville, SC 29605, USA;
| | - Juan A. Santamaria-Barria
- Department of Surgery, Division of Surgical Oncology, University of Nebraska Medical Center, Omaha, NE 68105, USA;
| | - Adam Khader
- Department of Surgery, Division of Surgical Oncology, Hunter Holmes McGuire Veterans Affairs Medical Center, Richmond, VA 23249, USA;
| | - Javier I. J. Orozco
- Department of Surgery, Saint John’s Cancer Institute at Providence St. John’s Health Center, Santa Monica, CA 90404, USA; (R.K.M.); (J.I.J.O.); (M.G.)
| | - Melanie Goldfarb
- Department of Surgery, Saint John’s Cancer Institute at Providence St. John’s Health Center, Santa Monica, CA 90404, USA; (R.K.M.); (J.I.J.O.); (M.G.)
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Gendoo DMA. Overview of Bioinformatics Software and Databases for Metabolic Engineering. Methods Mol Biol 2023; 2553:265-274. [PMID: 36227548 DOI: 10.1007/978-1-0716-2617-7_13] [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] [Indexed: 06/16/2023]
Abstract
The explosion of the "omics" era has introduced a growing number of sets and tools that facilitate molecular interrogation of the metabolome. These include various bioinformatics and pharmacogenomics resources that can be utilized independently or collectively to facilitate metabolic engineering across disease, clinical oncology, and understanding of molecular changes across larger systems. This review provides starting points for accessing publicly available data and computational tools that support assessment of metabolic profiles and metabolic regulation, providing both a depth-and-breadth approach toward understanding the metabolome. We focus in particular on pathway databases and tools, which provide in-depth analysis of metabolic pathways, which is at the heart of metabolic engineering.
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Affiliation(s)
- Deena M A Gendoo
- Centre for Computational Biology, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.
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Shen D, Hong Y, Feng Z, Chen X, Cai Y, Peng Q, Tu J. Development of dynamical network biomarkers for regulation in Epstein-Barr virus positive peripheral T cell lymphoma unspecified type. Front Genet 2022; 13:966247. [PMID: 36544484 PMCID: PMC9760704 DOI: 10.3389/fgene.2022.966247] [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: 06/10/2022] [Accepted: 11/24/2022] [Indexed: 12/12/2022] Open
Abstract
Background: This study was performed to identify key regulatory network biomarkers including transcription factors (TFs), miRNAs and lncRNAs that may affect the oncogenesis of EBV positive PTCL-U. Methods: GSE34143 dataset was downloaded and analyzed to identify differentially expressed genes (DEGs) between EBV positive PTCL-U and normal samples. Gene ontology and pathway enrichment analyses were performed to illustrate the potential function of the DEGs. Then, key regulators including TFs, miRNAs and lncRNAs involved in EBV positive PTCL-U were identified by constructing TF-mRNA, lncRNA-miRNA-mRNA, and EBV encoded miRNA-mRNA regulatory networks. Results: A total of 96 DEGs were identified between EBV positive PTCL-U and normal tissues, which were related to immune responses, B cell receptor signaling pathway, chemokine activity. Pathway analysis indicated that the DEGs were mainly enriched in cytokine-cytokine receptor interaction and chemokine signaling pathway. Based on the TF network, hub TFs were identified regulate the target DEGs. Afterwards, a ceRNA network was constructed, in which miR-181(a/b/c/d) and lncRNA LINC01744 were found. According to the EBV-related miRNA regulatory network, CXCL10 and CXCL11 were found to be regulated by EBV-miR-BART1-3p and EBV-miR-BHRF1-3, respectively. By integrating the three networks, some key regulators were found and may serve as potential network biomarkers in the regulation of EBV positive PTCL-U. Conclusion: The network-based approach of the present study identified potential biomarkers including transcription factors, miRNAs, lncRNAs and EBV-related miRNAs involved in EBV positive PTCL-U, assisting us in understanding the molecular mechanisms that underlie the carcinogenesis and progression of EBV positive PTCL-U.
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Affiliation(s)
- Dan Shen
- Department of Oncology, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yin Hong
- Department of Cardiothoracic Surgery, Suzhou BenQ Hospital, Suzhou, China
| | - Zhengyang Feng
- Department of Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiangying Chen
- Department of Radiotherapy and Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yuxing Cai
- Department of Radiotherapy and Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Qiliang Peng
- Department of Radiotherapy and Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China,*Correspondence: Jian Tu, ; Qiliang Peng,
| | - Jian Tu
- Department of Pathology, The Second Affiliated Hospital of Soochow University, Suzhou, China,*Correspondence: Jian Tu, ; Qiliang Peng,
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Gupta R, Kala N, Pai A, Malviya R. Bioinformatics Approach for Data Capturing: The Case of Breast Cancer. CURRENT CANCER THERAPY REVIEWS 2021. [DOI: 10.2174/1573394717666210203112941] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Background:
With the rapid evolution in advanced computer systems and various statistical
algorithms, it is now a days possible to analyze complex biological data. Bioinformatics is an
interface between computational and biological assemblies. It is applied in various fields of biological
as well as medical sciences.
Aim:
The manuscript aims to summarize the developments in the field of breast cancer research
through the applications of bioinformatics.
Methods:
Various search engines like google, science direct, Scopus, PubMed, etc., were used for
the literature survey.
Results:
It describes the bioinformatics analysis tools and models, which include mainly artificial
neural network models.
Conclusion:
Bioinformatics is the evolutionary approach that is used for the capturing of data from
the various case studies related to breast cancer.
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Affiliation(s)
- Ramji Gupta
- Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, Greater Noida, U.P.,India
| | - Nidhi Kala
- Saraswathi College of Pharmacy, Pilkhuwa, Hapur, U.P.,India
| | - Aravinda Pai
- Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka,India
| | - Rishabha Malviya
- Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, Greater Noida, U.P.,India
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Miloradovic D, Pavlovic D, Jankovic MG, Nikolic S, Papic M, Milivojevic N, Stojkovic M, Ljujic B. Human Embryos, Induced Pluripotent Stem Cells, and Organoids: Models to Assess the Effects of Environmental Plastic Pollution. Front Cell Dev Biol 2021; 9:709183. [PMID: 34540831 PMCID: PMC8446652 DOI: 10.3389/fcell.2021.709183] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 07/19/2021] [Indexed: 02/03/2023] Open
Abstract
For a long time, animal models were used to mimic human biology and diseases. However, animal models are not an ideal solution due to numerous interspecies differences between humans and animals. New technologies, such as human-induced pluripotent stem cells and three-dimensional (3D) cultures such as organoids, represent promising solutions for replacing, refining, and reducing animal models. The capacity of organoids to differentiate, self-organize, and form specific, complex, biologically suitable structures makes them excellent in vitro models of development and disease pathogenesis, as well as drug-screening platforms. Despite significant potential health advantages, further studies and considerable nuances are necessary before their clinical use. This article summarizes the definition of embryoids, gastruloids, and organoids and clarifies their appliance as models for early development, diseases, environmental pollution, drug screening, and bioinformatics.
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Affiliation(s)
- Dragana Miloradovic
- Department of Genetics, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
| | - Dragica Pavlovic
- Department of Genetics, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
| | - Marina Gazdic Jankovic
- Department of Genetics, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
| | - Sandra Nikolic
- Department of Genetics, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
| | - Milos Papic
- Department of Dentistry, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
| | - Nevena Milivojevic
- Laboratory for Bioengineering, Department of Science, Institute for Information Technologies, University of Kragujevac, Kragujevac, Serbia
| | - Miodrag Stojkovic
- Department of Genetics, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
- SPEBO Medical Fertility Hospital, Leskovac, Serbia
| | - Biljana Ljujic
- Department of Genetics, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
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Li PJ, Roose JP, Jablons DM, Kratz JR. Bioinformatic Approaches to Validation and Functional Analysis of 3D Lung Cancer Models. Cancers (Basel) 2021; 13:cancers13040701. [PMID: 33572297 PMCID: PMC7915264 DOI: 10.3390/cancers13040701] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 02/01/2021] [Accepted: 02/01/2021] [Indexed: 12/12/2022] Open
Abstract
3D models of cancer have the potential to improve basic, translational, and clinical studies. Patient-derived xenografts, spheroids, and organoids are broad categories of 3D models of cancer, and to date, these 3D models of cancer have been established for a variety of cancer types. In lung cancer, for example, 3D models offer a promising new avenue to gain novel insights into lung tumor biology and improve outcomes for patients afflicted with the number one cancer killer worldwide. However, the adoption and utility of these 3D models of cancer vary, and demonstrating the fidelity of these models is a critical first step before seeking meaningful applications. Here, we review use cases of current 3D lung cancer models and bioinformatic approaches to assessing model fidelity. Bioinformatics approaches play a key role in both validating 3D lung cancer models and high dimensional functional analyses to support downstream applications.
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Affiliation(s)
- P. Jonathan Li
- Department of Surgery, University of California, San Francisco, CA 94143, USA; (P.J.L.); (D.M.J.)
| | - Jeroen P. Roose
- Department of Anatomy, University of California, San Francisco, CA 94143, USA;
| | - David M. Jablons
- Department of Surgery, University of California, San Francisco, CA 94143, USA; (P.J.L.); (D.M.J.)
| | - Johannes R. Kratz
- Department of Surgery, University of California, San Francisco, CA 94143, USA; (P.J.L.); (D.M.J.)
- Correspondence:
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