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He M, Zhou X, Wang X. Glycosylation: mechanisms, biological functions and clinical implications. Signal Transduct Target Ther 2024; 9:194. [PMID: 39098853 PMCID: PMC11298558 DOI: 10.1038/s41392-024-01886-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 05/25/2024] [Accepted: 06/07/2024] [Indexed: 08/06/2024] Open
Abstract
Protein post-translational modification (PTM) is a covalent process that occurs in proteins during or after translation through the addition or removal of one or more functional groups, and has a profound effect on protein function. Glycosylation is one of the most common PTMs, in which polysaccharides are transferred to specific amino acid residues in proteins by glycosyltransferases. A growing body of evidence suggests that glycosylation is essential for the unfolding of various functional activities in organisms, such as playing a key role in the regulation of protein function, cell adhesion and immune escape. Aberrant glycosylation is also closely associated with the development of various diseases. Abnormal glycosylation patterns are closely linked to the emergence of various health conditions, including cancer, inflammation, autoimmune disorders, and several other diseases. However, the underlying composition and structure of the glycosylated residues have not been determined. It is imperative to fully understand the internal structure and differential expression of glycosylation, and to incorporate advanced detection technologies to keep the knowledge advancing. Investigations on the clinical applications of glycosylation focused on sensitive and promising biomarkers, development of more effective small molecule targeted drugs and emerging vaccines. These studies provide a new area for novel therapeutic strategies based on glycosylation.
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Affiliation(s)
- Mengyuan He
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, 250021, China
| | - Xiangxiang Zhou
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China.
- National Clinical Research Center for Hematologic Diseases, the First Affiliated Hospital of Soochow University, Suzhou, 251006, China.
| | - Xin Wang
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, 250021, China.
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China.
- National Clinical Research Center for Hematologic Diseases, the First Affiliated Hospital of Soochow University, Suzhou, 251006, China.
- Taishan Scholars Program of Shandong Province, Jinan, Shandong, 250021, China.
- Branch of National Clinical Research Center for Hematologic Diseases, Jinan, Shandong, 250021, China.
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2
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Sinha A, Ghosh S, Ghosh A, Ghosh A, Mathai S, Bhaumik J, Mukhopadhyay A, Maitra A, Biswas NK, Sengupta S. Unfurling the functional association between long intergenic noncoding RNAs (lincRNAs) and HPV16-related cervical cancer pathogenesis through weighted gene co-expression network analysis of differentially expressed lincRNAs and coding genes. Carcinogenesis 2024; 45:451-462. [PMID: 38446431 DOI: 10.1093/carcin/bgae019] [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: 10/12/2023] [Revised: 02/19/2024] [Accepted: 03/04/2024] [Indexed: 03/07/2024] Open
Abstract
Long intergenic noncoding RNAs (lincRNAs) do not overlap annotated coding genes and are located in intergenic regions, as opposed to antisense and sense-intronic lncRNAs, located in genic regions. LincRNAs influence gene expression profiles and are thereby key to disease pathogenesis. In this study, we assessed the association between lincRNAs and HPV16-positive cervical cancer (CaCx) pathogenesis using weighted gene co-expression network analysis (WGCNA) with coding genes, comparing differentially expressed lincRNA and coding genes (DElincGs and DEcGs, respectively) in HPV16-positive patients with CaCx (n = 44) with those in HPV-negative healthy individuals (n = 34). Our analysis revealed five DElincG modules, co-expressing and correlating with DEcGs. We validated a substantial number of such module-specific correlations in the HPV16-positive cancer TCGA-CESC dataset. Four such modules, displayed significant correlations with patient traits, such as HPV16 physical status, lymph node involvement and overall survival (OS), highlighting a collaborative effect of all genes within specific modules on traits. Using the DAVID bioinformatics knowledgebase, we identified the underlying biological processes associated with these modules as cancer development and progression-associated pathways. Next, we identified the top 10 DElincGs with the highest connectivity within each functional module. Focusing on the prognostic module hub genes, downregulated CTD-2619J13.13 expression was associated with poor patient OS. This lincRNA gene interacted with 25 coding genes of its module and was associated with such biological processes as keratinization loss and keratinocyte differentiation, reflecting severe disease phenotypes. This study has translational relevance in fighting various cancers with high mortality rates in underdeveloped countries.
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Affiliation(s)
- Abarna Sinha
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Sahana Ghosh
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Abhisikta Ghosh
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Arnab Ghosh
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Sonia Mathai
- Tata Medical Center, Kolkata, West Bengal, India
| | | | - Asima Mukhopadhyay
- Kolkata Gynecological Oncology Trials and Translational Research Group, Kolkata, West Bengal, India
| | - Arindam Maitra
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Nidhan K Biswas
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Sharmila Sengupta
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
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3
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Ghafouri F, Dehghanian Reyhan V, Sadeghi M, Miraei-Ashtiani SR, Kastelic JP, Barkema HW, Shirali M. Integrated Analysis of Transcriptome Profiles and lncRNA-miRNA-mRNA Competing Endogenous RNA Regulatory Network to Identify Biological Functional Effects of Genes and Pathways Associated with Johne's Disease in Dairy Cattle. Noncoding RNA 2024; 10:38. [PMID: 39051372 PMCID: PMC11270299 DOI: 10.3390/ncrna10040038] [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: 04/12/2024] [Revised: 06/25/2024] [Accepted: 06/27/2024] [Indexed: 07/27/2024] Open
Abstract
Paratuberculosis or Johne's disease (JD), a chronic granulomatous gastroenteritis caused by Mycobacterium avium subsp. paratuberculosis (MAP), causes huge economic losses and reduces animal welfare in dairy cattle herds worldwide. At present, molecular mechanisms and biological functions involved in immune responses to MAP infection of dairy cattle are not clearly understood. Our purpose was to integrate transcriptomic profiles and competing endogenous RNA (ceRNA) network analyses to identify key messenger RNAs (mRNAs) and regulatory RNAs involved in molecular regulation of peripheral blood mononuclear cells (PBMCs) for MAP infection in dairy cattle. In total, 28 lncRNAs, 42 miRNAs, and 370 mRNAs were identified by integrating gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. In this regard, we identified 21 hub genes (CCL20, CCL5, CD40, CSF2, CXCL8, EIF2AK2, FOS, IL10, IL17A, IL1A, IL1B, IRF1, MX2, NFKB1, NFKBIA, PTGS2, SOCS3, TLR4, TNF, TNFAIP3, and VCAM1) involved in MAP infection. Furthermore, eight candidate subnets with eight lncRNAs, 29 miRNAs, and 237 mRNAs were detected through clustering analyses, whereas GO enrichment analysis of identified RNAs revealed 510, 22, and 11 significantly enriched GO terms related to MAP infection in biological process, molecular function, and cellular component categories, respectively. The main metabolic-signaling pathways related to MAP infection that were enriched included the immune system process, defense response, response to cytokine, leukocyte migration, regulation of T cell activation, defense response to bacterium, NOD-like receptor, B cell receptor, TNF, NF-kappa B, IL-17, and T cell receptor signaling pathways. Contributions of transcriptome profiles from MAP-positive and MAP-negative sample groups plus a ceRNA regulatory network underlying phenotypic differences in the intensity of pathogenicity of JD provided novel insights into molecular mechanisms associated with immune system responses to MAP infection in dairy cattle.
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Affiliation(s)
- Farzad Ghafouri
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj 77871-31587, Iran; (F.G.); (V.D.R.); (S.R.M.-A.)
| | - Vahid Dehghanian Reyhan
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj 77871-31587, Iran; (F.G.); (V.D.R.); (S.R.M.-A.)
| | - Mostafa Sadeghi
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj 77871-31587, Iran; (F.G.); (V.D.R.); (S.R.M.-A.)
| | - Seyed Reza Miraei-Ashtiani
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj 77871-31587, Iran; (F.G.); (V.D.R.); (S.R.M.-A.)
| | - John P. Kastelic
- Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; (J.P.K.); (H.W.B.)
| | - Herman W. Barkema
- Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; (J.P.K.); (H.W.B.)
| | - Masoud Shirali
- School of Biological Sciences, Queen’s University Belfast, Belfast BT9 5AJ, UK
- Agri-Food and Biosciences Institute, Hillsborough BT26 6DR, UK
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Dehghanian Reyhan V, Ghafouri F, Sadeghi M, Miraei-Ashtiani SR, Kastelic JP, Barkema HW, Shirali M. Integrated Comparative Transcriptome and circRNA-lncRNA-miRNA-mRNA ceRNA Regulatory Network Analyses Identify Molecular Mechanisms Associated with Intramuscular Fat Content in Beef Cattle. Animals (Basel) 2023; 13:2598. [PMID: 37627391 PMCID: PMC10451991 DOI: 10.3390/ani13162598] [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: 06/16/2023] [Revised: 08/05/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
Intramuscular fat content (IMF), one of the most important carcass traits in beef cattle, is controlled by complex regulatory factors. At present, molecular mechanisms involved in regulating IMF and fat metabolism in beef cattle are not well understood. Our objective was to integrate comparative transcriptomic and competing endogenous RNA (ceRNA) network analyses to identify candidate messenger RNAs (mRNAs) and regulatory RNAs involved in molecular regulation of longissimus dorsi muscle (LDM) tissue for IMF and fat metabolism of 5 beef cattle breeds (Angus, Chinese Simmental, Luxi, Nanyang, and Shandong Black). In total, 34 circRNAs, 57 lncRNAs, 15 miRNAs, and 374 mRNAs were identified by integrating gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, 7 key subnets with 16 circRNAs, 43 lncRNAs, 7 miRNAs, and 237 mRNAs were detected through clustering analyses, whereas GO enrichment analysis of identified RNAs revealed 48, 13, and 28 significantly enriched GO terms related to IMF in biological process, molecular function, and cellular component categories, respectively. The main metabolic-signaling pathways associated with IMF and fat metabolism that were enriched included metabolic, calcium, cGMP-PKG, thyroid hormone, and oxytocin signaling pathways. Moreover, MCU, CYB5R1, and BAG3 genes were common among the 10 comparative groups defined as important candidate marker genes for fat metabolism in beef cattle. Contributions of transcriptome profiles from various beef breeds and a competing endogenous RNA (ceRNA) regulatory network underlying phenotypic differences in IMF provided novel insights into molecular mechanisms associated with meat quality.
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Affiliation(s)
- Vahid Dehghanian Reyhan
- Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj 77871-31587, Iran; (V.D.R.); (F.G.); (S.R.M.-A.)
| | - Farzad Ghafouri
- Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj 77871-31587, Iran; (V.D.R.); (F.G.); (S.R.M.-A.)
| | - Mostafa Sadeghi
- Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj 77871-31587, Iran; (V.D.R.); (F.G.); (S.R.M.-A.)
| | - Seyed Reza Miraei-Ashtiani
- Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj 77871-31587, Iran; (V.D.R.); (F.G.); (S.R.M.-A.)
| | - John P. Kastelic
- Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; (J.P.K.); (H.W.B.)
| | - Herman W. Barkema
- Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; (J.P.K.); (H.W.B.)
| | - Masoud Shirali
- Agri-Food and Biosciences Institute, Hillsborough BT26 6DR, UK
- School of Biological Sciences, Queen’s University Belfast, Belfast BT9 5AJ, UK
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5
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Monterde B, Rojano E, Córdoba-Caballero J, Seoane P, Perkins JR, Medina MÁ, Ranea JAG. Integrating differential expression, co-expression and gene network analysis for the identification of common genes associated with tumor angiogenesis deregulation. J Biomed Inform 2023; 144:104421. [PMID: 37315831 DOI: 10.1016/j.jbi.2023.104421] [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: 02/27/2023] [Revised: 06/05/2023] [Accepted: 06/09/2023] [Indexed: 06/16/2023]
Abstract
Angiogenesis is essential for tumor growth and cancer metastasis. Identifying the molecular pathways involved in this process is the first step in the rational design of new therapeutic strategies to improve cancer treatment. In recent years, RNA-seq data analysis has helped to determine the genetic and molecular factors associated with different types of cancer. In this work we performed integrative analysis using RNA-seq data from human umbilical vein endothelial cells (HUVEC) and patients with angiogenesis-dependent diseases to find genes that serve as potential candidates to improve the prognosis of tumor angiogenesis deregulation and understand how this process is orchestrated at the genetic and molecular level. We downloaded four RNA-seq datasets (including cellular models of tumor angiogenesis and ischaemic heart disease) from the Sequence Read Archive. Our integrative analysis includes a first step to determine differentially and co-expressed genes. For this, we used the ExpHunter Suite, an R package that performs differential expression, co-expression and functional analysis of RNA-seq data. We used both differentially and co-expressed genes to explore the human gene interaction network and determine which genes were found in the different datasets that may be key for the angiogenesis deregulation. Finally, we performed drug repositioning analysis to find potential targets related to angiogenesis inhibition. We found that that among the transcriptional alterations identified, SEMA3D and IL33 genes are deregulated in all datasets. Microenvironment remodeling, cell cycle, lipid metabolism and vesicular transport are the main molecular pathways affected. In addition to this, interacting genes are involved in intracellular signaling pathways, especially in immune system and semaphorins, respiratory electron transport and fatty acid metabolism. The methodology presented here can be used for finding common transcriptional alterations in other genetically-based diseases.
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Affiliation(s)
- Beatriz Monterde
- Departamento de Señalización Celular y Molecular, Instituto de Biomedicina y Biotecnología de Cantabria, Universidad de Cantabria-CSIC., C/Albert Einstein, 22, Santander, 39011, Spain
| | - Elena Rojano
- Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, Bulevar Louis Pasteur, 31, Málaga, 29010, Spain; Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina (IBIMA-Plataforma BIONAND), C/ Severo Ochoa, 35, Parque Tecnológico de Andalucía (PTA), Campanillas, Málaga, 29590, Spain
| | - José Córdoba-Caballero
- Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, Bulevar Louis Pasteur, 31, Málaga, 29010, Spain; Instituto de Investigación e Innovación Biomédica de Cádiz (INiBICA), Avda. Ana de Viya, 21, Cádiz, 11009, Spain
| | - Pedro Seoane
- Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, Bulevar Louis Pasteur, 31, Málaga, 29010, Spain; Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina (IBIMA-Plataforma BIONAND), C/ Severo Ochoa, 35, Parque Tecnológico de Andalucía (PTA), Campanillas, Málaga, 29590, Spain; CIBER de Enfermedades Raras (CIBERER), Avda. Monforte de Lemos, 3-5, Pabellón 11, Planta 0, Madrid, 28029, Spain.
| | - James R Perkins
- Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, Bulevar Louis Pasteur, 31, Málaga, 29010, Spain; Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina (IBIMA-Plataforma BIONAND), C/ Severo Ochoa, 35, Parque Tecnológico de Andalucía (PTA), Campanillas, Málaga, 29590, Spain; CIBER de Enfermedades Raras (CIBERER), Avda. Monforte de Lemos, 3-5, Pabellón 11, Planta 0, Madrid, 28029, Spain
| | - Miguel Ángel Medina
- Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, Bulevar Louis Pasteur, 31, Málaga, 29010, Spain; Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina (IBIMA-Plataforma BIONAND), C/ Severo Ochoa, 35, Parque Tecnológico de Andalucía (PTA), Campanillas, Málaga, 29590, Spain; CIBER de Enfermedades Raras (CIBERER), Avda. Monforte de Lemos, 3-5, Pabellón 11, Planta 0, Madrid, 28029, Spain
| | - Juan A G Ranea
- Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, Bulevar Louis Pasteur, 31, Málaga, 29010, Spain; Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina (IBIMA-Plataforma BIONAND), C/ Severo Ochoa, 35, Parque Tecnológico de Andalucía (PTA), Campanillas, Málaga, 29590, Spain; CIBER de Enfermedades Raras (CIBERER), Avda. Monforte de Lemos, 3-5, Pabellón 11, Planta 0, Madrid, 28029, Spain; Spanish National Bioinformatics Institute (INB/ELIXIR-ES), Instituto de Salud Carlos III (ISCIII), C/ Sinesio Delgado, 4, Madrid, 28029, Spain
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Schuurman AR, Butler JM, Michels EH, Otto NA, Brands X, Haak BW, Uhel F, Klarenbeek AM, Faber DR, Schomakers BV, van Weeghel M, de Vos AF, Scicluna BP, Houtkooper RH, Wiersinga WJ, van der Poll T. Inflammatory and glycolytic programs underpin a primed blood neutrophil state in patients with pneumonia. iScience 2023; 26:107181. [PMID: 37496676 PMCID: PMC10366455 DOI: 10.1016/j.isci.2023.107181] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 04/21/2023] [Accepted: 06/15/2023] [Indexed: 07/28/2023] Open
Abstract
Neutrophils are potent immune cells with key antimicrobial functions. Previous in vitro work has shown that neutrophil effector functions are mainly fueled by intracellular glycolysis. Little is known about the state of neutrophils still in the circulation in patients during infection. Here, we combined flow cytometry, stimulation assays, transcriptomics, and metabolomics to investigate the link between inflammatory and metabolic pathways in blood neutrophils of patients with community-acquired pneumonia. Patients' neutrophils, relative to neutrophils from age- and sex- matched controls, showed increased degranulation upon ex vivo stimulation, and portrayed distinct upregulation of inflammatory transcriptional programs. This neutrophil phenotype was accompanied by a high-energy state with increased intracellular ATP content, and transcriptomic and metabolic upregulation of glycolysis and glycogenolysis. One month after hospital admission, these metabolic and transcriptomic changes were largely normalized. These data elucidate the molecular programs that underpin a balanced, yet primed state of blood neutrophils during pneumonia.
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Affiliation(s)
- Alex R. Schuurman
- Center for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centers - Location AMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands
| | - Joe M. Butler
- Center for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centers - Location AMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands
| | - Erik H.A. Michels
- Center for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centers - Location AMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands
| | - Natasja A. Otto
- Center for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centers - Location AMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands
| | - Xanthe Brands
- Center for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centers - Location AMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands
| | - Bastiaan W. Haak
- Center for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centers - Location AMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands
| | - Fabrice Uhel
- Center for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centers - Location AMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands
| | - Augustijn M. Klarenbeek
- Center for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centers - Location AMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands
| | - Daniël R. Faber
- BovenIJ Hospital, Statenjachtstraat 1, 1034 CS Amsterdam, the Netherlands
| | - Bauke V. Schomakers
- Laboratory Genetic Metabolic Diseases, Amsterdam UMC, University of Amsterdam, Departments of Clinical Chemistry and Pediatrics, Amsterdam Gastroenterology Endocrinology Metabolism, 1105 AZ Amsterdam, the Netherlands
- Core Facility Metabolomics, Amsterdam UMC, 1105 AZ Amsterdam, the Netherlands
| | - Michel van Weeghel
- Laboratory Genetic Metabolic Diseases, Amsterdam UMC, University of Amsterdam, Departments of Clinical Chemistry and Pediatrics, Amsterdam Gastroenterology Endocrinology Metabolism, 1105 AZ Amsterdam, the Netherlands
- Core Facility Metabolomics, Amsterdam UMC, 1105 AZ Amsterdam, the Netherlands
| | - Alex F. de Vos
- Center for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centers - Location AMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands
| | - Brendon P. Scicluna
- Center for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centers - Location AMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands
- Department of Applied Biomedical Science, Faculty of Health Sciences, Mater Dei Hospital, University of Malta, Msida, Malta
| | - Riekelt H. Houtkooper
- Amsterdam Gastroenterology Endocrinology and Metabolism Institute, 1105 AZ Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences Institute, 1105 AZ Amsterdam, the Netherlands
| | - W. Joost Wiersinga
- Center for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centers - Location AMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands
- Division of Infectious Diseases, Amsterdam University Medical Centers - Location AMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands
| | - Tom van der Poll
- Center for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centers - Location AMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands
- Division of Infectious Diseases, Amsterdam University Medical Centers - Location AMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands
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7
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Ghosh A, Ghosh A, Sinha A, Mathai S, Bhaumik J, Mukhopadhyay A, Maitra A, Biswas NK, Majumder PP, Sengupta S. Identification of HPV16 positive cervical cancer subsets characterized by divergent immune and oncogenic phenotypes with potential implications for immunotherapy. Tumour Biol 2023; 45:55-69. [PMID: 37599552 DOI: 10.3233/tub-220035] [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: 08/22/2023] Open
Abstract
BACKGROUND Cervical cancers (CaCx), like many other cancer types, portray high molecular heterogeneity that affects response to therapy, including immunotherapy. In India and other developing countries, CaCx mortality rates are very high because women report to the clinics with advanced cancers in absence of organized screening programs. This calls for implementation of newer therapeutic regimens for CaCx, like immunotherapy, which is again not used commonly in such countries. OBJECTIVE Therefore, we focused on dissecting tumour immune heterogeneity, if any, identify immune gene-based biomarkers of heterogeneity and subsets of such cancers with the potential for immunotherapy. We also attempted to characterize the cancer-associated phenotypes of such subsets, including viral load, to decipher the relationship of tumour immunogenicity with oncogenicity. METHODS Employing RNA-seq analysis of 44 HPV16 positive CaCx patients, immune subtypes were identified by unsupervised hierarchical clustering of global immune-gene expression profiles. Proportions of tumor infiltrating immune cells in the tumor milieu were estimated, employing Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), using gene expression data from RNA-seq. The oncogenic phenotypes of the immune subtypes of CaCx were deciphered through differential gene expression (DEGs) and pathway enrichment analysis. Viral load was estimated through TaqMan-based qRT-PCR analysis. RESULTS Analysis revealed the presence of two immune subtypes of CaCx, A (26/44; 59.09%) and B (18/44; 40.90%). Compared to Subtype-A, Subtype-B portrayed overexpression of immune genes and high infiltration of immune cells, specifically CD8+ T cells (p < 0.0001). Besides, a significant correlation between PD-1 and PD-L1 co-expression among Subtype-B, as opposed to Subtype-A, confirmed the interactive roles of these immune checkpoint molecules in Subtype B. Stepwise discriminant analysis pin-pointed ten immune-genes that could classify 100% of the patients significantly (p < 0.0001) into the two immune subtypes and serve as potential biomarkers of CaCx immunity. Differential gene expression analysis between the subtypes unveiled that Subtype-B was more biologically aggressive than Subtype-A, reflecting loss of structural integrity and promotion of cancer progression. The viral load was significantly lower in Subtype-B (average viral load = 10.74/100 ng of genomic DNA) compared to Subtype-A (average viral load = 14.29/100 ng of genomic DNA). Thus viral load and the ten-gene panel underscore their association with immunogenicity and oncogenicity. CONCLUSION Our study provides strong evidence that only a subset, about 41% of HPV16 positive CaCx patients in India, portray immune enrichment of the tumor milieu coupled with aggressive phenotypes. Such subtypes are therefore likely to benefit through checkpoint molecule-based or tumor infiltrating lymphocyte-based immunotherapy, which could be a leap forward in tackling aggressive forms of such CaCx in India and other developing countries.
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Affiliation(s)
- Abhisikta Ghosh
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Arnab Ghosh
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Abarna Sinha
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Sonia Mathai
- Tata Medical Center, Kolkata, West Bengal, India
| | | | - Asima Mukhopadhyay
- Kolkata Gynecological Oncology Trials and Translational Research Group, Kolkata, West Bengal, India
| | - Arindam Maitra
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Nidhan K Biswas
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Partha P Majumder
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Sharmila Sengupta
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
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Mailem RC, Tayo LL. Drug Repurposing Using Gene Co-Expression and Module Preservation Analysis in Acute Respiratory Distress Syndrome (ARDS), Systemic Inflammatory Response Syndrome (SIRS), Sepsis, and COVID-19. BIOLOGY 2022; 11:biology11121827. [PMID: 36552336 PMCID: PMC9775208 DOI: 10.3390/biology11121827] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 11/28/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022]
Abstract
SARS-CoV-2 infections are highly correlated with the overexpression of pro-inflammatory cytokines in what is known as a cytokine storm, leading to high fatality rates. Such infections are accompanied by SIRS, ARDS, and sepsis, suggesting a potential link between the three phenotypes. Currently, little is known about the transcriptional similarity between these conditions. Herein, weighted gene co-expression network analysis (WGCNA) clustering was applied to RNA-seq datasets (GSE147902, GSE66890, GSE74224, GSE177477) to identify modules of highly co-expressed and correlated genes, cross referenced with dataset GSE160163, across the samples. To assess the transcriptome similarities between the conditions, module preservation analysis was performed and functional enrichment was analyzed in DAVID webserver. The hub genes of significantly preserved modules were identified, classified into upregulated or downregulated, and used to screen candidate drugs using Connectivity Map (CMap) to identify repurposed drugs. Results show that several immune pathways (chemokine signaling, NOD-like signaling, and Th1 and Th2 cell differentiation) are conserved across the four diseases. Hub genes screened using intramodular connectivity show significant relevance with the pathogenesis of cytokine storms. Transcriptomic-driven drug repurposing identified seven candidate drugs (SB-202190, eicosatetraenoic-acid, loratadine, TPCA-1, pinocembrin, mepacrine, and CAY-10470) that targeted several immune-related processes. These identified drugs warrant further study into their efficacy for treating cytokine storms, and in vitro and in vivo experiments are recommended to confirm the findings of this study.
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Affiliation(s)
- Ryan Christian Mailem
- School of Chemical, Biological, and Materials Engineering and Sciences and School of Graduate Studies, Mapúa University, Manila City 1002, Philippines
| | - Lemmuel L. Tayo
- School of Chemical, Biological, and Materials Engineering and Sciences and School of Graduate Studies, Mapúa University, Manila City 1002, Philippines
- School of Health Sciences, Mapúa University, Manila City 1002, Philippines
- Correspondence: ; Tel.: +63-02-247-5000 (ext. 3300)
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9
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Aberrant expression of KDM1A inhibits ferroptosis of lung cancer cells through up-regulating c-Myc. Sci Rep 2022; 12:19168. [PMID: 36357457 PMCID: PMC9649633 DOI: 10.1038/s41598-022-23699-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 11/03/2022] [Indexed: 11/11/2022] Open
Abstract
Ferroptosis is a cell death process caused by metabolic dysfunction with the feature of aberrant iron accumulation. Emerging studies have identified that ferroptosis is an important biological function involving in the tumorigenesis, and targeting ferroptosis could provide promising therapeutic targets for lung cancer. However, such therapeutic strategies show limited therapeutic effect owing to drug resistance and other unknown underlying mechanisms. In this study, lysine-specific demethylase 1 (LSD1/KDM1A) was found to be significantly upregulated in lung cancer cells and tissues. The patients with KDM1A downregulation displayed the good prognosis. Using gene set enrichment analysis (GSEA), we demonstrated that KDM1A-associated genes might participate in the regulation of cell ferroptosis and Myc signaling in lung cancer. Knockdown of KDM1A inhibited the level of c-Myc and increased the concentration of malondialdehyde (MDA) and irons in human lung cancer cells H1299 and A549. Downregulation of c-Myc could facilitate KDM1A knockdown-mediated ferroptosis. Our study has elucidated the effect of KDM1A/c-Myc regulatory axis in the ferroptosis resistance of lung cancer cells.
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10
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Dehghanian Reyhan V, Sadeghi M, Miraei-Ashtiani SR, Ghafouri F, Kastelic JP, Barkema HW. Integrated transcriptome and regulatory network analyses identify candidate genes and pathways modulating ewe fertility. GENE REPORTS 2022. [DOI: 10.1016/j.genrep.2022.101659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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11
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Zheng Y, Wang K, Li N, Zhang Q, Chen F, Li M. Prognostic and Immune Implications of a Novel Pyroptosis-Related Five-Gene Signature in Breast Cancer. Front Surg 2022; 9:837848. [PMID: 35656090 PMCID: PMC9152226 DOI: 10.3389/fsurg.2022.837848] [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: 12/17/2021] [Accepted: 04/15/2022] [Indexed: 11/24/2022] Open
Abstract
Background Breast cancer (BC) is the most common cancer among women worldwide, with enormous heterogeneity. Pyroptosis has a significant impact on the development and progression of tumors. Nonetheless, the possible correlation between pyroptosis-related genes (PRGs) and the BC immune microenvironment has yet to be investigated. Materials and methods In The Cancer Genome Atlas Breast Cancer cohort, 38 PRGs were shown to be significantly different between malignant and non-malignant breast tissues. The 38 PRGs’ consensus clustering grouped 1,089 individuals into two pyroptosis-related (PR) patterns. Using univariate and LASSO-Cox analyses, a PR five-gene predictive signature was constructed based on the differentially expressed genes between two clusters. The tools estimation of stromal and immune cells in malignant tumours using expression data (ESTIMATE), cell type identification by estimating relative subsets Of RNA transcripts (CIBERSORT), and single-sample gene set enrichment analysis (ssGSEA) were used to investigate the BC tumor microenvironment (TME). Results In TME, the two PR clusters displayed distinct clinicopathological characteristics, survival outcomes, and immunocyte infiltration features. The developed five-signature model (SEMA3B, IGKC, KLRB1, BIRC3, and PSME2) classified BC patients into two risk groups based on the estimated median risk score. Patients in the low-scoring category had a higher chance of survival and more extensive immunocyte infiltration. An external validation set can yield similar results. Conclusion Our data suggest that PRGs have a significant impact on the BC immunological microenvironment. The PR clusters and associated predictive signature stimulate additional research into pyroptosis in order to optimize therapeutic strategies for BC patients and their responses to immune therapy.
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Affiliation(s)
- Yuanyuan Zheng
- Department of Oncology, The Second Hospital of Dalian Medical University, Dalian, China
| | - Kainan Wang
- Department of Oncology, The Second Hospital of Dalian Medical University, Dalian, China
| | - Ning Li
- Department of Foreign Language, Dalian Medical University, Dalian, China
| | - Qianran Zhang
- Department of Breast Diseases, The Second Hospital of Dalian Medical University, Dalian, China
| | - Fengxi Chen
- Department of Oncology, The Second Hospital of Dalian Medical University, Dalian, China
| | - Man Li
- Department of Oncology, The Second Hospital of Dalian Medical University, Dalian, China
- Correspondence: Man Li
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12
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Bogner AN, Stiers KM, Tanner JJ. Structure, biochemistry, and gene expression patterns of the proline biosynthetic enzyme pyrroline-5-carboxylate reductase (PYCR), an emerging cancer therapy target. Amino Acids 2021; 53:1817-1834. [PMID: 34003320 PMCID: PMC8599497 DOI: 10.1007/s00726-021-02999-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 05/04/2021] [Indexed: 12/21/2022]
Abstract
Proline metabolism features prominently in the unique metabolism of cancer cells. Proline biosynthetic genes are consistently upregulated in multiple cancers, while the proline catabolic enzyme proline dehydrogenase has dual, context-dependent pro-cancer and pro-apoptotic functions. Furthermore, the cycling of proline and Δ1-pyrroline-5-carboxylate through the proline cycle impacts cellular growth and death pathways by maintaining redox homeostasis between the cytosol and mitochondria. Here we focus on the last enzyme of proline biosynthesis, Δ1-pyrroline-5-carboxylate reductase, known as PYCR in humans. PYCR catalyzes the NAD(P)H-dependent reduction of Δ1-pyrroline-5-carboxylate to proline and forms the reductive half of the proline metabolic cycle. We review the research on the three-dimensional structure, biochemistry, inhibition, and cancer biology of PYCR. To provide a global view of PYCR gene upregulation in cancer, we mined RNA transcript databases to analyze differential gene expression in 28 cancer types. This analysis revealed strong, widespread upregulation of PYCR genes, especially PYCR1. Altogether, the research over the past 20 years makes a compelling case for PYCR as a cancer therapy target. We conclude with a discussion of some of the major challenges for the field, including developing isoform-specific inhibitors, elucidating the function of the long C-terminus of PYCR1/2, and characterizing the interactome of PYCR.
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Affiliation(s)
- Alexandra N Bogner
- Department of Biochemistry, University of Missouri, Columbia, MO, 65211, USA
| | - Kyle M Stiers
- Department of Biochemistry, University of Missouri, Columbia, MO, 65211, USA
| | - John J Tanner
- Department of Biochemistry, University of Missouri, Columbia, MO, 65211, USA.
- Department of Chemistry, University of Missouri, Columbia, MO, 65211, USA.
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Hasankhani A, Bahrami A, Sheybani N, Fatehi F, Abadeh R, Ghaem Maghami Farahani H, Bahreini Behzadi MR, Javanmard G, Isapour S, Khadem H, Barkema HW. Integrated Network Analysis to Identify Key Modules and Potential Hub Genes Involved in Bovine Respiratory Disease: A Systems Biology Approach. Front Genet 2021; 12:753839. [PMID: 34733317 PMCID: PMC8559434 DOI: 10.3389/fgene.2021.753839] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 09/28/2021] [Indexed: 12/11/2022] Open
Abstract
Background: Bovine respiratory disease (BRD) is the most common disease in the beef and dairy cattle industry. BRD is a multifactorial disease resulting from the interaction between environmental stressors and infectious agents. However, the molecular mechanisms underlying BRD are not fully understood yet. Therefore, this study aimed to use a systems biology approach to systematically evaluate this disorder to better understand the molecular mechanisms responsible for BRD. Methods: Previously published RNA-seq data from whole blood of 18 healthy and 25 BRD samples were downloaded from the Gene Expression Omnibus (GEO) and then analyzed. Next, two distinct methods of weighted gene coexpression network analysis (WGCNA), i.e., module-trait relationships (MTRs) and module preservation (MP) analysis were used to identify significant highly correlated modules with clinical traits of BRD and non-preserved modules between healthy and BRD samples, respectively. After identifying respective modules by the two mentioned methods of WGCNA, functional enrichment analysis was performed to extract the modules that are biologically related to BRD. Gene coexpression networks based on the hub genes from the candidate modules were then integrated with protein-protein interaction (PPI) networks to identify hub-hub genes and potential transcription factors (TFs). Results: Four significant highly correlated modules with clinical traits of BRD as well as 29 non-preserved modules were identified by MTRs and MP methods, respectively. Among them, two significant highly correlated modules (identified by MTRs) and six nonpreserved modules (identified by MP) were biologically associated with immune response, pulmonary inflammation, and pathogenesis of BRD. After aggregation of gene coexpression networks based on the hub genes with PPI networks, a total of 307 hub-hub genes were identified in the eight candidate modules. Interestingly, most of these hub-hub genes were reported to play an important role in the immune response and BRD pathogenesis. Among the eight candidate modules, the turquoise (identified by MTRs) and purple (identified by MP) modules were highly biologically enriched in BRD. Moreover, STAT1, STAT2, STAT3, IRF7, and IRF9 TFs were suggested to play an important role in the immune system during BRD by regulating the coexpressed genes of these modules. Additionally, a gene set containing several hub-hub genes was identified in the eight candidate modules, such as TLR2, TLR4, IL10, SOCS3, GZMB, ANXA1, ANXA5, PTEN, SGK1, IFI6, ISG15, MX1, MX2, OAS2, IFIH1, DDX58, DHX58, RSAD2, IFI44, IFI44L, EIF2AK2, ISG20, IFIT5, IFITM3, OAS1Y, HERC5, and PRF1, which are potentially critical during infection with agents of bovine respiratory disease complex (BRDC). Conclusion: This study not only helps us to better understand the molecular mechanisms responsible for BRD but also suggested eight candidate modules along with several promising hub-hub genes as diagnosis biomarkers and therapeutic targets for BRD.
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Affiliation(s)
- Aliakbar Hasankhani
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Abolfazl Bahrami
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
- Biomedical Center for Systems Biology Science Munich, Ludwig-Maximilians-University, Munich, Germany
| | - Negin Sheybani
- Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, Tehran, Iran
| | - Farhang Fatehi
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Roxana Abadeh
- Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | | | | | - Ghazaleh Javanmard
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Sadegh Isapour
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Hosein Khadem
- Department of Agronomy and Plant Breeding, University of Tehran, Karaj, Iran
| | - Herman W. Barkema
- Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
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Abstract
Cancer is a genetic disease in which multiple genes are perturbed. Thus, information about the regulatory relationships between genes is necessary for the identification of biomarkers and therapeutic targets. In this review, methods for inference of gene regulatory networks (GRNs) from transcriptomics data that are used in cancer research are introduced. The methods are classified into three categories according to the analysis model. The first category includes methods that use pair-wise measures between genes, including correlation coefficient and mutual information. The second category includes methods that determine the genetic regulatory relationship using multivariate measures, which consider the expression profiles of all genes concurrently. The third category includes methods using supervised and integrative approaches. The supervised approach estimates the regulatory relationship using a supervised learning method that constructs a regression or classification model for predicting whether there is a regulatory relationship between genes with input data of gene expression profiles and class labels of prior biological knowledge. The integrative method is an expansion of the supervised method and uses more data and biological knowledge for predicting the regulatory relationship. Furthermore, simulation and experimental validation of the estimated GRNs are also discussed in this review. This review identified that most GRN inference methods are not specific for cancer transcriptome data, and such methods are required for better understanding of cancer pathophysiology. In addition, more systematic methods for validation of the estimated GRNs need to be developed in the context of cancer biology.
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Park Y, Heider D, Hauschild AC. Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence. Cancers (Basel) 2021; 13:3148. [PMID: 34202427 PMCID: PMC8269018 DOI: 10.3390/cancers13133148] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/16/2021] [Accepted: 06/21/2021] [Indexed: 12/18/2022] Open
Abstract
The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question.
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Affiliation(s)
- Youngjun Park
- Department of Mathematics and Computer Science, Philipps-University of Marburg, 35032 Marburg, Germany; (Y.P.); (D.H.)
| | - Dominik Heider
- Department of Mathematics and Computer Science, Philipps-University of Marburg, 35032 Marburg, Germany; (Y.P.); (D.H.)
| | - Anne-Christin Hauschild
- Department of Mathematics and Computer Science, Philipps-University of Marburg, 35032 Marburg, Germany; (Y.P.); (D.H.)
- Department of Medical Informatics, University Medical Center Göttingen, 37075 Göttingen, Germany
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