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Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer.
Bisht, Vartika; Nash, Katrina; Xu, Yuanwei; Agarwal, Prasoon; Bosch, Sofie; Gkoutos, Georgios V; Acharjee, Animesh.
Afiliación
  • Bisht V; Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TH, UK.
  • Nash K; MRC Health Data Research UK (HDR UK), Midlands B15 2TT, UK.
  • Xu Y; College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK.
  • Agarwal P; Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TH, UK.
  • Bosch S; MRC Health Data Research UK (HDR UK), Midlands B15 2TT, UK.
  • Gkoutos GV; Institute of Translational Medicine, University Hospitals Birmingham NHS, Foundation Trust, Birmingham B15 2TT, UK.
  • Acharjee A; KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science, 100 44 Stockholm, Sweden.
Int J Mol Sci ; 22(11)2021 May 28.
Article en En | MEDLINE | ID: mdl-34071236
Integrative multiomics data analysis provides a unique opportunity for the mechanistic understanding of colorectal cancer (CRC) in addition to the identification of potential novel therapeutic targets. In this study, we used public omics data sets to investigate potential associations between microbiome, metabolome, bulk transcriptomics and single cell RNA sequencing datasets. We identified multiple potential interactions, for example 5-aminovalerate interacting with Adlercreutzia; cholesteryl ester interacting with bacterial genera Staphylococcus, Blautia and Roseburia. Using public single cell and bulk RNA sequencing, we identified 17 overlapping genes involved in epithelial cell pathways, with particular significance of the oxidative phosphorylation pathway and the ACAT1 gene that indirectly regulates the esterification of cholesterol. These findings demonstrate that the integration of multiomics data sets from diverse populations can help us in untangling the colorectal cancer pathogenesis as well as postulate the disease pathology mechanisms and therapeutic targets.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Colorrectales / Redes y Vías Metabólicas / Metaboloma / Transcriptoma / Microbiota Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Int J Mol Sci Año: 2021 Tipo del documento: Article Pais de publicación: Suiza
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Colorrectales / Redes y Vías Metabólicas / Metaboloma / Transcriptoma / Microbiota Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Int J Mol Sci Año: 2021 Tipo del documento: Article Pais de publicación: Suiza