Literature DB >> 35230687

Pathway Analysis for Cancer Research and Precision Oncology Applications.

Alessandro La Ferlita1, Salvatore Alaimo1, Alfredo Ferro1, Alfredo Pulvirenti2.   

Abstract

With the advent of OMICs technologies, several bioinformatics methods have been developed to infer biological knowledge from such data. Pathway analysis methodologies help integrate multi-OMICs data and find altered function in known metabolic and signaling pathways. As widely known, such alterations promote the cancer cells' progression and the maintenance of the malignant state. In this chapter, we provide (i) a comprehensive description of the primary data sources for omics data, cancer "omics" projects, and precision oncology knowledge bases; (ii) a survey of the main biological pathway databases; (iii) and a global view of the principal pathway analysis tools and methodologies, describing their main characteristics and shortcomings highlighting their potential applications in cancer research and precision oncology.
© 2022. Springer Nature Switzerland AG.

Entities:  

Keywords:  Cancer; Functional Class Scoring; Metabolic pathways; Over-Representation Analysis; Pathway analysis; Pathway databases; Pathway topology-based analysis; Pathways; Phenotype simulation; Precision oncology; Signaling pathways

Mesh:

Year:  2022        PMID: 35230687     DOI: 10.1007/978-3-030-91836-1_8

Source DB:  PubMed          Journal:  Adv Exp Med Biol        ISSN: 0065-2598            Impact factor:   2.622


  104 in total

1.  KEGG: kyoto encyclopedia of genes and genomes.

Authors:  M Kanehisa; S Goto
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  The KEGG databases at GenomeNet.

Authors:  Minoru Kanehisa; Susumu Goto; Shuichi Kawashima; Akihiro Nakaya
Journal:  Nucleic Acids Res       Date:  2002-01-01       Impact factor: 16.971

Review 3.  Building the foundation for genomics in precision medicine.

Authors:  Samuel J Aronson; Heidi L Rehm
Journal:  Nature       Date:  2015-10-15       Impact factor: 49.962

4.  KEGG for integration and interpretation of large-scale molecular data sets.

Authors:  Minoru Kanehisa; Susumu Goto; Yoko Sato; Miho Furumichi; Mao Tanabe
Journal:  Nucleic Acids Res       Date:  2011-11-10       Impact factor: 16.971

5.  From genomics to chemical genomics: new developments in KEGG.

Authors:  Minoru Kanehisa; Susumu Goto; Masahiro Hattori; Kiyoko F Aoki-Kinoshita; Masumi Itoh; Shuichi Kawashima; Toshiaki Katayama; Michihiro Araki; Mika Hirakawa
Journal:  Nucleic Acids Res       Date:  2006-01-01       Impact factor: 16.971

6.  Reactome: a knowledgebase of biological pathways.

Authors:  G Joshi-Tope; M Gillespie; I Vastrik; P D'Eustachio; E Schmidt; B de Bono; B Jassal; G R Gopinath; G R Wu; L Matthews; S Lewis; E Birney; L Stein
Journal:  Nucleic Acids Res       Date:  2005-01-01       Impact factor: 16.971

7.  KEGG: new perspectives on genomes, pathways, diseases and drugs.

Authors:  Minoru Kanehisa; Miho Furumichi; Mao Tanabe; Yoko Sato; Kanae Morishima
Journal:  Nucleic Acids Res       Date:  2016-11-28       Impact factor: 16.971

8.  Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification.

Authors:  Salvatore Alaimo; Rosalba Giugno; Mario Acunzo; Dario Veneziano; Alfredo Ferro; Alfredo Pulvirenti
Journal:  Oncotarget       Date:  2016-08-23

Review 9.  Computational Oncology in the Multi-Omics Era: State of the Art.

Authors:  Guillermo de Anda-Jáuregui; Enrique Hernández-Lemus
Journal:  Front Oncol       Date:  2020-04-07       Impact factor: 6.244

10.  KEGG for representation and analysis of molecular networks involving diseases and drugs.

Authors:  Minoru Kanehisa; Susumu Goto; Miho Furumichi; Mao Tanabe; Mika Hirakawa
Journal:  Nucleic Acids Res       Date:  2009-10-30       Impact factor: 16.971

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