Hot Spots and Trends in the Relationship between Cancer and Obesity : A Systematic Review and Knowledge Graph Analysis

Le GAO*, Tian YANG, Ziru XUE, Chak Kwan Dickson CHAN

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

5 Citations (Scopus)

Abstract

Cancer is one of the most difficult medical problems in today’s world. There are many factors that induce cancer in humans, and obesity has become an important factor in inducing cancer. This study systematically and quantitatively describes the development trend, current situation and research hotspot of the relationship between cancer and obesity by using document statistics and knowledge graph visualization technology. Through the visualization technology analysis of knowledge graph in this study, the research hotspot and knowledge base source of the relationship between cancer and obesity in the last 20 years have been ascertained. Obesity-related factors, such as immunity, insulin, adiponectin, adipocytokines, nonalcoholic fatty liver and inflammatory reaction, may affect the occurrence of obesity and increase the risk of cancer. Obesity-related cancers include respiratory cancer, colorectal cancer, hepatocellular cancer, prostate cancer, gastric cancer, etc. Our research provides direction and basis for future research in this field, as well as technical and knowledge basis support for experts and researchers in related medical fields.
Original languageEnglish
Article number337
JournalLife
Volume13
Issue number2
Early online date27 Jan 2023
DOIs
Publication statusPublished - 27 Jan 2023

Bibliographical note

Funding Information:
This work was supported by the Wuyi university- Hong Kong- Macao research fund (grant number 2019WGALH23).

Publisher Copyright:
© 2023 by the authors.

Keywords

  • cancer
  • tumor
  • obseity
  • knowledge graph
  • visualization
  • obesity

Fingerprint

Dive into the research topics of 'Hot Spots and Trends in the Relationship between Cancer and Obesity : A Systematic Review and Knowledge Graph Analysis'. Together they form a unique fingerprint.

Cite this