Autor(es): La Vecchia, C ; Pelucchi, C ; Negri, E ; Bonzi, R ; Boffetta, P ; Camargo, MC ; Paula Curado, M ; Lunet, N ; Vioque, J ; Zhang, ZF
Data: 2021
Identificador Persistente: https://hdl.handle.net/10216/149767
Origem: Repositório Aberto da Universidade do Porto
The assessment of risk factors in cancer etiology is necessary for defining optimal preventive strategies, as well as for identifying high risk individuals, and it is therefore relevant for medical practice and cancer prevention. The Stomach cancer Pooling (StoP) Project is a consortium of epidemiological studies of gastric cancer (GC), established in year 2012. The StoP Project aims to examine the role of lifestyle, environmental and genetic determinants of GC through pooled analyses of subject-level data. The consortium is the major GC dataset globally, including original data from 35 studies – with case–control study design, including 5 nested case–control within cohort studies – conducted in the Americas, Asia and Europe (Table 1), for a total of about 13,500 cases and 32,000 controls, and it is continuously expanding. To date, the StoP Project contributed a detailed quantification of the risk of GC associated to several factors, including cigarette smoking (relative risk, RR, of 1.32 for heavy vs. never smokers), alcohol drinking (RR=1.48 for heavy vs. never drinkers), socio-economic status (RR=0.60 for high vs. low education), selected dietary factors (RR=1.30 for high vs. low meat intake; RR=0.65 for high vs. low vegetables consumption; RR=0.80 for high vs. low citrus fruit; RR=0.67 for high vs. low polyphenols intake) and occupational exposures (RR=1.70 for miners; RR=1.30 for construction workers; RR=1.33 for agricultural and animal husbandry workers; RR=1.41 for blacksmiths and machine-tool operators). Planned future developments are to analyze the role of rare exposures on GC risk and to examine risk factors in understudied patient subgroups (e.g., young onset GC, gastric cardia cancer, etc.); to integrate additional studies from East Asia; to develop a genome-wide modeling of polygenic risk score in GC; to include survival analyses and to apply machine learning methods in GC risk prediction and prognostication.
