Programme(s) to which this project applies:
|☑ MPhil/PhD||☒ MRes[Med]||☒ URIS|
In China, the incidence of esophageal cancer shows prominent geographical patterns with several endemic areas including Taihang Mountain range (TMR) of North China and southeastern littoral regions (e.g., northern Jiangsu and southeastern Guangdong). This suggests that context-specific environmental hazard to esophageal cancer development has played a critical role, awaiting investigation urgently.
Based on the background of multidisciplinary clues and the support of preliminary findings, this project hypothesizes that, 1) aridity and siliceous bauxite soil in TMR can promote the effective enrichment of phytolith in the form of amorphous silica thorns on husks of wheat (local staple crop), and those silica thorns can pollute flour during the processing of wheat grains; 2) eutrophication of the water body in the lower reaches of the Huaihe River in northern Jiangsu, and the upwelling ecosystem of Nanao Island in southeastern Guangdong, have led to nitrogen-rich surface water and so enhanced growth of phytoplanktons (especially diatoms) in their local waters, of which the needle-shaped diatom genera are often predominant species. The glass diatom frustules can remain in the gut of filter-feeding fish (i.e., trash fish due to low economic value) through food chain, and finally consumed by local residents. These biogenic silica needles have chances of being anchored in the esophagus, causing mechanical damage, chronic inflammation and abnormal cell proliferation, thereby promoting the especially high incidence of esophageal squamous cell carcinoma.
To test such hypothesis, our team will firstly design ecological comparison study to explore the differences of biogenic silica needles in environmental samples between the three high-incidence areas of esophageal cancer and their adjacent low-incidence areas, respectively; then, laboratory model is designed to simulate the exposure path to biogenic silica needles, analyzing their migration during wheat/fish processing and examining the possibility of their retention in the body esophagus; finally, two-phase carcinogenesis mice model is built to investigate the potential and possible mechanism of biogenic silica needles in promoting the occurrence of esophageal squamous cell carcinoma via chronic inflammation. Our project holds promise for offering new insights into research and practice of esophageal cancer prevention and treatment in China.
Dr LW Tian, School of Public Health
Dr Linwei Tian is an environmental epidemiologist with a focus on air pollution and health. He has been conducting field epidemiology and laboratory work on indoor air pollution and lung cancer in Xuan Wei County, which has the highest lung cancer rates among women in China. Identifying the carcinogenic agents in coal and its emissions would affect local intervention policies and gain insights into the carcinogenesis mechanisms.
Meanwhile, Dr Tian gains a strong research interest in exploring the potential role of food contamination by biogenic siliceous needles in the endemic cancer of esophagus in China. The geographic patterns of esophagus cancer endemics offers a unique natural experiment in assessing environmental carcinogenesis. Based on the previous evidence on glass (silica fiber) roots of cancer, Dr. Tian hypothesizes wheat bract-derived glass fibers and needle-shaped diatoms in guts of trash fish as a major causative factor of esophagus cancer endemic in North China and southern China, respectively.
Urbanized Hong Kong provides another unique setting to study air pollution and health. Its high density of people and vehicles, high-rise buildings, a rich resource of accessible environmental measurement and healthcare data, and various air pollution control policies offers a great opportunity for valuable environmental epidemiology. Compared with static data, time series data contain far more information at our disposal for the inference of causality. Dr. Tian has been trying to examine the earlier ambiguity and enhance causal inference of the environment-health associations by contrasting the traditional time series regression models with the recent methods of causal discovery from big data.
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