Predictive Value of Air Pollution and Metabolomics for Gastric Cancer.

The Development and Prognosis of Gastric Cancer Based on Air Pollution and Metabolomics

As one of the common malignant tumours worldwide, gastric cancer continues to have a high incidence and mortality rate, especially in Asia. Although a large number of studies have focused on its underlying genetic and lifestyle factors, the specific role of environmental factors in gastric cancer development and progression has not been fully elucidated. Air pollution, a growing environmental problem, and its major components such as PM2.5, PM10, and NO2 have been shown to be closely associated with the occurrence and development of many chronic diseases. Recent studies have gradually revealed the association between air pollution and certain cancer types (e.g., lung cancer), but its relationship with gastric cancer remains relatively unexplored. Against this background, the application of metabolomics provides new perspectives and methods to study the association between gastric cancer and environmental factors. Metabolomics is capable of systematically analysing the metabolite composition and changes in individuals under different environmental exposures, revealing the potential effects of environmental factors, such as air pollution, on individual metabolic functions. By combining air pollution data and metabolomics analysis, investigators can deeply explore the role of environmental factors in the occurrence, development and prognosis of gastric cancer, and thus provide a scientific basis for the development of prevention and treatment strategies.

In this study, clinical data, blood, urine and stool specimens were collected by including patients in the healthy control group and gastric cancer group. The air pollution indicators, metabolomics determination and macro genomic determination were extracted from clinical data, blood and stool respectively. The extracted air pollution, metabolomics and intestinal microbiota data were integrated and analysed. Based on machine learning, a comprehensive model was constructed to screen the key indicators that play a role in the development of gastric cancer and construct a prediction model for gastric cancer development. The aim of this study is to investigate how environmental, metabolic and microbiota factors affect the occurrence and development of gastric cancer. Secondly, metabolic markers specific to gastric cancer patients can also be identified, providing a basis for early diagnosis, early intervention and individualised treatment.

Inclusion Criteria:

• Diagnosis of gastric cancer confirmed by pathology or cytology;
• aged >18 years;
• not received chemotherapy, radiotherapy, targeted therapy or immunotherapy;
• post-operative pathological stages other than stage IV, or no liver, lung or other organs as confirmed by CT, MRI, B-ultrasound imaging.

Exclusion Criteria:

• Patients with systemic diseases such as severe cardiorespiratory insufficiency affecting the choice of treatment regimen;
• inappropriate for enrolment as assessed by the investigator;
• incomplete clinical data.