Integrated gene-metabolite association network analysis reveals key metabolic pathways in gastric adenocarcinoma
Botao Xu,
Yuying Shi,
Chuang Yuan,
Zhe Wang,
Qitao Chen,
Cheng Wang,
Jie Chai
Affiliations
Botao Xu
Department of Gastrointestinal Surgery, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
Yuying Shi
Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China; National Institute of Health Data Science of China, Shandong University, Jinan, 250000, China; National Science Library (Chengdu), Chinese Academy of Sciences, Chengdu, 610299, China
Chuang Yuan
Department of Biochemistry and Biophysics, School of Basic Medical Sciences, Peking University, Beijing, 100191, China
Zhe Wang
Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, Shandong, China
Qitao Chen
Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China; National Institute of Health Data Science of China, Shandong University, Jinan, 250000, China
Cheng Wang
Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China; National Institute of Health Data Science of China, Shandong University, Jinan, 250000, China; Corresponding author. Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China.
Jie Chai
Department of Gastrointestinal Surgery, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China; Corresponding author. Department of Gastrointestinal Surgery, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China.
Gastric adenocarcinoma is one of the most death cause cancers worldwide. Metabolomics is an effective approach for investigating the occurrence and progression of cancer and detecting prognostic biomarkers by studying the profiles of small bioactive molecules. To fully decipher the functional roles of the disrupted metabolites that modulate the cellular mechanism of gastric cancer, integrated gene-metabolite association network methods are critical to map the associations between metabolites and genes. In this study, we constructed a knowledge-based gene-metabolite association network of gastric cancer using the dysregulated metabolites and genes between gastric cancer patients and control group. The topological pathway analysis and gene-protein-metabolite-disease association analysis revealed four key gene-metabolite pathways which include eleven metabolites associated with modulated genes. The integrated gene-metabolite association network enables mechanistic investigation and provides a comprehensive overview regarding the investigation of molecular mechanisms of gastric cancer, which facilitates the in-depth understanding of metabolic biomarker roles in gastric cancer.
