| Resumo: | Maolin Hou,1,* Jinghua Chen,2,* Le Yang,3,* Lei Qin,3 Jie Liu,4 Haibo Zhao,5 Yujin Guo,6 Qing-Qing Yu,6 Qiujie Zhang5 1Department of Internal Medicine, Siziwangqi People’s Hospital, Wulancabu, 011800, People’s Republic of China; 2Department of Oncology, the First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Shandong Lung Cancer Institute, Jinan, 250000, People’s Republic of China; 3Department of Gastrointestinal Surgery, Jining, 272000, People’s Republic of China; 4Department of Pediatric Intensive Care Unit, Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University, Jinan, 250000, People’s Republic of China; 5Department of Oncology, Jining, 272000, People’s Republic of China; 6Department of Clinical Pharmacology, Jining, 272000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qiujie Zhang, Department of Oncology, Jining No. 1 People’s Hospital, Jining, 272000, People’s Republic of China, Email zhangqiujie86@163.com Qing-Qing Yu, Department of Clinical Pharmacology, Jining No. 1 People’s Hospital, Jining, 272000, People’s Republic of China, Email yuqingqing_lucky@163.comIntroduction: Gastric cancer, the fifth most common malignant tumor in the world, poses a serious threat to human health. However, the role of fatty acid metabolism (FAM) in gastric cancer remains incompletely understood. We aim to provide guidance for clinical decisions by utilizing public database of gastric adenocarcinoma to establish an FAM-related gene subtypes via machine learning algorithm.Methods: The intersection of FMGs from KEGG, Hallmark, and Reactome bioinformatics databases and the DEGs of the TCGA-STAD cohort was used to decompose the gene matrix related to establish FAM-related gene subtypes by NMF. Comparison of immune infiltrating differences between subtypes using ESTIMATE and Cibersort algorithms. The multifactor Cox regression to identify independent risk genes for patient prognosis based on the subtypes. A prognostic model including independent risk genes was built using random survival forest and Cox regression. IHC validation in gastric cancer and adjacent tissues confirmed the above gene expression level.Results: 71 DEGs related to FMGs of STAD were identified, which was used to established the FAM-related gene subtypes, C1 and C2. The immune infiltrating analysis showed that most immune features of C2 were significantly upregulated compared to C1. The independent risk genes were CGβ 8, UPK1B, and OR51G based on the subtypes. A gastric cancer prognostic model consisting of independent risk genes was constructed and patients were classified into high-risk and low-risk groups with survival differential analysis. Finally, IHC showed that CGβ 8 and UPK1B expression were upregulated in gastric cancer, while OR51G2 did not detect differences in expression.Conclusion: The study developed a machine learning-based gastric cancer prognosis risk model using FMGs. This model effectively stratifies patients according to their risk levels and provides valuable insights for clinical decision-making, enabling accurate evaluation of patient prognosis.Keywords: Gastric cancer, fatty acid metabolism-related genes, machine learning, genomics, signature
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