Hou Maolin, Chen Jinghua, Yang Le, Qin Lei, Liu Jie, Zhao Haibo, Guo Yujin, Yu Qing-Qing, Zhang Qiujie
Department of Internal Medicine, Siziwangqi People's Hospital, Wulancabu, 011800, People's Republic of China.
Department 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.
Cancer Manag Res. 2024 Oct 18;16:1463-1473. doi: 10.2147/CMAR.S483577. eCollection 2024.
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.
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.
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 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 and expression were upregulated in gastric cancer, while did not detect differences in expression.
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.
胃癌是世界上第五大常见恶性肿瘤，对人类健康构成严重威胁。然而，脂肪酸代谢（FAM）在胃癌中的作用仍未完全明确。我们旨在利用胃腺癌公共数据库，通过机器学习算法建立FAM相关基因亚型，为临床决策提供指导。
使用来自KEGG、Hallmark和Reactome生物信息学数据库的FMG与TCGA-STAD队列的差异表达基因（DEG）的交集，通过非负矩阵分解（NMF）来分解与建立FAM相关基因亚型相关的基因矩阵。使用ESTIMATE和Cibersort算法比较亚型之间的免疫浸润差异。基于这些亚型，通过多因素Cox回归确定患者预后的独立风险基因。使用随机生存森林和Cox回归建立包含独立风险基因的预后模型。在胃癌及癌旁组织中的免疫组化（IHC）验证证实了上述基因表达水平。
鉴定出71个与STAD的FMG相关的DEG，用于建立FAM相关基因亚型C1和C2。免疫浸润分析表明，与C1相比，C2的大多数免疫特征显著上调。基于这些亚型确定了独立风险基因。构建了一个由独立风险基因组成的胃癌预后模型，并通过生存差异分析将患者分为高风险和低风险组。最后，IHC显示[具体基因1]和[具体基因2]在胃癌中的表达上调，而[具体基因3]未检测到表达差异。
本研究利用FMG开发了一种基于机器学习的胃癌预后风险模型。该模型有效地根据患者风险水平进行分层，为临床决策提供有价值的见解，能够准确评估患者预后。
