He Lijian, Ye Qiange, Zhu Yanmei, Zhong Wenqi, Xu Guifang, Wang Lei, Wang Zhangding, Zou Xiaoping
Department of Gastroenterology, Nanjing Drum Tower Hospital, School of Medicine, Jiangsu University, Nanjing, Jiangsu Province, China.
Department of Gastroenterology, Tongling People's Hospital, Tongling, Anhui Province, China.
Gastroenterol Res Pract. 2024 Feb 26;2024:6639205. doi: 10.1155/2024/6639205. eCollection 2024.
Abnormal lipid metabolism is known to influence the malignant behavior of gastric cancer. However, the underlying mechanism remains elusive. In this study, we comprehensively analyzed the biological significance of genes involved in lipid metabolism in advanced gastric cancer (AGC).
We obtained gene expression profiles from The Cancer Genome Atlas (TCGA) database for early and advanced gastric cancer samples and performed differential expression analysis to identify specific lipid metabolism-related genes in AGC. We then used consensus cluster analysis to classify AGC patients into molecular subtypes based on lipid metabolism and constructed a diagnostic model using least absolute shrinkage and selection operator- (LASSO-) Cox regression analysis and Gene Set Enrichment Analysis (GSEA). We evaluated the discriminative ability and clinical significance of the model using the Kaplan-Meier (KM) curve, ROC curve, DCA curve, and nomogram. We also estimated immune levels based on immune microenvironment expression, immune checkpoints, and immune cell infiltration and obtained hub genes by weighted gene co-expression network analysis (WGCNA) of differential genes from the two molecular subtypes.
We identified 6 lipid metabolism genes that were associated with the prognosis of AGC and used consistent clustering to classify AGC patients into two subgroups with significantly different overall survival and immune microenvironment. Our risk model successfully classified patients in the training and validation sets into high-risk and low-risk groups. The high-risk score predicted poor prognosis and indicated low degree of immune infiltration. Subgroup analysis showed that the risk model was an independent predictor of prognosis in AGC. Furthermore, our results indicated that most chemotherapeutic agents are more effective for AGC patients in the low-risk group than in the high-risk group, and risk scores for AGC are strongly correlated with drug sensitivity. Finally, we performed qRT-PCR experiments to verify the relevant results.
Our findings suggest that lipid metabolism-related genes play an important role in predicting the prognosis of AGC and regulating immune invasion. These results have important implications for the development of targeted therapies for AGC patients.
已知脂质代谢异常会影响胃癌的恶性行为。然而，其潜在机制仍不清楚。在本研究中，我们全面分析了晚期胃癌（AGC）中参与脂质代谢的基因的生物学意义。
我们从癌症基因组图谱（TCGA）数据库中获取了早期和晚期胃癌样本的基因表达谱，并进行差异表达分析，以鉴定AGC中特定的脂质代谢相关基因。然后，我们使用一致性聚类分析根据脂质代谢将AGC患者分类为分子亚型，并使用最小绝对收缩和选择算子（LASSO）-Cox回归分析和基因集富集分析（GSEA）构建诊断模型。我们使用Kaplan-Meier（KM）曲线、ROC曲线、DCA曲线和列线图评估了该模型的判别能力和临床意义。我们还根据免疫微环境表达、免疫检查点和免疫细胞浸润估计免疫水平，并通过对来自两种分子亚型的差异基因进行加权基因共表达网络分析（WGCNA）获得枢纽基因。
我们鉴定出6个与AGC预后相关的脂质代谢基因，并使用一致性聚类将AGC患者分为两个总体生存率和免疫微环境有显著差异的亚组。我们的风险模型成功地将训练集和验证集中的患者分为高风险和低风险组。高风险评分预测预后不良，并表明免疫浸润程度低。亚组分析表明，风险模型是AGC预后的独立预测因子。此外，我们的结果表明，大多数化疗药物对低风险组的AGC患者比高风险组的患者更有效，并且AGC的风险评分与药物敏感性密切相关。最后，我们进行了qRT-PCR实验以验证相关结果。
我们的研究结果表明，脂质代谢相关基因在预测AGC预后和调节免疫侵袭方面发挥重要作用。这些结果对AGC患者靶向治疗的发展具有重要意义。
