PDF(Pubmed)
Abstract：
Globally, gastric cancer (GC) is a category of prevalent malignant tumors. Its high occurrence and fatality rates represent a severe threat to public health. According to recent research, lipid metabolism (LM) reprogramming impacts immune cells\' ordinary function and is critical for the onset and development of cancer. Consequently, the article conducted a sophisticated bioinformatics analysis to explore the potential connection between LM and GC.
We first undertook a differential analysis of the TCGA queue to recognize lipid metabolism-related genes (LRGs) that are differentially expressed. Subsequently, we utilized the LASSO and Cox regression analyses to create a predictive signature and validated it with the GSE15459 cohort. Furthermore, we examined somatic mutations, immune checkpoints, tumor immune dysfunction and exclusion (TIDE), and drug sensitivity analyses to forecast the signature\'s immunotherapy responses.
Kaplan-Meier (K-M) curves exhibited considerably longer OS and PFS (p<0.001) of the low-risk (LR) group. PCA analysis and ROC curves evaluated the model\'s predictive efficacy. Additionally, GSEA analysis demonstrated that a multitude of carcinogenic and matrix-related pathways were much in the high-risk (HR) group. We then developed a nomogram to enhance its clinical practicality, and we quantitatively analyzed tumor-infiltrating immune cells (TIICs) using the CIBERSORT and ssGSEA algorithms. The low-risk group has a lower likelihood of immune escape and more effective in chemotherapy and immunotherapy. Eventually, we selected BCHE as a potential biomarker for further research and validated its expression. Next, we conducted a series of cell experiments (including CCK-8 assay, Colony formation assay, wound healing assay and Transwell assays) to prove the impact of BCHE on gastric cancer biological behavior.
Our research illustrated the possible consequences of lipid metabolism in GC, and we identified BCHE as a potential therapeutic target for GC. The LRG-based signature could independently forecast the outcome of GC patients and guide personalized therapy.
We first undertook a differential analysis of the TCGA queue to recognize lipid metabolism-related genes (LRGs) that are differentially expressed. Subsequently, we utilized the LASSO and Cox regression analyses to create a predictive signature and validated it with the GSE15459 cohort. Furthermore, we examined somatic mutations, immune checkpoints, tumor immune dysfunction and exclusion (TIDE), and drug sensitivity analyses to forecast the signature\'s immunotherapy responses.
Kaplan-Meier (K-M) curves exhibited considerably longer OS and PFS (p<0.001) of the low-risk (LR) group. PCA analysis and ROC curves evaluated the model\'s predictive efficacy. Additionally, GSEA analysis demonstrated that a multitude of carcinogenic and matrix-related pathways were much in the high-risk (HR) group. We then developed a nomogram to enhance its clinical practicality, and we quantitatively analyzed tumor-infiltrating immune cells (TIICs) using the CIBERSORT and ssGSEA algorithms. The low-risk group has a lower likelihood of immune escape and more effective in chemotherapy and immunotherapy. Eventually, we selected BCHE as a potential biomarker for further research and validated its expression. Next, we conducted a series of cell experiments (including CCK-8 assay, Colony formation assay, wound healing assay and Transwell assays) to prove the impact of BCHE on gastric cancer biological behavior.
Our research illustrated the possible consequences of lipid metabolism in GC, and we identified BCHE as a potential therapeutic target for GC. The LRG-based signature could independently forecast the outcome of GC patients and guide personalized therapy.
摘要：
■全球，胃癌是一类常见的恶性肿瘤。它的高发病率和死亡率对公众健康构成了严重威胁。根据最近的研究,脂质代谢（LM）重编程影响免疫细胞的正常功能，对癌症的发生和发展至关重要。因此,本文进行了复杂的生物信息学分析，以探索LM和GC之间的潜在联系。
■我们首先对TCGA队列进行了差异分析，以识别差异表达的脂质代谢相关基因（LRG）。随后，我们利用LASSO和Cox回归分析创建了预测特征,并在GSE15459队列中进行了验证.此外，我们检查了体细胞突变，免疫检查点,肿瘤免疫功能障碍和排斥（TIDE），和药物敏感性分析，以预测签名的免疫治疗反应。
■卡普兰-迈耶（K-M）曲线显示出低风险（LR）组明显更长的OS和PFS（p<0.001）。PCA分析和ROC曲线评价模型的预测效能。此外,GSEA分析表明，高风险（HR）组中有许多致癌和基质相关的途径。然后，我们开发了列线图以增强其临床实用性，我们使用CIBERSORT和ssGSEA算法对肿瘤浸润免疫细胞（TIIC）进行定量分析。低风险组的免疫逃逸可能性较低，在化疗和免疫疗法中更有效。最终,我们选择BCHE作为进一步研究的潜在生物标志物,并验证其表达.接下来,我们进行了一系列细胞实验（包括CCK-8测定，集落形成测定,伤口愈合测定和Transwell测定）以证明BCHE对胃癌生物学行为的影响。
■我们的研究说明了GC中脂质代谢的可能后果，我们确定BCHE是GC的潜在治疗靶点。基于LRG的签名可以独立预测GC患者的预后并指导个性化治疗。
■我们首先对TCGA队列进行了差异分析，以识别差异表达的脂质代谢相关基因（LRG）。随后，我们利用LASSO和Cox回归分析创建了预测特征,并在GSE15459队列中进行了验证.此外，我们检查了体细胞突变，免疫检查点,肿瘤免疫功能障碍和排斥（TIDE），和药物敏感性分析，以预测签名的免疫治疗反应。
■卡普兰-迈耶（K-M）曲线显示出低风险（LR）组明显更长的OS和PFS（p<0.001）。PCA分析和ROC曲线评价模型的预测效能。此外,GSEA分析表明，高风险（HR）组中有许多致癌和基质相关的途径。然后，我们开发了列线图以增强其临床实用性，我们使用CIBERSORT和ssGSEA算法对肿瘤浸润免疫细胞（TIIC）进行定量分析。低风险组的免疫逃逸可能性较低，在化疗和免疫疗法中更有效。最终,我们选择BCHE作为进一步研究的潜在生物标志物,并验证其表达.接下来,我们进行了一系列细胞实验（包括CCK-8测定，集落形成测定,伤口愈合测定和Transwell测定）以证明BCHE对胃癌生物学行为的影响。
■我们的研究说明了GC中脂质代谢的可能后果，我们确定BCHE是GC的潜在治疗靶点。基于LRG的签名可以独立预测GC患者的预后并指导个性化治疗。
