Liu Wenxuan, Liu Li, Kuang Tianrui, Deng Wenhong
Department of General Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China.
J Cancer. 2025 Mar 10;16(7):2087-2102. doi: 10.7150/jca.104389. eCollection 2025.
Gastric cancer (GC) is one of the most prevalent malignant diseases worldwide. Abnormal metabolic reprogramming, particularly cholesterol metabolism, influences tumor development and treatment outcomes. This study investigates the predictive and functional significance of cholesterol metabolism-related genes in gastric cancer patients. Clinical and gene expression data related to cholesterol metabolism in gastric cancer were analyzed using datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). A predictive signature was developed and validated using LASSO, Cox regression, and the GSE26889 cohort, followed by evaluation with Kaplan-Meier analysis. A nomogram was constructed by integrating the signature with clinical factors and ssGSEA for immunological analysis. The role of NPC2 was investigated using western blot, qPCR, and cellular assays. We conducted a bioinformatics analysis of 50 genes associated with cholesterol metabolism in gastric cancer. Using the GEO and TCGA datasets, we identified 28 genes with differential expression in gastric cancer patients. Subsequent COX univariate and LASSO regression analyses of these 28 DEGs identified five genes (APOA1, APOC3, NPC2, CD36, and ABCA1) as independent prognostic risk factors. We then constructed a risk model for cholesterol metabolism genes, revealing that survival was worse in the high-risk group compared to the low-risk group, with more severe case staging outcomes. We conducted a comparative analysis of immune cells between the high-risk and low-risk groups, revealing distinct variations in immune cell type expression. We then developed a model using a correlation nomogram to illustrate these conclusions. We further examined the biological characteristics of NPC2. Immunohistochemistry and qPCR results showed that NPC2 exhibited significant protein and mRNA expression in gastric cancer tissues. We used siRNA technology to suppress NPC2, resulting in reduced viability, proliferation, and invasion capacity of gastric cancer cells, as determined by CCK-8, colony formation, wound healing, and Transwell assays. A risk signature comprising five cholesterol metabolism-related genes was constructed using bioinformatics to estimate outcomes and therapeutic responses in gastric cancer patients. The results suggest that NPC2 may serve as a novel biomarker for gastric cancer patients.
胃癌（GC）是全球最常见的恶性疾病之一。异常的代谢重编程，尤其是胆固醇代谢，会影响肿瘤的发展和治疗结果。本研究调查了胆固醇代谢相关基因在胃癌患者中的预测和功能意义。使用来自基因表达综合数据库（GEO）和癌症基因组图谱（TCGA）的数据集，分析了与胃癌中胆固醇代谢相关的临床和基因表达数据。使用LASSO、Cox回归和GSE26889队列开发并验证了一个预测特征，随后通过Kaplan-Meier分析进行评估。通过将该特征与临床因素和用于免疫分析的单样本基因集富集分析（ssGSEA）相结合，构建了一个列线图。使用蛋白质免疫印迹法、定量聚合酶链反应（qPCR）和细胞实验研究了NPC2的作用。我们对与胃癌中胆固醇代谢相关的50个基因进行了生物信息学分析。利用GEO和TCGA数据集，我们在胃癌患者中鉴定出28个差异表达基因。随后对这28个差异表达基因进行COX单变量和LASSO回归分析，确定了五个基因（载脂蛋白A1、载脂蛋白C3、NPC2、分化簇36（CD36）和三磷酸腺苷结合盒转运体A1（ABCA1））作为独立的预后危险因素。然后，我们构建了一个胆固醇代谢基因风险模型，结果显示高风险组的生存率低于低风险组，病例分期结果更严重。我们对高风险组和低风险组之间的免疫细胞进行了比较分析，发现免疫细胞类型表达存在明显差异。然后，我们使用相关列线图开发了一个模型来说明这些结论。我们进一步研究了NPC2的生物学特性。免疫组织化学和qPCR结果显示，NPC2在胃癌组织中表现出显著的蛋白质和mRNA表达。我们使用小干扰RNA（siRNA）技术抑制NPC2，通过细胞计数试剂盒-8（CCK-8）、集落形成、伤口愈合和Transwell实验测定，结果显示胃癌细胞的活力、增殖和侵袭能力降低。利用生物信息学构建了一个包含五个胆固醇代谢相关基因的风险特征，以评估胃癌患者的预后和治疗反应。结果表明，NPC2可能是胃癌患者的一种新型生物标志物。
