Chen Yi, Liao Yunmei, Huang Lang, Luo Zhibin
Department of Oncology, Chongqing General Hospital, Chongqing University, Chongqing, 401147, China.
Discov Oncol. 2024 Sep 27;15(1):482. doi: 10.1007/s12672-024-01374-6.
Gastric cancer (GC) is the third leading cause of cancer-related deaths globally. Despite advancements in treatment, the overall 5-year survival rate remains below 30%, particularly in advanced stages. Copper metabolism, vital for various cellular processes, has been linked to cancer progression, but its role in GC, especially at the single-cell level, is not well understood.
This study aims to investigate copper metabolism in GC by integrating single-cell RNA sequencing (scRNA-seq) data and developing a prognostic model based on copper metabolism-related gene (CMRG) expression. The study explores how copper metabolism affects the tumor microenvironment and identifies potential therapeutic targets.
scRNA-seq data from gastric cancer and normal tissues were analyzed using the Seurat package. Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) were used for dimensionality reduction and clustering. Non-negative matrix factorization (NMF) was employed for T cell subpopulation analysis. A high-dimensional weighted gene co-expression network analysis (HdWGCNA) identified key molecular features. LASSO regression and Random Survival Forest (RSF) techniques were used to create and validate a prognostic model. Survival analysis, immune microenvironment assessment, and drug sensitivity analysis were conducted.
Sixteen cell clusters and nine distinct cell types were identified, with T cells showing significant roles in cell communication. The NMF analysis of CD8 +T cells revealed five copper metabolism-related subtypes. The prognostic model based on nine CMRGs indicated significant survival differences between high- and low-risk groups. High-risk patients showed shorter survival times, increased immune cell infiltration, and altered immune responses. Drug sensitivity analysis suggested higher efficacy of certain drugs in high-CMRG patients.
胃癌（GC）是全球癌症相关死亡的第三大主要原因。尽管治疗取得了进展，但总体5年生存率仍低于30%，尤其是在晚期。铜代谢对各种细胞过程至关重要，已被证明与癌症进展有关，但其在胃癌中的作用，特别是在单细胞水平上，尚未得到充分了解。
本研究旨在通过整合单细胞RNA测序（scRNA-seq）数据来研究胃癌中的铜代谢，并基于铜代谢相关基因（CMRG）表达建立一个预后模型。该研究探讨了铜代谢如何影响肿瘤微环境，并确定潜在的治疗靶点。
使用Seurat软件包分析来自胃癌和正常组织的scRNA-seq数据。主成分分析（PCA）和均匀流形近似与投影（UMAP）用于降维和聚类。非负矩阵分解（NMF）用于T细胞亚群分析。高维加权基因共表达网络分析（HdWGCNA）确定关键分子特征。使用LASSO回归和随机生存森林（RSF）技术创建并验证预后模型。进行生存分析、免疫微环境评估和药物敏感性分析。
识别出16个细胞簇和9种不同的细胞类型，T细胞在细胞通讯中发挥重要作用。对CD8+T细胞的NMF分析揭示了5种与铜代谢相关的亚型。基于9个CMRG的预后模型显示高风险组和低风险组之间存在显著的生存差异。高风险患者的生存时间较短，免疫细胞浸润增加，免疫反应改变。药物敏感性分析表明某些药物在高CMRG患者中疗效更高。
