Discovery of a novel lipid metabolism-related gene signature to predict outcomes and the tumor immune microenvironment in gastric cancer by integrated analysis of single-cell and bulk RNA sequencing.
Discovery of a novel lipid metabolism-related gene signature to predict outcomes and the tumor immune microenvironment in gastric cancer by integrated analysis of single-cell and bulk RNA sequencing.
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通过单细胞和批量RNA测序的综合分析发现一种新的脂质代谢相关基因特征，以预测胃癌的结局和肿瘤免疫微环境。
DOI：
10.1186/s12944-023-01977-y
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发表时间：
2023-12-02
影响因子：
4.5
通讯作者：
Wei, Yushan
中科院分区：
文献类型：
--
作者：
Zhang, Jinze;Wang, He;Tian, Yu;Li, Tianfeng;Zhang, Wei;Ma, Li;Chen, Xiangjuan;Wei, Yushan
关键词：
胃癌（GC）是一个紧迫的全球临床问题，治疗选择很少且预后不良。胃癌的发病和扩散受到脂质代谢相关途径变化的显着影响。本研究旨在利用脂质代谢相关基因 (LMRG) 发现 GC 的预测特征，并检查其与肿瘤免疫微环境 (TIME) 的相关性。 GC 患者的转录组数据和临床信息是从 TCGA 和 GEO 数据库收集的。使用批量 RNA-seq 和单细胞 RNA 测序 (scRNA-seq) 分析 GC 样品的数据。为了鉴定与生存相关的差异表达 LMRG（DE-LMRG），进行了差异表达和预后研究。我们使用 LASSO 回归构建了预测签名，并在 TCGA 和 GSE84437 数据集上进行了测试。此外，还全面分析了预后特征与时间的相关性。在本研究中，我们在GC中鉴定了258个DE-LMRG，并进一步筛选了7个与生存相关的DE-LMRG。 scRNA-seq 的结果鉴定出三个分支之间的 688 个差异表达基因 (DEG)。使用上述两个基因组鉴定了两个关键基因（GPX3和NNMT）。此外，还开发了依赖于 GPX3 和 NNMT 的预测风险评分。 TCGA 和 GEO 数据集中的生存研究表明，低危患者的预后明显优于高危患者。此外，通过使用基于 TCGA 数据的校准图，该研究证明了预后列线图的强大预测能力，该列线图结合了风险评分以及各种临床因素。在高危组中，存在大量活跃的自然杀伤 (NK) 细胞、静止单核细胞、巨噬细胞、肥大细胞和活化的 CD4++T 细胞。总之，已经开发出双基因特征和预测列线图，为 GC 患者的一般生存提供准确的预后预测。这些发现有可能帮助医疗保健专业人员做出明智的医疗决策并提供个性化的治疗方法。
Gastric cancer (GC) is a pressing global clinical issue, with few treatment options and a poor prognosis. The onset and spread of stomach cancer are significantly influenced by changes in lipid metabolism-related pathways. This study aimed to discover a predictive signature for GC using lipid metabolism-related genes (LMRGs) and examine its correlation with the tumor immune microenvironment (TIME). Transcriptome data and clinical information from patients with GC were collected from the TCGA and GEO databases. Data from GC samples were analyzed using both bulk RNA-seq and single-cell sequencing of RNA (scRNA-seq). To identify survival-related differentially expressed LMRGs (DE-LMRGs), differential expression and prognosis studies were carried out. We built a predictive signature using LASSO regression and tested it on the TCGA and GSE84437 datasets. In addition, the correlation of the prognostic signature with the TIME was comprehensively analyzed. In this study, we identified 258 DE-LMRGs in GC and further screened seven survival-related DE-LMRGs. The results of scRNA-seq identified 688 differentially expressed genes (DEGs) between the three branches. Two critical genes (GPX3 and NNMT) were identified using the above two gene groups. In addition, a predictive risk score that relies on GPX3 and NNMT was developed. Survival studies in both the TCGA and GEO datasets revealed that patients categorized to be at low danger had a significantly greater prognosis than those identified to be at high danger. Additionally, by employing calibration plots based on TCGA data, the study demonstrated the substantial predictive capacity of a prognostic nomogram, which incorporated a risk score along with various clinical factors. Within the high-risk group, there was a noticeable abundance of active natural killer (NK) cells, quiescent monocytes, macrophages, mast cells, and activated CD4 + T cells. In summary, a two-gene signature and a predictive nomogram have been developed, offering accurate prognostic predictions for general survival in GC patients. These findings have the potential to assist healthcare professionals in making informed medical decisions and providing personalized treatment approaches.
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影响因子：
16.1
作者：
Butler LM;Perone Y;Dehairs J;Lupien LE;de Laat V;Talebi A;Loda M;Kinlaw WB;Swinnen JV
通讯作者：
Swinnen JV
单细胞测序在癌症研究中的应用：进展与展望
影响因子：
28.5
作者：
Lei Y;Tang R;Xu J;Wang W;Zhang B;Liu J;Yu X;Shi S
通讯作者：
Shi S
影响因子：
14.9
作者：
Li X;Wang CY
通讯作者：
Wang CY
影响因子：
6.2
作者：
Cronin, Kathleen A.;Scott, Susan;Firth, Albert U.;Sung, Hyuna;Henley, S. Jane;Sherman, Recinda L.;Siegel, Rebecca L.;Anderson, Robert N.;Kohler, Betsy A.;Benard, Vicki B.;Negoita, Serban;Wiggins, Charles;Cance, William G.;Jemal, Ahmedin
通讯作者：
Jemal, Ahmedin
脂质代谢对 CD4( ) T 细胞的影响
影响因子：
4.6
作者：
Cai F;Jin S;Chen G
通讯作者：
Chen G