Department of Gastroenterology, The First Affiliated Hospital of Ningbo University, 315020, Ningbo, China.
BMC Genomics. 2024 Mar 28;25(1):319. doi: 10.1186/s12864-024-10243-z.
Gastric cancer (GC) is one of the most common malignant tumors worldwide. Nevertheless, GC still lacks effective diagnosed and monitoring method and treating targets. This study used multi omics data to explore novel biomarkers and immune therapy targets around sphingolipids metabolism genes (SMGs).
LASSO regression analysis was performed to filter prognostic and differently expression SMGs among TCGA and GTEx data. Risk score model and Kaplan-Meier were built to validate the prognostic SMG signature and prognostic nomogram was further constructed. The biological functions of SMG signature were annotated via multi omics. The heterogeneity landscape of immune microenvironment in GC was explored. qRT-PCR was performed to validate the expression level of SMG signature. Competing endogenous RNA regulatory network was established to explore the molecular regulatory mechanisms.
3-SMGs prognostic signature (GLA, LAMC1, TRAF2) and related nomogram were constructed combing several clinical characterizes. The expression difference and diagnostic value were validated by PCR data. Multi omics data reveals 3-SMG signature affects cell cycle and death via several signaling pathways to regulate GC progression. Overexpression of 3-SMG signature influenced various immune cell infiltration in GC microenvironment. RBP-SMGs-miRNA-mRNAs/lncRNAs regulatory network was built to annotate regulatory system.
Upregulated 3-SMGs signature are excellent predictive diagnosed and prognostic biomarkers, providing a new perspective for future GC immunotherapy.
胃癌（GC）是全球最常见的恶性肿瘤之一。然而，GC 仍然缺乏有效的诊断和监测方法以及治疗靶点。本研究使用多组学数据来探索围绕神经酰胺代谢基因（SMGs）的新型生物标志物和免疫治疗靶点。
使用 LASSO 回归分析从 TCGA 和 GTEx 数据中筛选预后和差异表达的 SMGs。构建风险评分模型和 Kaplan-Meier 以验证预后 SMG 特征，并进一步构建预后列线图。通过多组学注释 SMG 特征的生物学功能。探索 GC 中免疫微环境的异质性景观。通过 qRT-PCR 验证 SMG 特征的表达水平。建立竞争性内源性 RNA 调控网络，探索分子调控机制。
构建了结合几个临床特征的 3-SMGs 预后特征（GLA、LAMC1、TRAF2）和相关列线图。通过 PCR 数据验证了表达差异和诊断价值。多组学数据表明，3-SMG 特征通过几种信号通路影响细胞周期和死亡，从而调节 GC 的进展。3-SMG 特征的过表达影响 GC 微环境中各种免疫细胞的浸润。构建 RBP-SMGs-miRNA-mRNAs/lncRNAs 调控网络以注释调控系统。
上调的 3-SMGs 特征是优秀的预测诊断和预后生物标志物，为未来的 GC 免疫治疗提供了新视角。
