Wang Anbin, Yang Hong, Zhu Yuming
Department of Hepatobiliary and Pancreatic Surgery, The Third Affiliated Hospital of Chongqing Medical University (Gener Hospital), Chongqing, 401120, China.
Curr Med Chem. 2025 Feb 12. doi: 10.2174/0109298673367232250102015441.
We aimed to develop a reliable prognostic tool related to glucagon-like peptide-1 (GLP-1) for guiding treatment of pancreatic cancer (PC).
The treatment strategies for PC being greatly advanced the prognosis of cancer still remains unfavorable.
To develop a RiskScore model for evaluating PC prognosis.
The bulk RNA-seq data of PC patients were obtained from the UCSCXena and GEO database, and the GSE156405 cohort was used for single-cell RNA-seq (scRNA- seq) analysis in the "Seurat" package. Firstly, the gene expression and mutation in the PC samples were analyzed to perform differentially expressed genes (DEGs) analysis using the "limma" package. The "survival" package was employed to conduct un/- multivariate Cox regression and Kaplan-Meier (KM) survival analysis. Secondly, a RiskScore model was developed and assessed using the "glmnet" and "timeROC" packages. Next, the CIBERSORT algorithm and the ssGSEA method were applied for immune infiltration analysis and calculation of the immune cell scores, respectively. Finally, pathway enrichment analysis was conducted using gene set enrichment analysis (GSEA).
Most GLP-1 signaling genes were overexpressed in the PC samples with multiple mutation types. LASSO analysis selected 3 GLP-1 genes for the development of a RiskScore model with a high classification accuracy (AUC >0.6). Notably, high-risk patients showed a significantly shorter survival time in both training and validation sets. In addition, as an independent factor, the RiskScore was further used to establish a nomogram model for the survival prediction of PC in clinical practice. The tumor microenvironment (TME) analysis revealed that low-risk patients with more abundant immune and stroma components had higher levels of anti-tumor immune cell infiltration (such as activated B and T cells), while the proliferation pathways (E2F targets, G2M checkpoint) were significantly activated in the high-risk groups. The genes in the RiskScore model may affect the survival of PC patients through modulating the activities of NK cells and macrophages.
We demonstrated that the GLP-1 signaling affected PC development and developed a reliable RiskSocre model for the prognosis assessment in PC. Our findings are expected to improve PC diagnosis and treatment in clinical practice.
我们旨在开发一种与胰高血糖素样肽-1（GLP-1）相关的可靠预后工具，用于指导胰腺癌（PC）的治疗。
尽管PC的治疗策略有了很大进展，但其癌症预后仍然不佳。
开发一种用于评估PC预后的风险评分模型。
从UCSCXena和GEO数据库获取PC患者的批量RNA测序数据，并使用“Seurat”软件包对GSE156405队列进行单细胞RNA测序（scRNA-seq）分析。首先，分析PC样本中的基因表达和突变情况，使用“limma”软件包进行差异表达基因（DEG）分析。采用“survival”软件包进行单因素/多因素Cox回归和Kaplan-Meier（KM）生存分析。其次，使用“glmnet”和“timeROC”软件包开发并评估风险评分模型。接下来，分别应用CIBERSORT算法和ssGSEA方法进行免疫浸润分析和免疫细胞评分计算。最后，使用基因集富集分析（GSEA）进行通路富集分析。
大多数GLP-1信号基因在具有多种突变类型的PC样本中过表达。LASSO分析选择了3个GLP-1基因用于开发具有高分类准确性（AUC>0.6）的风险评分模型。值得注意的是，高风险患者在训练集和验证集中的生存时间均显著缩短。此外，作为一个独立因素，风险评分进一步用于建立临床实践中PC生存预测的列线图模型。肿瘤微环境（TME）分析显示，免疫和基质成分更丰富的低风险患者具有更高水平的抗肿瘤免疫细胞浸润（如活化的B细胞和T细胞），而高风险组中增殖通路（E2F靶点通路、G2M检查点通路）显著激活。风险评分模型中的基因可能通过调节自然杀伤细胞和巨噬细胞的活性来影响PC患者的生存。
我们证明了GLP-1信号影响PC的发展，并开发了一种可靠的风险评分模型用于PC的预后评估。我们的研究结果有望改善临床实践中PC的诊断和治疗。
