BackgroundThe hunt for the molecular markers with specificity and sensitivity has been a hot area for the tumor treatment. Due to the poor diagnosis and prognosis of pancreatic cancer (PC), the excision rate is often low, which makes it more urgent to find the ideal tumor markers.MethodsRobust Rank Aggreg (RRA) methods was firstly applied to identify the differentially expressed genes (DEGs) between PC tissues and normal tissues from GSE28735, GSE15471, GSE16515, and GSE101448. Among these DEGs, the highly correlated genes were clustered using WGCNA analysis. The co-expression networks and molecular complex detection (MCODE) Cytoscape app were then performed to find the sub-clusters and confirm 35 candidate genes. For these genes, least absolute shrinkage and selection operator (lasso) regression model was applied and validated to build a diagnostic risk score model. Cox proportional hazard regression analysis was used and validated to build a prognostic model.ResultsBased on integrated transcriptomic analysis, we identified a 19 gene module (SYCN, PNLIPRP1, CAP2, GNMT, MAT1A, ABAT, GPT2, ADHFE1, PHGDH, PSAT1, ERP27, PDIA2, MT1H, COMP, COL5A2, FN1, COL1A2, FAP and POSTN) as a specific predictive signature for the diagnosis of PC. Based on the two consideration, accuracy and feasibility, we simplified the diagnostic risk model as a four-gene model: 0.3034*log2(MAT1A)-0.1526*log2(MT1H) + 0.4645*log2(FN1) -0.2244*log2(FAP), log2(gene count). Besides, a four-hub gene module was also identified as prognostic model = − 1.400*log2(CEL) + 1.321*log2(CPA1) + 0.454*log2(POSTN) + 1.011*log2(PM20D1), log2(gene count).ConclusionIntegrated transcriptomic analysis identifies two four-hub gene modules as specific predictive signatures for the diagnosis and prognosis of PC, which may bring new sight for the clinical practice of PC.

中文翻译：

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整合转录组分析揭示了参与胰腺癌诊断和预后的中心基因

研究背景寻找具有特异性和敏感性的分子标志物一直是肿瘤治疗的热点领域。由于胰腺癌（PC）的诊断和预后较差，切除率往往较低，这使得寻找理想的肿瘤标志物变得更加迫切。方法首先采用稳健排名聚合（RRA）方法来识别差异表达基因（来自 GSE28735、GSE15471、GSE16515 和 GSE101448 的 PC 组织和正常组织之间的 DEG）。在这些 DEG 中，使用 WGCNA 分析对高度相关的基因进行聚类。然后执行共表达网络和分子复合物检测 (MCODE) Cytoscape 应用程序来查找子簇并确认 35 个候选基因。对于这些基因，应用最小绝对收缩和选择算子（套索）回归模型并进行验证，以构建诊断风险评分模型。使用 Cox 比例风险回归分析并进行验证来构建预后模型。结果基于整合转录组分析，我们鉴定了 19 个基因模块（SYCN、PNLIPRP1、CAP2、GNMT、MAT1A、ABAT、GPT2、ADHFE1、PHGDH、PSAT1、ERP27、 PDIA2、MT1H、COMP、COL5A2、FN1、COL1A2、FAP 和 POSTN）作为 PC 诊断的特定预测特征。基于准确性和可行性两方面考虑，我们将诊断风险模型简化为四基因模型：0.3034*log2(MAT1A)-0.1526*log2(MT1H) + 0.4645*log2(FN1) -0.2244*log2(FAP) ，log2（基因计数）。此外，四中心基因模块也被确定为预后模型 = − 1.400*log2(CEL) + 1.321*log2(CPA1) + 0.454*log2(POSTN) + 1.011*log2(PM20D1), log2(基因计数)。结论整合转录组分析确定了两个四中心基因模块作为PC诊断和预后的特异性预测特征​​，可能为PC的临床实践带来新的视野。