Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, East Qingchun Road 3, Zhejiang, 310016, Hangzhou, China.
BMC Cancer. 2021 Apr 23;21(1):455. doi: 10.1186/s12885-021-08203-x.
Preoperative evaluation of lymph node (LN) state is of pivotal significance for informing therapeutic decisions in gastric cancer (GC) patients. However, there are no non-invasive methods that can be used to preoperatively identify such status. We aimed at developing a genomic biosignature based model to predict the possibility of LN metastasis in GC patients.
We used the RNA profile retrieving strategy and performed RNA expression profiling in a large GC cohort (GSE62254, n = 300) from Gene Expression Ominus (GEO). In the exploratory stage, 300 GC patients from GSE62254 were involved and the differentially expressed RNAs (DERs) for LN-status were determined using the R software. GC samples in GSE62254 were randomly allocated into a learning set (n = 210) and a verification set (n = 90). By using the Least absolute shrinkage and selection operator (LASSO) regression approach, a set of 23-RNA signatures were established and the signature based nomogram was subsequently built for distinguishing LN condition. The diagnostic efficiency, as well as the clinical performance of this model were assessed using the decision curve analysis (DCA). Metascape was used for bioinformatic analysis of the DERs.
Based on the genomic signature, we established a nomogram that robustly distinguished LN status in the learning (AUC = 0.916, 95% CI 0.833-0.999) and verification sets (AUC = 0.775, 95% CI 0.647-0.903). DCA demonstrated the clinical value of this nomogram. Functional enrichment analysis of the DERs was performed using bioinformatics methods which revealed that these DERs were involved in several lymphangiogenesis-correlated cascades.
In this study, we present a genomic signature based nomogram that integrates the 23-RNA biosignature based scores and Lauren classification. This model can be utilized to estimate the probability of LN metastasis with good performance in GC. The functional analysis of the DERs reveals the prospective biogenesis of LN metastasis in GC.
术前评估淋巴结（LN）状态对于告知胃癌（GC）患者的治疗决策至关重要。然而，目前尚无可以用于术前识别这种状态的非侵入性方法。我们旨在开发一种基于基因组生物标志物的模型，以预测 GC 患者 LN 转移的可能性。
我们使用 RNA 谱检索策略并在来自基因表达 Omnibus（GEO）的大型 GC 队列（GSE62254，n=300）中进行 RNA 表达谱分析。在探索性阶段，使用 R 软件确定 GSE62254 中 300 名 GC 患者的差异表达 RNA（DER）用于 LN 状态。GSE62254 中的 GC 样本被随机分配到学习集（n=210）和验证集（n=90）。通过使用最小绝对收缩和选择算子（LASSO）回归方法，建立了一组 23-RNA 特征，并随后建立了基于特征的列线图以区分 LN 情况。使用决策曲线分析（DCA）评估该模型的诊断效率和临床性能。使用 Metascape 对 DER 进行生物信息学分析。
基于基因组特征，我们建立了一个列线图，该列线图在学习集（AUC=0.916，95%CI 0.833-0.999）和验证集（AUC=0.775，95%CI 0.647-0.903）中均能稳健地区分 LN 状态。DCA 显示了该列线图的临床价值。使用生物信息学方法对 DER 进行功能富集分析，结果表明这些 DER 参与了几个淋巴管生成相关的级联反应。
在这项研究中，我们提出了一个基于基因组特征的列线图，该列线图整合了基于 23-RNA 生物标志物的评分和 Lauren 分类。该模型可用于估计 GC 中 LN 转移的概率，具有良好的性能。DER 的功能分析揭示了 GC 中 LN 转移的潜在发生。
