A Genomic-Clinicopathologic Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Gastric Cancer

Abstract

Background: Preoperative evaluation of the lymph node (LN) state is of pivotal significance for therapy strategy decisions in gastric cancer (GC) patients. However, there is lack of noninvasive method that can identify such status preoperatively. We aimed at developing a genomic biosignature based model to forecast the possibility of LN metastasis in GC patients.Methods: We employed the RNA profile retrieving strategy and conducted 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 R software. The GC samples in GSE62254 were randomly divided into a learning set (n = 210) and a verification set (n = 90). By performing the Least absolute shrinkage and selection operator (LASSO) regression approach, a set of 23-RNA signature was established and the signature based nomogram was subsequently built for distinguishing LN condition. The diagnostic effectiveness of this model was assessed, as well as the clinical performance subsequently assessed using the decision curve analysis (DCA). Metascape was used for bioinformatic analysis of the DERsResults: Based on this genomic signature, we established a nomogram which 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 conducted using bioinformatics methods which posited that these DERs were involved with several lymphangiogenesis-correlated cascades.Conclusions: Here, we present a genomic signature based nomogram that integrates the 23-RNA biosignature based scores and Lauren classification. This model can be readily utilized to estimate the possibility of LN metastasis with good performance in GC. The uncovering of the DERs reveals the prospective biogenesis of LN metastasis in GC.