Construction and Validation of a Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Gastric Cancer. (21st June 2021)

• Record Type: Journal Article
• Authors: Li, Shilong; Zhao, Zongxian; Yang, Huaxiang; Wang, Daohan; Sun, Weilin; Li, Shuliang; Zhang, Zhaoxiong; Fu, Weihua
• Is Part Of: Cancer control. Volume 28(2021)
• Publication Date: 2021-06-21
• Subjects: nomogram -- gastric cancer -- tumor microenvironment -- lymph node metastasis -- TCGA; Cancer -- Prevention -- Periodicals; Cancer -- Diagnosis -- Periodicals; Cancer -- Treatment -- Periodicals; Cancer -- Palliative treatment -- Periodicals; Cancer -- Prevention; Medical Oncology; Neoplasms -- prevention & control; Neoplasms -- therapy; Electronic journals; Periodicals; Periodicals; 616.994005
• DOI: 10.1177/10732748211027160
• Languages: English
• ISSNs: 1073-2748

Abstract

Background: Increasing evidence indicated that the tumor microenvironment (TME) plays a critical role in tumor progression. This study aimed to identify and evaluate mRNA signature involved in lymph node metastasis (LNM) in TME for gastric cancer (GC). Methods: Gene expression and clinical data were downloaded from The Cancer Genome Atlas (TCGA). The ESTIMATE algorithm was used to evaluate the TME of GC. The heatmap and Venn plots were applied for visualizing and screening out intersect differentially expressed genes (DEGs) involved in LNM in TME. Functional enrichment analysis, gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) network were also conducted. Furthermore, binary logistic regression analysis were employed to develop a 4-mRNAs signature for the LNM prediction. ROC curves were applied to validate the LNM predictive ability of the riskscore. Nomogram was constructed and calibration curve was plotted to verify the predictive power of nomogram. Results: A total of 88 LNM related DEGs were identified. Functional enrichment analysis and GSEA implied that those genes were associated with some biological processes, such as ion transportation, lipid metabolism and thiolester hydrolase activity. After univariate and multivariate logistic regression analysis, 4 mRNAs (RASSF2, MS4A2, ANKRD33B and ADH1B) were eventually screened out to develop a predictive model. ROC curves manifested the good performance of the 4-mRNAs signature. The proportion of patients with LNM in high-risk group was significantly higher than that in low-risk group. The C-index of nomogram from training and test cohorts were 0.865 and 0.765, and the nomogram was well calibrated. Conclusions: In general, we identified a 4-mRNAs signature that effectively predicted LNM in GC patients. Moreover, the 4-mRNAs signature and nomogram provide a guidance for the preoperative evaluation and postoperative treatment of GC patients.