Construction and Validation of a Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Gastric Cancer

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.

Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis

“…All the included studies were of low risk of bias with respect to the domain of participants and outcome, 19 (46.34%) studies had low risk of bias in predictors ( 32 , 34 , 37 , 38 , 40 , 42 , 44 , 54 – 56 , 60 , 62 , 63 , 66 – 71 ), 22 (53.66%) had unclear risk of bias due to that the prediction assessment was performed in the know of outcome data ( 33 , 35 , 36 , 39 , 41 , 43 , 45 – 53 , 57 – 59 , 61 , 64 , 65 , 72 ). As for the domain of analysis, the risk of bias in 16 studies was considered high ( 33 , 35 , 41 , 43 , 47 , 50 , 51 , 53 , 58 , 59 , 61 , 63 , 65 , 68 , 70 , 71 ), and the reasons were that (1): Insufficient sample size.…”

“…Eight studies lacked external validation in model development ( 35 , 50 , 51 , 53 , 59 , 63 , 65 , 70 ). Concern regarding ‘overall applicability’ was rated as low in 15 studies (36.59%) ( 32 , 34 , 37 , 38 , 40 , 42 , 44 , 54 – 56 , 60 , 62 , 66 , 67 , 69 ), high in 16 studies (39.02%) ( 33 , 35 , 41 , 43 , 47 , 50 , 53 , 58 , 59 , 61 , 63 , 65 , 68 , 70 , 71 ) and unclear in the remaining 10 (24.39%) ( 36 , 39 , 45 , 46 , 48 , 49 , 52 , 57 , 64 , 72 ). Risk of bias and applicability assessment were shown in Table 2 .…”

Prognostic prediction by a novel integrative inflammatory and nutritional score based on least absolute shrinkage and selection operator in esophageal squamous cell carcinoma

“…Nomogram is considered to be a reliable tool for integrating and quantifying significant risk factors for cancer prognosis ( 43 ). Nomogram, as a prognostic statistical model, can not only visually display the relevant indicators affecting the outcome in Cox PH regression analyses but also predict the survival probability through a simple graphical representation, which makes the prediction more simple and convenient ( 44 ).…”

Identification of critical prognosis signature associated with lymph node metastasis of stomach adenocarcinomas

“…An expression signature consisting of 10 angiogenesis-related genes (ITGAV, STC1, APOH, SLCO2A1, NRP1, POSTN, VTN, SERPINA5, LPL, KCNJ8) was shown to predict the prognosis of GC patients [ 11 ]. Researchers constructed a prognostic model including four genes (RASSF2, MS4A2, ANKRD33B, and ADH1B) to predict patient LNM based on The Cancer Genome Atlas (TCGA) STAD dataset [ 12 ]. However, the study was conducted only for the immune microenvironment and was not validated using an external dataset.…”