A nomogram model based on the number of examined lymph nodes–related signature to predict prognosis and guide clinical therapy in gastric cancer - Li, Huling; Lin, Dandan; Yu, Zhen; Li, Hui; Zhao, Shi; Hainisayimu, Tuersun; Liu, Lin; Wang, Kai (Frontiers in Immunology, 2022)
Background: The goal of this study was to assess the prognostic implications of ELN number and to construct an ELN-based risk signature and nomogram model to predict overall survival characteristics in GC patients. Methods: This inception cohort study included 19,317 GC patients from the US Surveillance, Epidemiology, and End Results (SEER) database, who were separated into a training and an internal validation group. Based on the RNA-seq data, LASSO-Cox regression analysis was utilized to construct ELNs-related DERNAs and immune cells prognostic signature in the TCGA cohort. A nomogram was successfully constructed based on the ELNs signature. The Meta-analysis, GEPIA database, and RT-qPCR were utilized to validate the RNA expression or abundance of prognostic genes and immune cells between GC tissues and normal GC tissues respectively. Results: Using the training set, a nomogram incorporating ELNs was built and proven good calibration and discrimination (C-index 95% confidence interval [CI], 0.714 [0.710-0.718]), which was validated in the internal validation set (0.720 [0.714-0.726]), the TCGA set (0.693 [0.662-0.724]) and the Chinese set (0.750 [0.720-0.782]), respectively. An ELNs-related signature model based on ELNs group, T cells regulatory (Tregs), Neutrophils, CDKN2B-AS1, H19, HOTTIP, LINC00643, MIR663AHG, TMEM236, ZNF705A and hsa-miR-135a-5p was constructed by LASSO-Cox regression analysis. Moreover, the expression of prognostic genes (LINC00643, TMEM236 and hsa-miR-135a-5p) displayed differences between GC tissues and adjacent non-tumor tissues. The C-index of the nomogram that can be used to predict the OS of GC patients was 0.710 (95% CI: 0.663-0.753). Conclusions: The signature might contain potential biomarkers for treatment response prediction for GC patients. we established a novel nomogram, which will facilitate clinical decision making in GC patients.