A nomogram model based on the number of examined lymph nodes-related signature to predict prognosis and guide clinical therapy in gastric cancer

Abstract

Background: Increasing evidence suggests that the number of examined lymph nodes (ELNs) is strongly linked to the survivorship of gastric cancer (GC). The goal of this study was to assess the prognostic implications of the ELNs number and to construct an ELNs-based risk signature and nomogram model to predict overall survival (OS) characteristics in GC patients.

Methods: This inception cohort study included 19,317 GC patients from the U.S. Surveillance, Epidemiology, and End Results (SEER) database, who were separated into a training group and an internal validation group. The nomogram was built with the training set, then internally verified with SEER data, and externally validated with two different data sets. Based on the RNA-seq data, ELNs-related DERNAs (DElncRNAs, DEmiRNAs, andDEmRNAs) and immune cells were identified. The LASSO-Cox regression analysis was utilized to construct ELNs-related DERNAs and immune cell prognostic signature in The Cancer Genome Atlas (TCGA) cohort. The OS of subgroups with high- and low-ELN signature was compared using the Kaplan-Meier (K-M) analysis. A nomogram was successfully constructed based on the ELNs signature and other clinical characteristics. The concordance index (C-index), calibration plot, receiver operating characteristic curve, and decision curve analysis (DCA) were all used to evaluate the nomogram model. The meta-analysis, the Gene Expression Profiling Interactive Analysis database, and reverse transcription-quantitative PCR (RT-qPCR) were utilized to validate the RNA expression or abundance of prognostic genes and immune cells between GC tissues and normal gastric tissues, respectively. Finally, we analyzed the correlations between immune checkpoints, chemotherapy drug sensitivity, and risk score.

Results: The multivariate analysis revealed that the high ELNs improved OS compared with low ELNs (hazard ratio [HR] = 0.659, 95% confidence interval [CI]: 0.626-0.694, p < 0.0001). Using the training set, a nomogram incorporating ELNs was built and proven to have good calibration and discrimination (C-index [95% CI], 0.714 [0.710-0.718]), which was validated in the internal validation set (C-index [95% CI], 0.720 [0.714-0.726]), the TCGA set (C-index [95% CI], 0.693 [0.662-0.724]), and the Chinese set (C-index [95% CI], 0.750 [0.720-0.782]). An ELNs-related signature model based on ELNs group, regulatory T cells (Tregs), neutrophils, CDKN2B-AS1, H19, HOTTIP, LINC00643, MIR663AHG, TMEM236, ZNF705A, and hsa-miR-135a-5p was constructed by the LASSO-Cox regression analysis. The result showed that OS was remarkably lower in patients with high-ELNs signature compared with those with low-ELN signature (HR = 2.418, 95% CI: 1.804-3.241, p < 0.001). This signature performed well in predicting 1-, 3-, and 5-year survival (AUC [95% CI] = 0.688 [0.612-0.763], 0.744 [0.659-0.830], and 0.778 [0.647-0.909], respectively). The multivariate Cox analysis illustrated that the risk score was an independent predictor of survival for patients with GC. 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). Both the calibration plots and DCA showed that the nomogram has good predictive performance. Moreover, the signature was significantly correlated with the N stage and T stage. According to our analysis, GC patients in the low-ELN signature group may have a better immunotherapy response and OS outcome.

Conclusions: We explored the prognostic role of ELNs in GC and successfully constructed an ELNs signature linked to the GC prognosis in TCGA. The findings manifested that the signature is a powerful predictive indicator for patients with GC. The signature might contain potential biomarkers for treatment response prediction for GC patients. Additionally, we identified a novel and robust nomogram combining the characteristics of ELNs and clinical factors for predicting 1-, 3-, and 5-year OS in GC patients, which will facilitate personalized survival prediction and aid clinical decision-making in GC patients.

Keywords: ELNs signature; chemotherapy; gastric cancer (GC); immunotherapy; nomogram; the number of lymph nodes examined (ELNs).

Figure 1

Flowchart illustrating gastric cancer patient selection for this study.

Figure 2

Flowchart of data analysis and experiment.

Figure 3

Survival analyses of OS in the ELNs group. (A) OS for high (>16) and low (≤16) ELNs in the training cohort. (B) OS for high (>16) and low (≤16) ELNs in the external validation cohort (The Cancer Genome Atlas). ELNs, the number of examined lymph nodes; OS, overall survival.

Figure 4

Nomogram to predict the OS of GC patients. ELNs, the number of examined lymph nodes; LN status, lymph node status; OS, overall survival; GC, gastric cancer. ***p < 0.001.

Figure 5

Evaluation of the nomogram. (A) The time-dependent AUC value of the nomogram in the training cohort (SEER), the internal validation cohort (SEER), the external validation cohort (TCGA), and the Chinese validation cohort. (B) The C-index of the nomogram in the training cohort (SEER), the internal validation cohort (SEER), the external validation cohort (TCGA), and the Chinese validation cohort. Calibration plots of the nomogram performed in the (C) SEER training, (D) the SEER internal validation, (E) the TCGA validation, and (F) the Chinese validation set, respectively.

Figure 6

A decision curve analysis constructed for the nomogram that depicted the clinical net benefit for each cohort. (A–C) SEER training. (D–F) SEER internal validation. (G–I) TCGA validation. (J–L) Chinese validation set. As shown by the horizontal blue solid line, all patients are assumed not to be treated, whereas the solid red line indicates that all patients are treated. In all different cohorts, the nomogram provided superior net benefit across a range of threshold probabilities for decision curve analysis.

Figure 7

The differentially expressed mRNAs, lncRNAs, and miRNAs between the high-ELN and low-ELN groups were identified using the “DESeq2” package with R. The cutoff that we set was log2 (foldchange) > 0.5 or < −0.5 and p < 0.05. (A, C, E) The volcano plots of differentially expressed mRNAs (n = 664), lncRNAs (n = 530), and miRNAs (n = 20). Blue and red dots represent downregulated genes and upregulated genes, respectively. (B, D, F) Heat maps of the differentially expressed lncRNAs, miRNAs, and mRNAs between the high- and low-ELN groups.

Figure 8

Sankey diagram of the competing endogenous RNA network in GC. Each rectangle represents a gene, and the connectedness of each gene is shown according to the size of the rectangle.

Figure 9

Analysis of the ELNs-related TIICs. (A) Distribution of 22 types of TIICs in gastric cancer. (B) Box plot displays the abundance differentiation of 22 types of immune cells between the GC samples with low- and high-ELN groups, and the significance test was carried out by the Wilcoxon rank-sum test. (C) Kaplan–Meier and log-rank test for seven immune cells passed the Wilcoxon rank-sum test. Four representative immune cells including plasma cells, neutrophils, and regulatory T cells (Tregs) are shown based on their respective optimal cutoff values (all p < 0.1). ELNs, the number of lymph nodes examined; TIICs, tumor-infiltrating immune cells; GC, gastric cancer. *p < 0.05; **p < 0.01.ns, no significance.

Figure 10

The correlation result of the coexpression analysis between tumor-infiltrating immune cells and DERNAs related to the prognosis of gastric cancer patients. (A) The coexpression heat map illustrated the coexpression patterns of 10 genes and 3 immune cells. (B–D) Neutrophils and hsa-miR-135a-5p (R = -0.13, p = 0.022), plasma cells and hsa-miR-135a-5p (R = 0.16, p = 0.0034), and regulatory T cells and APOA1 (R = 0.14, p =0.013). *p < 0.05; **p < 0.01; ***p < 0.001.

Figure 11

Identifying prognostic genes and cells for developing an ELNs signature. (A) LASSO coefficient profiles of the 14 survival-related factors in the TCGA cohort. (B) Selection of the optimal parameter (lambda.1se = 0.05892685) in the LASSO regression model. (C) The distribution of the ELNs signature between the high- and low-ELN groups using Mann–Whitney U-test. (D) Kaplan–Meier survival curve of patients with high- and low-ELN signature groups. (E) Distribution of the ELNs signature in the TCGA cohort. ELNs, the number of lymph nodes examined.

Figure 12

Reverse transcription–quantitative PCR result of eight RNAs expression in 30 pairs of gastric cancer tissues and adjacent non-tumor tissues. (A) H19. (B) HOTTIP. (C) CDKN2B-AS1. (D) LINC00643. (E) MIR663AHG. (F) TMEM236. (G) hsa-miR-135a-5p. (H) ZNF705A.

Figure 13

Comparison of TMEM236 expression at the protein level immunohistochemistry pictures. (A) normal (left) and (B, C) tumor (middle and right) tissues.

Figure 14

Prognostic analysis of the gastric cancer patients in the TCGA cohort. (A, B) Forest plots of univariate and multivariate Cox regression analysis between the ELNs signature and clinicopathological characteristics regarding OS in the TCGA cohort. (C–E) Time-dependent receiver operating characteristic analyses were constructed by the ELNs signature, ELNs group, age + T stage + M stage, etc., to show their prognostic ability in the TCGA cohort. ELNs, the number of lymph nodes examined; OS, overall survival.

Figure 15

Establishment and assessment of the nomogram. (A) The nomogram plot was built based on the ELNs signature, age, M stage, and T stage. (B) The calibration curves showed that the predicted OS of the nomogram is highly concordant with the actual OS. (C–E) DCAs of the nomogram for 1-, 3-, and 5-year OS in the TCGA cohort. OS, overall survival; DCA, decision curve analysis. *p < 0.05; **p < 0.01; ***p < 0.001.