AUTHOR=Luo Haiping , Yu Xiaoming , Li Xinming , Yin Dongzhi , Cao Yugang 

TITLE=Development and validation of a Log odds of negative lymph nodes/T stage ratio-based prognostic model for gastric cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1554270

DOI=10.3389/fonc.2025.1554270

ISSN=2234-943X

ABSTRACT=PurposeTo investigate the impact of the ratio of T stage to logarithm of negative lymph nodes (LONT) on the prognosis of gastric cancer patients, and to construct and evaluate CoxPH, RSF, and DeepSurv predictive models for their prognosis.MethodsA retrospective analysis of clinical, pathological, and prognostic data of patients with gastric cancer from the SEER cohort, TCGA cohort, and GSE62254 cohort was performed. Patients were divided into high-risk and low-risk groups based on the median LONT value. Kaplan-Meier survival curves and log-rank tests were used to compare survival differences between groups. Restricted cubic spline curves, univariate, and multivariate Cox regression analyses were conducted to assess the effect of LONT on patient prognosis. Simultaneously, We sought to develop and validate a novel nomogram based on LONT for predicting overall survival in individual patients with gastric cancer. The performance of the nomogram was evaluated based on the receiver operating characteristic (ROC) curve, calibration curve, and the decision curve analysis (DCA).Weighted gene coexpression network analysis (WGCNA) was used to screened co-expression modules and genes related to LONT, Then Pathway enrichment was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis for related genes. COX, RSF, and DeepSurv models were constructed using LONT and clinicopathological features to predict overall survival in gastric cancer patients and validated. The predictive performance of these models was evaluated using C-index, time-dependent AUC, and overall Brier score.ResultsIn the SEER, TCGA, and GSE62254 cohorts, gastric cancer patients with high LONT expression demonstrated significantly prolonged overall survival compared to those with low expression (P < 0.05). Elevated LONT levels were associated with improved cancer-specific survival in the SEER cohort, disease-specific survival in the TCGA cohort, and disease-free survival in the GSE62254 cohort (P < 0.05). A negative linear relationship between LONT and the hazard ratio for overall survival was observed (P < 0.05), confirming its role as an independent prognostic factor. In the SEER and GSE62254 datasets, LONT outperformed conventional clinicopathological features in predicting overall survival (P < 0.05). The LONT-integrated OS nomogram exhibited robust accuracy, supported by favorable C-index values, well-calibrated plots, and superior net benefit. The CoxPH model surpassed the traditional TNM staging system in discrimination (P < 0.05) while maintaining better calibration than RSF and DeepSurv models. Weighted gene co-expression network analysis (WGCNA) of the TCGA-STAD cohort (soft-threshold power β = 4, R, = 0.92) identified 23 modules, four of which (blue, grey60, red, tan) were strongly correlated with LONT status (|r| > 0.5, P < 0.05). Hub gene screening (|MM| > 0.8, |GS| > 0.1) prioritized 480 genes enriched in focal adhesion, ECM organization, and collagen assembly (P < 0.001, FDR < 0.05). Similarly, WGCNA of the GSE62254 cohort (o = 3, R, = 0.88) revealed three LONT-associated modules (black, blue, cyan; |r| > 0.5, P < 0.05), yielding 111 hub genes. Cross-cohort pathway analysis highlighted dysregulation of cGMP-PKG and relaxin signaling, as well as ECM-integrin interactions. Critically, tumors with low LONT exhibited transcriptional signatures indicative of disrupted ECM homeostasis, providing a mechanistic basis for their aggressive clinical behavior.ConclusionLONT is closely related to the overall survival (OS) of gastric cancer patients, and the COX model based on LONT can effectively predict the OS of these patients.