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Background <p>Prognostication for advanced gastric cancer remains sub-optimal because conventional staging systems overlook systemic inflammation and tumour-immune contexture. We investigated whether incorporating routine immune biomarkers into machine-learning survival models improves risk stratification.</p> Methods <p>A TRIPOD-compliant, single-centre cohort of 300 patients with histologically confirmed gastric adenocarcinoma (2020–2023) was analysed. Baseline variables comprised demographics, TNM stage, C-reactive protein (CRP), and four immune markers assayed on archival tissue: PD-L1 combined-positive score (CPS), microsatellite instability (MSI), tumour mutational burden (TMB), and quantitative CD8⁺-cell density. Missing values (&lt; 15%) were multiply imputed. Feature selection combined univariable screening (<i>P</i> &lt; 0.10), clinical judgement, and LASSO. An eight-variable multivariable Cox model and a tuned Random Survival Forest (RSF) were developed; performance was assessed using optimism-corrected C-index, time-dependent AUC, calibration, decision-curve, and clinical-impact analyses.</p> Results <p>Age, T stage, N stage, and CRP were independent adverse predictors, whereas PD-L1 CPS ≥ 1, MSI-H, TMB-high (≥ 10 mut Mb⁻¹), and high CD8⁺ density were protective. The combined Cox model achieved an optimism-corrected C-index of 0.78, representing a 0.05 improvement over the clinical-only model (<i>P</i> &lt; 0.001). RSF further improved discrimination to 0.82, with AUCs of 0.85, 0.83, and 0.81 at 12, 24, and 36 months, respectively. Decision-curve analysis showed that the RSF conferred a net-benefit gain of 0.06 at a 30% treatment threshold, equating to six additional correctly managed patients per 100. Kaplan-Meier curves confirmed significant survival separation for RSF-defined risk strata and for each immune marker (all log-rank <i>P</i> ≤ 0.041).</p> Conclusions <p>Integrating PD-L1, MSI, TMB, and CD8⁺ density with standard clinicopathological factors yields materially better survival prediction for gastric cancer, particularly when modelled with an explainable machine-learning approach. These readily assayable biomarkers could enable more precise prognostic stratification and treatment individualisation once validated externally.</p>

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Prognostic model for gastric cancer integrating immune biomarkers and machine learning approaches

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月1日

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  • Mingpai Ge,
  • Bowen Zheng,
  • Jiaqi Jiang,
  • Lu Cui,
  • Han Wang,
  • Lu Zhan,
  • Xusheng Chang,
  • Kai Yin,
  • Jun Xu,
  • Haobing Yu

摘要

Background

Prognostication for advanced gastric cancer remains sub-optimal because conventional staging systems overlook systemic inflammation and tumour-immune contexture. We investigated whether incorporating routine immune biomarkers into machine-learning survival models improves risk stratification.

Methods

A TRIPOD-compliant, single-centre cohort of 300 patients with histologically confirmed gastric adenocarcinoma (2020–2023) was analysed. Baseline variables comprised demographics, TNM stage, C-reactive protein (CRP), and four immune markers assayed on archival tissue: PD-L1 combined-positive score (CPS), microsatellite instability (MSI), tumour mutational burden (TMB), and quantitative CD8⁺-cell density. Missing values (< 15%) were multiply imputed. Feature selection combined univariable screening (P < 0.10), clinical judgement, and LASSO. An eight-variable multivariable Cox model and a tuned Random Survival Forest (RSF) were developed; performance was assessed using optimism-corrected C-index, time-dependent AUC, calibration, decision-curve, and clinical-impact analyses.

Results

Age, T stage, N stage, and CRP were independent adverse predictors, whereas PD-L1 CPS ≥ 1, MSI-H, TMB-high (≥ 10 mut Mb⁻¹), and high CD8⁺ density were protective. The combined Cox model achieved an optimism-corrected C-index of 0.78, representing a 0.05 improvement over the clinical-only model (P < 0.001). RSF further improved discrimination to 0.82, with AUCs of 0.85, 0.83, and 0.81 at 12, 24, and 36 months, respectively. Decision-curve analysis showed that the RSF conferred a net-benefit gain of 0.06 at a 30% treatment threshold, equating to six additional correctly managed patients per 100. Kaplan-Meier curves confirmed significant survival separation for RSF-defined risk strata and for each immune marker (all log-rank P ≤ 0.041).

Conclusions

Integrating PD-L1, MSI, TMB, and CD8⁺ density with standard clinicopathological factors yields materially better survival prediction for gastric cancer, particularly when modelled with an explainable machine-learning approach. These readily assayable biomarkers could enable more precise prognostic stratification and treatment individualisation once validated externally.