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<p>Gastric cancer detection remains challenging due to the lack of noninvasive early diagnostic tools. This study investigates profiling of serum volatile organic compounds (VOCs) using gas chromatography-ion mobility spectrometry (GC–IMS) for gastric cancer screening. Serum samples were obtained from 277 participants, including 123 patients with gastric cancer, 38 patients with precancerous diseases (PD), and 116 healthy controls (HC). In the model development group, Kruskal–Wallis tests showed that the levels of 19 VOCs differed significantly among gastric cancer, PD, and HC groups (p &lt; 0.05). Based on the VOCs that differed significantly, a support vector machine (SVM) model achieved the best performance among the six models tested. Using importance ranking and forward selection, 11 VOCs were selected for the final model, achieving 96.4% accuracy in the validation set and 92.9% in an independent test set, showing higher diagnostic accuracy than the traditional tumor marker carcinoembryonic antigen. The model also achieved 100% sensitivity and &gt; 90% specificity for detecting early gastric cancer in both the validation and test sets. Collectively, our findings suggest that GC–IMS–based serum VOC profiling may offer a potential noninvasive approach for gastric cancer detection.</p>

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GC–IMS-based analysis of serum volatile organic compounds for diagnosis of gastric cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月5日

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  • Yuxiao Zhao,
  • Yueming Xin,
  • Mai Mao,
  • Xin Zheng,
  • Fuwei Liang,
  • Xin Zhang,
  • Chengxi Sun,
  • Tong Liu,
  • Nannan Ning,
  • Helgi B. Schiöth,
  • Yanli Zhang,
  • Yi Zhang

摘要

Gastric cancer detection remains challenging due to the lack of noninvasive early diagnostic tools. This study investigates profiling of serum volatile organic compounds (VOCs) using gas chromatography-ion mobility spectrometry (GC–IMS) for gastric cancer screening. Serum samples were obtained from 277 participants, including 123 patients with gastric cancer, 38 patients with precancerous diseases (PD), and 116 healthy controls (HC). In the model development group, Kruskal–Wallis tests showed that the levels of 19 VOCs differed significantly among gastric cancer, PD, and HC groups (p < 0.05). Based on the VOCs that differed significantly, a support vector machine (SVM) model achieved the best performance among the six models tested. Using importance ranking and forward selection, 11 VOCs were selected for the final model, achieving 96.4% accuracy in the validation set and 92.9% in an independent test set, showing higher diagnostic accuracy than the traditional tumor marker carcinoembryonic antigen. The model also achieved 100% sensitivity and > 90% specificity for detecting early gastric cancer in both the validation and test sets. Collectively, our findings suggest that GC–IMS–based serum VOC profiling may offer a potential noninvasive approach for gastric cancer detection.