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Background <p>Early identification of gastric cancer (GC) risk among patients with precancerous lesions remains a major clinical challenge. This study aimed to develop and validate a minimally invasive mRNA-based classifier for predicting malignant transformation.</p> Methods <p>Three differentially expressed mRNAs (CCL3, CCL4, CXCL2) were identified through transcriptomic analyses of precancerous and cancerous gastric tissues and were further validated in tissue and serum samples. A logistic regression–based model which named risk signature assessment (RSA), was constructed using mRNA expression and key clinical variables.</p> Results <p>In tissue biopsy samples from 229 patients, the RSA model achieved excellent predictive performance, with an AUC of 0.853 in the training set and 0.836 in the validation set. Sensitivity and specificity reached 82.6% and 79.7%, respectively. The RSA model outperformed clinical models (AUC = 0.695) and provided better net clinical benefit across thresholds. In serum samples (n = 210), the model remained robust, yielding an AUC of 0.846 in the training cohort and 0.843 in the validation cohort, with consistent sensitivity (81.3%) and specificity (77.1%). Risk reclassification improved markedly: low-risk patients identified by the RSA model had a cancer conversion rate of only 2.9% (vs. 4.3% using clinical models), while high-risk patients had rates of 12.9% (vs. 12.2%).</p> Conclusion <p>This dual-platform mRNA classifier demonstrates strong potential for early, noninvasive identification of high-risk individuals with precancerous gastric lesions, offering a valuable tool for precision screening and clinical decision-making.</p>

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A tissue-to-blood mRNA classifier enables early detection of gastric cancer risk in precancerous lesions

  • 期刊论文

  • 发表日期：

    2026年3月25日

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  • Wenqian Ma,
  • Honghai Guo,
  • Tao Zheng,
  • Mingchang Miao,
  • Shuo Guo,
  • Jiangao Yu,
  • Ping’an Ding,
  • Qun Zhao

摘要

Background

Early identification of gastric cancer (GC) risk among patients with precancerous lesions remains a major clinical challenge. This study aimed to develop and validate a minimally invasive mRNA-based classifier for predicting malignant transformation.

Methods

Three differentially expressed mRNAs (CCL3, CCL4, CXCL2) were identified through transcriptomic analyses of precancerous and cancerous gastric tissues and were further validated in tissue and serum samples. A logistic regression–based model which named risk signature assessment (RSA), was constructed using mRNA expression and key clinical variables.

Results

In tissue biopsy samples from 229 patients, the RSA model achieved excellent predictive performance, with an AUC of 0.853 in the training set and 0.836 in the validation set. Sensitivity and specificity reached 82.6% and 79.7%, respectively. The RSA model outperformed clinical models (AUC = 0.695) and provided better net clinical benefit across thresholds. In serum samples (n = 210), the model remained robust, yielding an AUC of 0.846 in the training cohort and 0.843 in the validation cohort, with consistent sensitivity (81.3%) and specificity (77.1%). Risk reclassification improved markedly: low-risk patients identified by the RSA model had a cancer conversion rate of only 2.9% (vs. 4.3% using clinical models), while high-risk patients had rates of 12.9% (vs. 12.2%).

Conclusion

This dual-platform mRNA classifier demonstrates strong potential for early, noninvasive identification of high-risk individuals with precancerous gastric lesions, offering a valuable tool for precision screening and clinical decision-making.