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Background <p>Mast cell and nucleotide metabolism(NM) encodes the Stomach adenocarcinoma (STAD) progression and tumor immune microenvironment(TME) heterogeneity. Research targeting deciphering NM-mast cell axis in STAD can pave the way for the deeper understanding of STAD pathogenesis.</p> Methods <p>STAD stomach tissue bulk profiles(GSE26899 and GSE10326) were utilized for identification of Mast cell and nucleotide metabolism(MNM)-associated shared differentially expressed genes(DEGs) via integrative bioinformatic analysis, including Limma, ssGSEA and WGCNA. Next, Lasso-cox regression was cross-employed for elaboration of predictive model and MNM-associated hub gene identification in TCGA-STAD and GSE84437 bulk profiles for STAD patients. Besides, deep learning algorithm(SOM) was utilized for risk stratification for STAD patients in TCGA-STAD dataset. Indeed, we also elucidated the molecular and immune heterogeneity of hub gene at bulk(TCGA-STAD cohort) and single-cell transcriptomic(GSE158937) levels of STAD patients, especially in virtual malignant cells at spatial and temporal manners. Besides, GSCA database with ridge regression were cross-performed for identification of optimal therapeutic framework for STAD patient treatment and then validated by molecular docking. Finally, in vitro study examined the relationship between hub gene and STAD cancer cell proliferation, growth and metastasis.</p> Results <p>MNM can guide the STAD patient prognostic forecasting and risk stratification. Particularly, UCK2 can be considered as MNM-associated hub gene involved in STAD pathogenesis proved by in silico and in vitro studies. Indeed, HG-5-113-01 should be considered as drug reproposing framework targeting UCK2 for the treatment of STAD.</p> Conclusion <p>Our study first discovered the integration of MNM mechanisms and predictive roles for STAD patients by combining artificial intelligence(AI) and multi-omic studies.</p>

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Machine learning and multi-omic empowered risk stratification and therapeutic framework targeting nucleotide metabolism and mast cell for stomach adenocarcinoma patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月12日

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  • Yue Shi,
  • Jingwei Zhang,
  • Xiaoping Men,
  • Zhicun Yang,
  • Fang Wang

摘要

Background

Mast cell and nucleotide metabolism(NM) encodes the Stomach adenocarcinoma (STAD) progression and tumor immune microenvironment(TME) heterogeneity. Research targeting deciphering NM-mast cell axis in STAD can pave the way for the deeper understanding of STAD pathogenesis.

Methods

STAD stomach tissue bulk profiles(GSE26899 and GSE10326) were utilized for identification of Mast cell and nucleotide metabolism(MNM)-associated shared differentially expressed genes(DEGs) via integrative bioinformatic analysis, including Limma, ssGSEA and WGCNA. Next, Lasso-cox regression was cross-employed for elaboration of predictive model and MNM-associated hub gene identification in TCGA-STAD and GSE84437 bulk profiles for STAD patients. Besides, deep learning algorithm(SOM) was utilized for risk stratification for STAD patients in TCGA-STAD dataset. Indeed, we also elucidated the molecular and immune heterogeneity of hub gene at bulk(TCGA-STAD cohort) and single-cell transcriptomic(GSE158937) levels of STAD patients, especially in virtual malignant cells at spatial and temporal manners. Besides, GSCA database with ridge regression were cross-performed for identification of optimal therapeutic framework for STAD patient treatment and then validated by molecular docking. Finally, in vitro study examined the relationship between hub gene and STAD cancer cell proliferation, growth and metastasis.

Results

MNM can guide the STAD patient prognostic forecasting and risk stratification. Particularly, UCK2 can be considered as MNM-associated hub gene involved in STAD pathogenesis proved by in silico and in vitro studies. Indeed, HG-5-113-01 should be considered as drug reproposing framework targeting UCK2 for the treatment of STAD.

Conclusion

Our study first discovered the integration of MNM mechanisms and predictive roles for STAD patients by combining artificial intelligence(AI) and multi-omic studies.