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Molecular Diagnostics

Artificial intelligence-driven microRNA signature for early detection of gastric cancer: discovery and clinical functional exploration

British Journal of Cancer volume 132, pages 957–972 (2025)Cite this article

Subjects

Abstract

Background

Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide, with late-stage diagnoses frequently leading to poor outcomes. This underscores the need for effective early-stage gastric cancer (ESGC) diagnostics.

Methods

We introduce ESGCmiRD, an innovative artificial intelligence-driven strategy that identifies a miRNA signature for ESGC detection by integrating robust expression patterns, ESGC relevance, and regulatory capabilities of microRNA (miRNA) based on multiple networks. Expression and biological roles of miRNAs in GC were validated and explored via bioinformatics analysis and in vitro studies. miRNA-target interaction was confirmed by dual-luciferase reporter assay. Molecular docking predicted miRNA-drug binding affinities, assessing the miRNA signature’s therapeutic potential.

Results

ESGCmiRD identified a blood miRNA signature (miR-320b, miR-222-3p, miR-181a-5p, miR-103a-3p, miR-107) for ESGC detection, demonstrated high diagnostic accuracy with AUC values of 0.986, 0.977, 0.815, and 0.811 in the test and three validation sets (GSE211692, TCGA-STAD, and our cohort), respectively. The five miRNAs were overexpressed in ESGC plasma and directly target PTEN, promoting GC cell proliferation, migration, and invasion. Molecular docking suggested Paclitaxel had the strongest potential interaction with these miRNAs.

Conclusion

This method identifies a robust miRNA signature for ESGC detection and sheds light on gastric carcinogenesis mechanisms, opening doors for potential therapeutic strategies.

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Fig. 1: Overall study design flowchart.
Fig. 2: Identification of miRNA signature candidates for ESGC detection by ESGCmiRD.
Fig. 3: Biomarker acquisition through ESGCmiRD and validation by qRT-PCR.
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Data availability

All data generated or analysed are included in this article and its supplementary information files. The code can be found at https://github.com/ljc7878/ESGCmiRD.git.

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49.

    PubMed  Google Scholar 

  2. Li Y, Feng A, Zheng S, Chen C, Lyu J. Recent estimates and predictions of 5-year survival in patients with gastric cancer: a model-based period analysis. Cancer Control: J Moffitt Cancer Cent. 2022;29:10732748221099227.

    Google Scholar 

  3. Smyth EC, Nilsson M, Grabsch HI, van Grieken NC, Lordick F. Gastric cancer. Lancet. 2020;396:635–48.

    CAS  PubMed  Google Scholar 

  4. Joshi SS, Badgwell BD. Current treatment and recent progress in gastric cancer. Cancer J Clin. 2021;71:264–79.

    Google Scholar 

  5. Huang Z-B, Zhang H-T, Yu B, Yu D-H. Cell-free DNA as a liquid biopsy for early detection of gastric cancer. Oncol Lett. 2021;21:3.

    PubMed  Google Scholar 

  6. Ma S, Zhou M, Xu Y, Gu X, Zou M, Abudushalamu G, et al. Clinical application and detection techniques of liquid biopsy in gastric cancer. Mol Cancer. 2023;22:7.

    PubMed  PubMed Central  Google Scholar 

  7. Guo X, Peng Y, Song Q, Wei J, Wang X, Ru Y, et al. A liquid biopsy signature for the early detection of gastric cancer in patients. Gastroenterology. 2023;165:402–13.e13.

    CAS  PubMed  Google Scholar 

  8. Ignatiadis M, Sledge GW, Jeffrey SS. Liquid biopsy enters the clinic—implementation issues and future challenges. Nat Rev Clin Oncol. 2021;18:297–312.

    PubMed  Google Scholar 

  9. Lone SN, Nisar S, Masoodi T, Singh M, Rizwan A, Hashem S, et al. Liquid biopsy: a step closer to transform diagnosis, prognosis and future of cancer treatments. Mol Cancer. 2022;21:79.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Nikanjam M, Kato S, Kurzrock R. Liquid biopsy: current technology and clinical applications. J Hematol Oncol. 2022;15:131.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Alix-Panabières C, Pantel K. Liquid biopsy: from discovery to clinical application. Cancer Discov. 2021;11:858–73.

    PubMed  Google Scholar 

  12. Zhang Z, Wu H, Chong W, Shang L, Jing C, Li L. Liquid biopsy in gastric cancer: predictive and prognostic biomarkers. Cell Death Dis. 2022;13:903.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Farasati Far B, Vakili K, Fathi M, Yaghoobpoor S, Bhia M, Naimi-Jamal MR. The role of microRNA-21 (miR-21) in pathogenesis, diagnosis, and prognosis of gastrointestinal cancers: a review. Life Sci. 2023;316:121340.

    CAS  PubMed  Google Scholar 

  14. Guo T, Tang X-H, Gao X-Y, Zhou Y, Jin B, Deng Z-Q, et al. A liquid biopsy signature of circulating exosome-derived mRNAs, miRNAs and lncRNAs predict therapeutic efficacy to neoadjuvant chemotherapy in patients with advanced gastric cancer. Mol Cancer. 2022;21:216.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Yan W, Chen Y, Hu G, Shi T, Liu X, Li J, et al. MiR-200/183 family-mediated module biomarker for gastric cancer progression: an AI-assisted bioinformatics method with experimental functional survey. J Transl Med. 2023;21:163.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. So JBY, Kapoor R, Zhu F, Koh C, Zhou L, Zou R, et al. Development and validation of a serum microRNA biomarker panel for detecting gastric cancer in a high-risk population. Gut. 2021;70:829–37.

    CAS  PubMed  Google Scholar 

  17. Abe S, Matsuzaki J, Sudo K, Oda I, Katai H, Kato K, et al. A novel combination of serum microRNAs for the detection of early gastric cancer. Gastric Cancer. 2021;24:835–43.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.

    PubMed  PubMed Central  Google Scholar 

  19. Kolde R, Laur S, Adler P, Vilo J. Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics. 2012;28:573–80.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinforma. 2008;9:559.

    Google Scholar 

  21. Lin Y, Qi X, Chen J, Shen B. Multivariate competing endogenous RNA network characterization for cancer microRNA biomarker discovery: a novel bioinformatics model with application to prostate cancer metastasis. Precis Clin Med. 2022;5:pbac001.

    PubMed  PubMed Central  Google Scholar 

  22. Lin Y, Chen F, Shen L, Tang X, Du C, Sun Z, et al. Biomarker microRNAs for prostate cancer metastasis: screened with a network vulnerability analysis model. J Transl Med. 2018;16:134.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E. The role of site accessibility in microRNA target recognition. Nat Genet. 2007;39:1278–84.

    CAS  PubMed  Google Scholar 

  24. Huang H-Y, Lin Y-C-D, Cui S, Huang Y, Tang Y, Xu J, et al. miRTarBase update 2022: an informative resource for experimentally validated miRNA-target interactions. Nucleic Acids Res. 2022;50:D222–30.

    CAS  PubMed  Google Scholar 

  25. Yan W, Liu X, Wang Y, Han S, Wang F, Liu X, et al. Identifying drug targets in pancreatic ductal adenocarcinoma through machine learning, analyzing biomolecular networks, and structural modeling. Front Pharmacol. 2020;11:534.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Csardi G, Nepusz T. The Igraph software package for complex network research. InterJournal. Complex Systems; 2006. p. 1695.

  27. Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics J Integr Biol. 2012;16:284–7.

    CAS  Google Scholar 

  28. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-delta delta C(T)) method. Methods. 2001;25:402–8.

    CAS  PubMed  Google Scholar 

  29. Knox C, Wilson M, Klinger CM, Franklin M, Oler E, Wilson A, et al. DrugBank 6.0: the DrugBank Knowledgebase for 2024. Nucleic Acids Res. 2024;52:D1265–75.

    CAS  PubMed  Google Scholar 

  30. Kozomara A, Birgaoanu M, Griffiths-Jones S. miRBase: from microRNA sequences to function. Nucleic Acids Res. 2019;47:D155–62.

    CAS  PubMed  Google Scholar 

  31. Abramson J, Adler J, Dunger J, Evans R, Green T, Pritzel A, et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature. 2024;630:493–500.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31:455–61.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Ling H, Fabbri M, Calin GA. MicroRNAs and other non-coding RNAs as targets for anticancer drug development. Nat Rev Drug Discov. 2013;12:847–65.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Kim T, Croce CM. MicroRNA: trends in clinical trials of cancer diagnosis and therapy strategies. Exp Mol Med. 2023;55:1314–21.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Winkle M, El-Daly SM, Fabbri M, Calin GA. Noncoding RNA therapeutics—challenges and potential solutions. Nat Rev Drug Discov. 2021;20:629–51.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Sun Z, Zhang Q, Yuan W, Li X, Chen C, Guo Y, et al. MiR-103a-3p promotes tumour glycolysis in colorectal cancer via hippo/YAP1/HIF1A axis. J Exp Clin Cancer Res. 2020;39:250.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Leslie PL, Franklin DA, Liu Y, Zhang Y. p53 regulates the expression of LRP1 and apoptosis through a stress intensity-dependent microRNA feedback loop. Cell Rep. 2018;24:1484–95.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Wang P, Zhou Y, Wang J, Zhou Y, Zhang X, Liu Y, et al. miR-107 reverses the multidrug resistance of gastric cancer by targeting the CGA/EGFR/GATA2 positive feedback circuit. J Biol Chem. 2024;300:107522.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Li J, Yen C, Liaw D, Podsypanina K, Bose S, Wang SI, et al. PTEN, a putative protein tyrosine phosphatase gene mutated in human brain, breast, and prostate cancer. Science. 1997;275:1943–7.

    CAS  PubMed  Google Scholar 

  40. Xu W, Yang Z, Xie C, Zhu Y, Shu X, Zhang Z, et al. PTEN lipid phosphatase inactivation links the hippo and PI3K/Akt pathways to induce gastric tumorigenesis. J Exp Clin Cancer Res. 2018;37:198.

    PubMed  PubMed Central  Google Scholar 

  41. Messina B, Lo Sardo F, Scalera S, Memeo L, Colarossi C, Mare M, et al. Hippo pathway dysregulation in gastric cancer: from Helicobacter pylori infection to tumor promotion and progression. Cell Death Dis. 2023;14:21.

    PubMed  PubMed Central  Google Scholar 

  42. Baird RD, Tan DS, Kaye SB. Weekly paclitaxel in the treatment of recurrent ovarian cancer. Nat Rev Clin Oncol. 2010;7:575–82.

    CAS  PubMed  Google Scholar 

  43. Yoon HH. Ramucirumab plus paclitaxel for gastric cancer in China. Lancet Gastroenterol Hepatol. 2021;6:975–6.

    PubMed  Google Scholar 

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Acknowledgements

First of all, I would like to express my sincere gratitude to my supervisor, Prof. Weichang Chen, for your careful guidance and unwavering support, which greatly benefited me in the process of selecting the topic, designing the research methodology and writing the thesis. Your rigorous attitude and profound knowledge have provided endless insights into my research and pushed me to continuously strive for excellence. Meanwhile, I would like to thank my supervisors, Prof Yan Wenying and Prof Shi Tongguo, who have given me important guidance and support in my research journey, and whose high-level insights and rich research experience have enabled me to avoid many misunderstandings in my research and provided valuable support for my academic growth. During the sample collection process of this project, I am especially grateful to all the colleagues who participated in the sample collection work. Without their hard work and precision, this study could not have been carried out smoothly. Finally, I would like to thank all those who have helped and supported me throughout my academic career.

Funding

This research was supported by the Key Research and Development Programme of Jiangsu Province (BE2020656); Medical and Health Science and Technology Innovation Project of Suzhou (SKY2022010, SKYD2022097); Foundation of Suzhou Medical College of Soochow University (MP13405423, MX13401423); National Natural Science Foundation of China (82270561, 82073156); Jiangsu Provincial Medical Key Discipline (ZDXK202246); and the Priority Academic Programme Development of Jiangsu Higher Education Institutions (SKYD2022097).

Author information

Author notes

  1. These authors contributed equally: Jiachun Lu, Yuqi Chen.

Authors and Affiliations

  1. Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China

    Jiachun Lu, Xin Liu, Jiayu Wang, Yuxin He & Weichang Chen

  2. Jiangsu Institute of Clinical Immunology, The First Affiliated Hospital of Soochow University, Suzhou, China

    Jiachun Lu, Xin Liu, Jiayu Wang, Yuxin He, Tongguo Shi & Weichang Chen

  3. Department of Gastroenterology, The Fourth Affiliated Hospital of Soochow University, Suzhou, China

    Yuqi Chen

  4. Jiangsu Key Laboratory of Clinical Immunology, Soochow University, Suzhou, China

    Weichang Chen

  5. School of Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, China

    Wenying Yan

  6. Suzhou Key Lab of Multi-modal Data Fusion and Intelligent Healthcare, Suzhou, China

    Wenying Yan

  7. Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Suzhou, China

    Wenying Yan

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  1. Jiachun Lu
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Contributions

Jiachun Lu: writing—original draft, formal analysis, investigation, methodology, software, visualisation, data curation. Yuqi Chen: conceptualisation, methodology, investigation, resources. Jiayu Wang: conceptualisation, methodology, investigation. Yuxin He: methodology, investigation. Xin Liu: methodology, investigation. Tongguo Shi: project administration, methodology, supervision, validation, writing—review & editing. Weichang Chen: conceptualisation, funding acquisition, project administration, supervision, writing—review & editing. Wenying Yan: conceptualisation, methodology, project administration, supervision, funding acquisition, writing—review & editing.

Corresponding authors

Correspondence to Tongguo Shi, Weichang Chen or Wenying Yan.

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Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institution and with the 1964 Helsinki Declaration. Written informed consent was obtained from individual or guardian participants. The study was approved by the Bioethics Committee of the First Affiliated Hospital of Soochow University (Approval No. 2021068). All animal experiments were performed in accordance with the institutional guidelines of the Soochow Animal Care and Use Committee (No.202412A0046).

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Lu, J., Chen, Y., Liu, X. et al. Artificial intelligence-driven microRNA signature for early detection of gastric cancer: discovery and clinical functional exploration. Br J Cancer 132, 957–972 (2025). https://doi.org/10.1038/s41416-025-02984-9

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