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Development and validation of a prognostic and predictive 32-gene signature for gastric cancer

Author

Listed:
  • Jae-Ho Cheong

    (Yonsei University College of Medicine
    Yonsei University College of Medicine
    Yonsei University College of Medicine)

  • Sam C. Wang

    (University of Texas Southwestern Medical Center)

  • Sunho Park

    (Department of Artificial Intelligence and Informatics, Mayo Clinic)

  • Matthew R. Porembka

    (University of Texas Southwestern Medical Center)

  • Alana L. Christie

    (University of Texas Southwestern Medical Center)

  • Hyunki Kim

    (Yonsei University College of Medicine)

  • Hyo Song Kim

    (Yonsei University College of Medicine)

  • Hong Zhu

    (University of Texas Southwestern Medical Center)

  • Woo Jin Hyung

    (Yonsei University College of Medicine)

  • Sung Hoon Noh

    (Yonsei University College of Medicine)

  • Bo Hu

    (Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic)

  • Changjin Hong

    (Department of Artificial Intelligence and Informatics, Mayo Clinic)

  • John D. Karalis

    (University of Texas Southwestern Medical Center)

  • In-Ho Kim

    (The Catholic University of Korea)

  • Sung Hak Lee

    (The Catholic University of Korea)

  • Tae Hyun Hwang

    (Department of Artificial Intelligence and Informatics, Mayo Clinic
    Department of Immunology, Mayo Clinic)

Abstract

Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this retrospective analysis, we use our machine learning algorithm NTriPath to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identify four molecular subtypes that are prognostic for survival. We then built a support vector machine with linear kernel to generate a risk score that is prognostic for five-year overall survival and validate the risk score using three independent datasets. We also find that the molecular subtypes predict response to adjuvant 5-fluorouracil and platinum therapy after gastrectomy and to immune checkpoint inhibitors in patients with metastatic or recurrent disease. In sum, we show that the 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated using large patient cohorts in a prospective manner.

Suggested Citation

  • Jae-Ho Cheong & Sam C. Wang & Sunho Park & Matthew R. Porembka & Alana L. Christie & Hyunki Kim & Hyo Song Kim & Hong Zhu & Woo Jin Hyung & Sung Hoon Noh & Bo Hu & Changjin Hong & John D. Karalis & In, 2022. "Development and validation of a prognostic and predictive 32-gene signature for gastric cancer," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28437-y
    DOI: 10.1038/s41467-022-28437-y
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    Cited by:

    1. Yangzi Chen & Bohong Wang & Yizi Zhao & Xinxin Shao & Mingshuo Wang & Fuhai Ma & Laishou Yang & Meng Nie & Peng Jin & Ke Yao & Haibin Song & Shenghan Lou & Hang Wang & Tianshu Yang & Yantao Tian & Pen, 2024. "Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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