Nature Communications (Feb 2022)
Development and validation of a prognostic and predictive 32-gene signature for gastric cancer
- 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-Ho Kim,
- Sung Hak Lee,
- Tae Hyun Hwang
Affiliations
- Jae-Ho Cheong
- Department of Surgery, Yonsei University College of Medicine
- Sam C. Wang
- Division of Surgical Oncology, Department of Surgery, University of Texas Southwestern Medical Center
- Sunho Park
- Department of Artificial Intelligence and Informatics, Mayo Clinic
- Matthew R. Porembka
- Division of Surgical Oncology, Department of Surgery, University of Texas Southwestern Medical Center
- Alana L. Christie
- Department of Clinical Sciences, University of Texas Southwestern Medical Center
- Hyunki Kim
- Department of Pathology, Yonsei University College of Medicine
- Hyo Song Kim
- Department of Internal Medicine, Division of Medical Oncology, Yonsei University College of Medicine
- Hong Zhu
- Department of Clinical Sciences, University of Texas Southwestern Medical Center
- Woo Jin Hyung
- Department of Surgery, Yonsei University College of Medicine
- Sung Hoon Noh
- Department of Surgery, 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
- Division of Surgical Oncology, Department of Surgery, University of Texas Southwestern Medical Center
- In-Ho Kim
- Department of Internal Medicine, Division of Medical Oncology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea
- Sung Hak Lee
- Department of Hospital Pathology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea
- Tae Hyun Hwang
- Department of Artificial Intelligence and Informatics, Mayo Clinic
- DOI
- https://doi.org/10.1038/s41467-022-28437-y
- Journal volume & issue
-
Vol. 13,
no. 1
pp. 1 – 9
Abstract
The ability to predict the survival and response to treatment of cancer patients may improve patient care. Here, the authors generate a 32 gene signature that can predict the survival and response to treatment in gastric cancer patients.
