Machine Learning-Driven discovery of immunogenic cell Death-Related biomarkers and molecular classification for diabetic ulcers.

Journal: Gene
Published Date: Sep 17, 2024

Abstract

In this study, we redefine the diagnostic landscape of diabetic ulcers (DUs), a major diabetes complication. Our research uncovers new biomarkers linked to immunogenic cell death (ICD) in DUs by utilizing RNA-sequencing data of Gene Expression Omnibus (GEO) analysis combined with a comprehensive database interrogation. Employing a random forest algorithm, we have developed a diagnostic model that demonstrates improved accuracy in distinguishing DUs from normal tissue, with satisfactory results from ROC analysis. Beyond mere diagnosis, our model categorizes DUs into novel molecular classifications, which may enhance our comprehension of their underlying pathophysiology. This study bridges the gap between molecular insights and clinical practice. It sets the stage for transformative strategies in DUs management, marking a significant step forward in personalized medicine for diabetic patients.

Authors

  • Yun-Xi Cai
    Department of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China; Institute of Dermatology, Shanghai Academy of Traditional Chinese Medicine, Shanghai 201203, China.
  • Shi-Qi Li
    Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai 200443, China.
  • Hang Zhao
    Department of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China; Institute of Dermatology, Shanghai Academy of Traditional Chinese Medicine, Shanghai 201203, China.
  • Miao Li
    School of Computer Science and TechnologyHuazhong University of Science and Technology Wuhan 430074 China.
  • Ying Zhang
    Department of Nephrology, Nanchong Central Hospital Affiliated to North Sichuan Medical College, Nanchong, China.
  • Yi Ru
    Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai 200443, China.
  • Ying Luo
    School of Statistics, Beijing Normal University, Beijing, China.
  • Yue Luo
    Chengdu University of Traditional Chinese Medicine, No. 1166 Liutai Avenue, Wenjiang District, Chengdu 611137, China.
  • Xiao-Ya Fei
    Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai 200443, China.
  • Fang Shen
  • Jian-Kun Song
    Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai 200443, China.
  • Xin Ma
    Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China.
  • Jing-Si Jiang
    Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai 200443, China.
  • Le Kuai
    Department of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China; Institute of Dermatology, Shanghai Academy of Traditional Chinese Medicine, Shanghai 201203, China.
  • Xiao-Xuan Ma
    Department of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China; Institute of Dermatology, Shanghai Academy of Traditional Chinese Medicine, Shanghai 201203, China. Electronic address: 985841171@qq.com.
  • Bin Li
    Department of Magnetic Resonance Imaging (MRI), Beijing Shijitan Hospital, Capital Medical University, Beijing, China.

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