Integrative machine learning reveals the biological function and prognostic significance of α-ketoglutarate in gastric cancer.

Journal: Oncology letters
Published Date: Jun 11, 2025

Abstract

Gastric cancer (GC) has a poor response to treatment, an unfavorable prognosis and a lack of reliable biomarkers for predicting disease progression and therapeutic outcomes. α-Ketoglutarate (α-KG) is a critical metabolite involved in cellular energy metabolism and epigenetic regulation during tumor development, which has emerged as a potential prognostic biomarker for GC. The present study aimed to explore this potential using publicly available datasets from The Cancer Genome Atlas and Gene Expression Omnibus databases to analyze α-KG-related genes and establish the α-KG Index (AKGI). By assessing the predictive performance of the AKGI model, the results demonstrated its capability to predict survival outcomes in patients with GC. Notably, high AKGI scores were associated with worse prognoses. Building on these findings, the associations between AKGI and clinical variables, immune cell infiltration and tumor mutation characteristics were assessed, further identifying potential therapeutic drugs for patients with high AKGI scores. Additionally, by analyzing signaling pathways and biological functions correlated with AKGI, the regulatory mechanisms and biological roles of α-KG in GC were elucidated. The findings of these analyses were further evaluated using cellular experiments, where α-KG treatment was demonstrated to significantly inhibit GC cell proliferation, migration and invasion. In conclusion, the present study successfully constructed and validated the AKGI as a potential prognostic biomarker for GC. The findings indicate that AKGI can identify patients likely to benefit from immunotherapy, enhance diagnostic precision and improve clinical outcomes in GC management. Moreover, AKGI offers a valuable framework for advancing the understanding of the role and mechanisms of α-KG in GC.

Authors

  • Fangyuan Liu
    Clinical Medicine Research Center, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia Autonomous Region 010030, P.R. China.
  • Xuemeng Sun
    Clinical Medicine Research Center, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia Autonomous Region 010030, P.R. China.
  • Yun Zeng
    Key Laboratory of Integrated Rice-Fish Farming, Ministry of Agriculture and Rural Affairs, Shanghai Ocean University, Shanghai 201306, P.R. China.
  • Xiangyun Meng
    Clinical Medicine Research Center, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia Autonomous Region 010030, P.R. China.
  • Rongrong Zhang
    Department of Psychiatry Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China.
  • Liya Su
    Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.
  • Gang Liu
    Department of Interventional Radiology, Qinghai Red Cross Hospital, Xining, Qinghai, China.

Keywords

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