Research Article| Volume 274, P84-93, June 2023

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Metabolic–related gene signatures for survival prediction and immune cell subtypes associated with prognosis in intrahepatic cholangiocarcinoma

  • Zhe Jin
    Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, No. 1, Xinmin Street, Chaoyang District, Changchun, Jilin 130021, China
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  • Ya-Hui Liu
    Corresponding author.
    Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, No. 1, Xinmin Street, Chaoyang District, Changchun, Jilin 130021, China
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      • There were 143 metabolic gens that differentially expressed in the survival group comparing with the dead group.
      • SVM classifier based on 21 feature metabolic genes have a good performance in samples classifying.
      • RS prognostic prediction model constructed by 10 metabolic genes have great potential in prognostic prediction.
      • GSEA showed complement and coagulation cascades, PPAR signaling pathway and hematopoietic cell lineage were activated in high risk group.
      • High risk group had obviously lower counts of b cell naive and t cell CD4+ memory resting, while higher counts of b cell plasma and macrophage M2.



      Our study aimed to reveal the metabolic-related gene signatures for survival prediction and immune cell subtypes associated with IHCC prognosis.


      Differentially expressed metabolic genes were identified between survival group and dead group which were divided according to survival at discharge. Recursive feature elimination (RFE) and randomForest (RF) algorithms were applied to optimize the combination of feature metabolic genes, which were used to generate SVM classifier. Performance of SVM classifier was evaluated by receiver operating characteristic (ROC) curves. Gene set enrichment analysis (GSEA) was conducted to uncover the activated pathways in high risk group, and differences in immune cell distributions were revealed.


      There were 143 differentially expressed metabolic gens. RFE and RF identified 21 overlapping differentially expressed metabolic genes, and the constructed SVM classifier had excellent accuracy in training and validation dataset. RS survival prediction model was consisted of 10 metabolic genes. RS model had reliable predictive capability in the training and validation dataset. GSEA revealed 15 significant KEGG pathways that were relatively activated in the high risk group. High risk group had obviously lower counts of B cell naive and T cell CD4+ memory resting, while higher counts of B cell plasma and macrophage M2.


      Prognostic prediction model of 10 metabolic genes could accurately predict the prognosis of IHCC patients.


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