Development of a prognostic metabolic signature in stomach adenocarcinoma

• Yu Gong ^[1] ; Siyuan Wu ^[3] ; Sen Dong ^[4] ; Shuai Chen ^[1] ; Gengdi Cai ^[2] ; Kun Bao ^[2] ; Haojun Yang ^[1] ; Yuwen Jiao ^[1]
  1. [1] Nanjing Medical University

     Nanjing Medical University

     China

     
  2. [2] Dalian Medical University

     Dalian Medical University

     China

     
  3. [3] Department of Hepato-Biliary-Pancreatic Surgery, The Affiliated Changzhou No.2 People’s Hospital of Nanjing Medical University, Changzhou, China
  4. [4] Bengbu Medical University, China

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• Localización: Clinical & translational oncology, ISSN 1699-048X, Vol. 24, Nº. 8, 2022, págs. 1615-1630
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • Purpose The growth and aggressiveness of Stomach adenocarcinoma (STAD) is significantly affected by basic metabolic changes. This study aimed to identify metabolic gene prognostic signatures in STAD.

    Methods An integrative analysis of datasets from the Cancer Genome Atlas and Gene Expression Omnibus was performed. A metabolic gene prognostic signature was developed using univariable Cox regression and Kaplan–Meier survival analysis. A nomogram model was developed to predict the prognosis of STAD patients. Finally, Gene Set Enrichment Analysis (GESA) was used to explore the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways significantly associated with the risk grouping.

    Results A total of 327 metabolism-related differentially expressed genes were identified. Three subtypes of STAD were identified and nine immune cell types, including memory B cell, resting and activated CD4+ memory T cells, were significantly different among the three subgroups. A risk score model including nine survival-related genes which could separate high-risk patients from low-risk patients was developed. The prognosis of STAD patients likely benefited from lower expression levels of genes, including ABCG4, ABCA6, GPX8, KYNU, ST8SIA5, and CYP19A1. Age, radiation therapy, tumor recurrence, and risk score model status were found to be independent risk factors for STAD and were used for developing a nomogram. Nine KEGG pathways, including spliceosome, pentose phosphate pathway, and citrate TCA cycle were significantly enriched in GESA.

    Conclusion We propose a metabolic gene signature and a nomogram for STAD which might be used for predicting the survival of STAD patients and exploring prognostic markers.