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Identifification of amino acid metabolic signature to predict prognosis and guide clinical therapy in patients with Hepatocellular carcinoma
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  • Dalin Chen,
  • HuiZhong Jiang,
  • Bing Yang,
  • Bin Guo,
  • Dan Li,
  • Guangze Ye,
  • Zhu Yang,
  • Dongxin Tang,
  • FengXi Long
Dalin Chen
Guizhou University of Traditional Chinese Medicine
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HuiZhong Jiang
Guizhou University of Traditional Chinese Medicine
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Bing Yang
Guizhou University of Traditional Chinese Medicine
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Bin Guo
Guizhou University of Traditional Chinese Medicine
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Dan Li
Guizhou University of Traditional Chinese Medicine
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Guangze Ye
Guizhou University of Traditional Chinese Medicine
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Zhu Yang
Guizhou University of Traditional Chinese Medicine
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Dongxin Tang
Guizhou University of Traditional Chinese Medicine
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FengXi Long
Guizhou University of Traditional Chinese Medicine

Corresponding Author:[email protected]

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Abstract

Background The high heterogeneity of Hepatocellular carcinoma (HCC) has led to poor clinical outcomes. The critical role of amino acid metabolic reprogramming in tumor growth is gradually emerging. However, amino acid metabolism in HCC is less studied, and the mechanisms still need to be clarified. Methods We acquired transcriptome information on HCC patients from public databases. Amino acid metabolism-related genes (ACRGs) were used as prognostic markers. We built the prognosis-related ACRG_score model using Univariate/Multivariate COX and Lasso regression analyses following stratification by consensus clustering. Furthermore, we assigned HCC patients with high ACRG expression to the high-risk category and others to the low-risk category. We compared clinical characteristics, immune cell infiltration, somatic mutations, and immune checkpoint (IC) genes among the groups. Finally, drug sensitivity and molecular docking analyses were used to identify therapeutic candidates targeting the essential ACRG target proteins. Result We constructed a six-gene ACRG_score model that better predicted the survival prognosis for liver cancer patients, and we validated it using internal and external datasets. In HCC patients, ACRG_score are associated with clinicopathological characteristics and have proven to be an independent prediction factor. Nomogram and calibration curves illustrated the model could correctly forecast patient prognosis. In addition, immune infiltration, Tumor Mutational Burden (TMB), and ACRG_score were revealed to be significantly correlated. IC genes were also present. According to immunohistochemical analysis, HCC tissues had higher EZH2, SLC2A1, and SPP1 expression levels than normal tissues. Additionally, we identified seven candidate small-molecule medications that may bind four of the ACRG essential target proteins. Conclusions: In this study, the ACRG_score model was created to analyze the prognosis, TMB, IC, and therapy responsiveness for HCC patients. This model can predict patient prognosis, guide immunotherapy, provide clinical dosing suggestions, and supply valuable tools for individualized patient treatment.
07 Sep 2023Submitted to Cancer Reports
08 Sep 2023Submission Checks Completed
08 Sep 2023Assigned to Editor
08 Sep 2023Review(s) Completed, Editorial Evaluation Pending
12 Sep 2023Reviewer(s) Assigned