Construction of a prognostic model based on protein post-translational modification genes for prediction of immune characteristics and therapeutic response in hepatocellular carcinoma

By leveraging protein post-translational modification (PTM) genes, we developed a prognostic model for hepatocellular carcinoma (HCC), providing a novel approach for predicting patient outcomes and their response to immunotherapy. This model introduces a potentially valuable tool for improving clinical decision-making in HCC treatment. PTM-related genes were sourced from the MsigDB database, and key prognostic genes were identified using weighted gene co-expression network analysis (WGCNA) and univariate Cox regression analysis. Prognostic models were constructed through the evaluation of 101 machine learning algorithm combinations, and the model’s predictive accuracy was tested using an independent validation dataset. Additionally, we investigated differences in biological functions, immune status, mutation burden, immunotherapy responsiveness, and chemotherapy sensitivity across distinct risk groups. Finally, the functions of ANAPC7 and SAMD1 in HCC were verified by in vitro experiments. We identified 23 prognostic PTM genes, from which prognostic models were constructed using 9 key genes and 101 machine learning combinations. The external validation demonstrated that the models were highly accurate in predicting patient outcomes. Moreover, substantial differences were observed between high-risk and low-risk groups in terms of biological functions, immune cell infiltration, mutation burden, and sensitivity to both immunotherapy and chemotherapy. In vitro results showed that ANAPC7 and SAMD1 were able to promote the proliferation, invasion and migration of HCC. Our prognostic model, based on PTM-related genes, successfully predicts both the prognosis of HCC patients and their responsiveness to immunotherapy. This model has the potential to aid in the clinical management of HCC, guiding personalized treatment strategies.