Disulfidptosis is a newly discovered cellular programmed cell death mode. Presently, a considerable number of genes related to disulfidptosis remain undiscovered, and its significance in hepatocellular carcinoma remains unrevealed. We have developed a powerful analytical method called RF-GSEA for identifying potential genes associated with disulfidptosis. This method draws inspiration from gene regulation networks and graph theory, and it is implemented through a combination of random forest regression model and Gene Set Enrichment Analysis. Subsequently, to validate the practical application value of this method, we applied it to hepatocellular carcinoma. Based on the RF-GSEA method, we developed a disulfidptosis-related signature. Lastly, we looked into how the disulfidptosis-related signature is connected to HCC prognosis, the tumor microenvironment, the effectiveness of immunotherapy, and the sensitivity of chemotherapy drugs. The RF-GSEA method identified a total of 220 disulfidptosis-related genes, from which 7 were selected to construct the disulfidptosis-related signature. The high-disulfidptosis-related score group had a worse prognosis compared to the low-disulfidptosis-related score group and showed lower infiltration levels of immune-promoting cells. The high-disulfidptosis-related score group had a higher likelihood of benefiting from immunotherapy compared to the low-disulfidptosis-related score group. The RF-GSEA method is a powerful tool for identifying disulfidptosis-related genes. The disulfidptosis-related signature effectively predicts HCC prognosis, immunotherapy response, and drug sensitivity.

To identify the influencing factors and develop a predictive model for the risk of abnormal liver function in the automotive manufacturing industry works in Chongqing. Automotive manufacturing workers in Chongqing city surveyed during 2019–2021 were used as the study subjects. Logistic regression analysis was used to identify the influencing factors of abnormal liver function. A restricted cubic spline model was used to further explore the influence of the length of service. Finally, a deep neural network-based model for predicting the risk of abnormal liver function among workers was developed. Of all 6087 study subjects, a total of 1018 (16.7%) cases were detected with abnormal liver function. Increased BMI, length of service, DBP, SBP, and being male were independent risk factors for abnormal liver function. The risk of abnormal liver function rises sharply with increasing length of service below 10 years. AUC values of the model were 0.764 (95% CI: 0.746–0.783) and 0.756 (95% CI: 0.727–0.786) in the training and test sets, respectively. The other four evaluation indices of the DNN model also achieved good values.