Autophagy is a programmed cell degradation process that is involved in a variety of physiological and pathological processes including malignant tumors. Abnormal induction of autophagy plays a key role in the development of hepatocellular carcinoma (HCC). We established a prognosis prediction model for hepatocellular carcinoma based on autophagy related genes. Two hundred and four differentially expressed autophagy related genes and basic information and clinical characteristics of 377 registered hepatocellular carcinoma patients were retrieved from the cancer genome atlas database. Cox risk regression analysis was used to identify autophagy-related genes associated with survival, and a prognostic model was constructed based on this. A total of 64 differentially expressed autophagy related genes were identified in hepatocellular carcinoma patients. Five risk factors related to the prognosis of hepatocellular carcinoma patients were determined by univariate and multivariate Cox regression analysis, including TMEM74, BIRC5, SQSTM1, CAPN10 and HSPB8. Age, gender, tumor grade and stage, and risk score were included as variables in multivariate Cox regression analysis. The results showed that risk score was an independent prognostic risk factor for patients with hepatocellular carcinoma (HR = 1.475, 95% CI = 1.280–1.699, P < 0.001). In addition, the area under the curve of the prognostic risk model was 0.739, indicating that the model had a high accuracy in predicting the prognosis of hepatocellular carcinoma. The results suggest that the new prognostic risk model for hepatocellular carcinoma, established by combining the molecular characteristics and clinical parameters of patients, can effectively predict the prognosis of patients.
The use of clinical predictive modeling to guide clinical decision-making and thus provide accurate diagnosis and treatment services for patients has become a clinical consensus and trend. However, the models available for clinical use are more limited due to unstandardised research methods and poor quality of evidence. This paper introduces the development process of clinical prediction models from six aspects, data collection, model development, performance evaluation, model validation, model presentation and model updating, as well as the clinical prediction model research report statement and risk of bias assessment tools in order to provide methodological references for domestic researchers.
ObjectiveTo explore the method of constructing time-dependent variables of clinical prognostic model, and to combine marginal structure model with clinical prognostic model to provide a more accurate tool for individualized prognostic assessment of patients. MethodsThrough data simulation, a training dataset with sample size of 7 000 and a validation dataset with sample size of 3 000 were constructed, and the predictive efficacy of ignoring treatment model, baseline no-treatment model, baseline treatment prediction model and marginal structure prediction model were respectively compared under different follow-up times and different situations. ResultsAt 2 follow-up time points, there was no significant difference between the marginal structure prediction model and the baseline treatment prediction model, but they were higher than the neglected treatment model and the baseline no treatment model. At 5 follow-up time points, the prediction ability of the marginal structure prediction model was significantly higher than that of the other three prediction models. ConclusionIn the case of time-dependent treatment in the observational cohort, the change of treatment after baseline should be considered when constructing the clinical prognosis model, otherwise the prediction accuracy of the prognosis model will be reduced.