1.Exploration Of Constructing Prognostic Prediction Models For Hepatocellular Carcinoma Based On Laboratory Indicators 2.Preliminary Exploration Of MiRNA-25 For Hepatocellular Carcinoma

Background:In recent years,some prognostic analysises have been made in the treatment and diagnosis of hepatocellular carcinoma(HCC),but the overall survival(OS)is still not ideal.Mining and establishing predictive indicators and models related to the occurrence and development of HCC can help improve the survival management of HCC patients.Methods:The first part is to collect the clinical data and biochemical,immune and other laboratory test indicators of HCC patients admitted to the Cancer Hospital of the Chinese Academy of Medical Sciences from 2010 to 2017,establish a prognosis model of HCC patients through nomogram method,and evaluate the model through machine learning.In the second part,a retrospective analysis was conducted on patients who underwent liver cancer surgery at our hospital from 2010 to 2019,and preoperative clinical data and laboratory related indicators were collected.Using the cox regression method for univariate analysis,a scoring system was established to screen coagulation indicators related to the prognosis of HCC patients.Using propensity score matching method to remove the influence of other factors in each group.Use the Kaplan Meier method to analyze survival differences among different risk groups.Result:This study is divided into two parts.Part 1:A total of 402 HCC patients were included.They were randomly divided into training and validation cohorts with a 7:3 ratio.The model based on PVTT-TS-preALBLDH showed an area under the ROC curve(AUC)of 0.765,demonstrating optimal performance.The five machine learning models further confirmed the discriminative performance of the model in predicting the prognosis of HCC patients,with the logistic regression model having the highest area under the curve(AUC)value of 0.796.Part 2:576 patients with HCC were included,with a median follow-up time of 33.7 months(15.4 months to 60.9 months).Cox regression analysis showed that preoperative coagulation indicators PT and FIB were associated with the prognosis of hepatocellular carcinoma patients.The optimal cutoff values for PT and FIB are 11.4s and 3.8 g/L,respectively.A coagulation scoring system was established based on PT and FIB results to divide hepatocellular carcinoma patients into high-risk and low-risk groups.Kaplan Meier analysis showed significant differences in survival between the two groups(P<0.01);The propensity score matching was used to match the indicators with differences in Cox regression analysis between the low-risk and high-risk groups using a 1:1 ratio.After matching,Kaplan Meier analysis showed significant differences in survival between the high-risk and low-risk groups(P<0.01).The coagulation scoring system combined with indicators such as AFP,ALT,GGT and LDH showed the higher AUC values than those of individual indicators,respectively.Conclusion:This study systematically evaluated the value of clinical features and laboratory indicators in the prognosis of HCC patients,and constructed a prognostic model based on PVTT-TS-preALB-LDH.This model has good prognostic value for HCC patients and provides a reference basis for risk stratification of patients.The coagulation scoring system formed by the combination of PT and FIB has predictive value for the prognosis of hepatocellular carcinoma patients,providing reference data for clinical preoperative evaluation of the prognosis of hepatocellular carcinoma patients.