Construction of a Prediction Model for Disease Outcome after TACE in Moderate and Advanced Liver Cancer Based on Serum miR-126,LOXL4 and NLR

XIE Fangfang; FAN Jing; LI Jianmei

doi:10.3969/j.issn.1002-3429.2025.11.012

Abstract：Objective To construct a prediction model for disease outcome after transcatheter arterial chemoembolization(TACE)in patients with moderate and advanced liver cancer based on serum microRNA-126(miR-126),lysyl oxidase-like protein 4(LOXL4),and neutrophil-to-lymphocyte ratio(NLR).Methods A total of 106 patients with moderate and advanced liver cancer who underwent TACE from January 2018 to May 2022 were selected.These patients were categorized into the good prognosis group(n=65)and the poor prognosis group(n=41)based on their disease outcomes at 2 years after TACE.General information,disease-related data,and laboratory test results were collected from both groups.Multivariate logistic regression analysis was conducted to identify factors affecting the disease outcomes of patients with moderate and advanced liver cancer after TACE.Additionally,a Nomogram prediction model was constructed and its predictive performance was validated using calibration curves,decision curves,and receiver operating characteristic(ROC)curves.Results The proportion of clinical stage Ⅳ,lymph node metastasis,concurrent liver cirrhosis,as well as age,LOXL4 expression level,and NLR in the poor prognosis group were higher than those in the good prognosis group,while the proportion of high differentiation and the expression level of miR-126 were lower than those in the good prognosis group(P<0.05,P<0.01).Multivariate Logistic regression analysis showed that age,clinical stage,lymph node metastasis,concurrent liver cirrhosis,LOXL4 expression level,NLR,differentiation degree,and miR-126 expression were influencing factors for disease outcome after TACE in patients with moderate and advanced liver cancer(P<0.01).The Nomogram prediction model based on the above influencing factors for disease outcome of patients with moderate and advanced liver cancer after TACE had high consistency,accuracy and discrimination.The decision curve showed that the net clinical benefit of the prediction model was significant when the risk threshold was between 0.01 and 0.80.The ROC curve showed that the optimal cut-off value of the risk of poor prognosis of the model(risk score 0.336 scores)could divide patients into high-risk group(≥0.336 scores)and low-risk group(<0.336 scores).Conclusion Age,clinical stage,lymph node metastasis,concurrent liver cirrhosis,LOXL4 expression level,NLR,differentiation degree,and miR-126 expression level are influencing factors for disease outcome in patients with moderate and advanced liver cancer after TACE.The Nomogram prediction model established based on these factors exhibits good consistency,accuracy,and discrimination,which is helpful for clinicians to screen out early-stage high-risk individuals.