Multifaceted roles of lymphatic and blood endothelial cells in the tumor microenvironment of hepatocellular carcinoma:A comprehensive review

The tumor microenvironment is a complex network of cells,extracellular matrix,and signaling molecules that plays a critical role in tumor progression and metastasis.Lymphatic and blood vessels are major routes for solid tumor metastasis and essential parts of tumor drainage conduits.However,recent studies have shown that lymphatic endothelial cells(LECs)and blood endothelial cells(BECs)also play multifaceted roles in the tumor microenvironment beyond their structural functions,particularly in hepatocellular carcinoma(HCC).This comprehensive review summarizes the diverse roles played by LECs and BECs in HCC,including their involvement in angiogenesis,immune modulation,lymphangiogenesis,and metastasis.By providing a detailed account of the complex interplay between LECs,BECs,and tumor cells,this review aims to shed light on future research directions regarding the immune regulatory function of LECs and potential therapeutic targets for HCC.

Machine learning approach to predict acute kidney injury after liver surgery

BACKGROUND Acute kidney injury(AKI)after surgery appears to increase the risk of death in patients with liver cancer.In recent years,machine learning algorithms have been shown to offer higher discriminative efficiency than classical statistical analysis.AIM To develop prediction models for AKI after liver cancer resection using machine learning techniques.METHODS We screened a total of 2450 patients who had undergone primary hepatocellular carcinoma resection at Changzheng Hospital,Shanghai City,China,from January 1,2015 to August 31,2020.The AKI definition used was consistent with the Kidney Disease:Improving Global Outcomes.We included in our analysis preoperative data such as demographic characteristics,laboratory findings,comorbidities,and medication,as well as perioperative data such as duration of surgery.Computerized algorithms used for model development included logistic regression(LR),support vector machine(SVM),random forest(RF),extreme gradient boosting(XGboost),and decision tree(DT).Feature importance was also ranked according to its contribution to model development.RESULTS AKI events occurred in 296 patients(12.1%)within 7 d after surgery.Among the original models based on machine learning techniques,the RF algorithm had optimal discrimination with an area under the curve value of 0.92,compared to 0.87 for XGBoost,0.90 for DT,0.90 for SVM,and 0.85 for LR.The RF algorithm also had the highest concordance-index(0.86)and the lowest Brier score(0.076).The variable that contributed the most in the RF algorithm was age,followed by cholesterol,and surgery time.CONCLUSION Machine learning algorithms are highly effective in discriminating patients at high risk of developing AKI.The successful application of machine learning models may help guide clinical decisions and help improve the long-term prognosis of patients.