ObjectiveTo review the role of intestinal flora on the tumor microenvironment and the effect of both on the development of hepatocellular carcinoma (HCC), with a view to providing new ideas on the causes of HCC development and progression. MethodRelevant articles in the direction of intestinal flora and tumor microenvironment and HCC as well as the relationship between intestinal flora and tumor microenvironment in recent years were searched and summarized. ResultsThe tumor microenvironment played an important role in the occurrence, development and postoperative recurrence of HCC. The intestinal flora, as one of the important regulators of tumor microenvironment, could induce HCC by affecting the tumor microenvironment in addition to interacting with the liver through the gut-liver axis. ConclusionIntestinal flora can influence to HCC by regulating the tumor microenvironment, and its specific mechanism of action still needs to be further investigated, which can be a new direction for HCC research.
ObjectiveTo investigate the association of lipid metabolism and other markers with microvascular invasion in hepatocellular carcinoma (HCC) and to develop a preoperative prediction model from it. MethodsData from 389 HCC patients who underwent hepatectomy at First Hospital of Lanzhou University between January 2017 and March 2023 were retrospectively analyzed. These patients were divided into training group (n=272) and validation group (n=117) with a ratio of 7 : 3. The independent risk factors of microvascular invasion (MVI) were determined by univariate and multivariate logistic regression analysis, and the MVI prediction model was established. The prediction efficiency of the model was verified by the analysis of calibration curve, receiver operating characteristic (ROC) curve and decision curve. ResultsUnivariate and multivariate logistic regression analysis showed that the risk factors independently related to MVI before operation included total cholesterol, lactate dehydrogenase, body mass index, alpha-fetoprotein, carbohydrate antigen 125, hepatitis B DNA, maximum tumor diameter and albumin-bilirubin score. MVI prediction model was established based on the above eight risk factors, and its area under ROC curve in the training group and the validation group were 0.79 [95%CI (0.74, 0.84)] and 0.75 [95%CI (0.66, 0.84)] respectively. Calibration curve analysis showed that the prediction curve fitted well with the standard curve. ROC curve analysis showed that the MVI prediction model was efficient. Decision curve analysis confirmed that the MVI prediction model had significant clinical applications. ConclusionThis study identified independent correlations between total cholesterol levels, among other things, and MVI, and successfully developed and validated novel predictive model based on these indicators that can help physicians effectively identify individuals at high risk for MVI in patients with hepatocellular carcinoma preoperatively, leading to more rational treatment choices.