Back

The ratio of monocyte to apolipoprotein A1 is an independent predictor of hepatocellular carcinoma: a retrospective study

Su, C.; Zhu, X.; Lin, Z.; Wei, F.; li, l.

2025-05-31 gastroenterology
10.1101/2025.05.29.25328608 medRxiv
Show abstract

ObjectiveThe ratio of monocyte to apolipoprotein A1 (MAR) is a new diagnostic indicator of some chronic diseases, but there have been no reports on hepatocellular carcinoma (HCC). This study aimed to explore the predictive value of MAR in HCC. MethodsA total of 270 patients with HCC, 540 hepatitis B patients and 540 healthy volunteers were included. Laboratory data including monocyte counts and apolipoprotein A1 levels were retrospectively collected from electronic medical records. The truncation value, area under the curve (AUC) and Youden index of each significant variable were calculated using the receiver operating characteristic curve (ROC). The clinical value of MAR was analyzed by univariate and multivariate logistic regression. Correlation heatmap was used to analyze the correlation between each index and MAR. ResultsMAR level was the highest in the HCC, followed by hepatitis B disease, and the lowest in the healthy volunteer (P < 0.001), and it was found to be an independent risk factor for HCC, also an indicator to distinguish HCC from hepatitis B disease. The cut-off value of MAR for distinguishing HCC from hepatitis B was 0.53; the AUC was 0.762, while the cut-off value in the HCC and the healthy volunteer groups was 0.62; the AUC was 0.829. The increase in MAR level showed that the risk of HCC was 6.001 times higher than that of healthy volunteer (P < 0.001). MAR was correlated with indicators, such as lymphocytes (r = -0.075, P < 0.05), carcinoembryonic antigen (r = 0.171, P < 0.001), albumin (r = -0.445, P < 0.001). ConclusionsMAR is a new indicator for the differential diagnosis of HCC and hepatitis B disease, and also an independent risk factor for HCC. This is the first study to explore the clinical value of MAR in HCC.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.