Background: Liver cancer represents a most common and fatal cancer worldwide. Xianglian Pill (XLP) is an herbal formula holding great promise in clearing heat for treating diseases in an integrative and holistic way. However, due to the complex constituents and multiple targets, the exact molecular mechanisms of action of XLP are still unclear.

Purpose: This study is focused on hepatocellular carcinoma (HCC), the most common type of liver cancer. The aim of this study is to develop a fast and efficient model to investigate the anti-HCC effects of XLP, and its underlying mechanisms.

Materials and methods: HepG2, Hep3B, Mahlavu, HuH-7, or Li-7 cells were employed in the studies. The ingredients were analyzed using liquid chromatography tandem mass spectrometry (LC-MS). RNA sequencing combined with network pharmacology was used to elucidate the therapeutic mechanism of XLP in HCC via in silico and in vitro studies. An approach was constructed to improve the accuracy of prediction in network pharmacology by combining big data and omics.

Results: First, we identified 13 potential ingredients in the serum of XLP-administered rats using LC-MS. Then the network pharmacology was performed to predict that XLP demonstrates anti-HCC effects via targeting 94 genes involving in 13 components. Modifying the database thresholds might impact the accuracy of network pharmacology analysis based on RNA sequencing data. For instance, when the matching rate peak is 0.43, the correctness rate peak is 0.85. Moreover, 9 components of XLP and 6 relevant genes have been verified with CCK-8 and RT-qPCR assay, respectively.

Conclusion: Based on the crossing studies of RNA sequencing and network pharmacology, XLP was found to improve HCC through multiple targets and pathways. Additionally, the study provides a way to optimize network pharmacology analysis in herbal medicine research.