Investigating the molecular mechanism of Qizhu anticancer prescription in inhibiting hepatocellular carcinoma based on high-resolution mass spectrometry and network pharmacology.

ETHNOPHARMACOLOGICAL RELEVANCE: Of all primary liver cancer cases, hepatocellular carcinoma (HCC) accounts for about 90%. Most patients with HCC receive a diagnosis in the medium-to-late stages or with chronic liver disease, have lost the opportunity for radical treatment, such as surgical resection, and their 5-year survival rate is low. Qizhu Anticancer Prescription (QZACP) is an empirical formula composed of traditional Chinese herbs that can clinically relieve HCC symptoms, inhibit the progression of HCC, reduce recurrence rate, and prolong survival; however, its exact mode of action remains unknown. AIM OF THE STUDY: This study's purpose was to investigate the mode of action of QZACP in the prevention and treatment of HCC. MATERIALS AND METHODS: Initially, drug components in the QZACP decoction were analyzed using high-resolution mass spectrometry. A subcutaneous tumor xenograft model in nude mice was constructed to further analyze the active components of QZACP that had entered tumor tissues through oral administration. Potential targets of QZACP in the prevention and treatment of HCC were identified and then confirmed in vivo via network pharmacology and molecular docking. In addition, regulatory effects of QZACP on HCC cell proliferation and the cell cycle were detected using a CCK-8 assay and flow cytometry. RESULTS: High-resolution mass spectrometry revealed that the QZACP decoction contained deacetyl asperulosidic acid methyl ester (DAAME), paeoniflorin, calycosin-7-glucoside, liquiritin, glycyrrhizic acid, astragaloside IV, saikosaponin A, curdione, and atractylenolide II. In nude mice, QZACP could effectively inhibit the growth of subcutaneous tumors, where DAAME, paeoniflorin, liquiritin, and glycyrrhizic acid could enter liver cancer tissues after oral administration. Among these, DAAME was the most highly expressed in HCC tissues and may be an important active component of QZACP for inhibiting HCC. Utilizing network pharmacology, the targets of action of these four drug components were identified. After verification using western blotting, STAT3, VEGFA, JUN, FGF2, BCL2L1, AR, TERT, MMP7, MMP1, ABCB1, CA9, and ESR2 were identified as targets of QZACP inhibition in HCC. In vitro experiments revealed that QZACP inhibited the proliferation of HCC cells while inducing G0/G1 phase cell cycle arrest. In vivo experiments demonstrated that DAAME significantly inhibited HCC growth. After intersection of the 24 DAAME targets predicted using network pharmacology with the 435 HCC disease targets, only CA9 was identified as a DAAME-HCC crossover target. Molecular docking results revealed that the binding site of DAAME and CA9 had good stereo-complementarity with a docking score of -8.1 kcal/mol. Western blotting and immunohistochemical results also confirmed that DAAME significantly decreased CA9 protein expression in HCC. CONCLUSIONS: QZACP inhibits HCC by reducing the expression of STAT3, VEGFA, JUN, FGF2, BCL2L1, AR, TERT, MMP7, MMP1, ABCB1, CA9, and ESR2. DAAME may be an important active component of QZACP for the prevention and treatment of HCC, inhibiting it by targeting the expression of CA9.

Machine learning-based disulfidptosis-related lncRNA signature predicts prognosis, immune infiltration and drug sensitivity in hepatocellular carcinoma.

Disulfidptosis a new cell death mode, which can cause the death of Hepatocellular Carcinoma (HCC) cells. However, the significance of disulfidptosis-related Long non-coding RNAs (DRLs) in the prognosis and immunotherapy of HCC remains unclear. Based on The Cancer Genome Atlas (TCGA) database, we used Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression model to construct DRL Prognostic Signature (DRLPS)-based risk scores and performed Gene Expression Omnibus outside validation. Survival analysis was performed and a nomogram was constructed. Moreover, we performed functional enrichment annotation, immune infiltration and drug sensitivity analyses. Five DRLs (AL590705.3, AC072054.1, AC069307.1, AC107959.3 and ZNF232-AS1) were identified to construct prognostic signature. DRLPS-based risk scores exhibited better predictive efficacy of survival than conventional clinical features. The nomogram showed high congruence between the predicted survival and observed survival. Gene set were mainly enriched in cell proliferation, differentiation and growth function related pathways. Immune cell infiltration in the low-risk group was significantly higher than that in the high-risk group. Additionally, the high-risk group exhibited higher sensitivity to Afatinib, Fulvestrant, Gefitinib, Osimertinib, Sapitinib, and Taselisib. In conclusion, our study highlighted the potential utility of the constructed DRLPS in the prognosis prediction of HCC patients, which demonstrated promising clinical application value.