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Identification of the Key Active Pharmaceutical Ingredients of Yishen Qutong Granule, A Chinese Medicine Formula, In The Treatment of Primary Lung Cancer

Chen J, Feng L, Jin W, Chang J, Li J, Li H

Combinatorial chemistry & high throughput screening, 2023

Abstract

BACKGROUND: Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Traditional Chinese medicine (TCM) reportedly has potential therapeutic effects against LC.

OBJECTIVE: This study aimed to investigate the antitumor efficacy of Yishen Qutong granule (YSQTG) in primary LC treatment, to identify its key active pharmaceutical ingredients (APIs), and to explore its possible mechanism of action.

METHODS: The antitumor role of YSQTG was validated via cell function assays and a xenograft tumor model. Then, high-performance liquid chromatography-mass spectrometry (HPLC-MS) was performed to determine the objective precipitation components of YSQTG, followed by target prediction through reference to databases. Subsequently, the proportion of the predicted targets that underwent actual changes was identified via RNA-sequencing. Enrichment analysis was performed to explore the possible mechanisms of action. Hub genes were screened, and western blotting was used to verify their protein expression levels to identify the core target. Molecular docking between the active compounds and the verified core target was performed, combined with an evaluation of the potential efficacy of candidate compounds using meta-analysis to screen the candidate key APIs.

RESULTS: Experiments confirmed that YSQTG could inhibit LC cell proliferation, induce apoptosis in vitro, and inhibit lung tumor growth in vivo. HPLC-MS, RNA-seq, and enrichment analysis showed that oxidative stress-related…

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Evidence Hub reads this as a meta-analysis, done in animal and computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.

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