Back

Molecular mechanism of the anti-lung cancer effect of Jin Ning Fang based on network pharmacology and experimental verification

Wu, C.; Yu, Q.; Shou, W.; Zhang, K.; Li, Y.; Guo, W.; Bao, Q.

2021-07-27 cancer biology
10.1101/2021.07.26.453881 bioRxiv
Show abstract

BackgroundJin Ning Fang (JNF) is widely used as an adjuvant therapy for lung cancer. However, its molecular mechanism against lung cancer is still unclear. MethodsThe chemical compounds JNF were screened from the TCMSP database and its target proteins were then predicted. The genes related to lung cancer were collected from the CTD and DisGeNET databases. Next, targets were integrated with disease-related genes to obtain candidate genes. Functional enrichment and protein-protein interaction (PPI) analysis were also performed, followed by construction of pharmacological network. Meanwhile, Autodock was used to assess the affinity between targets and compound. Finally, the anti-cancer effect of JNF on lung cancer cells was detected and some predicted key genes was validated by using real-time PCR. ResultsTwenty-five overlapping targets were obtained, and pathway analysis showed that JNF might exert its anti-cancer function by regulating some biological pathways, such as apoptosis pathway. PPI and pharmacological network revealed several core targets (such as AKT1, AR, and ESR1) and three compounds (quercetin, calcium carbonate, and beta-sitosterol). Then, beta-sitosterol had a high affinity with AKT1, AR, and ESR1. Further in vitro experiments confirmed that JNF could inhibit proliferation and promote apoptosis of A549 cells. The expression of FDPS, PIM1, VCAM1, SLC29A1, NQO1, and ESR1 were significantly decreased, while mRNA level of AR and ANPEP were markedly increased after JNF treatment. ConclusionJNF may exert anti-lung cancer effect through multiple targets and pathways, and identified genes may be used as potential biomarkers for diagnosis and treatment of lung cancer.

Matching journals

The top 6 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.