Identification of ACHE as the hub gene targeting solasonine associated with non-small cell lung cancer (NSCLC) using integrated bioinformatics analysis

Abstract

Background: Solasonine, as a major biological component of Solanum nigrum L., has demonstrated anticancer effects against several malignancies. However, little is understood regarding its biological target and mechanism in non-small cell lung cancer (NSCLC).

Methods: We conducted an analysis on transcriptomic data to identify differentially expressed genes (DEGs), and employed an artificial intelligence (AI) strategy to predict the target protein for solasonine. Subsequently, genetic dependency analysis and molecular docking were performed, with Acetylcholinesterase (ACHE) selected as a pivotal marker for solasonine. We then employed a range of bioinformatic approaches to explore the relationship between ACHE and solasonine. Furthermore, we investigated the impact of solasonine on A549 cells, a human lung cancer cell line. Cell inhibition of A549 cells following solasonine treatment was analyzed using the CCK8 assay. Additionally, we assessed the protein expression of ACHE, as well as markers associated with apoptosis and inflammation, using western blotting. To investigate their functions, we employed a plasmid-based ACHE overexpression system. Finally, we performed dynamics simulations to simulate the interaction mode between solasonine and ACHE.

Results: The results of the genetic dependency analysis revealed that ACHE could be identified as the pivotal target with the highest docking affinity. The cell experiments yielded significant findings, as evidenced by the negative regulatory effect of solasonine treatment on tumor cells, as demonstrated by the CCK8 assay. Western blotting analysis revealed that solasonine treatment resulted in the downregulation of the Bcl-2/Bax ratio and upregulation of cleaved caspase-3 protein expression levels. Moreover, we observed that ACHE overexpression promoted the expression of the Bcl-2/Bax ratio and decreased cleaved caspase-3 expression in the OE-ACHE group. Notably, solasonine treatment rescued the Bcl-2/Bax ratio and cleaved caspase-3 expression in OE-ACHE cells compared to OE-ACHE cells without solasonine treatment, suggesting that solasonine induces apoptosis. Besides, solasonine exhibited its anti-inflammatory effects by inhibiting P38 MAPK. This was supported by the decline in protein levels of IL-1β and TNF-α, as well as the phosphorylated forms of JNK and P38 MAPK. The results from the molecular docking and dynamics simulations further confirmed the potent binding affinity and effective inhibitory action between solasonine and ACHE.

Conclusions: The findings of the current investigation show that solasonine exerts its pro-apoptosis and anti-inflammatory effects by suppressing the expression of ACHE.

Keywords: ACHE; Anti-inflammatory; Apoptosis; Bioinformatic; Solasonine.

Figure 1. Process of target exploration.

The 2D (A) and 3D (B) structures of solasonine. (C) Gene intersection among the DEG in the solasonine treatment dataset, DEG in NSCLC dataset, and predicted targets for solasonine. (D) Expression status of ACHE between tumor and normal groups of NSCLC. (E and F) correlation analysis of ACHE expression in CRISPR (E) and RNAi (F) scores.

Figure 2. Drug sensitivity of ACHE groups and biological enrichment for marker genes.

(A) Compounds with the significant drug-sensitive in IC50 and AUC scores (*p < 0.05, **p < 0.01) and corresponding targeting pathways. (B–D) The differences of IC50 values for these inducers in high/low level groups of ACHE. (B) Result for Venetoclax. (C) Result for ABT737. (D) Result for WEHI-539. (E and F) Biological enrichment analysis for the proteins with significant activity in biological process terms (E) and KEGG pathways (F).

Figure 3. The cell inhibition of A549 cells and the protein levels of ACHE with solasonine treatment.

(A) Cell inhibition of A549 treated with different concentrations of solasonine as determined by CCK8 assay. (B and C) Western blotting of ACHE protein expression in A549 and its bar diagram. Data are expressed as mean ± SD (n = 3). **p < 0.01 vs. the control group (0 µM solasonine).

Figure 4. Solasonine influences related protein expressions of apoptosis and inflammation.

(A) Protein expression levels of marker proteins of apoptosis, including Cleaved caspase-3, Bax, and Bcl-2 via western blotting. (B and C) The bar diagram shows the ratio of Bcl-2/Bax and Cleaved caspase‑3/GAPDH from densitometric analysis of three independent experiments. (D and E) Protein expression levels of IL-1β and TNF-α, and data analysis with bar diagram. Data are expressed as mean ± SD (n = 3). *p < 0.05, **p < 0.01 vs. the control group (0 µM solasonine).

Figure 5. The p38 MAPK pathway contributes to solasonine-induced apoptosis and inflammation in A549 cells.

(A–C) Protein expression levels of activated MAPKs, including p-JNK and p-p38 MAPK. Data are expressed as mean ± SD (n = 3). *p < 0.05, **p < 0.01 vs. the control group (0 μM solasonine). (D) The ACHE mRNA expression in different groups. Data are expressed as mean ± SD (n = 6). ^##p < 0.01 vs. the control group, ^%%p < 0.01 vs. the vector group. (E–G) Protein expression levels of ACHE and marker proteins of apoptosis and inflammation, including Bcl-2, Bax, Cleaved caspase 3, TNF-α, and IL-1β via western blotting and data analysis with bar diagram. A549 cells were transfected with ACHE over expression plasmid (lanes C and D) or with empty vector (lanes E and F). Cells were then incubated with (lanes B, D, and F) or without (lanes A, C, and E) 15 µM solasonine for 24 h before harvesting. Data are expressed as mean ± SD (n = 3). *p < 0.05 vs. the A group, ^#p < 0.05 vs. the A group, ^%p < 0.05 vs. the E group, ^@p < 0.05 vs. the C group.

Figure 6. Docking visualization and dynamics simulations of the solasonine-ACHE complex.

(A–D) Docking visualization. (E) RMSD profile for the simulated trajectory of the complex system in 50 ns dynamics simulations.

Figure 7. Major processes of this study.