**4. Discussion**

The conventional drug design approach follows the "one drug, one target" principle, whereas network pharmacology focuses on the relationship between drugs and diseases using a multi-targeted therapy approach [61]. This approach is innovative because it employs systems biology, network analysis, connectivity, and redundancy. Natural product studies have been effective in identifying new targets and uncovering unknown signaling pathways that interact with compounds [91]. The approach of network pharmacology offers fresh perspectives on the interconnectedness of therapeutic targets and diseases as a whole. It is a potent and encouraging method for understanding disease mechanisms at a systemic level and identifying possible bioactive ingredients [92]. The current study created a new network that provides an overview of the molecular mechanisms involved in the most prevalent potato glycoalkaloids.

A compound's ADME features predict its disposition inside an organism, contributing to its pharmacological (or toxicological) action [93, 94]. The study investigated the impact of glycosylation on the pharmacokinetic properties of potato glycoalkaloids using the SwissADME tool. The glycosylated forms of solanine and chaconine had higher molecular weights, lower LogSw values, and lower lipophilicity compared to the aglycone solanidine. The glycosylation state was negatively correlated with gastrointestinal (GI) absorption, and none of the glycosylated solanines or chaconines could penetrate the blood-brain barrier (BBB). The compounds were recognized and effluxed by P-glycoprotein (Pgp), which might be the cause of their anticancer activity. The glycosylation moieties also influenced the drug-likeness properties of the compounds (**Table 2**).

In the present study, drug targets were collected based on structure similarity and reverse pharmacophore mapping, 46 shared targets were discovered shared between both compounds. Cancer-related genes were collected by mining public databases, and 2925 unique genes were screened after removing duplicates. Among the drug targets and disease-related genes, 26 genes were shared between both datasets. GO and KEGG analysis identified several pathways and related diseases/disorders associated with the selected genes. The GO enrichment analysis showed the direct involvement of bioactive in the regulation of BC.

KEGG pathway analysis proved that PI3K-Akt1 pathway, HIF-1 pathway, and Th17 cell differentiation were among the most enriched pathways that are closely related to cancer development, progression, and drug resistance [83–90]. This helps to support that potato glycoalkaloids may be used for cancer treatment. For the GO analysis, enriched BP were cellular response to oxygen-containing compound, cellular response to endogenous stimulus, regulation of cell death, regulation of programmed cell death, and regulation of intracellular signal transduction, suggesting the property of glycoalkaloids as multi-target compounds.

To gain mechanistic insight into the drug targets, we used STRIG database to build PPI. The resulting PPI network consisted of 26 nodes and 38 edges, with an average node score of 2.92. PPI enrichment analysis revealed a P value of 3.65E−05 (**Figure 4A**). Applying the MCC algorithm in Cytohubba, we screened the top 10 hub genes in the network, namely, STAT3, TLR4, FGF2, IL2, NFKB1, AR, CHUK, TRIM24, NOS3, and KDM1A (**Figure 4B**). Survival analysis of hub genes identified several genes having prognostic significance in several types of cancers (**Figure 6**).
