Alpha-Solanine Anti-Tumor Effects in Non-Small Cell Lung Cancer Through Regulating the Glycolysis Pathway

Background: Lung cancer is a malignant tumor with a high incidence in China, especially non-small cell lung cancer (NSCLC), which is the main threat to human life, with terrible morbidity and mortality. The research on the treatment and mechanism of lung cancer and NSCLC has been the forefront and hotspot of research. Alpha-solanine (α-solanine) has anti-tumor effect, but its target and related mechanism remain to be elucidated. Methods: Network pharmacology was used to predict the possible targets and mechanisms of α-solanine on NSCLC. The targets were derived from TGCC database, and α-solanine targets were derived from PharmMapper. Gene-related potential pathways were identied by gene ontology and pathway enrichment analysis. Compound-target and target-pathway networks were constructed and validated by molecular biology and targeted energy metabolomics.Experiments were used to verify the results of network pharmacology in vitro, such as cell migration and invasion, cell apoptosis, immunouorescenceand western blot. Results: Network pharmacology showed that there were 130 potential targets of α-solanine and NSCLC, and the main core was the pathway of glycolysis. In addition, experiment results showed that α-solanine inhibited cell proliferation, migration, invasion and promoted cell apoptosis. At the same time, α-solanine mainly regulated cell proliferation and survival throughglycolytic signaling pathway, which included GPI, ALDOA, TPI1, PKLR, LDHA and ALDH 3 . After α-solanine treatment, the expression of the above-mentioned proteins was reduced. Conclusion: Combination of network pharmacological prediction and experimental verication, α-solanine affected the expression of related proteins and cell functions by regulating the glycolytic pathway, thereby achieving anti- NSCLC effects. and permeabilized with 0.1% Triton X-100 made in PBS for 15–20 minutes. Prepare a blocking solution of 5% normal goat serum in PBS for 30 minutes, then incubated with relevant antibodies (Dilute with 5% goat serum) overnight at 4 ℃ . Apply an appropriate uorophore-conjugated secondary antibody diluted in antibody dilution buffer to the coverslips and incubate for 1 hour in a moist, dark environment. Mount cover slips on microscope slides with Hydromount (National Diagnostics) containing DAPI (if desired) for nuclear staining.


Introduction
Lung cancer remains the leading cause of cancer-related deaths worldwide [1] . There are two main forms of lung cancer: small cell lung cancer and non-small cell lung cancer (NSCLC). NSCLC is the most common histological subtype, accounting for about 85% of all lung cancer cases. Compared with small cell lung cancer, the growth and division of NSCLC cells are slower, and the spread and metastasis are late [2] . However, NSCLC is not diagnosed until advanced disease appears. In 50-75% of patients, cough is the most common symptom, followed by hemoptysis, chest pain, and dyspnea [3] . The current treatments for NSCLC mainly include: (1) surgical treatment (2) chemotherapy (3) radiotherapy [4] (4) immunotherapy [2,5] (5) targeted therapy [6] . However, how to control the development of lung cancer and explore effective treatment methods are the main problems that need to be solved. Although targeted therapy is a popular direction for lung cancer, which has achieved curative effects in special populations, traditional chemotherapy or radiotherapy is still the main choice. Therefore, the development of new strategies to inhibit the growth of NSCLC cells and enhance the sensitivity of chemotherapy and radiotherapy is the focus of current medical research.
Solanine, also known as solanine toxin, and potato toxin, is a weakly basic glycoside composed of glucose residues and solanidine. It is widely found in Solanaceae plants such as potatoes, tomatoes and eggplants [7] . The α-solanine is the main biologically active component in solanine. What is more, αsolanine has shown anti-tumor cell metastasis activity in a number of tumor treatment experiments [8] . In recent years, more and more research showed that α-solanine had anti-proliferation effect on various human tumor cells in vitro [9] . The main anti-tumor mechanisms of α-solanine are: (1) inhibit cancer cell proliferation and metastasis [10,11] (2) cytotoxic effect [12,13] (3) induce tumor cell apoptosis [14] (4) inhibit the invasion of cancer cells into tissues [15] (5) enhance the sensitivity of tumor cells to radiotherapy and chemotherapy [16,17] (6) Enhance immunity [18] . Some scholars believed that α-solanine can play an anticancer effect in lung cells by inducing the expression of microRNA-138 (MIR-138). In addition, studies have addressed that α-solanine enhanced the chemotherapy sensitivity and radiosensitivity of A549 and H1299 cells by affecting the expression of MIR-138 and focal adhesion kinase (FAK). This indicated that the α-solanine/MIR-138/FAK cascade may be a potential target for the treatment of NSCLC [19] . Lin et al. explored that α-solanine inhibited the proliferation and migration of HepG2 cells induced by acetylcholine signal. They also demonstrated that α-solanine has the potential to inhibit liver cancer metastasis by regulating epithelial-mesenchymal transition (EMT), exosomes and miR-21 [20] . Acetylcholine can bind to niacin and physostigmine receptors on lung cancer cells, thereby accelerating their proliferation, migration and invasion [21] . Whereas, there is no research reported on the therapeutic effect of α-solanine on lung cancer and its related molecular mechanisms, the mechanism of α-solanine on NSCLC is still unclear. Therefore, the target of α-solanine, as well as the metabolite changes and metabolic pathways of α-solanine after acting on the body need to be further studied.
Based on the rapid development of omics and big data, network pharmacology integrates multiple emerging interdisciplinary disciplines such as systems biology and bioinformatics. It is an overall analysis of biological system network [22] . Network pharmacology techniques can combine drug action networks with biological networks [23] . Network pharmacology can quickly and e ciently predict and screen the effective components of traditional Chinese medicine (TCM), and analyze the possible interactions between components to explore the possibility of drug regulation [24] . Therefore, this study used network pharmacology to predict the targets of solanine acting on NSCLC and establish a multilevel "drug targets -disease" network. Then, bioinformatics methods were used to analyze the pathways and mechanisms corresponding to the target. According to the results of the analysis of network pharmacology validated using targeted metabolomics. Finally, the molecular biological experiments were used to verify the mechanism of α-solanine on NSCLC, and to provide the basis for the rational application and secondary development of α-solanine. The detailed technical strategy for the current study is shown in Fig. 1.

Collection And Analysis Of Gene Targets For Nsclc
The genes of NSCLC patients were obtained from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed gene analysis was performed in R software. Genes with a P value < 0.05 and |log2(fold change) | > 2 were considered to be of signi cantly differential expression between NSCLC tissue samples and normal samples. The R package "DEseq2" was used for differential expression analysis. The heatmap and volcano plot were drawn with "pheatmap" R package and "ggplot2" R package.

Screening of candidate targets and construction of network
The intersection targets of α-solanine and NSCLC were obtained by Venn tool(http://bioinformatics.psb.ugent.be/webtools/Venn/), namely the candidate genes of α-solanine for NSCLC targets. Then Cytoscape software (ver. 3.8.1) was used to construct drug and disease target network diagram to show the relationship between α-solanine and NSCLC.

Construction Of Ppi Network And Selection Of Core Genes
The obtained candidate targets of α-solanine and NSCLC were uploaded to online STRING11.0 (https://www.string-db.org/) to obtain forecasted protein-protein interactions (PPI), with the species set as "Homo sapiens". Then download the PPI network le and import it into Cytoscape for topology attribute analysis. CytoNCA was used to calculate degree centrality (DC), median centrality (BC), proximity centrality (CC), eigenvector centrality (EC), network centrality (NC) and local mean connectivity (LAC).
Based on the results above the median of the six parameters, the core target of the network was obtained.
Gene Ontology and Pathway Enrichment Analysis for NSCLC-Related Targets of α-solanine The candidate targets of α-solanine and NSCLC were bioinformatics analyzed by R software. Gene Ontology (GO) function enrichment and KEGG pathway analysis were performed using BiocManager, Dose, Cluster Pro ler and Enrichment Plot in R software. The top 20 items with the highest degree were presented in the form of bubble diagram.

Experimental Validation
Cell Culture and Drug Preparation Human Lung cancer A549 and PC-9 cells were obtained from the Type Culture Collection of the Chinese Academy of Sciences. The cells were cultured in RPMI-1640 medium (Gibco, Thermo Fisher Scienti c, Inc.) with 10% fetal bovine serum (FBS, Gibco, Thermo Fisher Scienti c, Inc.) and 1% streptomycin and penicillin (Gibco), and maintained at 37˚C in a humidi ed atmosphere containing 5% CO 2 . The α-solanine (J & K Scienti c) were dissolved in dimethyl sulfoxide (DMSO, Sigma-aldrich). The control group was treated with DMSO only under identical conditions.

Cell Viability Assay
In this study, 3x10 3 lung cancer cells were inoculated into 96-well plates, and 0, 10, 12, 14, 16, 18, 20, 22, 24 µg/mL concentrations of α-solanine with the same volume of DMSO were added after 24 h; At different time points during treatment, 10 µl CCK-8 (Dojindo) solution was added to the wells and incubated at 37˚C for 2h, and the optical density (OD) was measured by a plate reader (SpectraMax M5; Molecular Devices LLC) at the read mode of absorbance (450 nm).

Cell Migration And Invasion Assays
For cell migration assays, transwell lters (Costar) were uncoated with Matrigel on the upper surface of a polycarbonic membrane (6.5 mm diameter, 8 µm pore size). After being cultured for 24h in medium containing various concentrations of α-solanine (0, 12, 18 and 24 µg/mL), A549 and PC-9 cells (2×10 5 ) were resuspended in 200 µL of serum-free medium in the upper chambers, while 600-µL medium containing 10 % FBS (as a chemoattractant) was added to the bottom chamber. After incubating for 24 h at 37°C in a humidi ed incubator with 5 % CO 2 , cells in the upper chamber were carefully removed with a cotton swab. Cells that had migrated to the basal side of the membrane were xed with 4% polyformaldehyde, stained with crystal violet, mounted and dried at room temperature for 30 minutes. Then randomly selected three visual elds and counted and recorded the number of cells invading the Matrigel (3.9 µg/µL, 60-80 µL) with an inverted microscope at ×100 magni cation. Each test was performed in triplicate. With a similar principle and approach, we used Transwell lters with coated Matrigel on the upper surface of a polycarbonic membrane for a cell invasion assay.

Apoptosis Analysis By Flow Cytometry (Fcm)
Apoptosis was detected according to the manufacturer's instructions. Brie y, 3x10 5 A549 and PC-9 cells were inoculated into 6-well plates, after being cultured for 24h, then treated with 0, 12, 18, 24 µg/mL concentrations of α-solanine with the same volume of DMSO. After 24h of treatment, the cells were collected and washed with phosphate buffered saline (PBS), resuspended in binding buffer, labeled by Annexin V-FITC, incubated with propidium (PI), and apoptosis was detected by ow cytometry.
Immuno uorescent Staining A549 and PC-9 cells (2×10 5 ) were inoculated into 6-well plate (Corning), after being cultured for 24h in order to make the cells adhere and stabilize. Then the test group treated with α-solanine (18 µg/mL), an equal volume of DMSO was used as control. After 24h of treatment, the cells were collected and washed with PBS, then cells were xed with 4% polyformaldehyde, and permeabilized with 0.1% Triton X-100 made in PBS for 15-20 minutes. Prepare a blocking solution of 5% normal goat serum in PBS for 30 minutes, then incubated with relevant antibodies (Dilute with 5% goat serum) overnight at 4℃. Apply an appropriate uorophore-conjugated secondary antibody diluted in antibody dilution buffer to the coverslips and incubate for 1 hour in a moist, dark environment. Mount cover slips on microscope slides with Hydromount (National Diagnostics) containing DAPI (if desired) for nuclear staining.

Statistical Analysis
Statistical analysis was processed with GraphPad Prism 8 software. Data were expressed as the mean ± SD and analyzed using Student's t-test (for comparison between two groups) or one-way ANOVA with a post hoc Tukey test (for comparison among three or more groups). Differences between groups were considered to be statistically signi cant if values of P < 0.05.

Network Pharmacology Analysis
Results of genes in NSCLC and target genes of α-solanine From TCGA data analysis,7845 differentially expressed genes were found in NSCLC, of which 5343 genes were up-regulated and 2502 genes were down-regulated (Supplement Table S1). As shown in Fig. 2A and Fig. 2B, the distribution of differentially expressed genes was shown by heat map and volcano map. The structure of α-solanine was displayed in Fig. 2C. Then 399 target genes of α-solanine were obtained (Supplement Table S2). Further analysis revealed a total of 130 targets for α-solanine and NSCLC (Fig. 2D). The details of these 130 genes were shown in Supplement Table S3.

Results Of Ppi Network Construction And Core Gene
The candidate targets of α-solanine and NSCLC were introduced into the String website, and the network interaction diagram of them was obtained (Supplementary Figure S1). In the cytoscape interactive network of α-solanine and NSCLC there are 110 nodes and 712 edges. CytoNCA was used to obtain the core target network through DC, BC, CC, EC, NC and LAC parameters. In the core target network, α-solanine and NSCLC have 31 nodes and 395 edges, which mainly include GPI, ALDOA, TPI1, PKLR, LDHA and ALDH 3 , etc. (shown in Fig. 3).
Results of GO and KEGG enrichment analysis targeting between α-solanine and NSCLC GO enrichment analysis was conducted on the common potential target gene of α-solanine and NSCLC. The top10 signi cantly enriched terms in BP, CC, and MF categories were shown in Fig. 4A. The biological processes are mainly purine nucleotide metabolic process, the cellular components mainly include the cytoplasmic vesicle lumen, and the molecular functions mainly include amide binding, etc. KEGG pathway analysis mainly included Glycolysis/Gluconeogenesis and Biosynthesis of amino acids signaling pathway in Fig. 4B.
Experimental Validation α-solanine inhibited the growth, migration and invasion of A549 and PC-9 cells.
Various cell function experiments were rst carried out in order to verify the effect of α-solanine on NSCLC. When the concentration of α-solanine was increased (0, 10, 12, 14, 16, 18, 20, 22, 24 µg/mL), the viability of A549 and PC-9 cells was decreased in a dose-dependent and time-dependent manner at different time points (24 h, 48 h) (Fig. 5A). The IC50 values of α-solanine on A549 and PC-9 cell lines were 18 µg/mL, respectively. It can be seen from the Fig. 5B and Fig. 5C, α-solanine inhibited A549 and PC-9 cell migration and invasion in a dose-dependent manner. In brief, these data con rmed that α-solanine may inhibit the growth of NSCLC cells in vitro.

α-solanine Induces Apoptosis Of A549 And Pc-9 Cells
The purpose of this experiment was to prove that α-solanine inhibited NSCLC cell apoptosis. Annexin V-FITC/PI double staining was used to detect the apoptotic cells. As can be seen from the Fig. 6, after 24 h of α-solanine treatment, the proportion of early and late apoptotic cells in the 12 µg/mL group was 10.51 ± 0.34% of PC-9. In the 18 µg/mL group, the early and late apoptotic cells accounted for 2.40 ± 0.03% of A549 and 11.10 ± 0.59% of PC-9, In 24 µg/mL, the apoptosis rate of A549 was 11.91 ± 0.93%, and PC-9 was 15.67 ± 1.42%, both of which were signi cantly higher than those in 0 µg/mL group. The results displayed that α-solanine could promote the apoptosis of A549 and PC-9 cells.

The Result Of Targeted Energy Metabolite Detection
According to network pharmacological results, α-solanine may exert anti-tumor effect through energy metabolism pathway. To further verify the results, we carried out the detection of targeted energy metabolites. Firstly, the biological duplication among samples in the study group was investigated by means of sample correlation heat map (shown in Fig. 7A). The higher the correlation coe cient of intragroup samples relative to inter-group samples, the more reliable the differential metabolites obtained.
In this study, 65 energy metabolites were quanti able (Supplementary Table S4 and Supplementary Figure S2). Based on the assessment of OSC partial least squares-discriminant analysis (OPLS-DA), the pro le of metabolites of α-solanine group and control group could be clearly separated (shown in Fig. 7B). The S-plot of OPLS-DA showed the differential metabolites screened by variable importance in projection (VIP) value, with red dots representing VIP > 1 and green dots representing VIP < 1 (shown in Fig. 7C). Qualitative and quantitative analysis was conducted on the detected differential metabolites, and the metabolites with large difference in change times were shown, as shown in the bar chart of metabolites (Fig. 7D). Clustered heat maps reveal the total differences of differential metabolites (Fig. 7E). Differential metabolite ddle plots also showed changes in metabolites in the administration and control groups (Fig. 7F). To further evaluate the relevance of metabolic pathways that are altered by α-solanine treatment, the KEGG pathway database was used to identify pathway enrichment patterns for the signi cantly altered metabolites. The results showed that differential metabolites were mainly involved in biosynthesis of amino acids related pathways (Fig. 7G).

α-solanine Down-regulates Energy Metabolism Pathway Related Proteins Expression
In order to preliminarily verify the effect of α-solanine on energy metabolism pathway, immuno uorescence technology was used to detect the changes of related proteins in the control group and the administration group, and it was found that the expression of GPI, ALDOA, TPI1, PKLR, LDHA and ALDH 3 in the administration group was signi cantly reduced (Fig. 8). As shown in Fig. 9, A549 and PC-9 were treatment with α-solanine at 0, 12, 18 and 24 µg/mL signi cantly inhibited the energy metabolism pathway related proteins. The western blot results were consistent with immuno uorescence. These results suggested that α-solanine inhibited the growth of NSCLC through the energy metabolism pathway.

Discussion
NSCLC is a heterogeneous tumor, accounting for approximately 85% of all newly diagnosed lung cancers [26] . Due to inadequate screening programs and late onset of clinical symptoms, most patients are already in the advanced stage of disease when they are diagnosed. Therefore, the prognosis of patients is poor [27] . With the deepening of understanding of tumor molecular biology, the treatment of NSCLC has gradually entered the era of precise treatment based on tumor molecular and histological characteristics.
All this information makes possible the development of individualized molecular targeted therapy [28] . αsolanine is the main component of Solanum, which has a variety of pharmacological effects. In this study, the mechanism of α-solanine against NSCLC was studied by integrating network pharmacology and molecular biology techniques.
In drug discovery, methods based on network pharmacology have given us a deeper understanding of multi-layer drug information, which can provide support for the development of rational drug design and a better understanding of the mechanism of drug multiple actions [29] . For example, through network pharmacological analysis, it was found that the possible mechanisms of triptolide include regulation of immune and in ammatory responses, promotion of cell apoptosis and inhibition of tumor development [30] . Artemisia argyi may produce gastric mucosal protection through coordinated regulation of NF-kB, HIF-1, TNF, VEGF, Toll-like receptor signaling pathways and other biological pathways [31] . Using the method of network pharmacology, the mechanism of Tripterygium wilfordii in the treatment of acute myeloid leukemia is explained in detail, that is, the main pathways of treatment targets are Toll-like receptor signaling pathway, NF-κB signaling pathway and HIF-1 signaling pathway [32] . In this study, rst download the target of α-solanine from the PharmMapper database and the target of the treatment of NSCLC from the TCGA database, then use R software to perform differential analysis of different expressions to obtain candidate genes for α-solanine to act on NSCLC. Use Cytoscape software to construct a network diagram of drugs and diseases, and establish a multi-level "drug target-disease" network to reveal the relationship between α-solanine and NSCLC. Finally, GO analysis is conducted through candidate genes. From three aspects, the biological process is mainly involved in purine nucleotide metabolism. In terms of cell components, it is mainly related to the cytoplasmic vesicle cavity, and it is inseparable from the combination of amide in molecular function. At the same time, KEGG analysis found that the effect of α-solanine against NSCLC was mainly related to the metabolic pathway, especially the glycolytic pathway, indicating that α-solanine may produce anti-tumor effects through the energy metabolism pathway. It has been suggested that the occurrence, development and metastasis of cancer are highly regulated by transcription factors, and transcription regulators promote the regulation of energy metabolism pathways, which mainly depend on the tumor microenvironment [33] . The analysis found that rapamycin plays a key role in the connection between glycosphingolipids and glutamine metabolism and mitochondrial signaling [34] .
According to the experimental results, it can be concluded that A549 and PC-9 were inhibited with the increase of the administration concentration and the extension of the administration time, showing a concentration and time dependence. Finally, it was determined that the administration concentration of A549 and PC-9 was IC50, which was about 18 µg/mL. Further studies have shown that α-solanine inhibits the invasion and migration of A549 and PC-9, and increases with the increase of drug concentration. αsolanine induces apoptosis of A549 and PC-9, and the apoptotic rate increases with the increase of drug concentration. Previous studies have shown that the induction of cancer cell apoptosis by steroid alkaloids is one of the reasons for its anti-cancer effect [8,35,36] . This may also be an important reason why α-solanine exhibits anti-NSCLC effects.
According to the results of network pharmacology analysis, α-solanine may produce anti-tumor effects through metabolic pathways, especially energy metabolism. This study further detected the expression of energy metabolites through targeted metabolomics. At present, small molecule metabolites play an important role in cancer screening, diagnosis and prognosis [37] . It is also a good method for evaluating drug e cacy and mechanism of action [38] . The results of metabolomics experiments showed that the Administration group compared with the control group, the small molecule metabolites related to amino acid biosynthesis had signi cant changes. We focused on the rst 20 metabolites (Fig. 7D and Fig. 7F) with signi cant changes. Among them, 15 small-molecule metabolites increased in synthesis and the other 5 decreased. Combined with the KEGG pathway diagram and experiments, it can be obtained that αsolanine affects the synthesis and elimination of small molecule metabolites by inhibiting amino acid synthase and metabolic enzymes, that is, inhibiting synthase. The decrease in the synthesis of amino acids shows a decrease in the number of amino acids; on the contrary, the inhibition of amino acid metabolism enzymes reduces the elimination of amino acid metabolism and accumulates, which shows an increase in the number of amino acids.
In order to further verify that α-solanine may act on NSCLC through glycolysis pathway, related proteins such as GPI, ALDOA, TPI1, PKLR, LDHA and ALDH 3 in glycolysis pathway were studied. This is mutually con rmed with the core target in network pharmacology. In the immuno uorescence experiment, it can be seen that the expression of the above proteins which treated with α-solanine reduced. The western blot experiment was consistent with the immuno uorescence result. The above two experiments showed that the expression of GPI, ALDOA, TPI1, PKLR, LDHA and ALDH 3 decreased after A549 and PC-9 were treated with α-solanine, indicating the mechanism of α-solanine is related to these proteins and is consistent with the results of the research of network pharmacology.
The results showed that α-solanine may achieve the purpose of anti-tumor by affecting energy metabolism. Adequate amino acid supply is important for cancer to maintain its proliferation power. In addition to its direct role as a substrate for protein synthesis, it can also play a role in energy production, driving nucleoside synthesis and maintaining cell redox homeostasis [39] . At present, many differences in the metabolism of speci c amino acids in prostate cancer have been found. Other key requirements of the amino acid library come from serine, single carbon cycle, glycine synthesis pathway and folate metabolism, which are the main intermediates for the synthesis of nucleobases necessary for rapidly proliferating cells [40] . The currently known metabolic changes related to cancer can be roughly divided into six aspects [41] : (1) Glucose and amino acid intake disorders (2) Random access to nutrients (3) Biosynthesis using glycolysis/TCA cycle intermediates (4) Increased demand for nitrogen (5) Metabolitedriven gene regulation changes (6) Metabolism interaction with the microenvironment. Generally speaking, tumor cells metabolize glucose, lactic acid, pyruvate, hydroxybutyrate, acetic acid, and glutamine much faster than non-tumor cells [42] . Studies have pointed out that amino acid metabolism starvation therapy has become essential in the treatment of many tumors, and non-essential amino acids are particularly prominent [43] . In energy metabolism, glucose undergoes glycolysis in the cytoplasm to produce phosphoglucose and phosphoglycerate. These two substances can produce histidine, serine, glycine, and cysteine. Phosphoglycerate can be further metabolized to produce pyruvate, which can be converted into alanine, valine, and leucine. Then, if pyruvate enters the mitochondria and undergoes further oxidation, that is, enters the tricarboxylic acid cycle, α-ketoglutarate can be produced. αketoglutarate can provide backbone atoms for glutamate amino acids, which can be converted into glutamic acid, glutamine, and further into ornithine, citrulline, arginine, proline, and hydroxyproline acid. If ketoglutarate continues to be converted in the tricarboxylic acid cycle, it can be converted to oxaloacetic acid. Oxaloacetate can be converted into aspartic acid, asparagine, lysine, threonine, methionine, and isoleucine. In addition to glycolysis of glucose in the cytoplasm to form pyruvate, the pentose phosphate pathway can also produce tryptophan, phenylalanine, and tyrosine. Compared with normal cells, tumor cells need a lot of amino acids to synthesize their own substances in order to achieve rapid growth, invasion and migration [44] . By enhancing aerobic glycolysis, cancer cells provide more carbon intermediates for the synthesis of nucleotides, amino acids and lipids, and promote their anabolism. This experiment found through metabolomics that more than 10 amino acids including alanine, cysteine, serine, tyrosine, etc., increased after a certain concentration of α-solanine, and these amino acid metabolites and energy were signi cantly changed. The metabolic pathways are closely related. Among them, alanine, cysteine, serine and threonine are related to the energy metabolism intermediate product pyruvate, while asparagine, tyrosine, glutamine and arginine are respectively related to oxaloacetate in the citric acid cycle, Fumaric acid and α-ketoglutarate are related. Combining with the previous discussion and the results of metabolomics, it is found that the intermediate product of glycolysis pathway glyceraldehyde 3-phosphate rises, which can indicate that the expression of energy metabolism enzymes inhibited by α-solanine can cause disorder of its metabolic intermediates and interfere with cancer. Cell energy metabolism inhibits tumor cell proliferation, migration and invasion, and promotes apoptosis. The schematic diagram of the inhibition mechanism of α-solanine on NSCLC (Fig. 10).

Conclusion
In conclusion, the pharmacological mechanism of α-solanine inhibiting NSCLC was discussed by combining network pharmacological prediction and experimental veri cation. α-solanine may inhibit the proliferation and survival of NSCLC by regulating glycolysis related pathways. This study further suggests that the combination of network pharmacological prediction and experimental veri cation can provide a useful tool for the detailed description of the mechanism of action of TCM. The potential therapeutic effect of α-solanine on NSCLC may bene t from further clinical trials in patients with lung cancer.

DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any quali ed researcher.

ETHICS STATEMENT
No humans and animals were involved in the study.  The strategic process of the current study.  Process of topological screening for the PPI network. The yellow parts are the targets screened by calculation of DC, BC, CC, EC, NC and LAC parameters. The last network diagram represents the core gene network. The more important targets were regarded as the targets with the bigger ellipse and darker color.    Comparison of energy metabolism pro les between control group and administration group. (A) Intersample correlation diagram. Horizontal and vertical coordinates represent the corresponding sample names, and color represents the value of phase relation. (B) OPLS-DA score chart for metabolomic data of the two groups. (C)Opls-da s-plot. The abscissa represents the cocorrelation coe cient between principal components and metabolites, while the ordinate represents the correlation coe cient between principal components and metabolites. (D) Histogram of differential multiple of metabolites. (E) Clustering heat map of differential metabolites, red represents high metabolic content and green represents low content. (F) Violin diagrams of differential metabolites. The abscissa is the sample and the ordinate is the expression quantity. (G) KEGG classi cation of differential metabolites. The ordinate is the name of KEGG metabolic pathway. The abscissa is the number of metabolites annotated to the pathway and its ratio to the total number of metabolites annotated.

Figure 8
A549and PC-9 cells were treated with α-solanine (0,12,18,24 μg/mL) for 24 h; The proteins expression was detected using a confocal laser confocal microscope. The target proteins presented as green and the nuclei as blue using DAPI counterstaining. Fluorescence intensity was detected using Image J software.
(A) The proteins to be detected for A549. (B) The targets proteins for PC-9. (C) The statistical graphs for uorescence intensity of A549 and PC-9 cells.Full-length blots are presented in Supplementary Figure S3.