Anticancer activity of Stemona tuberosa (wild asparagus) against type-II human lung adenocarcinoma (A549) cells and identification of SRC inhibitor using integrated network pharmacology and molecular dynamic simulation

Abstract

Stemona tuberosa is widely recognized for its traditional applications as an anti-cancer agent. This study aimed to assess the anti-cancer properties of S. tuberosa in human lung adenocarcinoma A549 cells. Among the various solvent extracts of S. tuberosa, the methanolic extract showed the highest toxicity against A549 cells. The S. tuberosa extract elicited cytotoxic effects and suppressed colony formation in A549 cells in a dose-dependent manner. S. tuberosa activity was further supported by AO/EtBr staining, increased caspase 3/6 activity, upregulation of pro-apoptotic genes, DNA damage, and elevated lipid peroxidation, with decreasing antioxidant levels. LC–MS analysis identified 80 predominant secondary metabolites in the methanolic extracts of S. tuberosa. A network pharmacology study identified SRC as the primary target of compounds identified from S. tuberosa. SRC protein is crucial for advancing lung cancer because of its function in cell proliferation, survival, and metastasis. Among the various compounds identified from S. tuberosa extract, 4-Azatricyclo [4.3.1.13,8] undecan-5-one (ADE) (− 10.88 kcal/mol) and Dihydro-normorphine, 3-desoxy- (DNY) (− 10.83 kcal/mol) exhibited notable binding affinities for SRC. Further analysis using molecular dynamics simulations (100 ns) validated the stability of SRC-ligand complexes, with RMSD of 1.8 and 2.2 Å for ADE and DNY, respectively, alongside the establishment of essential hydrogen bonds with pivotal residues, including ASP408, ALA403, and THR438. Finally, gmx._MMPBSA showed favourable ΔGbind values for ADE (− 15.06 ± 0.11 kcal/mol) and DNY (− 15.66 ± 0.25 kcal/mol), which highlights the significant potential of ADE and DNY as effective SRC inhibitors, suggesting S. tuberosa as a novel candidate for cancer therapy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-02138-6.

Introduction

Cancer, with more than 36 major types, is the leading cause of mortality globally, exhibiting a notable fatality rate of 9.6 million people per year and responsible for approximately 10 million deaths in 2020. Lung cancer is the second most frequently diagnosed cancer and the predominant cause of cancer-related mortality in 2020, affecting nearly 2.22 million people and resulting in approximately 1.8 million fatalities [1]. The mechanisms underlying lung cancer development remain largely unclear; however, tobacco smoking, genetic predisposition, occupational exposure, alcohol consumption, and viral infections are recognized as significant risk factors for lung cancer [2]. Lung cancer is categorized into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) based on cellular histology. Approximately 85% of lung malignancies are classified as non-small cell lung cancer (NSCLC), which encompasses adenocarcinomas, squamous cell carcinomas, and large cell carcinomas. Despite recent advancements in lung cancer treatment, such as surgery, chemotherapy, and radiotherapy, the prognosis remains poor, with a 5-year survival rate of less than 15%, indicating the limited efficacy of the current therapies [3]. The combination of chemotherapy drugs with platinum compounds is the most frequently used therapy and ideal treatment choice for patients with NSCLC. Nonetheless, the effectiveness of this treatment remains constrained by drug resistance and adverse side effects associated with chemotherapeutic agents [4, 5]. Consequently, the development of more efficacious and less toxic anticancer drugs has emerged as a critical strategy for lung cancer therapy.

Herbal medicines have a long history of use in cancer treatment. In recent years, natural products derived from medicinal plants have received extensive attention as major sources of drugs for reducing chemotherapy-associated toxic side effects [6]. Numerous studies have focused on the potential of extracts from traditional medicinal herbs as alternative and complementary medications for the treatment of cancer [7–9]. Among the numerous chemicals assessed for anti-cancer properties, natural compounds from medicinal plants appear to be the most promising because of their safety, efficacy, and fewer side effects than synthetic drugs [10]. Medicinal plant-derived therapeutic medicines have been documented to function as anticancer agents in numerous experimental cancer models, and approximately 60% of the currently available anticancer drugs are derived from plant sources [11]. A549 cells are human alveolar basal squamous epithelial cells obtained from a 58-year-old Caucasian male [12], which grow as a monolayer in vitro and have been commonly used for the screening of the anticancer properties of various plant extracts in vitro [13–15].

Stemona tuberosa is an elegant plant belonging to the family Stemonaceae. It is native to China, South-East Asia, North-East India, and New Guinea, and it is among the 50 fundamental herbs used in traditional Chinese medicine [16]. S. tuberosa extracts have been documented to exert many pharmacological effects, including anti-inflammatory, anti-tussive, anti-tuberculotic, anti-fungal, anti-microbial, demulcent, and therapeutic effects in lung disorders [17]. Stemophenanthrenes and isopinosylvin A isolated from S. tuberosa have been shown to exhibit cytotoxic activity against certain human cancer cell lines (KB, MCF7, SK-LU-1, and HepG2) [18]. The dichloromethane fraction of S. tuberosa have also been reported for its anti-cancer activity against Medullary Thyroid Carcinoma via induction of apoptosis [19]. Nonetheless, the molecular mechanisms responsible for the cytotoxic effects of S. tuberosa remain unclear.

In-silico approaches such as ADMET profiling, network pharmacology, molecular docking, molecular dynamics simulations, and gmx_MMPBSA together function as an integrated computational framework for identifying active compounds in drug discovery. ADMET evaluates substances according to pharmacokinetic and toxicological characteristics, confirming drug-likeness before additional investigation. Network pharmacology elucidates essential molecular targets and pathways, especially advantageous for natural compounds exhibiting multi-target actions. Molecular docking offers a preliminary evaluation of binding affinity and interaction sites, whereas molecular dynamics simulations confirm ligand stability and interactions throughout time in physiological conditions. gmx_MMPBSA enhances binding free energy calculations by integrating entropic and solvation contributions [20]. Collectively, these techniques optimize compound selection, improving predictive accuracy and facilitating rapid experimental validation. In this study, the growth-inhibitory and apoptosis-based cytotoxic activity of S. tuberosa was studied in A549 (human lung adenocarcinoma) cells and further explored the molecular target(s) of active compounds from S. tuberosa and analysed their interaction using in silico approaches.

Methods

Chemicals and reagents

Trypsin–EDTA, Eagle’s Minimal Essential Medium (MEM), fetal bovine serum (FBS), 3-(4,5-dimethylthiazole-2-yl)-2, 5-diphenyl tetrazolium bromide (MTT), sodium bicarbonate, bovine serum albumin (BSA), glutathione (GSH) reduced, nicotinamide adenosine dinucleotide (NADH), nitroblue tetrazolium (NBT), Folin-ciocalteu’s reagent, n-butanol, thiobarbituric acid (TBA), potassium chloride (KCl), sodium chloride (NaCl), Triton X-100, acridine orange, ethidium bromide, phenazine methosulphate (PMS), dimethyl sulphoxide (DMSO), 1-chloro-2,4 dinitrobenzene (CDNB) and 5, 5’ dithio 2-nitrobenzoic acid (DTNB) were purchased from HiMedia Laboratories Pvt. Ltd. (Mumbai, India). L-Glutamine, phenol red, agarose (low gelling temperature), ethylenediamine tetra-acetic acid (EDTA), Trizma base, Trizma hydrochloride, and trichloroacetic acid (TCA) were purchased from Sigma Chemical Co., Bangalore, India. Fluorouracil (5FU) was obtained from GLS Pharma Ltd. (Hyderabad, India). The remaining chemicals were purchased from Merck Specialities Pvt., Ltd. (Mumbai, India).

Ethics and consent to participate

S. tuberosa is a wild plant collected from Phura village, Siaha District, Mizoram, India. The collection of the plant sample was done based on the Botanical Survey of India (BSI) guidelines, Shillong, Government of India. The plant was identified and authenticated by the Natural History Museum in Mizoram, India (accession no. NHMM-P/000349).

Preparation of S. tuberosa extracts

The roots were washed, chopped into pieces, dried in the shade at room temperature, and powdered. The pulverized roots were then defatted using petroleum ether in a Soxhlet apparatus at 40 °C for 30 cycles and dried overnight at 40 °C to remove all traces of petroleum ether. The powdered samples were further extracted with chloroform, methanol, and distilled water according to their increasing polarity at their respective boiling points using a Soxhlet apparatus for a minimum of 40 cycles each. The liquid extracts were filtered and concentrated using a rotary evaporator (Buchi, Germany) under reduced pressure at 40 °C for approximately 5 h and finally freeze-dried.

Cell lines and culture

Type II human lung adenocarcinoma cell line (A549 cells) was obtained from the National Centre for Cell Sciences (NCCS), Pune, India. The cells were maintained in MEM supplemented with 10% FBS and 1% L-glutamine in a humidified incubator with 5% CO₂ at 37 °C (Eppendorf, Hamburg, Germany).

Cytotoxicity assay

The cytotoxicity of S. tuberosa was estimated using the 3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyltetrazolium bromide (MTT) reduction assay [21]. Briefly, 1 × 10⁴ cells were seeded in flat-bottomed 96 well plates (Himedia Laboratories Pvt. Ltd., Mumbai, India) containing 100 µL MEM. The cells were allowed to adhere for 24 h at 37 °C with 5% CO₂ and treated with different concentrations (75–300 µg/mL) of various solvent extracts of S. tuberosa for 24, 48, and 72 h along with a control sample. At the end of the treatment, drug-containing media were removed, and cells were washed with FBS-free media. Next, 10 µL of MTT (5 mg/mL) was added to each well and incubated for another 2 h at 37 °C in a CO₂ incubator. The insoluble purple formazan crystals formed were dissolved in 100 μL of DMSO and incubated for 30 min. The absorbance of the solution was measured at 560 nm using a microplate reader (Spectramax m2e; Molecular Devices). Three independent experiments, consisting of three replicates, were performed for each treatment. Cytotoxicity was expressed as inhibition (%) and was calculated using the following formula:

% inhibition = Control-Treatment/Control X 100.

The methanolic extract of S. tuberosa (STME), the most effective extract in preliminary cytotoxicity screening using the MTT assay, was subsequently used for further experiments.

Liquid chromatography-mass spectrometry (LC–MS) analysis

Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) was used to determine the bioactive secondary metabolites present in the methanolic extract of S. tuberosa. Analysis was carried out using an ACCUCORE HPLC (C18, 150 × 2.1, 1.7 um) system coupled to a triple quadrupole tandem mass spectrometer (ACQ-TQD-QBB1152, Waters acuity PDA detector, Waters Corporation, Milford, MA, USA) equipped with an orthogonal ESI source. Mass acquisition spectra were recorded between 150–2000 m/z for both extracts. The source temperature was set to 120 °C and desolvation at 350 °C for ES + and ES-. Mobile phases A (water and 0.1% formic acid) and B (mixed acetonitrile and 0.1% formic acid) were used as eluents in a linear gradient flow. The injection volume was 5 µL and the flow rate was maintained at 0.3 mL/min throughout the gradient. Data were analyzed using OpenLynx™ and MassLynx™ Software.

Clonogenic assay

The effect of methanolic extract of S. tuberosa (STME) on the reproductive integrity of A549 cells was assessed using a clonogenic assay [22]. Exponentially growing cells were harvested from the stock culture by trypsinization, and 200 cells were seeded into individual Petri dishes containing 5 mL of media. After overnight adherence, the cells were treated with different concentrations of STME (75, 150, and 300 µg/mL) or 5FU at a dose of 100 µg/mL (positive control) for 48 h. The cells were then washed with sterile 1X PBS and cultured in fresh medium for another 11 days. The resultant colonies were stained with 1% crystal violet in methanol (w/v) for 30 min at room temperature, and colonies with more than 50 cells were counted using an inverted microscope. The plating efficiency (PE) and surviving fraction (SF) of A549 cells were calculated using the following formulae:

$$\text{PE}=\left(\text{Number}\text{of}\text{colonies}\text{counted}\times 100\right)/(\text{Number}\text{of}\text{cells}\text{seeded}).$$

$$\text{SF}=\left(\text{Number}\text{of}\text{colonies}\text{counted}\right)/\left(\text{Number}\text{of}\text{cells}\text{seeded}\right)\times (\text{Mean}\text{plating}\text{efficiency}).$$

Cell morphology analysis by fluorescence staining (apoptotic assay)

The ability of STME to induce apoptosis was studied using AO/EtBr staining. Briefly, 1 × 10⁵ A549 cells were seeded in several six-well plates containing 5 mL of medium. Cells were allowed to adhere overnight, and treated for 48 h with different concentrations of STME (75–300 µg/mL) or 5FU. Untreated controls were maintained in the culture medium alone. After treatment, the cells were washed with sterile 1X PBS and detached with 1X trypsin EDTA. The cells were pelleted and resuspended in 100 µL of FBS-free medium. Subsequently, 25 µL of cell suspension was stained with 2.5 µL each of acridine orange (100 µg/mL) and ethidium bromide (100 µg/mL) at a ratio of 1:1 for 2 min followed by gentle mixing. The morphology of apoptotic cells was then examined on a slide under a fluorescence microscope (Thermo Fisher Scientific, EVOS^R Fluorescence Imaging, AMEP-4615). A fluorescent cationic dye called acridine orange penetrates both living and dead cells, intercalating in double-stranded DNA to give the nuclei a green appearance. Only dead cells with damaged cytoplasmic membranes absorbed ethidium bromide, which gave the nucleus a yellowish-orange stain. Because of this, ethidium bromide-incorporating apoptotic cells display condensed and broken orange chromatin, whereas living cells have green nuclei. In contrast, orange nuclei of necrotic cells are structurally normal [23]. At least 300 cells were scored and the apoptotic index was determined as follows:

$$\text{Apoptotic}\text{index}\left(\%\right)=\text{number}\text{of}\text{apoptotic}\text{cells}\text{scored}\times 100/\text{Total}\text{number}\text{of}\text{cells}\text{counted}.$$

Assessment of genotoxicity of STME using comet assay

Alkaline single-cell gel electrophoresis (comet assay) is a simple method for the detection of DNA strand breaks in eukaryotic cells. The assay was performed using a previously described method, with minor modifications [24]. Briefly, 2 × 10⁴ A549 cells treated with different concentrations of STME or 5FU for 48 h, along with the untreated control, were suspended in 75 µL of 0.5% low-melting-point agarose (LMPA) prepared in 1X PBS and spread onto a frosted slide precoated with 1% normal-melting point agarose (NMPA), and covered with a coverslip. Once the gel solidified following incubation of the slide at 4 °C, the coverslip was gently removed, and a third layer of 90 µL 0.5% LMPA was added. The slides were then incubated for 2 h in a freshly prepared lysis solution (2.5 M NaCl, 100 mM Na₂EDTA, 10 mM Trizma base, 1% Triton X-100, and 10% DMSO, pH 10). After lysis, slides were placed in a horizontal electrophoresis tank filled with freshly prepared alkaline electrophoresis buffer (300 mM NaOH, 1 mM Na₂EDTA, pH13) for 20 min to allow unwinding of DNA. Electrophoresis was performed for 30 min at 24 V and 300 mA. The slides were then neutralized by washing with neutralization buffer (0.4 M Tris–HCl, pH 7.5) for 5 min. After neutralization, the slides were washed with distilled water and stained with an ethidium bromide (EtBr) solution (2 μg/mL) for 5 min. Each slide was prepared in triplicate, and 100 randomly selected cells from each slide were examined using a fluorescence microscope at × magnification of 200x. Image capture and analysis were performed using the Image J software.

Antioxidant assays

To estimate antioxidant enzyme activities and lipid peroxidation levels, 1 × 10⁶ cells were seeded in a T-25 flask containing 5 mL of media. At the end of 48 h treatments with STME or 5FU, drug-containing media was discarded and the cells were washed with sterile 1XPBS and harvested. The cancer cells were pelleted, sonicated (PCI Analytics Pvt. Ltd., Mumbai, India) and 5% homogenate was prepared using cold sterile PBS (pH-7.4) and used for biochemical estimations. Total protein contents were determined using a standard protocol [25] using bovine serum albumin as standard.

Glutathione (GSH) levels were measured by its reaction with DTNB in Ellman’s reaction to give a compound that absorbs light at 412 nm [26]. Briefly, 80 µL of the cell homogenate was mixed with 900 µL of 0.02 M sodium phosphate buffer and 20 µL of 10 mM DTNB and incubated for 2 min at room temperature. The blank consisted of distilled water instead of cell homogenate. The absorbance of the sample was read against blank at 412 nm in a UV–visible spectrophotometer (SW 3.5.1.0. Biospectrometer, Eppendorf India Ltd., Chennai). GSH concentration was calculated from the standard graph and expressed in μmol/mg protein.

Glutathione-s-transferase (GST) activity was measured using the standard method with slight modification [27]. Briefly, 50 µL of 20 mM CDNB was added to 850 µL of 0.1 M phosphate buffer (pH 6.5) and incubated for 10 min at 37 °C. Then, 50 µL each of 20 mM GSH and cell homogenate was added to the mixture. For blank, distilled water was added instead of cell homogenate. The absorbance of the sample was measured at 1 min interval for 5 min at 340 nm. GST activity was measured as, GST activity = (OD of test–OD of blank/9.6 × vol. of test sample) × 1000; where 9.6 is the molar extinction coefficient for GST.

Superoxide dismutase (SOD) activity was measured by the NBT reduction method [28]. Briefly, 100 µL each of cell homogenate and 186 µM PMS was mixed with 300 µL of 3 mM NBT and 200 µL of 780 µM NADH. The mixture was incubated for 90 s at 30 ºC and 1 mL of acetic acid and 4 mL of n-butanol were added to stop the reaction. The blank consisted of all the reagents, except the cell homogenate. The absorbance of the test and blank was measured at 560 nm and the enzyme activity was expressed in unit (1 unit = 50% inhibition of NBT reduction)/mg protein.

$$\%\text{inhibition}=\left(\text{OD}\text{of}\text{blank}-\text{OD}\text{of}\text{sample}/\text{OD}\text{of}\text{blank}\right)\times 100.$$

Lipid peroxidation (LPO) assay

Lipid peroxidation (LPO) was measured by the standard method with slight modification [29, 30]. Malondialdehyde (MDA), one of the toxic products formed from the oxidation of fatty acids such as polyunsaturated fatty acids and phospholipids, serves as a convenient index for determining the extent of the peroxidation reaction of lipids. MDA derived from LPO reacts with TBA to give a red fluorescent adduct absorbing at 535 nm. Briefly, cell homogenate was added to a mixture of 10% TCA, 0.8% TBA, and 0.02 N HCl in a 1:2 ratio. The mixture was boiled for 10 min, cooled immediately at room temperature, and centrifuged at 1000 rpm for 10 min. The supernatant was collected and its absorbance was read at 535 nm against blank. The MDA concentration of the sample was calculated using the extinction coefficient of 1.56 × 10⁶/M/cm.

qRT-PCR analysis of pro-apoptotic and anti-apoptotic gene expression

1 × 10⁶ A549 cells were seeded in 6-well plates containing 5 mL of MEM. Cells were allowed to adhere overnight at 37 °C with 5% CO₂ and treated with 150 µg/mL STME or 5FU for 48 h along with a control sample. After treatment, cells were washed and detached. The cells were then pelleted and total RNA was extracted using Tri reagent (BR Biochem, Life Science Pvt. Ltd, R1022). Extracted RNA was quantified using Nanodrop Spectrophotometer (Eppendorf Biophotometer Plus, Hamburg, Germany), and an RQ1 DNase kit (Promega, M198A, Madison, WI, USA) was used to remove the genomic contamination. cDNA was synthesized from 2 μg of total RNA using a first-strand cDNA synthesis kit (Thermo scientific, K1621; Lithuania, Europe). Gene-specific primers were designed using Primer 3, Boston, MA, USA. The primer sequences used in qRT-PCR analyses were: Bax, forward: 5′-TCCCCCCGAGAGGTCTTTT-3′ and reverse: 5′-CGGCCCCAGTTGAAGTTG-3′; Bid, forward: 5′-CCTTGCTCCGTGATGTCTTTC-3′ and reverse: 5′-GTAGGTGCGTAGGTTCTGGT-3′; p53_, forward: 5′-GTTCCGAGAGCTGAATGAGG-3′ and reverse: 5′-TCTGAGTCAGGCCCTTCTGT-3′; Bcl-X_L, forward: 5′-GGCCACTTACCTGAATGACC-3′ and reverse: 5′-AAGAGTGAGCCCAGCAGAAC-3′; Apaf-1, forward: 5′-AAGGTGGAGTACCACAGAGG-3′ and reverse: 5′-TCCATGTATGGTGACCCATCC-3′; DR5, forward: 5′-CAGAGGGATGGTCAAGGTCG-3′ and reverse: 5′-TGATGATGCCTGATTCTTTGTGG-3′; GAPDH, forward: 5′-GAGTCAACGGATTTGGTCGT-3′ and reverse: 5′-GACAAGCTTCCCGTTCTCAG-3′. qPCR was performed using Quant-Studio 5 (Thermo Fisher Scientific, Foster City, CA, USA). PCR reaction volume of 7 µL for each gene comprised of 1 µL each of cDNA, gene-specific forward and reverse primers, 3 µL PowerUp™ SYBR™ Green Master Mix (Thermo Fisher Scientific, A25742, Lithuania, Europe), and 1 µL of nuclease-free water (ThermoFisher Scientific, A19938, Bangalore, India). The cycling condition of qPCR was 1 cycle at 95 °C (20 s), 35 cycles at 95 °C (01 s), 60 °C (20 s), and 95 °C (01 s), additional melt curve plot step included 1 cycle of 60 °C (20 s) and 1 cycle of 95 °C (01 s) [31]. Afterward, melting curves were generated to confirm a single uniform peak. GAPDH gene was used as a reference gene for determining the relative expression levels of specific target genes. Each sample was run in duplicate along with non-template and negative RT controls. Relative gene expression was determined using the ΔΔCt method [32].

Caspase-3/6 activity assay

A quantitative enzymatic activity assay was carried out for caspase 3 and caspase 6 according to the manufacturer’s protocol (BioVision Incorporated, USA). Briefly, 5 × 10⁵ A549 cells were treated with 150 µg/mL STME or 5FU for 48 h in a 6-well plate, along with the untreated control. After treatment, the cells were washed, lysed in 50 μL of chilled lysis buffer, and incubated for 10 min on ice. The cell lysates were centrifuged at 15,000 × g for 1 min at 4 °C and the supernatant was collected. Total protein content was determined using Bradford assay [33]. The assay was performed in a total volume of 100 μl in 96-well plates. A total of 150 µg of protein from each sample was assayed for caspase-3/6 activity against the specific colorimetric substrates, DEVD-pNA for caspase-3 and VEID-pNA for caspase-6. The mixture was incubated for another 2 h at 37 °C, and the absorbance of free p-nitroanilide (pNA) produced via cleavage of specific substrates by activated caspase-3 and caspase-6 was measured at 405 nm using a microplate reader.

ADME prediction and druglikeness analysis

The Absorption, Distribution, Metabolism, and Excretion (ADME) aspects of a drug's pharmacokinetic profiles are necessary to determine its absorption, distribution, metabolism, and excretion in the body. The canonical Simplified Molecular Input Line Entry System (SMILES) of the phytocompounds identified from the methanolic extract of S. tuberosa was retrieved from PubChem and employed for subsequent analysis. Identifying drug candidates with therapeutic significance that may serve as safe and effective medications necessitates knowledge of their pharmacokinetics and physicochemical properties. Thus, the drug-likeness and bioavailability of the compounds were screened using Lipinski, Ghose, Veber, Egan, and Muegge’s rules with the swissADME software (https://www.SwissADME.ch). The phytocompounds that exhibited no violation of druglikeness were subsequently analyzed for their pharmacokinetic features using pkCSM software (https://biosig.lab.uq.edu.au/pkcsm/prediction).

Network pharmacology screening for potential gene target

Screening of potential targets

The target proteins of the selected phytocompounds were obtained from the Swiss Prediction Database (http://www.swisstargetprediction.ch/). Proteins associated with Non-Small Cell Lung Carcinoma (NSCLC) were obtained from GeneCard database (https://www.genecards.org/). Ultimately, Jvenn, a plug-in of the jQuery JavaScript library (https://jvenn.toulouse.inrae.fr/) was employed to identity the common targets of the selected phytocompounds and NSCLC [34].

Protein–protein interaction (PPI) network construction and core target screening

The protein–protein interaction network of common targets was subsequently constructed using the STRING database (https://string-db.org/). The results were downloaded in tsv.format and then imported into Cytoscape 3.10.2 software for additional analysis. The Cytohubba plug-in was employed to rank the associated nodes according to their degree value to identify possible protein targets.

Molecular docking, molecular dynamics (MD) simulation and gmx_MMPBSA

The three-dimensional (3D) structure of SRC was acquired from the Protein Data Bank (PDB) (https://www.rcsb.org/). The chemical structures of the selected bioactive compounds and the control were acquired from the NCBI PubChem database (http://pubchem.nlm.nih.gov/) as shown in Supplementary Data 1. Molecular docking was performed using the UCSF CHIMAERA (ver.1.17) plugin with VINA (ver. 1.1.2), and the resulting docked conformations were visualised using the Discovery Studio Visualiser [35]. MD simulations were conducted to assess the interaction strength and stability of the receptor-ligand complex for 100 ns. Key metrics, such as Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Hydrogen bonds (H-bond), and Solvent Accessible Surface Area (SASA), were executed using GROMACS (version 2024.4). The total binding free energy was calculated using gmx_MMPBSA (ver. 1.6.3) [20].

Statistical analysis

All data were expressed as mean ± standard error of the mean. One-way ANOVA followed by Tukey’s test was performed to test significant variations between control and treatment groups. SPSS ver.16.0 software (SPSS Inc, Chicago, Illinois, USA) and Graph Pad Prism ver. 6.0 were used for statistical and graphical analyses. Statistical significance was set at p < 0.05.

Results

The antiproliferative and cytotoxic effects of S. tuberosa extracts in A549 cells

To test the cytotoxic effects of various S. tuberosa extracts, A549 cells were treated with various doses for 24, 48, and 72 h. The inhibition (%) of A549 cells by different S. tuberosa extracts and 5FU was plotted against log doses to calculate IC₅₀ (Fig. 1A–C). The methanolic extract of S. tuberosa showed the highest cytotoxicity compared to the chloroform and aqueous extracts at all-time points. The cytotoxic activity was highest at 48 h of treatment with an IC₅₀ of 149.3 ± 8.55 µg/mL which does not differ statistically with that of the standard drug 5FU having an IC₅₀ of 123.8 ± 6.14 µg/mL (Fig. 1D). Additionally, the cytotoxicity of the methanolic extract of S. tuberosa was assessed against the normal murine cell line L929. The IC₅₀ could not be determined even at the dose 400 μg/mL, indicating the extract safety in a normal cell line. Thus, only the methanolic extract was used in the subsequent experiments.

Fig. 1.

A–C) Plots of log-doses of various extracts of S. tuberosa against inhibition (%) of A549 cells after 24, 48, and 72 h treatment for the calculation of IC₅₀. D Cytotoxic effects (IC₅₀) of different extracts of S. tuberosa on A549 cells after 24, 48, and 72 h treatment. STCE: S. tuberosa chloroform extract; STME: S. tuberosa methanolic extract; STAE: S. tuberosa aqueous extract; 5FU: 5-Fluorouracil. Values are expressed as Mean ± SEM. Different letters indicate significant variation between extracts at each treatment duration

Liquid chromatography-mass spectroscopy (LC–MS)

The methanolic extract of S. tuberosa (STME) obtained by Soxhlet extraction under optimal conditions was analyzed using LC-HRMS. An untargeted analysis was performed to determine the chemical profiles of the main compounds present in the extracts. The tentative identification of the phytochemicals was performed based on the MS data (accurate mass, isotopic distribution, and fragmentation pattern), commercial standards, and data found in the literature. The identified compounds in the methanolic extract of S. tuberosa, along with their exact mass, retention time, molecular formula of the deprotonated molecules, characteristic fragment ions corresponding to each compound, and their structures, are provided in Supplementary Data 2.

The inhibitory effect of STME on the clonogenicity of A549 cells

STME treatment effectively reduced the clonogenicity of A549 cells in a dose-dependent manner when compared with the untreated control (Fig. 2A). A549 cells treated with 75, 150, and 300 µg/mL of STME led to significant inhibition of colony-forming capability of the cells with the surviving fraction of 73.5%, 30.3%, and 1.5% respectively (Fig. 2B). At a dose of 300 µg/mL, STME inhibited colony formation in cancer cells as effectively as the standard drug 5FU.

Fig. 2.

A Inhibition of colony formation of A549 cells mediated by methanolic extract of S. tuberosa. B Effect of methanolic extract of S. tuberosa on the reproductive capability of A549 cells after 48 h treatment, expressed as a surviving fraction (SF). Control: A549 cells without treatment; STME 75, STME 150, and STME 300: A549 cells treated with 75, 150, and 300 µg/mL of methanolic extract of S. tuberosa respectively; 5FU-100: A549 cells treated with 100 µg/mL of 5-Fluorouracil (positive control). Values are expressed as Mean ± SEM. Different letters indicate significant variation

Morphological evidence of apoptosis induced by STME

To determine whether STME-induced inhibition of A549 cell growth occurred through apoptosis, acridine orange/ethidium bromide (AO/EB) dual staining was performed to identify and quantify apoptotic morphology. The treatment of A549 cells with different concentrations of STME (75–300 µg/mL) for 48 h resulted in a dose-dependent increase in the number of apoptotic cells. Fluorescence microscopy images of the STME-treated cells (Fig. 3A) revealed morphological alterations such as membrane blebbing, nuclear condensation, and nuclear fragmentation, which are distinct characteristics of apoptotic cells. A progressive increase in the apoptotic index (%) was also observed in cells treated with STME, where the percentage of dead cells was 27.1, 48.3, and 92.4% after treatment with STME at doses of 75, 150, and 300 µg/mL, respectively. Treatment of A549 cells with STME (300 µg/mL) resulted in a significantly higher apoptotic index than the standard drug 5FU (Fig. 3B).

Fig. 3.

A Acridine orange/Ethidium bromide (AO/EtBr) dual staining of A549 cells after treatment with different doses of methanolic extract of S. tuberosa for 48 h (green arrow shows the live cells, the blue arrow shows apoptotic cells with nuclear condensation, orange arrow shows apoptotic cells with nuclear fragmentation, and the red arrow shows apoptotic cells with membrane blebs). B Percentage of dead cells after treatment of A549 with methanolic extract of S. tuberosa. Control: A549 cells without treatment. STME 75, STME 150, and STME 300: A549 cells treated with 75, 150, and 300 µg/mL of methanolic extract of S. tuberosa respectively; 5FU-100: A549 cells treated with 100 µg/mL of 5-Fluorouracil (positive control). Values are expressed as Mean ± SEM. Different letters indicate significant variation

Induction of DNA strand breaks by STME

Alkaline comet assay was used to assess DNA damage in A549 cells after treatment with different doses of STME (75, 150, and 300 µg/mL) for 48 h. Our findings revealed that STME induced significant DNA damage in A549 cells in a dose-dependent manner, as evidenced by the increased tail length and olive moment in STME-treated groups compared to untreated controls (Fig. 4A–C). Moreover, STME treatment at 150 µg/mL resulted in similar increases in tail length and olive moment as the standard drug 5FU (Fig. 4B, C).

Fig. 4.

A Fluorescence images of Comets observed in control and A549 cells treated with different concentrations of STME. B, C The extent of DNA damage expressed in terms of Tail length and Olive moment. Control: A549 cells without treatment; STME 75, STME 150, and STME 300: A549 cells treated with 75, 150, and 300 µg/mL of methanolic extract of S. tuberosa respectively; 5FU-100: A549 cells treated with 100 µg/mL of 5-Fluorouracil. Values are expressed as Mean ± SEM. Different letters indicate significant variation

Antioxidants/oxidant status

Treatment of A549 cells with STME (150 µg/mL) significantly decreased glutathione (GSH) concentration and the activities of glutathione-s-transferase (GST) and superoxide dismutase (SOD) when compared to the untreated control (Fig. 5A–C). In an effort to investigate the effect of STME treatment on intracellular oxidant level, the level of lipid peroxidation (LPO) as a biomarker of oxidative stress was also assessed. According to our findings, STME treatment significantly raised the MDA level in A549 cells (Fig. 5D), suggesting that STME might raise the intracellular ROS levels through disruption of the antioxidant system. It was also found that 5FU and S. tuberosa extract had comparable effects on the antioxidant/oxidant status in A549 cells.

Fig. 5.

Effects of the methanolic extract of S. tuberosa on A glutathione (GSH) level; B glutathione-s-transferase (GST) activity; C superoxide dismutase (SOD) activity; and D lipid peroxidation (LPO) expressed in malondialdehyde (nmol/mg protein) in A549 cells after 48 h of treatment. Control: A549 cells without treatment; STME: A549 cells treated with 150 µg/mL of methanolic extract of S. tuberosa; 5FU: A549 cells treated with 100 µg/mL of 5-Fluorouracil. Values are expressed as Mean ± SEM. Different letters indicate significant variation

Effect of STME on the relative expression of pro-apoptotic and anti-apoptotic genes

The mRNA expression levels of both pro-apoptotic and anti-apoptotic genes were also investigated in A549 cells using qPCR. We found that treatment of A549 cells with 150 µg/mL STME for 48 h induced up-regulation of pro-apoptotic genes including Bax, Bid, p53 and Apaf-1 by 3.7, 18.1, 6.9 and 14.2 folds respectively when compared to the untreated control. The levels of mRNA expression of the pro-apoptotic gene DR5 and the anti-apoptotic gene Bcl-XL did not, however, differ significantly between the control and STME-treated groups (Fig. 6A–F).

Fig. 6.

Effects of the methanolic extract of S. tuberosa on mRNA expression levels of A Bax; B Bid; C p53; D DR5; E Apaf-1; and F Bcl-X_L in A549 cells after 48 h treatment. Control: A549 cells without treatment; STME: A549 cells treated with 150 µg/mL of methanolic extract of S. tuberosa; 5FU: A549 cells treated with 100 µg/mL of 5-Fluorouracil. Values are expressed as Mean ± SEM. Different letters indicate significant variation

Activation of caspase-3/6 by STME on A549 cells

Caspase-3/6 activities are crucial for the execution of apoptosis in cancer cells. Effect of STME on A549 cell apoptosis was assessed by measuring caspase-3/6 activity. When A549 cells were treated with 150 µg/mL of STME for 48 h, the activities of caspase-3 and caspase-6 increased significantly, by 2.4 and 2.3 folds, respectively, in comparison to the untreated control (Fig. 7A, B). Activity of caspase-3 was found to be significantly higher in STME treated group compared to the standard drug 5FU, which served as a positive control.

Fig. 7.

Effects of the methanolic extract of S. tuberosa on the activities of A Caspase-3 and B Caspase-6 in A549 cells after 48 h of treatment. Control: A549 cells without treatment; STME: A549 cells treated with 150 µg/mL of methanolic extract of S. tuberosa; 5FU: A549 cells treated with 100 µg/mL of 5-Fluorouracil. Values are expressed as Mean ± SEM. Different letters indicate significant variation

Druglikeness and oral bioavailability analysis of phytocompounds from S. tuberosa

The druglikeness and oral bioavailability of the compounds from S. tuberosa were determined using the SwissADME tool. The results indicated that all the 80 compounds displayed zero violation of all the druglikeness rules as per Lipinski, Ghose, Veber, Egan, and Muegge.

ADMET analysis

The ADMET characteristics of a specific drug provide information regarding its physicochemical and physiochemical characteristics. These are crucial factors that affect the overall pharmacokinetics of a drug. Based on their oral bioavailability properties (Supplementary Data 3 Fig. A–E), five compounds were selected for ADMET analysis using the SwissADME and pkCSM online servers, and the results are displayed in Supplementary Data 3.

Common targets of compounds of S. tuberosa and NSCLC

A total of 348 possible molecular targets of compounds from the methanolic extract of S. tuberosa were identified after analyzing their targets in the Swiss Target Prediction database. A total of 7183 targets linked to non-small cell lung carcinoma were also retrieved and compiled from the Genecard database. Mutual gene matching between the target genes of S. tuberosa compounds and those of NSCLC resulted in the identification of 208 genes (Fig. 8).

Fig. 8.

Venn diagram of proteins involved in NSCLC targeted by compounds from the methanolic extract of S. tuberosa. NSCLC non-small cell lung cancer, ST Stemona tuberosa

Analysis of PPI network

The total of 208 mutual targets were loaded into the STRING database. The analysis revealed that the network was composed of 208 nodes and 1955 edges (Fig. 9A). The network was analyzed and visualized via the Cytoscape software by assessing centrality and additional parameters. The targets were organized in circles based on the specifications provided. The CytoHubba plug-in was then used to select primary targets based on their significant role in the network, as indicated by the high centrality value. The top 10 target genes were SRC, EGFR, JUN, PPARG, MTOR, PTGS2, SIRT1, GSK3B, MDM2 and PARP (Fig. 9B).

Fig. 9.

A PPI Network of 208 protein targets. B The top 10 potential target networks ranked by degree value as generated by Cytoscape 3.10.2

Molecular docking on inhibitors of SRC for targeting lung cancer

Molecular docking demonstrated the significant binding affinities and pharmacological properties of the assessed compounds targeting SRC, as illustrated in Fig. 10A–E. Among the compounds assessed, ADE (− 10.88 kcal/mol) exhibited the most favourable binding energy, closely followed by DNY (− 10.83 kcal/mol), both demonstrating strong inhibitory constants (KI) of 11.28 nM and 11.43 nM, respectively. The data were comparable to the control ligand, DB, which exhibited a binding energy of − 11.12 kcal/mol and a KI of 36.1 nM. CDO exhibited substantial inhibitory potency, characterised by a binding energy of − 10.24 kcal/mol and a KI of 31.38 nM. Conversely, ETE (− 9.24 kcal/mol) and EH9 (− 8.53 kcal/mol) demonstrated moderate binding energies, with KI values of 167.34 nM and 562.51 nM, respectively. A docking binding free energy lower than − 5 kcal/mol has a high binding affinity and is regarded as having potential for drug development. Therefore, all the compounds in this study may exhibit drug potential against SRC. ADE and DNY are the most promising SRC inhibitor candidates for further development.

Fig. 10.

Interaction of different compounds with SRC protein active site. A N-(2,3-Epoxypropyl)-1,2,3,4,9,10-Hexahydroacridin-9-One (EHY); B Crinan,1,2-didehydro- (CDO); C 9-(2,3-Epoxypropoxy)-1,2,3,4-tetrahydroacridine (ETE); D Dihydro-normorphine, 3-desoxy- (DNY); and E 4-Azatricyclo [4.3.1.13,8] undecan-5-one (ADE)

Molecular dynamics simulation of inhibitory binding of compounds to SRC enzymes in lung cancer

MD simulations further offered comprehensive insights into the stability and interaction dynamics of the SRC-ligand complexes. The RMSD of the protein backbone stabilised at roughly 2.1 Å after the initial ten ns, signifying equilibrium and structural stability during the 100-ns simulation, with slight fluctuations of 0.2 Å, suggesting strong system equilibration as illustrated in Fig. 11A. RMSF analysis, as illustrated in Fig. 11B, indicated that most residues fluctuated below 1.5 Å, but loop areas had heightened flexibility between 1.8 and 2.5 Å. Conversely, essential residues inside the binding pocket exhibited negligible variations of 0.6 to 0.8 Å, indicating stable ligand interactions. Hydrogen bonds revealed stable ligand–protein hydrogen bonds, varying from 3 to 5 during the trajectory. ADE and DNY demonstrated the most prolonged enduring bonds, averaging 4.2 and 4.5, respectively, as shown in Fig. 11C. SASA analysis revealed continuous solvent exposure of the protein–ligand complexes, with average values of 150–160 nm² and fluctuations of 5 nm², indicating strong hydrophobic interactions and ligand packing as illustrated in Fig. 11D. As illustrated in Fig. 11E, PCA analysis demonstrated that the initial two main components explained 75–80% of the conformational variance, exhibiting diminished overlap between ligand-free and ligand-bound systems, suggesting restricted conformational drift and limited mobility in the ligand-bound state.

Fig. 11.

MD simulation of protein and ligands A RMSD, B RMSF, C H-bond, D SASA, and E PCA

Total binding energy using gmx_MMPBSA

The gmx_MMPBSA evaluated the binding free energies (ΔG_bind) of five compounds (ADE, CDO, DNY, EHE, and ETE) against the SRC lung cancer gene, thereby providing insights into their potential as therapeutic inhibitors (Table 1). The ΔG_bind − 6.54 kcal/mol suggested moderately strong ligand and receptor binding and more negative results equate to a higher binding affinity. In our study, EHE demonstrated the highest total binding energy (ΔG_bind − 24.08 ± 0.08 kcal/mol), indicating its robust binding affinity and potential efficacy as a lead agent. DNY and ETE exhibited ΔG_bind values of − 15.66 ± 0.25 kcal/mol and − 15.79 ± 0.13 kcal/mol, respectively, indicating moderate binding interactions. ADE and CDO exhibited diminished binding affinities with ΔG_bind values of − 15.06 ± 0.11 kcal/mol and − 6.55 ± 0.11 kcal/mol, respectively, signifying less advantageous interactions with the SRC target. The gas-phase contribution (ΔG_GAS) was most significant for CDO (− 57.38 ± 0.50 kcal/mol). Still, the solvation-free energy (ΔG_SOLV) was most significant for DNY (110.69 ± 0.67 kcal/mol), underscoring the varied energy contributions among the compounds (Bahadar et al., 2024).

Table 1.

gmx_MMPBSA analysis of 5 compounds against SRC protein

Ligands	Time-dependent energy profiles of SRC protein–ligand complexes in different states (ΔGGAS, ΔGSOLV, and ΔGbind) Over 1000 Simulation Frames (100 ns)	ΔGGAS	ΔGSOLV	ΔGbind
ADE		− 27.78 ± 0.28	12.72 ± 0.21	− 15.06 ± 0.11
CDO		− 57.38 ± 0.50	50.83 ± 0.45	− 6.55 ± 0.11
DNY		− 12 ± 0.90	110.6 ± 0.67	− 15.6 ± 0.25
EHE		− 39.8 ± 0.10	15.7 ± 0.10	− 24.08 ± 0.08
ETE		− 25.34 ± 0.18	9.57 ± 0.09	− 15.79 ± 0.13

Discussion

Recent studies have focused on drugs used in traditional medicine to improve the efficacy of cancer treatments [36]. Because of their efficacy and lesser side effects, natural products have drawn the attention of the pharmaceutical industry in recent decades as they have been explored as better cancer treatments. Despite numerous reports on the therapeutic benefits of S. tuberosa, investigations that look into the scope of S. tuberosa for cancer therapy are still limited. Cancer is a multifaceted disease characterized by a high rate of cell proliferation and strong resistance to death. The purpose of targeting cell proliferation is to kill cancer cells or stop the cell cycle using cytotoxic compounds. MTT assay is a quick and conventional method for assessing drug cytotoxicity in diverse cultured cells wherein a reduction of MTT can occur only in metabolically active cells. A549 cells treated with S. tuberosa extracts showed a dose-dependent increase in cytotoxicity, with the methanolic extract having the highest activity (IC₅₀:149.3 ± 8.55 µg/mL), which is comparable to that of 5FU (IC₅₀ of 123.8 ± 6.14 µg/mL). STME upon comparison to plant derived drugs such as taxol (IC₅₀: 30 µg/mL) and vinblastine (IC₅₀: 3.4 µg/mL), still exhibit lower cytotoxicity. However, these plant derived drugs have also been reported for their resistance against A549 cells [37–39]. Cancer cells' ability to form colonies allows them to interact and grow into solid tumors. This ability is also exhibited by A549 cells, which however was ameliorated by treatment with STME as shown by the clonogenic assays in Fig. 2. Clonogenic assay showed the ability of cancer cells to not only form colonies but showed the resilience and survival potential of cancer cells against harsh conditions. Treatment of A549 cells with 300 µg/mL of STME inhibited the colony formation comparable to the standard drug 5FU indicating the efficiency of the plant against cancer cell proliferation.

Understanding the precise processes by which anticancer agents exert their effects has become a significant strategy for evaluating and developing anticancer drugs. Apoptosis is an essential and highly regulated cell death mechanism that serves to eliminate ailing cells without causing injury to the normal cells, and loss of its regulation underlies numerous pathologies including cancer [40]. Any substance that causes apoptosis is considered to be a promising cancer chemotherapeutic treatment [41]. The methanolic extract of S. tuberosa induced apoptosis as evidenced from the AO/EtBr dual staining and comet assays (Figs. 3, 4). A549 cells treated with different concentration of STME exhibited characteristic apoptotic morphology with brightly orange red and condensed nuclei compared to the untreated control cells (Fig. 3A). Numerous studies have revealed that various anticancer drugs function by inducing apoptosis in cancer cells, and multiple compounds derived from medicinal plants have been shown to have anticancer activity via apoptosis induction [42]. Thus, STME's apoptosis-inducing activity necessitates additional exploration of the compound(s) involved and their mechanism of action, as apoptosis is the most preferred pathway for drug-induced cellular death. Most of the chemotherapy drug works by causing genotoxic stress to cancer cells and thereby inducing apoptosis [43]. Similar to the chemotherapy drugs in the market STME induced significant DNA damage in A549 cells in a dose-dependent manner (Fig. 4). The genotoxic effects of STME are consistent with the lipid peroxidation results, as malondialdehyde, the end product of LPO, can create mutagenic adducts that consequently led to DNA damage [44]. Thus, the DNA damage observed upon STME treatment could be a result of oxidative stress caused by the plant extract. In fact, several plant-derived anti-cancer drugs have also been found to have similar effects across different cancer types [45, 46].

The development and progression of many diseases, including cancer, is associated with elevated levels of intracellular reactive oxygen species (ROS), which can occur due to either increased ROS production or a weakened antioxidant defense system [47–49]. Because of the delicate nature of the levels of ROS in cancer cells, a mild increase may lead to cancer progression whilst a relatively higher levels of ROS may trigger the apoptotic pathway [50]. Many studies have highlighted that elevated ROS levels can trigger apoptosis, often with plant extracts or natural compounds playing a key role in this process. In many instances, plant extracts and plant derived compounds induced ROS production leads to DNA damage thereby activating the apoptotic pathway, similar to the way chemotherapeutic drugs like 5-FU and doxorubicin induces apoptosis [51–53]. Plants such as Momordica charantia [54], Rhynchosia rufescens [55], Malva pseudolavatera [56] have been reported to induce cytotoxic effects in cancer cells by increasing ROS production.

In normal cells, ROS levels are typically well-regulated by the antioxidant systems enzymatic (such as CAT, SOD, GPx and GST) and non-enzymatic (such as GSH, ascorbic acid and lipoic acid). However, the antioxidant systems are generally deregulated in cancer cells resulting in oxidative stress that are generally detected by reduced GSH, GST, SOD and increased lipid peroxidation. In our study, treatment of A549 cells with 150 µg/mL STME showed reduced glutathione level and the activities of antioxidant enzymes including GST and SOD. Consistently, STME treatment showed significant increase in the levels of lipid peroxidation which could be due to the decreased antioxidant activities resulting in accumulation of cellular ROS (Fig. 5D). Subsequently, the increased ROS level could attribute to the cytotoxic effects of STME on A549 cells.

Through their interactions, the pro- and anti-apoptotic proteins of the Bcl-2 (B-cell lymphoma/leukemia-2) family play crucial roles in mitochondria-mediated intrinsic apoptosis by controlling the permeabilization of the mitochondrial membrane [57]. The ratio of pro- and anti-apoptotic Bcl-2 proteins may affect how sensitive cells are to apoptotic stimuli. The permeability transition (PT) pore is created by an overabundance of pro-apoptotic bcl-2 proteins at the mitochondrial surface. This results in the hierarchical release of pro-apoptotic proteins like cytochrome c, Smac/Diablo, and apoptosis inducing factor (AIF), which in turn triggers the downstream activation of caspase-3 and caspase-6 [58]. Therefore, alterations in these genes' expression have a role in the etiology and development of malignancies, offering targets for the development of anti-cancer drugs. To determine whether Bcl-2 family proteins are involved in STME induced apoptosis, the relative mRNA expression of pro-apoptotic genes (Bax, Bid and p53) and anti-apoptotic genes (PARP, DR5 and Bcl-X_L) were assessed by qPCR techniques. Up-regulation of pro-apoptotic genes in the current study suggests the possibility that the mitochondrial pathway might contribute to the modulation of STME-induced apoptosis in A549 cells. Altered expression of anti-apoptotic genes such as PARP, DR5 and Bcl-X_L and pro-apoptotic genes such as Bax, Bid and p53 by several plant extracts have been documented earlier in A549 cells [54, 59–61].

By cleaving a number of important proteins, including intra-nuclear proteins, poly (ADP ribose) polymerase (PARP), and inhibitor of caspase-activated DNase (ICAD), caspase-3 and caspase-6 play a crucial role in the execution of both the intrinsic (the mitochondrial mediated) and extrinsic (the death receptor mediated) apoptotic pathways [62]. This cleavage facilitates the cell's disintegration into apoptotic morphological changes, such as nuclear fragmentation, chromatin condensation, and cell shrinkage [40]. Therefore, a potent biomarker for cells going through apoptosis is the activation of caspase-3 and caspase-6. Increased caspase-3 and caspase-6 activities in A549 cells following STME treatment strongly indicated that STME-induced apoptosis was executed through a caspase-dependent pathway. The proposed mechanism of the actions of the methanolic extract of S. tuberosa is summarised in Fig. 12.

Fig. 12.

The proposed mechanism of the actions of the methanolic extract of S. tuberosa in A549 cells

Network pharmacology which identified 208 common targets between S. tuberosa compounds and NSCLC, were analyzed based on their relevance to both the compounds and the disease. Key targets involved in cancer-related pathways such as cell proliferation, apoptosis, and metastasis were prioritized on the basis of their functional significance. The STRING database also constructed a protein–protein interaction network comprising 208 nodes and 1955 edges. Within this network, targets with high degree of centrality, indicating strong connectivity, were considered essential for disease progression. The network was further analyzed using Cytoscape software, where parameters such as centrality were assessed to determine the most significant targets. The CytoHubba plug-in identified the top 10 hub genes such as SRC, EGFR, JUN, PPARG, MTOR, PTGS2, SIRT1, GSK3B, MDM2, and PARP. Among these, SRC was selected as the primary target due to its highest degree of centrality, emphasizing its central role in the PPI network. As a well-established oncogene in NSCLC, SRC drives tumor proliferation, metastasis, and therapy resistance. Moreover, its druggable ATP-binding pocket makes it an attractive target for small-molecule inhibitors, reinforcing its selection for therapeutic investigation. SRC is an essential kinase in the pathophysiology of NSCLC and is often overexpressed or hyperactivated, promoting tumour formation, invasion, and metastasis. It integrates signals from growth factor receptors such as EGFR, frequently mutated in lung cancer, to promote cancer cell survival and proliferation. Moreover, SRC kinase has been found to suppress the apoptotic p53 pathway via phosphorylation of HIPK2 and moving the kinase to the cytoplasm. It can further decrease the phosphorylation of p53 Ser46 and apoptosis activation. [63, 64]. SRC, distinguished by its unique ATP-binding pocket and acknowledged druggability, is an attractive target for molecular docking studies. Inhibition of SRC can also impede these oncogenic pathways, diminishing tumour development, metastasis, and chemoresistance. SRC inhibitions have shown promise in both preclinical and clinical investigations, particularly when used in conjunction with chemotherapy or targeted treatments. Therefore, by targeting SRC, compounds isolated from S. tuberosa may augment the effectiveness of current NSCLC therapies and assist in surmounting drug resistance [65]. Our finding from the molecular docking study underscores the therapeutic potential of S. tuberosa extract by targeting SRC to impede lung cancer growth and emphasise the need for further evaluation of compounds from S. tuberosa such as ADE and DNY in drug development protocols.

Results of the MD simulations highlight the dynamic stability and advantageous interaction characteristics of the SRC-ligand complexes. The consistent RMSD values underscored the structural integrity of the protein–ligand complexes throughout the simulation. Minimal variations in RMSF for essential binding pocket residues indicate robust anchoring of ligands. At the same time, the sustained presence of 3–5 hydrogen bonds underscores the vital importance of these interactions in preserving complex stability. The stable SASA values further corroborate the dense arrangement of ligands within the binding pocket, maintaining the integrity of the hydrophobic core of the receptor. PCA validated that ligand binding limited structural flexibility, an advantageous trait for therapeutic candidates aimed at SRC. The 100 ns MD simulation time was warranted by system equilibration, convergence of binding interactions, and computational practicality. The stabilization of RMSD within the initial 10 ns indicated system equilibration. In contrast, critical metrics such as RMSF, hydrogen bonds, and SASA exhibited consistent trends, hence demonstrating the convergence of ligand–protein interactions. A 100 ns period offered a compromise between capturing biologically relevant interactions and ensuring computational performance, making it an appropriate interval for evaluating ligand binding and dynamic behaviour [66]. The stability of ligand-SRC complexes was evaluated using multiple structural and interaction-based metrics. RMSD analysis revealed minimal structural deviations, with ADE (1.8 Å) and DNY (2.2 Å) remaining stable throughout the simulation, indicating the overall integrity of the ligand–protein complexes. RMSF values showed low fluctuations (0.6–0.8 Å) in the binding pocket residues, suggesting that ligand interactions do not induce significant structural instability. Hydrogen bond analysis confirmed the formation of 4–5 stable hydrogen bonds between ADE/DNY and key residues (e.g., ASP408, ALA403, THR438), reinforcing the strength of ligand binding. SASA values remained consistent (150–160 nm²), highlighting stable hydrophobic interactions and favorable ligand accommodation within the binding site Additionally, PCA analysis demonstrated that the first two components accounted for 75–80% of conformational variance, with limited structural drift. These results collectively confirm that the ligand-SRC complexes exhibit stable binding interactions, supporting their potential for further drug development. Furthermore, the GMX_MMPBSA results highlight EHE as the most promising choice for SRC targeting because of its superior ΔG_bind binding, indicating substantial binding affinity and significant therapeutic potential. The moderate ΔG_bind bound values of DNY and ETE indicate their potential for further optimisation and development. ADE and CDO, which possess diminished ΔG_bind values, may require structural alterations to improve their binding affinities. The unique energy contributions of ΔG_GAS and ΔG_SOLV among the compounds emphasize the necessity of comprehending the equilibrium between the gas phase and solvation effects in developing efficient inhibitors. These findings establish a foundation for prioritizing EHE in subsequent experimental and computational investigations. In contrast, DNY and ETE warrant ongoing examination for their therapeutic potential against SRC in lung cancer.

Conclusion

In conclusion, this study highlights the potential of S. tuberosa methanolic extract (STME) as a promising candidate for lung cancer therapy, particularly against A549 cells. The extract exhibited significant cytotoxicity, reduced clonogenicity, induced apoptosis, and disrupted the antioxidant defense system, thereby increasing the intracellular ROS levels. These mechanisms collectively contribute to cancer cell death, demonstrating the efficacy of the extract in targeting the key pathways in cancer progression.

Molecular docking and dynamic simulations further emphasized the therapeutic potential of STME-derived compounds, particularly ADE, DNY, and EHE, in targeting SRC, a crucial kinase in non-small cell lung cancer. The stable interactions, high binding affinities, and promising energy profiles of these compounds underline a strong foundation for their development as SRC inhibitors. While our findings highlight the potential of S. tuberosa as a promising source of bioactive compounds with anticancer properties, further validation through in vivo and clinical studies is necessary to confirm its applicability for cancer treatment.

Author contributions

C. Lalmuansangi, Lalfakawmi, Fanai Nghakliana, and Hmingremhlua Sailo were responsible for data collection, formal analysis, interpretation, and original draft preparation. Zothan Siama, Amit Kumar Trivedi, Nachimuthu Senthil Kumar were involved in statistical analysis and technical support, conceptualization, methodology, supervision of the project, and revision of the manuscript. Lalchhandami Tochhawng, Kiran R Kharat and Balachandar Vellingiri were responsible for technical support, ensuring accuracy and reliability of the results, and contributing to the revision of the final manuscript. All authors have read and approved the final manuscript.

Data availability

The manuscript contains all of the data produced during this investigation. On request, the corresponding author can provide additional data supporting the findings of this study. The data analysis software and bioinformatics tools are openly available, and the article includes comprehensive procedures to ensure reproducibility.

Declarations

Consent for publication

The publication of this work is approved by all authors. We affirm that the work is unique and has not been published or submitted for publication anywhere else. The authors accept the journal's terms and conditions for the manuscript's publication and distribution.

Competing interests

The authors declare no competing interests.