Evaluation of Optimal Harvesting Time for Selaginella doederleinii Hieron at a Fixed Location Using HPLC Fingerprinting and In Vitro Antiproliferation Assays

ABSTRACT

In this study, a strategy for determining the optimal harvesting time of medicinal plants was proposed and implemented, using the antitumor herb Selaginella doederleinii Hieron (S. doederleinii) as a case study, based on HPLC fingerprinting profiles and in vitro activity assays. A total of 12 batches of S. doederleinii samples were collected from a fixed geographic location from January to December. An HPLC‐based fingerprint was then established, followed by similarity analysis, principal component analysis (PCA), and partial least squares discriminant analysis (PLS‐DA) on the 80% ethanol extracts of these samples. Meanwhile, in vitro viability was assessed using Calcein/PI cell viability assays and Western blotting analysis. By combining the chemometrics and biological activity results, it is inferred that the optimal harvesting time for S. doederleinii is in April and May. Furthermore, the following compounds were tentatively identified as quality control markers for this medicinal herb at the fixed geographic location: 2″,3″‐Dihydro‐3′,3‴‐biapigenin, 3′,3‴‐binaringenin, Delicaflavone, 2,3‐Dihydrohinokiflavone, Chrysocauloflavone I, and 2″,3″‐Dihydro‐3′,3‴‐biapigenin methyl ether. This study provides a feasible strategy for selecting harvesting time of medicinal plants grown at a fixed geographic location.

1. Introduction

Currently, the harvesting time of medicinal plants is typically determined based on traditional harvesting experience, personal subjective factors, or the content of only a few components in the plants [1]. However, this approach is not comprehensive. The selection of the optimal harvesting time should prioritize achieving the best therapeutic effects as the fundamental goal, thereby ensuring the provision of high‐quality medicinal materials for clinical use. The chemical composition and pharmacological activities of different plants can vary significantly at different stages of their growth cycle [2, 3]. Therefore, scientifically determining the harvesting time can significantly enhance the content of active ingredients in the harvested herbs, increase their medicinal value, and ensure that the medicinal materials used for clinical treatment achieve the optimal therapeutic efficiency. This not only supplements and optimizes traditional harvesting experience, but also deepens and applies modern pharmaceutical research for folk medicine in practice.

Selaginella doederleinii is the dried whole herb of S. doederleinii Hieron, belonging to the family Selaginellaceae. It has been traditionally used for its heat‐clearing, detoxifying, anti‐inflammatory, and antitumor properties. In folk medicine, it is commonly used to treat choriocarcinoma, non‐small cell lung cancer (NSCLC), pharyngeal cancer, and gastrointestinal cancers [4, 5, 6]. S. doederleinii is rich in biflavonoid compounds, which are its key pharmacologically active components. Previous studies have identified the main bioflavonoids as Amentoflavone, Robustaflavone, 2″,3″‐Dihydro‐3′,3‴‐biapigenin, 3′,3‴‐Binaringenin, Delicaflavone, Chrysocauloflavone I, Heveaflavone, and 7,4′,7″,4‴‐Tetra‐O‐methyl‐amentoflavone [6, 7]. Sui et al. [8] reported that the biflavonoid extract of S. doederleinii exhibits potential antitumor activity against NSCLC through activation of the mitochondrial apoptosis pathway. Additionally, S. doederleinii induced both autophagy and apoptosis in colorectal cancer cells via AMPKα and caspase‐dependent signaling pathways [9]. Delicaflavone, a key active compound in S. doederleinii, exerts its antitumor effects by inhibiting NSCLC through the Akt/mTOR/p70S6K signaling pathway [10]. Yao, et al. [11] demonstrated that Delicaflavone induces apoptosis in HeLa cells via the mitochondrial pathway, thereby exerting anticervical cancer effects. These findings suggest that S. doederleinii could exert its pharmacological effects through multiple targets and multiple pathways, driven by its diverse active components. Variations in the content of active components at different harvesting times can lead to differences in the therapeutic efficacy of S. doederleinii against cancers [12]. Furthermore, seasonal changes may influence its chemical composition, which can significantly affect its therapeutic activity. However, to date, no research has been reported on the optimal harvesting time of S. doederleinii, which is not conducive to its quality control and further drug development and research.

In this study, an integrated approach combining high‐performance liquid chromatography (HPLC) fingerprinting, chemometric analysis, and in vitro bioactivity evaluation was employed to investigate the optimal harvesting time and potential quality control markers of medicinal plants. This approach builds upon previously reported strategies that integrate HPLC fingerprinting with chemometric methods for quality evaluation, while further incorporating bioactivity data to provide additional functional relevance. Compared with conventional methods, which often rely on empirical experience or focus on a limited number of compounds, this approach enables a more objective assessment of overall chemical variation among samples using principal component analysis (PCA) and partial least squares discriminant analysis (PLS‐DA). Furthermore, by integrating fingerprint data with bioactivity results, compounds with higher importance in sample discrimination—based on variable importance projection (VIP) values—were preliminarily associated with pharmacological effects, providing a more comprehensive basis for selecting the optimal harvesting period and candidate quality markers [13]. Taking S. doederleinii as an example (Scheme 1), HPLC‐based fingerprinting profiles were obtained for the 80% ethanol extracts (EESD) of 12 batches of samples collected from January to December at a fixed geographic location. Similarity analysis, PCA and PLS‐DA were then performed to evaluate the chemical variation among samples. This approach is consistent with previously reported strategies for quality evaluation of traditional Chinese medicines [14]. Furthermore, the anti‐proliferative effects of EESD from the twelve months on PC‐9 cells were evaluated by a Calcein/PI cell viability assay, and the effects on apoptosis‐related protein expression (Bax/Bcl‐2) were examined by Western blotting (WB). Overall, the present study not only identifies the optimal harvesting time and proposes potential quality control markers for this medicinal herb, but also provides a practical approach for selecting harvesting time based on activity evaluation under a fixed geographic location, which may help improve the utilization efficiency of medicinal plant resources.

SCHEME 1.

A strategy for selecting harvesting time of medicinal plants using HPLC fingerprinting profiles and in vitro activity assays with S. doederleinii as a case study.

2. Results and Discussion

2.1. EESD HPLC Fingerprinting Profiles, Similarity Analysis, and Identification of Potential Quality Control Makers

All samples used in this study were collected from Qingtan Village, Yangzhong Town, Jiaocheng District, Ningde City, Fujian Province, China. This location is at an altitude of 340 m, characterized by a mild climate and abundant rainfall. Preliminary studies optimized HPLC conditions, including column type, detection wavelength, mobile phase, and flow rate. Based on these findings, the current study employed an Agilent Zorbax SB‐C18 column (100 × 4.6 mm, 3.5 µm), with a detection wavelength of 270 nm and a mobile phase consisting of acetonitrile and 0.5% acetic acid aqueous solution for gradient elution [15]. Ten common peaks were observed in the HPLC chromatograms of EESD samples from January to December (Figure 1A). By comparison with authentic reference standards under consistent liquid chromatography‐tandem mass spectrometry (LC‐MS/MS) conditions, the MS/MS fragmentation patterns of these peaks matched those of the corresponding reference compounds, providing definitive experimental evidence for their structural identification. As a result, five biflavonoids were identified: Amentoflavone (peak 1), Robustaflavone (peak 2), 2″,3″‐Dihydro‐3′,3‴‐biapigenin (peak 3), 3′,3‴‐binaringenin (peak 4), and Delicaflavone (peak 5) with retention times of 9.8, 11.6, 15.2, 16.3, and 22.2 min, respectively, aided by their standard references (Figure 1B).

FIGURE 1.

(A) Characteristic HPLC chromatogram of EESD. (B) HPLC chromatogram of reference standards (Peak 1: Amentoflavone, Peak 2: Robustaflavone, Peak 3: 2″,3″‐Dihydro‐3′,3‴‐biapigenin, Peak 4: 3′,3‴‐binaringenin, and Peak 5:Delicaflavone).

The fingerprinting profiles were analyzed using the software of the Traditional Chinese Medicine Chromatographic Fingerprint Similarity Evaluation System (2012 edition, China). The similarity results were presented in Figure 2A, and the fingerprint similarity among the monthly samples was evaluated accordingly [16]. The similarity values for samples collected across 12 months were all greater than 0.97. The peak areas of common components varied among samples from different months, leading to distinct similarity values for each month (Figure 2B). Subsequently, a cluster similarity analysis was conducted on the 10 common components across the 12‐month samples. Significant differences were observed in the peak areas of these 10 common components across different months, indicating notable variations in their relative content of these components (Figure 2C). In the heat map, the intensity of the colors reflects the magnitude of the peak area for each component: a redder color indicates a larger peak area, while a bluer color indicates a smaller peak area. The numbers in the figure also represent the peak area values of the corresponding components in each sample. The results reveal that the EESD samples from April, May, and October clustered into one group, while those from January, September, and December formed another group. Samples from March and November were grouped together, and the remaining monthly samples were clustered into a separate group. As the Euclidean distance continued to increase, the samples from January, February, March, June, July, August, September, November, and December could cluster together.

FIGURE 2.

(A) Three‐dimensional overlay of HPLC chromatograms of EESD samples from January to December. (B) Similarity clustering heatmap of HPLC fingerprints of EESD samples from January to December. (C) Clustering heatmap of peak areas of ten major components in EESD samples from January to December by HPLC chromatography. (D) Score plot of PCA.

In PCA, higher contribution rates indicate a better representation of sample variation [17]. PCA performed on the compound peak areas across 12 months (Figure 2D) showed that PC1 and PC2 explained 84.8% and 8.2% of the total variance, respectively, accounting for a cumulative variance of 93%. This suggests that these two principle components sufficiently captured the variation in the constituents of S. doederleinii. Along the PC1 axis, samples were clustered into three groups: samples from April, May, and October, located on the right side of the plot, formed one group; samples from February, June, July, and September concentrated in the middle of the PCA score plot, constituted a second group; and the remaining monthly samples were classified into a third group.

PLS‐DA is a supervised multivariate statistical method that integrates PCA, canonical correlation, and regression analysis and can highlight intergroup differences and reveal robust features affecting model performance [18]. Based on the PCA results, we grouped the sample data from April, May, and October into one set, and consolidated the data from the remaining months into another set for PLS‐DA analysis (Figure S1). The results showed that the VIP scores for peaks 3, 4, 5, 6, 7, and 8 exceeded 1 (Figure S1), indicating that these components played a highly significant role in sample classification. The relative contents of these peaks in the herb may substantially influence its therapeutic effects and quality characteristics. Given the potential importance of these peak components, we further identified peaks 6, 7, and 8 using liquid chromatography‐mass spectrometry (LC‐MS) with reference to a previous report [6]. Figure 3A presents the total ion chromatogram (TIC) of a 1 mg/mL EESD solution in ethanol. In the TIC scan, peaks 6, 7, and 8 at retention times of 25.6, 27.2, and 28.9 min, respectively showed [M‐H]⁻ ions at m/z 539, 539, and 553 in negative ion mode, as well as [M+H]⁺ ions at m/z 541, 541, and 555 in positive ion mode. The MS/MS fragmentation patterns of peaks 6, 7, and 8 were highly consistent with those reported in the previous study, allowing us to deduce them as 2,3‐Dihydrohinokiflavone, Chrysocauloflavone I, and 2″,3″‐Dihydro‐3′,3‴‐biapigenin methyl ether, respectively [6] (Figure 3B–D).

FIGURE 3.

(A) TIC of EESD in 1 mg/mL ethanol at negative mode. (B), (C), and (D) represent the MS/MS fragments attribution of these peak components (left), referring to 2,3‐Dihydrohinokiflavone, Chrysocauloflavone I and 2″,3″‐Dihydro‐3′,3‴‐biapigenin methyl ether, and their MS/MS spectra (right).

2.2. Inhibitory Effect of EESD on PC‐9 Cell Proliferation

Different concentrations of EESD were applied to PC‐9 cells, and cell viability was assessed following 72 h of treatment. Bright‐field microscopy revealed an increase in necrotic cells with EESD treatment. Green fluorescence (Calcein) marked live cells, while red fluorescence (PI) indicated dead cells. Merged images clearly distinguished live from dead cells [19] (Figures 4A,B and 5A,B). After treatment, green fluorescence diminished, red fluorescence intensified, and the number of live cells decreased. Under bright‐field microscopy, cells exhibited deformation, membrane rupture, and shrinkage. A significant increase in dead cells was observed following treatment with 50, 100, and 200 µg/mL EESD. Notably, the dead‐to‐live cell ratio was higher in April and May groups than in other monthly groups for 100 and 200 µg/mL EESD treatments. The results demonstrated that at 100 and 200 µg/mL, both the April and May groups exhibited significantly higher dead‐to‐live ratios compared with the August and November groups (p < 0.05). At 50 µg/mL, the April group remained statistically significant, while the May group showed an increasing trend but did not reach statistical significance.

FIGURE 4.

(A) Fluorescence image of the control group of PC‐9 cells. (B) Fluorescence image of PC‐9 cells treated with 50 µg/mL EESD from 12 months. (C) Quantitative results and ratios of live and dead cells of the control group of PC‐9 cells treated with the 0 µg/mL EESD. (D) Quantitative results and ratios of live and dead cells of PC‐9 cells treated with 50 µg/mL EESD samples. (*p < 0.05).

FIGURE 5.

(A) Fluorescence image of PC‐9 cells treated with 100 µg/mL EESDs from 12 months. (B) Fluorescence image of PC‐9 cells treated with 200 µg/mL EESD from 12 months. (C) Quantitative results and ratios of live and dead cells of PC‐9 cells treated with 100 µg/mL EESD from 12 months. (D) Quantitative results and ratios of live and dead cells of PC‐9 cells treated with 200 µg/mL EESD from 12 months.

2.3. Effects of EESD From January to December on Apoptosis Related Protein Ratios

After treating PC‐9 cells and BEAS‐2B cells with increasing concentrations of EESD, a concentration of 200 µg/mL exhibited a significantly inhibitory effect on PC‐9 cells and a certain level of toxicity to BEAS‐2B cells, primarily demonstrating an antitumor effect (Figure 6A,B). EESD collected from each month was applied to PC‐9 cells to evaluate the expression of apoptosis‐related proteins Bax and Bcl‐2 (Figure 6C). The ratio of Bax to Bcl‐2 protein expression in PC‐9 cells treated with the EESD from April was higher than those from August and November (p < 0.05). These results further suggest that EESD from April induces a higher apoptosis rate in tumor cells compared to EESD from other months. To further analyze the differences, samples from April and May were categorized as the high‐activity group, while the others were assigned to the low‐activity group. These groups were subjected to unpaired statistical test comparing the means of their total peak areas for peaks 2–8. The results revealed significant differences (p < 0.01) between the high‐activity and low‐activity groups (Figure S2).

FIGURE 6.

(A) Cell survival rate of PC‐9 cells after treatment with increasing concentrations of EESD. (B) Cell survival rate of BEAS‐2B cells after treatment with increasing concentrations of EESD. (C) WB bands and Expression levels (D) of Bax and Bcl‐2 proteins in PC‐9. Data are expressed as mean ± SEM (n = 3), and represents statistical differences between groups (**p < 0.01, ***p < 0.001, ****p < 0.0001, vs. 0 µg/ml group; ns, no significance, vs. 0 µg/ml group; ^# p < 0.05).

3. Discussion

There are numerous types of active components in herbal medicine materials, but their content is greatly influenced by various factors, particularly changes in origin and season. To ensure the stable quality of herbal medicine materials, it is essential to explore the optimal harvesting time and determine the components of quality control indicators. In this study, S. doederleinii was selected as the research material. To minimize the influence of environmental variability on its chemical composition, all samples were collected from a fixed site in Qingtan Village, Fujian Province, China, which is one of the main distribution areas of this species. Additionally, sampling was carried out monthly over a full year (January to December) to capture seasonal variations, while avoiding differences caused by variations in collection sites.

HPLC fingerprinting was used, combined with similarity analysis, PCA, and PLS‐DA, to examine the chemical variation across different months. The in vitro results showed that extracts collected in April and May had stronger anti‐proliferative effects on PC‐9 cells than those from other months. However, this was not fully consistent with the chemometric results, where the October sample was grouped together with those from April and May. This difference suggests that similar chemical profiles do not necessarily lead to similar biological effects. It is possible that differences in component ratios, or the contribution of minor constituents, play a role here. Overall, the link between chemical composition and activity does not seem to be a simple one.

The six biflavonoids showed high VIP values in the PLS‐DA model, suggesting their contribution to the differences among samples from different months. However, no clear linear relationship was observed between the peak area of individual compounds and the apoptosis index (Bax/Bcl‐2 ratio) (Figure S3), indicating that the activity cannot be explained by a single component alone. Both chemical composition and bioactivity exhibited similar seasonal changes, with higher activity observed in April and May. The overall levels of these compounds were also higher during this period, suggesting that they are associated with the activity variation at the extract level. These biflavonoids are therefore better regarded as potential markers reflecting overall activity‐related changes, rather than direct predictors of bioactivity, and further work is needed to clarify their individual roles. It should also be noted that no validation using isolated compounds was performed in this study, and their individual contributions remain to be clarified. This also leaves open the possibility that other, uncharacterized compounds, or interactions are involved.

The WB results support this to some extent. The April extract increased the Bax/Bcl‐2 ratio more clearly, suggesting a stronger pro‐apoptotic effect. For May, the same trend was observed, although the difference was not statistically significant. This may be related to experimental variability or dose‐related effects.

Based on both the chemical data and the biological results, April and May seem to be more suitable harvesting periods for S. doederleinii from this particular location. The six components discussed above could be considered as potential quality markers under these conditions.

This seasonal pattern may be related to plant growth. In spring, the production and accumulation of secondary metabolites are often more active, which could help explain the higher activity seen in April and May. At the same time, it should be kept in mind that the chemical composition of medicinal plants is influenced by many factors, such as climate, soil, and altitude. Because of this, the current findings are likely to be location‐specific. Further work with samples from other regions would be needed before making more general conclusions.

Delicaflavone has already been reported to have antitumor activity in several studies. The other components identified here, including 2″,3″‐Dihydro‐3′,3‴‐biapigenin, 3′,3‴‐binaringenin, 2,3‐Dihydrohinokiflavone, Chrysocauloflavone I, and 2″,3″‐Dihydro‐3′,3‴‐biapigenin methyl ether have been less studied. They may also contribute to the observed effects, but this still needs to be confirmed. In addition, only one cancer cell line (PC‐9) was used in this study, which limits how far these results can be extended.

4. Conclusions

In this study, we put forward a strategy for selecting the harvesting time of medicinal plants using HPLC fingerprinting and in vitro activity assays at a fixed geographic location. Taking S. doederleinii as an example, we applied this strategy as a case study. The results showed that the recommended harvesting time for S. doederleinii is in April and May, and 2″,3″‐Dihydro‐3′,3‴‐biapigenin, 3′,3‴‐binaringenin, Delicaflavone, 2,3‐Dihydrohinokiflavone, Chrysocauloflavone I, and 2″,3″‐Dihydro‐3′,3‴‐biapigenin methyl ether are tentatively identified as quality control markers for this medicinal herb at the fixed geographic location. Overall, the present study provides a feasible approach for selecting the harvesting time of medicinal plants based on HPLC fingerprinting profiles and activity assays at a fixed geographic location.

5. Materials and Methods

5.1. Materials

All herb samples S1−S12 (harvesting time: 15/01/2024, 14/02/2024, 14/03/2024, 18/04/2023, 17/05/2023, 15/06/2023, 14/07/2023, 14/08/2023, 14/09/2023, 15/10/2023, 15/11/2023, and 14/12/2023) were collected monthly from Qingtan Village, Yangzhong Town, Jiaocheng District, Ningde City, Fujian Province in China. Each batch consisted of no less than 2 kg of fresh whole herb and was authenticated by Professor Hong Yao in School of Pharmacy, Fujian Medical University, as S. doederleinii from the Selaginellaceae family. The voucher specimens for S1−S12 (batch numbers: 240115, 240214, 240314, 230418, 230517, 230615, 230714, 230814, 230914, 231015, 231115, and 231214, respectively) have been deposited at the Laboratory of Department of Pharmaceutical Analysis, Fujian Medical University (Fuzhou, China). The herbs were dried, pulverized, passed through a No. 3 sieve, and stored in sealed containers. Approximately 100 g of powdered S. doederleinii was extracted with 500 mL of 80% ethanol by refluxing at 85 °C for 1.5 h. The extract was filtered, concentrated using a rotary evaporator, and freeze–dried for 24 h to obtain the EESD. Amentoflavone, Robustaflavone, 2″,3″‐Dihydro‐3′,3‴‐biapigenin, 3′,3‴‐binaringenin and Delicaflavone were isolated and prepared in the laboratory according to our previous report [7] with HPLC purity > 95%.

PC‐9 cells (Identifier CSTR: 19375.09.3101 HUMSCSP5085) and BEAS‐2B cells (Identifier CSTR: 19375.09.3101HUMSCSP5067) purchased in the Shanghai National Identification Cell Culture Collection. DMEM (MA0212) and RPMI 1640 (0215) were purchased from MeilunBio (Shanghai, China), and Fetal Bovine Serum (FSP500) was purchased from ExCell Bio (Suzhou, China)

The Bcl‐2 (ET1702‐53) and Bax (ET1603‐34) antibodies were purchased from HUABIO (Hangzhou, China). β‐tubulin (GB11017‐100) antibody was purchased from Servicebio (Wuhan, China). Calcein/PI Cell Viability/Cytotoxicity Assay Kit (C2015M) was obtained from BEYOTIME (Shanghai, China). The IgG‐HRP antibody (A0208), BeyoECL Star (P0018AS), QuickBlock Western Primary Antibody Diluent (P0256‐500 mL) and QuickBlock Western Secondary Antibody Diluent (P0258‐500 mL) were purchased from Beyotime Biotechnology (Shanghai, China). The Cell Counting Kit‐8 (MA0218) was purchased from MeilunBio (Shanghai, China).

5.2. HPLC Conditions

For HPLC analysis, a Shimadzu HPLC system consists of a LC‐15C pump, a SPD‐20A UV detector and a column oven were used. Analysis was performed on a XB‐C18 (100 × 4.6 mm, 3.5 µm) (Welch Materials Inc.) with mobile phase: acetonitrile (A) and 0.5% acetic acid water (B); gradient elution program: 0–4 min, 30%–40% A; 4–23 min, 40%–42% A; 23–25 min, 42%–48% A; 25–38 min, 48%–68% A; 38–42 min, 68%–78% A; 42–55 min, 78%–98% A. Detection wavelength: 270 nm; flow rate: 0.6 mL/min; column temperature: 30°C; injection volume: 5 µL.

5.3. HPLC‐Mass Spectrometry (MS) Conditions

A Shimadzu LC‐MS 8040 triple quadrupole mass spectrometer (Shimadzu, Japan) was used for HPLC‐MS/MS analysis. The HPLC conditions were the same as “2.2 HPLC Conditions”. The MS conditions were as follows: block heating temperature, 400°C; desolvation line temperature, 250°C; dry gas (nitrogen), 15 mL/min; and auxiliary gas (nitrogen), 3 mL/min. The mass scan range was set as mass to charge (m/z) 100–1500. LC‐MS/MS spectra were obtained for product ion scan through collision‐induced dissociation with argon in the collision cell to offer fragment masses information about interesting precursor ions. The collision energies for MS/MS analysis were −25 and 25 V for positive and negative ion mode, respectively.

5.4. Cytotoxicity and Activity Assay

PC‐9 cells were cultured in RPMI‐1640 medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin‐streptomycin. A precisely measured amount of freeze–dried EESD powder was dissolved in DMSO to prepare a stock solution of 200 mg/mL, stored at 4°C in a sealed container, and diluted to 200 µg/mL with complete culture medium before use.

PC‐9 cells were seeded in 12‐well plates at a density of 2 × 10⁵ cells per well in 1 mL of complete culture medium and incubated overnight at 37°C in a 5% CO₂ incubator. Cells were treated with EESD at concentrations of 0, 50, 100, and 200 µg/mL for 72 h. After treatment, the culture medium was discarded, cells were washed twice with 1 mL PBS, and an appropriate volume of Calcein/PI working solution was added. Cells were incubated in the dark at 37°C for 25 min. The staining solution was then removed, and the cells were observed and imaged under a fluorescence microscopy (Leica, Wetzlar, Germany). The live images and dead images were quantified respectively using Image J, and the threshold was uniformly selected as the default value to obtain the corresponding IntDen value. Then, by comparison, the cell viability under the corresponding processing conditions could be obtained.

BEAS‐2B cells were cultured in DMEM medium supplemented with 10% FBS and 1% penicillin‐streptomycin. A precisely measured amount of freeze–dried EESD powder was dissolved in DMSO to prepare a stock solution of 250 mg/mL, stored at 4°C in a sealed container, and diluted to 200 µg/mL with complete culture medium before use. PC‐9 and BEAS‐2B cells were seeded in 96‐well plates at a density of 2 × 10³ cells per well. 100 µL of complete culture medium was added to each well and incubated overnight in a 37°C, 5% CO₂ incubator. Cells were then treated with a series of gradient concentrations of EESD for 72 h. After processing, CCK‐8 detection solution was added and the absorbance was measured at 450 nm wavelength at an appropriate time.

5.5. WB Assay

Cells in the logarithmic growth phase were collected using trypsin digestion, resuspended into single‐cell suspensions, counted, and seeded uniformly into 100 mm culture dishes at a density of 1.2 × 10⁶ cells per dish. When cell confluence reached 70%–80%, they were treated with EESD at 200 µg/mL for 72 h. Total cellular protein was then extracted on ice, then diluted with loading sample buffer and heated treat at 95°C for 5 min. After that, all the protein samples were divided with the effect of denaturing sodium dodecyl sulfate‐polyacrylamide gel electrophoresis, blocked with 5% skim milk at room temperature for 2 h, the primary antibody stored overnight in a 4‐degree refrigerator, and the secondary antibody for 2 h [20, 21, 22].

5.6. Data Analysis

PCA, clustering analysis and PLS‐DA were performed using RStudio software, the results of the activity and vitality determination were analyzed using Graphpad Prism 10.1.2 software. All quantitative data were expressed as the average of three replicates. The t‐test was used to compare intergroup differences, with p < 0.05 considered statistically significant.

Author Contributions

Huilin Qin: writing – original draft, methodology, and investigation. Zhijie Chen: writng – original draft, methodology, and investigation. Jie Zhang: formal analysis and software. Zhenzhen Li: formal analysis and software. Mike Hu: formal analysis and software. Yan Hu: formal analysis and software. Hong Yao: conceptualization, supervision, and writing – review and editing. Jianyong Huang: conceptualization, supervision, and writing – review and editing.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supplementary file 1: cbdv71474‐sup‐0001‐SuppMat.docx

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.