Introduction

Strobilanthes sarcorrhiza is a perennial herbaceous plant in the Acanthaceae family, also known as ‘Caitoushen’ in Chinese, which is mainly distributed in the hilly and mountainous areas of southern Zhejiang province1. Its fleshy roots are the main medicinal parts, which have the multiple efficacies of nourishing yin, clearing heat, tonifying kidney, and have often been used to treat hepatitis, nephritis, toothache, and other diseases2 In addition to its medicinal uses, the roots of S. sarcorrhiza are often also made into medicinal dishes in the Wenzhou area of Zhejiang. Its favorable activities have attracted more and more attention to S. sarcorrhiza. Many researches have been conducted to found that phenols, alkaloids, and terpenoids were the main ingredients in S. sarcorrhiza3,4,5. A study further indicated that the phenolic extract of S. sarcorrhiza can prevent diabetic nephropathy in mice by regulating NF-κB/IL-1β signaling and glycerophospholipid metabolism6.

Active ingredients are the material basis for the quality of Chinese medicinal materials, mostly belong to the secondary metabolites. It is roughly estimated that about 1 million different secondary metabolites are produced within the plant kingdom7. These metabolites also are easily influenced by external conditions and undergo changes. Therefore, it is difficult to achieve the scientific evaluation of the quality of Chinese herb by traditional evaluation mode8,9. The metabolomics, a comprehensive and systematic method, can be used to precisely identify and accurately quantify all secondary metabolites in medicinal plants of different species, genotypes and ecological types10. For example, pingbeimine B, yibeinoside C and other species-specific markers of eight Fritillaria species were discovered by metabolomics analysis11. Chrysanthemum morifolium from different cultivars and regions also could be well distinguished by morphology and metabolite profiles12. Network pharmacology has become a critical methodology for elucidating the complex therapeutic mechanisms of traditional Chinese medicine, which exerts multi-component, multi-target synergistic effects. This approach systematically identifies disease- and compound-associated targets through network topology analysis, constructs compound-protein-disease interaction networks, and investigates pharmacological regulation in high-throughput contexts13. Moreover, molecular docking, as a complementary technique, verifies the binding affinity and interaction patterns between key differential metabolites identified by metabolomics and core targets predicted by network pharmacology, effectively eliminating false-positive results and providing molecular evidence for the rationality of quality differences14. Notably, the integrated application of these three technologies synergistically harnesses their respective strengths, overcoming the limitations of single metabolomic analysis and standalone network pharmacology, thereby establishing a systematic paradigm for elucidating the material basis and molecular mechanisms of quality variations in medicinal plants.

In Zhejiang province, the artificial cultivation of S. sarcorrhiza has been achieved. According to its reproductive characteristics, the fleshy roots of annual S. sarcorrhiza have been often used as breeding materials for cultivation. Despite the existence of several S. sarcorrhiza origins in current production, few studies have been conducted to analyze them comparatively. In this study, two S. sarcorrhiza cultivars were collected from southern Zhejiang, and compared in terms of plant morphology, chemical composition and pharmacological activity. We found there were significant difference between the two origins, which have their own emphasis on efficacy. Our research has laid a theoretical foundation for the development and utilization of the resources of S. sarcorrhiza, which is helpful for achieving targeted development and breeding of S. sarcorrhiza.

Materials and methods

Plant materials

Strobilanthes sarcorrhiza (Caitoushen) plant materials from different provenances were collected from cultivation bases in Yongjia County (YJ) and Gaolou County (GL), Zhejiang Province, China, with the prior permission of the base managers. All plant materials were authenticated by Professor Qingsong Shao from the Key Laboratory of Traditional Chinese Medicine Resource Protection and Innovative Utilization of Zhejiang Province. Voucher specimens have been deposited in the laboratory with the accession numbers CTS-YJ-2024 and CTS-GL-2024, respectively.

Plant phenotype determination

The plant height, root length, leaf length, leaf width, and canopy diameter of S. sarcorrhiza were measured using a ruler and vernier caliper. Concurrently, the fresh weight of its fleshy roots was determined. All measurements were performed with a minimum of three biological replicates, each biological replicate comprised at least 5 individual plants.

Determination of total flavonoids and total phenolic content

The fleshy roots of S. sarcorrhiza were freeze-dried and pulverized. Subsequently, 0.5 g of the powdered material was accurately weighed and subjected to ultrasonic-assisted extraction with 6 mL of 95% ethanol in a temperature-controlled water bath maintained at 50 °C for 30 min. After centrifugation, the supernatant was collected. This extraction procedure was repeated twice under identical conditions. The resulting supernatants were pooled and combined to constitute the test sample solution for subsequent analytical purposes. Total flavonoid content (TFC) was quantified via aluminum nitrate‑sodium nitrite colorimetry, using rutin as the reference standard. Briefly, the sample solution was reacted sequentially with sodium nitrite and aluminum nitrate, and alkalized with sodium hydroxide to induce color formation; the absorbance was then detected at 510 nm, with TFC calculated against the rutin calibration curve. For total phenolic content (TPC), the Folin-Ciocalteu colorimetric method was adopted with gallic acid as the reference standard. The sample solution was incubated with Folin-Ciocalteu reagent and sodium carbonate at ambient temperature, and the absorbance was measured at 765 nm. TPC was subsequently computed based on the gallic acid calibration curve. All determinations were done in triplicate, and relative standard deviation (RSD) was calculated to ensure result reliability and method precision15.

Metabolite content determination

An appropriate quantity of plant specimen was mixed with pre-chilled methanol/acetonitrile/aqueous solution (2:2:1, v/v). The mixture underwent vortex mixing and low-temperature ultrasonication for 30 min, followed by stationary incubation at −20 °C for 10 min. Subsequent centrifugation was performed at 14,000 g and 4 °C for 20 min. The supernatant was collected and vacuum-dried. For mass spectrometric analysis, the residue was reconstituted with 100 µL acetonitrile-aqueous solution (acetonitrile: water = 1:1, v/v), vortexed, and centrifuged at 14,000 g (4 °C) for 15 min. The resulting supernatant was subjected to LC-MS analysis. Chromatographic separation was achieved using an Agilent 1290 Infinity LC system equipped with a HILIC chromatographic column. Operational parameters included: column temperature maintained at 25 °C, flow rate set to 500 µL/min, and injection volume of 2 µL. The mobile phase consisted of two components: Phase A - aqueous solution containing 25 mM ammonium acetate and 25 mM ammonia; Phase B - acetonitrile. The gradient elution conditions were set as follows: 0 min 95% B, 0.5 min 95% B, 7 min 65% B, 8 min 40% B, 9 min 40% B, 9.1 min 95% B, 12 min 95%. To avoid instrument detection signal fluctuations, continuous sample analysis was conducted in random order. Quality control (QC) samples were inserted into the sample queue to monitor system stability and experimental data reliability.

An AB Triple TOF 6600 mass spectrometer was employed to acquire primary and secondary mass spectra of samples. ESI source conditions after UHPLC separation were as follows: nebulizer-assisted heating gas 1 (Gas1): 60, Gas2: 60, curtain gas (CUR): 30 psi, ion source temperature: 600 °C, spray voltage (ISVF): ±5500 V (both positive and negative modes); primary m/z detection range: 60–1000 Da, secondary product ion m/z range: 25–1000 Da, primary scan accumulation time: 0.20 s/spectra, secondary scan accumulation time: 0.05 s/spectra. Secondary mass spectra were acquired via data-dependent acquisition (IDA) with peak intensity screening, declustering potential (DP): ±60 V (both modes), collision energy (CE): 35 ± 15 eV. IDA settings: dynamic exclusion of isotopic ions within 4 Da, and 10 fragment spectra acquired per scan.

Metabolomics analysis

Raw mass spectrometry data were converted to mzXML format using ProteoWizard MSConvert and further processed with the open-source XCMS package. Peak picking was performed with the centWave algorithm at the following settings: m/z tolerance = 10 ppm, peak width = c(10, 60), prefilter = c(10, 100). Peak grouping was implemented with parameters set as bw = 5, mzwid = 0.025, and minfrac = 0.5. Isotopic and adduct annotations were conducted using the CAMERA toolbox. Extracted ion features were filtered, retaining only variables with non-zero measurements accounting for more than 50% of values in at least one group. Metabolite identification was achieved by matching accurate m/z values (< 10 ppm) and MS/MS spectra against an in-house library established with authentic reference standards. Metabolite quantification was performed via peak area and EIC intensity. The relative contents of all identified metabolites were then analyzed using multivariate statistical methods, including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA). Differentially accumulated metabolites (DAMs) were screened using the criteria of Variable Importance in Projection (VIP) ≥ 1 and |Log2FoldChange| ≥ 1.

Network pharmacology analysis

The differential flavonoids, polyphenols, and alkaloids were systematically screened using the TCMSP database and SwissADME platform. Potential targets of the identified components were predicted through SwissTargetPrediction. Disease-related targets were retrieved from the GeneCards database using “nephritis” and “dental pain” as keywords, with candidate disease targets selected based on relevance scores exceeding the median value16. A Venn diagram was generated via Venny 2.1 to visualize overlapping targets between component-related and disease-related targets. Subsequently, protein-protein interaction (PPI) networks of shared targets were constructed using the STRING database to elucidate potential therapeutic mechanisms.

Functional and pathway enrichment analysis

Common targets between components and diseases were analyzed using Metascape for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment17. The Micro bioinformatics platform was employed to visualize cellular components (CC), molecular functions (MF), biological processes (BP), and signaling pathways.

Molecular docking validation

The 3D structures of active components were obtained from PubChem and optimized using Chem3D. Protein structures of core targets were downloaded from the RCSB PDB database, preprocessed with AutoDockTools (hydrogens added, water molecules removed, charges calculated), and docked with ligands. The docking box was centered on key residues of the active pocket with coordinates (x = 23.581 Å, y = 9.73 Å, z = 58.824 Å), dimensions of 40 Å × 40 Å × 40 Å, and grid spacing of 1.0 Å. Docking parameters were set as follows: num_modes = 20, energy_range = 5 kcal/mol, and exhaustiveness = 8 (default). The conformation with the optimal binding free energy (ΔG) was selected as the most possible binding mode, and its key interactions were visualized and analyzed using PyMOL 2.5.7.

Results

Different phenotypes of two S. sarcorrhiza origins

Based on previous resource investigation, we found significant differences in plant morphology between S. sarcorrhiza from Yongjia (YJ) and Gaolou (GL), as shown in Fig. 1. By measuring the phenotype data of two plant origins (Table 1), it was found that YJ has a plant height of approximately 50.4 cm, which is about three times the height of GL. The root length of YJ is about 50 cm, which is about twice the root length of GL. In addition, the leaf length, leaf width, and crown width of YJ are significantly greater than GL. In terms of weight, the mass of YJ fleshy roots is about 4.38 g, which is about four times the mass of GL, indicating a significant difference in the external quality of two types S. sarcorrhiza.

Table 1 Plant phenotypic parameters of S. sarcorrhiza.
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Fig. 1
Fig. 1
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Phenotype and active ingredient content of S. sarcorrhiza. (A, B) The plant phenotype of S. sarcorrhiza from YJ and GL, respectively; (C) The total flavonoids content of S. sarcorrhiza from YJ and GL; (D) The total phenolic content of S. sarcorrhiza from YJ and GL. Significantly different values (P < 0.05) between groups were indicated with different lowercase letters.

Different content of active ingredients in two S. sarcorrhiza origins

To evaluate the intrinsic quality differences between two origins of S. sarcorrhiza (YJ and GL), we quantitatively analyzed the total flavonoids and phenolics in their fleshy roots (Fig. 1C, D). The results revealed distinct variations, with YJ containing 5.6 mg/g total flavonoids compared to 6.0 mg/g in GL (P < 0.05). Notably, GL exhibited a 2.6-fold higher total phenolic content (37.4 mg/g) than YJ (13.9 mg/g), underscoring substantial divergence in phytochemical composition between the two origins.

Significant differences of metabolites in two S. sarcorrhiza origins

Metabolomic analysis using high-resolution mass spectrometry identified 23,728 metabolites, with 3,259 annotated in public databases. Among these, 91 flavonoids, 35 phenolics, and 41 alkaloids were structurally characterized. Multivariate analyses (HCA, PCA, and OPLS-DA) demonstrated clear separation between YJ and GL samples (Fig. 2), indicating germplasm-specific metabolic profiles. Comparative analysis identified 565 differentially accumulated metabolites (DAMs), including 690 metabolites enriched in GL and 189 in YJ (Fig. 3A). Based on the results of metabolite identification, the key DAMs may include Antipain, Lappaconitine, Oleanonic acid, and Pantethine (Fig. 3B).

Fig. 2
Fig. 2
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Multivariate analyses of metabolites in S. sarcorrhiza. (A) Hierarchical clustering analysis of identified metabolites; (B) Principal component analysis of identified metabolites; (C) OPLS-DA score map of identified metabolites.

Fig. 3
Fig. 3
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The differentially accumulated metabolites between YJ and GL. (A) The volcano plots of metabolites comparing GL to YJ; (B) The four metabolites with the highest degree of upregulation and downregulation.

Among the differential metabolites, we identified 19 flavonoids, hecogenin, ononin, cirsimaritin, daidzein, isosakuranetin, and apigenin were YJ-enriched, while GL accumulated higher levels of sinensetin (5,6,7,3,,4,-pentamethoxyflavone), psoralidin, tangeretin, rotenone, vitexin, phlorizin, and typhaneoside (Fig. 4A). In addition, there were significant differences in the content of 12 phenolics and 4 alkaloids between the two germplasm, such as Dopamine, homogentisic acid, anethole, vindoline, and tetrahydroharmine (Fig. 4B, C).

Fig. 4
Fig. 4
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The differential flavonoids, phenolics, and alkaloids between YJ and GL. (A) The heatmap of differential flavonoids; (B) The heatmap of differential phenolics; (C) The heatmap of differential alkaloids.

Therapeutic propensity of YJ germplasm in nephropathy

The 23 high-content components of YJ were screened through SwissTargetPrediction to obtain 500 potential targets, which were subsequently intersected with 1,332 nephritis-related potential targets, and yielded 94 “YJ high-content components-nephritis” targets (Fig. 5A). By setting a degree value ≥ 30 as the threshold, vindoline and apigenin were identified as the key active components of YJ for nephritis treatment from the “component-disease-target” network. The protein-protein interaction (PPI) network, generated using the STRING database and visualized via Cytoscape, comprised 86 nodes and 362 edges (Fig. 5B). The core targets, epidermal growth factor receptor (EGFR), were screened based on a degree value ≥ 30, suggesting their pivotal roles in YJ-mediated nephritis regulation.

GO and KEGG analyses via Metascape revealed that the YJ high-content component-nephritis network was associated with 185 molecular functions, 449 biological processes, and 195 cellular components. Key molecular functions included protein kinase activity and kinase binding. Biological processes predominantly involved positive regulation of responses to external stimuli and cellular responses to nitrogen compounds. Cellular components were enriched in membrane rafts and perinuclear cytoplasmic regions (Fig. 5C). KEGG pathway analysis identified 221 signaling pathways, including cancer pathways, lipid and atherosclerosis pathways, and the AGE-RAGE signaling pathway (Fig. 5D).

Fig. 5
Fig. 5
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The network pharmacology analysis of metabolites enriched in YJ. (A) The Venn diagram of components targets and nephritis targets; (B) Protein-protein interaction network diagram of nephritis disease; (C) GO analysis of nephritis disease; (D) KEGG analysis of nephritis disease.

The molecular docking of vindoline and apigenin with the EGFR target revealed binding energies of −7.4 kcal/mol and − 7.7 kcal/mol, respectively. These results demonstrate that both vindoline and apigenin may effectively bind to EGFR. The specific docking modes are illustrated in the Fig. 6.

Fig. 6
Fig. 6
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Molecular docking results between active ingredients in YJ and EGFR target. (A) Molecular docking results between vindoline and EGFR target; (B) Molecular docking results between apigenin and EGFR target.

GL germplasm demonstrates analgesic potential in dental pain

The 263 potential targets of GL bioactive components intersected with 1223 potential targets of toothache, resulting in 76 “GL high-content component-dental pain” targets. Network construction in Cytoscape highlighted tangeretin and sinensetin (5,6,7,3’,4’-pentamethoxyflavone) as key components (degree ≥ 20) for GL-mediated toothache alleviation. The PPI network for toothache regulation contained 64 nodes and 225 edges (Fig. 7A, B). Steroid receptor coactivator (SRC) emerged as core targets (degree ≥ 25). GO analysis of the GL high-content component-toothache network revealed 165 molecular functions, 349 biological processes, and 128 cellular components. Molecular functions emphasized protein kinase activity and tyrosine kinase activity. Biological processes included cellular responses to nitrogen compounds and positive regulation of phosphorus metabolism. Cellular components were linked to membrane rafts and dendrites (Fig. 7C). KEGG analysis identified 173 pathways, such as cancer pathways, the PI3K-AKT signaling pathway, and the HIF-1 signaling pathway (Fig. 7D). Molecular docking results indicated that tangeretin and sinensetin exhibited binding energies of −5.5 and − 5.4 kcal/mol with SRC, respectively (Fig. 8), suggesting that they can effectively bind to SRC. This comprehensive multi-omics approach elucidates germplasm-specific therapeutic orientations of S. sarcorrhiza, providing a scientific basis for its targeted application in precision phytotherapy.

Fig. 7
Fig. 7
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The network pharmacology analysis of metabolites enriched in GL. (A) The Venn diagram of components targets and dental pain targets; (B) Protein-protein interaction network diagram of dental pain; (C) GO analysis of dental pain; (D) KEGG analysis of dental pain.

Fig. 8
Fig. 8
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Molecular docking results between active ingredients in GL and SCR target. A: Molecular docking results between tangeretin and SCR target. B: Molecular docking results between sinensetin and SCR target.

Discussion

This study systematically investigates the phytochemical diversity and genetic basis of S. sarcorrhiza through integrated metabolomics and pharmacognostic approaches. The utilization of LC-MS-coupled metabolomics afforded systematic and high-throughput phytochemical characterization, which not only corroborated the distribution of well-recognized bioactive components (i.e., polyphenols and alkaloids) in the target plant but also underscored the markedly improved resolution and throughput of modern analytical techniques relative to traditional labor-intensive isolation protocols18. Beyond global metabolite profiling, our chemometric analysis further identified antipain, lappaconitine, oleanonic acid, and pantetheine as potential discriminatory metabolites, which hold great promise as reliable chemical biomarkers for distinguishing between two different germplasms of S. sarcorrhiza. These findings provide a valuable metabolic foundation for the germplasm evaluation and chemotaxonomic investigation of this species. Currently, multiple studies have shown that key biomarkers have been discovered in many medicinal plants using metabolomics techniques, it is also one of the advantages of metabolomic analysis19,20,21.

The observed metabolic divergence between YJ and GL ecotypes provides critical insights into genotype-environment interactions. YJ ecotypes prioritize biomass accumulation, especially enhanced root mass, while GL variants channel metabolic flux toward phenolic biosynthesis22. This differentiation causes significant disparities in bioactive phenolics, which are widely recognized as the key determinants of medicinal efficacy in herbal plants23. Specifically, YJ ecotypes maintain higher primary metabolic flux to support rapid growth but exhibit lower phenolic accumulation, whereas GL variants allocate more metabolic resources to enrich core bioactive phenolics that underpin antioxidant, anti-inflammatory, and other crucial therapeutic properties. Consistent with ecological resource allocation theory, this trade-off between growth and defense metabolism reflects distinct adaptive strategies of the two ecotypes to their respective cultivation environments24,25. Importantly, this metabolic divergence establishes a solid theoretical and practical basis for targeted germplasm selection and region-specific cultivation, which are essential to balance yield and medicinal quality in large-scale medicinal plant production, aligning with prior research on quality optimization of medicinal plants26.

Network pharmacology analysis identified EGFR as the core target mediating the therapeutic effects of YJ on nephritis. EGFR localizes to membrane rafts and the perinuclear region, a subcellular distribution that is essential for its activation and sustained signaling—consistent with its well-documented cholesterol-dependent activation and endocytic trafficking27. Subsequent pathway enrichment analysis further linked EGFR to the AGE-RAGE signaling axis, through which EGFR can cross-regulate lipid metabolism and inflammatory responses28. These findings collectively suggest that YJ may alleviate nephritis through EGFR-mediated modulation of the AGE-RAGE signaling pathway, and its multi-pathway regulatory properties provide a direction for further mechanistic investigations. Furthermore, network pharmacology predicted SRC as the core target through which GL exerts its regulatory effects on dental pain. As a key non-receptor tyrosine kinase, SRC was significantly enriched in the PI3K-AKT signaling pathway. Accumulating evidence has indicated an association between SRC and oral diseases29. This suggests that GL may regulate the pathophysiological processes of dental pain by targeting SRC and its associated signaling pathways, with the underlying mechanisms closely linked to SRC-mediated signal transduction.

The preferential accumulation of vindoline and apigeninin YJ ecotypes correlates with nephroprotective effects. Vindoline is an indole alkaloid distributed in Catharanthus roseus, which has significant antioxidant, anti-inflammatory and anti-diabetes activities. Studies have shown that vindoline has a better effect on type 2 diabetes induced renal tissue oxidation and inflammation30,31. Apigenin is a common flavonoid found in many fruits and vegetables, has potential anti-inflammatory and antioxidant properties. Apigenin can effectively treat liver, lung, heart, kidney, nervous system diseases32,33. It was found that apigenin ameliorates hypercholesterolemia-induced renal injury by modulating the renal KIM-1, Fn1, and Nrf2 signaling pathways34. Conversely, GL-derived tangeretin and Sinensetin enrichment suggests superior neuroprotection and anti-inflammatory efficacy for dental applications. Among them, tangeretin has shown therapeutic effects in alleviating oxidative stress, neuroinflammation, and neuronal damage in various neurodegenerative diseases35,36. Sinensetin is a naturally occurring polymethoxyflavone that has been found to effectively alleviate oxidative stress and inflammatory response in cultured human periodontal ligament cells, and has a significant improvement effect on toothache37. However, network pharmacology and molecular docking represent merely predictive in silico bioinformatic approaches, rather than providing definitive experimental evidence. Accordingly, the concrete material basis responsible for the therapeutic effects, as well as the precise molecular mechanisms underlying these pharmacological activities, still require rigorous validation through a series of welldesigned in vitro and in vivo experiments. Furthermore, toxicity, bioavailability, and optimal therapeutic concentrations also are critical determinants affecting the therapeutic efficacy of herb, which cannot be solely ascribed to the concentration of active metabolites38. Comprehensive experimental validation is therefore indispensable to fully substantiate and corroborate the conclusions derived from the present study.

This chemotypic differentiation creates opportunities for precision cultivation - developing YJ-type materials for chronic renal disorders and GL-type materials for oro-dental formulations. From a breeding perspective, the identification of key metabolic QTLs controlling root biomass and phenolic biosynthesis could enable marker-assisted selection39. Future work should integrate transcriptomics to elucidate the regulatory networks underlying these chemotypic variations. The findings underscore the importance of germplasm authentication in traditional medicine standardization and provide a roadmap for phytochemical-oriented cultivar development.

Conclusions

This study conducted a comprehensive comparative analysis of two origins of Strobilanthes sarcorrhiza, revealing significant differences in morphological characteristics, active component content, metabolite profiles (varieties and levels), and pharmacological efficacy. Through integrated network pharmacology and molecular docking analyses, we identified vindoline and apigenin as key bioactive compounds exhibiting high binding affinity with the EGFR target, suggesting their therapeutic potential for nephritis. Concurrently, tangeretin and demonstrated specific interactions with the SRC target, elucidating their molecular mechanism in alleviating dental pain symptoms. The systematic investigation of phytochemical composition and multi-target pharmacological mechanisms not only enriches the understanding of S. sarcorrhiza medicinal properties but also provides a theoretical foundation for developing plant-derived therapeutics for inflammatory disorders.