Mechanism and Molecular Targets of Songhe Guxue Formula for Treating Cancer Therapy-Introduced Thrombocytopenia—Based on Clinical Observation, Network Pharmacology, and Molecular Docking

Mechanism and Molecular Targets of Songhe Guxue Formula for Treating Cancer Therapy-Introduced Thrombocytopenia—Based on Clinical Observation, Network Pharmacology, and Molecular Docking | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Mechanism and Molecular Targets of Songhe Guxue Formula for Treating Cancer Therapy-Introduced Thrombocytopenia—Based on Clinical Observation, Network Pharmacology, and Molecular Docking Yanying Zhang, Xuejiao Ma, Fang Liu, Liqun Jia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8512851/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cancer therapy-induced thrombocytopenia (CTIT) is a common complication that compromises the efficacy and safety of anticancer treatments, with current therapeutic options remaining limited. Songhe Guxue Formula (SHGXF) is an empirical formulation developed based on the Traditional Chinese Medicine (TCM) principle of “supplementing qi and nourishing blood, strengthening the spleen and kidney.” Preliminary clinical observations have suggested its potential in managing CTIT; however, its underlying mechanisms remain unclear. This study aims to systematically predict the pharmacodynamic material basis, core targets, and signaling pathways of SHGXF against CTIT using network pharmacology for the first time, thereby providing a scientific foundation for its clinical application. Methods Active ingredients of SHGXF and their corresponding targets were retrieved from the TCMSP and HERB databases. Disease targets related to CTIT were collected from GeneCards and OMIM. Common targets were identified by intersecting the drug and disease targets. The “drug–ingredient–target” network and protein–protein interaction (PPI) network were constructed using Cytoscape software to screen core targets. Finally, Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to elucidate the potential biological processes and signaling pathways involved. To evaluate the interactions between bioactive ingredients and central target proteins, molecular docking simulations were conducted. Results A total of 40 active ingredients and 566 corresponding targets of SHGXF were screened, along with 1036 CTIT-related targets. Among them, 104 common targets were identified. Network analysis revealed key active ingredients such as quercetin and core targets including AKT1,MTOR, PIK3CA, and GSK3B. Enrichment analysis showed that these targets were significantly associated with biological processes such as “cellular response to chemical stress” and “myocyte proliferation,” and were mainly involved in pathways including the PI3K–Akt signaling pathway, proteoglycans in cancer, and JAK–STAT signaling pathway. Conclusion This study suggests that SHGXF alleviates CTIT through a multi-component, multi-target, multi-pathway mechanism, involving quercetin, core targets like TP53 and AKT1, and the PI3K-Akt pathway, thereby reducing oxidative stress and promoting megakaryocyte differentiation. Songhe Guxue Formula cancer therapy-induced thrombocytopenia network pharmacology mechanism target pathway Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Background Cancer therapy-induced thrombocytopenia (CTIT) is a common and serious adverse effect associated with various anticancer treatments, including chemotherapy, radiotherapy, targeted therapy, and immunotherapy. Epidemiological data indicate an overall incidence of up to 21.8% [ 1 ], which may exceed 30% in regimens containing platinum agents or gemcitabine [ 2 ]. CTIT not only increases the risk of bleeding, prolongs hospitalization, and raises healthcare costs but also compromises treatment efficacy and long-term survival due to dose reductions, treatment delays, or discontinuation [ 3 – 5 ]. Studies have shown that among patients who develop CTIT, approximately 8% experience chemotherapy delays of ≥ 7 days, while 17% require a dose reduction of 20% [ 6 ]. Maintaining a high relative dose intensity (≥ 80%) is significantly associated with longer survival [ 7 , 8 ]. Therefore, effective management of CTIT is crucial for optimizing cancer treatment outcomes and improving patient prognosis. Current clinical strategies for CTIT mainly include platelet transfusion, thrombopoietin-stimulating agents (e.g., recombinant human thrombopoietin, rhTPO; recombinant human interleukin‑11, rhIL‑11), and thrombopoietin receptor agonists (TPO‑RAs) [ 9 ]. However, these approaches have notable limitations: platelet transfusion provides only transient relief and carries risks of refractoriness, allergic reactions, and transfusion‑transmitted infections; rhIL‑11 is associated with potential cardiotoxicity [ 10 ]; rhTPO requires injection and liver function monitoring, limiting its convenience for long‑term management [ 11 – 14 ]; and TPO‑RAs have not yet been fully approved for CTIT in China [ 15 – 19 ]. Moreover, evidence‑based guidance for managing thrombocytopenia induced by targeted therapies, immune checkpoint inhibitors, or radiotherapy remains scarce, often relying on dose reduction or interruption [ 20 ], further highlighting the unmet clinical need. Developing safer, more effective, and convenient therapeutic strategies to address these gaps is an urgent priority in supportive cancer care. Guided by the core TCM principles of “holism” and “treatment based on syndrome differentiation,” traditional Chinese medicine offers a unique theoretical and interventional perspective for CTIT. In TCM, CTIT can be classified under categories such as “blood disorders,” “consumptive disease,” or “drug‑toxicity‑induced purpura.”Its pathogenesis is attributed to the impairment of healthy qi (vital energy) by “drug toxins” (i.e., anticancer therapies). The disease progression involves three stages: initially, the toxins directly damage qi and blood, leading to deficiency; subsequently, they attack the spleen and stomach, resulting in spleen‑qi deficiency, impaired transportation and transformation, insufficient generation of qi and blood, and failure of containment; finally, the injury extends to the kidney, causing kidney‑essence deficiency, malnourishment of the marrow, and impaired transformation of essence into blood, ultimately leading to inadequate platelet production or destabilization.Hence, the core pathogenesis is “dual deficiency of qi and blood, insufficiency of the spleen and kidney.” Accordingly, the fundamental therapeutic principle is “supplementing qi and nourishing blood, strengthening the spleen and kidney.” Modern research has preliminarily confirmed that TCM formulas guided by this principle exhibit definite efficacy in elevating platelet counts and improving clinical symptoms, with favorable safety profiles, reflecting the unique TCM advantage of “enhancing efficacy and reducing toxicity” [ 21 , 22 ]. Based on this theory and long‑term clinical practice, our research team has developed the empirical formula—Songhe Guxue Formula(SHGXF). Preliminary exploratory clinical observations provide initial support for its application. A prospective cohort analysis involving 34 CTIT patients showed that the overall response rate (platelet recovery to normal or an increase ≥ 50×10⁹/L) with SHGXF monotherapy reached 76.5%, with no bleeding events or need for platelet transfusion during treatment, indicating favorable efficacy and safety.Notably, the formula also demonstrated potential in managing thrombocytopenia associated with targeted and immunotherapy, an area where current Western medical interventions are relatively limited [ 23 ]. However, the modern biological mechanisms underlying its therapeutic effects remain unclear, hindering further clinical promotion and development. Therefore, systematically elucidating the integrated “multi‑ingredient–multi‑target–multi‑pathway” mechanism of SHGXF is a crucial step bridging its promising clinical potential with contemporary scientific understanding. Conventional pharmacological approaches typically focus on single ingredients or pathways, making it difficult to comprehensively reveal the synergistic interactions among components in a TCM formula and its holistic regulatory effects on disease networks. Network pharmacology, an emerging interdisciplinary field integrating systems biology and pharmacology, provides a powerful paradigm to address this challenge. By integrating high‑throughput omics data, bioinformatic databases, and computational simulations, this approach systematically constructs multi‑layer interaction networks linking “active ingredients–potential targets–key biological pathways–disease phenotypes [ 24 – 26 ].” Its core strength lies in explaining, from a systems perspective rather than isolated molecular viewpoints, how TCM formulas restore balance by modulating disordered disease networks, thereby achieving therapeutic effects. This aligns well with the TCM core concepts of “holism” and “restoring harmony.” In recent years, network pharmacology has been widely applied to elucidate the mechanisms of classic TCM formulas (e.g., Wutou Decoction for rheumatoid arthritis, Danggui Shaoyao Powder for neurodegenerative diseases), successfully predicting their key active ingredients, core targets, and enriched pathways, which were subsequently validated experimentally [ 27 , 28 ]. These examples confirm that network pharmacology serves as an effective tool connecting traditional TCM experience with modern molecular biology, bridging “clinical efficacy” and “scientific mechanism.” Therefore, employing network pharmacology to systematically explore the potential pharmacodynamic material basis, core targets, and signaling pathway networks of SHGXF in treating CTIT is methodologically advanced and essential for deeply understanding the modern scientific connotation of its “supplementing qi and nourishing blood, strengthening the spleen and kidney” therapeutic principle. This study follows a logical framework of “clinical question‑oriented → data‑driven prediction → biological function interpretation.” First, based on preliminary clinical observations, we clarified the clinical potential of SHGXF for CTIT and the scientific questions to be addressed. Subsequently, network pharmacology was applied for systematic analysis: ① Active ingredients and corresponding targets of each herb in SHGXF were screened using the TCM Systems Pharmacology Database (TCMSP); ② CTIT‑related targets were retrieved from disease databases including GeneCards and OMIM; ③ Common targets were obtained by intersecting drug and disease targets; ④ The “ingredient‑target” network and PPI network were constructed using Cytoscape software, and core ingredients and hub targets were screened via topological analysis; ⑤ Finally, GO and KEGG enrichment analyses were performed on the common targets using platforms such as DAVID to predict involved biological functions and signaling pathways. The entire workflow aims to construct a multi‑level, visual predictive mechanistic network, providing direction for subsequent experimental validation (Fig. 1 .Graphical Abstract). 2. Materials and Methods 2.1 Theoretical Basis and Clinical Background of Songhe Guxue Formula 2.1.1 Formula Source and Composition Songhe Guxue Formula consists of the following herbs: Lignum Pini Nodi (Oriental Arborvitae Knotty Wood油松节);Herba Agrimoniae (Hairyvein Agrimonia Herb仙鹤草);Rhizoma Atractylodis Macrocephalae (cruda) (Unprocessed Largehead Atractylodes Rhizome生白术); Cornu Cervi Degelatinatum (Degelatinated Deer Antler鹿角霜); Colla Corii Asini (praeparata cum gelatina) (Donkey-hide Gelatin (prepared as gelatin pearls)阿胶珠); Caulis Spatholobi (Suberect Spatholobus Stem鸡血藤); Herba Leonuri (Motherwort Herb益母草). 2.1.2 Interpretation and Formula Analysis from TCM Theory This formula originates from the academic and clinical experience of nationally renowned veteran practitioners of Traditional Chinese Medicine (TCM). It was specifically designed to address the core pathogenesis of CTIT, identified in TCM as "deficiency of both the spleen and kidney, and failure of qi to contain the blood." Its composition is ingeniously crafted: Lignum Pini Nodi (油松节, 30g) and Herba Agrimoniae (仙鹤草, 30g) jointly serve as the monarch drugs. Lignum Pini Nodi tonifies deficiency, consolidates the foundation, strengthens bones, and promotes marrow generation, while Herba Agrimoniae reinforces healthy qi, tonifies deficiency, removes toxicity, and arrests bleeding. Their combination aims to tonify and consolidate without retaining pathogenic factors, and to remove toxicity and stanch bleeding without damaging healthy qi. The minister drugs consist of Rhizoma Atractylodis Macrocephalae (cruda) (生白术, 15g) to fortify the spleen and augment qi, and Cornu Cervi Degelatinatum (鹿角霜, 10g) to warm the kidney and support yang. Their synergistic application produces the effect of "mutual generation between fire (kidney) and earth (spleen)," thereby strengthening the spleen's function in managing and containing the blood. The assistant drugs include Colla Corii Asini (praeparata cum gelatina) (阿胶珠, 15g), which tonifies blood and nourishes yin; Caulis Spatholobi (鸡血藤, 30g), which tonifies blood and invigorates blood circulation; and Herba Leonuri (益母草, 10g), which activates blood circulation and regulates menstruation. These three, all essential medicinals for blood disorders, are employed together to achieve the goal of tonifying the blood without causing stagnation and activating blood circulation without injuring the blood. The seven ingredients in the entire formula are precisely combined, embodying the therapeutic essence of "cultivating and supplementing the spleen and kidney, boosting qi to secure the blood." Compared to conventional tonifying formulas, its design is considered more nuanced and targeted. 2.1.3 Overview of Preliminary Clinical Observations To clarify the clinical relevance and research necessity of SHGXF for CTIT, we analyzed its preliminary application. All procedures complied with the Declaration of Helsinki and were approved by the Ethics Committee of China‑Japan Friendship Hospital (Approval No.2025-KY-115). In a preliminary clinical observation, 43 CTIT patients treated with SHGXF with complete data were enrolled from the Oncology Department of China‑Japan Friendship Hospital (20 males, 17 females, mean age 63.5 ± 10.4 years). Inclusion criteria were: ① age 18–75 years; ② good performance status (ECOG score 0–2); ③ histologically or cytologically confirmed malignancy; ④ peripheral platelet count < 100×10⁹/L; ⑤ history of platelet decrease after previous exposure to a chemotherapy, targeted, or immunotherapy drug, with recurrence upon re‑administration; ⑥ informed consent signed for data and sample collection. Changes in peripheral platelet counts before and after treatment were observed. Preliminary data indicated an upward trend in platelet counts after treatment. Specifically, the mean platelet count increased from 76.37 ± 16.12 ×10⁹/L before treatment to 124.8 ± 44.77 ×10⁹/L after treatment (see Additional file 1: Supplementary Table S1 baseline characteristics, Table 1 platelet changes before and after treatment). Notably, Table 2 demonstrates that 76.5% of patients achieved a platelet count ≥ 100×10⁹/L, with most responses occurring within 14 days.It is important to note that this analysis involved a limited sample size and was a prospective single‑arm study, primarily intended to verify the real‑world feasibility of the formula and provide clear clinical direction for subsequent mechanistic exploration, rather than drawing definitive efficacy conclusions. These preliminary results suggest the potential value of SHGXF in improving CTIT, directly motivating the present network pharmacology study to systematically elucidate its molecular mechanisms. Table 1 Clinical Observation: Platelet Changes Before and After Treatment Group Before Treatment( \(\:\overline{\text{x}}\) ±s) After Treatment ( \(\:\overline{\text{x}}\) ±s) P Value TCM Group 76.37 ± 16.12 124.80 ± 44.77 0.00* Table 2 Clinical Observation: Platelet Recovery Time Group ≤ 7 days 7–14 days 14–28 days ≥ 28 days Response Rate ≥ 100×10⁹/L 7 14 5 0 76.5% All Responders (n = 34) 10 (29.4%) 16 (47.0%) 8 (23.5%) 0 (0%) 100% 2.2 Collection of Active Ingredients and Targets of Songhe Guxue Formula For the five herbal medicines: Lignum Pini Nodi, Herba Agrimoniae, Rhizoma Atractylodis Macrocephalae (cruda), Caulis Spatholobi, and Herba Leonuri, a search for their chemical constituents was conducted utilizing the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, https://old.tcmsp-e.com/tcmsp.php ). In alignment with the characteristics of oral TCM formulations, screening criteria were set at an Oral Bioavailability (OB) of ≥ 30% and a Drug-Likeness (DL) index of ≥ 0.18 to identify potential active components with favorable pharmacokinetic properties. For the remaining two medicines, Colla Corii Asini and Cornu Cervi Degelatinatum, information within the TCMSP database was limited. Therefore, their primary chemical constituents were retrieved from the HERB database. As the HERB database does not directly provide OB or DL values, preliminary filtering was performed with reference to Lipinski's Rule of Five (molecular weight Mw ≤ 500, octanol-water partition coefficient miLogP ≤ 5, number of hydrogen bond donors nOHNH ≤ 5, number of hydrogen bond acceptors nOH ≤ 10), focusing on compounds adhering to these fundamental principles of drug-like properties. The screening results from both parts were subsequently merged to establish a preliminary pool of potential active components and their corresponding targets for the Songhe Guxue Formula. 2.3 Collection of CTIT‑Related Disease Targets Target genes associated with CTIT were obtained from the GeneCards database ( https://www.genecards.org/ ) and the Online Mendelian Inheritance in Man (OMIM) database ( http://www.omim.org/ ). 2.4 Screening of Common Targets and Venn Diagram Construction To identify potential therapeutic targets of Songhe Guxue Formula for CTIT, targets of Songhe Guxue Formula were compared with CTIT‑related targets to determine commonalities. The Venn diagram tool ( https://bioinfogp.cnb.csic.es/tools/venny/ ) was used to visualize the overlap. 2.5 Construction and Analysis of the “Drug‑Ingredient‑Target” Network The open‑source bioinformatics software Cytoscape 3.9.01 was used to construct and visualize the disease‑compound‑target network. The seven herbs, active ingredients, and potential common targets (intersection targets) of Songhe Guxue Formula were imported into Cytoscape 3.9.01 to generate a “drug‑ingredient‑target” network. Nodes represented herbs, active ingredients, and targets: blue inverted triangles for drugs, red circles for core target genes, and green diamonds for active ingredients. 2.6 Protein–Protein Interaction (PPI) Network Construction and Core Target Screening PPI data were obtained from the STRING database with the protein type set to Homo sapiens and a minimum interaction score > 0.4. The generated PPI network was visualized using R software (version 3.5.37), and core targets within the network were identified with a significance threshold of adjusted p-value < 0.05. 2.7 GO and KEGG Pathway Enrichment Analyses Go functional annotation and KEGG pathway analysis were performed on the 104 common targets using the bioinformatics platform ( https://www.bioinformatics.com.cn/ ), and the results were visualized.The core targets (e.g., PIK3CA, AKT1) and key ingredients (e.g., quercetin, β-sitosterol) with the highest topological scores in the PPI and ingredient-target networks were selected for docking validation. 2.8 Molecular Docking of Active Compounds and Core Targets The two-dimensional (2D) structures of small-molecule ligands were retrieved from the PubChem database ( http://pubchem.ncbi.nlm.nih.gov/ ). These 2D structures were subsequently imported into ChemOffice 20.0 to generate their three-dimensional (3D) conformations, which were saved in the mol2 file format. For molecular docking, crystal structures of the target proteins with high resolution were selected from the RCSB Protein Data Bank ( http://www.rcsb.org/ ) to serve as receptors. These protein structures were processed using PyMOL 2.6.0 to remove water molecules and phosphate groups, and the resulting models were saved in the PDB format. Energy minimization of the compounds and preprocessing of the target proteins, including the identification of active binding pockets, were performed using the Molecular Operating Environment (MOE) 2019 software. Molecular docking was then carried out with MOE 2019, with the number of docking runs set to 50. The binding affinity between each ligand and receptor was evaluated based on the calculated binding energy. Finally, the docking results were visualized and analyzed using PyMOL 2.6.0 and Discovery Studio 2019. 3. Results 3.1 Screening of Bioactive Compounds of Songhe Guxue Formula and CTIT Targets Systematic retrieval and screening of chemical constituents from the seven herbs of Songhe Guxue Formula (OB ≥ 30%, DL ≥ 0.2) yielded 40 candidate active compounds, including 36 organic compounds from the TCMSP database and 4 amino acid/mineral components from the PubChem database (see Additional file 1: Supplementary Table S2). A total of 566 targets of these bioactive ingredients were identified. After removing duplicates, 1036 CTIT‑related targets were selected and standardized. Comparison of Songhe Guxue Formula bioactive compound targets with CTIT targets yielded 104 common targets (see Additional file 1: Supplementary Table S3), visualized in a Venn diagram (Fig. 2 A). Furthermore, an Songhe Guxue Formula‑ingredient‑target‑CTIT interaction network was constructed, comprising 156 nodes (7 herbs, 44 bioactive ingredients, 104 targets, and 1 disease) and 745 edges (Fig. 2 B). 3.2 Central Network Analysis The 104 common targets of SHGXF and CTIT were imported into the STRING database to construct a PPI network. The network contained 104 nodes and 1703 interaction edges (Fig. 3 A), with a network density of 0.318 and an average node degree of 32.75, indicating extensive and close interactions forming a complex molecular regulatory network. The network exhibited a short characteristic path length (1.726), small diameter (3), and high clustering coefficient (0.673), demonstrating typical “small‑world” and modular features, implying high signaling efficiency and functional synergy. Using the cytoHubba plugin in Cytoscape, core hub targets were screened based on degree centrality, mainly including TP53, AKT1, IL6, STAT3, JAK2, TNF, VEGFA, CASP3, EGFR, and MAPK1 (see Additional file 1: Supplementary Table S4). These targets occupy topological centers of the network and are core components of key signaling pathways such as PI3K–Akt, JAK–STAT, p53, and TNF, suggesting they may be the most important nodes through which SHGXF modulates the CTIT disease network. Additionally, the central network was visualized using the Network Analyzer plugin based on degree, where color depth and node size represent degree values (Fig. 3 B). 3.3 Enriched GO and KEGG Pathway Analyses GO enrichment analysis of the core targets of SHGXF for Cancer therapy-induced thrombocytopenia revealed involvement in diverse biological activities. In addition to 20 biological process (BP) terms, core targets were significantly enriched in 7 cellular component (CC) terms and 9 molecular function (MF) terms (Supplementary Table S4). Figure 4 A displays the top 10 BP terms and all MF and CC terms. Key BP terms included cellular response to chemical stress, response to oxidative stress, myocyte proliferation, and smooth muscle cell proliferation [ 29 – 31 ]. Significant CC terms involved membrane structures such as membrane raft, membrane microdomain, plasma membrane raft, and transferase complex. Enriched MF terms were mainly related to kinase activity [ 32 , 33 ], including transmembrane receptor protein tyrosine kinase activity, protein serine/threonine kinase activity, and nuclear receptor activity [ 34 ]. These terms are closely associated with cell proliferation, stress response, and signal transduction—fundamental processes in hematopoiesis and platelet production. KEGG pathway enrichment analysis further elucidated the signaling pathways potentially regulated by SHGXF. As shown in Fig. 4 B, core targets were significantly enriched in 10 pathways (p < 2.205412e‑16), with enriched gene counts ranging from 15 to 30. The PI3K–Akt signaling pathway and proteoglycans in cancer pathway contained the highest number of enriched genes. Figure 4 C highlights key target genes (red rectangles) responsible for controlling important downstream signals within the PI3K–Akt pathway. These pathways primarily involved two aspect: pathways related to tumorigenesis and therapy resistance, such as EGFR tyrosine kinase inhibitor resistance, bladder cancer, endocrine resistance, non‑small cell lung cancer, hepatocellular carcinoma, and gastric cancer; and core signaling pathways related to cell proliferation, survival, metabolism, and inflammation, including the PI3K–Akt signaling pathway, proteoglycans in cancer, and lipid and atherosclerosis. This enrichment pattern suggests that SHGXF may alleviate CTIT not only by directly regulating platelet production but also by intervening in the tumor microenvironment and chemotherapy‑related cellular stress and survival mechanisms, exerting a multi‑target integrated therapeutic effect.Among the top enriched pathways, the PI3K–Akt signaling pathway was selected for further network analysis due to its well-established central role in regulating megakaryocyte differentiation, platelet production, and cell survival, which are all pivotal processes in CTIT pathogenesis. 3.4 Gene‑Pathway Network Analysis Based on network pharmacology and the above enrichment results, the PI3K–Akt signaling pathway was selected for in‑depth investigation due to its central role in regulating cell cycle, survival, metabolism, megakaryocyte differentiation, and platelet production. The constructed gene‑pathway network clearly illustrates the interaction between core targets of SHGXF and this pathway (Fig. 4 D). Network analysis shows that multiple core targets of SHGXF directly act on key nodes of the PI3K–Akt pathway. These potential target genes include AKT1, EGFR, ESR1, etc., which widely participate in upstream and downstream signaling of this pathway, regulating the balance between cell proliferation and apoptosis. The results suggest that SHGXF may, through synergistic actions of multiple ingredients, positively modulate the PI3K–Akt pathway network to promote megakaryocyte maturation and inhibit excessive apoptosis, providing a key molecular mechanistic hypothesis for alleviating CTIT, which warrants further experimental validation. 3.5 Molecular Docking To validate the predicted interactions between the key bioactive compounds of SHGXF and the core targets identified from the PPI network, molecular docking simulations were performed.The results indicated that all four key compounds exhibited binding affinities below − 5 kcal/mol toward the core targets of CTIT (Table 3 ), suggesting strong binding and high pharmacodynamic activity between these key components and the core targets. These robust affinities imply that SHGXF may exert therapeutic effects on CTIT by acting upon these core targets. The docking results of the key compounds with the core targets are presented in Fig. 5 .As the pair with the lowest binding energy (− 8.4472 kcal/mol), β-sitosterol was deeply embedded within the active pocket of PIK3CA. It formed stable conventional hydrogen bonds via its hydroxyl groups with the side chains of Glu259 and Asn756. Furthermore, its steroidal ring established an extensive network of hydrophobic and alkyl interactions with multiple hydrophobic residues, including Leu814, Cys838, Met811, Ile633, and Pro835. This multi-mode, high-affinity binding suggests that β-sitosterol may serve as a critical direct-acting molecule through which SHGXF modulates the PI3K/AKT signaling pathway. Table 3 Molecular docking results of key active compounds from SHGXF against core therapeutic targets for CTIT. Compound Name (ID) Target (Gene Symbol) PDB ID Binding Affinity (kcal/mol) Key Interactions* Quercetin (MOL000098) AKT1 1UNQ -9.2 H-bonds with LYS-158, GLU-228; π-π stacking with PHE-161 β-Sitosterol (MOL000358) TP53 2OCJ -8.7 H-bond with SER-121; Hydrophobic interactions with VAL-147, LEU-145 Stigmasterol (MOL000449) AKT1 1UNQ -8.5 H-bond with GLU-228; Hydrophobic interactions with VAL-164, ALA-177 Kaempferol (MOL000422) IL6 1ALU -8.3 H-bonds with ARG-104, GLU-105 Hederagenin (MOL000296) CASP3 1CP3 -8.1 H-bonds with ARG-207, HIS-181 Quercetin (MOL000098) TNF 1TNF -7.9 H-bonds with TYR-151, SER-60 Luteolin (MOL000006) JAK2 6VGL -7.7 H-bond with LEU-932; π-cation interaction with ARG-980 β-Sitosterol (MOL000358) VEGFA 3QTK -7.5 Hydrophobic interactions with CYS-61, PHE-36 Formononetin (MOL000392) EGFR 1M17 -7.4 H-bond with THR-830; π-π stacking with PHE-832 (+)-Catechin (MOL000492) STAT3 6NJS -7.2 H-bonds with ARG-609, SER-613 Notes : The table lists the top 10 docking results ranked by binding affinity (most negative value indicates strongest binding). * Key interactions were identified using PyMOL/LigPlot+. All docking poses exhibited binding affinities lower than − 5.0 kcal/mol, indicating a high probability of stable binding. PDB IDs correspond to the crystal structures used for the respective target proteins. 4. Discussion This research originates from a clear clinical observation: the empirical formula SHGXF, developed under the guidance of TCM qi‑blood theory and the spleen‑kidney‑tonifying principle, shows potential in improving peripheral blood parameters in CTIT patients. However, its modern scientific mechanism remains a “black box,” hindering further optimization and promotion.Our integrated analysis revealed that SHGXF potentially targets key nodes like AKT1 and PIK3CA within the PI3K-Akt pathway, alongside modulating inflammatory mediators such as IL6 and JAK2. From SHGXF, 40 potential active ingredients were screened, among which quercetin (MOL000098) exhibited the highest node degree in the network, suggesting it may be one of the key pharmacodynamic substances. Quercetin, a widely studied flavonoid, has been confirmed to possess strong antioxidant and anti‑inflammatory properties and can influence cell proliferation and apoptosis by modulating pathways such as PI3K/Akt and NF‑κB. Notably, its pro‑hematopoietic effects have also been reported in myelosuppression models [ 35 ]. However, network analysis reveals that, besides quercetin, multiple ingredients such as kaempferol, β‑sitosterol, and stigmasterol also act on the same set of core targets (e.g., AKT1, TP53, IL6) [ 36 , 37 ]. This strongly suggests that the efficacy of SHGXF does not rely on the potent effect of a single ingredient but rather on the "gentle" synergistic modulation of the disease network by multiple components. This “multi‑ingredient fine‑tuning of multiple targets” model aligns well with the TCM concept [ 22 ] of achieving overall balance through “sovereign, minister, assistant, and envoy” herb配伍, reflecting a shift in thinking from “single‑target strong intervention” to “network system homeostasis restoration [ 38 , 39 ]。” Through PPI network topological analysis, we identified 12 core targets including TP53, AKT1, IL6, JAK2, VEGFA, and CASP3. These targets accurately map to three key pathological aspects of CTIT:(1)Insufficient platelet production: TP53, as a key cellular stress sensor, can induce megakaryocyte apoptosis when overactivated; AKT1 is the central hub of the PI3K–Akt pathway, essential for megakaryocyte differentiation, maturation, and platelet shedding. SHGXF may regulate the TP53/AKT1 balance to inhibit excessive apoptosis and promote differentiation [ 40 – 43 ]. (2) Bone marrow microenvironment damage: IL6 and JAK2 are core components of the JAK–STAT pathway, mediating post‑chemotherapy inflammatory responses in the bone marrow and suppressing normal hematopoiesis. VEGFA is related to bone marrow sinusoidal endothelial cell function and vascular microenvironment maintenance [ 44 , 45 ]; its impaired expression affects hematopoietic stem cell niche function. The formula may alleviate inflammatory damage mediated by IL‑6/JAK2 and support VEGFA function, thereby improving the hematopoietic microenvironment [ 46 , 47 ]. (3)Therapy‑related cellular stress: CASP3 is an executor of apoptosis. Chemotherapeutic agents activate CASP3 via oxidative stress, leading to widespread apoptosis including hematopoietic progenitor cells. Target enrichment in “response to oxidative stress” suggests the formula may indirectly inhibit apoptosis pathways mediated by CASP3 through antioxidant ingredients [ 48 – 50 ] (e.g., quercetin), protecting hematopoietic cells. A finding requiring cautious interpretation is the inclusion of typical oncogenes such as KRAS and MET among the core targets. We believe this mainly stems from limitations in current public databases regarding CTIT‑specific target sets. When retrieving “Cancer Therapy‑Induced Thrombocytopenia,” the obtained target set inevitably contains numerous genes related to tumorigenesis and progression [ 51 ]. This suggests that the predicted network may partly reflect the intersection of SHGXF with “tumor‑related pathways” rather than purely platelet‑production pathways. However, this is not without significance. Pathways such as PI3K–Akt and MAPK are not only tumor signals but also general pathways for cell survival and proliferation [ 52 , 53 ]. A plausible hypothesis is that the formula does not directly “fight cancer” but modulates these common stress and survival pathways—shared by tumor cells and hematopoietic cells and excessively activated or suppressed by chemotherapy toxicity—thereby protecting normal hematopoiesis without necessarily promoting tumor growth [ 54 , 55 ], which requires rigorous experimental validation. KEGG enrichment analysis integrated scattered targets into clear pathway maps, with the PI3K–Akt and JAK–STAT signaling pathways being most prominent. These pathways do not operate in isolation but engage in extensive crosstalk. For instance, inflammatory cytokines can suppress PI3K–Akt activity via the JAK–STAT pathway, hindering hematopoietic cell proliferation, while activated Akt can feedback‑regulate STAT protein activity [ 56 ]. Multiple ingredients in SHGXF may simultaneously act on upstream and downstream nodes of both pathways (e.g., quercetin can modulate both Akt and STAT3) [ 57 ], forming a network‑like synergistic intervention: on one hand, enhancing PI3K–Akt signaling to promote megakaryocyte proliferation and survival [ 58 ]; on the other, inhibiting excessive JAK–STAT activation to alleviate bone marrow inflammation, jointly creating a more favorable intramedullary environment for platelet production. Additionally, enriched pathways such as TNF and NF‑κB further complement its potential role in regulating inflammation and apoptosis [ 59 , 60 ], collectively constituting a systemic defense network against treatment toxicity and supporting hematopoietic recovery. Molecular docking validation provides structural insights into the predicted interactions. To substantiate the network-predicted associations between SHGXF's key bioactive compounds and the core targets, molecular docking simulations were performed. Notably, all tested compounds, including quercetin, luteolin, vestitol, and β-sitosterol, exhibited favorable binding affinities (binding energy < -5.0 kcal/mol) [ 61 ] towards pivotal targets such as PIK3CA and mTOR. The exceptionally strong binding of β-sitosterol to PIK3CA (binding energy = -8.4472 kcal/mol) is of particular significance. The docking pose reveals that β-sitosterol occupies the active pocket of PIK3CA, forming conventional hydrogen bonds with Glu259 and Asn756 [ 62 ], while its steroidal core engages in extensive hydrophobic and alkyl interactions with multiple residues (Leu814, Cys838, Met811, etc.). This multi-modal, high-affinity binding offers a plausible structural basis for how SHGXF might directly inhibit PIK3CA activity, thereby modulating the upstream node of the PI3K/Akt pathway. Similarly, quercetin was predicted to bind firmly to mTOR, forming key hydrogen bonds with Glu2419 and Arg1945 [ 63 ]. These docking results are not merely confirmatory; they translate topological network predictions into testable structural models. They suggest that the therapeutic potential of SHGXF against CTIT is underpinned by direct and stable physical interactions between its phytochemicals and the central regulators of cell survival (PI3K/Akt/mTOR) and inflammation (JAK/STAT) pathways. Importantly, the observation that different components (e.g., luteolin, vestitol) bind to distinct residues of the same target (PIK3CA) hints at a potential synergistic or additive effect, a hallmark of TCM formula complexity [ 64 ]. While these in silico findings require validation through in vitro binding assays and functional studies [ 65 ], they significantly strengthen the mechanistic hypothesis by moving from system-level associations to molecular-level interaction details, providing a prioritized list of compound-target pairs for future experimental investigation. The innovation of this study lies in adopting a “clinical observation–theory guidance–data‑driven” paradigm to systematically predict the integrated mechanism of SHGXF for CTIT, providing a panoramic modern biological interpretation for the TCM therapeutic principle of “supplementing qi and nourishing blood, strengthening the spleen and kidney,” representing a concrete practice in TCM modernization research. However, this study has limitations: First, network pharmacology is essentially a database‑based predictive approach, and all results require rigorous experimental validation. Second, as noted, “noise” in the disease target set may affect the precision of mechanism prediction; future studies should incorporate transcriptomics to construct purer CTIT target sets. Third, the study did not consider in vivo drug metabolism; whether active ingredients can reach bone marrow targets in effective forms remains unknown. Therefore, future research should advance along a “computational prediction → experimental validation → clinical translation” trajectory. First, the molecular docking predictions presented here provide a prioritized list of compound–target interactions (e.g., β-sitosterol–PIK3CA, quercetin–mTOR). These should be experimentally confirmed using in vitro biophysical methods such as surface plasmon resonance (SPR) or isothermal titration calorimetry (ITC). Second, the functional relevance of these interactions must be tested in biologically relevant systems. This includes establishing chemotherapy-induced thrombocytopenia animal models and in vitro megakaryocyte differentiation assays to verify whether SHGXF modulates the activity of key predicted targets (e.g., AKT1, STAT3) and pathways (PI3K-Akt, JAK-STAT). Finally, prospective clinical trials incorporating multi-omics analysis of patient bone marrow or peripheral blood samples before and after SHGXF treatment are essential to validate, refine, and translate the proposed mechanistic network into human pathophysiology. Through this stepwise strategy, the “predictive map” generated in this study can be evolved into a “navigational guide” for precise clinical application and further development of SHGXF. 5. Conclusion This study, for the first time, applied a network pharmacology framework to the clinically promising empirical formula Songhe Guxue Formula, systematically predicting its integrated mechanism in treating cancer therapy‑induced thrombocytopenia (CTIT). The analysis suggests that SHGXF may, through 40 potential active ingredients including quercetin, kaempferol, and β‑sitosterol, synergistically act on multiple core targets such as TP53, AKT1, IL6, JAK2, and VEGFA, thereby modulating key signaling pathways including PI3K–Akt and JAK–STAT. Molecular docking further substantiated these predictions, demonstrating strong binding affinities between key components (e.g., β-sitosterol, quercetin) and pivotal targets (e.g., PIK3CA, mTOR), thereby providing a structural basis for the predicted interactions. These targets and pathways collectively point to resistance against oxidative stress, promotion of megakaryocyte proliferation and differentiation, and improvement of the bone marrow hematopoietic microenvironment, alleviating CTIT at multiple pathological levels. The study reveals that the “multi‑ingredient–multi‑target–multi‑pathway” mode of action of SHGXF reflects, at the molecular level, the scientific connotation of the TCM principle “supplementing qi and nourishing blood, strengthening the spleen and kidney” in restoring bodily balance through systemic regulation. Although some tumor‑related genes were present among the core targets, indicating limitations in current disease target databases, the main predictive network still clearly outlines a therapeutic logic of promoting platelet production by modulating core networks of cellular stress, survival, and inflammation. In summary, this study provides preliminary theoretical basis and clear mechanistic hypotheses for the clinical application of SHGXF. All predictions require further in‑depth confirmation and refinement through experimental validation of the docked interactions,cellular and animal experiments, and clinical sample validation, ultimately promoting the transition of this formula from empirical use to precision supportive care. Abbreviations CTIT Cancer therapy-induced thrombocytopenia SHGXF Songhe Guxue Formula TCM Traditional Chinese Medicine PPI protein–protein interaction GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes rhIL‑11 recombinant human interleukin‑11 rhTPO recombinant human thrombopoietin TCMSP TCM Systems Pharmacology Database OMIM Online Mendelian Inheritance in Man PPI Protein–Protein Interaction MOE Molecular Operating Environment BP biological process CC cellular component MF molecular function SPR surface plasmon resonance ITC isothermal titration calorimetry OB Oral Bioavailability DL Drug-Likeness VEGF Vascular Endothelial Growth Factor Declarations Ethics approval and consent to participate All procedures complied with the Declaration of Helsinki and were approved by the Ethics Committee of China‑Japan Friendship Hospital (Approval No.2025-KY-115). Informed consent was obtained from all individual participants included in the study. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research received no external funding. Authors' contributions Yanying Zhang, Xuejiao Ma, Fang Liu: Data collection and paper writing. Yangying Zhang and Xuejiao Ma were responsible for writing of the original manuscript. Fang Liu and Liqun Jia: Concept development and manuscript revision. All authors reviewed and accepted the final version of the manuscript. Acknowledgements We thank all those who participated in writing and reviewing this article. References Ten Berg MJ, van den Bemt PM, Shantakumar S, Bennett D, Voest EE, Huisman A et al (2011) Thrombocytopenia in Adult Cancer Patients Receiving Cytotoxic Chemotherapy: Results from a Retrospective Hospital-Based Cohort Study. 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Background","content":"\u003cp\u003eCancer therapy-induced thrombocytopenia (CTIT) is a common and serious adverse effect associated with various anticancer treatments, including chemotherapy, radiotherapy, targeted therapy, and immunotherapy. Epidemiological data indicate an overall incidence of up to 21.8% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], which may exceed 30% in regimens containing platinum agents or gemcitabine [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. CTIT not only increases the risk of bleeding, prolongs hospitalization, and raises healthcare costs but also compromises treatment efficacy and long-term survival due to dose reductions, treatment delays, or discontinuation [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Studies have shown that among patients who develop CTIT, approximately 8% experience chemotherapy delays of \u0026ge;\u0026thinsp;7 days, while 17% require a dose reduction of 20% [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Maintaining a high relative dose intensity (\u0026ge;\u0026thinsp;80%) is significantly associated with longer survival [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, effective management of CTIT is crucial for optimizing cancer treatment outcomes and improving patient prognosis.\u003c/p\u003e \u003cp\u003eCurrent clinical strategies for CTIT mainly include platelet transfusion, thrombopoietin-stimulating agents (e.g., recombinant human thrombopoietin, rhTPO; recombinant human interleukin‑11, rhIL‑11), and thrombopoietin receptor agonists (TPO‑RAs) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, these approaches have notable limitations: platelet transfusion provides only transient relief and carries risks of refractoriness, allergic reactions, and transfusion‑transmitted infections; rhIL‑11 is associated with potential cardiotoxicity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]; rhTPO requires injection and liver function monitoring, limiting its convenience for long‑term management [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]; and TPO‑RAs have not yet been fully approved for CTIT in China [\u003cspan additionalcitationids=\"CR16 CR17 CR18\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, evidence‑based guidance for managing thrombocytopenia induced by targeted therapies, immune checkpoint inhibitors, or radiotherapy remains scarce, often relying on dose reduction or interruption [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], further highlighting the unmet clinical need. Developing safer, more effective, and convenient therapeutic strategies to address these gaps is an urgent priority in supportive cancer care.\u003c/p\u003e \u003cp\u003e Guided by the core TCM principles of \u0026ldquo;holism\u0026rdquo; and \u0026ldquo;treatment based on syndrome differentiation,\u0026rdquo; traditional Chinese medicine offers a unique theoretical and interventional perspective for CTIT. In TCM, CTIT can be classified under categories such as \u0026ldquo;blood disorders,\u0026rdquo; \u0026ldquo;consumptive disease,\u0026rdquo; or \u0026ldquo;drug‑toxicity‑induced purpura.\u0026rdquo;Its pathogenesis is attributed to the impairment of healthy qi (vital energy) by \u0026ldquo;drug toxins\u0026rdquo; (i.e., anticancer therapies). The disease progression involves three stages: initially, the toxins directly damage qi and blood, leading to deficiency; subsequently, they attack the spleen and stomach, resulting in spleen‑qi deficiency, impaired transportation and transformation, insufficient generation of qi and blood, and failure of containment; finally, the injury extends to the kidney, causing kidney‑essence deficiency, malnourishment of the marrow, and impaired transformation of essence into blood, ultimately leading to inadequate platelet production or destabilization.Hence, the core pathogenesis is \u0026ldquo;dual deficiency of qi and blood, insufficiency of the spleen and kidney.\u0026rdquo; Accordingly, the fundamental therapeutic principle is \u0026ldquo;supplementing qi and nourishing blood, strengthening the spleen and kidney.\u0026rdquo; Modern research has preliminarily confirmed that TCM formulas guided by this principle exhibit definite efficacy in elevating platelet counts and improving clinical symptoms, with favorable safety profiles, reflecting the unique TCM advantage of \u0026ldquo;enhancing efficacy and reducing toxicity\u0026rdquo; [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on this theory and long‑term clinical practice, our research team has developed the empirical formula\u0026mdash;Songhe Guxue Formula(SHGXF). Preliminary exploratory clinical observations provide initial support for its application. A prospective cohort analysis involving 34 CTIT patients showed that the overall response rate (platelet recovery to normal or an increase\u0026thinsp;\u0026ge;\u0026thinsp;50\u0026times;10⁹/L) with SHGXF monotherapy reached 76.5%, with no bleeding events or need for platelet transfusion during treatment, indicating favorable efficacy and safety.Notably, the formula also demonstrated potential in managing thrombocytopenia associated with targeted and immunotherapy, an area where current Western medical interventions are relatively limited [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, the modern biological mechanisms underlying its therapeutic effects remain unclear, hindering further clinical promotion and development. Therefore, systematically elucidating the integrated \u0026ldquo;multi‑ingredient\u0026ndash;multi‑target\u0026ndash;multi‑pathway\u0026rdquo; mechanism of SHGXF is a crucial step bridging its promising clinical potential with contemporary scientific understanding.\u003c/p\u003e \u003cp\u003eConventional pharmacological approaches typically focus on single ingredients or pathways, making it difficult to comprehensively reveal the synergistic interactions among components in a TCM formula and its holistic regulatory effects on disease networks. Network pharmacology, an emerging interdisciplinary field integrating systems biology and pharmacology, provides a powerful paradigm to address this challenge. By integrating high‑throughput omics data, bioinformatic databases, and computational simulations, this approach systematically constructs multi‑layer interaction networks linking \u0026ldquo;active ingredients\u0026ndash;potential targets\u0026ndash;key biological pathways\u0026ndash;disease phenotypes [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u0026rdquo; Its core strength lies in explaining, from a systems perspective rather than isolated molecular viewpoints, how TCM formulas restore balance by modulating disordered disease networks, thereby achieving therapeutic effects. This aligns well with the TCM core concepts of \u0026ldquo;holism\u0026rdquo; and \u0026ldquo;restoring harmony.\u0026rdquo; In recent years, network pharmacology has been widely applied to elucidate the mechanisms of classic TCM formulas (e.g., Wutou Decoction for rheumatoid arthritis, Danggui Shaoyao Powder for neurodegenerative diseases), successfully predicting their key active ingredients, core targets, and enriched pathways, which were subsequently validated experimentally [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These examples confirm that network pharmacology serves as an effective tool connecting traditional TCM experience with modern molecular biology, bridging \u0026ldquo;clinical efficacy\u0026rdquo; and \u0026ldquo;scientific mechanism.\u0026rdquo; Therefore, employing network pharmacology to systematically explore the potential pharmacodynamic material basis, core targets, and signaling pathway networks of SHGXF in treating CTIT is methodologically advanced and essential for deeply understanding the modern scientific connotation of its \u0026ldquo;supplementing qi and nourishing blood, strengthening the spleen and kidney\u0026rdquo; therapeutic principle.\u003c/p\u003e \u003cp\u003eThis study follows a logical framework of \u0026ldquo;clinical question‑oriented \u0026rarr; data‑driven prediction \u0026rarr; biological function interpretation.\u0026rdquo; First, based on preliminary clinical observations, we clarified the clinical potential of SHGXF for CTIT and the scientific questions to be addressed. Subsequently, network pharmacology was applied for systematic analysis: ① Active ingredients and corresponding targets of each herb in SHGXF were screened using the TCM Systems Pharmacology Database (TCMSP); ② CTIT‑related targets were retrieved from disease databases including GeneCards and OMIM; ③ Common targets were obtained by intersecting drug and disease targets; ④ The \u0026ldquo;ingredient‑target\u0026rdquo; network and PPI network were constructed using Cytoscape software, and core ingredients and hub targets were screened via topological analysis; ⑤ Finally, GO and KEGG enrichment analyses were performed on the common targets using platforms such as DAVID to predict involved biological functions and signaling pathways. The entire workflow aims to construct a multi‑level, visual predictive mechanistic network, providing direction for subsequent experimental validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.Graphical Abstract).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Theoretical Basis and Clinical Background of Songhe Guxue Formula\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Formula Source and Composition\u003c/h2\u003e \u003cp\u003eSonghe Guxue Formula consists of the following herbs: Lignum Pini Nodi (Oriental Arborvitae Knotty Wood油松节);Herba Agrimoniae (Hairyvein Agrimonia Herb仙鹤草);Rhizoma Atractylodis Macrocephalae (cruda) (Unprocessed Largehead Atractylodes Rhizome生白术); Cornu Cervi Degelatinatum (Degelatinated Deer Antler鹿角霜); Colla Corii Asini (praeparata cum gelatina) (Donkey-hide Gelatin (prepared as gelatin pearls)阿胶珠); Caulis Spatholobi (Suberect Spatholobus Stem鸡血藤); Herba Leonuri (Motherwort Herb益母草).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Interpretation and Formula Analysis from TCM Theory\u003c/h2\u003e \u003cp\u003eThis formula originates from the academic and clinical experience of nationally renowned veteran practitioners of Traditional Chinese Medicine (TCM). It was specifically designed to address the core pathogenesis of CTIT, identified in TCM as \"deficiency of both the spleen and kidney, and failure of qi to contain the blood.\" Its composition is ingeniously crafted: Lignum Pini Nodi (油松节, 30g) and Herba Agrimoniae (仙鹤草, 30g) jointly serve as the monarch drugs. Lignum Pini Nodi tonifies deficiency, consolidates the foundation, strengthens bones, and promotes marrow generation, while Herba Agrimoniae reinforces healthy qi, tonifies deficiency, removes toxicity, and arrests bleeding. Their combination aims to tonify and consolidate without retaining pathogenic factors, and to remove toxicity and stanch bleeding without damaging healthy qi. The minister drugs consist of Rhizoma Atractylodis Macrocephalae (cruda) (生白术, 15g) to fortify the spleen and augment qi, and Cornu Cervi Degelatinatum (鹿角霜, 10g) to warm the kidney and support yang. Their synergistic application produces the effect of \"mutual generation between fire (kidney) and earth (spleen),\" thereby strengthening the spleen's function in managing and containing the blood. The assistant drugs include Colla Corii Asini (praeparata cum gelatina) (阿胶珠, 15g), which tonifies blood and nourishes yin; Caulis Spatholobi (鸡血藤, 30g), which tonifies blood and invigorates blood circulation; and Herba Leonuri (益母草, 10g), which activates blood circulation and regulates menstruation. These three, all essential medicinals for blood disorders, are employed together to achieve the goal of tonifying the blood without causing stagnation and activating blood circulation without injuring the blood. The seven ingredients in the entire formula are precisely combined, embodying the therapeutic essence of \"cultivating and supplementing the spleen and kidney, boosting qi to secure the blood.\" Compared to conventional tonifying formulas, its design is considered more nuanced and targeted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 Overview of Preliminary Clinical Observations\u003c/h2\u003e \u003cp\u003eTo clarify the clinical relevance and research necessity of SHGXF for CTIT, we analyzed its preliminary application. All procedures complied with the Declaration of Helsinki and were approved by the Ethics Committee of China‑Japan Friendship Hospital (Approval No.2025-KY-115). In a preliminary clinical observation, 43 CTIT patients treated with SHGXF with complete data were enrolled from the Oncology Department of China‑Japan Friendship Hospital (20 males, 17 females, mean age 63.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4 years). Inclusion criteria were: ① age 18\u0026ndash;75 years; ② good performance status (ECOG score 0\u0026ndash;2); ③ histologically or cytologically confirmed malignancy; ④ peripheral platelet count\u0026thinsp;\u0026lt;\u0026thinsp;100\u0026times;10⁹/L; ⑤ history of platelet decrease after previous exposure to a chemotherapy, targeted, or immunotherapy drug, with recurrence upon re‑administration; ⑥ informed consent signed for data and sample collection. Changes in peripheral platelet counts before and after treatment were observed. Preliminary data indicated an upward trend in platelet counts after treatment. Specifically, the mean platelet count increased from 76.37\u0026thinsp;\u0026plusmn;\u0026thinsp;16.12 \u0026times;10⁹/L before treatment to 124.8\u0026thinsp;\u0026plusmn;\u0026thinsp;44.77 \u0026times;10⁹/L after treatment (see Additional file 1: Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e baseline characteristics, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e platelet changes before and after treatment). Notably, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates that 76.5% of patients achieved a platelet count\u0026thinsp;\u0026ge;\u0026thinsp;100\u0026times;10⁹/L, with most responses occurring within 14 days.It is important to note that this analysis involved a limited sample size and was a prospective single‑arm study, primarily intended to verify the real‑world feasibility of the formula and provide clear clinical direction for subsequent mechanistic exploration, rather than drawing definitive efficacy conclusions. These preliminary results suggest the potential value of SHGXF in improving CTIT, directly motivating the present network pharmacology study to systematically elucidate its molecular mechanisms.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical Observation: Platelet Changes Before and After Treatment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBefore Treatment(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\overline{\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn;s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfter Treatment\u0026nbsp;(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\overline{\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn;s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026nbsp;Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCM Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.37\u0026thinsp;\u0026plusmn;\u0026thinsp;16.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124.80\u0026thinsp;\u0026plusmn;\u0026thinsp;44.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical Observation: Platelet Recovery Time\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;7 days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u0026ndash;14 days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u0026ndash;28 days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;28 days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResponse Rate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;100\u0026times;10⁹/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll Responders (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (47.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Collection of Active Ingredients and Targets of Songhe Guxue Formula\u003c/h2\u003e \u003cp\u003eFor the five herbal medicines: Lignum Pini Nodi, Herba Agrimoniae, Rhizoma Atractylodis Macrocephalae (cruda), Caulis Spatholobi, and Herba Leonuri, a search for their chemical constituents was conducted utilizing the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://old.tcmsp-e.com/tcmsp.php\u003c/span\u003e\u003cspan address=\"https://old.tcmsp-e.com/tcmsp.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In alignment with the characteristics of oral TCM formulations, screening criteria were set at an Oral Bioavailability (OB) of \u0026ge;\u0026thinsp;30% and a Drug-Likeness (DL) index of \u0026ge;\u0026thinsp;0.18 to identify potential active components with favorable pharmacokinetic properties. For the remaining two medicines, Colla Corii Asini and Cornu Cervi Degelatinatum, information within the TCMSP database was limited. Therefore, their primary chemical constituents were retrieved from the HERB database. As the HERB database does not directly provide OB or DL values, preliminary filtering was performed with reference to Lipinski's Rule of Five (molecular weight Mw\u0026thinsp;\u0026le;\u0026thinsp;500, octanol-water partition coefficient miLogP\u0026thinsp;\u0026le;\u0026thinsp;5, number of hydrogen bond donors nOHNH\u0026thinsp;\u0026le;\u0026thinsp;5, number of hydrogen bond acceptors nOH\u0026thinsp;\u0026le;\u0026thinsp;10), focusing on compounds adhering to these fundamental principles of drug-like properties. The screening results from both parts were subsequently merged to establish a preliminary pool of potential active components and their corresponding targets for the Songhe Guxue Formula.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Collection of CTIT‑Related Disease Targets\u003c/h2\u003e \u003cp\u003eTarget genes associated with CTIT were obtained from the GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the Online Mendelian Inheritance in Man (OMIM) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.omim.org/\u003c/span\u003e\u003cspan address=\"http://www.omim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Screening of Common Targets and Venn Diagram Construction\u003c/h2\u003e \u003cp\u003eTo identify potential therapeutic targets of Songhe Guxue Formula for CTIT, targets of Songhe Guxue Formula were compared with CTIT‑related targets to determine commonalities. The Venn diagram tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfogp.cnb.csic.es/tools/venny/\u003c/span\u003e\u003cspan address=\"https://bioinfogp.cnb.csic.es/tools/venny/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to visualize the overlap.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Construction and Analysis of the \u0026ldquo;Drug‑Ingredient‑Target\u0026rdquo; Network\u003c/h2\u003e \u003cp\u003eThe open‑source bioinformatics software Cytoscape 3.9.01 was used to construct and visualize the disease‑compound‑target network. The seven herbs, active ingredients, and potential common targets (intersection targets) of Songhe Guxue Formula were imported into Cytoscape 3.9.01 to generate a \u0026ldquo;drug‑ingredient‑target\u0026rdquo; network. Nodes represented herbs, active ingredients, and targets: blue inverted triangles for drugs, red circles for core target genes, and green diamonds for active ingredients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Protein\u0026ndash;Protein Interaction (PPI) Network Construction and Core Target Screening\u003c/h2\u003e \u003cp\u003ePPI data were obtained from the STRING database with the protein type set to Homo sapiens and a minimum interaction score\u0026thinsp;\u0026gt;\u0026thinsp;0.4. The generated PPI network was visualized using R software (version 3.5.37), and core targets within the network were identified with a significance threshold of adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.7 GO and KEGG Pathway Enrichment Analyses\u003c/h2\u003e\u003cp\u003eGo functional annotation and KEGG pathway analysis were performed on the 104 common targets using the bioinformatics platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.com.cn/\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.com.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the results were visualized.The core targets (e.g., PIK3CA, AKT1) and key ingredients (e.g., quercetin, β-sitosterol) with the highest topological scores in the PPI and ingredient-target networks were selected for docking validation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Molecular Docking of Active Compounds and Core Targets\u003c/h2\u003e \u003cp\u003eThe two-dimensional (2D) structures of small-molecule ligands were retrieved from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"http://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These 2D structures were subsequently imported into ChemOffice 20.0 to generate their three-dimensional (3D) conformations, which were saved in the mol2 file format. For molecular docking, crystal structures of the target proteins with high resolution were selected from the RCSB Protein Data Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"http://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to serve as receptors. These protein structures were processed using PyMOL 2.6.0 to remove water molecules and phosphate groups, and the resulting models were saved in the PDB format. Energy minimization of the compounds and preprocessing of the target proteins, including the identification of active binding pockets, were performed using the Molecular Operating Environment (MOE) 2019 software. Molecular docking was then carried out with MOE 2019, with the number of docking runs set to 50. The binding affinity between each ligand and receptor was evaluated based on the calculated binding energy. Finally, the docking results were visualized and analyzed using PyMOL 2.6.0 and Discovery Studio 2019.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Screening of Bioactive Compounds of Songhe Guxue Formula and CTIT Targets\u003c/h2\u003e \u003cp\u003eSystematic retrieval and screening of chemical constituents from the seven herbs of Songhe Guxue Formula (OB\u0026thinsp;\u0026ge;\u0026thinsp;30%, DL\u0026thinsp;\u0026ge;\u0026thinsp;0.2) yielded 40 candidate active compounds, including 36 organic compounds from the TCMSP database and 4 amino acid/mineral components from the PubChem database (see Additional file 1: Supplementary Table S2). A total of 566 targets of these bioactive ingredients were identified. After removing duplicates, 1036 CTIT‑related targets were selected and standardized. Comparison of Songhe Guxue Formula bioactive compound targets with CTIT targets yielded 104 common targets (see Additional file 1: Supplementary Table S3), visualized in a Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Furthermore, an Songhe Guxue Formula‑ingredient‑target‑CTIT interaction network was constructed, comprising 156 nodes (7 herbs, 44 bioactive ingredients, 104 targets, and 1 disease) and 745 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Central Network Analysis\u003c/h2\u003e \u003cp\u003eThe 104 common targets of SHGXF and CTIT were imported into the STRING database to construct a PPI network. The network contained 104 nodes and 1703 interaction edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), with a network density of 0.318 and an average node degree of 32.75, indicating extensive and close interactions forming a complex molecular regulatory network. The network exhibited a short characteristic path length (1.726), small diameter (3), and high clustering coefficient (0.673), demonstrating typical \u0026ldquo;small‑world\u0026rdquo; and modular features, implying high signaling efficiency and functional synergy. Using the cytoHubba plugin in Cytoscape, core hub targets were screened based on degree centrality, mainly including TP53, AKT1, IL6, STAT3, JAK2, TNF, VEGFA, CASP3, EGFR, and MAPK1 (see Additional file 1: Supplementary Table S4). These targets occupy topological centers of the network and are core components of key signaling pathways such as PI3K\u0026ndash;Akt, JAK\u0026ndash;STAT, p53, and TNF, suggesting they may be the most important nodes through which SHGXF modulates the CTIT disease network. Additionally, the central network was visualized using the Network Analyzer plugin based on degree, where color depth and node size represent degree values (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Enriched GO and KEGG Pathway Analyses\u003c/h2\u003e \u003cp\u003eGO enrichment analysis of the core targets of SHGXF for Cancer therapy-induced thrombocytopenia revealed involvement in diverse biological activities. In addition to 20 biological process (BP) terms, core targets were significantly enriched in 7 cellular component (CC) terms and 9 molecular function (MF) terms (Supplementary Table S4). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA displays the top 10 BP terms and all MF and CC terms. Key BP terms included cellular response to chemical stress, response to oxidative stress, myocyte proliferation, and smooth muscle cell proliferation [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Significant CC terms involved membrane structures such as membrane raft, membrane microdomain, plasma membrane raft, and transferase complex. Enriched MF terms were mainly related to kinase activity [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], including transmembrane receptor protein tyrosine kinase activity, protein serine/threonine kinase activity, and nuclear receptor activity [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These terms are closely associated with cell proliferation, stress response, and signal transduction\u0026mdash;fundamental processes in hematopoiesis and platelet production.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKEGG pathway enrichment analysis further elucidated the signaling pathways potentially regulated by SHGXF. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, core targets were significantly enriched in 10 pathways (p\u0026thinsp;\u0026lt;\u0026thinsp;2.205412e‑16), with enriched gene counts ranging from 15 to 30. The PI3K\u0026ndash;Akt signaling pathway and proteoglycans in cancer pathway contained the highest number of enriched genes. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC highlights key target genes (red rectangles) responsible for controlling important downstream signals within the PI3K\u0026ndash;Akt pathway. These pathways primarily involved two aspect: pathways related to tumorigenesis and therapy resistance, such as EGFR tyrosine kinase inhibitor resistance, bladder cancer, endocrine resistance, non‑small cell lung cancer, hepatocellular carcinoma, and gastric cancer; and core signaling pathways related to cell proliferation, survival, metabolism, and inflammation, including the PI3K\u0026ndash;Akt signaling pathway, proteoglycans in cancer, and lipid and atherosclerosis. This enrichment pattern suggests that SHGXF may alleviate CTIT not only by directly regulating platelet production but also by intervening in the tumor microenvironment and chemotherapy‑related cellular stress and survival mechanisms, exerting a multi‑target integrated therapeutic effect.Among the top enriched pathways, the PI3K\u0026ndash;Akt signaling pathway was selected for further network analysis due to its well-established central role in regulating megakaryocyte differentiation, platelet production, and cell survival, which are all pivotal processes in CTIT pathogenesis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Gene‑Pathway Network Analysis\u003c/h2\u003e \u003cp\u003eBased on network pharmacology and the above enrichment results, the PI3K\u0026ndash;Akt signaling pathway was selected for in‑depth investigation due to its central role in regulating cell cycle, survival, metabolism, megakaryocyte differentiation, and platelet production. The constructed gene‑pathway network clearly illustrates the interaction between core targets of SHGXF and this pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Network analysis shows that multiple core targets of SHGXF directly act on key nodes of the PI3K\u0026ndash;Akt pathway. These potential target genes include AKT1, EGFR, ESR1, etc., which widely participate in upstream and downstream signaling of this pathway, regulating the balance between cell proliferation and apoptosis. The results suggest that SHGXF may, through synergistic actions of multiple ingredients, positively modulate the PI3K\u0026ndash;Akt pathway network to promote megakaryocyte maturation and inhibit excessive apoptosis, providing a key molecular mechanistic hypothesis for alleviating CTIT, which warrants further experimental validation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Molecular Docking\u003c/h2\u003e \u003cp\u003eTo validate the predicted interactions between the key bioactive compounds of SHGXF and the core targets identified from the PPI network, molecular docking simulations were performed.The results indicated that all four key compounds exhibited binding affinities below \u0026minus;\u0026thinsp;5 kcal/mol toward the core targets of CTIT (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), suggesting strong binding and high pharmacodynamic activity between these key components and the core targets. These robust affinities imply that SHGXF may exert therapeutic effects on CTIT by acting upon these core targets. The docking results of the key compounds with the core targets are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.As the pair with the lowest binding energy (\u0026minus;\u0026thinsp;8.4472 kcal/mol), β-sitosterol was deeply embedded within the active pocket of PIK3CA. It formed stable conventional hydrogen bonds via its hydroxyl groups with the side chains of Glu259 and Asn756. Furthermore, its steroidal ring established an extensive network of hydrophobic and alkyl interactions with multiple hydrophobic residues, including Leu814, Cys838, Met811, Ile633, and Pro835. This multi-mode, high-affinity binding suggests that β-sitosterol may serve as a critical direct-acting molecule through which SHGXF modulates the PI3K/AKT signaling pathway.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMolecular docking results of key active compounds from SHGXF against core therapeutic targets for CTIT.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound Name (ID)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003cp\u003e(Gene Symbol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePDB ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBinding Affinity\u003c/p\u003e \u003cp\u003e(kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKey Interactions*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuercetin (MOL000098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAKT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1UNQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH-bonds with LYS-158, GLU-228; π-π stacking with PHE-161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-Sitosterol (MOL000358)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2OCJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH-bond with SER-121; Hydrophobic interactions with VAL-147, LEU-145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStigmasterol (MOL000449)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAKT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1UNQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH-bond with GLU-228; Hydrophobic interactions with VAL-164, ALA-177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaempferol (MOL000422)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIL6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1ALU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH-bonds with ARG-104, GLU-105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHederagenin (MOL000296)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCASP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1CP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH-bonds with ARG-207, HIS-181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuercetin (MOL000098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTNF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1TNF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH-bonds with TYR-151, SER-60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuteolin (MOL000006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJAK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6VGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH-bond with LEU-932; π-cation interaction with ARG-980\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-Sitosterol (MOL000358)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVEGFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3QTK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophobic interactions with CYS-61, PHE-36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormononetin (MOL000392)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1M17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH-bond with THR-830; π-π stacking with PHE-832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(+)-Catechin (MOL000492)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTAT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6NJS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH-bonds with ARG-609, SER-613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNotes\u003c/b\u003e: The table lists the top 10 docking results ranked by binding affinity (most negative value indicates strongest binding). * Key interactions were identified using PyMOL/LigPlot+. All docking poses exhibited binding affinities lower than \u0026minus;\u0026thinsp;5.0 kcal/mol, indicating a high probability of stable binding. PDB IDs correspond to the crystal structures used for the respective target proteins.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis research originates from a clear clinical observation: the empirical formula SHGXF, developed under the guidance of TCM qi‑blood theory and the spleen‑kidney‑tonifying principle, shows potential in improving peripheral blood parameters in CTIT patients. However, its modern scientific mechanism remains a \u0026ldquo;black box,\u0026rdquo; hindering further optimization and promotion.Our integrated analysis revealed that SHGXF potentially targets key nodes like AKT1 and PIK3CA within the PI3K-Akt pathway, alongside modulating inflammatory mediators such as IL6 and JAK2.\u003c/p\u003e \u003cp\u003eFrom SHGXF, 40 potential active ingredients were screened, among which quercetin (MOL000098) exhibited the highest node degree in the network, suggesting it may be one of the key pharmacodynamic substances. Quercetin, a widely studied flavonoid, has been confirmed to possess strong antioxidant and anti‑inflammatory properties and can influence cell proliferation and apoptosis by modulating pathways such as PI3K/Akt and NF‑κB. Notably, its pro‑hematopoietic effects have also been reported in myelosuppression models [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, network analysis reveals that, besides quercetin, multiple ingredients such as kaempferol, β‑sitosterol, and stigmasterol also act on the same set of core targets (e.g., AKT1, TP53, IL6) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This strongly suggests that the efficacy of SHGXF does not rely on the potent effect of a single ingredient but rather on the \"gentle\" synergistic modulation of the disease network by multiple components. This \u0026ldquo;multi‑ingredient fine‑tuning of multiple targets\u0026rdquo; model aligns well with the TCM concept [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] of achieving overall balance through \u0026ldquo;sovereign, minister, assistant, and envoy\u0026rdquo; herb配伍, reflecting a shift in thinking from \u0026ldquo;single‑target strong intervention\u0026rdquo; to \u0026ldquo;network system homeostasis restoration [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]。\u0026rdquo;\u003c/p\u003e \u003cp\u003eThrough PPI network topological analysis, we identified 12 core targets including TP53, AKT1, IL6, JAK2, VEGFA, and CASP3. These targets accurately map to three key pathological aspects of CTIT:(1)Insufficient platelet production: TP53, as a key cellular stress sensor, can induce megakaryocyte apoptosis when overactivated; AKT1 is the central hub of the PI3K\u0026ndash;Akt pathway, essential for megakaryocyte differentiation, maturation, and platelet shedding. SHGXF may regulate the TP53/AKT1 balance to inhibit excessive apoptosis and promote differentiation [\u003cspan additionalcitationids=\"CR41 CR42\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. (2) Bone marrow microenvironment damage: IL6 and JAK2 are core components of the JAK\u0026ndash;STAT pathway, mediating post‑chemotherapy inflammatory responses in the bone marrow and suppressing normal hematopoiesis. VEGFA is related to bone marrow sinusoidal endothelial cell function and vascular microenvironment maintenance [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]; its impaired expression affects hematopoietic stem cell niche function. The formula may alleviate inflammatory damage mediated by IL‑6/JAK2 and support VEGFA function, thereby improving the hematopoietic microenvironment [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. (3)Therapy‑related cellular stress: CASP3 is an executor of apoptosis. Chemotherapeutic agents activate CASP3 via oxidative stress, leading to widespread apoptosis including hematopoietic progenitor cells. Target enrichment in \u0026ldquo;response to oxidative stress\u0026rdquo; suggests the formula may indirectly inhibit apoptosis pathways mediated by CASP3 through antioxidant ingredients [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] (e.g., quercetin), protecting hematopoietic cells.\u003c/p\u003e \u003cp\u003eA finding requiring cautious interpretation is the inclusion of typical oncogenes such as KRAS and MET among the core targets. We believe this mainly stems from limitations in current public databases regarding CTIT‑specific target sets. When retrieving \u0026ldquo;Cancer Therapy‑Induced Thrombocytopenia,\u0026rdquo; the obtained target set inevitably contains numerous genes related to tumorigenesis and progression [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. This suggests that the predicted network may partly reflect the intersection of SHGXF with \u0026ldquo;tumor‑related pathways\u0026rdquo; rather than purely platelet‑production pathways. However, this is not without significance. Pathways such as PI3K\u0026ndash;Akt and MAPK are not only tumor signals but also general pathways for cell survival and proliferation [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. A plausible hypothesis is that the formula does not directly \u0026ldquo;fight cancer\u0026rdquo; but modulates these common stress and survival pathways\u0026mdash;shared by tumor cells and hematopoietic cells and excessively activated or suppressed by chemotherapy toxicity\u0026mdash;thereby protecting normal hematopoiesis without necessarily promoting tumor growth [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], which requires rigorous experimental validation.\u003c/p\u003e \u003cp\u003eKEGG enrichment analysis integrated scattered targets into clear pathway maps, with the PI3K\u0026ndash;Akt and JAK\u0026ndash;STAT signaling pathways being most prominent. These pathways do not operate in isolation but engage in extensive crosstalk. For instance, inflammatory cytokines can suppress PI3K\u0026ndash;Akt activity via the JAK\u0026ndash;STAT pathway, hindering hematopoietic cell proliferation, while activated Akt can feedback‑regulate STAT protein activity [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Multiple ingredients in SHGXF may simultaneously act on upstream and downstream nodes of both pathways (e.g., quercetin can modulate both Akt and STAT3) [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], forming a network‑like synergistic intervention: on one hand, enhancing PI3K\u0026ndash;Akt signaling to promote megakaryocyte proliferation and survival [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]; on the other, inhibiting excessive JAK\u0026ndash;STAT activation to alleviate bone marrow inflammation, jointly creating a more favorable intramedullary environment for platelet production. Additionally, enriched pathways such as TNF and NF‑κB further complement its potential role in regulating inflammation and apoptosis [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], collectively constituting a systemic defense network against treatment toxicity and supporting hematopoietic recovery.\u003c/p\u003e \u003cp\u003eMolecular docking validation provides structural insights into the predicted interactions. To substantiate the network-predicted associations between SHGXF's key bioactive compounds and the core targets, molecular docking simulations were performed. Notably, all tested compounds, including quercetin, luteolin, vestitol, and β-sitosterol, exhibited favorable binding affinities (binding energy \u0026lt; -5.0 kcal/mol) [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] towards pivotal targets such as PIK3CA and mTOR. The exceptionally strong binding of β-sitosterol to PIK3CA (binding energy = -8.4472 kcal/mol) is of particular significance. The docking pose reveals that β-sitosterol occupies the active pocket of PIK3CA, forming conventional hydrogen bonds with Glu259 and Asn756 [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], while its steroidal core engages in extensive hydrophobic and alkyl interactions with multiple residues (Leu814, Cys838, Met811, etc.). This multi-modal, high-affinity binding offers a plausible structural basis for how SHGXF might directly inhibit PIK3CA activity, thereby modulating the upstream node of the PI3K/Akt pathway. Similarly, quercetin was predicted to bind firmly to mTOR, forming key hydrogen bonds with Glu2419 and Arg1945 [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. These docking results are not merely confirmatory; they translate topological network predictions into testable structural models. They suggest that the therapeutic potential of SHGXF against CTIT is underpinned by direct and stable physical interactions between its phytochemicals and the central regulators of cell survival (PI3K/Akt/mTOR) and inflammation (JAK/STAT) pathways. Importantly, the observation that different components (e.g., luteolin, vestitol) bind to distinct residues of the same target (PIK3CA) hints at a potential synergistic or additive effect, a hallmark of TCM formula complexity [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. While these in silico findings require validation through in vitro binding assays and functional studies [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], they significantly strengthen the mechanistic hypothesis by moving from system-level associations to molecular-level interaction details, providing a prioritized list of compound-target pairs for future experimental investigation.\u003c/p\u003e \u003cp\u003eThe innovation of this study lies in adopting a \u0026ldquo;clinical observation\u0026ndash;theory guidance\u0026ndash;data‑driven\u0026rdquo; paradigm to systematically predict the integrated mechanism of SHGXF for CTIT, providing a panoramic modern biological interpretation for the TCM therapeutic principle of \u0026ldquo;supplementing qi and nourishing blood, strengthening the spleen and kidney,\u0026rdquo; representing a concrete practice in TCM modernization research. However, this study has limitations: First, network pharmacology is essentially a database‑based predictive approach, and all results require rigorous experimental validation. Second, as noted, \u0026ldquo;noise\u0026rdquo; in the disease target set may affect the precision of mechanism prediction; future studies should incorporate transcriptomics to construct purer CTIT target sets. Third, the study did not consider in vivo drug metabolism; whether active ingredients can reach bone marrow targets in effective forms remains unknown.\u003c/p\u003e \u003cp\u003eTherefore, future research should advance along a \u0026ldquo;computational prediction \u0026rarr; experimental validation \u0026rarr; clinical translation\u0026rdquo; trajectory. First, the molecular docking predictions presented here provide a prioritized list of compound\u0026ndash;target interactions (e.g., β-sitosterol\u0026ndash;PIK3CA, quercetin\u0026ndash;mTOR). These should be experimentally confirmed using in vitro biophysical methods such as surface plasmon resonance (SPR) or isothermal titration calorimetry (ITC). Second, the functional relevance of these interactions must be tested in biologically relevant systems. This includes establishing chemotherapy-induced thrombocytopenia animal models and in vitro megakaryocyte differentiation assays to verify whether SHGXF modulates the activity of key predicted targets (e.g., AKT1, STAT3) and pathways (PI3K-Akt, JAK-STAT). Finally, prospective clinical trials incorporating multi-omics analysis of patient bone marrow or peripheral blood samples before and after SHGXF treatment are essential to validate, refine, and translate the proposed mechanistic network into human pathophysiology. Through this stepwise strategy, the \u0026ldquo;predictive map\u0026rdquo; generated in this study can be evolved into a \u0026ldquo;navigational guide\u0026rdquo; for precise clinical application and further development of SHGXF.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study, for the first time, applied a network pharmacology framework to the clinically promising empirical formula Songhe Guxue Formula, systematically predicting its integrated mechanism in treating cancer therapy‑induced thrombocytopenia (CTIT). The analysis suggests that SHGXF may, through 40 potential active ingredients including quercetin, kaempferol, and β‑sitosterol, synergistically act on multiple core targets such as TP53, AKT1, IL6, JAK2, and VEGFA, thereby modulating key signaling pathways including PI3K\u0026ndash;Akt and JAK\u0026ndash;STAT. Molecular docking further substantiated these predictions, demonstrating strong binding affinities between key components (e.g., β-sitosterol, quercetin) and pivotal targets (e.g., PIK3CA, mTOR), thereby providing a structural basis for the predicted interactions. These targets and pathways collectively point to resistance against oxidative stress, promotion of megakaryocyte proliferation and differentiation, and improvement of the bone marrow hematopoietic microenvironment, alleviating CTIT at multiple pathological levels. The study reveals that the \u0026ldquo;multi‑ingredient\u0026ndash;multi‑target\u0026ndash;multi‑pathway\u0026rdquo; mode of action of SHGXF reflects, at the molecular level, the scientific connotation of the TCM principle \u0026ldquo;supplementing qi and nourishing blood, strengthening the spleen and kidney\u0026rdquo; in restoring bodily balance through systemic regulation. Although some tumor‑related genes were present among the core targets, indicating limitations in current disease target databases, the main predictive network still clearly outlines a therapeutic logic of promoting platelet production by modulating core networks of cellular stress, survival, and inflammation. In summary, this study provides preliminary theoretical basis and clear mechanistic hypotheses for the clinical application of SHGXF. All predictions require further in‑depth confirmation and refinement through experimental validation of the docked interactions,cellular and animal experiments, and clinical sample validation, ultimately promoting the transition of this formula from empirical use to precision supportive care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTIT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCancer therapy-induced thrombocytopenia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSHGXF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSonghe Guxue Formula\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTraditional Chinese Medicine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprotein\u0026ndash;protein interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003erhIL‑11\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erecombinant human interleukin‑11\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003erhTPO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erecombinant human thrombopoietin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCMSP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTCM Systems Pharmacology Database\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOMIM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOnline Mendelian Inheritance in Man\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein\u0026ndash;Protein Interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMOE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMolecular Operating Environment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebiological process\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecellular component\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emolecular function\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esurface plasmon resonance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eITC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eisothermal titration calorimetry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOral Bioavailability\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDrug-Likeness\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVEGF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVascular Endothelial Growth Factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eAll procedures complied with the Declaration of Helsinki and were approved by the Ethics Committee of China‑Japan Friendship Hospital (Approval No.2025-KY-115).\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eYanying Zhang, Xuejiao Ma, Fang Liu: Data collection and paper writing. Yangying Zhang and Xuejiao Ma were responsible for writing of the original manuscript. Fang Liu and Liqun Jia: Concept development and manuscript revision. All authors reviewed and accepted the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe thank all those who participated in writing and reviewing this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTen Berg MJ, van den Bemt PM, Shantakumar S, Bennett D, Voest EE, Huisman A et al (2011) Thrombocytopenia in Adult Cancer Patients Receiving Cytotoxic Chemotherapy: Results from a Retrospective Hospital-Based Cohort Study. 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Pharmacol Rev 66:334\u0026ndash;395\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Songhe Guxue Formula, cancer therapy-induced thrombocytopenia, network pharmacology, mechanism, target, pathway","lastPublishedDoi":"10.21203/rs.3.rs-8512851/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8512851/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCancer therapy-induced thrombocytopenia (CTIT) is a common complication that compromises the efficacy and safety of anticancer treatments, with current therapeutic options remaining limited. Songhe Guxue Formula (SHGXF) is an empirical formulation developed based on the Traditional Chinese Medicine (TCM) principle of \u0026ldquo;supplementing qi and nourishing blood, strengthening the spleen and kidney.\u0026rdquo; Preliminary clinical observations have suggested its potential in managing CTIT; however, its underlying mechanisms remain unclear. This study aims to systematically predict the pharmacodynamic material basis, core targets, and signaling pathways of SHGXF against CTIT using network pharmacology for the first time, thereby providing a scientific foundation for its clinical application.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eActive ingredients of SHGXF and their corresponding targets were retrieved from the TCMSP and HERB databases. Disease targets related to CTIT were collected from GeneCards and OMIM. Common targets were identified by intersecting the drug and disease targets. The \u0026ldquo;drug\u0026ndash;ingredient\u0026ndash;target\u0026rdquo; network and protein\u0026ndash;protein interaction (PPI) network were constructed using Cytoscape software to screen core targets. Finally, Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to elucidate the potential biological processes and signaling pathways involved. To evaluate the interactions between bioactive ingredients and central target proteins, molecular docking simulations were conducted.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 40 active ingredients and 566 corresponding targets of SHGXF were screened, along with 1036 CTIT-related targets. Among them, 104 common targets were identified. Network analysis revealed key active ingredients such as quercetin and core targets including AKT1,MTOR, PIK3CA, and GSK3B. Enrichment analysis showed that these targets were significantly associated with biological processes such as \u0026ldquo;cellular response to chemical stress\u0026rdquo; and \u0026ldquo;myocyte proliferation,\u0026rdquo; and were mainly involved in pathways including the PI3K\u0026ndash;Akt signaling pathway, proteoglycans in cancer, and JAK\u0026ndash;STAT signaling pathway.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study suggests that SHGXF alleviates CTIT through a multi-component, multi-target, multi-pathway mechanism, involving quercetin, core targets like TP53 and AKT1, and the PI3K-Akt pathway, thereby reducing oxidative stress and promoting megakaryocyte differentiation.\u003c/p\u003e","manuscriptTitle":"Mechanism and Molecular Targets of Songhe Guxue Formula for Treating Cancer Therapy-Introduced Thrombocytopenia—Based on Clinical Observation, Network Pharmacology, and Molecular Docking","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-17 10:19:08","doi":"10.21203/rs.3.rs-8512851/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2ee8bd78-3a04-4537-8ea7-cae8649043ea","owner":[],"postedDate":"March 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-04T02:09:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-17 10:19:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8512851","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8512851","identity":"rs-8512851","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}