Based on Bioinformatics and Network Pharmacology Analyses, The Multi-System Mechanism by which Ge Gen Qin Lian Decoction Intervenes in Chemotherapy-induced Diarrhea through the Regulation of Key Genes and Signaling Pathways is Elucidated

Introduction

Chemotherapy remains a cornerstone of cancer treatment; however, reducing its side effects is essential for improving patients’ quality of life.¹ Chemotherapy-induced diarrhea (CID) is one of the most common adverse effects of antineoplastic drugs, and it accounts for more than 80% of chemotherapy-related side effects.² Severe cases of CID (graded as 3 or 4) occur in over 30% of patients who are affected by this condition.³ Additionally, cancer itself has been reported to increase the risk of CID following radiation therapy, which may manifest as small bowel obstruction, abdominal pain, increased frequency of bowel movements, and loss of bowel control—symptoms that may persist for years or even a decade.⁴ CID primarily arises from the toxic effects of chemotherapeutic drugs on rapidly proliferating gastrointestinal epithelial cells, resulting in intestinal mucosal inflammation and disruption of the gut microbial balance. ⁵, Key mechanisms include apoptosis, upregulation of pro-inflammatory factors, and the spread of inflammation.⁶ These complex physiological processes pose significant challenges for the clinical management of CID.

Current therapeutic strategies for CID primarily involve pharmacological interventions, including the long-acting extended-release (LAR) formulation of octreotide^{7, 8} and loperamide.⁹ Octreotide (LAR) has shown efficacy as a second-line prophylactic treatment for CID,⁸ reducing its incidence and severity.¹⁰ However, its effectiveness can vary depending on patient characteristics, chemotherapy type, and study design, necessitating further systematic evaluation.⁷ Loperamide, an affordable and orally administered drug, is widely used for mild (grade 1 or 2) diarrhea.¹¹ However, its efficacy in severe cases is limited,^{12, 13} and long-term use may be associated with a rare risk of serious cardiac events.¹⁴

From a TCM perspective, chemotherapy is considered a method of “attacking the evil,” which aligns with the TCM principles of “eliminating toxins” or “expelling pathogens”.¹⁵ However, while chemotherapy targets the disease, it also damages the body’s “positive qi,” particularly affecting spleen and stomach function, leading to deficiencies in qi, blood, yin, and bodily fluids.¹⁶ TCM integrates a holistic approach with modern scientific methodologies.¹⁷ utilizing both macro and micro diagnostic indicators to enhance the objectivity and applicability of treatment strategies.¹⁸ The combination of herbal medicine, acupuncture, moxibustion, and massage with chemotherapy has been shown to improve treatment efficacy.¹⁹ enhance immune function, reduce side effects, and restore the balance of yin and yang, thereby optimizing therapeutic outcomes and improving patients’ quality of life.²⁰

Gegen-Qinlian Decoction (GQD), a classical formula from Zhang Zhongjing’s Treatise on Cold Damage Disorders, composed of Pueraria lobata, Scutellaria baicalensis, Coptis chinensis, and Glycyrrhiza uralensis. GQD exhibits anti-inflammatory, antioxidant, and antimicrobial properties and has demonstrated therapeutic efficacy in conditions such as type 2 diabetes mellitus (T2DM)²¹ and infectious lung injury.²² Moreover, GQD has been reported to enhance the efficacy of anticancer drugs while mitigating their adverse effects, particularly in alleviating CID.²³ The mechanisms underlying GQD’s therapeutic effects involve multiple signaling pathways. Its flavonoids and alkaloids suppress the activation of NF-κB and MAPK signaling pathways, reducing inflammatory cytokine release and alleviating inflammation.²⁴ Additionally, GQD modulates the Nrf2/HO-1 pathway to enhance cellular antioxidant capacity, mitigate oxidative stress, and protect the integrity of the epithelial barrier.²⁵ Glycyrrhiza uralensis and its metabolites contribute to cell membrane repair, reinforce barrier function, and regulate chemotherapy-induced gut microbiota imbalances.^{26, 27} Furthermore, GQD may promote tissue repair and enhance drug tolerance via the PI3K-AKT signaling pathway.²⁸

Due to its anti-inflammatory, antioxidant, barrier-protective, and microbiota-modulating effects, GQD has been suggested as a promising therapeutic option for CID. This study integrates network pharmacology, molecular docking, and multidimensional data analysis to systematically explore the therapeutic potential of GQD in CID treatment. By analyzing its core components and major active ingredients, we aim to elucidate its mechanisms of action and key therapeutic targets. This research not only clarifies the potential benefits of GQD for CID but also provides a scientific foundation for its clinical application, paving the way for future drug development targeting this condition. The research methodology is illustrated in Figure 1.

Figure 1: The mechanisms underlying chemotherapy-induced diarrhea and the corresponding treatment strategies proposed in this study Click here to View Figure

Materials and Methods

Screening of targets for core drug active ingredients

Using the TCMSP Database and Analysis Platform²⁹（https://www.tcmsp-e.com/）, we retrieved the active components of the four herbs in Gegen Qinlian Decoction . The screening criteria were established as follows: molecular weight³⁰（MW）≤500、oral bioavailability³¹（OB）≥30% and drug-like properties ³²（DL）≥0.18. The identified active components were then imported into the “Swiss Target Prediction” database, where the biological species was specified as “Homo sapiens” and the prediction threshold was set to “Probability > 0”. This process yielded the target data associated with the active components.

Screening of disease targets associated with Chemotherapy-induced diarrhea

Using “chemotherapy-induced diarrhea” as the keyword, conduct a search for associated targets in the Gene Card³³ database（https://www.genecards.org）. Within this database, a higher score indicates a stronger correlation between the target and the condition. Finally, utilize a Venn diagram tool³⁴ (https://bioinformatics.com.cn) to identify the common intersection of targets related to “disease-active ingredients”.³⁵

Construction of a ‘core drug-active ingredient-CID disease target’ network

The “core drug–active compound–CID disease target” network was built using Cytoscape 3.10.0, with node color and size adjusted according to the Degree value.

Drawing of PPI networks

The overlapping targets between core drug targets and disease-related targets were uploaded to the STRING³⁶ database (https://cn.stringdb.org/) to generate a PPI network. The species was specified as “Homo sapiens,” and the minimum required interaction score was set to ≥0.4. The obtained PPI network was subsequently imported into Cytoscape 3.10.0 for in-depth analysis. Key targets were identified by evaluating three topological metrics: DC, CC, and BC, which reflect the significance and connectivity of each node within the network. Furthermore, the MCODE plugin in Cytoscape 3.10.0 was applied to cluster and simplify the PPI network, using the following parameters: minimum MCODE score > 2, Node Score Cutoff = 0.2, k-core = 2, and maximum depth = 100.

Construction of organ-targets network

The treatment of diarrhea involves the coordinated function of multiple organs and tissues. However, the metabolic mechanisms of Ge Gen Qin Lian Decoction (GGQLD) in the body remain incompletely understood. To investigate its impact across multiple organs and tissues, we leveraged the Bio GPS³⁷ database to retrieve mRNA expression data of the target genes in various human tissues and organ systems. The Bio GPS database provides comprehensive data on gene expression in normal human tissues, with a Z-value greater than 5 indicating significant gene expression in a specific organ or tissue. The organ-target network was constructed using Cytoscape 3.10.0 software.

GEO dataset gene expression differential analysis.

The GEO³⁸ datasets（https://www.ncbi.nlm.nih.gov/）is a public repository for gene expression and high-throughput molecular data, encompassing clinical information from a wide range of diseases. Researchers can utilize this resource to retrieve mRNA expression profiles for various conditions.³⁹ In the study of chemotherapy-induced diarrhea, we selected the GSE11223 dataset from the GEO database for analysis. This dataset comprises 202 samples, including 67 from patients with chemotherapy-induced diarrhea and 135 from healthy controls. DEGs between the patient and control groups were identified using the GEO2R tool, applying a threshold of log2 fold change > 1.2 and an adjusted p-value < 0.01. This analysis provides a foundation for elucidating the molecular mechanisms underlying chemotherapy-induced diarrhea.

GO enrichment assay and KEGG pathway analysis

The potential target sites of the core drugs were submitted to the Metscape⁴⁰ platform (https://metascape.org/) with the organism set as “Homo sapiens”. The analysis was conducted with the following settings: a minimum overlap (Min Overlap) of 3, a P value cutoff of 0.01, and a minimum enrichment ratio (Min Enrichment) of 1.5. Following this, Gene Ontology (GO) and KEGG pathway enrichment analyses were carried out. The results were ranked based on log P values for enrichment, and the top 20 entries were selected. Bar charts were generated using relevant bioinformatics tools. The enriched pathways and targets were further analyzed by submitting them to the CNS Knowall platform (https://cnsknowall.com) to construct a “target-pathway” Sankey diagram.⁴¹ To clearly depict the interconnections among compounds, targets, and signaling pathways, a “compound–target–pathway” network was constructed using Cytoscape 3.10.0, followed by comprehensive analysis and assessment. Finally, cancer-related pathways were visualized using the KEGG PATHWAY⁴² database (https://www.kegg.jp).

Gene Set Enrichment Analysis (GSEA)

Enrichment analysis aids in identifying the molecular pathways potentially involved in GQD treatment of CID. Building on the results of KEGG enrichment analysis, GSEA analysis was employed to evaluate the expression patterns and overall trends of these pathways between the disease and control groups. In GSEA analysis,⁴³ pathway enrichment significance is determined by an adjusted p-value < 0.05 and a q-value < 0.25, ensuring the statistical reliability and biological relevance of the findings. Through GSEA analysis, key signaling pathways that GQD may target can be thoroughly elucidated, offering new insights into the molecular mechanisms underlying its therapeutic effects on CID.

Gene survival analysis

In the GEPIA2 ^[44],we analyzed the overall survival (OS) of target genes across all cancer types. Based on the node degree values in the PPI network, the top two key genes were identified for further investigation. In the survival analysis, patients were divided into high-expression and low-expression groups, with each group representing half of the total cohort. The HR along with its 95% CI was computed to evaluate the association between gene expression levels and survival outcomes.

Molecular docking

The five most active compounds were obtained from the TCMSP database and converted into PDB format using Open Babel 3.1. The crystal structures of the two highest-ranking proteins were then retrieved from the RCSB Protein Data Bank ^[45] (https://www.rcsb.org). The protein structures were prepared using Auto Dock Tools 1.5.6 by adding hydrogen atoms, assigning partial atomic charges, and eliminating water molecules. The binding site for molecular docking was defined based on the location of the original ligand within the protein. After removing the original ligand, the prepared protein structure files were generated for subsequent docking studies. All active compounds were then docked into the predefined binding site using Auto Dock Tools 1.5.6. Finally, the docking results were visualized and analyzed using PyMOL 2.5 software.

Results

GQD active ingredients and targets

In the TCMSP database, a total of 129 active components and 275 targets were identified. The active components of Ge Gen Qin Lian Decoction are summarized in Table S1. As shown in Table S1, Pueraria lobata (GG) contributed 3 active components, Glycyrrhiza uralensis (GC) contributed 83, Scutellaria baicalensis (HQ) contributed 37, and Coptis chinensis (HL) contributed 13. Notably, β-sitosterol (MOL000358) is a common component shared between Pueraria lobata and Scutellaria baicalensis, while formononetin (MOL000392) is shared between Pueraria lobata and Glycyrrhiza uralensis. Additionally, berberine, epiberberine, and quercetin are common components found in Coptis chinensis, Scutellaria baicalensis, and Glycyrrhiza uralensis.

Disease targets

A total of 810 disease targets associated with chemotherapy-induced diarrhea were retrieved from the GeneCards database. The intersection of these targets with those corresponding to the core drugs identified in Section 2.1 was then determined. Ultimately, 36 common drug-disease targets were identified (see Figure 2), as detailed in Table 1.

Table 1: Targets shared by GQD and chemotherapeutic diarrhea

Figure 2: Venn diagram illustrating the overlap between core drug active ingredient targets and disease-related targets. Click here to View Figure

Core Drug-Active Ingredient-CID Disease Target’ network

The “core drug–active ingredient–CID target” network was built using Cytoscape software (as shown in Figure 2), consisting of 311 nodes and 3,169 edges in total. The analysis revealed that the top five active ingredients ranked by node degree were berberine, coptisine, quercetin, β-sitosterol, and formononetin (see Table 2).

Table 2: GQD active ingredient information. Click here to View Table

Draw a PPI network diagram

To explore the underlying mechanisms of Ge Gen Qin Lian Decoction in managing chemotherapy-induced diarrhea, a PPI network was established based on the identified target genes and analyzed using topological centrality metrics, including DC、BC and CC. According to the results presented in Figure 3A, applying a threshold of DC ≥ 1 yielded a network comprising 34 nodes and 109 edges. Further refinement with stringent criteria of DC ≥ 10, BC > 0.03, and CC > 0.54 resulted in a subnetwork of 8 key targets connected by 22 edges. The most significant targets identified were TNF, EGFR, ESR1, SIRT1, ABCB1, NR3C1, COMT, and MAOB. To visually represent the degree centrality of these pivotal targets, a horizontal bar chart was generated for the PPI network (Figure S2). Additionally, cluster analysis using the MCODE plugin in Cytoscape was conducted to identify highly interconnected subnetworks, which were subsequently categorized into two groups (Figure S3).

Figure 3: PPI network diagram of disease targets corresponding to active ingredients (A: PPI network, where node size and color intensity reflect the degree centrality in the network; B: Network diagram of 8 core targets identified from 34 initial targets using DC, BC, and CC metrics through PPI network topology analysis) Click here to View Figure

Construction and Analysis of the Organ-Target Network

Analysis of mRNA expression levels for the 37 targets within the PPI network, based on data from the BioGPS database, revealed that all 37 targets showed increased mRNA expression in tissues associated with diarrhea. Specifically, significant increases were observed in Liver (degree: 49), Whole Blood (degree: 36), CD14+ Monocytes (degree: 36), Small Intestine (degree: 35), Trigeminal Ganglion (degree: 34), and Colon (degree: 34). As illustrated in Figure 4, the tissue-target network comprises 51 nodes and 475 edges. The majority of these targets showed high expression levels in the tissues, indicating a strong association between these organs and chemotherapy-induced diarrhea targets. Further analysis revealed that the 14 involved tissues and organs are closely linked to immune function, blood circulation, and metabolism, suggesting that the therapeutic effects of Ge Gen Qin Lian Decoction may involve systemic organ systems.

Figure 4: Tissue-target network diagram of PPI targets (red rhombuses indicate tissue locations, and blue hexagons represent target proteins). Click here to View Figure

Differential gene expression analysis of the GEO database

We obtained a gene dataset related to CID from the GEO database and conducted differential expression analysis using the GEO2R analysis tool, drawing a volcano plot of gene differential expression. In the volcano plot, the differential expression of the top-ranked genes in the PPI was displayed, among which TNF, EGFR, and ESR1 were all significantly downregulated. Furthermore, through the identification of differentially expressed genes using a significance threshold of p < 0.01 and intersecting them with the genes from the PPI network, a final set of 22 overlapping genes was obtained. These shared genes are likely to be critically involved in the initiation and progression of CID, offering valuable insights for uncovering its underlying molecular mechanisms.

Figure 5: Volcano plot depicting differentially expressed genes from GSE11223 (with blue indicating down-regulated genes and orange indicating up-regulated genes), along with a Venn diagram illustrating the overlap between these differentially expressed genes and GQD-CID targets. Click here to View Figure

To visually illustrate the results of differential gene expression (DGE) analysis, this study generated several visualization charts, including an adjusted p-value distribution plot (Adjusted P-value counts), a gene expression density plot (Expression density plot), a UMAP dimensionality reduction clustering plot (UMAP plot), and a moderated t-statistic plot (Moderated t statistic plot), as presented in Figure S4. These visualizations facilitate the evaluation of data quality, sample distribution, and the reliability of the analysis outcomes. (A) Adjusted p-value distribution plot: This chart evaluates the overall distribution of p-values from the differential expression analysis (DEA). A high concentration of genes with p-values in the P < 0.05 range suggests the presence of numerous significantly differentially expressed genes, reflecting strong statistical power. (B) Gene expression density plot: This plot assesses the effectiveness of data standardization. Ideally, the density curves of all samples should overlap extensively, indicating that technical variations have been effectively minimized and data comparability has been enhanced. (C) UMAP dimensionality reduction clustering plot: This visualization illustrates the overall relationships among samples. If the diseased and control groups form distinct clusters in the UMAP space, it indicates significant differences in gene expression patterns between the two groups, supporting the rationale for subsequent differential gene screening. (D) Moderated t-statistic plot: This chart evaluates the distribution characteristics of the moderated t-statistic calculated using the limma method. Ideally, data points should align closely with the theoretically predicted straight line, confirming that the statistical calculations in the differential expression analysis conform to theoretical expectations and enhancing result credibility. In conclusion, the findings in Figure S4 demonstrate effective data standardization, clear sample grouping, and sound statistical analysis, further reinforcing the accuracy and reliability of the research results.

GO enrichment analysis

GO enrichment analysis was conducted through the Metscape database, and a total of 194 entries were screened out, including 124 BP, 24 CC, and 46 MF (see Figure S5).

To further explore the potential mechanisms of Ge Gen Qin Lian Decoction in treating chemotherapy-induced diarrhea, we conducted a visualization analysis based on the top 20 items ranked by P-values. The results covered three main categories: BP  (Figure 6A), CC (Figure 6B), and MF (Figure 6C). Specifically, the key biological processes involved included inflammatory response and lipopolysaccharide response (Figure 6A); the primary cellular components affected were the plasma membrane, cell surface, and extrachromatin (Figure 6B); and the primary molecular functions identified included estrogen response element binding, nuclear receptor activity, and enzyme binding (Figure 6C). Additionally, the degree of enrichment was quantified through GO functional enrichment analysis, with enrichment scores presented in Figure 6D.

Figure 6: GO enrichment analysis results (A) Bar chart for BP; (B) Bar chart for CC; (C) Bar chart for MF. The x-axis and y-axis represent gene count and functional category, respectively, with bar colors indicating P values—darker red corresponds to higher significance. Click here to View Figure

KEGG pathway analysis

KEGG pathway enrichment analysis was conducted on the 37 intersection genes, resulting in the identification of 28 pathways. The 20 most significantly enriched pathways were selected based on P-values and the number of associated genes (as shown in Figure 7). Subsequent analysis using KEGG bubble plots and pathway classification charts indicated that the core targets were predominantly linked to metabolic pathways, pathways related to cancer, and cytomegalovirus infection pathways. To more intuitively illustrate the relationships between components, targets, and pathways, a “component-target-pathway” network diagram was constructed (see Figure S6), comprising 97 nodes and 580 edges. Among these, the top five targets—PRKCA, EGFR, TNF, MAOB, and ESR2—were involved in 16, 11, 6, 6, and 5 pathways, respectively. Subsequently, a Sankey diagram was created using the CNS Knowall platform (refer to Figure S9). Finally, the cancer-related pathway was mapped within the KEGG PATHWAY database (see Figure S9), emphasizing key signaling pathways including PI3K-AKT, MAPK, and apoptosis.

Figure 7: KEGG Enrichment Analysis Results (A) Bubble chart displaying the top 20 enriched pathways based on KEGG analysis; (B) Functional classification of the top 20 pathways. Click here to View Figure

Enrichment analysis

In conjunction with KEGG enrichment analysis, this study focused on the alterations in the PI3K-AKT and MAPK signaling pathways, as illustrated in Figure 8. The enrichment analysis revealed that both pathways were significantly upregulated in the CID-related gene set, indicating their potential critical roles in the pathogenesis and progression of CID. Activation of the PI3K-AKT and MAPK pathways may promote cell proliferation, enhance inflammatory responses, and regulate the expression of pro-inflammatory factors, thereby exacerbating inflammatory damage. Furthermore, these pathways are critically involved in modulating immune responses and maintaining cell viability, which may impact intestinal barrier integrity and the secretion of inflammatory factors. Overall, these results shed new light on the molecular basis of CID and suggest promising avenues for developing targeted therapeutic interventions.

Figure 8: Gene Enrichment Analysis Diagram Click here to View Figure

Gene Survival analysis

To investigate the impact of genes associated with chemotherapy-induced diarrhea on survival in various cancers, we conducted survival analysis using the GEPIA2 database for the EGFR (Figure S10A) and TNF (Figure S10B) genes. Patients were divided into high-expression and low-expression groups, each representing half of the cohort, and hazard ratios together with their corresponding 95% confidence intervals were computed. The results demonstrated that patients with lower expression levels of EGFR and TNF generally exhibited prolonged survival. These findings indicate that the two genes may be critically involved in cancer prognosis and hold promise as therapeutic targets for enhancing patient survival and reducing chemotherapy-associated diarrhea.

Molecular docking

To verify the targets identified via network pharmacology, molecular docking was conducted to evaluate the binding interactions between the selected active compounds and their corresponding targets. The top five targets and the top five compounds were selected for docking analysis based on their degree centrality rankings in the PPI network. The docking scores are presented in Figure S11. Notably, MOL000358 (β-sitosterol), MOL001458 (coptisine), and MOL002897 (berberine) exhibited relatively high binding affinities with both proteins, as detailed in Table 3.

Table 3: Docking Scores between TNF and EGFR and the Top 3 Active Ingredients in GQD

Analysis of the Combined Model

To investigate the binding modes and structural basis of interactions between active components and core targets, we utilized PyMOL for visualization analysis. Specifically, we examined the binding modes of the top three active ingredients listed in Table . The results revealed that beta-sitosterol exhibited the lowest binding energy when interacting with both TNF and EGFR targets, with values of -9.08 kcal/mol and -8.89 kcal/mol, respectively. In the TNF target, beta-sitosterol formed π-alkyl interactions with the key amino acid residue TYR151 ^[46]. Meanwhile, coptisine and epiberberine engaged in π-π stacking interactions with the critical residue TYR59 of the TNF target. The EGFR target interacted with beta-sitosterol via several non-covalent bonds, such as π-π stacking, π-alkyl, hydrophobic, and van der Waals interactions. Furthermore, coptisine and epiberberine established hydrogen bonds with the critical MET769 residue ^[47] of EGFR and simultaneously engaged in π-σ interactions with LEU694. These findings highlight the distinct binding patterns of different active components, particularly in their interactions with key amino acid residues (see Figure 9), providing a foundation for further research into the mechanisms of action and structural optimization of these compounds.

Figure 9 illustrates the binding modes of the three most active compounds with the highest docking scores to TNF (panels A, B, C) and EGFR (panels D, E, F). The labeling of panels A-F corresponds to the entries in Table 3. Click here to View Figure

Discussion

Chemotherapy is a cornerstone of cancer treatment ^[48]. For instance, CPT-11, a camptothecin derivative and TOP1 inhibitor, forms CPT-11–TOP1–DNA complexes to block DNA replication, inducing cell cycle arrest and apoptosis ^[49]. Its metabolite SN-38 enhances anticancer effects by upregulating p53 ^[50] and pro-apoptotic proteins (e.g., Bax, caspase-3, caspase-9), while downregulating Bcl-xL ^[51]. Similarly, 5-FU metabolites inhibit thymidylate synthase, disrupt RNA/DNA synthesis, and induce apoptosis ^[52]. However, these agents damage intestinal epithelial cells, disrupting production-apoptosis balance, leading to barrier dysfunction and increased CID risk ^[53]. Thus, integrative strategies that target cancer and mitigate side effects are essential.

Despite the availability of clinical drugs for CID treatment, their efficacy remains limited, highlighting the need for more effective therapeutic strategies. Previous studies have explored approaches to mitigate chemotherapy-induced intestinal toxicity, including modulation of inflammatory bowel disease mechanisms ^[54]、 optimization of CPT-11 metabolism ^[55] and inhibition of oxidative stress ^[56]. However, these treatments are often administered only after the onset of diarrhea, forcing patients to interrupt chemotherapy, which not only compromises treatment efficacy but also significantly reduces their quality of life. Therefore, developing prophylactic or early-intervention therapies for CID has become an urgent clinical priority.

TCM offers a unique perspective on disease management, emphasizing individualized treatment based on the balance of qi, blood, yin, and yang. From a Western medicine standpoint, TCM compounds exert anticancer effects primarily by inhibiting tumor growth and metastasis while enhancing immune function ^{[57, 58]}. In addition to their potential in cancer therapy, herbal formulations serve as complementary and alternative medicines (CAM), alleviating chemotherapy-induced side effects and cancer-related fatigue (CRF) ^{[59, 60]}. Given the growing interest in integrative oncology, TCM-based interventions are increasingly recognized as promising therapeutic strategies ^[61].

Integrating TCM with modern oncology treatments has demonstrated benefits in symptom relief, improved quality of life, recurrence prevention, and prolonged survival ^[62]. TCM is rooted in a holistic approach, emphasizing individualized, evidence-based treatment, while Western medicine traditionally follows standardized treatment protocols. However, with the advent of personalized medicine, Western medical concepts are gradually aligning with TCM’s systemic regulatory principles ^[63]. Recent advances in evidence-based medicine have contributed to the standardization of TCM research methodologies, enhancing their scientific credibility and facilitating their integration into modern healthcare systems ^{[64, 65]}. Network pharmacology serves as a crucial bridge between TCM and modern evidence-based medicine. By employing systems biology approaches, it enables multi-level and multi-target analyses of TCM formulations, addressing limitations in traditional research methodologies and supporting the modernization of TCM ^[66]. Additionally, network pharmacology can integrate large-scale biological data, construct drug-target-disease interaction networks, and elucidate the multi-component, synergistic mechanisms of TCM formulas, thereby improving the reproducibility and scientific validation of TCM-based treatments. Clinically, it can optimize herbal formulations, predict drug-drug interactions, identify active ingredients with high efficacy and low toxicity, and guide personalized treatment strategies. Moreover, in drug discovery, network pharmacology accelerates active ingredient screening, target validation, and mechanism exploration, providing a scientific basis for innovative pharmaceutical development.

In this study, we constructed compound-target interaction networks using TCMSP, Gene Cards, and Swiss Target Prediction databases. Our results indicate that GQD exerts a multi-target, multi-pathway, and multi-organ regulatory effect in CID treatment by acting on key target genes such as TNF, EGFR, ESR1, SIRT1, and ABCB1. The primary mechanisms of GQD involve intestinal tissue repair, inhibition of intestinal epithelial apoptosis, and cancer cell apoptosis induction, contributing to CID relief. Based on PPI network analysis and a review of existing literature, GQD exerts anti-inflammatory effects by modulating the PI3K/AKT/NF-κB signaling cascade, leading to reduced production of pro-inflammatory cytokines such as TNF-α, IL-1β, and IL-6, decreased neutrophil infiltration, and attenuation of oxidative stress. Moreover, GQD activates the Keap1/Nrf2 pathway, which strengthens the intestinal barrier by boosting antioxidant defenses and promoting the expression of tight junction proteins like ZO-1 and Occludin, thereby safeguarding intestinal epithelial cells and their mucosal secretions. ^[28]. Notably, TNF and its associated pro-inflammatory cytokines (e.g., IL-1β, COX-2, ICAM-1) play a pivotal role in CID pathogenesis. GQD’s ability to downregulate these inflammatory mediators suggests a protective effect against intestinal inflammation ^[67]. Furthermore, moderate modulation of EGFR and its downstream signaling pathways, including reduced PI3K and p-AKT expression, may help attenuate inflammation and further reinforce intestinal barrier function, contributing to CID symptom relief ^{[68, 69]}.

Our findings also indicate that GQD modulates biological processes such as inflammatory response regulation, MAPK pathway activation, and intracellular signaling transduction, further supporting its role in CID management. Given that CID development is primarily driven by intestinal epithelial apoptosis and excessive inflammatory responses, targeting these pathways represents a key therapeutic strategy. KEGG pathway enrichment analysis suggests that in addition to its impact on cancer-related pathways, GQD may positively influence cancer prognosis while alleviating chemotherapy-induced intestinal inflammation.

While this study provides insights into GQD’s potential mechanisms in CID treatment through network pharmacology, it primarily relies on bioinformatics analyses. Although this study is exploratory, it provides a solid scientific foundation for future research on the application of GQD in managing CID. Nevertheless, further in vivo and in vitro experiments are required to directly validate the proposed active compounds, critical targets, and underlying molecular mechanisms.

Conclusion

Network pharmacology analysis reveals that Gegen-Qinlian Decoction (GQD) exerts a therapeutic effect on chemotherapy-induced diarrhea (CID) by targeting TNF, EGFR, ESR1, SIRT1, and ABCB1, and modulating key signaling pathways such as PI3K/AKT, NF-κB, MAPK, and TNF-related pathways. Through a multi-organ and multi-target regulatory mechanism, GQD contributes to intestinal barrier maintenance, inflammation suppression, and epithelial repair, aligning with TCM’s holistic view that emphasizes systemic regulation and overall balance.

However, this study is primarily based on bioinformatics approaches, necessitating further experimental validation in preclinical and clinical settings to confirm its efficacy and mechanism of action. Future research should focus on identifying GQD’s specific active components, validating their targets, and elucidating their action networks to strengthen the clinical applicability of this formula. The findings of this study provide a novel scientific perspective for advancing research and development of GQD as a potential treatment for chemotherapy-induced diarrhea.

Funding Sources

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Conflict of Interest

The author(s) do not have any conflict of interest.

Data Availability Statement

This statement does not apply to this article.

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