Exploring the Reproductive Mechanisms of Fertility-Boosting No.1 and Fertility-Preserving Tang by Network pharmacology and molecular docking

Exploring the Reproductive Mechanisms of Fertility-Boosting No.1 and Fertility-Preserving Tang by 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 Research Article Exploring the Reproductive Mechanisms of Fertility-Boosting No.1 and Fertility-Preserving Tang by Network pharmacology and molecular docking Lin Jiao, Lijuan Jiang, Xingxiu Zhan, Yanping Qian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4529291/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 Despite global economic growth and health care and education improvements, the global birth rate has remained negative. How to increase fertility has become a common global challenge. Fertility-boosting No. 1 Tang (FB1T) and Fertility-preserving Tang (FPT) are clinically effective prescriptions of traditional Chinese medicine, which play important roles in improving the sperm quality of boys and the embryo loading rate of women to the process of fertilization of sperms and eggs, but the mechanism of their action is still unclear. Methods For insight into the molecular mechanism of FB1T and FPT in reproduction, we used a network pharmacology approach to analyze it with recurrent miscarriage (RM) as the disease representative. Then, we analyzed the potential protein targets signaling pathways looking for therapeutic mechanisms between FB1T and FPT and RSA by drug-target network respectively. Finally, AutoDock Vina was selected for molecular docking validation. Results From the OMIM, DisGeNET, and GeneCards databases, we identified 1933 targets for Recurrent Miscarriage (RM). Post-ADME screening, 96 active components and 467 targets in FB1T, along with 137 active components and 327 targets in FPT were recognized. A total of 286 active component targets in FB1T and 230 in FPT overlapped with RM targets. PPI analysis revealed top targets like TNF, AKT1, IL6, TP53, IL1B, ESR1, STAT3, EGFR, CASP3, JUN, CTNNB1, and MMP9. These targets are associated with 124 and 99 signalling pathways in FB1T and FPT respectively, including the AGE-RAGE signaling pathway and chemical carcinogenesis-receptor activation. Quercetin, kaempferol, and luteolin were identified as the primary active components in both FB1T and FPT for RM treatment. We hypothesize FB1T and FPT may activate NF-kB through the AGE-RAGE signaling pathway, inhibiting pro-inflammatory cytokines such as IL-1β, IL-6, and TNFα, thereby offering therapeutic benefits for RM. Molecular docking further verified that quercetin, kaempferol, and luteolin have strong binding activities with proteins involved in the AGE-RAGE signaling pathway. Conclusions The material basis of FB1T and FPT for the treatment of RM is quercetin, kaempferol, and luteolin. The mechanism may be to enhance oxidative stress resistance and improve anxiety and ovarian function by inhibiting the AGE-RAGE signaling pathway for the treatment of RM. Total fertility rates recurrent miscarriage Different treatment for the same illness Chinese herbal formula AGE-RAGE signaling pathway Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Throughout history, the issue of population has always been a concern for the stability of human societies, with population expansion or underpopulation ultimately leading to social upheaval or even collapse (Castello, 2012 ; De Zordo et al., 2022 ). However, since the Industrial Revolution, human science and technology have progressed rapidly, productivity has risen as never before, and the world's population has grown by leaps and bounds as a result (Parant, 1990 ). Although the global population is now the largest ever at 7.7 billion and rising, total fertility rates (TFRs) has now almost halved and global TFRs is steadily declining, with 183 of the world's 195 countries and territories projected to have TFRs below replacement level by 2100 (Vollset et al., 2020 ; Neyer et al., 2022 ). Banister (1987) proposed that the swift drop in China’s TFRs from 5.8 in 1970 to 2.8 in 1979 was, for the most part, accepted by the Chinese population. This acceptance was presumably due to preceding shifts in the family structure and societal norms (Lavely and Freedman, 1990 ; Vollset et al., 2020 ). However, over the last 40 years, China's TFRs has continued to decline, and in 2022, only 9.56 million people will be born in China, 8.15 million fewer than in 2000, resulting in negative endogenous population growth (De Zordo et al., 2022 ). Besides the exclusion of uncertainties such as politics, economy, wars, and epidemics, the effects of work pressure, environmental degradation, irregular work and rest, and unhealthy diets have made recurrent miscarriage (RM) one of the main focuses of global TFRs research (Bhattacharya and Bhattacharya, 2009 ). RM traditionally refers to 3 or more consecutive pregnancy losses within the first 28 weeks of gestation with the same partner. However, current beliefs suggest that patients experiencing 2 consecutive miscarriages face a comparable risk of recurrence to those with 3 consecutive miscarriages (Deng et al., 2022 ). Reflecting this, the European Society of Human Reproduction and Embryology stipulates that 2 or more miscarriages should be the criterion for RM diagnosis (Stirrat, 1990 ). Similarly, the American Society for Reproductive Medicine defines RM as 2 or more pregnancy losses occurring before the 20th week of gestation (Deng et al., 2022 ). RM occurs in 1–2% of all couples attempting to conceive (Bender et al., 2018 ). Astonishingly, the root causes of almost half of all RM cases remain unidentified, with immune factors implicated in 80% of these unaccounted instances (Li et al., 2021 ). Acknowledged causes can be traced back to key factors like maternal immunological conditions (including autoimmunity and alloimmune reactions), thrombophilic factors (both genetic predispositions and acquired thrombophilia), anatomical irregularities of the uterus, and endocrine abnormalities (Tise and Byers, 2021 ). With the complex origin and 50% of unexplained RM, the development of effective treatments and the improvement of live birth rates for RM patients are drawing significant clinical attention (Alijotas-Reig and Garrido-Gimenez, 2013 ; Ke, 2014 ). Prescription for Fertility-preserving Tang (FPT) are formulations developed through the clinical practice of Prof. Liangying Zhang, a nationally renowned practitioner of traditional Chinese medicine (TCM). Prof. Zhang, with nearly five decades of experience in gynecological clinics, teaching, and scientific research, has found these formulations to be highly effective in treating RM (Jiang et al., 2011 ). FPT is composed of Codonopsis pilosula (Franch.) Nannf. (DangShen, DS), Atractylodes macrocephala Koidz.(BaiZhu, BZ), Angelica sinensis (Oliv.) Diels (DangGui, DG), Rehjnannia glutinosa Libosch. (ShuDiHuang, SDH), Cuscuta chinensis Lam. (TuSiZi, TSZ), Dipsacus asper Wall. ex Henry (XuDuan, XD), Psoralea corylifolia L. (BuGuZhi, BGZ), Ligustrum lucidum Ait. (NüZhenZi, NZZ), Rubus chingii Hu (FuPengZi, FPZ), Litchi chinensis Sonn.(NanShaShen, NSS). Fertility-boosting No. 1 Tang (FB1T) was created by Prof. Jiang Lijuan, Director of the Gynecology Department of Yunnan Provincial Hospital of TCM, the academic successor of Prof. Zhang Liangying, the fourth batch of famous veteran TCM practitioners, according to the characteristics of the clinical use of the FB1T, which is more in line with today's clinical characteristics of RSA, and has a good effect in the treatment of RSA in the clinic. FB1T is composed of DS, BZ, XD), Astragalus membranaceus (Fisch.) Bge. var. mongholicus (Bge.) Hsiao HuangQi, HQ), Eclipta prostrata L. (MoHanLian, MHL), Taxillus chinensi (DC.) Danse (SangJiSheng, SJS), SDH, TSZ, Eucommia ulmoides Oliv. (DuZhong, DZ), Dioscorea opposita Thunb. (Shan Yan, SY), Boehmeria nivea (L.) Gaud. (ZhuMaGen, ZMG), Amomum villosum Lour. (ShaRen, SR), Perilla frutescens (L.) Britt (ZiSuGen, ZSG). TCM treatments can be tailored according to the patient’s characteristics and changes in the external environment, thus illustrating the principle of individualized prescriptions for the same disease (Sun et al., 2022 ; Zhang et al., 2019 ). This principle is best exemplified by FPT and FB1T. Both have shown positive effects on RSA, although their therapeutic material basis and the mechanism of their action remain obscure (Jiang et al., 2011 ). Previously, we discovered that FB1T can mediate maternal-fetal immune tolerance and prevent the onset of RM by inhibiting the positive feedback loop of the IL-23/Th17 immunoinflammatory axis and regulating the Th17/Treg cell balance (Xingxiu et al., 2022 ). It is yet to be determined whether FB1T has other mechanisms of action and if FB1T and FPT share the same mechanism of action. Furthermore, multidrug research is a fundamental strategy for the clinical application of TCM. It is widely assumed that the effective components of TCMs are either the chemical constituents of the drugs or their metabolites (He et al., 2022 ). However, TCMs typically contain thousands of compounds, and these components may vary due to environmental factors or preparation methods. Additionally, the original compounds may undergo comprehensive biological transformations when administered to humans or animals, resulting in a diverse range of metabolites. Therefore, the exhaustive screening of all drug-related compounds from biological matrices poses a substantial challenge. As molecular biology continues to advance, it opens up exciting new pathways to study the composition and activity of compounds found in herbs (Wang et al., 2021 ). In this current research endeavor, our objective is to identify the similarities and differences in the active constituents, therapeutic targets, and mechanisms of action of FPT and FB1T in treating RM. Accordingly, we initially identified potential therapeutic active ingredients, targets, and mechanisms of action using network pharmacological analysis. This was followed by virtual validation via molecular docking. Our findings will not only provide theoretical foundations for employing “different formulas for similar patients” in traditional Chinese medicine but also furnish scientific data to support the reproductive clinical application of FB1T and FPT. Materials and methods 1.1 Analysis of the correlation between FB1T and FPT and RM Utilizing the TCM Network formulaology and Pharmacology Analysis System, TCMNPAS ( http://54.223.75.62:3838/ ), we examined the associations between FPT and FB1T, its constituent elements, and the molecular underpinnings of RM. The specific procedure was as follows: RM ( PRG092)), FPT and FB1T were input into the system; the databases 'HIT’, 'TCMID’, 'STITCH’, and TCMSP’ were selected; the Quantitative Estimate of Drug-likeness (QED) index was set to 0.2; a drug relevance threshold was set at 400; and the compound target significance was set at P < 0.05 (He et al., 2022 ). We also conducted enrichment analyses using Disease ontology DO enrichment. All other parameters were left at their default settings. 1.2 Screening for active components and target proteins analysis Using TCMSP ( http://tcmspw.com/index.php ), TCM Database@Taiwan ( http://tcm.cmu.edu.tw/zh-tw/ ), and BATAMAN-TCM databases ( http://bionet.ncpsb.org/batman-tcm/ ), searches were conducted for active components and targets of FPT and FB1T. Each prescription component was used as input, with criteria set for oral availability (OB) of ≥ 30% and drug similarity (DL) of ≥ 0.18. A score > 80 in the BATAMANTCM database was used as a screening criterion (Duan et al., 2023 ). Unverified active ingredient targets were obtained from the SwissTarget Prediction database ( http://swisstargetprediction.ch ). PubChem Database ( https://pubchem.ncbi.nlm.nih.gov ) was utilized to import active ingredients in mol2 format, species set to human, and the top 10 targets with the highest fit score were obtained. The Uniprot database ( https://www.uniprot.org/ ) was then used to correct and convert targets into gene names for further mechanistic research. 1.3 Screening of RM-related targets analysis The GeneCards ( https://www.genecards.org/ ), DisGENET ( https://www.disgenet.org/ ), OMIM ( https://omim.org/ ), and TTD databases ( http://db.idrblab.net/ttd/ ) were searched using “RM” as the term to identify related genes with human genes as the background. An information table was generated, and redundant data were removed. Venn diagrams were created using Venny 2.1 ( http://bioinfogp.cnb.csic.es/tools/venny/ ) to visualize the overlapping targets of FB1T, FPT, and RM, which will be further analyzed in Cytoscape 3.9.1 along with their corresponding bioactive compounds. 1.4 Protein-Protein Interaction network constrution analysis To identify hub targets and their interactions in FPT and FB1T, the STRING database ( https://string-db.org/ ) was utilized to construct a protein-protein interaction (PPI) network. The protein type was specified as Homo sapiens, and a medium confidence level (0.70) was chosen. The PPI network was further analyzed using Cytoscape_v3.9.1 for molecule network analysis. 1.5 Enrichment analysis and pathway analysis Core targets were subjected to pathway annotation using the DAVID database. GO biological process (BP), cell component (CC), and molecular function (MF) enrichment analyses, as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, were conducted. The significance threshold was set at P ≤ 0.0001 with a count of > 5 for enriched terms (He et al., 2022 ). 1.6 Enrichment analysis and pathway analysis The active components and their targets in FB1T and FPT, along with RM-associated targets and KEGG pathways, were inputted into Cytoscape 3.9.1. Isolated components that did not intersect with their targets were filtered out. A “drug-target-pathway” table was imported into Cytoscape to generate network pharmacology figures, where the nodes represent the components and the degree value indicates the number of associations with predicted nodes. Higher degree values indicate greater importance of the target (He et al., 2022 ; He et al., 2022 ). 1.7 Molecular docking analysis The 3D structures of the target proteins were retrieved from the PDB database ( https://www.rcsb.org ). Water molecules and basic ligands were eliminated from the target protein using PyMOL. Subsequently, the proteins were processed through AutoDock Tools 1.5.6 for hydrogenation, charge estimations, and nonpolar hydrogen combinations. The resulting data was saved in PDBQT format. We then employed AutoDock Vina for molecular docking, utilizing the CMD command character. The docking results were visualized with PyMOL. The conformation exhibiting the highest affinity was selected as the final docking conformation, with the best binding energy results visualized using PyMOL 2.5.0. Furthermore, we used the PLIP database ( https://plip-tool.biotec.tu-dresden.de/ ) to analyze hydrogen and hydrophobic bonds in the binding of the ligand and receptor. Result 3.1 Intrinsic association of FB1T and FPT for the treatment of RM To investigate whether there is a relationship between FB1T and FPT and MAFLD, we used the TCMNPAS database to establish gene co-associations between the RM and FB1T and FPT ( Figure.1A-B ). FB1T was found to be related mainly to diabetes mellitus, atherosclerosis, rheumatoid arthritis, obesity, endometriosis, prostate cancer, asthma, breast cancer, hypertension, schizophrenia. FPT was found to be related mainly to Diabetes mellitus, atherosclerosis, rheumatoid arthritis, prostate cancer, breast cancer, endometriosis, obesity, asthma, alzheimer's disease, hypertension ( Figure.1C ). Included among these chronic inflammatory conditions impacting the endometrium, including endometriosis, adenomyosis, and chronic endometritis, lead to alterations in endometrial receptivity. These disorders are often linked to early pregnancy losses and may potentially contribute to RM. This was consistent with the results of clinical and laboratory studies and provided evidence for further questions addressed in this study (Cocksedge et al., 2009 ). 3.2 Potential active ingredients and targets of FB1T and FPT After the screening process, FB1T contained 96 pharmacological components and 2963 targets, with 467 potential drug targets obtained after target integration and deduplication. FPT, on the other hand, had 137 pharmacological active ingredients and 2712 targets, resulting in 327 targets after target integration and deduplication. 3.3 Network of RM-Related Targets The onset and development of RM is associated with the regulation of multiple genes. Studying gene-gene and gene-environment interactions can help elucidate the pathogenesis of RM. In this study, a total of 1933 genes were obtained from the OMIM, DisGeNET and GeneCards databases ( Figure S1 ) . Of these, there were 286 overlapping genes with FBIT and 230 overlapping genes with FPT ( Figure.1D-E) . 3.4 Construction of PPI network and core target To uncover the mechanism of action of FB1T and FPTT, we constructed a Protein-Protein Interaction (PPI) network using genes common to both FB1T and FPTT with RM, as illustrated in Fig. 2 . We used Degree Centrality (DC) = 78, Betweenness Centrality (BC) = 0.00121137, and Closeness Centrality (CC) = 0.50994575 to screen the PPI network of FPTT, resulting in a total of 60 targets. The top 10 core targets were determined based on the DC data, as displayed in Fig. 2 A. The highly ranked TNF, AKT1, IL6, TP53, IL1B, ESR1, STAT3, EGFR, CASP3, and SRC may play a crucial role in RM treatment (Table 1 ). The PPI network of FB1T, screened with DC = 26, BC = 0.002332105, and CC = 0.38172166, yielded a total of 46 targets. The top 10 core targets, ranked according to the DC data, are shown in Fig. 2 B. The highly ranked STAT3, EGFR, TP53, IL6, AKT1, TNF, IL1B, JUN, CTNNB1, and MMP9 are likely to play a key role in RM treatment (Table 2 ). Table 1 Target proteins with potentially critical roles in FB1T treatment of RM Name BC CC CC TNF 0.03355152 0.72122762 173 AKT1 0.03171513 0.71938776 172 IL6 0.03597074 0.71755725 171 TP53 0.02929201 0.70149254 164 IL1B 0.0211341 0.68613139 153 ESR1 0.03452849 0.67625899 147 STAT3 0.01663487 0.67625899 147 EGFR 0.01812182 0.67625899 147 CASP3 0.01036834 0.66509434 142 SRC 0.03810248 0.66509434 140 Table 2 Target proteins with potentially critical roles in FPT treatment of RM Name BC CC DC STAT3 0.02568604 0.91836735 41 EGFR 0.02674133 0.91836735 41 TP53 0.02380634 0.9 40 IL6 0.02590984 0.9 40 AKT1 0.02334625 0.9 40 TNF 0.0210385 0.8490566 37 IL1B 0.01833839 0.8490566 37 JUN 0.01580513 0.83333333 36 CTNNB1 0.0153337 0.81818182 35 MMP9 0.01346267 0.78947368 33 3.5 Construction of the FB1T and FPT component-target network Using the Cytoscape 3.9.1 software, active component-target relationships obtained were inputted to generate a network diagram, as depicted in Fig. 3 . In this figure, the V-shape symbolizes the herbal formula, the rhombus signifies the formula’s composition, the parallelogram represents the active ingredient, the triangle denotes the disease, and the circle symbolizes the target gene. Figure 3 . Bioactive compounds and corresponding targets network of FB1T (A) and FPT (B) in treating RM. RM, recurrent miscarriage. FPT, Fertility-preserving Tang. FB1T, Fertility- boosting No. 1 Tang. MOL000380, (6a R ,11a R )-9, 10-dimethoxy-6a, 11a-dihydro-6H-benzofurano[3, 2-c]chromen-3-ol. MOL000371, 3, 9-di- O -methylnissolin. MOL003896, 7-Methoxy-2-methyl isoflavone. MOL000378, 7- O -methylisomucronulatol.MOL004941, (2 R ) -7-hydroxy-2- (4-hydroxyphenyl) chroman-4-one. MOL007059, 3- β -hydroxymethyllenetanshiquinone. MOL008391, 5, α -stigmastan-3, 6-dione. MOL003389, 3'- O -methylorobol. MOL009053, 4- [(2S,3R) -5-[(E)-3-hydroxyprop-1-enyl]-7-methoxy-3-methylol-2,3-dihydrobenzofuran-2-yl] -2-methoxy-phenol. DS, DangShen. BZ, BaiZhu. DG, DaangGui. SDH, ShuDiHuang. TSZ, TuSiZi. XD, XuDuan. BGZ, BuGuZhi. NZZ, NüZhenZi. FPZ, FuPengZi. NSS, NanShaShen. HQ, HuangQi. MHL, MoHanLian.SJS, SangJiSheng. SDH, ShuDiHuang. DZ, DuZhong. SY, ShanYan. ZMG, ZhuMaGen. SR, ShaRen. ZSG, ZiSuGen. 3.6 Enrichment analysis of GO annotation and KEGG pathway In the PPI network, 286 targets associated with FB1T were identified. GO analysis was performed with a significance threshold of P 5, resulting in 211 biological processes (GO-BP), 37 cell components (GO-CC), and 50 molecular functions (GO-MF). The top 10 results, depicted in Fig. 4 A, revealed that GO-BP terms mainly included positive regulation of transcription, negative regulation of apoptosis, signal transduction, and cell proliferation. GO-CC terms primarily involved cellular components such as the plasma membrane, cytoplasm, nucleus, and extracellular region. GO-MF terms included functions like enzyme binding, protein kinase activity, and ligand-activated DNA binding (Fig. 4 A). 230 targets associated with FPT were extracted from the PPI network, and a GO analysis was conducted with a significance threshold of P 5. The analysis yielded 170 GO-BP, 31 GO-CC, and 45 GO-MF. The top 10 results, depicted in Fig. 5 A, showed that GO-BP terms primarily included positive regulation of transcription, negative regulation of apoptosis, signal transduction, and gene expression. GO-CC terms involved cellular components such as the plasma membrane, cytoplasm, nucleus, and extracellular space. GO-MF terms encompassed functions such as protein binding, enzyme binding, and protein kinase activity. ( Figure S2 A and Figure S2 B ) Following RM screening for FB1T treatment, the KEGG analysis revealed 124 major signaling pathways. The top 10 pathways are shown in Fig. 4 B. We speculate that the primary mechanisms of FB1T against RM are related to the AGE-RAGE signaling pathway in diabetic complications, chemical carcinogenesis-receptor activation, EGFR tyrosine kinase inhibitor resistance, fluid shear stress and atherosclerosis, human cytomegalovirus infection, hepatitis B, iL-17 signaling pathway, kaposi sarcoma-associated herpesvirus infection, lipid and atherosclerosis, and prostate cancer. The KEGG analysis after RM screening for FPT treatment showed 99 major signaling pathways, with the top 10 displayed in Fig. 5 B. The leading mechanisms of FB1T against RM are hypothesized to be linked to the AGE-RAGE signaling pathway in diabetic complications, chemical carcinogenesis-receptor activation, fluid shear stress and atherosclerosis, hepatitis B, human cytomegalovirus infection, kaposi sarcoma-associated herpesvirus infection, lipid and atherosclerosis, pancreatic cancer, prostate cancer, and proteoglycans in cancer. 3.7 FB1T(A) and FPT (B) active ingredient - RM central target-pathway network analysis We established an “active ingredient-RM target-pathway network” by merging the “active ingredient-target” and “target-pathway network” using the Merge tool in Cytoscape 3.9.1. Nodes with interconnections were retained and filtered by selecting values greater than the Degree Centrality (DC) median, and the results are depicted in Fig. 6 . In Fig. 6 A, the DC equals 5. After screening, the top five pathways include lipid and atherosclerosis (DC = 37), chemical carcinogenesis-receptor activation (DC = 35), Kaposi sarcoma-associated herpesvirus infection (DC = 35), AGE-RAGE signaling pathway (DC = 30), and fluid shear stress and atherosclerosis (DC = 28). Furthermore, human cytomegalovirus infection (DC = 41) emerged as a top pathway. The top three active ingredients are quercetin (DC = 46), kaempferol (DC = 30), and luteolin (DC = 29), while the top three targets are PTGS2 (DC = 19), RELA (DC = 16), and RXRA (DC = 16). In Fig. 6 B, the DC equals 5. After screening, the top five pathways are Kaposi sarcoma-associated herpesvirus infection (DC = 33), human cytomegalovirus infection (DC = 32), lipid and atherosclerosis (DC = 31), chemical carcinogenesis-receptor activation (DC = 27), and AGE-RAGE signaling pathway (DC = 22). The top three active ingredients are quercetin (DC = 42), kaempferol (DC = 23), and luteolin (DC = 24). The top targets are PTGS2 (DC = 28), MAPK14 (DC = 19), and GSK3B (DC = 17). 3.8 Molecular docking analysis We demonstrated molecular docking of the main active ingredients quercetin, kaempferol, and luteolin with the AGE-RAGE signaling pathway, and found that quercetin, kaempferol, and luteolin had both good binding activity with AKT1(PDB:6HHF), JUN (PDB:3U86), TNF (PDB:1FT4), PTGS2 (PDB:5IKV), AGER (PDB:7LMW), and STAT3 (PDB:6NUQ), as shown in Table 3 . Further we found that the top3 binding energies were the binding of AKT1 to quercetin, luteolin, and kaempferol with binding energies of -9.56kcal/mol, -9.11kcal/mol, and − 9.08kcal/mol, respectively, as shown in Fig. 7 A-C. The docking binding site of quercetin and AKT1 molecule is at THR-211 , ASN-54 , and GLN-79 . The docking binding site of luteolin and AKT1 molecule is at THR-211 , ASN-54 , and VAL-271 . The docking binding site of kaempferol and AKT1 is at ASN-54 and SER-205. Table 3 Molecular docking binding energy/kcal.mol − 1 AKT1 JUN TNF PTGS2 AGER STAT3 Quercetin -9.56 -8.00 -4.82 -8.47 -4.91 -5.57 Luteolin -9.11 -7.67 -4.80 -8.51 -4.85 -5.68 Kaempferol -9.08 -8.09 -4.65 -8.34 -4.57 -5.49 Discussion “Different treatment for the same disease” refers to the concept that the same condition can be addressed with various treatments, often achieving similar results (Sun et al., 2020 ). This concept is particularly prevalent in TCM, but its inherent connections are still not fully understood. Network pharmacology is a cross-disciplinary field that merges traditional pharmacology, bioinformatics, chemoinformatics, and biology, which is extensively used to explore the biological basis of syndromes, active compounds, the underlying mechanisms of formulas, and even the new indications and innovative drugs development (Zhang et al., 2019 ). Utilizing network pharmacology, this study focuses on FB1T and FPT as examples of RM treatment, a condition significantly impacting the global population’s TFRs. The goal is not only to explore the underlying basis and action mechanisms of FB1T and FPT in treating RM but also to better understand the scientific concept of "different treatment for the same disease". Our results suggest that the key components in RM treatment by FB1T and FPT are quercetin, kaempferol, and luteolin. These active ingredients appear to influence a common signaling pathway, demonstrating the interconnection between different treatments for the same condition. RM is a significant factor impacting TFRs (Neyer et al., 2022 ; Stirrat, 1990 ). This study aims to understand how FB1T and FPT treatments enhance TFRs. We first offer genetic data support for the application of FB1T and FPT in treating RM through DO enrichment. We identified a total of 1933 disease targets via various databases, with 286 and 230 overlapping genes with FB1T and FPT, respectively. While the top ten targets of FB1T and FPT for RM differ post-PPI enrichment, they both align within the AGE-RAGE signaling pathway following KEGG enrichment. Advanced Glycation End products (AGEs) are pro-inflammatory molecules inducing intracellular oxidative stress and inflammation upon binding with their cell membrane receptors, RAGE (Asadipooya and Uy, 2019 ; Bao et al., 2015 ). Produced by non-enzymatic glycation of macromolecules with a reducing sugar (Maillard reaction), AGEs can instigate protein damage through excessive oxidative stress and a significant increase in Reactive Oxygen Species (ROS) (Asadipooya and Uy, 2019 ). The soluble form of RAGE (sRAGE) is a result of either RAGE gene splicing or protease-based cleavage of membrane-bound RAGE (Asadipooya and Uy, 2019 ). Acting as a decoy for AGEs, sRAGE can inhibit AGE-RAGE interaction and its subsequent pro-inflammatory signaling. In many cases, sRAGE is considered an anti-inflammatory receptor (Bao et al., 2015 ). It interacts with various receptors, particularly RAGE, triggering the activation of multiple signals, including PI3K/Akt, MAPK/ERK, Src/RhoA, and JAK/STAT (Bao et al., 2015 ). This complex signal activation elevates NF-kB and other transcription factors, along with ROS production (Shen et al., 2020 ). The relationship between antiphospholipid antibody syndrome, uterine abnormalities, parental chromosomal abnormalities, polycystic ovary syndrome (PCOS), and RM is well-established (Cocksedge et al., 2009 ). Women with PCOS often exhibit systemic chronic inflammatory conditions, both systemically and at the ovarian level, as demonstrated by elevated serum/ovarian AGEs and increased pro-inflammatory RAGE in ovarian tissue (Pertynska-Marczewska et al., 2015 ). The presence of sRAGE in follicular fluid and its potential protective role against AGE-induced ovarian function damage were also noted (Roness et al., 2014 ). Our study also enriched several inflammation-related genes, including TNF, AKT1, JUN, and IL-1β.Thus, we hypothesize that FB1T and FPT might enhance oxidative stress resistance and provide ovarian protection against RM by inhibiting the AGE-RAGE signaling pathway. Recent studies suggest RM is often linked to structural and neurochemical changes in the brain, potentially resulting in depression and anxiety (Chen et al., 2020 ). For many women and their partners, miscarriage can be a significant, life-changing event, often leading to ‘state anxiety’ that an emotional state characterized by feelings of apprehension, worry, nervousness, and physical symptoms such as an increased heart rate and rapid breathing (Chen et al., 2020 ; Quenby et al., 2021 ). Evidence indicates that women with RM experience mild to moderate depressive symptoms and higher incidences of depression compared to those who experienced normal childbirth (Farren et al., 2020 ). Interestingly, suppressing the AGE-RAGE signaling pathway has been shown to not only reduce inflammation but also exhibit anti-anxiety and anti-depressive effects (Wang et al., 2021 ). Early studies reported elevated levels of proinflammatory cytokines, such as IL-1β, IL-6, and TNF, in depressive patients’ blood, while anti-inflammatory agents showed antidepressant effects (Kohler et al., 2016 ). The genes ESR1 , PTGS2 , IL-1β , MMP9 , IL-6 , and TNF , which are associated with depression and memory impairment, were also identified in this study, suggesting that FB1T and FPT treatments may affect RM through anxiolysis. Among the multiple signaling pathways associated with FB1T and FPT’s anti-depression function predicted by KEGG analysis, inflammation-related signaling pathways are well-documented in depression. The leading signaling pathway from KEGG enrichment is the AGE-RAGE pathway, implicated in various pathological conditions, including diabetic complications, fluid shear stress, and atherosclerosis. These are closely related to RM and AGE-RAGE signaling pathways. Hence, we hypothesize that FB1T and FPT may mediate the activation of NF-kB through the AGE-RAGE signaling pathway and inhibit pro-inflammatory cytokines such as IL-1β, IL-6, and TNFα, thereby exerting antidepressant effects and potentially offering therapeutic benefits for RM. Conclusion This study identifies quercetin, kaempferol, and luteolin as the key active components in FB1T and FPT, used to treat Recurrent Miscarriage (RM). Their potential mechanism likely involves enhancing oxidative stress resistance, reducing anxiety, and improving ovarian function, achieved by inhibiting the AGE-RAGE signaling pathway. These findings not only support the use of FB1T and FPT in reducing RM and increasing birth rates, but also enrich the traditional Chinese medical concept of “different treatment for the same disease.” However, the precise role of these components in key genes requires further validation. Declarations Author contributions Conceptualization: Lijuan Jiang; Data curation: Lin Jiao; Formal analysis: Lin Jiao; Funding acquisition: Lijuan Jiang, Yanping Qian; Investigation: Xingxiu Zhan; Software: Lin Jiao; Supervision: Lijuan Jiang; Validation: Lijuan Jiang; Roles/Writing - original draft: Lin Jiao, Xingxiu Zhan; Writing - review and Editing: Lijuan Jiang. All the authors contributed to the article revision, read, and approved the submitted version. Declaration of competing interest The authors declare that they have no conflicts of interest. Funding This work was supported by the National Natural Science Foundation of China (82060882) and Yunnan Provincial Science and Technology Department Science and Technology Program (202301AZ070001-077). Data statement The datasets used or analyzed relating to this study are available from the corresponding author on reasonable request. References Alijotas-Reig, J. & Garrido-Gimenez, C. (2013), "Current concepts and new trends in the diagnosis and management of recurrent miscarriage", Obstet Gynecol Surv 68, 6, 445-66. doi:10.1097/OGX.0b013e31828aca19. Asadipooya, K. & Uy, E. M. (2019), "Advanced Glycation End Products (AGEs), Receptor for AGEs, Diabetes, and Bone: Review of the Literature", Journal of the Endocrine Society 3, 10, 1799-1818. doi:10.1210/js.2019-00160. Bao, J. M., He, M. Y., Liu, Y. W., Lu, Y. J., Hong, Y. Q., Luo, H. H., Ren, Z. L., Zhao, S. C. & Jiang, Y. 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(2022), "The Influence of Traditional Chinese Medicine Fetal Protection Decoction on the IL-23/Th17 Immune Inflammatory Axis in a Mouse Model of Spontaneous Abortion", Journal of Central South University (Medical Sciences) 47, 11, 1532-1539. doi.10.16254/j.cnki.53-1120/r.2011.11.001 Zhang, R., Zhu, X., Bai, H. & Ning, K. (2019), "Network Pharmacology Databases for Traditional Chinese Medicine: Review and Assessment", Frontiers in Pharmacology 1010.3389/fphar.2019.00123. Additional Declarations No competing interests reported. Supplementary Files FigureS1.tif Figure S1. PPI network diagram of 1933 RM targets. FigureS2.tif Figure S2. (A) GO-MF enrichment analysis; (B) GO-CC enrichment analysis. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4529291","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":314321742,"identity":"1509620f-af12-4689-8e35-acfe18772178","order_by":0,"name":"Lin Jiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIie3RMQrCMBiG4ZSCXQJZE/QQEUErBHoQl0yZdHKp0CFTpx5A8RS9wV8CnQKucXLoBXoBwYqLW9NNMO+ch+QjCIVCP1imbz3QnGKSaE/CI1hCasWCVeBLYljBqVSCO+lJNjOQ4KzB7NLVDhViN0q2FUBzzg0mc3VMUasOetS4Rhv6vuW6X9NIGw/y6JB5lgbzu/UkHFoErFSYO+xJmLYIqBWYVcMW6bOFIBv3w1dmJDG16wvhseX7kVROOf4hU0UoFAr9Ry8BfEekAC4cgAAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Lin","middleName":"","lastName":"Jiao","suffix":""},{"id":314321743,"identity":"40c3d7f0-6d03-4d7a-90b8-eba3a1df4ef4","order_by":1,"name":"Lijuan Jiang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lijuan","middleName":"","lastName":"Jiang","suffix":""},{"id":314321744,"identity":"3abc6c30-52f7-462e-b24e-6e6bac85884e","order_by":2,"name":"Xingxiu Zhan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xingxiu","middleName":"","lastName":"Zhan","suffix":""},{"id":314321746,"identity":"08ae7c3c-ef2e-4ec4-b105-e8c23a17bd3e","order_by":3,"name":"Yanping Qian","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yanping","middleName":"","lastName":"Qian","suffix":""}],"badges":[],"createdAt":"2024-06-04 15:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4529291/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4529291/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58659994,"identity":"68653250-cf7a-4df7-966c-426ab6639ee5","added_by":"auto","created_at":"2024-06-19 12:14:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1100926,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntrinsic association of FB1T and FPT for the treatment of RM. \u003c/strong\u003e(A) Composition of the FPTT formula. (B) Composition of the FB1T formula. (C) Analysis of Disease DO Sample for FPTT and FB1T with RM. (D) Venn diagram of RM gene targets for FPTT treatment.(E) Venn diagram of RM gene targets for FPT treatment.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4529291/v1/32303188fa91bc9c7aa1c9f3.png"},{"id":58660500,"identity":"d84a92fd-f8de-4445-8f3c-6eb7889ca3f9","added_by":"auto","created_at":"2024-06-19 12:22:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4543058,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein-protein interaction network of the potential targets. \u003c/strong\u003e(A) FB1T treatment of RM intersecting gene PPI networks. (B) FPT treatment of RM intersecting gene PPI networks. RM, recurrent miscarriage. FPT, Fertility-preserving Tang. FB1T, Fertility- boosting No. 1 Tang. DS, DangShen. BZ\u003cem\u003e, \u003c/em\u003eBaiZhu. DG, DaangGui. SDH, ShuDiHuang. TSZ, TuSiZi. XD, XuDuan. BGZ, BuGuZhi. NZZ, NüZhenZi. FPZ, FuPengZi. NSS, NanShaShen. HQ, HuangQi. MHL, MoHanLian.SJS, SangJiSheng. SDH, ShuDiHuang. DZ, DuZhong. SY, ShanYan. ZMG, ZhuMaGen. SR, ShaRen. ZSG, ZiSuGen.\u003c/p\u003e","description":"","filename":"Firure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4529291/v1/7ffc6d65a10c0ab8e0423cc1.png"},{"id":58659995,"identity":"fa2e1cf9-ceac-41d3-9f90-101ddbc7175b","added_by":"auto","created_at":"2024-06-19 12:14:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4816353,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBioactive compounds and corresponding targets network of FB1T (A) and FPT (B) in treating RM. \u003c/strong\u003eRM, recurrent miscarriage. FPT, Fertility-preserving Tang. FB1T, Fertility- boosting No. 1 Tang. MOL000380, (6a\u003cem\u003eR\u003c/em\u003e,11a\u003cem\u003eR\u003c/em\u003e)-9, 10-dimethoxy-6a, 11a-dihydro-6H-benzofurano[3, 2-c]chromen-3-ol. MOL000371, 3, 9-di-\u003cem\u003eO\u003c/em\u003e-methylnissolin. MOL003896, 7-Methoxy-2-methyl isoflavone. MOL000378, 7-\u003cem\u003eO\u003c/em\u003e-methylisomucronulatol.MOL004941, (2\u003cem\u003eR\u003c/em\u003e) -7-hydroxy-2- (4-hydroxyphenyl) chroman-4-one. MOL007059, 3-\u003cem\u003eβ\u003c/em\u003e-hydroxymethyllenetanshiquinone. MOL008391, 5, \u003cem\u003eα\u003c/em\u003e-stigmastan-3, 6-dione. MOL003389, 3'-\u003cem\u003eO\u003c/em\u003e-methylorobol. MOL009053, 4- [(2S,3R) -5-[(E)-3-hydroxyprop-1-enyl]-7-methoxy-3-methylol-2,3-dihydrobenzofuran-2-yl] -2-methoxy-phenol. DS, DangShen. BZ\u003cem\u003e, \u003c/em\u003eBaiZhu. DG, DaangGui. SDH, ShuDiHuang. TSZ, TuSiZi. XD, XuDuan. BGZ, BuGuZhi. NZZ, NüZhenZi. FPZ, FuPengZi. NSS, NanShaShen. HQ, HuangQi. MHL, MoHanLian.SJS, SangJiSheng. SDH, ShuDiHuang. DZ, DuZhong. SY, ShanYan. ZMG, ZhuMaGen. SR, ShaRen. ZSG, ZiSuGen.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4529291/v1/6195d826ea476055d8b9ef27.png"},{"id":58659997,"identity":"96971c4f-3153-4eaa-afcb-b77862f5c1d3","added_by":"auto","created_at":"2024-06-19 12:14:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2308012,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO and KEGG enrichment analysis of RM hub targets for FB1T treatment.\u003c/strong\u003e (A) GO enrichment analysis of hub targets. (B) KEGG pathway enrichment analysis of hub targets. GO, Gene Ontology. GO-BP, GO-biological processes. GO-CC, GO-cell components. GO-MF, GO-molecular functions. KEGG, Kyoto Encyclopedia of Genes and Genomes. RM, recurrent miscarriage. FB1T, Fertility- boosting No. 1 Tang.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4529291/v1/71dc9a53f95b001bef78aa3e.png"},{"id":58660501,"identity":"76771dbf-ffa0-40b2-9f23-e9e36d6c5cfa","added_by":"auto","created_at":"2024-06-19 12:22:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":304153,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO and KEGG enrichment analysis of RM hub targets for FPT treatment.\u003c/strong\u003e (A) GO-MF enrichment analysis of hub targets. (B) KEGG pathway enrichment analysis of hub tar gets. GO, Gene Ontology. GO-BP, GO-biological processes. GO-CC, GO-cell components. GO-MF, GO-molecular functions. KEGG, Kyoto Encyclopedia of Genes and Genomes. RM, recurrent miscarriage. FPT, Fertility-preserving Tang.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4529291/v1/d9c420135afc98d56605157d.png"},{"id":58659999,"identity":"c1710e01-3e46-4f52-b801-baf62ca61164","added_by":"auto","created_at":"2024-06-19 12:14:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4661845,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFB1T（A) and FPT (B) active ingredient - RM central target-pathway network analysis.\u003c/strong\u003e RM, recurrent miscarriage. FPT, Fertility-preserving Tang. FB1T, Fertility- boosting No. 1 Tang. MOL000380, (6a\u003cem\u003eR\u003c/em\u003e,11a\u003cem\u003eR\u003c/em\u003e)-9, 10-dimethoxy-6a, 11a-dihydro-6H-benzofurano[3, 2-c] chromen-3-ol. MOL007059, 3-\u003cem\u003eβ\u003c/em\u003e-hydroxymethyllenetanshiquinone. MOL000436, (Z) -1- (2,4-dihydroxyphenyl) -3-(4-hydroxyphenyl) prop-2-en-1-one. MOL000371, 3, 9-di- \u003cem\u003eO \u003c/em\u003e- methylnissolin. MOL000378, 7- \u003cem\u003eO \u003c/em\u003e-methylisomucronulatol. MOL009031, Cinchonan-9-al, 6'- methoxy-, (9\u003cem\u003eR\u003c/em\u003e)-. MOL003389, 3'- \u003cem\u003eO \u003c/em\u003e-Methylorobol. MOL008240, (\u003cem\u003eE\u003c/em\u003e)- 3 - [ 4- [(1\u003cem\u003eR\u003c/em\u003e,2\u003cem\u003eR\u003c/em\u003e) -2- hydroxy - 2- (4-hydroxy-3-methoxy-phenyl)-1-methylol-ethoxy]-3-methoxy-phenyl]acrolein.\u003c/p\u003e","description":"","filename":"Firure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4529291/v1/726af35b43c30e8296c7ad44.png"},{"id":58660001,"identity":"8b65ce74-945d-46a9-bcad-4c877dfcd898","added_by":"auto","created_at":"2024-06-19 12:14:22","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2698599,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular models of quercetin, kaempferol, and luteolin binding to its predicted protein targets AKT1(PDB:6HHF).\u003c/strong\u003e Proteins AKT1 are shown interacting with a quercetin（A), luteolin (B), and kaempferol (C)molecule, represented by a blue stick model. Lines represent residues in the binding sites. The light dashed lines represent hydrogen bonds and the interaction distances are indicated next to the bonds.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4529291/v1/02143202174345cd6a75384e.png"},{"id":70317382,"identity":"ee2d05c1-648b-4687-b987-c356b207c190","added_by":"auto","created_at":"2024-12-02 06:04:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":20381001,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4529291/v1/ded4c98e-3a85-4c6e-b38b-3381e632fbe2.pdf"},{"id":58660006,"identity":"28b64460-29e2-44a2-b66e-73b004c9b4e6","added_by":"auto","created_at":"2024-06-19 12:14:22","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20518636,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1. PPI network diagram of 1933 RM targets.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4529291/v1/0423382722ff3df7b050f642.tif"},{"id":58660004,"identity":"a412ff11-c431-4a64-bd7e-b20ff615dbd0","added_by":"auto","created_at":"2024-06-19 12:14:22","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":30226140,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2. (A) GO-MF enrichment analysis; (B) GO-CC enrichment analysis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-4529291/v1/a2083076bcbef1510e0eb0bf.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Reproductive Mechanisms of Fertility-Boosting No.1 and Fertility-Preserving Tang by Network pharmacology and molecular docking","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThroughout history, the issue of population has always been a concern for the stability of human societies, with population expansion or underpopulation ultimately leading to social upheaval or even collapse (Castello, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; De Zordo et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, since the Industrial Revolution, human science and technology have progressed rapidly, productivity has risen as never before, and the world's population has grown by leaps and bounds as a result (Parant, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Although the global population is now the largest ever at 7.7\u0026nbsp;billion and rising, total fertility rates (TFRs) has now almost halved and global TFRs is steadily declining, with 183 of the world's 195 countries and territories projected to have TFRs below replacement level by 2100 (Vollset et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Neyer et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Banister (1987) proposed that the swift drop in China\u0026rsquo;s TFRs from 5.8 in 1970 to 2.8 in 1979 was, for the most part, accepted by the Chinese population. This acceptance was presumably due to preceding shifts in the family structure and societal norms (Lavely and Freedman, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Vollset et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, over the last 40 years, China's TFRs has continued to decline, and in 2022, only 9.56\u0026nbsp;million people will be born in China, 8.15\u0026nbsp;million fewer than in 2000, resulting in negative endogenous population growth (De Zordo et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBesides the exclusion of uncertainties such as politics, economy, wars, and epidemics, the effects of work pressure, environmental degradation, irregular work and rest, and unhealthy diets have made recurrent miscarriage (RM) one of the main focuses of global TFRs research (Bhattacharya and Bhattacharya, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). RM traditionally refers to 3 or more consecutive pregnancy losses within the first 28 weeks of gestation with the same partner. However, current beliefs suggest that patients experiencing 2 consecutive miscarriages face a comparable risk of recurrence to those with 3 consecutive miscarriages (Deng et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Reflecting this, the European Society of Human Reproduction and Embryology stipulates that 2 or more miscarriages should be the criterion for RM diagnosis (Stirrat, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Similarly, the American Society for Reproductive Medicine defines RM as 2 or more pregnancy losses occurring before the 20th week of gestation (Deng et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). RM occurs in 1\u0026ndash;2% of all couples attempting to conceive (Bender et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Astonishingly, the root causes of almost half of all RM cases remain unidentified, with immune factors implicated in 80% of these unaccounted instances (Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Acknowledged causes can be traced back to key factors like maternal immunological conditions (including autoimmunity and alloimmune reactions), thrombophilic factors (both genetic predispositions and acquired thrombophilia), anatomical irregularities of the uterus, and endocrine abnormalities (Tise and Byers, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). With the complex origin and 50% of unexplained RM, the development of effective treatments and the improvement of live birth rates for RM patients are drawing significant clinical attention (Alijotas-Reig and Garrido-Gimenez, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ke, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrescription for Fertility-preserving Tang (FPT) are formulations developed through the clinical practice of Prof. Liangying Zhang, a nationally renowned practitioner of traditional Chinese medicine (TCM). Prof. Zhang, with nearly five decades of experience in gynecological clinics, teaching, and scientific research, has found these formulations to be highly effective in treating RM (Jiang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). FPT is composed of \u003cem\u003eCodonopsis pilosula\u003c/em\u003e (Franch.) Nannf. (DangShen, DS), \u003cem\u003eAtractylodes macrocephala\u003c/em\u003e Koidz.(BaiZhu, BZ), \u003cem\u003eAngelica sinensis\u003c/em\u003e (Oliv.) Diels (DangGui, DG), \u003cem\u003eRehjnannia glutinosa\u003c/em\u003e Libosch. (ShuDiHuang, SDH), \u003cem\u003eCuscuta chinensis\u003c/em\u003e Lam. (TuSiZi, TSZ), \u003cem\u003eDipsacus asper\u003c/em\u003e Wall. ex Henry (XuDuan, XD), \u003cem\u003ePsoralea corylifolia\u003c/em\u003e L. (BuGuZhi, BGZ), \u003cem\u003eLigustrum lucidum\u003c/em\u003e Ait. (N\u0026uuml;ZhenZi, NZZ), \u003cem\u003eRubus chingii\u003c/em\u003e Hu (FuPengZi, FPZ), Litchi chinensis Sonn.(NanShaShen, NSS). Fertility-boosting No. 1 Tang (FB1T) was created by Prof. Jiang Lijuan, Director of the Gynecology Department of Yunnan Provincial Hospital of TCM, the academic successor of Prof. Zhang Liangying, the fourth batch of famous veteran TCM practitioners, according to the characteristics of the clinical use of the FB1T, which is more in line with today's clinical characteristics of RSA, and has a good effect in the treatment of RSA in the clinic. FB1T is composed of DS, BZ, XD), \u003cem\u003eAstragalus membranaceus\u003c/em\u003e (Fisch.) Bge. var. mongholicus (Bge.) Hsiao HuangQi, HQ), \u003cem\u003eEclipta prostrata\u003c/em\u003e L. (MoHanLian, MHL), \u003cem\u003eTaxillus chinensi\u003c/em\u003e (DC.) Danse (SangJiSheng, SJS), SDH, TSZ, \u003cem\u003eEucommia ulmoides\u003c/em\u003e Oliv. (DuZhong, DZ), \u003cem\u003eDioscorea opposita\u003c/em\u003e Thunb. (Shan Yan, SY), Boehmeria nivea (L.) Gaud. (ZhuMaGen, ZMG), \u003cem\u003eAmomum villosum\u003c/em\u003e Lour. (ShaRen, SR), \u003cem\u003ePerilla frutescens\u003c/em\u003e (L.) Britt (ZiSuGen, ZSG).\u003c/p\u003e \u003cp\u003eTCM treatments can be tailored according to the patient\u0026rsquo;s characteristics and changes in the external environment, thus illustrating the principle of individualized prescriptions for the same disease (Sun et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This principle is best exemplified by FPT and FB1T. Both have shown positive effects on RSA, although their therapeutic material basis and the mechanism of their action remain obscure (Jiang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Previously, we discovered that FB1T can mediate maternal-fetal immune tolerance and prevent the onset of RM by inhibiting the positive feedback loop of the IL-23/Th17 immunoinflammatory axis and regulating the Th17/Treg cell balance (Xingxiu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is yet to be determined whether FB1T has other mechanisms of action and if FB1T and FPT share the same mechanism of action. Furthermore, multidrug research is a fundamental strategy for the clinical application of TCM. It is widely assumed that the effective components of TCMs are either the chemical constituents of the drugs or their metabolites (He et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, TCMs typically contain thousands of compounds, and these components may vary due to environmental factors or preparation methods. Additionally, the original compounds may undergo comprehensive biological transformations when administered to humans or animals, resulting in a diverse range of metabolites. Therefore, the exhaustive screening of all drug-related compounds from biological matrices poses a substantial challenge.\u003c/p\u003e \u003cp\u003eAs molecular biology continues to advance, it opens up exciting new pathways to study the composition and activity of compounds found in herbs (Wang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this current research endeavor, our objective is to identify the similarities and differences in the active constituents, therapeutic targets, and mechanisms of action of FPT and FB1T in treating RM. Accordingly, we initially identified potential therapeutic active ingredients, targets, and mechanisms of action using network pharmacological analysis. This was followed by virtual validation via molecular docking. Our findings will not only provide theoretical foundations for employing \u0026ldquo;different formulas for similar patients\u0026rdquo; in traditional Chinese medicine but also furnish scientific data to support the reproductive clinical application of FB1T and FPT.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e1.1 Analysis of the correlation between FB1T and FPT and RM\u003c/p\u003e\n\u003cp\u003eUtilizing the TCM Network formulaology and Pharmacology Analysis System, TCMNPAS (\u003cspan\u003e\u003cspan\u003ehttp://54.223.75.62:3838/\u003c/span\u003e\u003c/span\u003e), we examined the associations between FPT and FB1T, its constituent elements, and the molecular underpinnings of RM. The specific procedure was as follows: RM ( PRG092)), FPT and FB1T were input into the system; the databases \u0026apos;HIT\u0026rsquo;, \u0026apos;TCMID\u0026rsquo;, \u0026apos;STITCH\u0026rsquo;, and TCMSP\u0026rsquo; were selected; the Quantitative Estimate of Drug-likeness (QED) index was set to 0.2; a drug relevance threshold was set at 400; and the compound target significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (He et al., \u003cspan\u003e2022\u003c/span\u003e). We also conducted enrichment analyses using Disease ontology DO enrichment. All other parameters were left at their default settings.\u003c/p\u003e\n\u003cp\u003e1.2 Screening for active components and target proteins \u003cstrong\u003eanalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing TCMSP (\u003cspan\u003e\u003cspan\u003ehttp://tcmspw.com/index.php\u003c/span\u003e\u003c/span\u003e), TCM Database@Taiwan (\u003cspan\u003e\u003cspan\u003ehttp://tcm.cmu.edu.tw/zh-tw/\u003c/span\u003e\u003c/span\u003e), and BATAMAN-TCM databases (\u003cspan\u003e\u003cspan\u003ehttp://bionet.ncpsb.org/batman-tcm/\u003c/span\u003e\u003c/span\u003e), searches were conducted for active components and targets of FPT and FB1T. Each prescription component was used as input, with criteria set for oral availability (OB) of \u0026ge;\u0026thinsp;30% and drug similarity (DL) of \u0026ge;\u0026thinsp;0.18. A score\u0026thinsp;\u0026gt;\u0026thinsp;80 in the BATAMANTCM database was used as a screening criterion (Duan et al., \u003cspan\u003e2023\u003c/span\u003e). Unverified active ingredient targets were obtained from the SwissTarget Prediction database (\u003cspan\u003e\u003cspan\u003ehttp://swisstargetprediction.ch\u003c/span\u003e\u003c/span\u003e). PubChem Database (\u003cspan\u003e\u003cspan\u003ehttps://pubchem.ncbi.nlm.nih.gov\u003c/span\u003e\u003c/span\u003e) was utilized to import active ingredients in mol2 format, species set to human, and the top 10 targets with the highest fit score were obtained. The Uniprot database (\u003cspan\u003e\u003cspan\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003c/span\u003e) was then used to correct and convert targets into gene names for further mechanistic research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Screening of RM-related targets analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GeneCards (\u003cspan\u003e\u003cspan\u003ehttps://www.genecards.org/\u003c/span\u003e\u003c/span\u003e), DisGENET (\u003cspan\u003e\u003cspan\u003ehttps://www.disgenet.org/\u003c/span\u003e\u003c/span\u003e), OMIM (\u003cspan\u003e\u003cspan\u003ehttps://omim.org/\u003c/span\u003e\u003c/span\u003e), and TTD databases (\u003cspan\u003e\u003cspan\u003ehttp://db.idrblab.net/ttd/\u003c/span\u003e\u003c/span\u003e) were searched using \u0026ldquo;RM\u0026rdquo; as the term to identify related genes with human genes as the background. An information table was generated, and redundant data were removed. Venn diagrams were created using Venny 2.1 (\u003cspan\u003e\u003cspan\u003ehttp://bioinfogp.cnb.csic.es/tools/venny/\u003c/span\u003e\u003c/span\u003e) to visualize the overlapping targets of FB1T, FPT, and RM, which will be further analyzed in Cytoscape 3.9.1 along with their corresponding bioactive compounds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 Protein-Protein Interaction network constrution analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify hub targets and their interactions in FPT and FB1T, the STRING database (\u003cspan\u003e\u003cspan\u003ehttps://string-db.org/\u003c/span\u003e\u003c/span\u003e) was utilized to construct a protein-protein interaction (PPI) network. The protein type was specified as Homo sapiens, and a medium confidence level (0.70) was chosen. The PPI network was further analyzed using Cytoscape_v3.9.1 for molecule network analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.5 Enrichment analysis and pathway analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCore targets were subjected to pathway annotation using the DAVID database. GO biological process (BP), cell component (CC), and molecular function (MF) enrichment analyses, as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, were conducted. The significance threshold was set at P\u0026thinsp;\u0026le;\u0026thinsp;0.0001 with a count of \u0026gt;\u0026thinsp;5 for enriched terms (He et al., \u003cspan\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.6 Enrichment analysis and pathway analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe active components and their targets in FB1T and FPT, along with RM-associated targets and KEGG pathways, were inputted into Cytoscape 3.9.1. Isolated components that did not intersect with their targets were filtered out. A \u0026ldquo;drug-target-pathway\u0026rdquo; table was imported into Cytoscape to generate network pharmacology figures, where the nodes represent the components and the degree value indicates the number of associations with predicted nodes. Higher degree values indicate greater importance of the target (He et al., \u003cspan\u003e2022\u003c/span\u003e; He et al., \u003cspan\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e1.7 Molecular docking analysis\u003c/p\u003e\n\u003cp\u003eThe 3D structures of the target proteins were retrieved from the PDB database (\u003cspan\u003e\u003cspan\u003ehttps://www.rcsb.org\u003c/span\u003e\u003c/span\u003e). Water molecules and basic ligands were eliminated from the target protein using PyMOL. Subsequently, the proteins were processed through AutoDock Tools 1.5.6 for hydrogenation, charge estimations, and nonpolar hydrogen combinations. The resulting data was saved in PDBQT format. We then employed AutoDock Vina for molecular docking, utilizing the CMD command character. The docking results were visualized with PyMOL. The conformation exhibiting the highest affinity was selected as the final docking conformation, with the best binding energy results visualized using PyMOL 2.5.0. Furthermore, we used the PLIP database (\u003cspan\u003e\u003cspan\u003ehttps://plip-tool.biotec.tu-dresden.de/\u003c/span\u003e\u003c/span\u003e) to analyze hydrogen and hydrophobic bonds in the binding of the ligand and receptor.\u003c/p\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e3.1 Intrinsic association of FB1T and FPT for the treatment of RM\u003c/h2\u003e\n \u003cp\u003eTo investigate whether there is a relationship between FB1T and FPT and MAFLD, we used the TCMNPAS database to establish gene co-associations between the RM and FB1T and FPT (\u003cstrong\u003eFigure.1A-B\u003c/strong\u003e). FB1T was found to be related mainly to diabetes mellitus, atherosclerosis, rheumatoid arthritis, obesity, endometriosis, prostate cancer, asthma, breast cancer, hypertension, schizophrenia. FPT was found to be related mainly to Diabetes mellitus, atherosclerosis, rheumatoid arthritis, prostate cancer, breast cancer, endometriosis, obesity, asthma, alzheimer\u0026apos;s disease, hypertension (\u003cstrong\u003eFigure.1C\u003c/strong\u003e). Included among these chronic inflammatory conditions impacting the endometrium, including endometriosis, adenomyosis, and chronic endometritis, lead to alterations in endometrial receptivity. These disorders are often linked to early pregnancy losses and may potentially contribute to RM. This was consistent with the results of clinical and laboratory studies and provided evidence for further questions addressed in this study (Cocksedge et al., \u003cspan\u003e2009\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e\u003cstrong\u003e3.2 Potential active ingredients and targets of\u003c/strong\u003e FB1T and FPT\u003c/h2\u003e\n \u003cp\u003eAfter the screening process, FB1T contained 96 pharmacological components and 2963 targets, with 467 potential drug targets obtained after target integration and deduplication. FPT, on the other hand, had 137 pharmacological active ingredients and 2712 targets, resulting in 327 targets after target integration and deduplication.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e3.3 Network of RM-Related Targets\u003c/h2\u003e\n \u003cp\u003eThe onset and development of RM is associated with the regulation of multiple genes. Studying gene-gene and gene-environment interactions can help elucidate the pathogenesis of RM. In this study, a total of 1933 genes were obtained from the OMIM, DisGeNET and GeneCards databases (\u003cstrong\u003eFigure \u003cspan\u003eS1\u003c/span\u003e)\u003c/strong\u003e. Of these, there were 286 overlapping genes with FBIT and 230 overlapping genes with FPT (\u003cstrong\u003eFigure.1D-E)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e3.4 Construction of PPI network and core target\u003c/h2\u003e\n \u003cp\u003eTo uncover the mechanism of action of FB1T and FPTT, we constructed a Protein-Protein Interaction (PPI) network using genes common to both FB1T and FPTT with RM, as illustrated in Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e. We used Degree Centrality (DC)\u0026thinsp;=\u0026thinsp;78, Betweenness Centrality (BC)\u0026thinsp;=\u0026thinsp;0.00121137, and Closeness Centrality (CC)\u0026thinsp;=\u0026thinsp;0.50994575 to screen the PPI network of FPTT, resulting in a total of 60 targets. The top 10 core targets were determined based on the DC data, as displayed in Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eA. The highly ranked TNF, AKT1, IL6, TP53, IL1B, ESR1, STAT3, EGFR, CASP3, and SRC may play a crucial role in RM treatment (Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). The PPI network of FB1T, screened with DC\u0026thinsp;=\u0026thinsp;26, BC\u0026thinsp;=\u0026thinsp;0.002332105, and CC\u0026thinsp;=\u0026thinsp;0.38172166, yielded a total of 46 targets. The top 10 core targets, ranked according to the DC data, are shown in Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eB. The highly ranked STAT3, EGFR, TP53, IL6, AKT1, TNF, IL1B, JUN, CTNNB1, and MMP9 are likely to play a key role in RM treatment (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cbr\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eTarget proteins with potentially critical roles in FB1T treatment of RM\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eName\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eBC\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eCC\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eCC\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eTNF\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.03355152\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.72122762\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e173\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eAKT1\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.03171513\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.71938776\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e172\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eIL6\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.03597074\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.71755725\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e171\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eTP53\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.02929201\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.70149254\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e164\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eIL1B\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.0211341\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.68613139\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e153\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eESR1\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.03452849\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.67625899\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e147\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eSTAT3\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.01663487\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.67625899\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e147\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eEGFR\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.01812182\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.67625899\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e147\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eCASP3\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.01036834\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.66509434\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e142\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eSRC\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.03810248\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.66509434\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e140\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\u003cbr\u003e\u003cbr\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eTarget proteins with potentially critical roles in FPT treatment of RM\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eName\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eBC\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eCC\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eDC\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eSTAT3\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.02568604\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.91836735\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e41\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eEGFR\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.02674133\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.91836735\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e41\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eTP53\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.02380634\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e40\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eIL6\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.02590984\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e40\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eAKT1\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.02334625\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e40\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eTNF\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.0210385\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.8490566\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e37\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eIL1B\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.01833839\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.8490566\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e37\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eJUN\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.01580513\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.83333333\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e36\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eCTNNB1\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.0153337\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.81818182\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e35\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eMMP9\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.01346267\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.78947368\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e33\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\u003cbr\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e3.5 Construction of the FB1T and FPT component-target network\u003c/h2\u003e\n \u003cp\u003eUsing the Cytoscape 3.9.1 software, active component-target relationships obtained were inputted to generate a network diagram, as depicted in Fig. \u003cspan\u003e3\u003c/span\u003e. In this figure, the V-shape symbolizes the herbal formula, the rhombus signifies the formula\u0026rsquo;s composition, the parallelogram represents the active ingredient, the triangle denotes the disease, and the circle symbolizes the target gene.\u003c/p\u003e\n \u003cp\u003eFigure\u003cspan\u003e3\u003c/span\u003e. \u003cstrong\u003eBioactive compounds and corresponding targets network of FB1T (A) and FPT (B) in treating RM.\u003c/strong\u003e RM, recurrent miscarriage. FPT, Fertility-preserving Tang. FB1T, Fertility- boosting No. 1 Tang. MOL000380, (6a\u003cem\u003eR\u003c/em\u003e,11a\u003cem\u003eR\u003c/em\u003e)-9, 10-dimethoxy-6a, 11a-dihydro-6H-benzofurano[3, 2-c]chromen-3-ol. MOL000371, 3, 9-di-\u003cem\u003eO\u003c/em\u003e-methylnissolin. MOL003896, 7-Methoxy-2-methyl isoflavone. MOL000378, 7-\u003cem\u003eO\u003c/em\u003e-methylisomucronulatol.MOL004941, (2\u003cem\u003eR\u003c/em\u003e) -7-hydroxy-2- (4-hydroxyphenyl) chroman-4-one. MOL007059, 3-\u003cem\u003e\u0026beta;\u003c/em\u003e-hydroxymethyllenetanshiquinone. MOL008391, 5, \u003cem\u003e\u0026alpha;\u003c/em\u003e-stigmastan-3, 6-dione. MOL003389, 3\u0026apos;-\u003cem\u003eO\u003c/em\u003e-methylorobol. MOL009053, 4- [(2S,3R) -5-[(E)-3-hydroxyprop-1-enyl]-7-methoxy-3-methylol-2,3-dihydrobenzofuran-2-yl] -2-methoxy-phenol. DS, DangShen. BZ, BaiZhu. DG, DaangGui. SDH, ShuDiHuang. TSZ, TuSiZi. XD, XuDuan. BGZ, BuGuZhi. NZZ, N\u0026uuml;ZhenZi. FPZ, FuPengZi. NSS, NanShaShen. HQ, HuangQi. MHL, MoHanLian.SJS, SangJiSheng. SDH, ShuDiHuang. DZ, DuZhong. SY, ShanYan. ZMG, ZhuMaGen. SR, ShaRen. ZSG, ZiSuGen.\u003c/p\u003e\u003cbr\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e3.6 Enrichment analysis of GO annotation and KEGG pathway\u003c/h2\u003e\n \u003cp\u003eIn the PPI network, 286 targets associated with FB1T were identified. GO analysis was performed with a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 and a count of \u0026gt;\u0026thinsp;5, resulting in 211 biological processes (GO-BP), 37 cell components (GO-CC), and 50 molecular functions (GO-MF). The top 10 results, depicted in Fig. \u003cspan\u003e4\u003c/span\u003eA, revealed that GO-BP terms mainly included positive regulation of transcription, negative regulation of apoptosis, signal transduction, and cell proliferation. GO-CC terms primarily involved cellular components such as the plasma membrane, cytoplasm, nucleus, and extracellular region. GO-MF terms included functions like enzyme binding, protein kinase activity, and ligand-activated DNA binding (Fig. \u003cspan\u003e4\u003c/span\u003eA).\u003c/p\u003e\u003cbr\u003e\n \u003cp\u003e230 targets associated with FPT were extracted from the PPI network, and a GO analysis was conducted with a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 and a count of \u0026gt;\u0026thinsp;5. The analysis yielded 170 GO-BP, 31 GO-CC, and 45 GO-MF. The top 10 results, depicted in Fig. \u003cspan\u003e5\u003c/span\u003eA, showed that GO-BP terms primarily included positive regulation of transcription, negative regulation of apoptosis, signal transduction, and gene expression. GO-CC terms involved cellular components such as the plasma membrane, cytoplasm, nucleus, and extracellular space. GO-MF terms encompassed functions such as protein binding, enzyme binding, and protein kinase activity. (\u003cstrong\u003eFigure \u003cspan\u003eS2\u003c/span\u003eA and Figure \u003cspan\u003eS2\u003c/span\u003eB\u003c/strong\u003e)\u003c/p\u003eFollowing RM screening for FB1T treatment, the KEGG analysis revealed 124 major signaling pathways. The top 10 pathways are shown in Fig. \u003cspan\u003e4\u003c/span\u003eB. We speculate that the primary mechanisms of FB1T against RM are related to the AGE-RAGE signaling pathway in diabetic complications, chemical carcinogenesis-receptor activation, EGFR tyrosine kinase inhibitor resistance, fluid shear stress and atherosclerosis, human cytomegalovirus infection, hepatitis B, iL-17 signaling pathway, kaposi sarcoma-associated herpesvirus infection, lipid and atherosclerosis, and prostate cancer. The KEGG analysis after RM screening for FPT treatment showed 99 major signaling pathways, with the top 10 displayed in Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003eB. The leading mechanisms of FB1T against RM are hypothesized to be linked to the AGE-RAGE signaling pathway in diabetic complications, chemical carcinogenesis-receptor activation, fluid shear stress and atherosclerosis, hepatitis B, human cytomegalovirus infection, kaposi sarcoma-associated herpesvirus infection, lipid and atherosclerosis, pancreatic cancer, prostate cancer, and proteoglycans in cancer.\u003cbr\u003e\u003cbr\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e3.7 \u003cstrong\u003eFB1T(A) and FPT (B) active ingredient - RM central target-pathway network analysis\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eWe established an \u0026ldquo;active ingredient-RM target-pathway network\u0026rdquo; by merging the \u0026ldquo;active ingredient-target\u0026rdquo; and \u0026ldquo;target-pathway network\u0026rdquo; using the Merge tool in Cytoscape 3.9.1. Nodes with interconnections were retained and filtered by selecting values greater than the Degree Centrality (DC) median, and the results are depicted in Fig. \u003cspan\u003e6\u003c/span\u003e. In Fig. \u003cspan\u003e6\u003c/span\u003eA, the DC equals 5. After screening, the top five pathways include lipid and atherosclerosis (DC\u0026thinsp;=\u0026thinsp;37), chemical carcinogenesis-receptor activation (DC\u0026thinsp;=\u0026thinsp;35), Kaposi sarcoma-associated herpesvirus infection (DC\u0026thinsp;=\u0026thinsp;35), AGE-RAGE signaling pathway (DC\u0026thinsp;=\u0026thinsp;30), and fluid shear stress and atherosclerosis (DC\u0026thinsp;=\u0026thinsp;28). Furthermore, human cytomegalovirus infection (DC\u0026thinsp;=\u0026thinsp;41) emerged as a top pathway. The top three active ingredients are quercetin (DC\u0026thinsp;=\u0026thinsp;46), kaempferol (DC\u0026thinsp;=\u0026thinsp;30), and luteolin (DC\u0026thinsp;=\u0026thinsp;29), while the top three targets are PTGS2 (DC\u0026thinsp;=\u0026thinsp;19), RELA (DC\u0026thinsp;=\u0026thinsp;16), and RXRA (DC\u0026thinsp;=\u0026thinsp;16). \u003cstrong\u003eIn\u003c/strong\u003e Fig. \u003cspan\u003e6\u003c/span\u003eB, the DC equals 5. After screening, the top five pathways are Kaposi sarcoma-associated herpesvirus infection (DC\u0026thinsp;=\u0026thinsp;33), human cytomegalovirus infection (DC\u0026thinsp;=\u0026thinsp;32), lipid and atherosclerosis (DC\u0026thinsp;=\u0026thinsp;31), chemical carcinogenesis-receptor activation (DC\u0026thinsp;=\u0026thinsp;27), and AGE-RAGE signaling pathway (DC\u0026thinsp;=\u0026thinsp;22). The top three active ingredients are quercetin (DC\u0026thinsp;=\u0026thinsp;42), kaempferol (DC\u0026thinsp;=\u0026thinsp;23), and luteolin (DC\u0026thinsp;=\u0026thinsp;24). The top targets are PTGS2 (DC\u0026thinsp;=\u0026thinsp;28), MAPK14 (DC\u0026thinsp;=\u0026thinsp;19), and GSK3B (DC\u0026thinsp;=\u0026thinsp;17).\u003c/p\u003e\u003cbr\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.8 Molecular docking analysis\u003c/h2\u003e\n \u003cp\u003eWe demonstrated molecular docking of the main active ingredients quercetin, kaempferol, and luteolin with the AGE-RAGE signaling pathway, and found that quercetin, kaempferol, and luteolin had both good binding activity with AKT1(PDB:6HHF), JUN (PDB:3U86), TNF (PDB:1FT4), PTGS2 (PDB:5IKV), AGER (PDB:7LMW), and STAT3 (PDB:6NUQ), as shown in Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e. Further we found that the top3 binding energies were the binding of AKT1 to quercetin, luteolin, and kaempferol with binding energies of -9.56kcal/mol, -9.11kcal/mol, and \u0026minus;\u0026thinsp;9.08kcal/mol, respectively, as shown in Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003eA-C. The docking binding site of quercetin and AKT1 molecule is at \u003cem\u003eTHR-211\u003c/em\u003e, \u003cem\u003eASN-54\u003c/em\u003e, and \u003cem\u003eGLN-79\u003c/em\u003e. The docking binding site of luteolin and AKT1 molecule is at \u003cem\u003eTHR-211\u003c/em\u003e, \u003cem\u003eASN-54\u003c/em\u003e, and \u003cem\u003eVAL-271\u003c/em\u003e. The docking binding site of kaempferol and AKT1 is at \u003cem\u003eASN-54\u003c/em\u003e and \u003cem\u003eSER-205.\u003c/em\u003e\u003c/p\u003e\u003cbr\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMolecular docking binding energy/kcal.mol\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eAKT1\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eJUN\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eTNF\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003ePTGS2\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eAGER\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eSTAT3\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eQuercetin\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e-9.56\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e-8.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e-4.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e-8.47\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e-4.91\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e-5.57\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eLuteolin\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e-9.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e-7.67\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e-4.80\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e-8.51\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e-4.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e-5.68\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eKaempferol\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e-9.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e-8.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e-4.65\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e-8.34\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e-4.57\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e-5.49\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u0026ldquo;Different treatment for the same disease\u0026rdquo; refers to the concept that the same condition can be addressed with various treatments, often achieving similar results (Sun et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). This concept is particularly prevalent in TCM, but its inherent connections are still not fully understood. Network pharmacology is a cross-disciplinary field that merges traditional pharmacology, bioinformatics, chemoinformatics, and biology, which is extensively used to explore the biological basis of syndromes, active compounds, the underlying mechanisms of formulas, and even the new indications and innovative drugs development (Zhang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Utilizing network pharmacology, this study focuses on FB1T and FPT as examples of RM treatment, a condition significantly impacting the global population\u0026rsquo;s TFRs. The goal is not only to explore the underlying basis and action mechanisms of FB1T and FPT in treating RM but also to better understand the scientific concept of \u0026quot;different treatment for the same disease\u0026quot;. Our results suggest that the key components in RM treatment by FB1T and FPT are quercetin, kaempferol, and luteolin. These active ingredients appear to influence a common signaling pathway, demonstrating the interconnection between different treatments for the same condition.\u003c/p\u003e\n\u003cp\u003eRM is a significant factor impacting TFRs (Neyer et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Stirrat, \u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e). This study aims to understand how FB1T and FPT treatments enhance TFRs. We first offer genetic data support for the application of FB1T and FPT in treating RM through DO enrichment. We identified a total of 1933 disease targets via various databases, with 286 and 230 overlapping genes with FB1T and FPT, respectively. While the top ten targets of FB1T and FPT for RM differ post-PPI enrichment, they both align within the AGE-RAGE signaling pathway following KEGG enrichment. Advanced Glycation End products (AGEs) are pro-inflammatory molecules inducing intracellular oxidative stress and inflammation upon binding with their cell membrane receptors, RAGE (Asadipooya and Uy, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bao et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Produced by non-enzymatic glycation of macromolecules with a reducing sugar (Maillard reaction), AGEs can instigate protein damage through excessive oxidative stress and a significant increase in Reactive Oxygen Species (ROS) (Asadipooya and Uy, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe soluble form of RAGE (sRAGE) is a result of either RAGE gene splicing or protease-based cleavage of membrane-bound RAGE (Asadipooya and Uy, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Acting as a decoy for AGEs, sRAGE can inhibit AGE-RAGE interaction and its subsequent pro-inflammatory signaling. In many cases, sRAGE is considered an anti-inflammatory receptor (Bao et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). It interacts with various receptors, particularly RAGE, triggering the activation of multiple signals, including PI3K/Akt, MAPK/ERK, Src/RhoA, and JAK/STAT (Bao et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). This complex signal activation elevates NF-kB and other transcription factors, along with ROS production (Shen et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The relationship between antiphospholipid antibody syndrome, uterine abnormalities, parental chromosomal abnormalities, polycystic ovary syndrome (PCOS), and RM is well-established (Cocksedge et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). Women with PCOS often exhibit systemic chronic inflammatory conditions, both systemically and at the ovarian level, as demonstrated by elevated serum/ovarian AGEs and increased pro-inflammatory RAGE in ovarian tissue (Pertynska-Marczewska et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). The presence of sRAGE in follicular fluid and its potential protective role against AGE-induced ovarian function damage were also noted (Roness et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Our study also enriched several inflammation-related genes, including TNF, AKT1, JUN, and IL-1\u0026beta;.Thus, we hypothesize that FB1T and FPT might enhance oxidative stress resistance and provide ovarian protection against RM by inhibiting the AGE-RAGE signaling pathway.\u003c/p\u003e\n\u003cp\u003eRecent studies suggest RM is often linked to structural and neurochemical changes in the brain, potentially resulting in depression and anxiety (Chen et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). For many women and their partners, miscarriage can be a significant, life-changing event, often leading to \u0026lsquo;state anxiety\u0026rsquo; that an emotional state characterized by feelings of apprehension, worry, nervousness, and physical symptoms such as an increased heart rate and rapid breathing (Chen et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Quenby et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Evidence indicates that women with RM experience mild to moderate depressive symptoms and higher incidences of depression compared to those who experienced normal childbirth (Farren et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Interestingly, suppressing the AGE-RAGE signaling pathway has been shown to not only reduce inflammation but also exhibit anti-anxiety and anti-depressive effects (Wang et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Early studies reported elevated levels of proinflammatory cytokines, such as IL-1\u0026beta;, IL-6, and TNF, in depressive patients\u0026rsquo; blood, while anti-inflammatory agents showed antidepressant effects (Kohler et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). The genes \u003cem\u003eESR1\u003c/em\u003e, \u003cem\u003ePTGS2\u003c/em\u003e, \u003cem\u003eIL-1\u0026beta;\u003c/em\u003e, \u003cem\u003eMMP9\u003c/em\u003e, \u003cem\u003eIL-6\u003c/em\u003e, and \u003cem\u003eTNF\u003c/em\u003e, which are associated with depression and memory impairment, were also identified in this study, suggesting that FB1T and FPT treatments may affect RM through anxiolysis. Among the multiple signaling pathways associated with FB1T and FPT\u0026rsquo;s anti-depression function predicted by KEGG analysis, inflammation-related signaling pathways are well-documented in depression. The leading signaling pathway from KEGG enrichment is the AGE-RAGE pathway, implicated in various pathological conditions, including diabetic complications, fluid shear stress, and atherosclerosis. These are closely related to RM and AGE-RAGE signaling pathways. Hence, we hypothesize that FB1T and FPT may mediate the activation of NF-kB through the AGE-RAGE signaling pathway and inhibit pro-inflammatory cytokines such as IL-1\u0026beta;, IL-6, and TNF\u0026alpha;, thereby exerting antidepressant effects and potentially offering therapeutic benefits for RM.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identifies quercetin, kaempferol, and luteolin as the key active components in FB1T and FPT, used to treat Recurrent Miscarriage (RM). Their potential mechanism likely involves enhancing oxidative stress resistance, reducing anxiety, and improving ovarian function, achieved by inhibiting the AGE-RAGE signaling pathway. These findings not only support the use of FB1T and FPT in reducing RM and increasing birth rates, but also enrich the traditional Chinese medical concept of \u0026ldquo;different treatment for the same disease.\u0026rdquo; However, the precise role of these components in key genes requires further validation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization:\u0026nbsp;Lijuan Jiang; Data curation: Lin Jiao; Formal analysis: Lin Jiao; Funding acquisition:\u0026nbsp;Lijuan Jiang, Yanping Qian; Investigation:\u0026nbsp;Xingxiu Zhan; Software: Lin Jiao; Supervision: Lijuan Jiang; Validation: Lijuan Jiang; Roles/Writing - original draft: Lin Jiao, Xingxiu Zhan; Writing - review and Editing:\u0026nbsp;Lijuan Jiang.\u0026nbsp;All the authors contributed\u0026nbsp;to the article revision, read, and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (82060882) and Yunnan Provincial Science and Technology Department Science and Technology Program (202301AZ070001-077).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used or analyzed relating to this study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlijotas-Reig, J. \u0026amp; Garrido-Gimenez, C. 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(2019), \u0026quot;Network Pharmacology Databases for Traditional Chinese Medicine: Review and Assessment\u0026quot;, \u003cem\u003eFrontiers in Pharmacology\u003c/em\u003e\u003cem\u003e \u003c/em\u003e1010.3389/fphar.2019.00123.\u003c/li\u003e\n\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":"Total fertility rates, recurrent miscarriage, Different treatment for the same illness, Chinese herbal formula, AGE-RAGE signaling pathway","lastPublishedDoi":"10.21203/rs.3.rs-4529291/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4529291/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDespite global economic growth and health care and education improvements, the global birth rate has remained negative. How to increase fertility has become a common global challenge. Fertility-boosting No. 1 Tang (FB1T) and Fertility-preserving Tang (FPT) are clinically effective prescriptions of traditional Chinese medicine, which play important roles in improving the sperm quality of boys and the embryo loading rate of women to the process of fertilization of sperms and eggs, but the mechanism of their action is still unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFor insight into the molecular mechanism of FB1T and FPT in reproduction, we used a network pharmacology approach to analyze it with recurrent miscarriage (RM) as the disease representative. Then, we analyzed the potential protein targets signaling pathways looking for therapeutic mechanisms between FB1T and FPT and RSA by drug-target network respectively. Finally, AutoDock Vina was selected for molecular docking validation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFrom the OMIM, DisGeNET, and GeneCards databases, we identified 1933 targets for Recurrent Miscarriage (RM). Post-ADME screening, 96 active components and 467 targets in FB1T, along with 137 active components and 327 targets in FPT were recognized. A total of 286 active component targets in FB1T and 230 in FPT overlapped with RM targets. PPI analysis revealed top targets like TNF, AKT1, IL6, TP53, IL1B, ESR1, STAT3, EGFR, CASP3, JUN, CTNNB1, and MMP9. These targets are associated with 124 and 99 signalling pathways in FB1T and FPT respectively, including the AGE-RAGE signaling pathway and chemical carcinogenesis-receptor activation. Quercetin, kaempferol, and luteolin were identified as the primary active components in both FB1T and FPT for RM treatment. We hypothesize FB1T and FPT may activate NF-kB through the AGE-RAGE signaling pathway, inhibiting pro-inflammatory cytokines such as IL-1β, IL-6, and TNFα, thereby offering therapeutic benefits for RM. Molecular docking further verified that quercetin, kaempferol, and luteolin have strong binding activities with proteins involved in the AGE-RAGE signaling pathway.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe material basis of FB1T and FPT for the treatment of RM is quercetin, kaempferol, and luteolin. The mechanism may be to enhance oxidative stress resistance and improve anxiety and ovarian function by inhibiting the AGE-RAGE signaling pathway for the treatment of RM.\u003c/p\u003e","manuscriptTitle":"Exploring the Reproductive Mechanisms of Fertility-Boosting No.1 and Fertility-Preserving Tang by Network pharmacology and molecular docking","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-19 12:14:17","doi":"10.21203/rs.3.rs-4529291/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":"1fa66d3d-6bbc-41b4-afd9-b418d7757672","owner":[],"postedDate":"June 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-02T05:39:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-19 12:14:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4529291","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4529291","identity":"rs-4529291","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}