Network pharmacology and Mendel randomization analysis method of chlorogenic acid for resisting lithangiuria

Abstract

The invention discloses a network pharmacology and Mendel randomization analysis method for resisting lithangiuria by chlorogenic acid, and belongs to the technical field of biomedical engineering, and the method comprises the following steps: taking an intersection of a potential action target of chlorogenic acid and a lithangiuria target as a candidate target; performing pathway enrichment analysis and screening on each candidate target spot to determine a plurality of first pathways; for each first path, calculating a target importance score of each candidate target and performing screening according to the target importance score, and obtaining the screened candidate target as a key target; respectively performing Mendel stochastic analysis on each key target spot to obtain a causal effect estimated value of each key target spot and the urinary calculus risk, screening all the first pathways according to the causal effect estimated value, taking the screened first pathways as target pathways, and taking the key target spots in the target pathways as target targets. The method can be used for efficiently and accurately screening and determining the core regulatory pathway playing the anti-lithangiuria role and the real key target to which the core regulatory pathway belongs.

Description

Technical Field

[0001] This invention belongs to the field of biomedical engineering technology, specifically relating to a network pharmacology and Mendelian randomization analysis method for chlorogenic acid in the treatment of urinary tract stones. Background Technology

[0002] Urinary tract stones are a common and refractory disease of the urinary system, including kidney stones and ureteral stones. Their incidence is increasing globally, with a high recurrence rate, severely impacting patients' quality of life and increasing the medical burden. Current clinical treatment mainly involves surgical stone removal, extracorporeal shock wave lithotripsy, and symptomatic drug therapy. However, surgery is highly invasive and carries a high risk of postoperative complications. Traditional drugs (such as citrate preparations and thiazide diuretics) have problems such as single-target action, significant side effects, and poor long-term tolerability, making it difficult to meet the clinical demand for safe and effective anti-stone drugs. Therefore, screening for multi-target, low-toxicity anti-stone active ingredients from natural products and systematically elucidating their mechanisms of action has become a research hotspot in the prevention and treatment of urinary tract stones. Chlorogenic acid (CGA) is a polyphenolic active ingredient widely found in natural plants such as honeysuckle, eucommia, and coffee. It possesses various pharmacological activities, including anti-inflammatory, antioxidant, antibacterial, and metabolic regulation, and exhibits high biosafety, making it a promising candidate for applications in the food, health product, and pharmaceutical fields. Recent studies have revealed that chlorogenic acid may exert its potential anti-stone effects by inhibiting the formation of calcium salt crystals in urine, reducing urinary tract mucosal inflammation, and regulating renal metabolic enzyme activity. However, successfully transforming this complex and multifunctional natural product into a targeted drug candidate currently faces a key technological bottleneck: how to efficiently and accurately screen and identify the core regulatory pathways and their true key targets for its anti-urinary tract stone-exerting effects from a vast array of potential biomolecules and complex interaction networks.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0005] This disclosure provides a network pharmacology and Mendelian randomization analysis method for chlorogenic acid's anti-calculi effect, which can efficiently and accurately screen and identify the core regulatory pathways and their true key targets for its anti-calculi effect.

[0006] In some embodiments, the network pharmacology and Mendelian randomization analysis method for chlorogenic acid's anti-urinary calculi treatment includes: Step S101: Obtain potential targets of chlorogenic acid and targets of urinary tract stones, and take the intersection of potential targets and targets of urinary tract stones as candidate targets. Step S102: Perform pathway enrichment analysis on each candidate target and screen based on the error rate to identify multiple first pathways; Step S103: For each first pathway, calculate the target importance score of each corresponding candidate target, and filter all corresponding candidate targets based on the target importance score to obtain the filtered candidate targets as key targets. Step S104: Perform Mendelian randomization analysis on each key target to obtain the causal effect estimate between each key target and the risk of urinary tract stones. Step S105: Based on the estimated values ​​of each causal effect, all first pathways are screened to obtain the screened first pathways as target pathways, and the key targets within the target pathways are taken as target targets.

[0007] The beneficial effects of this invention are as follows: By using the intersection of potential targets of chlorogenic acid and targets for urinary tract stones as candidate targets, irrelevant targets can be filtered out, ensuring that all candidate targets are direct targets for the anti-stone effects of chlorogenic acid. Further pathway enrichment analysis and screening of candidate targets allows for mapping discrete targets to specific pathways, ensuring that the selected first pathway has a statistically significant association with the anti-urinary tract stone effect of chlorogenic acid, thus guaranteeing the scientific validity and reliability of the first pathway. Within each first pathway, target importance scores are calculated to screen key targets within that pathway, identifying those at the core of the pathway signal. Mendelian randomization analysis is then performed on each key target to assess its causal effect estimate with the risk of urinary tract stones, providing causal evidence for the targets (i.e., key targets) predicted by network pharmacology.

[0008] In this way, by identifying candidate targets through intersection analysis as the starting point for network pharmacology analysis, and then mapping discrete targets to biological pathways through pathway enrichment analysis, the first pathway is screened based on various causal effect estimates to obtain the target pathway. Key targets within this pathway are then identified as target targets, providing a complete chain of evidence from correlation prediction to causal evidence for the study of chlorogenic acid's mechanism of action. Through a multi-level screening strategy involving target intersection screening, pathway enrichment screening, and target importance scoring, the core regulatory pathways (i.e., target pathways) and their corresponding key targets (i.e., target targets) that exert their anti-urinary calculi effect can be efficiently and accurately screened and identified.

[0009] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0010] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein: Figure 1 This is a flowchart of a network pharmacology and Mendelian randomization analysis method for chlorogenic acid in the treatment of urinary tract stones, provided by the present invention; Figure 2 This is a flowchart of a method for determining the quality of an invention. Detailed Implementation

[0011] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0012] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0013] Unless otherwise stated, the term "multiple" means two or more.

[0014] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0015] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0016] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.

[0017] Combination Figure 1As shown, this disclosure provides a network pharmacology and Mendelian randomization analysis method for chlorogenic acid's anti-urinary calculi treatment, including: Step S101: Obtain potential targets of chlorogenic acid and targets of urinary tract stones, and take the intersection of potential targets and targets of urinary tract stones as candidate targets.

[0018] In this way, by systematically acquiring the potential target group of chlorogenic acid (drug) and the relevant target group of urinary tract stones (disease), we can use them as the starting point for network pharmacology analysis to construct a "drug-disease" target interaction network.

[0019] In this embodiment, chlorogenic acid includes 5-caffeoylquinic acid, 4-caffeoylquinic acid, etc.

[0020] Specifically, obtaining potential targets for chlorogenic acid includes: obtaining the standard SMILES string or Mol file of chlorogenic acid, i.e., obtaining the chemical structure of chlorogenic acid. The chemical structure of chlorogenic acid is then input into multiple prediction platforms (such as SwissTarget Prediction, TCMSP database, BATMAN-TCM database, etc.), and merged and deduplicated to obtain potential targets for chlorogenic acid. No specific limitations are imposed here.

[0021] Specifically, identifying targets for urinary tract stones includes: using text mining tools such as PubTator and SemRep to search for literature containing both urinary tract stone terminology and gene / protein terminology, and extracting frequently co-occurring genes to obtain targets for urinary tract stones. Alternatively, it involves searching for gene expression profile datasets (such as GSE numbers) of patients with urinary tract stones (kidney tissue, urine sediment cells, blood, etc.) and healthy controls. Differential expression analysis is then performed to screen for significantly upregulated or downregulated genes. These differentially expressed genes are responders or participants in the disease state, and thus represent targets for urinary tract stones. No further limitations are imposed here.

[0022] Step S102: Perform pathway enrichment analysis on each candidate target and screen based on the error rate to identify multiple first pathways.

[0023] In this way, pathway enrichment analysis (such as GO and KEGG) maps discrete candidate targets onto biological pathways to reveal the key biological pathway network through which chlorogenic acid affects urinary tract stones via the synergistic effect of its target group, thereby revealing the networked functional modules of chlorogenic acid drug action.

[0024] Step S103: For each first pathway, calculate the target importance score of each corresponding candidate target, and screen all corresponding candidate targets based on the target importance score to obtain the screened candidate targets as key targets.

[0025] In this way, by quantifying the importance of candidate nodes, the centrality of network nodes can be quantified, thereby identifying the pivotal targets (i.e. key targets) in the network, which reflects the deepening from "having candidate targets" to "finding key targets".

[0026] Specifically, for each first pathway, candidate targets with a target importance score ≥ a preset importance threshold are designated as their corresponding key targets. Since Mendelian randomization analysis is computationally intensive, target importance scoring is used for screening to balance important candidate targets with reduced computational load and improved efficiency.

[0027] Each candidate target corresponding to the first pathway represents a candidate target within that first pathway.

[0028] Step S104: Perform Mendelian randomization analysis on each key target to obtain the causal effect estimate between each key target and the risk of urinary tract stones.

[0029] Step S105: Based on the estimated values ​​of each causal effect, all first pathways are screened to obtain the screened first pathways as target pathways, and the key targets within the target pathways are taken as target targets.

[0030] Specifically, for each primary pathway, the sum of the estimated causal effects of all its key targets is calculated. All primary pathways are then ranked in descending order based on the sum of their estimated causal effects. A predetermined number of primary pathways ranked highest are selected as target pathways. The sum of estimated causal effects characterizes the overall strength of the primary pathway's influence on disease risk at the genetic causal level. Thus, the higher the ranking, the more key targets within that primary pathway have been validated to have causal effects, or the stronger the causal effects of these targets. This indicates that the primary pathway is a core signaling pathway with significant causal evidence, i.e., a core regulatory pathway.

[0031] The network pharmacology and Mendelian randomization analysis method for chlorogenic acid's anti-urinary calculi treatment provided in this disclosure is employed. By using the intersection of potential targets of chlorogenic acid and targets for urinary calculi as candidate targets, irrelevant targets can be filtered out, ensuring that all candidate targets are direct targets for chlorogenic acid's potential anti-calculi effect. Furthermore, pathway enrichment analysis and screening of candidate targets allow for mapping discrete targets to specific pathways, ensuring that the selected first pathway has a statistically significant correlation with the anti-urinary calculi effect of chlorogenic acid, thus guaranteeing the scientific validity and reliability of the first pathway. Within each first pathway, target importance scores are calculated to identify key targets within that pathway, thus identifying those at the core of the pathway signal. Mendelian randomization analysis is then performed on each key target to assess its causal effect estimate with the risk of urinary calculi, providing causal evidence for the targets (i.e., key targets) predicted by network pharmacology. In this way, by identifying candidate targets through intersection analysis as the starting point for network pharmacology analysis, and then mapping discrete targets to biological pathways through pathway enrichment analysis, the first pathway is screened based on various causal effect estimates to obtain the target pathway. Key targets within this pathway are then identified as target targets, providing a complete chain of evidence from correlation prediction to causal evidence for the study of chlorogenic acid's mechanism of action. Through a multi-level screening strategy involving target intersection screening, pathway enrichment screening, and target importance scoring, the core regulatory pathways (i.e., target pathways) and their corresponding key targets (i.e., target targets) that exert their anti-urinary calculi effect can be efficiently and accurately screened and identified. This contributes to promoting the development and application of chlorogenic acid as a candidate drug for anti-urinary calculi.

[0032] Preferably, multiple first pathways are determined based on the error occurrence rate, including: Based on the error rate, multiple secondary pathways were identified, and parameter values ​​for each secondary pathway were obtained in various comparison dimensions. The comparison dimensions included: target coverage, disease association strength, and pathway synergy. For any two second paths, the superiority or inferiority is determined according to the parameter values ​​of each comparison dimension and the second path that is determined to be superior is counted as one win. The cumulative number of wins for each second pathway in all pairwise comparisons is calculated, and the cumulative number of wins is used as the pathway importance score for the second pathway. Based on the importance score of each pathway, all second pathways are screened, and the screened second pathways are used as first pathways to obtain multiple first pathways.

[0033] By screening based on error rate, multiple secondary pathways can be quickly identified from a massive pool of pathways, achieving initial pathway screening. Then, each initially screened secondary pathway is comprehensively evaluated based on target enrichment, disease relevance, and pathway synergy, determining the parameter values ​​for each secondary pathway across various comparison dimensions. Based on these parameter values, a competitive ranking mechanism using pairwise comparisons and cumulative wins is employed to determine the pathway importance score. This importance score is then used to further screen secondary pathways, resulting in multiple primary pathways and a second round of screening, making the selected primary pathways more robust and reliable.

[0034] Specifically, multiple second pathways are identified based on error rate screening, including: performing pathway enrichment analysis on the candidate target set to obtain the original enrichment significance P-values ​​of multiple third pathways; correcting the original enrichment significance P-values ​​of multiple third pathways for error detection rate and calculating the error detection rate (FDR) value corresponding to each third pathway; and, based on a preset FDR threshold, selecting all third pathways with FDR values ​​less than the preset FDR threshold, and using these selected third pathways as second pathways to identify multiple second pathways.

[0035] Preferably, combined with Figure 2 As shown in the embodiments of this disclosure, a method for determining superiority or inferiority is provided. Based on the parameter values ​​of each comparison dimension, superiority or inferiority is determined according to multi-dimensional comparison rules, including: Step S201: Determine the current comparison dimension according to the preset priority order; Step S202: Based on the parameter values ​​of each comparison dimension, calculate the absolute value of the parameter difference between the two second paths in the current comparison dimension; Step S203: If the absolute value of the parameter difference is ≥ the corresponding preset difference threshold, then the second pathway with the larger parameter value is determined to be superior, and the comparison ends. If the absolute value of the parameter difference is less than the corresponding preset difference threshold, and the priority of the current comparison dimension is not the lowest level, then return to steps S201 to S203. If the absolute value of the parameter difference is less than the corresponding preset difference threshold, and the priority of the current comparison dimension is the lowest level, then the two second paths are determined to be a tie, and the comparison ends.

[0036] The priority order ensures that the second pathway is sufficiently superior in the most critical dimension (i.e., the high-priority comparison dimension) when determining its merits. Only when the high-priority dimension cannot be determined will the next priority comparison dimension be considered. The difference threshold requires that the difference between two second pathways must meet a minimum difference (i.e., the difference threshold) to be accepted. This effectively prevents unstable ranking results caused by small random fluctuations in parameter values ​​or calculation errors, improving the robustness of the comparison results. Thus, by using a preset priority order and difference threshold, the complex multi-dimensional comparison of merits is transformed into a standardized, automated comparison process, ensuring that the comparison results between any pairwise second pathways are objective, repeatable, and robust.

[0037] Preferably, the parameter values ​​for target coverage of the second pathway are determined by the following method: For each second pathway, the following steps are performed: Obtain the first core target set for urinary tract stones; determine the candidate target set included in the second pathway; calculate the parameter values ​​of the target coverage of the second pathway: in, Characterizing the second pathway In terms of target coverage parameter values, Characterizing the second pathway The set of candidate targets, Characterizing the first core target set, Represents the set of candidate targets The number of elements in the intersection with the first core target set D. This represents the number of elements in the first core target set D.

[0038] The parameter value for target coverage of the second pathway is in the range of [0,1], indicating the proportion of the total core disease targets covered by the second pathway. A higher value indicates a more direct and extensive association between the second pathway and the known pathological mechanisms of urinary tract stones. This allows for the efficient and accurate determination of the parameter value for target coverage of the second pathway.

[0039] Preferably, the parameter values ​​for the second pathway in disease association are determined by the following method: For each second pathway, perform the following steps: Obtain disease gene association scores, differential expression scores, and literature co-occurrence scores; The disease gene association score, differential expression score, and literature co-occurrence score were normalized to obtain normalized disease gene association score, normalized differential expression score, and normalized literature co-occurrence score. The normalized disease gene association score, normalized differential expression score, and normalized literature co-occurrence score were weighted and summed to determine the parameter value of the second pathway in disease association.

[0040] Among them, the disease gene association score represents the score of known and validated association evidence from authoritative clinical and genetic databases (such as DisGeNET); the differential expression score represents the score of differential perturbation evidence from omics data (such as transcriptomics and proteomics) between the disease (i.e., urinary tract stones) and normal controls; and the literature co-occurrence score represents the score of emerging and potential association evidence mined from the latest and most extensive biomedical literature.

[0041] In this way, the parameter values ​​of the second pathway in disease association are determined from three dimensions: disease gene association score, differential expression score, and literature co-occurrence score. These parameter values ​​include known knowledge of the "gold standard", dynamic data reflecting the current pathological state, and the latest research findings, thereby avoiding bias or information lag caused by relying on a single source of evidence and realizing a comprehensive and three-dimensional assessment of the association between pathway and disease.

[0042] Specifically, obtaining disease gene association scores includes: obtaining the disease gene set (i.e., obtaining a list of genes known to be associated with urinary tract stones from authoritative disease-gene association databases (such as DisGeNET, OMIM, ClinVar). Typically, the DisGeNET gene-disease association score (range 0-1) and pathway gene set (i.e., all genes corresponding to the current second pathway) are used. A hypergeometric test (or Fisher's exact test) is used to assess whether the pathway gene set P significantly enriches genes in the disease gene set D, obtaining the P-value for enrichment analysis. The significance P-value of the enrichment analysis is converted into a score (which can be expressed as -log10(P)). value (The larger the value, the more significant the enrichment and the stronger the association), thus obtaining a disease gene association score.

[0043] Specifically, obtaining differential expression scores includes: obtaining genome-wide gene expression differential statistics based on transcriptomic data of urinary tract stones and controls; using gene set enrichment analysis to calculate the standardized enrichment score and statistical significance of the pathway gene set corresponding to the second pathway; and calculating the differential expression score based on the absolute value of the standardized enrichment score and the negative logarithm of the statistical significance.

[0044] Specifically, the co-occurrence score of the literature is obtained by: obtaining the disease terminology set for urinary tract stones; obtaining the pathway terminology set corresponding to the second pathway; wherein, the pathway terminology set includes the name, aliases and gene or protein names contained in the second pathway; based on the biomedical literature database, searching for co-occurring literature in which terms from the disease terminology set and terms from the pathway terminology set appear together in the same literature; counting the number of co-occurring literatures, and performing a logarithmic transformation on the number of co-occurring literatures to obtain the literature co-occurrence score.

[0045] Preferably, the comparison dimension also includes PPI network topology importance. The parameter value for the second path's importance in the PPI network topology is determined as follows: For each second path, the following steps are performed: Obtain the genome-wide protein-protein interaction network as the global network. Extract the protein list corresponding to the second pathway (i.e., obtain the official gene list (i.e., pathway gene set) of the second pathway from the pathway database, map these gene symbols to their encoded proteins to obtain the corresponding protein list), and obtain all interactions between each protein in the protein list from the global network to construct the pathway network corresponding to the second pathway (i.e., the PPI network corresponding to the second pathway). Calculate the topological feature index of the pathway network, and based on each topological feature index, determine the parameter values ​​of the topological importance of the second pathway in the PPI network.

[0046] The topological characteristic indicators include at least one of the following: the average degree centrality of all nodes in the pathway subnetwork in the global network, the module degree score, the average clustering coefficient, and the average betweenness center. The weighted sum of these topological characteristic indicators can be used as a parameter value for the topological importance of the second pathway in the PPI network. The PPI network (Protein-Protein Interaction Network) characterizes a protein-protein interaction network.

[0047] Specifically, obtaining the genome-wide protein-protein interaction network as a global network includes: downloading the protein-protein interaction information file from the STRING database website, performing data cleaning and standardization (such as identifier unification, confidence filtering, and redundancy removal), and converting the processed data into a graph in graph theory to obtain the global network. In this graph, each node is a unique, standardized protein identifier; and each edge represents a filtered pair of protein interactions.

[0048] In this way, by constructing a global network, a unified benchmark can be established for path topology analysis, thereby ensuring the fairness of the calculation of PPI network topology importance for all second paths.

[0049] Preferably, the parameter value of the second pathway in pathway synergy is determined by the following method: Based on each second path, a path node graph is constructed; where a node represents a second path, if there is at least one common candidate target between two second paths, then an undirected edge is established between the corresponding graph nodes, and the edge weight of the undirected edge represents the number of common candidate targets. For any graph node within the pathway node graph, determine the sum of the corresponding edge weights and the number of candidate target points, and The quotient of the sum of the corresponding edge weights and the number of candidate targets is used as the parameter value of the corresponding second path in terms of path synergy.

[0050] In this way, by constructing all the second pathways as a pathway node graph and using the number of the same candidate targets between two second pathways as the edge weight, the parameter value of the second pathway in pathway synergy can be quantified.

[0051] Preferably, the priority order is determined in the following manner: Obtain the ranking results of each expert based on the importance of each comparison dimension; Based on all ranking results, calculate the average ranking value and ranking consistency for each comparison dimension; For each comparison dimension, if the ranking consistency index is less than the preset consistency threshold, a penalty adjustment is made to the average ranking value, and the adjusted ranking value is used as the target ranking value. If the ranking consistency index is greater than or equal to the preset consistency threshold, the average ranking value will be used as the target ranking value. Priority order is determined based on the target ranking values ​​of each comparison dimension.

[0052] Multiple experts ranked each comparative dimension individually, and the average ranking value and ranking consistency for each dimension were calculated. For each comparative dimension, if the ranking consistency index was less than the consistency threshold, it indicated that the experts had significant differences in their rankings of that dimension. Therefore, a punitive adjustment was made to avoid the decision-making risks of forcibly assigning a high priority to a comparative dimension when experts disagreed, reflecting the principle of prudence. If the ranking consistency index was greater than or equal to the consistency threshold, it indicated that the experts had a clear consensus on the ranking of that comparative dimension. In this way, the priority order was determined based on the target ranking value of each comparative dimension, integrating the knowledge and perspectives of different experts. This made the final priority order more reflective of the general understanding in the research field, significantly improving the objectivity and authority of the decision (i.e., the priority order).

[0053] Specifically, the ranking consistency index can be 1 / (standard deviation + ε), where ε is a small constant (divided by zero). Alternatively, it can be 1 - coefficient of variation. That is, for each comparison dimension, calculate its standard deviation, and then use 1 / (standard deviation + ε) as the ranking consistency index. Or, calculate the coefficient of variation (CV) (the ratio of standard deviation to mean, used to eliminate dimensions). The smaller the CV, the higher the consistency. CV=0 indicates that all experts gave exactly the same ranking (perfect consistency), and then use 1 - coefficient of variation as the ranking consistency index.

[0054] Preferably, for each first pathway, the target importance score of each corresponding candidate target is calculated, including: For each first pathway, perform the following steps: Determine the PPI network corresponding to the first path; For each candidate target corresponding to the first path, the degree center and betweenness center of the node corresponding to the candidate target are determined based on the PPI network corresponding to the first path. The degree center and betweenness center are then weighted and summed to obtain the target importance score of the candidate target.

[0055] In this way, by determining the path PPI network (i.e. the PPI network corresponding to the first path), calculating the degree center and betweenness centrality of each candidate target point in the corresponding node of the network, and performing a weighted summation, the two topological feature indicators are summed into a target importance score, so as to efficiently and scientifically quantify the target importance of each candidate target point.

[0056] It is understandable that determining the PPI network corresponding to the first pathway is a relatively mature existing technology, and will not be elaborated upon here. The corresponding node represents the node corresponding to the candidate target within the corresponding PPI network.

[0057] Preferably, for each first pathway, the target importance score of each corresponding candidate target is calculated, including: Obtain the first core target set corresponding to urinary tract stones; For each first pathway, perform the following steps: The first core target set is matched with the targets within the first pathway to obtain the matched second core target set, and Determine the PPI network corresponding to the first path, and For each candidate target corresponding to the first path, based on the PPI network corresponding to the first path, calculate the average shortest path distance from the candidate target to all nodes corresponding to the second core target set to obtain the average distance of the candidate target, and perform a negative transformation or inverse transformation on the average distance to obtain the target importance score of the candidate target.

[0058] If the average shortest path distance from a candidate target to multiple core disease targets (i.e., the set of second core targets) is very small, then it is highly likely to be an upstream regulatory factor or signal relay station located at a critical intersection. In this way, the target importance assessment is cleverly transformed from a topological problem into a pathological problem. By calculating the distance in the network space, the efficiency of disease intervention (i.e., urinary tract stones) can be quantified, thereby enabling the determination of the target importance score of each candidate target within each first pathway from a pathological perspective.

[0059] It is understandable that the second core target set corresponds to all nodes representing the nodes of each target within the second core target set on the PPI network.

[0060] Specifically, the first core target set corresponding to urinary tract stones is obtained, including: screening out the genes with the strongest genetic association or causal evidence with urinary tract stones from authoritative disease databases (such as DisGeNET) to form a core target set for the disease.

[0061] Preferably, Mendelian randomization analysis is performed on each key target to obtain an estimate of the causal effect between each key target and the risk of urinary tract stones, including: For each key target, perform the following steps: Based on key targets, identify SNPs that meet preset conditions to form a target SNP set; Using the target SNP set as an instrumental variable and urinary tract stones as the outcome variable, Mendelian randomization analysis was performed to obtain an estimate of the causal effect between the key target and the risk of urinary tract stones.

[0062] Network pharmacology-predicted targets (i.e., key targets) are essentially based on correlation assumptions. Therefore, by introducing Mendelian randomization analysis, causal evidence can be provided for network pharmacology, thus addressing the uncertainty of its predictions. This allows drug development teams to prioritize funding and time for these doubly validated targets, significantly reducing the risk of failure on low-potential targets and optimizing the overall development pipeline.

[0063] In this embodiment, the target SNP set is used as an instrumental variable, and the "inverse variance weighting method" is used as the main analysis method to perform Mendelian randomization analysis.

[0064] For ease of understanding, this disclosure provides an example of Mendelian randomization analysis (using a key target as an example): From the genome-wide association study (GWAS) data of the "protein quantity trait loci (pQTLs)" corresponding to the key target, all single nucleotide polymorphisms (SNPs) significantly associated with the level of the target protein (i.e., the protein corresponding to the key target) in plasma or tissue are obtained. These obtained SNPs are then screened, i.e., those meeting preset conditions are selected to form a target SNP set. Each SNP within this target SNP set is a qualified instrumental variable, strongly correlated with the target protein level, and independent of each other. The preset conditions include a significance threshold, independence requirements, and strength requirements. For example, the significance threshold: SNPs with a genome-wide significance level of association with protein levels are selected (i.e., P < 5 × 10⁻⁶). -8 Independence requirements: To avoid redundancy caused by linkage disequilibrium (LD), the initially selected SNPs are clustered using LD (e.g., with r² < 0.001 as a condition), and only the SNP with the smallest p-value within each LD block is retained as an independent instrumental variable; Strength requirements: The F-statistic for each SNP is calculated (F = (beta / SE)², usually requiring F > 10 to ensure the use of strong instrumental variables and avoid bias from weak instrumental variables). Then, from the urolithiasis GWAS summary data, the association estimates (β, SE, p-value, etc.) between each SNP in the target SNP set and the risk of urolithiasis are extracted. Using these SNPs as instrumental variables, Mendelian randomization analysis is performed using the inverse variance weighting method as the main analytical method to obtain the β of this key target. MR (Estimated causal effect).

[0065] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made to it in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. A network pharmacology and Mendelian randomization analysis method for chlorogenic acid's anti-urinary calculi treatment, characterized in that, include: Step S101: Obtain potential targets of chlorogenic acid and targets of urinary tract stones, and take the intersection of potential targets and targets of urinary tract stones as candidate targets. Step S102: Perform pathway enrichment analysis on each candidate target and screen based on the error rate to identify multiple first pathways; Step S103: For each first pathway, calculate the target importance score of each corresponding candidate target, and filter all corresponding candidate targets based on the target importance score to obtain the filtered candidate targets as key targets. Step S104: Perform Mendelian randomization analysis on each key target to obtain the causal effect estimate between each key target and the risk of urinary tract stones. Step S105: Based on the estimated values ​​of each causal effect, all first pathways are screened to obtain the screened first pathways as target pathways, and the key targets within the target pathways are taken as target targets.

2. The method according to claim 1, characterized in that, The filtering based on error occurrence rate identifies multiple first pathways, including: Based on the error rate, multiple secondary pathways were identified, and parameter values ​​for each secondary pathway were obtained in various comparison dimensions. The comparison dimensions included: target coverage, disease association strength, and pathway synergy. For any two second paths, the superiority or inferiority is determined according to the parameter values ​​of each comparison dimension and the second path that is determined to be superior is counted as one win. The cumulative number of wins for each second pathway in all pairwise comparisons is calculated, and the cumulative number of wins is used as the pathway importance score for the second pathway. Based on the importance score of each pathway, all second pathways are screened, and the screened second pathways are used as first pathways to obtain multiple first pathways.

3. The method according to claim 2, characterized in that, The method of determining superiority or inferiority based on parameter values ​​across various comparison dimensions and according to multidimensional comparison rules includes: Step S201: Determine the current comparison dimension according to the preset priority order; Step S202: Based on the parameter values ​​of each comparison dimension, calculate the absolute value of the parameter difference between the two second paths in the current comparison dimension; Step S203: If the absolute value of the parameter difference is ≥ the corresponding preset difference threshold, then the second pathway with the larger parameter value is determined to be superior, and the comparison ends. If the absolute value of the parameter difference is less than the corresponding preset difference threshold, and the priority of the current comparison dimension is not the lowest level, then return to steps S201 to S203. If the absolute value of the parameter difference is less than the corresponding preset difference threshold, and the priority of the current comparison dimension is the lowest level, then the two second paths are determined to be a tie, and the comparison ends.

4. The method according to claim 2, characterized in that, The parameter values ​​for the second pathway in disease association were determined using the following methods: For each second pathway, perform the following steps: Obtain disease gene association scores, differential expression scores, and literature co-occurrence scores; The disease gene association score, differential expression score, and literature co-occurrence score were normalized to obtain normalized disease gene association score, normalized differential expression score, and normalized literature co-occurrence score. The normalized disease gene association score, normalized differential expression score, and normalized literature co-occurrence score were weighted and summed to determine the parameter value of the second pathway in disease association.

5. The method according to claim 2, characterized in that, The parameter values ​​for pathway synergy of the second pathway are determined using the following methods: Based on each second path, a path node graph is constructed; where a node represents a second path, if there is at least one common candidate target between two second paths, then an undirected edge is established between the corresponding graph nodes, and the edge weight of the undirected edge represents the number of common candidate targets. For any graph node within the pathway node graph, determine the sum of the corresponding edge weights and the number of candidate target points, and The quotient of the sum of the corresponding edge weights and the number of candidate targets is used as the parameter value of the corresponding second path in terms of path synergy.

6. The method according to claim 3, characterized in that, The priority order is determined in the following manner: Obtain the ranking results of each expert based on the importance of each comparison dimension; Based on all ranking results, calculate the average ranking value and ranking consistency for each comparison dimension; For each comparison dimension, if the ranking consistency index is less than the preset consistency threshold, a penalty adjustment is made to the average ranking value, and the adjusted ranking value is used as the target ranking value. If the ranking consistency index is greater than or equal to the preset consistency threshold, the average ranking value will be used as the target ranking value. Priority order is determined based on the target ranking values ​​of each comparison dimension.

7. The method according to claim 1, characterized in that, For each first pathway, the target importance score for each corresponding candidate target is calculated, including: For each first pathway, perform the following steps: Determine the PPI network corresponding to the first path; For each candidate target corresponding to the first path, the degree center and betweenness center of the node corresponding to the candidate target are determined based on the PPI network corresponding to the first path. The degree center and betweenness center are then weighted and summed to obtain the target importance score of the candidate target.

8. The method according to claim 1, characterized in that, For each first pathway, the target importance score for each corresponding candidate target is calculated, including: Obtain the first core target set corresponding to urinary tract stones; For each first pathway, perform the following steps: The first core target set is matched with the targets within the first pathway to obtain the matched second core target set, and Determine the PPI network corresponding to the first path, and For each candidate target corresponding to the first path, based on the PPI network corresponding to the first path, calculate the average shortest path distance from the candidate target to all nodes corresponding to the second core target set to obtain the average distance of the candidate target, and perform a negative transformation or inverse transformation on the average distance to obtain the target importance score of the candidate target.

9. The method according to claim 1, characterized in that, The step of performing Mendelian randomization analysis on each key target to obtain causal effect estimates between each key target and the risk of urinary tract stones includes: For each key target, perform the following steps: Based on key targets, identify SNPs that meet preset conditions to form a target SNP set; Using the target SNP set as an instrumental variable and urinary tract stones as the outcome variable, Mendelian randomization analysis was performed to obtain an estimate of the causal effect between the key target and the risk of urinary tract stones.

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