# Network Pharmacology-Based Study on the Active Component and Mechanism of the Anti-Non-Invasive and Invasive Bladder Urothelial Carcinoma Effects of Zhuling Jisheng Decoction

Authors: Chenyu Ma; Jianlong Wang; Na Zhao; Zhenya Pan; Yi Lu; Miao Cheng; Min Deng
Journal: Computational and Mathematical Methods in Medicine
 (2021)

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## Abstract

Zhuling Jisheng decoction is employed for the treatment of bladder urothelial cancer in clinical practice of traditional Chinese medicine. However, there are few studies on its precise mechanism. For the antibladder cancer action of Zhuling Jisheng decoction, a network pharmacological technique was used to design a component/target/pathway molecular regulatory network. The TCMSP dataset was used to identify the chemical makeup of Zhuling Jisheng decoction, which was then analyzed and assessed for oral bioavailability and pharmacological similarity. The chemical composition of Zhuling Jisheng decoction was identified through the TCMSP database, and it was evaluated and screened based on oral bioavailability and drug similarity. The GEO database was searched for genes associated with urothelial bladder carcinoma, and gene targets associated with bladder urothelial cancer resistance were chosen by comparison. The function and linked pathways of the target genes were examined and screened using annotation, visualization, and a comprehensive discovery database. The impact of Zhuling Jisheng decoction on urothelial bladder cancer was studied using Cytoscape software to create a component/target/pathway network. Finally, 69 and 55 target genes were discovered for noninvasive bladder urothelial cancer and invasive bladder urothelial cancer, respectively. In noninvasive urothelial cancer, 118 pathways were highly enriched, including the TNF signaling pathway and the IL-17 signaling route. 103 pathways were highly enriched in invasive urothelial cancer, including the p53 signaling route, bladder cancer route, and calcium signaling route. There were 18 and 15 drug targets associated with noninvasive and invasive bladder urothelial carcinoma prognoses. Many signaling pathways directly act on tumours, and indirect pathways inhibit the development of bladder urothelial carcinoma. This research establishes a scientific foundation for further research into the framework of action of Zhuling Jisheng decoction in the therapy of bladder urothelial cancer.


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## Body

## 1. Introduction

The most frequent malignant tumour in the urinary system is bladder cancer. Each year, more than 470,000 people in the world are diagnosed with bladder cancer for the first time [1]. Furthermore, men are three to four times as likely as women to get bladder cancer [2]. Urothelial cell carcinoma, squamous cell carcinoma, and adenocarcinoma are the most prevalent bladder malignancies, followed by metastatic malignancies, small cell carcinomas, and carcinosarcomas. The most frequent urological malignancy of human bladder cancers, urothelial carcinoma of the bladder, accounts for more than 90% of all bladder cancers, divided into two categories focused on the clinical and morphological characteristics [3]. The majority of urothelial carcinomas constitute noninvasive papillary tumours (PTA stage). These tumours are typically highly differentiated, develop slightly without spreading widely, and have an excellent clinical prognosis, while the remaining one-third of urothelial carcinomas (staged pT1 and above) are invasive tumours with weak differentiation, a rapid rate of progression, and the capacity to metastasize. Bladder cancer has the feature of “three highs:” a high morbidity rate, a high postoperative recurrence rate, and a high fatality rate.Western medicine relies heavily on surgery to treat bladder cancer, combined with radiotherapy and chemotherapy. But the side effects are considerable, and it is not ideal after healing [4]. Traditional Chinese medicine treatments for cancer show unique advantages in controlling the development of cancer lesions, prolonging the survival time for patients and improving the quality of life. Previous studies have studied the anticancer mechanism of traditional Chinese medicine in five directions: inhibiting cancer cell proliferation, directly killing cancer cells, reducing cancer cell metastasis ability, inducing cancer cell apoptosis, and inhibiting tumour blood vessel formation [5]. For example, Yiqi Jianpi Huaji decoction has the effect of reversing multidrug resistance [6]. In gastric cancer rats, Huayu Jiedu Fang can lower the levels of IL-6, IL-10, and TNF-α, as well as the inflammatory response [7]. Weipixiao, a traditional Chinese medicine ingredient, inhibits lung cancer cell angiogenesis by decreasing “hypoxia-inducible factor (HIF-1) and vascular endothelial growth factor (VEGF)” [8]. As a result, traditional Chinese medicine offers a valuable supplement to developing innovative cancer therapies.Zhuling Jisheng decoction (ZLJSD), a compound prescription for bladder cancer based on traditional Chinese medicine theory, containsPolyporus umbellatus (Pers.) Fries, Astragali Cpmplanatisemen, Herba Taxilli, Pseudobulbus Cremastrae Seu Pleiones, and Hedyotis Diffusa Herba, which can treat bladder cancer [9]. Resent research shows that Polyporus umbellatus contains polysaccharides and steroids. Among them, Polyporus umbellatus polysaccharides have effects of antitumour and improve immunity [10]. Pseudobulbus Cremastrae Seu Pleiones contains phenanthrene, glycosides, and aromatic compounds, which can be utilized to antitumour, antiangiogenesis, and antihypertensive [11]. Herba Taxilli contains flavonoids and volatile oils, which can be used for anti-inflammatory and antitumour effects; lowering blood pressure, blood sugar, and blood lipids; and enhancing memory [12]. Astragali Cpmplanatisemen contains chemical components such as flavonoids, triterpenoid saponins, polysaccharides, sterols, organic acids, and smaller elements such as zinc and manganese, which can be utilized to lower blood pressure and improve cellular immune function, anti-inflammatory, anticancer, and antioxidant effects [13]. Hedyotis Diffusa Herba mainly includes terpenes, anthraquinones, flavones, sterols, polysaccharides, organic acids, and alkanes, which can have anti-inflammatory, antioxidant, and neuroprotective effects [14]. Although ZLJSD is used clinically for the treatment of bladder cancer but the peculiar medicinal substances and mechanisms are not clear, so, further investigation is required.The results of the substantial data study include network pharmacology. Network pharmacology’s multicomponent, multitarget, and multichannel network constructive thinking is more suited than traditional single-component, single-target thinking for the complex characteristics of the traditional Chinese medicine complex. From a certain point of view, network pharmacology as a new method provides a large amount of information on the pharmacological effects of single-flavour and Chinese compound medicine. It can analyze traditional Chinese medicine recommendations from a modern pharmacological perspective. It can also assist in researching and developing current Chinese drugs and new dosage forms. As a result, integrating different components, different target characteristics, and network pharmacology in traditional Chinese medicine prescription might help researchers better understand the material foundation and mechanism of action in illness therapy [15]. Sun et al. employed network pharmacology technology to identify the active components and signaling pathways in Dachengqi decoction and investigate the molecular framework of DCQD in the therapy of acute pancreatitis. The results show that 17 prompt ingredients of DCQD can be promoted by “regulating the PI3K/AKT signaling pathway” apoptosis and inhibition of pathological damage and inflammation to improve acute pancreatitis [16]. Tang et al. used network pharmacology methods to find “37 potential targets of the Mahuang Fuzi Xixin decoction for treating allergic rhinitis, including drug similarity assessment, oral bioavailability prediction, multiple drug-target predictions, and network analysis of active ingredients and potential targets.” The objective has something to do with allergic rhinitis. In addition, four of the expected components have been discovered as having anti-inflammatory properties [17]. Shi et al. used network pharmacology and validation studies to investigate the method of Danggui Buxue decoction’s blood-enriching action for treating blood deficiency syndrome and discovered that it works by increasing immunity and increasing immunity hematopoiesis simultaneously [18]. The studies above demonstrated that network pharmacology has gradually begun to penetrate various aspects of traditional Chinese medicine research, exploring new aspects of traditional Chinese medicine and systematically explaining the component-target relationship and pharmacological mechanism of single and compound traditional Chinese medication. Collecting traditional Chinese medicine ingredients, predicting the target of the action, clarifying the action mechanism of single Chinese medicine, and discovering the latest indications of traditional Chinese medicine have important application value.The method and molecular targets of ZLJSD in the therapy of bladder urothelial carcinoma were investigated using a network pharmacological approach in this research study. The TCMSP database was used to identify the active chemicals of ZLJSD and their targets. Then, to find bladder urothelial cancer-related sites, we looked at the differently expressed genes among patients with bladder urothelial cancer and healthy people. The likely mechanism of “ZLJSD therapy of BLCA was investigated using gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and survival curve.” It will be beneficial to explore the processes of traditional Chinese medicine formulas, uncover pharmacological targets, and serve as a guide for future therapeutic procedures.


## 2. Materials and Methods

### 2.1. Active Ingredient Screening

We utilized the traditional Chinese medicine system’s pharmacological databases and analytic platforms (TCMSP,http://lsp.nwu.edu.cn/tcmsp.php) [19] to identify the chemical elements of ZLJSD. This database is used to obtain composition data, such as the composition number, “molecular name, molecular weight, fat-water partition coefficient, number of hydrogen bond donor acceptors, oral bioavailability (OB), intestinal epithelial permeability, blood-brain barrier (BBB), drug similarity (DL), and drug half-life” (HL). We chose to have oral bioavailability OB≥30% and drug similarity DL≥0.18 [20].


### 2.2. Identify Potential Targets

To discover the matching targets of ZLJSD, these 33 potential compounds were entered towards the DrugBank database (https://www.drugbank.ca/) [21]. After removing 6 compounds that were not linked to any target, 27 were finally selected. A total of 875 targets for 27 compounds were collected.


### 2.3. Targets Related to Bladder Urothelial Cancer

Differentially expressed genes in patients with BLCA are by the GEO databank (https://www.ncbi.nlm.nih.gov/geo/) from series GSE40355 and number of samples: GSM991923-GSM991930 control samples, 8 cases, GSM991931-GSM991938 samples of 8 noninvasive tumour samples (2x pTaG1 and 6x pTaG2), and GSM991939-GSM991946 of 8 invasive samples of tumour (1x pT1G1, 4x pT1G3, and 3x pT2G3). Genes with FDR values<0.05 and log2foldchange>1 were regarded to have BLCA linked and differential objectives.


### 2.4. Network Construction

ZLJSD’s complicated target network was built and visualized in Cytoscape 3.7.2 software. The STRING database is a database that holds information on direct and indirect protein interactions. For the degree of interaction, each protein is under aP value. A higher value leads to more confidence in the interaction of protein. Genes relevant to the therapy of ZLJSD bladder cancer have been uploaded for protein interaction to the online STRING database. Data were selected and imported into the software Cytoscape to generate the PPI network for ZLJSD with a high confidence score of 0.7 detect System-level protein–protein interactions. The core PPI network of ZLJSD’s calculated objective and the noninvasive and invasive BLCA targets were shown using the Cytoscape MCODE program.


### 2.5. Bioinformatic Analysis

GO analysis was done utilizing a Database for “Annotation, Visualization, and Integrated Discovery” (DAVID) (https://david.ncifcrf.gov, v6.8) to evaluate the biological function molecular function and cellular component. In the genes (FDR<0.05), functional categories have been enriched and the top 20 functioning categories of GO have been picked. For pathway analysis alone, DAVID that assigned the Kyoto Encyclopedia of Genes and Genome (KEGG) database was utilized. For subsequent investigation, pathways with significant FDR<0.05 alterations were discovered [22].


### 2.6. Relationship between Key Target Genes and Patient Prognosis

Prognostic Analysis of the Cancer Genome Atlas (TCGA) is a project NHGRI and NCI cooperation. The TCGA database is an associated project (NCI). TCGA carried out 33 tumours by sequencing. In this work, the BLCA grouping in TCGA analysis analyzes and draws Kaplan-Meier survival curve forecasts in the forecast of noninvasive urothelial cancer and invasive urothelial cancer, which examines the association of important target genes with the forecast.


## 2.1. Active Ingredient Screening

We utilized the traditional Chinese medicine system’s pharmacological databases and analytic platforms (TCMSP,http://lsp.nwu.edu.cn/tcmsp.php) [19] to identify the chemical elements of ZLJSD. This database is used to obtain composition data, such as the composition number, “molecular name, molecular weight, fat-water partition coefficient, number of hydrogen bond donor acceptors, oral bioavailability (OB), intestinal epithelial permeability, blood-brain barrier (BBB), drug similarity (DL), and drug half-life” (HL). We chose to have oral bioavailability OB≥30% and drug similarity DL≥0.18 [20].


## 2.2. Identify Potential Targets

To discover the matching targets of ZLJSD, these 33 potential compounds were entered towards the DrugBank database (https://www.drugbank.ca/) [21]. After removing 6 compounds that were not linked to any target, 27 were finally selected. A total of 875 targets for 27 compounds were collected.


## 2.3. Targets Related to Bladder Urothelial Cancer

Differentially expressed genes in patients with BLCA are by the GEO databank (https://www.ncbi.nlm.nih.gov/geo/) from series GSE40355 and number of samples: GSM991923-GSM991930 control samples, 8 cases, GSM991931-GSM991938 samples of 8 noninvasive tumour samples (2x pTaG1 and 6x pTaG2), and GSM991939-GSM991946 of 8 invasive samples of tumour (1x pT1G1, 4x pT1G3, and 3x pT2G3). Genes with FDR values<0.05 and log2foldchange>1 were regarded to have BLCA linked and differential objectives.


## 2.4. Network Construction

ZLJSD’s complicated target network was built and visualized in Cytoscape 3.7.2 software. The STRING database is a database that holds information on direct and indirect protein interactions. For the degree of interaction, each protein is under aP value. A higher value leads to more confidence in the interaction of protein. Genes relevant to the therapy of ZLJSD bladder cancer have been uploaded for protein interaction to the online STRING database. Data were selected and imported into the software Cytoscape to generate the PPI network for ZLJSD with a high confidence score of 0.7 detect System-level protein–protein interactions. The core PPI network of ZLJSD’s calculated objective and the noninvasive and invasive BLCA targets were shown using the Cytoscape MCODE program.


## 2.5. Bioinformatic Analysis

GO analysis was done utilizing a Database for “Annotation, Visualization, and Integrated Discovery” (DAVID) (https://david.ncifcrf.gov, v6.8) to evaluate the biological function molecular function and cellular component. In the genes (FDR<0.05), functional categories have been enriched and the top 20 functioning categories of GO have been picked. For pathway analysis alone, DAVID that assigned the Kyoto Encyclopedia of Genes and Genome (KEGG) database was utilized. For subsequent investigation, pathways with significant FDR<0.05 alterations were discovered [22].


## 2.6. Relationship between Key Target Genes and Patient Prognosis

Prognostic Analysis of the Cancer Genome Atlas (TCGA) is a project NHGRI and NCI cooperation. The TCGA database is an associated project (NCI). TCGA carried out 33 tumours by sequencing. In this work, the BLCA grouping in TCGA analysis analyzes and draws Kaplan-Meier survival curve forecasts in the forecast of noninvasive urothelial cancer and invasive urothelial cancer, which examines the association of important target genes with the forecast.


## 3. Results

### 3.1. Composite Target Network Analysis

The potential compounds were identified for twenty-seven ZLJSD (Table1). As Figure 1(a) shows, the GE Odds Database, 2485 of which were upregulated and 3104 downregulated, shows 5589 genes related to noninvasive BLCA. As shown in Figure 1(b), 4,460 distinct genes are in invasive BLCA with upregulation of 2173 and downregulation of 22,287 of them. As Figure 1(c) shows, there are 181 distinctive genes, upregulated by 158 and downregulated by 23, for invasive versus noninvasive BLCA. ZLJSD’s composition network was built utilizing the tested compounds and their objectives. As shown in Figure 2(a), there were 90 nodes (21 compounds for ZLJSD and 69 compound goals) in the drug and noninvasive-urothelial carcinoma network, as well as 166 borders indicating interactions between compounds. As shown in Figure 2(b), 76 nodes (21 compounds and 55 medicines in ZLJSD) and 147 edges indicate that the compound objective interaction is present in the medication and invasive-urothelial carcinoma network. Within noninvasive & invasive BLCA, the Venn diagram shows 39 intersection targets of ZLJSD (Table 2).Table 1
The main chemicals in compound ZLJSD.
Medical herbsMol IDCompoundsMWStructuresCompositionBaihuasheshecao (Hedyotis Diffusa Herba)MOL001659Poriferasterol412.69C29H48OBaihuasheshecao (Hedyotis Diffusa Herba)MOL0016702-Methoxy-3-methyl-9,10-anthraquinoe252.28C16H12O3Baihuasheshecao (Hedyotis Diffusa Herba)MOL000449Stigmasterol412.69C29H48OBaihuasheshecao (Hedyotis Diffusa Herba)MOL000358Beta-sitosterol414.71C29H50OBaihuasheshecao (Hedyotis Diffusa Herba)MOL000098Quercetin302.24C15H10O7Sangjisheng (Herba Taxilli)MOL000359Sitosterol414.71C29H50OShancigu (Pseudobulbus Cremastrae Seu Pleiones)MOL000098Quercetin302.24C15H10O7Shancigu (Pseudobulbus Cremastrae Seu Pleiones)MOL000358Beta-sitosterol414.71C29H50OShancigu (Pseudobulbus Cremastrae Seu Pleiones)MOL000449Stigmasterol412.69C29H48OShancigu (Pseudobulbus Cremastrae Seu Pleiones)MOL0079912-Methoxy-9,10-dihydrophenanthrene-4,5-diol242.09C15H14O3Shayuanzi (Astragali Cpmplanatisemen)MOL000184NSC63551412.69C29H48OShayuanzi (Astragali Cpmplanatisemen)MOL000358Beta-sitosterol414.71C29H50OShayuanzi (Astragali Cpmplanatisemen)MOL000392Formononetin268.27C16H12O4Shayuanzi (Astragali Cpmplanatisemen)MOL000417Calycosin284.26C16H12O5Shayuanzi (Astragali Cpmplanatisemen)MOL004564Kaempferia300.26C16H12O6Shayuanzi (Astragali Cpmplanatisemen)MOL009278Laricitrin332.263C16H12O8Shayuanzi (Astragali Cpmplanatisemen)MOL009289Calycosin-7-O-beta-D-glucopyranoside446.41C22H22O10Shayuanzi (Astragali Cpmplanatisemen)MOL000098Quercetin302.24C15H10O7Zhuling (Polyporus Umbellatus (Pers) Fr.)MOL000279Cerevisterol430.66C28H46O3Zhuling (Polyporus Umbellatus (Pers) Fr.)MOL000282Ergosta-7,22E-dien-3beta-ol398.66C28H46OZhuling (Polyporus Umbellatus (Pers) Fr.)MOL000797(22e,24r)-Ergosta-7,22-dien-3-one396.72C30H30OZhuling (Polyporus Umbellatus (Pers) Fr.)MOL000798Ergosta-7,22-diene-3β-ol398.66C28H46OZhuling (Polyporus Umbellatus (Pers) Fr.)MOL000816Ergosta-7,22-dien-3-one396.66C28H44OZhuling (Polyporus Umbellatus (Pers) Fr.)MOL000817Ergosta-5,7,22-trien-3-ol396.66C28H44OZhuling (Polyporus Umbellatus (Pers) Fr.)MOL000822Polyporusterone G458.68C29H46O4MW: molecular weight.Figure 1
Heat map of the distinctly expressed genes. The color dots reflect the genes expressed substantially. Red represents upregulated genes and blue represents downregulated genes. (a) Normal samples with noninvasive BLCA, (b) normal samples with invasive BLCA, and (c) invasive sample with noninvasive BLCA sample.
(a)(b)(c)Figure 2
Construction of the candidate-target network for ZLJSD against BLCA. (a) ZLJSD treatment of noninvasive BLCA and (b) ZLJSD treatment invasive BLCA. The pink represents targets; the yellow, blue, gray, purple, green, and rose-red represent the compounds fromAstragali Cpmplanatisemen, Polyporus Umbellatus (Pers) Fr., multiple drugs, Hedyotis Diffusa Herba, Herba Taxilli, and Pseudobulbus Cremastrae Seu Pleiones, respectively.
(a)(b)Table 2
Targets of ZLJSD in noninvasive and invasive bladder urothelial carcinoma.
NamesTotalElementsInvasive BLCA & noninvasive BLCA39PTGER3, MMP2, CDK1, FOS, PRKCB, NR3C2, RXRA, PPARG, PTGS1, F3, CHEK2, BCL2, CCL2, PRKCA, SLC2A4, IL-6, PTGS2, BIRC5, PPP3CA, TP63, CCNB1, ADRA2A, CYP1B1, CXCL2, F7, ADH1C, KCNH2, CHRM3, ADRA1D, CAV1, JUN, PGR, CHRM2, RUNX1T1, CCNA2, CASP8, ERBB3, CALM1, and MAOBNoninvasive BLCA30CYP1A2, PPARD, NFKBIA, IGFBP3, CHUK, COL3A1, IGF2, RELA, VCAM1, COL1A1, CYP3A4, TNFSF15, SCN5A, PLAT, SLPI, NCF1, SLC6A4, IKBKB, DPP4, HAS2, MMP3, IL-1B, ICAM1, SELE, IRF1, SOD1, ODC1, PCOLCE, ADRA1B, and GJA1Invasive BLCA16IL-4, CHEK1, ACACA, EIF6, MMP1, STAT1, PSMD3, ADRB2, VEGFA, TOP2A, CDK2, AHR, CASP3, E2F1, E2F2, and ERBB2


### 3.2. Construction of the PPI Network

To examine the working link between the target genes and the differential genes in BLCA, a PPI network was established utilizing the STRING database and visual synthesis was carried out using Cytoscape. The PPI network was used to examine the possible 69 ZLJSD goal genes for noninvasive BLCA therapy. Results indicated that the connection between protein and function was 90 nodes and 166 edges (Figure3(a)). The MCODE plug-in confirms the list of working PPI network modules and the relevant significant ones (Figure 3(b)). Module 1 (score: 6000) comprises 6 nodes and 15 edges. The seed gene is PRKCA; module 2 (score: 4.5) is composed of 5 nodes and 9 edges; the seed gene is IL-1β; module 3 (score: 4) is composed of 4 nodes and 6 edges, and the seed gene is BIRC5. Module 4 (score 3) consists of 3 nodes and 3 edges, and the seed gene is CHRM2. Module 5 (score 3) consists of 3 nodes and 3 edges, and the seed gene is ADRA1B. Module 6 (score 2.8) consists of 6 nodes and 7 edges, and the seed gene is MMP2. The potential 55 target genes for treatment of invasive-BLCA in ZLJSD, showing 76 nodes and 147 edges (Figure 3(c)), are the interaction between protein and function. The MCODE plug-in is utilized to confirm the list of PPI network functional modules and the corresponding meaningful modules listed (Figure 3(d)). Module 1 (score: 4.444) comprises 10 nodes and 20 edges. The seed gene is CHRM2; module 2 (score: 3.333) consists of 4 nodes and 5 edges, and the seed gene is CDK1.Figure 3
PPI network analysis of the potential ZLJSD target genes for BLCA therapy. (a) Demonstrates the target protein PPI network of ZLJSD in noninvasive BLCA targets. (b) Illustrates the analysis of MCODE just after the construction of the ZLJSD target protein PPI while (c) illustrates the ZLJSD invasive BLCA PPI network target protein. (d) Illustrates the MCODE test after the ZLJSD target protein PPI has been constructed.
(a)(b)(c)(d)


### 3.3. GO and KEGG Enrichment Analysis

The DAVID pathway boosted analysis was conducted by the GO and KEGG on 69 benchmarks for the recognized noninvasive urothelial carcinoma and 55 targets for invasive urothelial carcinoma. Cellular components and molecular functions for applicant target GO have been assessed based on biological processes. As shown in Figure4(a), 718 GO conditions in noninvasive urothelial cancer (FDR<0.05), 620 in biological pathways, 43 in cellular constituents, and 55 in molecule functions were highly enriched. GO keywords that are highly abundant in biological mechanisms in cellular constituents and molecular activities comprise cell proliferation control, organic reaction, endogenous stimulus reaction, and steroid hormone stimulus reaction. Invasive urothelial carcinoma (FDR<0.05), 483 biological mechanisms, 51 cell components, and 45 molecular activities were highly enriched by 579 GO keywords, as shown in Figure 4(b). In additional Table 1, the data evaluated by GO is displayed. Including cell proliferation, apoptosis regulation programmed mortality control of cells, and organic reaction, these GO keywords are significantly abundant in biologic processes, cell components, and molecular function. Common BP ZLJSD was fortified with 404, 31 for CC, and 38 for MF for the therapy of noninvasive BLCA. The data may be found in Table 2.Figure 4
Gene ontology terms for potential ZLJSD targets against BLCA. The top eight GO functional categories were chosen, with an FDR of 0.05 (a) in the noninvasive BLCA, (b) in the invasive BLCA, BP for the biological process, (c) for cellular component, MF for molecular function.
(a)(b)During the therapy of BLCA, the KEGG pathway study revealed the pathways that were greatly influenced by ZLJSD. As indicated in Figure5(a), there have been 118 highly enriched pathways (FDR<0.05, TNF signal pathway), IL-17 signal route, AGE-RAGE signaling route in diabetic problems, NF-kappa B signaling system, etc. in the therapy and therapy of noninvasive urothelial cancer. Data are given in additional Table 3 for the KEGG pathway analysis. 103 substantially enriched pathways (FDR<0.05), comprising pathway p53s, bladder cancer, and pathways of calcium signaling, were found as shown in Figure 5(b) for the therapy of invasive urothelial carcinoma. Data are included in additional Table 3 for KEGG pathway analysis. 93 frequent approaches of treating noninvasive and invasive BLCAs for KEGG are enhanced.Figure 5
Enrichment of putative ZLJSD targets with BLCA by the KEGG pathway. Pathways have been found that have substantial alterations inFDR<0.05. The spot size is the frequency of genes as well as the color is FDR, (a) in BLCA and (b) in noninvasive BLCA.
(a)(b)Table 3
KEGG pathway analysis. 93 frequent approaches of treating noninvasive and invasive BLCAs for KEGG are enhanced.
IDDescriptionGene ratioBg ratioP valueP.adjustq valueGene IDCounthsa05161Hepatitis B14/54162/80161.74E−123.56E−101.45E−10BCL2/JUN/CASP3/CASP8/PRKCA/FOS/IL-6/STAT1/PRKCB/BIRC5/E2F1/E2F2/CDK2/CCNA214hsa04933AGE-RAGE signaling pathway in diabetic complications11/54100/80163.99E−114.09E−091.66E−09BCL2/JUN/CASP3/PRKCA/VEGFA/MMP2/IL-6/STAT1/F3/CCL2/PRKCB11hsa05167Kaposi sarcoma-associated herpes virus infection13/54186/80161.79E−101.23E−084.97E−09PTGS2/JUN/CASP3/CASP8/VEGFA/FOS/IL-6/STAT1/CXCL2/E2F1/E2F2/CALM1/PPP3CA13hsa04657IL-17 signaling pathway10/5494/80164.73E−102.42E−089.83E−09PTGS2/JUN/CASP3/CASP8/FOS/IL-6/MMP1/CCL2/CXCL2/IL-410hsa05163Human cytomegalovirus infection13/54225/80161.90E−097.80E−083.16E−08PTGS2/CASP3/CASP8/PRKCA/VEGFA/IL-6/CCL2/PTGER3/PRKCB/E2F1/E2F2/CALM1/PPP3CA13hsa04218Cellular senescence11/54160/80166.48E−092.22E−078.99E−08IL-6/CDK1/CCNB1/CHEK2/E2F1/E2F2/CDK2/CCNA2/CHEK1/CALM1/PPP3CA11hsa05170Human immunodeficiency virus 1 infection12/54212/80161.11E−083.24E−071.32E−07BCL2/JUN/CASP3/CASP8/PRKCA/FOS/CDK1/PRKCB/CCNB1/CHEK1/CALM1/PPP3CA12hsa04115p53 signaling pathway8/5472/80162.13E−085.46E−072.21E−07BCL2/CASP3/CASP8/CDK1/CCNB1/CHEK2/CDK2/CHEK18hsa04020Calcium signaling pathway11/54193/80164.60E−081.05E−064.25E−07CHRM3/CHRM2/ADRB2/ADRA1D/PRKCA/ERBB2/PTGER3/PRKCB/ERBB3/CALM1/PPP3CA11hsa05169Epstein-Barr virus infection11/54201/80166.99E−081.43E−065.81E−07BCL2/JUN/CASP3/CASP8/IL-6/STAT1/PSMD3/E2F1/E2F2/CDK2/CCNA211hsa05219Bladder cancer6/5441/80162.64E−074.91E−061.99E−06VEGFA/MMP2/MMP1/ERBB2/E2F1/E2F26hsa05031Amphetamine addiction7/5469/80163.30E−075.64E−062.29E−06MAOB/JUN/PRKCA/FOS/PRKCB/CALM1/PPP3CA7hsa04659Th17 cell differentiation8/54107/80164.84E−077.64E−063.10E−06RXRA/JUN/FOS/IL-6/STAT1/AHR/IL-4/PPP3CA8hsa04668TNF signaling pathway8/54112/80166.89E−079.85E−063.99E−06PTGS2/JUN/CASP3/CASP8/FOS/IL-6/CCL2/CXCL28hsa05206MicroRNAs in cancer12/54310/80167.21E−079.85E−063.99E−06PTGS2/BCL2/CASP3/PRKCA/VEGFA/TP63/ERBB2/PRKCB/CYP1B1/E2F1/E2F2/ERBB312hsa01521EGFR tyrosine kinase inhibitor resistance7/5479/80168.42E−071.08E−054.37E−06BCL2/PRKCA/VEGFA/IL-6/ERBB2/PRKCB/ERBB37hsa04110Cell cycle8/54124/80161.50E−061.80E−057.28E−06CDK1/CCNB1/CHEK2/E2F1/E2F2/CDK2/CCNA2/CHEK18hsa05166Human T-cell leukemia virus 1 infection10/54219/80161.58E−061.80E−057.28E−06JUN/FOS/IL-6/CHEK2/E2F1/E2F2/CDK2/CCNA2/CHEK1/PPP3CA10hsa05222Small cell lung cancer7/5492/80162.38E−062.57E−051.04E−05PTGS2/RXRA/BCL2/CASP3/E2F1/E2F2/CDK27hsa05323Rheumatoid arthritis7/5493/80162.56E−062.62E−051.06E−05JUN/VEGFA/FOS/IL-6/MMP1/CCL2/CXCL27hsa05162Measles8/54138/80163.36E−063.28E−051.33E−05BCL2/JUN/CASP3/CASP8/FOS/IL-6/STAT1/CDK28hsa01522Endocrine resistance7/5498/80163.64E−063.39E−051.38E−05BCL2/JUN/FOS/MMP2/ERBB2/E2F1/E2F27hsa05223Non-small cell lung cancer6/5466/80164.68E−064.18E−051.69E−05RXRA/PRKCA/ERBB2/PRKCB/E2F1/E2F26hsa04625C-type lectin receptor signaling pathway7/54104/80165.42E−064.63E−051.88E−05PTGS2/JUN/CASP8/IL-6/STAT1/CALM1/PPP3CA7hsa01524Platinum drug resistance6/5473/80168.47E−066.94E−052.82E−05BCL2/CASP3/CASP8/ERBB2/BIRC5/TOP2A6hsa05140Leishmaniasis6/5477/80161.16E−059.11E−053.69E−05PTGS2/JUN/FOS/STAT1/PRKCB/IL-46hsa04970Salivary secretion6/5490/80162.84E−050.0002153978.74E−05CHRM3/ADRB2/ADRA1D/PRKCA/PRKCB/CALM16hsa04370VEGF signaling pathway5/5459/80164.36E−050.0003194330.000129578PTGS2/PRKCA/VEGFA/PRKCB/PPP3CA5hsa04215Apoptosis—multiple species4/5432/80165.75E−050.0003988470.000161792BCL2/CASP3/CASP8/BIRC54hsa05205Proteoglycans in cancer8/54204/80165.84E−050.0003988470.000161792CASP3/PRKCA/VEGFA/MMP2/ERBB2/CAV1/PRKCB/ERBB38hsa04921Oxytocin signaling pathway7/54153/80166.71E−050.0004440380.000180123PTGS2/JUN/PRKCA/FOS/PRKCB/CALM1/PPP3CA7hsa05160Hepatitis C7/54155/80167.29E−050.000467110.000189483RXRA/CASP3/CASP8/STAT1/E2F1/E2F2/CDK27hsa04066HIF-1 signaling pathway6/54109/80168.37E−050.0005197910.000210852BCL2/PRKCA/VEGFA/IL-6/ERBB2/PRKCB6hsa04725Cholinergic synapse6/54112/80169.73E−050.0005868990.000238075CHRM3/CHRM2/BCL2/PRKCA/FOS/PRKCB6hsa04151PI3K-Akt signaling pathway10/54354/80160.0001044780.0006119440.000248234RXRA/CHRM2/BCL2/PRKCA/VEGFA/IL-6/ERBB2/ERBB3/CDK2/IL-410hsa04726Serotonergic synapse6/54115/80160.0001127280.0006419220.000260395PTGS1/PTGS2/MAOB/CASP3/PRKCA/PRKCB6hsa05164Influenza A7/54170/80160.0001304140.0007225670.000293108CASP3/CASP8/PRKCA/IL-6/STAT1/CCL2/PRKCB7hsa04010MAPK signaling pathway9/54295/80160.0001355060.000728730.000295608JUN/CASP3/PRKCA/VEGFA/FOS/ERBB2/PRKCB/ERBB3/PPP3CA9hsa05214Glioma5/5475/80160.0001386360.000728730.000295608PRKCA/PRKCB/E2F1/E2F2/CALM15hsa05133Pertussis5/5476/80160.0001476460.0007382280.000299461JUN/CASP3/FOS/IL-6/CALM15hsa05212Pancreatic cancer5/5476/80160.0001476460.0007382280.000299461VEGFA/STAT1/ERBB2/E2F1/E2F25hsa05152Tuberculosis7/54180/80160.0001860860.0009082770.000368441BCL2/CASP3/CASP8/IL-6/STAT1/CALM1/PPP3CA7hsa04621NOD-like receptor signaling pathway7/54181/80160.0001925750.000918090.000372422BCL2/JUN/CASP8/IL-6/STAT1/CCL2/CXCL27hsa04114Oocyte meiosis6/54128/80160.0002032290.0009468640.000384094PGR/CDK1/CCNB1/CDK2/CALM1/PPP3CA6hsa04926Relaxin signaling pathway6/54129/80160.0002120490.0009660020.000391857JUN/PRKCA/VEGFA/FOS/MMP2/MMP16hsa04728Dopaminergic synapse6/54132/80160.0002403370.0010710650.000434476MAOB/PRKCA/FOS/PRKCB/CALM1/PPP3CA6hsa04012ErbB signaling pathway5/5485/80160.0002503980.0010921620.000443034JUN/PRKCA/ERBB2/PRKCB/ERBB35hsa05210Colorectal cancer5/5486/80160.0002645050.0011296560.000458243BCL2/JUN/CASP3/FOS/BIRC55hsa04210Apoptosis6/54136/80160.0002825960.0011822880.000479593BCL2/JUN/CASP3/CASP8/FOS/BIRC56hsa04915Estrogen signaling pathway6/54138/80160.000305810.0012538210.00050861PGR/BCL2/JUN/FOS/MMP2/CALM16hsa04510Focal adhesion7/54199/80160.0003444290.0013844710.000561608BCL2/JUN/PRKCA/VEGFA/ERBB2/CAV1/PRKCB7hsa04658Th1 and Th2 cell differentiation5/5492/80160.0003622660.0014156560.000574258JUN/FOS/STAT1/IL-4/PPP3CA5hsa05203Viral carcinogenesis7/54201/80160.0003659990.0014156560.000574258JUN/CASP3/CASP8/CDK1/CDK2/CCNA2/CHEK17hsa04912GnRH signaling pathway5/5493/80160.0003809060.0014460320.000586581JUN/PRKCA/MMP2/PRKCB/CALM15hsa05224Breast cancer6/54147/80160.0004294810.0016007910.000649358PGR/JUN/FOS/ERBB2/E2F1/E2F26hsa05226Gastric cancer6/54149/80160.0004616360.0016645290.000675213RXRA/BCL2/ERBB2/E2F1/E2F2/CDK26hsa05215Prostate cancer5/5497/80160.000462820.0016645290.000675213BCL2/ERBB2/E2F1/E2F2/CDK25hsa04923Regulation of lipolysis in adipocytes4/5455/80160.0004864210.0017192470.000697409PTGS1/PTGS2/ADRB2/PTGER34hsa04914Progesterone-mediated oocyte maturation5/5499/80160.0005084460.0017666340.000716632PGR/CDK1/CCNB1/CDK2/CCNA25hsa05134Legionellosis4/5457/80160.0005577940.0019057980.000773083CASP3/CASP8/IL-6/CXCL24hsa05142Chagas disease (American trypanosomiasis)5/54102/80160.000583140.0019281260.000782141JUN/CASP8/FOS/IL-6/CCL25hsa05146Amoebiasis5/54102/80160.000583140.0019281260.000782141CASP3/PRKCA/IL-6/PRKCB/CXCL25hsa04620Toll-like receptor signaling pathway5/54104/80160.0006373320.0020738570.000841257JUN/CASP8/FOS/IL-6/STAT15hsa04928Parathyroid hormone synthesis, secretion, and action5/54106/80160.0006952210.002226880.00090333RXRA/BCL2/PRKCA/FOS/PRKCB5hsa05321Inflammatory bowel disease (IBD)4/5465/80160.0009187630.0028976380.001175422JUN/IL-6/STAT1/IL-44hsa04720Long-term potentiation4/5467/80160.0010298490.0031987740.001297577PRKCA/PRKCB/CALM1/PPP3CA4hsa05165Human papillomavirus infection8/54330/80160.0015051950.0046054480.001868192PTGS2/CASP3/CASP8/VEGFA/STAT1/E2F1/CDK2/CCNA28hsa04971Gastric acid secretion4/5475/80160.0015694770.004731510.001919329CHRM3/PRKCA/PRKCB/CALM14hsa04380Osteoclast differentiation5/54128/80160.0016230430.0048220840.00195607JUN/PPARG/FOS/STAT1/PPP3CA5hsa04960Aldosterone-regulated sodium reabsorption3/5437/80160.001909820.0055142680.002236853NR3C2/PRKCA/PRKCB3hsa05143African trypanosomiasis3/5437/80160.001909820.0055142680.002236853PRKCA/IL-6/PRKCB3hsa04662B cell receptor signaling pathway4/5482/80160.0021814180.0062109830.002519474JUN/FOS/PRKCB/PPP3CA4hsa05235PD-L1 expression and PD-1 checkpoint pathway in cancer4/5489/80160.0029413310.0082599010.003350615JUN/FOS/STAT1/PPP3CA4hsa05132Salmonella infection6/54214/80160.0029934120.0082925610.003363863BCL2/JUN/CASP3/CASP8/FOS/IL-66hsa04024cAMP signaling pathway6/54216/80160.0031349750.0083964270.003405996CHRM2/ADRB2/JUN/FOS/PTGER3/CALM16hsa04261Adrenergic signaling in cardiomyocytes5/54149/80160.003153780.0083964270.003405996ADRB2/ADRA1D/BCL2/PRKCA/CALM15hsa04932Nonalcoholic fatty liver disease (NAFLD)5/54149/80160.003153780.0083964270.003405996RXRA/JUN/CASP3/CASP8/IL-65hsa04713Circadian entrainment4/5497/80160.0040118340.0105439230.004277124PRKCA/FOS/PRKCB/CALM14hsa05231Choline metabolism in cancer4/5498/80160.0041619020.0107998730.00438095JUN/PRKCA/FOS/PRKCB4hsa04064NF-kappa B signaling pathway4/54102/80160.0048002270.0123005820.00498971PTGS2/BCL2/PRKCB/CXCL24hsa04022cGMP-PKG signaling pathway5/54167/80160.005129590.0128570650.005215446ADRB2/ADRA1D/ADRA2A/CALM1/PPP3CA5hsa04660T cell receptor signaling pathway4/54104/80160.0051428260.0128570650.005215446JUN/FOS/IL-4/PPP3CA4hsa05014Amyotrophic lateral sclerosis (ALS)3/5457/80160.0065418740.016157640.006554319BCL2/CASP3/PPP3CA3hsa05145Toxoplasmosis4/54112/80160.0066772990.0162957880.006610358BCL2/CASP3/CASP8/STAT14hsa05202Transcriptional misregulation in cancer5/54186/80160.0080357340.01938030.007861585RXRA/PPARG/IL-6/RUNX1T1/CCNA25hsa04919Thyroid hormone signaling pathway4/54119/80160.0082472010.019433060.007882987RXRA/PRKCA/STAT1/PRKCB4hsa04935Growth hormone synthesis, secretion, and action4/54119/80160.0082472010.019433060.007882987PRKCA/FOS/STAT1/PRKCB4hsa04152AMPK signaling pathway4/54120/80160.00848960.0195546970.007932329PPARG/ACACA/SLC2A4/CCNA24hsa05135Yersinia infection4/54120/80160.00848960.0195546970.007932329JUN/FOS/IL-6/CCL24hsa04924Renin secretion3/5469/80160.0110693250.0251029480.010182967ADRB2/CALM1/PPP3CA3hsa05130Pathogenic Escherichia coli infection5/54202/80160.0112459510.0251029480.010182967JUN/CASP3/CASP8/FOS/IL-65hsa04068FoxO signaling pathway4/54131/80160.0114698530.0251029480.010182967IL-6/CCNB1/SLC2A4/CDK24hsa04650Natural killer cell-mediated cytotoxicity4/54131/80160.0114698530.0251029480.010182967CASP3/PRKCA/PRKCB/PPP3CA4hsa05120Epithelial cell signaling in Helicobacter pylori infection3/5470/80160.011510620.0251029480.010182967JUN/CASP3/CXCL23hsa04270Vascular smooth muscle contraction4/54132/80160.0117701780.0253988060.010302982ADRA1D/PRKCA/PRKCB/CALM14hsa03320PPAR signaling pathway3/5476/80160.0143709840.0306880390.01244855RXRA/PPARG/MMP13hsa05204Chemical carcinogenesis3/5482/80160.0176005080.037196950.015088878PTGS2/ADH1C/CYP1B13hsa04911Insulin secretion3/5486/80160.0199609580.0416686820.016902829CHRM3/PRKCA/PRKCB3hsa04934Cushing syndrome4/54155/80160.0201229240.0416686820.016902829AHR/E2F1/E2F2/CDK24hsa04540Gap junction3/5488/80160.0212037420.0434676720.017632586PRKCA/CDK1/PRKCB3hsa04310Wnt signaling pathway4/54160/80160.0223216820.0453063840.018378456JUN/PRKCA/PRKCB/PPP3CA4hsa04630JAK-STAT signaling pathway4/54162/80160.0232409160.0467096850.018947703BCL2/IL-6/STAT1/IL-44hsa00350Tyrosine metabolism2/5436/80160.0242448720.0482543570.019574296ADH1C/MAOB2


### 3.4. Genetic Pathway Network Analysis

A network of gene pathways was built on a very rich network and genes that control these pathways, as shown in Figure6. The BC noninvasive urothelial carcinoma topological study of 20 pathways and 34 genes was carried out. Green is the destination gene, while pink is the network path. The most extensive core goal gene has been found in Figure 6(a). Invasive urothelial cell carcinoma topological analysis using BC was carried out in 20 pathways and 41 genes, as shown in Figure 6(b). The target gene is blue, and the network route is yellow. The diagram in the network demonstrates that IL-6 has the greatest core gene. They might be a significant BLCA ZLJSD target.Figure 6
ZLJSD gene-pathway network against BLCA. (a) In noninvasive BLCA, a topological study of 20 pathways and 34 genes was performed using betweenness centrality. Target genes are shown by blue ellipses, and pathways are represented by green ellipses. (b) Invasive BLCA performed a topological study of 20 pathways and 41 genes using betweenness centrality. The pink ellipses reflect pathways, whereas the green ellipses indicate target genes. The higher the size, the greater the centrality betweenness.
(a)(b)


### 3.5. Prognostic Analysis of ZLJSD Targets

The TCGA database contains expression and clinical data for 33 tumours. Prognostic analysis using the TCGA database to explore the impact of core genes on the prognosis of BLCA found that survival analysis of noninvasive urothelial carcinoma showed that 18 targets are associated with prognostic survival as shown in Figure7; the overall survival of patients with high gene expression and those with lower than low expression include ADRA1D, ADRA1A, CALM1, CHRM2, COL3A1, CYP1B1, DPP4, GJA1, JUN, MAOB, PGR, PTGER3, PTGS1, RUNX1T1, and SLC2A4. The general survival of high-gene activity patients is greater than of low expression, including PPARG and TNFSF15. The analysis of survival of invasive urothelial cancer is shown in Figure 8. There have fifteen targets related to prognostic survival. The general survival of high-gene activity patients is lower than those with low expression, ACACA, ADRA1D, ADRA2A, CALM1, CHRM2, CYP1B1, JUN, MAOB, PGR, PTGER3, PTGS1, RUNX1T1, and SLC2A4; the overall survival of patients with high gene expression is higher than those with low expression, including PPARG and VEGFA. The variation (logrank P<0.05) was significant statistically. The findings of the examination of other genes were not significant statistically.Figure 7
18 drug targets are associated with prognosis in noninvasive urothelial cancer.Figure 8
15 drug targets associated with prognosis in invasive urothelial cancer.


## 3.1. Composite Target Network Analysis

The potential compounds were identified for twenty-seven ZLJSD (Table1). As Figure 1(a) shows, the GE Odds Database, 2485 of which were upregulated and 3104 downregulated, shows 5589 genes related to noninvasive BLCA. As shown in Figure 1(b), 4,460 distinct genes are in invasive BLCA with upregulation of 2173 and downregulation of 22,287 of them. As Figure 1(c) shows, there are 181 distinctive genes, upregulated by 158 and downregulated by 23, for invasive versus noninvasive BLCA. ZLJSD’s composition network was built utilizing the tested compounds and their objectives. As shown in Figure 2(a), there were 90 nodes (21 compounds for ZLJSD and 69 compound goals) in the drug and noninvasive-urothelial carcinoma network, as well as 166 borders indicating interactions between compounds. As shown in Figure 2(b), 76 nodes (21 compounds and 55 medicines in ZLJSD) and 147 edges indicate that the compound objective interaction is present in the medication and invasive-urothelial carcinoma network. Within noninvasive & invasive BLCA, the Venn diagram shows 39 intersection targets of ZLJSD (Table 2).Table 1
The main chemicals in compound ZLJSD.
Medical herbsMol IDCompoundsMWStructuresCompositionBaihuasheshecao (Hedyotis Diffusa Herba)MOL001659Poriferasterol412.69C29H48OBaihuasheshecao (Hedyotis Diffusa Herba)MOL0016702-Methoxy-3-methyl-9,10-anthraquinoe252.28C16H12O3Baihuasheshecao (Hedyotis Diffusa Herba)MOL000449Stigmasterol412.69C29H48OBaihuasheshecao (Hedyotis Diffusa Herba)MOL000358Beta-sitosterol414.71C29H50OBaihuasheshecao (Hedyotis Diffusa Herba)MOL000098Quercetin302.24C15H10O7Sangjisheng (Herba Taxilli)MOL000359Sitosterol414.71C29H50OShancigu (Pseudobulbus Cremastrae Seu Pleiones)MOL000098Quercetin302.24C15H10O7Shancigu (Pseudobulbus Cremastrae Seu Pleiones)MOL000358Beta-sitosterol414.71C29H50OShancigu (Pseudobulbus Cremastrae Seu Pleiones)MOL000449Stigmasterol412.69C29H48OShancigu (Pseudobulbus Cremastrae Seu Pleiones)MOL0079912-Methoxy-9,10-dihydrophenanthrene-4,5-diol242.09C15H14O3Shayuanzi (Astragali Cpmplanatisemen)MOL000184NSC63551412.69C29H48OShayuanzi (Astragali Cpmplanatisemen)MOL000358Beta-sitosterol414.71C29H50OShayuanzi (Astragali Cpmplanatisemen)MOL000392Formononetin268.27C16H12O4Shayuanzi (Astragali Cpmplanatisemen)MOL000417Calycosin284.26C16H12O5Shayuanzi (Astragali Cpmplanatisemen)MOL004564Kaempferia300.26C16H12O6Shayuanzi (Astragali Cpmplanatisemen)MOL009278Laricitrin332.263C16H12O8Shayuanzi (Astragali Cpmplanatisemen)MOL009289Calycosin-7-O-beta-D-glucopyranoside446.41C22H22O10Shayuanzi (Astragali Cpmplanatisemen)MOL000098Quercetin302.24C15H10O7Zhuling (Polyporus Umbellatus (Pers) Fr.)MOL000279Cerevisterol430.66C28H46O3Zhuling (Polyporus Umbellatus (Pers) Fr.)MOL000282Ergosta-7,22E-dien-3beta-ol398.66C28H46OZhuling (Polyporus Umbellatus (Pers) Fr.)MOL000797(22e,24r)-Ergosta-7,22-dien-3-one396.72C30H30OZhuling (Polyporus Umbellatus (Pers) Fr.)MOL000798Ergosta-7,22-diene-3β-ol398.66C28H46OZhuling (Polyporus Umbellatus (Pers) Fr.)MOL000816Ergosta-7,22-dien-3-one396.66C28H44OZhuling (Polyporus Umbellatus (Pers) Fr.)MOL000817Ergosta-5,7,22-trien-3-ol396.66C28H44OZhuling (Polyporus Umbellatus (Pers) Fr.)MOL000822Polyporusterone G458.68C29H46O4MW: molecular weight.Figure 1
Heat map of the distinctly expressed genes. The color dots reflect the genes expressed substantially. Red represents upregulated genes and blue represents downregulated genes. (a) Normal samples with noninvasive BLCA, (b) normal samples with invasive BLCA, and (c) invasive sample with noninvasive BLCA sample.
(a)(b)(c)Figure 2
Construction of the candidate-target network for ZLJSD against BLCA. (a) ZLJSD treatment of noninvasive BLCA and (b) ZLJSD treatment invasive BLCA. The pink represents targets; the yellow, blue, gray, purple, green, and rose-red represent the compounds fromAstragali Cpmplanatisemen, Polyporus Umbellatus (Pers) Fr., multiple drugs, Hedyotis Diffusa Herba, Herba Taxilli, and Pseudobulbus Cremastrae Seu Pleiones, respectively.
(a)(b)Table 2
Targets of ZLJSD in noninvasive and invasive bladder urothelial carcinoma.
NamesTotalElementsInvasive BLCA & noninvasive BLCA39PTGER3, MMP2, CDK1, FOS, PRKCB, NR3C2, RXRA, PPARG, PTGS1, F3, CHEK2, BCL2, CCL2, PRKCA, SLC2A4, IL-6, PTGS2, BIRC5, PPP3CA, TP63, CCNB1, ADRA2A, CYP1B1, CXCL2, F7, ADH1C, KCNH2, CHRM3, ADRA1D, CAV1, JUN, PGR, CHRM2, RUNX1T1, CCNA2, CASP8, ERBB3, CALM1, and MAOBNoninvasive BLCA30CYP1A2, PPARD, NFKBIA, IGFBP3, CHUK, COL3A1, IGF2, RELA, VCAM1, COL1A1, CYP3A4, TNFSF15, SCN5A, PLAT, SLPI, NCF1, SLC6A4, IKBKB, DPP4, HAS2, MMP3, IL-1B, ICAM1, SELE, IRF1, SOD1, ODC1, PCOLCE, ADRA1B, and GJA1Invasive BLCA16IL-4, CHEK1, ACACA, EIF6, MMP1, STAT1, PSMD3, ADRB2, VEGFA, TOP2A, CDK2, AHR, CASP3, E2F1, E2F2, and ERBB2


## 3.2. Construction of the PPI Network

To examine the working link between the target genes and the differential genes in BLCA, a PPI network was established utilizing the STRING database and visual synthesis was carried out using Cytoscape. The PPI network was used to examine the possible 69 ZLJSD goal genes for noninvasive BLCA therapy. Results indicated that the connection between protein and function was 90 nodes and 166 edges (Figure3(a)). The MCODE plug-in confirms the list of working PPI network modules and the relevant significant ones (Figure 3(b)). Module 1 (score: 6000) comprises 6 nodes and 15 edges. The seed gene is PRKCA; module 2 (score: 4.5) is composed of 5 nodes and 9 edges; the seed gene is IL-1β; module 3 (score: 4) is composed of 4 nodes and 6 edges, and the seed gene is BIRC5. Module 4 (score 3) consists of 3 nodes and 3 edges, and the seed gene is CHRM2. Module 5 (score 3) consists of 3 nodes and 3 edges, and the seed gene is ADRA1B. Module 6 (score 2.8) consists of 6 nodes and 7 edges, and the seed gene is MMP2. The potential 55 target genes for treatment of invasive-BLCA in ZLJSD, showing 76 nodes and 147 edges (Figure 3(c)), are the interaction between protein and function. The MCODE plug-in is utilized to confirm the list of PPI network functional modules and the corresponding meaningful modules listed (Figure 3(d)). Module 1 (score: 4.444) comprises 10 nodes and 20 edges. The seed gene is CHRM2; module 2 (score: 3.333) consists of 4 nodes and 5 edges, and the seed gene is CDK1.Figure 3
PPI network analysis of the potential ZLJSD target genes for BLCA therapy. (a) Demonstrates the target protein PPI network of ZLJSD in noninvasive BLCA targets. (b) Illustrates the analysis of MCODE just after the construction of the ZLJSD target protein PPI while (c) illustrates the ZLJSD invasive BLCA PPI network target protein. (d) Illustrates the MCODE test after the ZLJSD target protein PPI has been constructed.
(a)(b)(c)(d)


## 3.3. GO and KEGG Enrichment Analysis

The DAVID pathway boosted analysis was conducted by the GO and KEGG on 69 benchmarks for the recognized noninvasive urothelial carcinoma and 55 targets for invasive urothelial carcinoma. Cellular components and molecular functions for applicant target GO have been assessed based on biological processes. As shown in Figure4(a), 718 GO conditions in noninvasive urothelial cancer (FDR<0.05), 620 in biological pathways, 43 in cellular constituents, and 55 in molecule functions were highly enriched. GO keywords that are highly abundant in biological mechanisms in cellular constituents and molecular activities comprise cell proliferation control, organic reaction, endogenous stimulus reaction, and steroid hormone stimulus reaction. Invasive urothelial carcinoma (FDR<0.05), 483 biological mechanisms, 51 cell components, and 45 molecular activities were highly enriched by 579 GO keywords, as shown in Figure 4(b). In additional Table 1, the data evaluated by GO is displayed. Including cell proliferation, apoptosis regulation programmed mortality control of cells, and organic reaction, these GO keywords are significantly abundant in biologic processes, cell components, and molecular function. Common BP ZLJSD was fortified with 404, 31 for CC, and 38 for MF for the therapy of noninvasive BLCA. The data may be found in Table 2.Figure 4
Gene ontology terms for potential ZLJSD targets against BLCA. The top eight GO functional categories were chosen, with an FDR of 0.05 (a) in the noninvasive BLCA, (b) in the invasive BLCA, BP for the biological process, (c) for cellular component, MF for molecular function.
(a)(b)During the therapy of BLCA, the KEGG pathway study revealed the pathways that were greatly influenced by ZLJSD. As indicated in Figure5(a), there have been 118 highly enriched pathways (FDR<0.05, TNF signal pathway), IL-17 signal route, AGE-RAGE signaling route in diabetic problems, NF-kappa B signaling system, etc. in the therapy and therapy of noninvasive urothelial cancer. Data are given in additional Table 3 for the KEGG pathway analysis. 103 substantially enriched pathways (FDR<0.05), comprising pathway p53s, bladder cancer, and pathways of calcium signaling, were found as shown in Figure 5(b) for the therapy of invasive urothelial carcinoma. Data are included in additional Table 3 for KEGG pathway analysis. 93 frequent approaches of treating noninvasive and invasive BLCAs for KEGG are enhanced.Figure 5
Enrichment of putative ZLJSD targets with BLCA by the KEGG pathway. Pathways have been found that have substantial alterations inFDR<0.05. The spot size is the frequency of genes as well as the color is FDR, (a) in BLCA and (b) in noninvasive BLCA.
(a)(b)Table 3
KEGG pathway analysis. 93 frequent approaches of treating noninvasive and invasive BLCAs for KEGG are enhanced.
IDDescriptionGene ratioBg ratioP valueP.adjustq valueGene IDCounthsa05161Hepatitis B14/54162/80161.74E−123.56E−101.45E−10BCL2/JUN/CASP3/CASP8/PRKCA/FOS/IL-6/STAT1/PRKCB/BIRC5/E2F1/E2F2/CDK2/CCNA214hsa04933AGE-RAGE signaling pathway in diabetic complications11/54100/80163.99E−114.09E−091.66E−09BCL2/JUN/CASP3/PRKCA/VEGFA/MMP2/IL-6/STAT1/F3/CCL2/PRKCB11hsa05167Kaposi sarcoma-associated herpes virus infection13/54186/80161.79E−101.23E−084.97E−09PTGS2/JUN/CASP3/CASP8/VEGFA/FOS/IL-6/STAT1/CXCL2/E2F1/E2F2/CALM1/PPP3CA13hsa04657IL-17 signaling pathway10/5494/80164.73E−102.42E−089.83E−09PTGS2/JUN/CASP3/CASP8/FOS/IL-6/MMP1/CCL2/CXCL2/IL-410hsa05163Human cytomegalovirus infection13/54225/80161.90E−097.80E−083.16E−08PTGS2/CASP3/CASP8/PRKCA/VEGFA/IL-6/CCL2/PTGER3/PRKCB/E2F1/E2F2/CALM1/PPP3CA13hsa04218Cellular senescence11/54160/80166.48E−092.22E−078.99E−08IL-6/CDK1/CCNB1/CHEK2/E2F1/E2F2/CDK2/CCNA2/CHEK1/CALM1/PPP3CA11hsa05170Human immunodeficiency virus 1 infection12/54212/80161.11E−083.24E−071.32E−07BCL2/JUN/CASP3/CASP8/PRKCA/FOS/CDK1/PRKCB/CCNB1/CHEK1/CALM1/PPP3CA12hsa04115p53 signaling pathway8/5472/80162.13E−085.46E−072.21E−07BCL2/CASP3/CASP8/CDK1/CCNB1/CHEK2/CDK2/CHEK18hsa04020Calcium signaling pathway11/54193/80164.60E−081.05E−064.25E−07CHRM3/CHRM2/ADRB2/ADRA1D/PRKCA/ERBB2/PTGER3/PRKCB/ERBB3/CALM1/PPP3CA11hsa05169Epstein-Barr virus infection11/54201/80166.99E−081.43E−065.81E−07BCL2/JUN/CASP3/CASP8/IL-6/STAT1/PSMD3/E2F1/E2F2/CDK2/CCNA211hsa05219Bladder cancer6/5441/80162.64E−074.91E−061.99E−06VEGFA/MMP2/MMP1/ERBB2/E2F1/E2F26hsa05031Amphetamine addiction7/5469/80163.30E−075.64E−062.29E−06MAOB/JUN/PRKCA/FOS/PRKCB/CALM1/PPP3CA7hsa04659Th17 cell differentiation8/54107/80164.84E−077.64E−063.10E−06RXRA/JUN/FOS/IL-6/STAT1/AHR/IL-4/PPP3CA8hsa04668TNF signaling pathway8/54112/80166.89E−079.85E−063.99E−06PTGS2/JUN/CASP3/CASP8/FOS/IL-6/CCL2/CXCL28hsa05206MicroRNAs in cancer12/54310/80167.21E−079.85E−063.99E−06PTGS2/BCL2/CASP3/PRKCA/VEGFA/TP63/ERBB2/PRKCB/CYP1B1/E2F1/E2F2/ERBB312hsa01521EGFR tyrosine kinase inhibitor resistance7/5479/80168.42E−071.08E−054.37E−06BCL2/PRKCA/VEGFA/IL-6/ERBB2/PRKCB/ERBB37hsa04110Cell cycle8/54124/80161.50E−061.80E−057.28E−06CDK1/CCNB1/CHEK2/E2F1/E2F2/CDK2/CCNA2/CHEK18hsa05166Human T-cell leukemia virus 1 infection10/54219/80161.58E−061.80E−057.28E−06JUN/FOS/IL-6/CHEK2/E2F1/E2F2/CDK2/CCNA2/CHEK1/PPP3CA10hsa05222Small cell lung cancer7/5492/80162.38E−062.57E−051.04E−05PTGS2/RXRA/BCL2/CASP3/E2F1/E2F2/CDK27hsa05323Rheumatoid arthritis7/5493/80162.56E−062.62E−051.06E−05JUN/VEGFA/FOS/IL-6/MMP1/CCL2/CXCL27hsa05162Measles8/54138/80163.36E−063.28E−051.33E−05BCL2/JUN/CASP3/CASP8/FOS/IL-6/STAT1/CDK28hsa01522Endocrine resistance7/5498/80163.64E−063.39E−051.38E−05BCL2/JUN/FOS/MMP2/ERBB2/E2F1/E2F27hsa05223Non-small cell lung cancer6/5466/80164.68E−064.18E−051.69E−05RXRA/PRKCA/ERBB2/PRKCB/E2F1/E2F26hsa04625C-type lectin receptor signaling pathway7/54104/80165.42E−064.63E−051.88E−05PTGS2/JUN/CASP8/IL-6/STAT1/CALM1/PPP3CA7hsa01524Platinum drug resistance6/5473/80168.47E−066.94E−052.82E−05BCL2/CASP3/CASP8/ERBB2/BIRC5/TOP2A6hsa05140Leishmaniasis6/5477/80161.16E−059.11E−053.69E−05PTGS2/JUN/FOS/STAT1/PRKCB/IL-46hsa04970Salivary secretion6/5490/80162.84E−050.0002153978.74E−05CHRM3/ADRB2/ADRA1D/PRKCA/PRKCB/CALM16hsa04370VEGF signaling pathway5/5459/80164.36E−050.0003194330.000129578PTGS2/PRKCA/VEGFA/PRKCB/PPP3CA5hsa04215Apoptosis—multiple species4/5432/80165.75E−050.0003988470.000161792BCL2/CASP3/CASP8/BIRC54hsa05205Proteoglycans in cancer8/54204/80165.84E−050.0003988470.000161792CASP3/PRKCA/VEGFA/MMP2/ERBB2/CAV1/PRKCB/ERBB38hsa04921Oxytocin signaling pathway7/54153/80166.71E−050.0004440380.000180123PTGS2/JUN/PRKCA/FOS/PRKCB/CALM1/PPP3CA7hsa05160Hepatitis C7/54155/80167.29E−050.000467110.000189483RXRA/CASP3/CASP8/STAT1/E2F1/E2F2/CDK27hsa04066HIF-1 signaling pathway6/54109/80168.37E−050.0005197910.000210852BCL2/PRKCA/VEGFA/IL-6/ERBB2/PRKCB6hsa04725Cholinergic synapse6/54112/80169.73E−050.0005868990.000238075CHRM3/CHRM2/BCL2/PRKCA/FOS/PRKCB6hsa04151PI3K-Akt signaling pathway10/54354/80160.0001044780.0006119440.000248234RXRA/CHRM2/BCL2/PRKCA/VEGFA/IL-6/ERBB2/ERBB3/CDK2/IL-410hsa04726Serotonergic synapse6/54115/80160.0001127280.0006419220.000260395PTGS1/PTGS2/MAOB/CASP3/PRKCA/PRKCB6hsa05164Influenza A7/54170/80160.0001304140.0007225670.000293108CASP3/CASP8/PRKCA/IL-6/STAT1/CCL2/PRKCB7hsa04010MAPK signaling pathway9/54295/80160.0001355060.000728730.000295608JUN/CASP3/PRKCA/VEGFA/FOS/ERBB2/PRKCB/ERBB3/PPP3CA9hsa05214Glioma5/5475/80160.0001386360.000728730.000295608PRKCA/PRKCB/E2F1/E2F2/CALM15hsa05133Pertussis5/5476/80160.0001476460.0007382280.000299461JUN/CASP3/FOS/IL-6/CALM15hsa05212Pancreatic cancer5/5476/80160.0001476460.0007382280.000299461VEGFA/STAT1/ERBB2/E2F1/E2F25hsa05152Tuberculosis7/54180/80160.0001860860.0009082770.000368441BCL2/CASP3/CASP8/IL-6/STAT1/CALM1/PPP3CA7hsa04621NOD-like receptor signaling pathway7/54181/80160.0001925750.000918090.000372422BCL2/JUN/CASP8/IL-6/STAT1/CCL2/CXCL27hsa04114Oocyte meiosis6/54128/80160.0002032290.0009468640.000384094PGR/CDK1/CCNB1/CDK2/CALM1/PPP3CA6hsa04926Relaxin signaling pathway6/54129/80160.0002120490.0009660020.000391857JUN/PRKCA/VEGFA/FOS/MMP2/MMP16hsa04728Dopaminergic synapse6/54132/80160.0002403370.0010710650.000434476MAOB/PRKCA/FOS/PRKCB/CALM1/PPP3CA6hsa04012ErbB signaling pathway5/5485/80160.0002503980.0010921620.000443034JUN/PRKCA/ERBB2/PRKCB/ERBB35hsa05210Colorectal cancer5/5486/80160.0002645050.0011296560.000458243BCL2/JUN/CASP3/FOS/BIRC55hsa04210Apoptosis6/54136/80160.0002825960.0011822880.000479593BCL2/JUN/CASP3/CASP8/FOS/BIRC56hsa04915Estrogen signaling pathway6/54138/80160.000305810.0012538210.00050861PGR/BCL2/JUN/FOS/MMP2/CALM16hsa04510Focal adhesion7/54199/80160.0003444290.0013844710.000561608BCL2/JUN/PRKCA/VEGFA/ERBB2/CAV1/PRKCB7hsa04658Th1 and Th2 cell differentiation5/5492/80160.0003622660.0014156560.000574258JUN/FOS/STAT1/IL-4/PPP3CA5hsa05203Viral carcinogenesis7/54201/80160.0003659990.0014156560.000574258JUN/CASP3/CASP8/CDK1/CDK2/CCNA2/CHEK17hsa04912GnRH signaling pathway5/5493/80160.0003809060.0014460320.000586581JUN/PRKCA/MMP2/PRKCB/CALM15hsa05224Breast cancer6/54147/80160.0004294810.0016007910.000649358PGR/JUN/FOS/ERBB2/E2F1/E2F26hsa05226Gastric cancer6/54149/80160.0004616360.0016645290.000675213RXRA/BCL2/ERBB2/E2F1/E2F2/CDK26hsa05215Prostate cancer5/5497/80160.000462820.0016645290.000675213BCL2/ERBB2/E2F1/E2F2/CDK25hsa04923Regulation of lipolysis in adipocytes4/5455/80160.0004864210.0017192470.000697409PTGS1/PTGS2/ADRB2/PTGER34hsa04914Progesterone-mediated oocyte maturation5/5499/80160.0005084460.0017666340.000716632PGR/CDK1/CCNB1/CDK2/CCNA25hsa05134Legionellosis4/5457/80160.0005577940.0019057980.000773083CASP3/CASP8/IL-6/CXCL24hsa05142Chagas disease (American trypanosomiasis)5/54102/80160.000583140.0019281260.000782141JUN/CASP8/FOS/IL-6/CCL25hsa05146Amoebiasis5/54102/80160.000583140.0019281260.000782141CASP3/PRKCA/IL-6/PRKCB/CXCL25hsa04620Toll-like receptor signaling pathway5/54104/80160.0006373320.0020738570.000841257JUN/CASP8/FOS/IL-6/STAT15hsa04928Parathyroid hormone synthesis, secretion, and action5/54106/80160.0006952210.002226880.00090333RXRA/BCL2/PRKCA/FOS/PRKCB5hsa05321Inflammatory bowel disease (IBD)4/5465/80160.0009187630.0028976380.001175422JUN/IL-6/STAT1/IL-44hsa04720Long-term potentiation4/5467/80160.0010298490.0031987740.001297577PRKCA/PRKCB/CALM1/PPP3CA4hsa05165Human papillomavirus infection8/54330/80160.0015051950.0046054480.001868192PTGS2/CASP3/CASP8/VEGFA/STAT1/E2F1/CDK2/CCNA28hsa04971Gastric acid secretion4/5475/80160.0015694770.004731510.001919329CHRM3/PRKCA/PRKCB/CALM14hsa04380Osteoclast differentiation5/54128/80160.0016230430.0048220840.00195607JUN/PPARG/FOS/STAT1/PPP3CA5hsa04960Aldosterone-regulated sodium reabsorption3/5437/80160.001909820.0055142680.002236853NR3C2/PRKCA/PRKCB3hsa05143African trypanosomiasis3/5437/80160.001909820.0055142680.002236853PRKCA/IL-6/PRKCB3hsa04662B cell receptor signaling pathway4/5482/80160.0021814180.0062109830.002519474JUN/FOS/PRKCB/PPP3CA4hsa05235PD-L1 expression and PD-1 checkpoint pathway in cancer4/5489/80160.0029413310.0082599010.003350615JUN/FOS/STAT1/PPP3CA4hsa05132Salmonella infection6/54214/80160.0029934120.0082925610.003363863BCL2/JUN/CASP3/CASP8/FOS/IL-66hsa04024cAMP signaling pathway6/54216/80160.0031349750.0083964270.003405996CHRM2/ADRB2/JUN/FOS/PTGER3/CALM16hsa04261Adrenergic signaling in cardiomyocytes5/54149/80160.003153780.0083964270.003405996ADRB2/ADRA1D/BCL2/PRKCA/CALM15hsa04932Nonalcoholic fatty liver disease (NAFLD)5/54149/80160.003153780.0083964270.003405996RXRA/JUN/CASP3/CASP8/IL-65hsa04713Circadian entrainment4/5497/80160.0040118340.0105439230.004277124PRKCA/FOS/PRKCB/CALM14hsa05231Choline metabolism in cancer4/5498/80160.0041619020.0107998730.00438095JUN/PRKCA/FOS/PRKCB4hsa04064NF-kappa B signaling pathway4/54102/80160.0048002270.0123005820.00498971PTGS2/BCL2/PRKCB/CXCL24hsa04022cGMP-PKG signaling pathway5/54167/80160.005129590.0128570650.005215446ADRB2/ADRA1D/ADRA2A/CALM1/PPP3CA5hsa04660T cell receptor signaling pathway4/54104/80160.0051428260.0128570650.005215446JUN/FOS/IL-4/PPP3CA4hsa05014Amyotrophic lateral sclerosis (ALS)3/5457/80160.0065418740.016157640.006554319BCL2/CASP3/PPP3CA3hsa05145Toxoplasmosis4/54112/80160.0066772990.0162957880.006610358BCL2/CASP3/CASP8/STAT14hsa05202Transcriptional misregulation in cancer5/54186/80160.0080357340.01938030.007861585RXRA/PPARG/IL-6/RUNX1T1/CCNA25hsa04919Thyroid hormone signaling pathway4/54119/80160.0082472010.019433060.007882987RXRA/PRKCA/STAT1/PRKCB4hsa04935Growth hormone synthesis, secretion, and action4/54119/80160.0082472010.019433060.007882987PRKCA/FOS/STAT1/PRKCB4hsa04152AMPK signaling pathway4/54120/80160.00848960.0195546970.007932329PPARG/ACACA/SLC2A4/CCNA24hsa05135Yersinia infection4/54120/80160.00848960.0195546970.007932329JUN/FOS/IL-6/CCL24hsa04924Renin secretion3/5469/80160.0110693250.0251029480.010182967ADRB2/CALM1/PPP3CA3hsa05130Pathogenic Escherichia coli infection5/54202/80160.0112459510.0251029480.010182967JUN/CASP3/CASP8/FOS/IL-65hsa04068FoxO signaling pathway4/54131/80160.0114698530.0251029480.010182967IL-6/CCNB1/SLC2A4/CDK24hsa04650Natural killer cell-mediated cytotoxicity4/54131/80160.0114698530.0251029480.010182967CASP3/PRKCA/PRKCB/PPP3CA4hsa05120Epithelial cell signaling in Helicobacter pylori infection3/5470/80160.011510620.0251029480.010182967JUN/CASP3/CXCL23hsa04270Vascular smooth muscle contraction4/54132/80160.0117701780.0253988060.010302982ADRA1D/PRKCA/PRKCB/CALM14hsa03320PPAR signaling pathway3/5476/80160.0143709840.0306880390.01244855RXRA/PPARG/MMP13hsa05204Chemical carcinogenesis3/5482/80160.0176005080.037196950.015088878PTGS2/ADH1C/CYP1B13hsa04911Insulin secretion3/5486/80160.0199609580.0416686820.016902829CHRM3/PRKCA/PRKCB3hsa04934Cushing syndrome4/54155/80160.0201229240.0416686820.016902829AHR/E2F1/E2F2/CDK24hsa04540Gap junction3/5488/80160.0212037420.0434676720.017632586PRKCA/CDK1/PRKCB3hsa04310Wnt signaling pathway4/54160/80160.0223216820.0453063840.018378456JUN/PRKCA/PRKCB/PPP3CA4hsa04630JAK-STAT signaling pathway4/54162/80160.0232409160.0467096850.018947703BCL2/IL-6/STAT1/IL-44hsa00350Tyrosine metabolism2/5436/80160.0242448720.0482543570.019574296ADH1C/MAOB2


## 3.4. Genetic Pathway Network Analysis

A network of gene pathways was built on a very rich network and genes that control these pathways, as shown in Figure6. The BC noninvasive urothelial carcinoma topological study of 20 pathways and 34 genes was carried out. Green is the destination gene, while pink is the network path. The most extensive core goal gene has been found in Figure 6(a). Invasive urothelial cell carcinoma topological analysis using BC was carried out in 20 pathways and 41 genes, as shown in Figure 6(b). The target gene is blue, and the network route is yellow. The diagram in the network demonstrates that IL-6 has the greatest core gene. They might be a significant BLCA ZLJSD target.Figure 6
ZLJSD gene-pathway network against BLCA. (a) In noninvasive BLCA, a topological study of 20 pathways and 34 genes was performed using betweenness centrality. Target genes are shown by blue ellipses, and pathways are represented by green ellipses. (b) Invasive BLCA performed a topological study of 20 pathways and 41 genes using betweenness centrality. The pink ellipses reflect pathways, whereas the green ellipses indicate target genes. The higher the size, the greater the centrality betweenness.
(a)(b)


## 3.5. Prognostic Analysis of ZLJSD Targets

The TCGA database contains expression and clinical data for 33 tumours. Prognostic analysis using the TCGA database to explore the impact of core genes on the prognosis of BLCA found that survival analysis of noninvasive urothelial carcinoma showed that 18 targets are associated with prognostic survival as shown in Figure7; the overall survival of patients with high gene expression and those with lower than low expression include ADRA1D, ADRA1A, CALM1, CHRM2, COL3A1, CYP1B1, DPP4, GJA1, JUN, MAOB, PGR, PTGER3, PTGS1, RUNX1T1, and SLC2A4. The general survival of high-gene activity patients is greater than of low expression, including PPARG and TNFSF15. The analysis of survival of invasive urothelial cancer is shown in Figure 8. There have fifteen targets related to prognostic survival. The general survival of high-gene activity patients is lower than those with low expression, ACACA, ADRA1D, ADRA2A, CALM1, CHRM2, CYP1B1, JUN, MAOB, PGR, PTGER3, PTGS1, RUNX1T1, and SLC2A4; the overall survival of patients with high gene expression is higher than those with low expression, including PPARG and VEGFA. The variation (logrank P<0.05) was significant statistically. The findings of the examination of other genes were not significant statistically.Figure 7
18 drug targets are associated with prognosis in noninvasive urothelial cancer.Figure 8
15 drug targets associated with prognosis in invasive urothelial cancer.


## 4. Discussion

We discovered that ZLJSD could significantly improve the symptoms of bladder cancer patients, and we believe that this is due to its antibladder cancer activity. As a result, we use network pharmacological approaches to investigate probable processes.We employed oral bioavailability (OB) of 30 percent and drug similarity (DL) of 0.18 to find 21 substances with disease targets: “beta-sitosterol, quercetin, kaempferol, and stigmasterol,” as per the herb-disease network. Beta-sitosterol, for example, efficiently reduces tumour cell development by decreasing proliferation and has antibladder cancer effects [23]. Kaempferol is a flavonoid that suppresses bladder cancer cell development via the c-Met/p38 signaling pathway, hence preventing bladder cancer development, invasion, and metastasis [24].Quercetin, is a flavonoid, a kind of polyphenolic substance that suppresses bladder cancer cell proliferation, cell proliferation, and hypermethylation. The decrease of mutant P53 and survivin proteins may be connected to quercetin-induced apoptosis [25]. In a nutshell, these active components form the foundation for ZLJSD’s potential anticancer action on bladder cancers.We used the target-pathway network, together with the findings of the GO analysis, to hypothesize that the probable strategy of bladder cancer treatment by ZLJSD is connected to biological functions such as inflammatory response, proliferation, angiogenesis, apoptosis, and cell senescence.


### 4.1. Cell Proliferation

Aberrant cell proliferation is a crucial feature of malignancies. The interphase and mitosis of the preparatory phase should be carried out by cell growth and separation. Currently, the overexpression of cyclin and the loss of cyclin-dependent kinase (CKI) inhibitors in the stage are frequently associated with cell malignancy. Cyclin-related kinase 1 (CDK1) is a protein kinase in serines/threonines that plays a vital function in the cell proliferation regulation at a G2/M cell-cycle stage. The required prerequisite for all eukaryotic cells to divide has been taken into consideration till now. CDK1 inactivation of CDK1 causes mitosis to cease [26]. CCNB1 has been acknowledged as a useful screening tool in diagnosing patients with a high recurrence probability and intravesical reaction who are nonmuscular invasive bladder cancer [27]. Downregulation of DNA reparation-related checkpoint protein kinase 2 (CHEK2) is associated with poor prognostic for certain cancers. Studies on the CHEK2 involvement in BLCA have shown that a decrease of CHEK2 expression is an independent progression predictor and an inadequate progressive disease-free survival [28]. CDK1 inhibition of the expression by quercetin and kaempferol may lead to the action of zLJSD on cell proliferation, which further regulates the signaling pathway of the cell cycle. Quercetin can proactively influence DNA repair through CCNB1, CHEK2, and cell cycle effects. Furthermore, quercetin can modulate MAPK via PRKCB and FOS to regulate cell proliferation more.


### 4.2. Apoptosis

The suppression of apoptosis plays an essential part in the genesis and progression of malignancies in increased cell proliferation [29]. Another of the important aspects of apoptosis is the production and regulation of the BCL-2 family. The BCL-2 is an apoptosis protein inhibitor, a PI3K-AKT signal route, and a signal route for the P53 apoptosis regulator [30, 31]. In ZLJSD, beta-sitosterol, quercetin, and kaempferol are substances that operate on BCL-2. In addition, in the CASP cascade, the direct regulation of CASP8 and CASP3 by beta-sitosterol, quercetin, and kaempferol may be an essential component of ZLJSD in regulating apoptosis [32].


### 4.3. Inflammation

Stimulation of inflammatory reactions can lead to the release of tumours into an inflammatory microenvironment of many elements which support their proliferation [33]. TNF is an inflammatory factor that is influential. It can contribute not just to apoptosis but also to cell survival and growth signals. In the inflammatory response [34], NF-βB is a crucial factor. The apoptosis protein BCL-2 not only may access the nucleus and speed up the subsequent development of inflammatory molecules like IL-1B, IL-6, and PTGS2 but also prevent apoptosis. Quercetin and kaempferol components in ZLJSD can impact the overall inflammatory reaction through inhibitors of RELA upstream expression [35, 36]. The NF-ŢB signaling route and the IL-6 downregulated route may be implicated. IL-1B suppresses NF-ŢB release either indirectly or directly and suppresses the production of inflammatory downstream proteins. Three-six-six. Research demonstrates that the participation of specific inflammatory variables has a tight relation between cancer pains [37]. The pain reduction given by ZLJSD bladder cancer tumours can also be linked to its anti-inflammatory activity, accordingly.


### 4.4. Angiogenesis

Angiogenesis depends on tumour development and metastasis [38]. The tumour microenvironment (TME) has a basic property of hypoxia and can activate the HIF hypoxia gene transcription triggering the proangiogenic factor VEGF production [39]. VEGF is an important proangiogenic factor. When utilized in conjunction with VEGFR, many downstream signal molecules like PLC-α may be activated. PKC, PI3K, MAPK, etc., as well as a signal, may eventually be transmitted towards the cell nucleus and hence achieve the biological impact of VEGF [40]. Quercetin in ZLJSD may affect the expression levels of PTGS2, PRKCA, and PRKCB in the VEGF pathway by acting on HIF1A, VEGFA, and the expression levels of 2 - methoxy-3 - methyl-9,10 - anthraquinone, stigmasterol, and β-sitosterol. The expression of quercetin PPP3CA reduces angiogenesis and thus exerts anti-cancer effect.


### 4.5. Cell Senescence

Cell senescence is an irreversible stopping cell cycle system that reacts to several types of cellular stress [41]. Cell ageing can be evident as a significant process for countering cancer. During signal transmission inside the cell senescence signalization pathway, CHEK1 and CHEK2 are essential kinases. It can activate p53 phosphorylation in numerous locations and let p53 trigger cell cycle arrest via downstream target transcript [42]. Four-one-four-one senescence-related cells, known as the senescence-secreted phenotype, release cytokines, chemokines, extra cell matrix protease, and growth factors (SASP). “NF-κB and C/EBPβ” regulate mainly SASP through direct modulation of the primary inflammation producing SASP regulators (such as IL-6 and IL-8). Secreted SASP factors can improve stability by providing immune system signals and immune recruitment. For instance, “plasminographic activator 1 (PAI1), interleukin 6 (IL-6), interleukin 8 (IL-8), insulin-like growth factor binding protein7,” cell cycle arrests, carcinogenesis (IGFBP7), and growth factor-β (TGF-β) transformation can lead to cancer cell apoptosis [43]. Secreted SASP also promotes carcinogenesis by generating VEGF or increasing angiogenesis through induction of epithelial transition from one malignancy to another [44]. Therefore, it is necessary to design more potential ageing therapies for a clearly defined method of SASP components. The active components in “formonetin, calycosin, and kaempferide ZLJSD” may operate on the cellular senescence pathway and suppress carcinogenesis using CHEK1, quercetin, CHEK2, RELA, etc.


## 4.1. Cell Proliferation

Aberrant cell proliferation is a crucial feature of malignancies. The interphase and mitosis of the preparatory phase should be carried out by cell growth and separation. Currently, the overexpression of cyclin and the loss of cyclin-dependent kinase (CKI) inhibitors in the stage are frequently associated with cell malignancy. Cyclin-related kinase 1 (CDK1) is a protein kinase in serines/threonines that plays a vital function in the cell proliferation regulation at a G2/M cell-cycle stage. The required prerequisite for all eukaryotic cells to divide has been taken into consideration till now. CDK1 inactivation of CDK1 causes mitosis to cease [26]. CCNB1 has been acknowledged as a useful screening tool in diagnosing patients with a high recurrence probability and intravesical reaction who are nonmuscular invasive bladder cancer [27]. Downregulation of DNA reparation-related checkpoint protein kinase 2 (CHEK2) is associated with poor prognostic for certain cancers. Studies on the CHEK2 involvement in BLCA have shown that a decrease of CHEK2 expression is an independent progression predictor and an inadequate progressive disease-free survival [28]. CDK1 inhibition of the expression by quercetin and kaempferol may lead to the action of zLJSD on cell proliferation, which further regulates the signaling pathway of the cell cycle. Quercetin can proactively influence DNA repair through CCNB1, CHEK2, and cell cycle effects. Furthermore, quercetin can modulate MAPK via PRKCB and FOS to regulate cell proliferation more.


## 4.2. Apoptosis

The suppression of apoptosis plays an essential part in the genesis and progression of malignancies in increased cell proliferation [29]. Another of the important aspects of apoptosis is the production and regulation of the BCL-2 family. The BCL-2 is an apoptosis protein inhibitor, a PI3K-AKT signal route, and a signal route for the P53 apoptosis regulator [30, 31]. In ZLJSD, beta-sitosterol, quercetin, and kaempferol are substances that operate on BCL-2. In addition, in the CASP cascade, the direct regulation of CASP8 and CASP3 by beta-sitosterol, quercetin, and kaempferol may be an essential component of ZLJSD in regulating apoptosis [32].


## 4.3. Inflammation

Stimulation of inflammatory reactions can lead to the release of tumours into an inflammatory microenvironment of many elements which support their proliferation [33]. TNF is an inflammatory factor that is influential. It can contribute not just to apoptosis but also to cell survival and growth signals. In the inflammatory response [34], NF-βB is a crucial factor. The apoptosis protein BCL-2 not only may access the nucleus and speed up the subsequent development of inflammatory molecules like IL-1B, IL-6, and PTGS2 but also prevent apoptosis. Quercetin and kaempferol components in ZLJSD can impact the overall inflammatory reaction through inhibitors of RELA upstream expression [35, 36]. The NF-ŢB signaling route and the IL-6 downregulated route may be implicated. IL-1B suppresses NF-ŢB release either indirectly or directly and suppresses the production of inflammatory downstream proteins. Three-six-six. Research demonstrates that the participation of specific inflammatory variables has a tight relation between cancer pains [37]. The pain reduction given by ZLJSD bladder cancer tumours can also be linked to its anti-inflammatory activity, accordingly.


## 4.4. Angiogenesis

Angiogenesis depends on tumour development and metastasis [38]. The tumour microenvironment (TME) has a basic property of hypoxia and can activate the HIF hypoxia gene transcription triggering the proangiogenic factor VEGF production [39]. VEGF is an important proangiogenic factor. When utilized in conjunction with VEGFR, many downstream signal molecules like PLC-α may be activated. PKC, PI3K, MAPK, etc., as well as a signal, may eventually be transmitted towards the cell nucleus and hence achieve the biological impact of VEGF [40]. Quercetin in ZLJSD may affect the expression levels of PTGS2, PRKCA, and PRKCB in the VEGF pathway by acting on HIF1A, VEGFA, and the expression levels of 2 - methoxy-3 - methyl-9,10 - anthraquinone, stigmasterol, and β-sitosterol. The expression of quercetin PPP3CA reduces angiogenesis and thus exerts anti-cancer effect.


## 4.5. Cell Senescence

Cell senescence is an irreversible stopping cell cycle system that reacts to several types of cellular stress [41]. Cell ageing can be evident as a significant process for countering cancer. During signal transmission inside the cell senescence signalization pathway, CHEK1 and CHEK2 are essential kinases. It can activate p53 phosphorylation in numerous locations and let p53 trigger cell cycle arrest via downstream target transcript [42]. Four-one-four-one senescence-related cells, known as the senescence-secreted phenotype, release cytokines, chemokines, extra cell matrix protease, and growth factors (SASP). “NF-κB and C/EBPβ” regulate mainly SASP through direct modulation of the primary inflammation producing SASP regulators (such as IL-6 and IL-8). Secreted SASP factors can improve stability by providing immune system signals and immune recruitment. For instance, “plasminographic activator 1 (PAI1), interleukin 6 (IL-6), interleukin 8 (IL-8), insulin-like growth factor binding protein7,” cell cycle arrests, carcinogenesis (IGFBP7), and growth factor-β (TGF-β) transformation can lead to cancer cell apoptosis [43]. Secreted SASP also promotes carcinogenesis by generating VEGF or increasing angiogenesis through induction of epithelial transition from one malignancy to another [44]. Therefore, it is necessary to design more potential ageing therapies for a clearly defined method of SASP components. The active components in “formonetin, calycosin, and kaempferide ZLJSD” may operate on the cellular senescence pathway and suppress carcinogenesis using CHEK1, quercetin, CHEK2, RELA, etc.


## 5. Conclusion

Our research predicts the objective of the composition of the ZLJSD by the network pharmacology approach and analyzes its possible anti-BLCA effect mechanism. This approach may address four aspects: antagonizes the systemic inflammatory response and inhibits angiogenesis by decreasing anomalous cell growth and boosting apoptosis. We possess a reason to believe that their anticancer mechanisms for bladder tumours are the indirect or direct synergy between efforts on many multipurpose and several different paths. Further testing, however, is crucial to expose the impact of ZLJSD on antibladder cancer.



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*Source: 7667707-2021-12-31.xml*