Network Pharmacology-based Elucidation of Molecular Biological Mechanisms of Kanglaite Injection for Treatment of Pancreatic Ductal Adenocarcinoma

Background: Kanglaite injection (KLTi) has shown good clinical efficacy in the treatment of pancreatic ductal adenocarcinoma (PDAC). However, its molecular biological mechanisms are still unclear. This study used network pharmacology approach to investigate the molecular biological mechanisms of KLTi. Methods: Compounds in KLTi were screened using TCMSP and drug targets were obtained from the DRUGBANK. Next, the GEO database was searched for differentially expressed genes in cancerous tissues and healthy tissues of PDAC patients to identify targets. Subsequently, the protein-protein interaction data of KLTi and PDAC targets were constructed by BisoGenet. A visual analysis was done to extract KLTi candidate genes for PDAC. The candidate genes were enriched using GO and KEGG by Metascape, and the gene-pathway network was constructed to further screen the key genes. Results: A total of 10 active compounds and 36 drug targets were screened for KLTi, 919 differentially expressed genes associated with PDAC were identified from GEO, and 139 KLTi candidate genes against PDAC were excavated by BisoGenet. The gene-pathway network showed RELA , NFKB1 , IKBKG , JUN , MAPK1 , TP53 , and AKT1 as the core genes, predicting that KLTi intervenes in PDAC by acting on these genes. Conclusions: Our study suggested that KLTi plays an anti-PDAC role by intervening in the cell cycle, inducing apoptosis, regulating protein binding, inhibiting nerve invasion, and down-regulating the NF-κB, MAPK, and PI3K-Akt signaling pathways. In addition, it might also directly participate in the pancreatic cancer pathway. These results provide new evidence and therapeutic direction for subsequent clinical applications and basic research on KLTi in PDAC. transcription factors and inhibiting protease activity. Results from the GO and KEGG analysis showed that KLTi exerted anti-PDAC effects by regulating cell cycle, inducing apoptosis, and participating in cancer-related pathways, neurotrophin signaling pathway, MAPK signaling pathway, and PI3K-Akt signaling pathway and their interactions. Through the mining of network pharmacology, this study found that KLTi treatment of PDAC could directly regulate the pancreatic cancer pathway, thus providing scientific evidence for the rational application of KLTi for PDAC in clinical practice. These results also provide a theoretical foundation for subsequent basic experiments.

4 TCM induces its effects by acting via multiple targets, pathways, and compounds that are involved in various aspects of disease progression. Thus, it is not possible to decode the integrity and multidirectional functionality of TCM treatment by analyzing a single pathway mechanism. The network pharmacology approach emphasizes the multi-way regulation of a specific pathway, analyzes its mutual relationship by constructing the inter-compounds network, and thoroughly examines the key nodes in the network. Network pharmacology has proven to systematically elaborate the material basis and mechanism of action of TCM. Hence, it is currently used to investigate the mechanism of action and new drug development of TCM and its compound prescriptions. Therefore, in this study, the network pharmacology method was used to explore the potential active compounds, key gene targets, basic pharmacological effects, and molecular biological mechanisms of KLTi intervention in PDAC.

Materials And Methods Screening of Active Compounds and Targets in KLT
We identified the total chemical composition of KLTi from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, Version 2.3 http://lsp.nwu.edu.cn/tcmsp.php).According to the traditional ADME (absorption, distribution, metabolism and excretion) screening principle, there are two core indicators used for screening of compounds, viz. oral bioavailability (OB) and drug likeness (DL). Since KLTi is administered intravenously, it did not require screening of OB, that is specific to orally administered drugs. The screening condition for DL was set as ≥ 0.18. Compounds related to KLTi that demonstrated antitumor activity, as confirmed from previous studies, were collected to supplement and improve the results and obtain candidate compounds. Candidate compounds were then matched to drug targets in the DRUGBANK database (Version 5.1.5, https://www.drugbank.ca/) and corrected to standard genes names, using the Uniprot database (https://www.uniprot.org/). Cytoscape 3.7.2 was employed to construct a compound-target network of KLTi for the selected compounds and targets. In this network, the nodes represented compounds or targets and the edges represented relationship of interactions.
The network was analyzed to study the relationship between the important compounds and targets in KLTi, with the help of Cytoscape's built-in network analyzer tool, focusing on the degree of connectivity-the more connected the degree, the greater were the number of involved biological functions, and the higher was its importance. The workflow of the network pharmacology analysis performed in this study is depicted in Fig. 1.

Identification of PDAC-related Targets
The differential expressed genes in cancerous tissues and healthy tissues of PDAC patients were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) series (GSE15471, samples: GSM388115-GSM388153 and GSM388076-GSM388114). Disease targets of PDAC were screened under the conditions of adjusted P-value < 0.05 and |logFC| > 1, and the gene markers with significant differentially expressed genes corresponded to gene names.

Construction of PPI Network and Screening of Key Targets
Based on the built-in function of BisoGenet of Cytoscape3.7.2, protein-protein interaction (PPI) network between KLTi and PDAC was constructed and visualized. The intersection network of two PPI networks was extracted by the Merge function of Cytoscape and the attribute values of each node in the intersection network were analyzed using CytoNCA [] . The median k 1 of the connectivity degree was calculated and all nodes with a connectivity degree greater than 2 times k 1 were selected and termed as "Hit hubs." The properties of each node of the Hit hubs network were calculated to obtain three medians k 2 , l 2 , and m 2 for connectivity degree centrality (DC), closeness centrality (CC), and betweenness centrality (BC), respectively. All nodes whose node properties were simultaneously greater than k 2 , l 2 , and m 2 were screened as candidate genes.

Pathway Enrichment Analysis
The Metascape platform (http://metascape.org/) integrates several reliable databases such as gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Uniprot for pathway enrichment analysis of gene targets and is updated monthly to ensure data accuracy. GO and KEGG analyses of candidate genes can be performed with the help of this platform. GO utilizes three parameters; namely molecular function, biological process, and cellular component to interpret the anti-tumor biological process of candidate genes. KEGG signaling pathway enrichment analysis examines the main anti-tumor signaling pathways involved in candidate genes. The top 20 GO and KEGG processes, with significant differences, were screened and the results were visualized and analyzed with R software. Based on the relevant targets mapped by KEGG results, gene-pathway networks were constructed to further screen key target genes for KLTi treatment of PDAC.

Active Compounds of KLTi
A total of 38 compounds were identified in TCMSP, based on the chemical composition of coix lacryma-jobi. Candidate compounds with DL ≥ 0.18 (18 active compounds) were included in the study.
Two compounds, olein and MBOA, that had antitumor activity as reported by previous studies [] , were excluded since their DL < 0.18. A total of 36 drug targets were matched in the DrugBank database. Of these, 10 active compounds were mapped to the corresponding targets, while 8 compounds did not match to any target (Table 1). A compound-target network was constructed, based on KLTi's active compounds and drug targets (Fig. 2). This network contained 46 nodes (10 compounds in KLTi and 36 drug targets) and 64 edges. The top four key active compounds in KLTi were stigmasterol, mandenol, sitosterol alpha1, and isoarborinol and their respective degree and DL values were 26, 20, 5, and 5 and 76%, 19%, 78%, and 77%.

PDAC-related Targets
A total of 919 PDAC-related targets were identified from the GEO database. Among these, 709 were up-regulated genes and 210 were down-regulated genes. As shown in Fig. 3, a volcano plot was created to show the distribution of differentially expressed genes. A heat map of expression for these differential genes is shown in Fig. 4.  Fig. 5(A). According to data statistics, the median degree of all nodes was 37, which was filtered with DC > 68 to obtain Fig. 5  Since the roles of proteins in PPI networks are reciprocal, they are usually classified as undirected graphs. The presence of regions with high partial density in complex networks of PPI is referred to as community or module. The network inside the module is the potential subnetwork of the PPI network, which has a higher density of subnetwork connections and less regional partial connections. Thus, the module can be considered as a biologically meaningful set, which has two components. First is the protein complex, consisting of multiple proteins to form a complex, which then plays a biological role.

Candidate Genes for KLTi Treatment of PDAC
The other is the functional module, comprising proteins located in the same pathway but with closer interactions. Therefore, to analyze the mechanism of KLTi in the treatment of PDAC more precisely, it was necessary to further identify its intrinsic module after obtaining the core PPI network. The module was obtained by analyzing the interaction relationship through the molecular complex detection algorithm, as shown in Fig. 6. Based on the p value, the biological processes of the three best scores in the PPI network and module were retained and functionally described. The functional descriptions are shown in Table 2.

Gene-Pathway Network
The gene-pathway network was constructed based on the significant difference in KEGG pathways and genes that regulated these pathways. It included 20 signaling pathways, 57 genes, and 253 relationships (Fig. 9). From the network, it was observed that RELA and NFKB1 had the largest degree (19). The other genes with large degrees were IKBKG, JUN, MAPK1, AKT1, and TP53 (17, 16, 15, 12, and 10, respectively). These genes might be the core target genes for KLTi intervention in PDAC. TCM injection is a preparation composed of multiple compounds corresponding to many targets.

Discussion
Further, there may be synergistic or antagonistic effects between targets. Hence, it is difficult to elucidate the mechanism of action of KLTi using conventional approaches that use drug-targetdisease framework. Network pharmacology is based on various types of biological information databases. Through the network analysis of drugs and diseases, it is possible to explain the overall mechanism of action and compound information of TCM and related compounds. Network pharmacology emphasizes the study of multi-target pathways, which is consistent with the overall concept of TCM.

Potential Active Compounds
The top four compounds obtained from the compound-target network of KLTi were stigmasterol, mandenol, sitosterol alpha1, and isoarborinol. The degree values of stigmasterol and mandenol were found to be significantly superior compared to others. Moreover, these compounds exhibited a high degree of biological activity and could map multiple drug targets. Hence, they were designated as the

Conclusions
In this study, we used the network pharmacology approach to conduct a preliminary investigation on the mechanism of action of KLTi in PDAC.

Availability Of Data And Materials
The data and materials used to support the findings of this study are available from the corresponding author upon request.

Availability of data and materials
The data and materials used to support the findings of this study are available from the corresponding author upon request. Heat map of differentially expressed genes. Kanglaite injection treatment pancreatic ductal adenocarcinoma core protein-protein interaction network internal potential module network. Gene-Pathway Network of Kanglaite injection against pancreatic ductal adenocarcinoma.
(The topological analysis of 20 pathways and 57 genes was carried out with Degree. Yellow diamonds represent target genes, light blue squares represent signaling pathways, and a bigger size represents a larger Degree)

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