Acinonyx jubatus) are a keystone predator of savanna systems in Africa, yet their populations have dramatically declined due to pressures such as human-wildlife conflict, loss of habitat, and most notably the illegal trade in live cheetah cubs as pets. We provide the most extensive dataset relevant to seized and non-intercepted illegal trade in live cheetahs and cheetah parts for the decade 2010-2019, spanning over 300 sources and 56 countries in Africa, the Middle East, Asia, Europe, Oceania and North America. It includes 1,884 individual incidents involving at least 4,000 cheetahs or cheetah parts or products likely or confirmed to breach national laws or CITES regulations. While the covert nature of illegal trade of any kind makes it extremely difficult to capture its true volume, we believe that the information contained in this dataset demonstrates the need for a more in-depth look into illegal cheetah trade, including sustainability assessments with emphasis in regions where cheetah populations are small and widely exploited, such as the Horn of Africa, as this dataset suggests. Ultimately, such actions could lead to improved enforcement and legal frameworks, and provide a guide for CITES actions involving international cooperation and demand reduction efforts.Cheetahs ( Over the last century, the geographic distributional range of cheetahs has seen a reduction of over 90%. Today, the known global cheetah population in the wild is estimated at only 7,100 adults and adolescents •The reports on illegal activities involving cheetahs contained in this dataset will be useful to understanding the global nature, extent, potential trafficking routes, and impact of this trade. This, in turn, could be used by law enforcement agencies, environmental investigative organizations, and non-government organizations working on the ground to promote action and raise awareness.•This dataset may contribute to further research that can lead to improved enforcement and legal frameworks through international cooperation and demand reduction efforts.1Acinonyx jubatus) presented with this paper, covers the last ten years  of seizures, as well as non-intercepted, alleged (asserted without proof) or suspected (believed to exist) illegal cheetah trade incidents known to us worldwide, including live cheetahs or cheetah parts  and derivatives .The global dataset for illegal cheetah trade  listed the cheetah under its Appendix I category: the most endangered plants and animals. Under Appendix I, wild cheetahs cannot be traded commercially, but captive-bred cheetahs can be traded by facilities registered with CITES All countries included in this dataset are Parties to CITES, except for Somaliland. Somaliland is a self-proclaimed autonomous region of Somalia with its own government institutions. As it is a legal jurisdiction of critical importance to combatting illegal trade of cheetahs, it is included as a separate entity under countries for the purposes of this dataset.This dataset was compiled utilizing over 300 sources, including direct communications with field informants, veterinarians, and cheetah owners; open-source information including official government and CITES  reports, databases, reports and media articles; and the authors’ field work. Additionally, we include data obtained through e-commerce and social media platforms and apps where wildlife is offered for sale.The dataset includes 1,884 individual incidents involving at least 4,184 cheetahs . The data spans 56 countries, 15 of which are cheetah-range countries assumed to play the role of source or transit countries, with Somaliland (42.4%), Kenya (12.7%), and Ethiopia (10.2%) being the most represented in terms of cheetah units. Iran, with a remaining population of less than 50 cheetahs While all advertisements were harvested from open sources/public accounts, special care was taken to ensure that no data use policies were breached. Consequently, any identifiers that could reveal, or lead to revealing, information about subjects linked to this research subjects were anonymized. This procedure was also followed to protect the identity of individual sources or people linked to listed incidents (see Ethics Statement).The covert nature of illicit activities makes it difficult to identify every instance. Hence, this dataset is likely to capture only a fraction of the actual trade while the numbers of traded cheetahs may be significantly larger.2Decades of work researching threats facing cheetahs had suggested that one of these threats was the illegal trade in cheetahs, mainly for their parts and derivatives. In 2005 we received our first direct report of live cheetah cubs being sold illegally in a remote area of Ethiopia and coordinated their confiscation. Stemming from this incident, as well as the decrease in wild cheetah populations across their range Thus, this dataset consists of information obtained through our field work or through networks set up on the field. We also compiled data reported by government representatives and CITES, databases, field informants, veterinarians, cheetah owners, rescue facilities, and media articles. Additionally, we included data obtained through e-commerce and social media platforms where wildlife are offered for sale Given the covert nature of illegal activities, and that this is the first research of its kind in terms of timespan and multiplicity of sources, we approached this research in an organic manner, i.e., we searched all sources of information available to us through the methods described below. In most cases, each search led us to additional sources, which were included going forward.Our sources were classified as follows and each was processed based on the methodology described in To protect the privacy of informants or presumed “Persons of Interest”, all details that might reveal individuals’ personal information were omitted from this dataset and were assigned codes.2.12.1.1All records from official sources were assumed to be true. These include CITES web site documents , all of which contain data from CITES national authorities or Intergovernmental Organizations (IGO) such as the United Nations Office for Drugs and Crime.Court records were obtained either through internet searches when the name or names of arrestees were mentioned in media articles, or through periodic searches for the word “cheetah” in web sites that publish court records.Direct reports from government or enforcement officials which lacked evidence of a seizure, such as specific details of disposal or official documentation, were further researched online or through personal interviews to avoid including erroneous information. Additional verifications were also performed if confusion on the species in question was suspected, e.g., leopards, servals or caracal cubs reported as cheetahs, or leopard skins reported as cheetah skins, by searching for original images.2.1.2In addition to the CITES trade database , we consulted two reliable databases: International Cheetah Studbook (ICSB) and TRAFFIC International Wildlife Trade Portal, which are maintained by reputable organizations.The ICSB contains firsthand reports by cheetah-holding facilities around the world. Facilities provide information about the date when they received a cheetah and birth type . Factors such as whether the facility is in a cheetah-range country or not, whether the birth type is unknown or of wild origin, and the circumstances of a transfer  were considered. We focused on transfers of wild-born or unknown birth type cheetahs into facilities in non-range countries and compared them with CITES trade database entries to determine whether a transfer was permitted or reported as a confiscation or seizure (source “I”). ICSB entries were also cross-checked with seizures reported by CITES national authorities through other means such as CITES questionnaires or meeting reports.The TRAFFIC International Wildlife Trade Portal is a database that includes open-source instances of illegal wildlife trade. Data from this database were also compared to other sources, since some incidents sourced from this tool included records from the CITES database or Management Authorities’ biennial reports, as well as media articles. We attempted to confirm the latter by cross-checking with other sources, such as court records or police reports, and graded each incident accordingly (see 1.6. Incident Grading).2.1.3Our field work data are based on firsthand incidents consisting mainly of helping governments with the rescue or disposal of seized animals. Data based on this work are rated true. Through field work we are often approached by cheetah dealers. Working alongside authorities, we attempt to obtain physical evidence of the existence of cheetahs . We maintain detailed records and evidence of all such incidents, including those where seized cheetahs are placed under our care.Similarly, we are commonly approached by rescue facilities, veterinarians or owners seeking advice for cheetah care. In such cases we conduct informal interviews to obtain information on the animals, as well as images or videos to better assist them and as evidence.Field informants are an important source of data. Over the years we built a network of confidential informants who either have ties with exotic animal dealers or live in areas where cheetahs are commonly trafficked or sold. We attempt to obtain evidence of all reports from confidential informants who have personally seen or come across a case of actual or potential illegal cheetah trade. In addition to their credibility, informants’ reports are graded based on the level of detail provided, as well as evidence such as images or videos. Finally, field research, firsthand media and investigative reports are also included in this category.2.1.4In this category we include research papers, second-hand media articles, and direct reports from people or organizations not directly involved in an incident, e.g., NGO/government staff doing field work in known wildlife trafficking areas, who often come across reports of illegal cheetah trade. Every effort was made to confirm reports by secondary sources with follow-up searches for official records, substantiation with images or video, and evidence of their primary source including declarations by government officials in the case of seizures reported by media.We included in our dataset the result of thousands of internet searches over the 10-year period. We set up Google Alerts with the search terms “illegal wildlife”, “pet trade”, “cheetah” AND “illegal”, “pet cheetah”, “cheetahs” AND “trade”, “cheetah” AND “confiscated” and “cheetah trafficking.” An average of 10 alerts were received by email daily and included mainly media articles, blogs and Facebook posts.jagluiperd (Afrikaans), image (Amharic), شيتا (Arabic), guépard (French), loài báo (Vietnamese), یوزپلنگ (Persian), harimad, harimacad or harima'ad , guepardo (Spanish/Portuguese). Where arrests were reported, online court records were searched periodically. Original images were particularly important as in some cases leopards or other cat species were mistaken for cheetahs, whether due to translation or confusion between species.We reviewed relevant items to assess their veracity utilizing criteria such as timeliness, level of detail, original versus stock images and declarations by government officials that included name and title. Further internet searches for the same topic were made to find the primary source of an article using keywords from the initial article or specific names of arrestees when available. The word “cheetah” in foreign languages was used to locate additional information relevant to specific countries, e.g., 2.1.5Exotic pets are very popular in the Middle East Additionally, we monitored sellers’ accounts that were brought to our attention by confidential informants who had dealt with them or are part of their networks. One of these informants provided us with Instagram usernames of the five largest sellers in the UAE. Posts on these accounts yielded hundreds of new discoveries given that sellers often post comments on other sellers’ posts to publicize that they, too, have cheetahs for sale. These were also added to the research.Our research into online advertisements goes beyond 2010 and has yielded over 60,000 files  that include other CITES-listed species. To protect their privacy, advertisers and URLs to their posts were anonymized and assigned unique codes to avoid identifiers that could lead to revealing personal information.We set up a dedicated account on Instagram, which was found to be the most used platform to advertise cheetahs during this research, to follow posts; however, we did not interact with the users.The second most used platform found through this research is the Saudi eCommerce web site haraj.com.sa, followed by the Kuwaiti phone app 4sale, both popular for buying and selling multiple items including animals. A Kuwaiti collaborator monitored 4sale through random searches for 35 months (December 2012 to October 2015) and provided us with screen captures of all cheetah offerings found. Although there are other mobile phone apps used in the Gulf countries, these were not monitored during the dataset period as no local SIM card was available.During our searches we identified accounts that shared the same telephone/WhatsApp numbers. These accounts were merged and cataloged under the listed numbers. The numbers were further searched on Google to identify real names, location, email address, as well as additional advertisements and information on other businesses or accounts connected to them. Every detail found through these searches was recorded. All identifying data was anonymized after collection.Special care was paid to avoid recording scam posts, i.e., animals do not exist, but money is required as a deposit. To this end, we used reverse Google Image Search to ensure that images were original, as most scammers utilize pictures previously published by others. We searched Google for telephone numbers and email addresses and looked for indicators that are common with legitimate sellers, like wording and pictures or videos of them with the animals.We found that cheetahs are occasionally advertised in more than one account with the same or similar images but different telephone/WhatsApp numbers. We strived to avoid duplications by performing cursory manual comparisons of over 60,000 images from different accounts saved in our archives by sorting them by similar dates, prices, description of the cheetahs or common denominators, such as furniture and carpeting or jewelry, and conditions in which the animals were showcased. We merged similar advertisements and recorded the earliest publication date with all telephone/WhatsApp numbers and URLs in connection with the same advertisement.Short advertisements were screen captured, while longer ones were saved as PDF files. All were assigned a filename consisting of the date they were posted (YYYY-MM-DD) and a brief description of the post, e.g., 3 2-mo cubs [price], with their URLs added to the file properties. In the case of advertisements containing videos, we saved those that were more descriptive of the advertisement than the displayed image, e.g., number of cheetahs offered were only evident on the video. The saved files were then recorded in a spreadsheet with all relevant information: URL(s), user ID and telephone/WhatsApp number(s), and a Google translation of the post. While Google translations are poor, they provide enough information to determine the nature of a post.For the most prolific sellers who post frequent advertisements, we made every effort to compare markings on the cheetahs to minimize the risk of double-counting them. Due to the frequency with which some sellers delete posts, make their pages private or close them to reopen new ones, we downloaded entire Instagram pages of some of those sellers who appeared more active to facilitate searches and preserve evidence.We have noticed a decrease in the number of online advertisements in some of the Gulf States in the last three years, possibly due to growing regulations on the possession of predators in the Gulf States, e.g., a 2017 UAE law prohibiting private ownership of exotic and dangerous pets without a license. Sellers still using open-source platforms avoid including the word “sale” or prices. Instead they respond to sales enquiries by inviting contact through WhatsApp or private message. Consequently, advertisements that include a telephone/WhatsApp number or ask to be contacted privately were considered offers to sell and were included in this dataset.Many sellers began avoiding the internet altogether or use apps such as SnapChat, where posted videos disappear after being viewed. Furthermore, sellers have formed chat groups on WhatsApp –an end-to-end encrypted app; membership is approved only for known buyers or sellers and direct referrals. Reports from a confidential source belonging to one of these chat groups were included in the dataset. Based on information provided by this confidential source, sellers are highly suspicious of being contacted by someone with whom they have never dealt before, and more so, by someone who does not speak Arabic. As such, we made the decision of not attempting to infiltrate their networks directly and instead focus on gathering data.2.2Given our specific aim to quantify the volume of illegal cheetah trade, we adapted the fields in our dataset from various methods utilized by TRAFFIC, the Wildlife Conservation Society, the International Fund for Animal Welfare, and the Wildlife Justice Commission, with whom we have collaborated and shared data over the years. Each incident of actual, alleged or suspected illegal cheetah trade was entered into an Excel spreadsheet organized into six sections described below.2.2.11.Seizures: Cases where cheetahs were reported as taken/seized by authorities2.Possession/trade: Alleged or suspected cases of cheetahs being held illegally and not intercepted, including those offered for sale or pets reported directly by the owners. However, cheetahs displayed as pets on social media are not included as these could be a duplication of cheetahs advertised for sale.Each incident in this dataset was assigned an ID number composed of the researcher's initials, the year when the incident occurred, and a 3-digit number for each incident, e.g., PT-2018-XXX. For this research we selected two types of incidents:2.2.2All incidents were recorded by the country where they occurred. Countries were assigned role codes  based on their location and the incident description. Roles could be a combination of two when one could not be assigned with certainty, e.g., origin and/or transit (O/T) or transit and/or destination (T/D). In the decision process we considered whether countries were in cheetah range as well as empirical deductions. When countries of origin, transit or destination were known, these were entered in the corresponding fields. In all cases where a report included a description of the location, this was entered under location type.2.2.3Each incident was graded based on TRAFFIC guidelines, which were adapted to the specific characteristics of incidents reported in this dataset. Grading is assigned as follows:These gradings were based on the ultimate means of verification, which included searching for original photos or video and official records, whether a seller is well known, or the reliability of a source, among others.2.2.4Reliability of a source is an important element to define whether an incident requires further clarification, especially in the cases where the source is not directly involved in the incident . We periodically searched the internet to answer questions regarding the validity of recorded incidents, i.e., court records or databases. As such, we included fields to describe the original source of each report, as well as the ultimate source used to verify an incident.Reliability ratings were assigned only to the ultimate source, which on occasions was the same as the original source, based on the following factors: known or unknown source, direct experience with a source, timeliness of a report and details/evidence provided.2.2.5This section includes a full description of an incident, including the number and type of specimens involved (in full cheetah units), whether they were seized or surrendered, number of animals known to be alive or death, including those that died from trade-related consequences  after confiscation. Cheetah products and parts are classified as dead. If the number of cheetahs was not specified or deducible due to the nature of the product , we entered UNK. All cheetahs for which details on disposal or fate were unknown were listed as LTF, or lost to follow up. Live cheetahs offered for sale are considered LTF unless a seller specifies that the cheetah died or was sold. Sold cheetahs were classified as alive.2.2.6The last section of the dataset includes actions taken by the authors or reported by sources, such as circumstances of an arrest. Known outcomes, such as court decisions and disposal of the animals are entered in the relevant field. In seizure cases, we recorded the detecting agency and the Person of Interest (POI) when reported. Each person involved in an illegal cheetah trade incident was anonymized and given a POI code, while their actual details were kept confidential as part of the Limited Dataset (LDS) (see Ethics Statement). We also included a column for other details such as URLs  where information was found or confirmed.This dataset includes data collected from open-source social media and eCommerce platforms that has been fully anonymized. It does not include Limited Data Sets (LDS). LDS consist of any information about the people reporting or involved in an incident or online advertisement, and which can reveal names, telephone numbers, account IDs or any other personal information. These have been assigned identifying codes for the purpose of this dataset. Consequently, all data obtained from the internet complies with data redistribution policies from the platforms.No funding was received for this research. The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.


In this study, the data mining method was used to screen the core Chinese materia medicas (CCMMs) against primary liver cancer (PLC), and the potential mechanisms of CCMMs in treating PLC were analyzed based on network pharmacology.  Traditional Chinese medicine (TCM) prescriptions for treating PLC were obtained from a famous TCM doctor in Shenzhen, China. According to the data mining technique, the TCM Inheritance Support System (TCMISS) was applied to excavate the CCMMs in the prescriptions. Then, bioactive ingredients and corresponding targets of CCMMs were collected using three different TCM online databases, and target genes of PLC were obtained from GeneCards and OMIM. Afterwards, common targets of CCMMs and PLC were screened. Furthermore, a network of CCMMs bioactive ingredients and common target gene was constructed by Cytoscape 3.7.1, and gene ontology (GO) and signaling pathways analyses were performed to explain the mechanism of CCMMs in treating PLC. Besides, protein-protein interaction (PPI) analysis was used to identify key target genes of CCMMs, and the prognostic value of key target genes was verified using survival analysis.  A total of 15 high-frequency Chinese materia medica combinations were found, and CCMMs  were identified by TCMISS. A total of 40 bioactive ingredients  of CCMMs were obtained, and 202 common target genes of CCMMs and PLC were screened. GO analysis indicated that biological processes of CCMMs were mainly involved in response to drug, response to ethanol, etc. Pathway analysis demonstrated that CCMMs exerted its antitumor effects by acting on multiple signaling pathways, including PI3K-Akt, TNF, and MAPK pathways. Also, some key target genes of CCMMs were determined by PPI analysis, and four genes  were found to be correlated with survival in PLC patients.  Based on data mining and network pharmacology methods, our results showed that the therapeutic effect of CCMMs on PLC may be realized by acting on multitargets and multipathways related to the occurrence and development of PLC. Primary liver cancer (PLC) is one of the most common and fatal malignant tumors. In 2012, 782,500 new cases of PLC were reported worldwide, being responsible for an estimated 745,000 deaths globally, and PLC ranked as the second leading cause of death from cancer . The mosDue to the limitations of present therapies, complementary and alternative medicines have been more utilized for the treatment of PLC in the past few decades . In ChinAt present, the network pharmacology approach can be used to interpret the relationship between diseases, drugs, and targets, showing the network of drug targets from a holistic view . Also, iFirst of all, TCM prescriptions for the treatment of PLC were collected from the clinical practice of a famous TCM doctor, and then a database of TCM prescriptions for treating PLC was established. Secondly, data mining software named the TCM Inheritance Support System (TCMISS) was used to extract the CCMMs. Subsequently, the ingredients and target genes of the CCMMs were obtained from three different databases. Target genes related to PLC were searched in GeneCards and OMIM databases. In addition, the common target genes of CCMMs and PLC were screened. Furthermore, potential mechanism of CCMMs in treating liver cancer was analyzed, and survival analyses of key target genes were performed. The overall flowchart for this study is displayed in All TCM prescriptions for the treatment of PLC were acquired from Shenzhen traditional Chinese Medicine Hospital affiliated to Guangzhou University of Chinese Medicine (date: from October 2009 to August 2019), which were prescribed by Professor Chensheng Ouyang, a famous TCM doctor in Shenzhen, China. The inclusion criterion of TCM prescriptions was the first diagnosis was PLC, and exclusion criteria were (1) duplicate prescriptions of the same patients; (2) prescriptions were primarily for treating acute symptoms such as cold, cough, diarrhea, and vomiting; and (3) external prescriptions. Based on data mining methods, TCMISS was applied to extract the high-frequency Chinese materia medicas and CCMMs of the prescriptions. TCMISS is software widely used in TCM prescription data analyses, which integrates general statistics, text mining, association rules, and complex system entropy clustering methods , 16. ThrThe association rules method in TCMISS was used to identify the high-frequency combinations of Chinese materia medicas, and then CCMMs were obtained. Specifically, the “prescription analysis” module of “data analysis” section in TCMISS was selected to identify the frequency and the combinations of Chinese materia medicas. Analysis of prescription rules was carried out under the condition that support the degree was 200 and the confidence score was greater than or equal to 0.95.http://tcmspw.com/tcmsp.php) [http://www.tcmip.cn/) [http://bionet.ncpsb.org/batman-tcm/) [https://www.uniprot.org/).TCM Systems Pharmacology Database and Analysis Platform  , Integramip.cn/) , and Bioan-tcm/) , were utan-tcm/) . Besideshttps://www.genecards.org/) [https://omim.org/) [The databases “GeneCards” (ds.org/)  and “Onlim.org/)  were seahttp://bioinformatics.psb.ugent.be/webtools/Venn/). In order to fully understand the molecular mechanism, the network of CCMMs bioactive ingredients and common targets was constructed by using Cytoscape 3.7.1.Common target genes of CCMMs and PLC were screened using the Draw Venn Diagram online tool (https://david.ncifcrf.gov/) [R software (version 3.6.0). Furthermore, top 20 key KEGG pathways were identified according to the order of gene count. In the light of the biological functions of common targets and correlative signaling pathways, the mechanism of CCMMs against PLC was discussed. In GO and KEGG analyses, a false discovery rate (FDR) below 0.05 was considered significant.GO and Kyoto Encyclopedia of Genes and Genomics (KEGG) signaling pathway analyses for common target genes were performed by the Database for Annotation, Visualization, and Integrated Discovery  . In addihttps://string-db.org/) [The common target genes were input into Search Tool for the Retrieval of Interacting Genes (STRING) database (db.org/)  to constdb.org/)  in Cytoshttp://kmplot.com/analysis/) to explore the association between key genes expression and prognosis in patients with PLC. The Kaplan–Meier Plotter includes data on survival prognosis of liver cancer in The Cancer Genome Atlas (TCGA) database [p value less than 0.05 was considered significant.After obtaining the key target genes from the PPI network, we used the Kaplan–Meier Plotter  with the DC > 90 and OS rate were analyzed by the K–M method. Survival curves of the key genes are shown in Figures The results of data mining demonstrated that CCMMs for PLC comprised six Chinese materia medicas, including Radix Alba, Radix Bupleuri, Macrocephalae Rhizoma, Coicis Semen, Poria, and Curcumae Radix. According to TCM theories, these CCMMs can be applied to disperse stagnated liver Qi, reinforce Qi, and nourish Xue (blood). In TCM, the pathogenesis of PLC is usually thought to be caused by liver Qi stagnation and deficiency of qi and Xue. Thus, data mining results are in line with the theories of TCM. Furthermore, the results of network pharmacology revealed that the bioactive ingredients of CCMMs, including quercetin, kaempferol, naringenin, isorhamnetin, beta-sitosterol, and stigmasterol were related to more target genes, which were the key bioactive ingredients. Quercetin has been proved to induce apoptosis of liver cancer cells (SMMC-7721), and its mechanism could be related to the inhibition of activation of Akt by PTEN gene overexpression . KaempfeIn the present study, GO and KEGG analyses were used to predict the underlying mechanism of CCMMs in treating PLC. The result of GO analysis indicated that common targets were significantly enriched in the mitochondrion, extracellular exosome, membrane raft, cytosol, and extracellular space, showing that these genes are involved in various cell metabolisms. As for GO-MF, the genes were involved in drug binding, identical protein binding, protein binding, protein homodimerization activity, and enzyme binding, which are correlated with the liver metabolic function. In GO-BP, common target genes were most enriched in response to hypoxia, positive regulation of transcription from RNA polymerase II promoter, response to lipopolysaccharide, response to ethanol, and response to drug, suggesting that CCMMs may be associated with cell apoptosis, liver metabolism, immune system, and drug metabolism. KEGG analysis showed that CCMMs may produce anti-PLC effects via multiple pathways, including pathways in cancer, hepatitis B, PI3K-Akt, TNF, MAPK, hepatitis C, HIF-1, toll-like receptor, and Ras signaling pathway. Previous studies have reported that the expression of Akt in PLC cells is significantly higher than that in normal liver tissues , and ChiSeveral key target genes of CCMMs were identified by PPI network construction, and four of the top nine genes were found to be correlated with OS in PLC patients. An experimental study showed that overexpression of AKT1 may promote the migration and invasion of PLC cells (HepG2) . InflammIn our study, the CCMMs for the treatment of PLC were identified from a large number of clinical prescriptions using the data mining approach, which may provide a useful reference for the clinical practice of TCM. Furthermore, the network pharmacology method was applied to acquire potential targets and signaling pathways of CCMMs in the treatment of PLC. According to the results of network pharmacology, CCMMs may exert antitumor effects on PLC by inhibiting the proliferation, invasion, and metastasis of PLC cells and by inducing cell apoptosis in PLC. Moreover, some of the pathways  and target genes  may provide a novel approach for studying the mechanism of Chinese materia medicas in treating PLC. In the present study, the K–M survival analysis was used to verify the prognostic value of key genes in PPI, and four key genes of CCMMs were found to be significantly related to OS in patients with PLC, which made the results of network pharmacology more reliable. However, the results in this paper depend on the reported ingredients and targets of CCMMs. With the development of experimental technology, more and more molecular targets for PLC therapy as well as new ingredients of Chinese materia medicas will be discovered, which may be helpful to enrich the results of this paper. Besides, future experiments are required to validate the findings of the present study.In conclusion, based on the combination of data mining and network pharmacology, we predicted that the therapeutic effect of CCMMs on PLC may be realized by acting on multitargets and multipathways related to the occurrence and development of PLC, which laid a foundation for further experimental study.


As the incidence of nonalcoholic fatty liver disease (NAFLD) increases globally, nonalcoholic steatohepatitis (NASH) has become the second common cause of liver transplantation for liver diseases. Recent evidence shows that Roux-en-Y gastric bypass (RYGB) surgery obviously alleviates NASH. However, the mechanism underlying RYGB induced NASH improvement is still elusive.We obtained datasets, including hepatic gene expression data and histologic NASH status, at baseline and 1 year after RYGB surgery. Differentially expressed genes (DEGs) were identified comparing gene expression before and after RYGB surgery in each dataset. Common DEGs were obtained between both datasets and further subjected to functional and pathway enrichment analysis. Protein–protein interaction (PPI) network was constructed, and key modules and hub genes were also identified.IGF1, JUN, FOS, LDLR, TYROBP, DUSP1, CXCR4, ATF3, CXCL2, EGR1, SAA1, CTSS, and PPARA were identified as hub genes, and three functional modules were also extracted.In the present study, GSE106737 and GSE83452 datasets were included. One hundred thirty common DEGs (29 up-regulated and 101 down-regulated) were identified between GSE106737 and GSE83452 datasets. KEGG analysis showed that mineral absorption, IL-17 signaling pathway, osteoclast differentiation, and TNF signaling pathway were significantly enriched. Based on the PPI network, This study identifies the global gene expression change in the liver of NASH patients before and after RYGB surgery in a bioinformatic method. Our findings will contribute to the understanding of molecular biological changes underlying NASH improvement after RYGB surgery. In the past few decades, obesity and its comorbidities are becoming the leading causes of death around the world . MeanwhiSo far, bariatric surgery is the most effective approach to treat obesity, which can also alleviate its comorbidities, such as type II diabetes mellitus (T2DM), hypertension, and hyperlipidemia . Roux-enRYGB surgery is thought to be the malabsorptive procedure that bypasses a great portion of the intestine leading to nutrient malabsorption and weight loss . In the Microarray technology is an essential tool to illustrate gene expression patterns in multiple diseases, which can help us understand the biology and molecular mechanisms of diseases more efficiently. Microarray technology has also been used in the liver of NASH patients and animal models to detect differentially expressed genes (DEGs), screen disease biomarkers, or find new therapeutic targets , 12. By http://www.ncbi.nlm.nih.gov/geo). Datasets must meet the main inclusion criteria: 1) Datasets assessed transcriptome information in the liver specimen of patients who undergone RYGB surgery; 2) Datasets included liver specimens during RYGB surgery and paired liver specimens collected through liver biopsy 1-year post-operation; 3) All cases should be pathological proven NASH before RYGB surgery and NASH regression 1 year post-operation; 4) Datasets must contain at least 10 cases and paired follow-ups. In the GSE106737 dataset, 21 cases matched the inclusion criteria. In the GSE83452 dataset, 16 cases matched the inclusion criteria. All microarray analysis in the GSE106737 and the GSE83452 were performed on the GPL16686 platform. All gene expression patterns were originated from the open-access GEO database, so our study did not require Ethics Committee approval.The gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database (http://bioinformatics.psb.ugent.be/webtools/Venn/) to get the common DEGs.The Gene expression matrix and attached annotation document for GSE106737 and GSE83452 datasets were downloaded from the GEO database. GSE files were divided into Baseline  and Follow-up (same patients with NASH improvement 1 year after RYGB surgery) groups. All microarray data had already been corrected and normalized by the RMA method. Gene IDs in the matrix were annotated with gene symbols by R package through related annotation documents. Mean values were preserved if the gene symbols matched with multiple probes. The DEGs were detected between liver biopsies at baseline and 1 year after RYGB surgery in each microarray by limma (linear models for microarray) R package. In each dataset, |log fold change (FC)| > 0.5 and P-value < 0.05 were regarded as the threshold value to determine DEGs. Then, DEGs in each dataset were upload to Venn software online  is a recognized method to annotate the function of genes detected through high throughput transcriptomic or genomic data. Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database resource including genomes, diseases, biological pathways, and other bioinformation. In this study, Bioconductor clusterProfiler package was used to carried out GO and KEGG analysis for common DEGs . Biologihttps://string-db.org/) (version 11) is a frequently used software online to predict the interaction between proteins and proteins . There existed 37 Baseline  and 37 paired Follow-up (same patients with NASH improvement 1 year after RYGB surgery) for further analysis. The detailed information of included cases was shown in Initially, GSE106737 and GSE83452 were included according to the inclusion criteria. However, to obtain high-quality data, we further screen both datasets to get cases that match inclusion criteria  and 101 DEGs were down-regulated (logFC < 0). Venn diagram was shown in P-value= 5.97E-13) was the most significantly enriched biological process (BP), followed by cellular response to cadmium ion process , zinc ion homeostasis process , and so on. Cell component (CC) and metabolic function (MF) of DEGs were mainly related to lipoprotein homeostasis and inflammatory signaling pathway, respectively. The detailed information of GO analysis was shown in P-value= 3.75E-08), followed by IL-17 signaling pathway , osteoclast differentiation , etc. The top 10 pathways of KEGG analysis were shown in All common DEGs were analyzed by the clusterProfiler package to illustrate the biological functions and pathways related to DEGs. The results demonstrated that cellular zinc ion homeostasis process , Module 2 , and Module 3 . All genes belonged to these three modules were down-regulated common DEGs. Modules were shown in In the past few decades, NAFLD has become the most common chronic liver disease around the world , 19. AltIGF1, JUN, FOS, LDLR, TYROBP, DUSP1, CXCR4, ATF3, CXCL2, EGR1, SAA1, CTSS, and PPARA were identified as the hub genes and ranked by the degree value using CytoHubba plug-in APP in Cytoscape. Among 13 hub genes, only IGF1 and PPARA were up-regulated, while others were down-regulated. In consistence with this conclusion, a previous study had demonstrated that PPARα activation might be the mechanism underlying NASH improvement after RYGB surgery , which was combined with Jun , Fos , activating transcription factor (Atf) and musculoaponeurotic fibrosarcoma (Maf) proteins to form homodimer or heterodimer is a dimeric leucine zipper (bZIP) transcription factor , activated transcription factors regulating hepatic lipid homeostasis , including MT1, MT2, MT3, and MT4 isoforms, played a pivotal role in heavy metal toxicity protection, metal homeostasis, and oxidative stress regulation  The gastrointestinal anatomy was altered after RYGB surgery, so more bile acid (BA) reach ileum and stimulate enteric GLP-1 and PYY release through activating the local L-cells . FurtherThere existed several limitations of the present study, which should be noted. First and foremost, due to the difficulty in collecting the hepatic specimens after RYGB surgery, we did not validate the genes in the pathway highlighted by the present study. This limited the strength of our results in interpreting the mechanism underlying RYGB surgery induced NASH alleviation. Second, we could not get enough demographic and clinical information for the patients included in our study. Considering body weight, body mass index (BMI), and obesity related comorbidities such as type 2 diabetes mellitus were all risk factors for NASH. There existed confounding factors in the analysis in the present study. Third, hepatic tissue was the heterogeneous mixture of hepatocytes and mesenchymal cells, and the cell composition might be influenced during specimen collection. Finally, the metabolic change in the liver after bariatric surgery is a dynamic process. Analysis at a single time point after surgery ignored the full picture of the hepatic remodeling process.IGF1, JUN, FOS, LDLR, TYROBP, DUSP1, CXCR4, ATF3, CXCL2, EGR1, SAA1, CTSS, and PPARA, and the possible involved pathways are IL-17 signaling pathway, osteoclast differentiation pathway, chemokine signaling pathway, viral protein interaction with cytokine and cytokine receptor pathway, Toll-like receptor signaling pathway, TNF signaling pathway and mineral absorption pathway. Most DEGs and enriched signaling pathways are involved in the inflammatory response, immunoreaction, and lipid homeostatic regulation. These results provide pivotal transcriptome information underlying RYGB surgery induced NASH alleviation.In conclusion, in the present study, we exhibit the global profile of DEGs and corresponding signaling pathways, which may mediate RYGB surgery induced NASH improvement. In the process of RYGB surgery induced NASH improvement, the possible key genes are The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.This work was supported by the Support Program for Young and Middle-aged Scientific and Technological Innovation Talent (Grant No. RC200607); 2020 “Double First Class”: Support Plan for the Iconic Achievement of Clinical Medicine (Grant No. 3110200345).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Gaining biological insights into molecular responses to treatments or diseases from omics data can be accomplished by gene set or pathway enrichment methods. A plethora of different tools and algorithms have been developed so far. Among those, the gene set enrichment analysis (GSEA) proved to control both type I and II errors well. In recent years the call for a combined analysis of multiple omics layers became prominent, giving rise to a few multi-omics enrichment tools. Each of these has its own drawbacks and restrictions regarding its universal application.Here, we present the multiGSEA package aiding to calculate a combined GSEA-based pathway enrichment on multiple omics layers. The package queries 8 different pathway databases and relies on the robust GSEA algorithm for a single-omics enrichment analysis. In a final step, those scores will be combined to create a robust composite multi-omics pathway enrichment measure. multiGSEA supports 11 different organisms and includes a comprehensive mapping of transcripts, proteins, and metabolite IDs.https://github.com/yigbt/multiGSEA and at bioconductor: https://bioconductor.org/packages/multiGSEA.With multiGSEA we introduce a highly versatile tool for multi-omics pathway integration that minimizes previous restrictions in terms of omics layer selection, pathway database availability, organism selection and the mapping of omics feature identifiers. multiGSEA is publicly available under the GPL-3 license at  When measuring molecular responses to a certain treatment or gaining insights into clinical phenotypes, gene set or pathway enrichment techniques are tools of first choice to infer mechanistic biological information from high-dimensional molecular omics data. Through different statistical techniques, such as over-representation analysis (ORA) or gene set enrichment analysis (GSEA), these methods are capable of identifying specific sets of genes or molecular response/signaling pathways that are triggered upon a certain treatment or disease. These sets might represent specific molecular functions, as defined by Gene Ontology (GO) , biologip value-based pathway enrichment but moreover was the only method not found to produce any false prediction [Up to date nearly one hundred algorithms have been developed for gene set or pathway enrichment, each of which has its own strengths and weaknesses. In principle, these methods can be grouped into two distinct classes: (1) pure gene set enrichment, where the algorithms solely focus on a plain list of features and (2) topology-based enrichment, where algorithms include additional information derived from pathway or network databases, e.g., which genes or proteins are directly connected and how they influence each other. There are several comprehensive reviews on this topic available, see for example , 5. BesiOver the last decade, combined analysis of molecular responses through the integration of multiple omics types has become prevalent, e.g. combining transcriptomics, proteomics, and metabolomics. This is becoming necessary since single-omics analyses will only measure biomolecules of a specific type and will often not even detect its entirety but only a subset thereof. Furthermore, the response time and the life-span of biomolecules varies substantially within and between single omics layers. Thus, only the combined analysis of several molecular layers through multi-omics measurements reliably allows to uncover a significant fraction of cellular effects .PaintOmics [IMPaLA [PaintOmics, for example, is capable of including several different omics layers into its pathway enrichment analysis, but solely relies on pathway definitions from the KEGG database. Furthermore, impacted pathways are determined based on Fisher’s exact test, which was shown to be particularly prone to reporting false positive pathway enrichments [IMPaLA on the contrary supports the analysis of a range of different pathway databases but is limited to two different omics input layers, allowing to integrate either transcriptome and metabolome or proteome and metabolome. While PaintOmics is applicable to several model organisms  IMPaLA is restricted to human pathway definitions only.There are a few integration tools available that incorporate pathway knowledge to interpret multi-omics datasets like intOmics  or IMPaL [IMPaLA . These michments . IMPaLA Here we introduce the multiGSEA R package that provides multi-omics-based pathway enrichment employing the robust GSEA algorithm and allowing to use pathway or gene set definitions from several curated databases. In its current version multiGSEA is applicable to a combination of transcriptome, proteome, and metabolome data measured in 11 different organisms, including human, mouse, or rat.In principle, the workflow of the multiGSEA package is composed of three essential steps: 1) prepare pathway definitions and omics data (2) single omics gene set enrichment analysis (3) combined multi-omics enrichment. These steps are graphically outlined in Fig.  prepare Over the last decades, several pathway databases have been established. Some of which are peer-reviewed and manually curated while others follow a community-based approach to develop and refine pathways. However, often these databases contain their own format in which pathway definitions are provided, making it cumbersome to include multiple databases in an analysis workflow. The ‘graphite‘ ‘R‘ package  was desiDepending on the database, pathway features (nodes) are encoded with different ID formats. The KEGG database, for example, uses Entrez Gene IDs for transcripts and proteins while KEGG Compound IDs are used for metabolites. The Reactome database on the contrary stores transcripts and proteins by means of Uniprot identifiers, while ChEBI IDs are used for metabolites. Further metabolite ID formats are CAS numbers and Pubchem IDs. To solve this issue, especially when dealing with multiple pathway databases in a single analysis, we implemented an ID mapping for features of all three supported omics layers. Transcriptomics and proteomics features can be mapped to the following formats: Entrez Gene IDs, Uniprot IDs, Gene Symbols, RefSeq, or Ensembl IDs. The mapping procedure is accomplished by means of the AnnotationDbi Bioconductor package  and depeMetabolomic features can be mapped to Comptox Dashboard specific IDs , CAS numbers, Pubchem IDs , KEGG Compound IDs, HMDB IDs, or ChEBI IDs. For enhanced usability we encapsulated this comprehensive metabolite mapping data set in a stand-alone AnnotationHub package called metaboliteIDmapping . In its p value. The pre-processing step has to be done externally and is not part of the multiGSEA package.Measured omics data are necessary for the calculation of gene set enrichment scores. These data have to be loaded for each of the omics layers that have been defined in the previous step of extracting pathway-specific features from external databases. Prior to the enrichment score computation, a differential expression analysis has to be performed such that all omics features have an associated fold change and GSEA in its original form was first described to shed light on the mechanistic basis of Type 2 Diabetes mellitus [p values. Ranking metrics are then used to calculate the so-called local statistic. In multiGSEA, a ranked feature list is calculated based on the direction of the fold-change and the log-transformed p value. Different ranking metrics can be chosen individually and this choice can have strong effects on the outcome of the gene set enrichment analysis [GSEA algorithm. Its performance is achieved through implementing a cumulative GSEA-statistic calculation allowing to reuse random gene set samples between different input pathways [In a second step, multiGSEA calculates the enrichment score by applying the fgsea R package  on each mellitus . The updmellitus  two yearanalysis . In the pathways .p values, and adjusted p values for each omics layer separately.After the second part of the multiGSEA workflow, each downloaded pathway has been assigned fgsea-based enrichment scores, p value over multiple omics layers. Because no single approach for aggregating p values performs best under all circumstances, Loughin proposed basic recommendations on which method to use depending on structure and expectation of the problem [p values should be emphasized, Fisher’s method should be chosen. In cases where p values should be treated equally, Stouffer’s method is preferable. If large p values should be emphasized, the user should select Edgington’s method. Figure To more comprehensively measure a pathway response, multiGSEA provides different approaches to compute an aggregated  problem . If smalp values from k independent tests (here up to three omics layers) to calculate a test statistic k tests are true, the test statistic will follow a p values which results in a bias for aggregated p values from multiple studies on the same null hypothesis [p value is close to 1 and yet the combined p value is still considered to be significant, simply because the other single p value is small enough.A first option is the Fisher’s combined probability test, which uses the  freedom . Fisher’pothesis . This caentclass1pt{minimap values, ranging from 0 to 1, can be uniquely matched with a value in Z, representing a standard normal deviate, and vice versa. Each p value k independent tests (here omics layer) is converted into deviates To circumvent this asymmetry, multiGSEA can also apply alternative methods: the Z-transform test and the weighted Z-transform test. The first algorithm is also called Stouffer’s method. Both versions make use of the fact that evidence . As can entclass1pt{minimaThe weighted version of this method is defined by:le sizes , 28.p values. It was developed to combine probability values through an additive approach [k being the number of individual studies. However, this is a rather conservative estimate resulting in combined probability values that are too high when A third alternative method was created by Edgington and relies on untransformed approach :\documenentclass1pt{minimaA recommendable review on those three  aggregation methods was published by Heard and Rubin-Delanchy  alongsidp value combination, which are provided through the metap R package [p value combination. However, the weighted Z-transform method was reported superior regarding type II errors [multiGSEA allows to choose from all above described approaches for  package . Both FiI errors . We are p values, these can be adjusted for multiple testing. Since appropriate methods are available in R base packages, multiGSEA does not provide its own implementation. Type I and type II errors depend on each other and thus reducing type I errors through a p value adjustment will likely increase the chance of making a type II error and an appropriate trade-off has to be made [After computing combined  be made , 33.p values and adjusted p values.Finally, multiGSEA outputs a plain table listing the pathways with their single-omics and aggregated multi-omics In the following, we will illustrate a use case scenario on human mitochondrial stress data. A comprehensive vignette of the multiGSEA package can be found in our git repository or at the Bioconductor package website.Please visit the repository page to report issues, request features or provide other feedback.For installation we recommend two ways: (i) use the BiocManager package from Bioconductor: (ii) use the devtools library  to insta(i). We furthermore need to load the multi-omics data (ii) and we have to download the pathway definitions where the enrichment should be calculated on (iii).At the beginning we need to set up several prerequisites. This includes loading the package itself and those packages that are needed to map omics feature IDs such as transcript IDs or metabolite IDs (i) Load the multiGSEA R package and the packages that are needed for the mapping of omics features IDs: Depending on the organism and omics layer for which an enrichment should be calculated the users might need different packages that provide the necessary ID mapping information. In principle, the mapping of transcript and protein IDs relies on the AnnotationDbi package . In this(ii) Load the multi-omics data sets: The multi-omics data set that is used in this example was originally published in its raw form by Quirós et al. . In thisRight after multiple omics data sets are loaded, we need to create a suitable data structure and calculate the ranked omics features for the following GSEA calculation. multiGSEA works with nested lists where each sublist represents an omics layer. Such a data structure is initialized with the initOmicsDataStructure command: p value [The feature ranks are calculated separately for each of the applied omics layers. In this example we simply use the signed and logarithm transformed p value  derived Please note that every other ranking metric is, of course, possible as well, and the choice is up to the user. According to Zyla et al. this decision does also have critical effects on the outcome of the gene set enrichment analysis (iii) Retrieve pathway definitions and map features to the same ID format as in your omics measurements: In this use case we merely retrieved KEGG-based pathway definitions but depending on the organism being analyzed up to eight different pathway databases can be queried. The function getMultiOmicsFeatures retrieves the pathway definitions from the specified databases, extracts the omics features thereof, and maps those features to the ID format that is been used in the omics data. Here, it maps Entrez Gene IDs and KEGGCOMP IDs that are used in KEGG pathways for transcripts/proteins and metabolites, respectively, towards Gene Symbols and HMDB identifiers, respectively.Now that we have ranked omics features and pre-formatted pathway definitions, we can calculate GSEA-based pathway enrichments for each omics layer separately by means of multiGSEA: p values for a collection of feature sets. This speedup compared to the original GSEA implementation is basically accomplished because generated random gene sets are shared between different input pathways.The pathway enrichment within multiGSEA is done by the fgsea package . This paThe returned data frame enrichment_scores contains nested lists for each analyzed omics layer and each omics-specific sublist contains the complete gene set enrichment analysis for its respective layer.p values and adjusted p values: For further analysis, the function extractPvalues creates a simple data frame where each row represents a pathway and columns represent omics related p values and the adjusted p values. As explained in the workflow section covering the combination of multiple omics pathway enrichment p values : Platform independentProgramming language: ROther requirements: NoLicense: GNU GPL V3.Any restrictions to use by non-academics: No


It is an inedible renewable carbonaceous resource that is very rich in pentose and hexose sugars. The ability of microorganisms to use lignocellulosic sugars can be exploited for the production of biofuels and chemicals, and their concurrent biotechnological processes could advantageously replace petrochemicals’ processes in a medium to long term, sustaining the emerging of a new economy based on bio-based products from renewable carbon sources. One of the major issues to reach this objective is to rewire the microbial metabolism to optimally configure conversion of these lignocellulosic-derived sugars into bio-based products in a sustainable and competitive manner. Systems’ metabolic engineering encompassing synthetic biology and evolutionary engineering appears to be the most promising scientific and technological approaches to meet this challenge. In this review, we examine the most recent advances and strategies to redesign natural and to implement non-natural pathways in microbial metabolic framework for the assimilation and conversion of pentose and hexose sugars derived from lignocellulosic material into industrial relevant chemical compounds leading to maximal yield, titer and productivity. These include glycolic, glutaric, mesaconic and 3,4-dihydroxybutyric acid as organic acids, monoethylene glycol, 1,4-butanediol and 1,2,4-butanetriol, as alcohols. We also discuss the big challenges that still remain to enable microbial processes to become industrially attractive and economically profitable.Lignocellulose is the most abundant biomass on earth with an annual production of about 2 × 10 The expected rarefaction of these resources prompted us to innovate in alternative and sustainable solutions, which in particular must revive the natural cycles of carbon and nitrogen. One of the cornerstones of this new societal and economical deal coined ‘Bioeconomy’ [ric tons . This feric tons . In addirabinose . Microorrabinose  and fungrabinose  or via trabinose –10  and hexose (d-glucose) sugars as depicted in Fig. d-xylose and l-arabinose are metabolized by an oxidoreductive pathway  reductase, followed by a dehydrogenation catalyzed by an NAD+-dependent xylitol (or l-arabitol) dehydrogenase to yield d-xylulose and l-xylulose, respectively. d-Xylulose is readily phosphorylated on carbon 5 by a d-xylulokinase to d-xylulose-5-P, which fuels the CCM at the level of fructose-6-P and glyceraldehyde-3-phosphate (GAP) through transketolase/transaldolase-catalyzed reactions. Prior to its incorporation into CCM, l-xylulose derived from l-arabinose must be reduced into xylitol by an NADPH-dependent l-xylulose reductase followed by an oxidation catalyzed by an NAD+-xylitol dehydrogenase [Pichia stipitis, d-xylulose can enter the non-oxidative pentose phosphate pathway via another route, which involves its reduction into d-arabinitol, followed by oxidation to d-ribulose and then phosphorylation to ribulose-5-P [d-xylose and l-arabinose by filamentous fungi is characterized by a dissimilarity in the use of redox cofactors while the process is redox neutral. This dissimilarity of redox cofactors is still considered as a major problem for efficient fermentation of pentose sugars into ethanol by metabolically engineered yeasts [Prokaryotic and eukaryotic microorganisms have elaborated different pathways to assimilate pentose , the assimilation of pentose sugars also starts with their uptake mediated by active transporters [d-xylulose or l-ribulose. d-xylulose further enters the CCM via Xu5P, by direct phosphorylation catalyzed by a d-xylulokinase. Alternatively Xu5P can be obtained from l-ribulose through a phosphorylation and epimerization catalyzed by a l-ribulokinase and a l-ribulose 5-P 4-epimerase, respectively  are catabolized by a so-called non-phosphorylating pentose (NP) pathway to either α-2-ketoglutarate (αKG) or pyruvate and glycoladehyde. The conversion of pentose into αKG takes place by the Weimberg pathway [d-xylose or l-arabinose) into pentono-(d/l)-lactone (d-xylonolactone or l-arabinolactone) by a specific pentose dehydrogenase . These intermediates are then hydrated by a specific lactonase into their corresponding pentonic acid, followed by a dehydration into a 2-keto-3-deoxy l or d-pentanoate (KDP) catalyzed by specific d-xylonate or l-arabinonate dehydratase. A subsequent dehydratation catalyzed by a 2-keto-deoxy l- or d-pentanoate dehydratase (KDPH) gives rise to α-ketoglutaric semialdehyde also termed 2,5-dioxopentanoate (DOP). The last reaction of this pathway is catalyzed by an α-ketoglutaric semialdehyde dehydrogenase that yields α-ketoglutarate, which fuels CCM at the level of the tricarboxylic acid cycle (TCA) [Sulfolobus sp., engaging a highly oxygen-sensitive glycoladehyde oxidoreductase and a soluble NADH-dependent glyoxylate reductase [+ or NADP+ as well as the co-existence of the Weimberg and Dahms route in a same microorganism. In this respect, Nunn et al. [Sulfobolus solfataricus and acidocaldarius employ the same enzymes for the oxidation of d-xylose and l-arabinose to generate 2-keto-3-deoxy-d-xylonate (KDX) and 2-keto-3-deoxy-l-arabinonate (kDa), which thereafter are cleaved by a same KDP aldolase into pyruvate and glyceraldehyde [Haloferax volcanii, d-xylose is mainly metabolized by the Weimberg pathway and the xylose dehydrogenase has almost no activity on other C5 sugars [In a third pathway, which is the privilege of archeabacteria  , 44. An le (TCA)  and is teductase . Althougn et al.  showed taldehyde . In thesaldehyde , whereas5 sugars .E. coli strains, each engineered to preferentially catabolize a different hexose–glucose, galactose, or mannose [As stated above, many microorganisms are able to assimilate pentose and hexose sugars. However, this capability is not economically viable with the biotech industry sector aiming at producing bio-based chemicals from these lignocellulosic sugars, due to several constraints as follows. First, pentose assimilation is often repressed in the presence of glucose , 50. Thi mannose , as well mannose . Second,E. coli, yeast or C. glutamicum [d-xylonate (KDX) or 2-keto-3-l-arabinonate (KDA) obtained from d-xylose or l-arabinose through the three reaction steps involving a dehydrogenase, a lactonase and a dehydratase  pathway present in archeabacteria  in indusutamicum –60 is aninto KDX , 61. As https://www.grandviewresearch.com/industry-analysis/bio-based-platform-chemicals-market).The bio-based chemicals that are considered at length in this review as they were reported to be produced through these NP pathways are listed in Table E. coli, two genes yjhH and yagE encode a functional KDPA [yjhH yagE mutant was unable to grow on d-xylonate [yagE is likely more active than that encoded by yjhH since levels of GA were higher in engineered strains with yagE [aldA [E. coli [yqhD encoding a broad-substrate NADPH-aldehyde reductase [yjgB also termed ahr encoding another broad-spectrum NADPH-aldehyde reductase. The discrepancy between these two reports is unclear. At variance to these studies, Wang et al. [fucO, which encodes an NADH-aldehyde reductase, was the most efficient in the production of EG. These authors explained their results by considering a higher NADH availability in their engineered strain due to the deletion of aldA and arcA, a gene encoding a redox regulator under microaerobic condition and whose deletion results in derepression of oxygen-repressed genes [E. coli strains for EG and for GA production.The NP_Dahms pathway starts by the aldolytic cleavage of KDX or KDA into glycolaldehyde and pyruvate catalyzed by a 2-keto-3-deoxy-pentanoate aldolase (KDPA). While pyruvate can be used as a carbon source for growth, glycoaldehyde is either oxidized into glycolic acid (GA) or reduced into ethylene glycol (EG)  as rate-limiting enzyme of this pathway. Cabulong et al. [xylB encoding XDH together with an overexpression of yjhG encoding the endogenous XD. Reduction of xylB expression was also carried out by Chae et al. [E)  Table . This YErding to  as the rd-xylose   and 3,4-dihydroxybutyric acid . These two metabolites actually revealed the presence of endogenous aldo–keto reductase and 2-ketoacid decarboxylase that compete with KDPA for KDX, respectively (see below for further details). Their accumulation was mainly ascribed to the inefficient catalytic activity of xylonate dehydratase due to low availability of iron in yeast cytosol. Indeed, this enzyme belongs to LlvD/EDD protein family that requires iron for activity [FRA2 encoding a cytosolic protein implicated in the regulation of iron, resulted in a 13-fold increase of the XD activity, which was accompanied by a still modest increase of EG (14 mg/L) from d-xylose.The yeast promoter . In addiactivity . Supportactivity  who founaldA encoding an NAD+-dependent glycolaldehyde reductase [E. coli through the glyoxylate shunt (GS). Therefore, a linkage between GS and the NP_Dahms pathway at the level of pyruvate might be envisioned, resulting in the production of 2 mol GA per mole of pentose , xylulose-5-P kinase (xylB) and glycolate oxidase (glcDEF). Module 1 was a low-copy plasmid carrying genes encoding the NP-Dahms pathway, i.e., xdh-yagF-yagE-aldA as well as grhA encoding a glyoxylate reductase (GLR). Module 2 was a high-copy plasmid carrying genes favoring GS, namely aceA encoding isocitrate lyase and aceK, which codes for the bifunctional isocitrate dehydrogenase kinase/phosphatase to reduce isocitrate dehydrogenase activity. A production of GA at 0.46 g/g d-xylose was obtained with this engineered E. coli strain. Same yield on d-xylose was obtained by Liu et al. [ackA encoding acetate kinase was deleted instead of overexpressing aceK.As already described above, GA is the other relevant bio-based product that can be obtained from pentose through the NP_Dahms. At first glance, it only requires the rerouting of glycolaldehyde by the overexpression of eductase . Howeverse Table . This liu et al.  followinE. coli strain from glucose that initially grew poorly on glucose, likely due to a high expression of aceK causing a blockage of TCA at the isocitrate dehydrogenase level. Following adaptive evolution by repeating transfer to mineral glucose (M9) medium, a producer strain with improved growth rate was isolated and shown to produce 54 g/L GA at 63% of the pathway yield  is 1.7 mol glycolic acid/mol glucose instead of 2 since not all NADPH is fulfilled by glycolysis. On the other hand, C. glutamicum does not possess a glyoxylate reductase. On top of that, glyoxylate shunt is inactive under glucose growth condition. Therefore, GA was produced using an engineered C. glutamicum deleted for aceB encoding malate synthase, attenuated for expression of icd encoding isocitrate dehydrogenase and overexpressing the E. coli grhA encoding glyoxylate reductase. Due to this attenuation of icd, which impairs TCA cycle, acetate had to be added in culture medium. A production of 5.3 g/L GA was obtained according to a 1:1 C-mole ratio between glucose and acetate [S. cerevisiae and K. lactis for GA production. The metabolic engineering in both yeasts requested to remove genes encoding malate synthase and cytosolic isocitrate dehydrogenase and to express the A. thaliana GLYR1 encoding the glyoxylate reductase. d-Xylose was used as the carbon source to avoid glucose repression of the glyoxylate shunt [S. cerevisiae while it was 15 times higher with the engineered K. lactis. The observed difference was explained by the fact that K. lactis exhibits neither glucose repression of respiration nor a Crabtree effect, which is known to divert carbon and reducing power into ethanol [Production of GA from glucose by ttempted , 77. How acetate . The grote shunt . The tit ethanol .d/l)-pentanoate is dehydrated by a specific 2-keto-3-deoxy-d-xylonate dehydratase (KdxD) or a 2-keto-3-deoxy-l-arabinonate dehydratase (KdaD) to yield 2,5-dioxopentanoate (DOP). DOP is a strategic platform because it can be oxidized into α-ketoglutarate, from which a panel of other bio-based chemicals can be produced  unable to produce α-ketoglutarate from glucose by TCA. Therefore, growth rescue of this mutant on d-xylose or l-arabinose exclusively relied on the expression of genes clusters isolated from various Archea that harbor the NP_Weimberg pathway. An interesting result that came out from this screening was to find that the dehydratase enzyme encoded by xylX (for d-xylose degradation pathway) or araE (for l-arabinose degradation pathway) was clearly the bottleneck enzyme in this pathway.In a second route, which we called NP_Weimberg pathway, the 2-keto-3-deoxy-(E. coli strain with three compatible plasmids. Plasmid 1 (medium-copy) carried NP_Weimberg pathway genes to produce DOP from d-xylose, genes encoding glutamate mutase (GlmE) and methyl aspartate lyase  to generate mesaconate from DOP were cloned in a plasmid 2 (high-copy), and plasmid 3 (low-copy) carried four genes necessary for the regeneration and reactivation enzymatic system for coenzyme 12 that is essential for GlmE activity. This strain was moreover deleted for yagE and yjhH encoding KDPA to eliminate Dahms route and for sucA to avoid loss of α-ketoglutarate downstream in the TCA cycle. Altogether, the engineered strain was able to produce 14.7 g/L of MSA after 2 days at a yield of 0.74 g/g d-xylose, which corresponds to 85% of the pathway yield YP , a combination of XI with NP_Weimberg should be the best since the calculated pathway yield of this arrangement is 0.625 mol glutaric acid per mole xylose. Wang et al. [E. coli with either one or a combination of two or of the three pathways. They found that the combination of NP_Weimberg and XI yielded the best producer strain, as expected from the model. Even though the titer was only 602 mg/L, it was 5 times and 2 times higher than using XI alone or NP_Weimberg + Dahms, respectively. Additional improvements were attempted by restricting XI pathway to the production of acetyl-CoA while NP_Weimberg was dedicated to production of α-ketoglutarate by deleting gltA encoding citrate synthase. Unexpectedly, these genetic interventions did not improve yield and titer, probably because they affected cell growth. In addition, a likely reason for low yield and titer of glutaric acid may be found in the promiscuity activity of the a-ketoacid decarboxylase encoded by Lactococcus lactis kivD as this enzyme can readily decarboxylate upstream intermediates of the pathway, namely 2-keto-3-deoxy-d-xylonate and DOP (see below). Thus, in addition to the problem of providing enough acetyl-CoA in the pathway [d-xylose. Pathways engineering for de novo biosynthesis of glutaric acid from glucose via α-ketoglutarate by E. coli have been also reported [l-lysine overproducing C. glutamicum strain. This lysine-based pathway offers a theoretical yield of 0.75 mol/mol glucose, which is higher than the α-ketoglutarate pathway  from an enzymatic screening of various bacterial 2-keto acid decarboxylases to convert DOP into butanedial. Assuming YqhD as the most active NADPH-dependent aldehyde reductase in E. coli [E. coli strain equipped with the NP_Weimberg pathway genes, that also overexpressed kivD and was deleted for competing pathways . However, this engineered strain produced high amount of 1,2,4-butanetriol (BTO), whereas no BTO was produced using l-arabinose, as the carbon source. These results were explained by the fact that KDC from Lactococcus lactis was even more active on KDX than on DOP and was inactive on KDA [V411I variant exhibiting catalytic efficiency 15-fold reduced on KDX and twofold increased on DOP [V411I variant in the engineered E. coli equipped with the NP_Weimberg pathway resulted in a production of 0.37 g BDO per g d-xylose. Interestingly, this yield was in the same range as that obtained with a metabolically engineered E. coli strain for BDO production from glucose [The production of 1,4-butanediol (BDO) from C5 sugars has been also attempted through the NP-Weimberg pathway , and pre E. coli , these ae on KDA . Therefod on DOP . Replace glucose   3,4-dehydroxybutyraldehyde constitutes a third non-natural route that can lead to the production of 1,2,4-butanetriol (BTO) or 3,4-dihydrobutyric acid . This metabolite can be further cyclized at acidic pH to give rise to 3-hydroxybutyrolactone (γHBL)  or arabinose dehydrogenase (ArDH) from Caulobacter crescenti  and 1.1 g/L from direct fermentation of rice straw hydrolysate have been reported [E. coli [BOL2 encoding a cytosolic protein involved in the repression of iron regulon [tTYW1, whose effect was reported to increase cytosolic iron, resulted in an improved production of BTO from d-xylose with a 24% molar yield  by a succinate semialdehyde dehydrogenase encoded by sucD from Porphyromonas gingivalis. The last step is the reduction of MSA into 2,4-dihydroxybutyric acid that was found to be catalyzed by 4-hydroxybutyrate dehydrogenase encoded by 4-hbB from Porphyromonas gingivalis. Module 2 was constructed with abfT-2 of Porphyromonos gingivalis encoding 4-hydroxybutyrate COA transferase and adhE2 of Clostridium acetobutylicum that encodes the bifunctional aldehyde/alcohol dehydrogenase. Upon transformation of E. coli strain BL21 with these two compatible plasmids, a tiny amount (120 ng/L) of BTO was detected. Although the feasibility of this non-natural pathway was demonstrated, many side products like BDO and 4-hydroxybutyrate were also accumulated, indicating that most of the requested enzymes for this non-natural pathway showed poor specificity for the intermediates of this pathway [pct from Megasphaera elsdenii, a thiolase and acyl-CoA reductase encoded by btkB and phaB, respectively, from Cupriavidus necator and finally a thioesterase encoded by the endogenous E. coli tesB gene. In addition, overexpression of acetyl-CoA synthetase could be required to recover acetate into acetyl-CoA. Overall, this pathway could optimally produce 1 mol 3,4-DHBA from 1 mol glucose with the generation of 2 mol reduced equivalents and 1 mol ATP  pathway in microbial systems for conversion of lignocellulosic sugars into bio-based products presents two main difficulties that may hinder use of these microbial strains for industrial exploitation. A first limitation is the strong dependency of the xylonate/arabinonate dehydratase to iron, which strongly affects in vivo activity, and explains high and eventually toxic accumulation of xylonic acid in engineered strains harboring the NP pathway. The second limitation relates to the fact that only pentose sugars can be converted into bio-based product through the NP. To circumvent the first limitation and to partially solve the second one, non-natural pathways have been recently designed that enable the production of bio-based products from lignocellulosic sugars at higher production yield and productivity. The example presented in this review will focus on the bioproduction of EG and GA.E. coli, C. glutamicum or yeast for the production of EG and GA from pentose and glucose  and DHAP (C3), which requires an aldolase that turns out to be rather specific for the pentose-1P produced. The mammalian aldolase B (AldoB) was found to be very efficient to cleave xylulose-1P, but ineffective on l-ribulose-1P [fucA and rhaD were used to cleave d-ribulose-1P and l-xylulose-1P [yqhD but its maximal conversion from glycolaldehyde also needed overexpression of fucO and deletion of aldA [aldA but also the deletion of glcD encoding the membrane-associated glycolate oxidase [E. coli, in which competing pathways of xylose and arabinose into PPP were deleted, resulted in the production of EG and GA that reached 85 to 90% of the maximal yield expected (1 mol/mol sugar) . The fim et al.  identifil. [khkC  as very s’ group  trusted tabolism  to yieldulose-1P , whereasulose-1P   activity that has been obtained by in vivo evolutionary engineering. Because of this high XI activity, xylulose accumulates in the cells and can be illegitimately phosphorylated into its C1 carbon by phosphofructo-6-kinase encoded by PFK1/2 [XKS1-encoded xylulose-5-P kinase favored GE production. A production of 4 g/L EG from 20 g/L xylose was obtained with such an XI-evolved ∆xks1 strain that also overexpressed an E. coli aldehyde reductase encoded by fucO [Xylulose-1-Phosphate (X1P) and Ribulose-1-Phosphate (R1P) pathways have been tentatively expressed in the yeast eport by , a weak  xylitol . Anothery PFK1/2 . In addi by fucO .E. coli strain and after some genetic modifications that was intended to favor the carbon flux in the serine pathway, the engineered strain produced 4.1 g/L EG from 20 g/L glucose which corresponded to 30% of the pathway yield  were retained by this screening test. Similarly, an active amino acid oxidase (AAO) and serine decarboxylase (SDC) were screened based on the production of EG using the same C. glutamicum strain as above but incubated with ethanolamine and serine, respectively. This screening identified the monoamineoxidase from Arthrobacter sp. and serine decarboxylase from Arabidopsis thaliana as the best candidates in route 2. Subsequently, these pathways were introduced in a C. glutamicum strain that had been engineered from hyperproduction of serine, notably by deletion of sdA encoding serine deaminase and overexpressing the serABC operon. These authors showed that a strain that expressed route 2 was roughly 3 times more efficient than that carrying route 1 for EG production from glucose. However, a higher production of EG was obtained in a strain that contained both route [E. coli constructed by Gu et al. [As stated above, a limitation of these non-natural pathways is that only the two-carbon fraction is diverted into the products, leaving the C3 fraction (DHAP) into the biomass. Thus, to increase the yield, it might be worth to recapture this C3 fraction to glycolaldehyde. A possibility is through the serine pathway, which can lead to glycolaldehyde from DHAP via 8 reaction steps from which the first five enzymatic reactions correspond to the natural pathway for serine biosynthesis. From serine, there are actually two different routes to yield glycolaldehyde  has been investigated for the production of GA from both xylose and glucose. In theory, this combination shall produce 2 mol GA/mol of sugar with a positive redox and ATP balance . This reengineering of glycolytic pathway can lead to the maximal conversion 1 mol of glucose into 3 mol of acetate [kdsD and a class I aldolase encoded by fsaA/fsaB to generate glycolaldehyde and GAP from Xu5P. While glycolaldehyde is oxidized into GA, GAP can be shuttled back into the non-oxidative pentose phosphate pathway via transketolase and transaldolase reaction. This cycling scheme also generates 3 mol of NADH that needs to be reoxidized to allow a continuous running of the pathway. The reoxidation of NADH requires an active respiratory chain, which is coupled to the synthesis of ATP. This ATP provision is useful for the uptake and the phosphorylation of sugars, as well as for strain stability and maintenance. The in vitro and in vivo functions of the glycoptimus pathway have been validated although the yield of GA from glucose and xylose did not exceed 30% of the maximal yield expected. This poor efficiency of the pathway could be due to several problems and notably that the blockage of the lower part of glycolysis at the level of GAPDH may be detrimental for cell viability. In addition, many competitive pathways siphoning intermediates of the cycle have not been dismantled yet in the engineered strains and carbon flux in the kdsD-fsaA pathway needs to be optimised [A general feature of metabolic pathways is their inherent carbon loss as CO acetate . Accordiptimised . FinallyThe sustainability and competitiveness of biotechnological processes for the production of bio-based chemicals from renewable carbon sources critically depend on the need to equip the microbial platform with efficient metabolic pathways able to readily transform these carbon resources into bio-based products at titer, rate and yield (TRY) that are economically viable. For high-volume, low-priced chemicals such as those considered here, the higher these three performance indices are, the more competitive biotechnological processes become compared to petrochemical processes. Even though many microorganisms on earth are able to assimilate pentose and hexose sugars, their carbon metabolism is not optimally fashioned to achieve these performances. This necessitates to rewire their actual metabolic network, to plug  pathways that exist in other organisms or to create new ones , while ensuring cellular homoeostasis. Metabolic diversity found in Nature, combined with enzymes’ promiscuity, provides inspiration for implementing non-natural or constructing synthetic metabolic pathways that could meet these goals.E. coli, Corynebacterium glutamicum or S. cerevisiae empower these microbial cells with strong capability to convert lignocellulosic sugars into a wide range of added-value chemicals. Although these microbial processes are far from industrial performances, there are strong indications that this objective could be soon reached. First, these pathways are orthogonal to the natural metabolic networks, which in theory make them insensitive to endogenous metabolic and genetic regulation, while being deliberately controllable by mean of biosensors/biocontrollers [In this review, we showed that plugging the non-phosphorylating pentose pathway or implementing synthetic pentose-1-P pathway in the industrially tractable organism trollers . This ortrollers . Second,2 and methane from landfills. Although the conversion of these carbon sources into bio-based chemicals still largely depends on traditional metabolic paths, new routes must be designed and constructed that should be orthogonal to the central carbon metabolism without perturbing the cellular homoeostasis. As for instance, new route for methane and CO2 assimilation into ethanol has been proposed by Liao and coworkers [Lactococcus lactis, whose promiscuity has been exploited to decarboxylate KdxD, DOP and α-adipate, but concomitantly generated metabolic side products due to this promiscuous activity [E. coli aspartate kinase encoded by lysC from its natural substrate—aspartate—to malate [E. coli prior to the screen [http://www.altar.bio/about-us/). The use of this technique requires a phenotypic screening assay that exclusively relies on the vital function of the enzyme of interest. As an example, this strategy has been used to evolve an amino acid transaminase that can convert l-homoserine into the non-natural intermediate 2-keto-4-hydroxybutyrate (HOB), which thereafter could transfers its one carbon unit (CHO) to tetrahydrofolate (H4F), replacing the natural transfer C1-moieties from serine and glycine to H4F in an E. coli [There still remain many challenges ahead to better improve microbial cell factories and concurrent biotechnology processes to make them industrially more appealing. We anticipate at least three major actions that must be actively worked on. The first one dealt with further rewiring of carbon metabolism for the conversion of all available renewable carbon resources, which include no solely lignocellulosic biomass, but COoworkers . The samoworkers ). Innovaoworkers . A seconactivity . Nevertho malate . Howevere screen , 131. Al E. coli . Ferment E. coli . This as E. coli , 135. BeAdditional file 1: Table S1. Enzymes and corresponding genes that has been used in the expression of the that has been used in the Dahms, Weimberg and KDC-dependent non phosphorylating (NP) pathway for the production of bio-based commodity chemicals from hexose and pentose sugars by E. coli as described in Figs. Table S2. Production titer and yield of bio-based chemicals from lignocellulosic sugars by engineered microorganisms. Table S3. Enzymes and corresponding genes that have been used in the xylulose-1P and ribulose-1-P synthetic pathways for the production of bio-based commodity chemicals from pentose sugars. Table S4. Thermodynamic yield and pathway yield of bio-based chemicals from sugars.


As an effective prescription for gastric cancer (GC), Compound Kushen Injection (CKI) has been widely used even though few molecular mechanism analyses have been carried out.In this study, we identified 16 active ingredients and 60 GC target proteins. Then, we established a compound-predicted target network and a GC target protein-protein interaction (PPI) network by Cytoscape 3.5.1 and systematically analyzed the potential targets of CKI for the treatment of GC. Finally, molecular docking was applied to verify the key targets. In addition, we analyzed the mechanism of action of the predicted targets by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses.The results showed that the potential targets, including CCND1, PIK3CA, AKT1, MAPK1, ERBB2, and MMP2, are the therapeutic targets of CKI for the treatment of GC. Functional enrichment analysis indicated that CKI has a therapeutic effect on GC by synergistically regulating some biological pathways, such as the cell cycle, pathways in cancer, the PI3K-AKT signaling pathway, the mTOR signaling pathway, and the FoxO signaling pathway. Moreover, molecular docking simulation indicated that the compounds had good binding activity to PIK3CA, AKT1, MAPK1, ERBB2, and MMP2 in vivo.This research partially highlighted the molecular mechanism of CKI for the treatment of GC, which has great potential in the identification of the effective compounds in CKI and biomarkers to treat GC. Helicobacter pylori (H. pylori) infection, smoking, high salt intake, and other dietary factors, can increase the risk of GC [Gastric cancer (GC) is one of the most common malignant tumors, and its mortality rate ranks third in the world, making it the fifth most frequently diagnosed cancer according to the latest report of Global Cancer Statistics GLOBOCAN 2018). The global incidence rate varies widely, with the highest incidence rates in East Asia and Eastern Europe, while North America, parts of Africa and Northern Europe have the lowest incidence [018. The sk of GC . Combinesk of GC . Howeversk of GC , 6. As ask of GC . Compounsk of GC . Kushen sk of GC . Studiessk of GC . CKI maisk of GC . Researcsk of GC . CKI cansk of GC . The chesk of GC . The netsk of GC . As a nesk of GC . TCM netsk of GC .Previous studies have confirmed the effectiveness of CKI in the treatment of GC , 21 but http://bidd.nus.edu.sg/group/ttd/ttd.asp) is a database that allows users to search for targets, biomarkers, and drugs related to various disease conditions. Online Mendelian Inheritance in Man (OMIM) [http://www.omim.org/) is an online database of continuously updated human genes and genetic diseases. Pharmacogenomics Knowledge Base (PharmGKB) [http://www.pharmgkb.org) is a database of high-quality pharmacogenomic data that provides information on genetic variation, pharmacokinetics, and pharmacodynamic pathways, as well as data on the effects of mutations on drug-related phenotypes. The disease gene search engine with evidence sentences (DigSEE) [http://210.107.182.61/geneSearch/) database can directly query the links between diseases, genes and biological processes. DisGeNET [http://www.disgenet.org) is one of the most comprehensive genetic disease association databases. The above databases were searched for “gastric cancer” as a keyword to collect proteins related to GC.The Therapeutic Target Database (TTD)  (http://n (OMIM)  (http://harmGKB)  (http://(DigSEE)   to obtain a compound-predicted target network map. Cytoscape is bioinformatics analysis software that visualizes biological pathways and intermolecular interaction networks. It provides a basic set of data integration, analysis and visualization capabilities for complex network analyses [The 3D chemical structure data of 16 active ingredients were imported into the Search Tool for Interactions of Chemicals (STITCH), SuperPred, and SwissTargetPrediction databases for retrieval. STITCH shows the link between proteins and small molecules in biological processes, visualizing them in a network, and provides information such as the strength of their association; it can also be used to search for potential targets for compounds . SuperPranalyses .https://string-db.org/) is a database of known and predicted protein interactions that contains direct and indirect associations of proteins. The number and quality of interacting proteins can be set according to their confidence settings, and it has a score for each protein interaction. The higher the score is, the higher the confidence of the protein interaction [Homo sapiens” was selected as the confidence data with a scoring value greater than 0.7. Then, the data were introduced into Cytoscape to construct a protein-protein interaction (PPI) network related to GC and a PPI network related to the predicted target of the compound.Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) 10.5  to perform GO functional enrichment analyses and KEGG pathway enrichment analyses on the potential key targets in the CKI network for the treatment of GC.To illustrate the role of the key potential targets in gene function and signaling pathways, this study used the Database for Annotation, Visualization and Integrated Discovery (DAVID) v 6.8  (https:/http://systemsdock.unit.oist.jp/iddp/home/index) is a web server based on the docking program designed by AutoDock VINA with a high-precision scoring function. It has the characteristics of high precision and fast speed. The docking score for systemsDock is the negative logarithm of the experimental dissociation/inhibition constant (pKd/pKi), which directly indicates the binding strength.The molecular docking of systemsDock   related to GC were retrieved from the TTD, OMIM, PharmGKB, DigSEE, and DisGeNET databases  [Fourteen of the potential targets were directly related to the CKI active components, and these 14 targets were imported into the Protein Data Bank (PDB) (sb.org/)  databasesb.org/) . The docsb.org/) .Fig. 5DFDR < 0.01 and P < 0.01). KEGG function enrichment analysis was performed by using the DAVID platform, and the roles of the 43 target proteins involved in the Module 1 and Module 2 networks in signaling pathways were studied. A total of 76 KEGG entries were determined based on the FDR and P values (FDR < 0.01 and P < 0.01)  is an important cell cycle regulatory protein that primarily controls the transition of proteins from G1 to S in the cell cycle. The overexpression of CCND1 can cause a variety of tumors, increasing tumor cell proliferation, and affecting the prognosis of cancer patients . Kaplan-The clustering analysis was carried out by using the MCODE algorithm in Cytoscape, and two modules were obtained by clustering. Among them, Module 1 had a higher number of nodes, edges, and scores, which may be the main functional module. To further understand the overall process of CKI in the treatment of GC, the GO and KEGG enrichment functions of the two modules obtained in this study were analyzed.In the GO functional enrichment analysis, the target proteins were divided into different functional modules. Module 1 had the most biological process-related entries, including the regulation of cell mitosis, cell proliferation and transcription, protein phosphorylation, and protein signaling. Module 2 contained 13 related items, showing a strong correlation with phosphatidylinositol and its kinase-mediated signaling.In the KEGG pathway enrichment analysis, the different modules obtained 76 related items by FDR < 0.01 screening analysis. The 76 related pathways included cancer pathways and disease pathways, most of which were related to signaling pathways, such as the PI3K-AKT signaling pathway, FoxO signaling pathway, mTOR signaling pathway, ErbB signaling pathway, AMPK signaling pathway, and HIF-1 signaling pathway. Therefore, the predicted mechanism of CKI for the treatment of GC is related to these pathways.PI3K signaling is a critical regulator of many important cellular processes, including cell growth, metabolism, survival, metastasis, and resistance to chemotherapy. PI3K and AKT proteins are significantly overexpressed in tumor tissues. AKT is a downstream effector of PTEN/PI3K, and the inactivation of PTEN will result in an abnormal activation of AKT. p-AKT is an activated form of AKT that plays an important role in cell proliferation, differentiation and survival. Studies have found that it is abnormally activated in various tumors, such as lung cancer, breast cancer, and colorectal cancer. mTOR is located downstream of the PI3K/AKT/mTOR signaling pathway. After activation, it mediates important downstream signaling molecules and affects cell proliferation and inhibits apoptosis. High mTOR expression is found in many malignant tumors , 56, 57.The limitations of our study were as follows: Firstly, our study has some limitations because it focuses on genes that were obtained data in the network databases, without regard to gender, age, tumor classification, and staging. Secondly, our data research results need to be verified by corresponding experimental studies.In summary, this study applied the network pharmacology method to study complex network relationships regarding the multicomponent, multitarget and multichannel aspects of CKI for the treatment of GC. We predicted that CKI controls the growth and metastasis of GC cells by regulating vital targets such as CCND1, PIK3CA, and AKT1, as well as important related pathways. The results of the study preliminarily verified and predicted the molecular mechanism of CKI against GC but still need further experimental verification. The results provide a basis for further exploration of the mechanism of action and of CKI for the treatment of GC and provides a reference for the study of the more complex mechanism of action of this Chinese herbal compound.Additional files 1: Table S1. CKI Compound.Additional files 2: Table S2. Target of GC.Additional files 3: Table S3. Target of CKI Compound.