Constipation commonly occurs during childhood, and more than 95% of cases are classified as functional constipation. If not effectively treated, 20% of patients with childhood constipation can continue to exhibit symptoms into adulthood, which seriously affects their mental health and quality of life. The main feature of acupuncture or acupoint stimulation, a special branch of traditional Chinese medicine, is the selection of different acupoints for different diseases, and many worthy guidelines have been established for matching acupoints. The back-shu and front-mu point combination adheres to an important acupoint compatibility law that has been used since its proposal 2,500 years ago but has not yet been verified by the modern evidence-based experiments. This study focused on the back-shu and front-mu point combination using the Dachangshu (BL25) and Tianshu (ST25) points as examples to explore possible research methods for network acupoint-based stimulation based on existing evidence and to elucidate the mechanisms induced by BL25 and ST25 in the treatment of functional constipation in children (FCC). The study found that BL25 and ST25 have 20 common targets, namely, AQP8, DRD2, VIP, TAC1, IL6R, TNF, FOS, KIT, CHAT, HTR3A, GAS8, SOD3, TRPV1, MPO, CALCA, IL1B, P2RX7, NPY2R, IL10RA, and TPH1, and these targets may provide a strategy for the combined usage of BL25 and ST25. In addition, BL25 and ST25 can affect FCC treatment through inflammation-relatedTh17-cell differentiation, the NF-kappa B signaling pathway, and the Toll-like receptor signaling pathway. Adipocytokines or leptin may also comprise the mechanism through which BL25 and ST25 regulate FCC. In addition, BL25 and ST25 regulate FCC through 13 core targets, namely, NFKBIA, RELA, TNF, IKBKB, IRAK1, TLR4, MYD88, TNFRSF1A, IL1R1, TLR2, IL1B, TRAF6, and TNFRSF1B. In short, this study provides new ideas and methods for studying the mechanism of acupuncture points. Constipation commonly occurs during childhood, with an incidence ranging from 3% to 30%, and more than 95% of all cases are classified as functional constipation . The medThe traditional Chinese medicine (TCM) theory has gradually developed over a long period based on medical practices guided by ancient simple Chinese material principles and dialectics of nature and includes complex theoretical systems and treatment methods. Acupuncture or acupoint stimulation is a special branch of TCM. The selection of different points for different diseases is the main feature of acupuncture or acupoint stimulation, and many important laws have been established for matching acupoints. A typical method is the back-shu and front-mu point combination, which has been considered the classic acupoint combination applied in the clinic since its proposal in the “Classic of Questioning” 2500 years ago and is still used today. Simply, the back-shu and front-mu point combination is based on the yin and yang theory in Chinese medicine. The abdomen (which belongs to yin according to the TCM theory) and the back (which belongs to yang according to the TCM theory) are selected as acupoint stimulation targets to balance yin and yang and thereby regulate visceral functions. Some studies have investigated the regulatory mechanisms of the back-shu and front-mu points, including the Feishu point (back-shu point) and the Zhongfu point (front-mu), which can reduce inflammatory exudation and inflammatory cell infiltration in the lung tissues of asthmatic mice . The WeiBL25 was selected as the research object because it belongs to the bladder meridian of foot-taiyang, which is also the dorsal point of the large intestine. BL25 is located at the same level as the inferior border of the spinous process of the fourth lumbar vertebra (L4), 1.5 B-cun lateral to the posterior median line. According to the TCM theory, targeting BL25 can cure abdominal pain, bloating, diarrhea, and constipation, among other ailments. Previous studies have provided ample information about BL25, and the evidence shows that targeting this point can enhance jejunal movement in rats with constipation and diarrhea , that moIn brief, this study aimed to assess the back-shu and front-mu point combination using BL25 and ST25 as examples to explore network acupoint research methods based on existing evidence and to elucidate the mechanisms induced by stimulation at BL25 and ST25 in the treatment of FCC. The results of this study should provide new ideas and methods for acupuncture research. The research route is shown in We manually searched the CNKI, Wanfang, VIP, PubMed, and Web of Science databases to collect information on targets of BL25 and ST25, and the search time was from the time of database creation to July 1, 2021. Using a search method involving subject headings and free words and taking a PubMed search based on BL25 as an example, the search formula was (((((((((((((((((((((((((((Acupuncture Points [MeSH Terms])) OR (Electroacupuncture [MeSH Terms])) OR (Moxibustion [MeSH Terms])) OR (Acupuncture Therapy [MeSH Terms])) OR (Acupuncture Point)) OR ) OR ) OR (Acupoints)) OR (Acupoint)) OR (Acupuncture Treatment)) OR (Acupuncture Treatments)) OR ) OR ) OR (Pharmacoacupuncture Treatment)) OR ) OR (Pharmacoacupuncture Therapy)) OR ) OR (Acupotomy)) OR (Acupotomies)) AND (((BL25) OR (B25)) OR (Dachangshu)). Reexamination, debugging, double-checking, and omission repair were performed by special personnel.https://www.UniProt.org/) for correction and conversion into gene names. UniProt is a nonredundant protein sequence database with the most complete sequence data and the richest annotation information in the world. Since its establishment at the beginning of this century, this database has provided valuable resources for the field of life sciences. More than 500,000 sequences in the Swiss-Prot sublibrary were manually reviewed and annotated, and all corrected data in this study were obtained from the Swiss-Prot sublibrary. The target information for the two acupoints was then uploaded to the website (https://www.tcmmodel.com).The two acupoint targets were entered separately into the UniProt database (https://www.genecards.org) to identify disease-related genes, and those related to both the acupoint targets and FCC were retained. Cytoscape 3.8.2 software was then used to prepare the figure.The key phrase “functional constipation in children” was searched in the GeneCards database (https://string-db.org/) database, and their functional enrichment was assessed based on Kyoto Encyclopedia of Genes and Genomes (KEGG) and local STRING network clusters. A bioinformatics website (https://www.bioinformatics.com.cn) was then used to prepare the figure.The above-described genes were inputted into the String . In brief, local STRING network clusters were reenriched based on the STRING, GO, KEGG, Reactome, UniProt, Pfam, SMART, and InterPro databases, which may provide a more comprehensive description of the gene enrichment status. Therefore, we selected the local STRING network clusters for reenrichment.The local STRING network cluster enrichment analysis included precomputed protein clusters derived from a hierarchical clustering of the full STRING network using an average linkage algorithm. The cluster names were derived automatically based on the consensus protein annotations from the GO, KEGG, Reactome, UniProt, Pfam, SMART, and InterPro databases. All clusters and their hierarchical tree are available for download in the download section of STRING  pathways that have been proven to be directly or indirectly related to FCC; (2) pathways with the same or similar mechanisms present in both the enrichment methods; and (3) multiple pathways with the same or a similar meaning.After retrieval, removal of redundant data, and correction based on the UniProt database, a total of 56 BL25 targets and 122 ST25 targets were obtained, as shown in After removing the redundancy among the BL25 and ST25 targets, an analysis of the FCC genes from the GeneCards database that intersected with the abovementioned targets revealed 115 targets that were related to the two acupoints and diseases. Detailed information is provided in Supplementary The abovementioned targets were entered into the STRING website, and the analysis function was used to obtain enrichment information for 141 KEGG pathways. Detailed information is provided in Supplementary κBA variant leads to EDA-ID; the IκBA variant leads to EDA-ID and death-inducing signaling complex assembly; TAK1 activates NF-κB by phosphorylation and activation of the IKK complex; and death-inducing signaling complex assemblies are closely related to the NF-κB signaling pathway. The enrichment of local STRING network clusters also revealed that the NF-κB signaling pathway is correlated with acupoints and FCC.A total of 59 local STRING network cluster enrichment items were obtained, as shown in Supplementary κB signaling pathway [The enrichment analysis of the local STRING network clusters also showed several pathways that are related to inflammation, such as the interleukin-1 receptor complex and IRAK4 deficiency (TLR5); IRAK4 deficiency (TLR2/4) and interleukin-1 receptor complex; interleukin-1-mediated signaling pathway and TIR domain; MyD88 deficiency (TLR2/4); and MyD88-dependentToll-like receptor signaling pathway and interleukin-1 conserved site pathways. All of these pathways are related to IL-1 or MyD88; IL-1 is a well-known proinflammatory factor , and MyD pathway . In addiκB signaling, Toll-like receptor signaling, IKBA variant leads to death-inducing signaling complex assembly, TAK1 activation of NF-κB by phosphorylation and activation of the IKK complex and death-inducing signaling complex assembly, interleukin-1 receptor complex, IRAK4 deficiency (TLR5), IRAK4 deficiency (TLR2/4), interleukin-1 receptor complex, interleukin-1-mediated signaling pathway, TIR domain, MyD88 deficiency (TLR2/4), MyD88-dependentToll-like receptor signaling pathway, and interleukin-1 conserved site pathways. Second, adipocytokines and leptin appear to be associated with the acupoint regulation of FCC, including through adipocytokine signaling, the orexin receptor family, prepro-orexin, and leptin signaling, and erythropoietin activates PLCG pathways.In short, enrichment analyses of the KEGG and local STRING network clusters revealed two important points. First, the potential mechanisms through which the two acupoints regulate FCC appear to be related to inflammatory pathways, including the following: NF-Therefore, we subsequently aimed to analyze the abovementioned 13 signaling pathways to determine commonalities.To identify the commonalities among the abovementioned 13 pathways, the pathway-target relationship network was constructed using Cytoscape. As shown in According to the TCM theory, the back-shu point refers to the acupoints at which the energies of the five internal organs and six FCs are inflicted upon the back. In subjects with visceral diseases, the corresponding back-shu points often have abnormal reactions, such as sensitivity and tenderness, and stimulation of these acupoints can be used to treat corresponding visceral diseases. BL25 corresponds to the passage of qi in the large intestine through the back, and stimulation of BL25 can regulate the pathological changes in the large intestine. Front-mu acupoints are those at which the vital energies of the viscera are infused into the chest and abdomen, and “mu” means gathering and confluence. Similar to the back-shu point, when the viscera are diseased, stimulation of the front-mu acupoint usually results in abnormal manifestations such as sensitivity and tenderness. ST25 is also the front-mu acupoint of the large intestine. In clinical treatment, the back-shu and front-mu points are often used in combination due to their similar effects. Despite being used for thousands of years, Chinese researchers have not provided evidence-based experimental results that sufficiently support the effectiveness of this combination therapy. This study aimed to analyze the existing evidence and revealed that BL25 and ST25 have 20 common targets, namely, AQP8, DRD2, VIP, TAC1, IL6R, TNF, FOS, KIT, CHAT, HTR3A, GAS8, SOD3, TRPV1, MPO, CALCA, IL1B, P2RX7, NPY2R, IL10RA, and TPH1. These targets may underlie how BL25 and ST25 can be used together to enhance the therapeutic effect. In addition, because the BL5 and ST25 combination is based on the specific embodiment of back-shu and front-mu points, other acupoints belonging to the back-shu and front-mu point combination may also have similar matching targets, and this possibility may represent a direction of future research on acupoint compatibility.FCC can be attributed to eating habits (too much milk or too little fiber), changes in the intestinal flora, and psychological and behavioral factors secondary to systemic diseases or drug use, among other factors . Among tIn this study, KEGG and local STRING network cluster analyses revealed several interesting potential functions of BL25 and ST25.Enterococcus, Escherichia coli, and Klebsiella [Constipation is a common problem in childhood, affecting approximately 3% of children in the world, and up to 30% of children in some environments suffer from constipation. Constipation is defined as infrequent bowel movements (<2 times per week), reduced bowel frequency, occurrence of fecal incontinence, fecal retention, painful or hard bowel movements, or large-diameter feces . FCC accebsiella , overproebsiella . In thisebsiella , 37. Ourα [As one of the CD4+ T-cell subsets, Th17 cells play a dual role in the pathogenesis of inflammation-related bowel disease (mainly proinflammatory). These cells can not only protect the intestinal mucosa by maintaining the balance of the immune microenvironment but also aggravate the intestinal inflammatory response through proinflammatory cytokines. Th17 cells can secrete IL-17, IL-21, and IL-22 and the proinflammatory cytokines IL-1, IL-6, IL-18, and TNF-α . Among tα . The potIn addition, the NF-kappa B signaling pathway has long been regarded as a typical proinflammatory signaling pathway, mainly based on the role of NF-kappa B in the expression of proinflammatory genes, including cytokines, chemokines, and adhesion molecules . Due to β cells. Due to the ability of recombinant leptin to reduce appetite under normal or low circulating leptin conditions, recombinant leptin has been developed as a weight-loss drug for the treatment of obesity [Adipocytokines are peptides that signal the functional status of the adipose tissue to the targets in the brain, liver, pancreas, immune system, vasculature, muscle, and other tissues. Adipokines, including leptin, adiponectin, fibroblast growth factor 21, retinol-binding protein 4, dipeptidyl peptidase 4, bone morphogenetic protein-4, BMP-7, vaspin, apelin, and proanulin, play an important role in the function of the adipose tissue and obesity-related diseases. Leptin is a 16-kDa protein composed of 167 amino acids . Leptin  obesity , 49.C gamma (PLCG). This finding may indicate that BL25 and ST25 can regulate FCC through adipocytokine or leptin, and although more evidence is needed, this study at least provides a new idea.At present, weight loss is considered an alarm symptom of gastrointestinal disease, but the relationship between obesity and gastrointestinal symptoms is unclear . HoweverApproximately less than 5% of children with constipation have organic changes, and these organic changes include anorectal malformations, megacolon, neurological abnormalities, or endocrine and metabolic disorders. More than 95% of children with constipation have no obvious organic causes and are considered to have FCC. However, this study found that leishmaniasis, Chagas disease (American trypanosomiasis), malaria, African trypanosomiasis, toxoplasmosis, hepatitis B, osteoclast differentiation, and measles may be related to FCC, but the relationship between these diseases and FCC is currently poorly understood. However, according to the theory of Chinese medicine, humans should be considered as a whole, and the various tissues and organs of the human body can cooperate with each other to complete various complex physiological functions. Therefore, it does not seem impossible that the occurrence of one disease will eventually cause the occurrence of another disease. Of course, more research is needed on the internal links between these diseases and FCC. We only provide the current results obtained through bioinformatics analysis, and we expect that more scholars will explore and further decipher these findings.Our PPI analysis also revealed that the proteins involved in the abovementioned regulatory functions are NFKBIA, RELA, TNF, IKBKB, IRAK1, TLR4, MYD88, TNFRSF1A, IL1R1, TLR2, IL1B, TRAF6, and TNFRSF1B, as shown in Any strong or weak external stimulus can alter the physiological functions of the human body. The most basic changes are those related to protein conformation and output caused by gene expression, which result in various positive or negative bodily states. Based on this information, the stimulation of BL25 and ST25 will inevitably change the human body, and exploring the changes in genes or proteins produced after the stimulation of BL25 and ST25 is thus meaningful.Based on the existing evidence, this study revealed the targets of BL25 and ST25 and performed enrichment analyses, as shown in BL25 and ST25 exert a synergistic effect.The pathway through which BL25 and ST25 regulate FCC is closely related to inflammation and adipocytokine or leptin.The findings indirectly prove the usability of the method of matching points based on the back-shu and front-mu point combination.At present, there are many difficulties in acupuncture research, such as the difficulty of determining the targets of acupoints, and the theory of acupoint compatibility has not been effectively proven. This research explored published articles, collected the existing BL25 and ST25 targets for further research, and explored the connotation of combinations of back-shu and front-mu points for TCM. This research draws the following conclusions:In short, this study provides a new method and idea for exploring the mechanism induced by acupuncture points and provides a new research direction for the modernization of Chinese medicine, particularly acupuncture and moxibustion.


Scutellaria baicalensis (SB) against hepatocellular carcinoma (HCC).  The aim of the study was to use a network pharmacological method and experimental validation to examine the mechanism of  The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) and GeneCards were used for screening of targets of SB for the treatment of HCC. Cytoscape (3.7.2) software was used to construct the “drug-compound-intersection target interaction” interaction network. The STING database was used to analyze the interactions of the previous intersecting targets. The results were visualized and processed by performing GO (Gene Ontology) enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) signaling pathway enrichment analysis at the target sites. The core targets were docked with the active components by AutoDockTools-1.5.6 software. We used cellular experiments to validate the bioinformatics predictions.  A total of 92 chemical components and 3258 disease targets including 53 intersecting targets were discovered. The results showed that wogonin and baicalein, the main chemical components of SB, could inhibit the viability and proliferation of hepatocellular carcinoma cells, promote apoptosis through the mitochondrial apoptotic pathway, and effectively act on AKT1, RELA, and JUN targets.  SB has multiple components and targets in the treatment of HCC, providing possible potential targets for the treatment of HCC and providing a basis for further research. With roughly 906,000 new cases and 830,000 deaths from primary liver cancer worldwide in 2020, it is the sixth most prevalent cancer and the third main cause of cancer death globally, with typically three times as many men as women affected . The maiArtemisia annua and thus effectively acted on malaria patients [Many Chinese traditional herbs can have specific efficacy in disease treatment, but the specific mechanism of action is not clear. Network pharmacology has enabled some herbal components to be explored and studied, allowing more specific components to be discovered and studied, and the combination with molecular docking technology is beneficial to discover new drug targets and active drug components . For exapatients . Previoupatients . Cyber ppatients .Scutellaria baicalensis (SB) has been used in China for thousands of years and was recorded in Shennong ben cao jing  as early as 200 AD, where it was used to treat liver and lung problems and wind-cold fever [ld fever , 8. Modeld fever , 10. Theld fever . This imThis study determined the primary active ingredients in SB for the treatment of HCC based on network pharmacology and obtained its potential targets. This research established the molecular mechanism of SB and supported it with cytological tests, giving SB a rationale for use in the treatment of HCC. The overall structure flowchart is shown in https://tcmspw.com/tcmsp.php). Oral bioavailability (OB) and drug likeness (DL) were used as the screening criteria, and chemical components with OB ≥ 30% or DL ≥ 0.18 were selected.The chemical components of SB were searched by the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) (https//www.uniprot.org/).The corresponding targets of the obtained compounds were collected in the TCMSP database, and the obtained targets were subjected to gene name transformation and specification in the UniProt database (https://www.genecards.org/) database for genes related to hepatocellular carcinoma using the keyword “Hepatocellular Carcinomas” and obtained the targets related to hepatocellular carcinoma after deduplication.We searched the GeneCards (https://www.liuxiaoyuyuan.cn/), the targets of SB component action and the targets related to hepatocellular carcinoma were intersected, and the overlapping targets were the targets related to the possible action of SB in hepatocellular carcinoma.Using Venny2.1 (Cytoscape (3.7.2) software was used to construct the “drug-compound-intersection target interaction” interaction network. The nodes in the network diagram represent the disease, drug, active ingredients, and targets, respectively. The interactions between active ingredient and target, target and disease, or target and drug were represented by the connection of edges. A network analysis, a plug-in of Cytoscape, was used to analyze the topological properties of the network, and the network was arranged according to the degree value to obtain a network graph that can characterize the relationships between drugs, diseases, ingredients, and targets.https://string-db.org/) was used to analyze the interactions of the previous intersecting targets, import the target information into the database, select multipleproteins, set the species to Homo sapiens, set the confidence level to 0.9, hide the scattered nodes, and obtain the protein-protein interaction of the intersecting target genes. The protein-protein interaction (PPI) network of intersecting target genes was obtained. Cytoscape 3.7.0 software was applied to visualize the results, and topological analysis was performed with the aid of CytoNCA plug-in. The core targets were calculated and visualized with the median values of the parameters of mediator centrality, proximity centrality, degree centrality, eigenvector centrality, local average connectivity, and network centrality as the filtering conditions, respectively. These six parameters represent the topological importance of each individual node in the whole network, and the larger their quantified values, the more important the node is in that network.The STING database (P < 0.05. We use Sangerbox online processing tool (https://www.sangerbox.com/), a comprehensive and interactive friendly platform for clinical bioinformatics analysis. They used SpringCloud framework, java 11,10.2.30-MariaDB to build the website, website backend, and database. R 3.6.3, R 4.0.1, Perl, and JavaScript languages were applied to write and convert to arithmetic and analytical scripts.The results were visualized and processed by performing GO (Gene Ontology) enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) signaling pathway enrichment analysis at the target sites, and the differences were considered statistically significant at https://pubchem.ncbi.nlm.nih.gov/), import it into AutoDockTools-1.5.6 software, check its spatial structure for errors, add the atomic charges, and assign the atomic types; all flexible keys are changed to default rotatable and saved in the PDBQT format as docking ligands. We select the core target of the previous screening, look for the 3D concept of the active ingredient in the UniProt database (https://www.uniprot.org/), enter the query PDB id into the PDB database (https://www.rcsb.org/), download the 3D structure of its corresponding protein, and save it in the PDB format. We import this 3D structure into AutoDockTools-1.5.6 software, perform water molecule removal, hydrogen addition, charge addition, and various optimizations, then set the grid box (bird box) as the default value and save it, and finally save the optimized protein in the PDBQT format as a pair of acceptors. According to the coordinates of the ligand in the target protein and the target protein activity pocket that had been set, molecular docking was performed using AutoDock Vina 1.1.2 to obtain the results, and the obtained results were imported into PyMOL software for visualization of the results.We download the mol2 format file of the 2D structure of the screened key compound from the PubChem database  were dissolved in DMSO, and the drug solutions of 0.65 μM, 1.25 μM, 2.5 μM, 5 μM, and 10 μM were configured.The HepG2 cell line was employed in cell experiments of this study. Cells were cultured in DMEM  containing 10% fetal bovine serum  at 37°C with 5% COμM as a concentration gradient, the cells were treated with wogonin and baicalein. After 24 hours, 10 μl CCK8 solution was added to each well. The absorbance at a wavelength of 450 nm was measured after 1 hour using a microplate reader.Cell viability was detected by employing the CCK8 assay . HCC cells in the logarithmic growth phase were seeded in 96-well plates, and 3 replicate wells were taken from each group. Cells were grown adherent and then replaced, and having taken 0.625∼10 −ΔΔCt. The previous experiments were repeated three times independently. The sequences of primers were as follows: Jun forward, 5′-ACTCGGACCTCCTCACCT-3′ and Jun reverse, 5′-CGTTGCTGGACTGGATTAT-3′; RELA forward, 5′-AGAGCAGCGTGGGGACTA-3′ and RELA reverse, 5′-ATGGGATGAGAAAGGACAGG-3′; AKT1 forward, 5′-CAAGGTGATCCTGGTGAA-3′ and AKT1 reverse, 5′-CGTGGGTCTGGAAAGAGT-3′.The mRNA level of key target molecules was detected by qPCR. The cells were seeded in six-well plates and pretreated with wogonin and baicalein for 24 h, respectively. Total RNA was extracted with Trizol , and cDNA was synthesized by a reverse transcription reagent . The internal reference group was GAPDH, and the results were expressed as 2The intracellular ROS levels were measured using a reactive oxygen species assay kit . Briefly, the cells were seeded in 6-well plates and exposed to wogonin and baicalein for 24 h. Following the treatment, the cells were incubated with DCFH-DA for 20 min at 37°C and then observed using Olympus IX71 fluorescence microscopy (Japan).J and Sanger box 3.0. All data were presented as the mean ± SD. The one-way analysis of variance was applied for comparisons between the groups, and P < 0.05 indicated that the difference was statistically significant.Statistical analysis was performed with GraphPad Prism 6.0. Image processing is conducted by image A total of 105 compounds was founded in SB by TCMSP search, and 36 active ingredients were obtained after screening. The basic information about the active ingredients of SB is shown in The keyword “Hepatocellular Carcinomas” was used to collect 3,258 genes for related diseases in the GeneCards database. The screened 92 baicalin targets were put into the online website Venny2.1 to take the intersection with 3,258 primary liver cancer targets, and a total of 53 common targets were collected .The “drug-compound-intersection target” interaction network is shown in Homo sapiens, the confidence level was set to 0.95, and the PPI graph was plotted , 25 cellular components (CC), and 87 molecular functions (MF). The BP results suggest that it may be related to response to inorganic substance, apoptotic signaling pathway, and response to bacterium. The CC results suggest that it may be related to transcription regulator complex, cytoplasmic vesicle lumen, and vesicle lumen. The MF results suggest that it may be related to protein domain specific binding, transcription factor binding, and DNA-binding transcription factor binding. KEGG analysis of key targets of SB for hepatocellular carcinoma showed that the key targets were enriched in a total of 238 pathways, including those in various cancers, such as small cell lung cancer and colorectal cancer, involving hepatitis B, hepatitis C, Epstein–Barr virus, influenza A and PI3K-Akt signaling pathway, AGE-RAGE signaling pathway in diabetic complications, IL-17 signaling pathway, TNF signaling pathway, and p53 signaling pathway. The bubble plots are sorted by the gene ratio, the higher the number of genes in the top pathway, the larger the corresponding bubble in the plot, as shown in Figure B. JUN, RELA, and AKT1 were ranked in the top 5 of the core genes of the PPI network, which indicated that JUN, RELA, and AKT1 might be the key targets of SB for the treatment of hepatocellular carcinoma.β-sitosterol and JUN, RELA, and AKT1 are used as examples to analyze their possible modes of action with receptors  of wogonin and baicalein were employed to act on HepG2 cells and Huh7 cells. After 24 hours, the effect of wogonin and baicalein on HepG2 cells and Huh7 cell viability were then observed. The CCK8 results demonstrated  levels, as shown in To verify the targets obtained from bioinformatic analysis, we used qPCR to detect the expression of JUN, RELA, and AKT1 mRNA after 24 h of drug treatment and found that JUN, RELA, and AKT1 mRNAs were significantly higher than those of the untreated group, as shown in HCC is a common cancer with extremely high incidence and mortality rates worldwide, and patients are often diagnosed at an advanced stage. Surgical treatment options are not applicable to these advanced HCC patients, making the development of new drugs critical for patients with advanced disease , 13. Chi2O2 production and Ca2+ release [Network pharmacology is an emerging paradigm to systematically reveal the multicomponent and network regulation of prescriptions and has been widely applied to the screening of bioactive compounds and the study of efficacy mechanisms of various Chinese medicine prescriptions . We dete release , Wang et release , and Hua release . There i release . This isIn this study, we investigated the drug-component-target-disease interactions of SB in the treatment of HCC using network pharmacology. SB may induce apoptosis in HCC cells by modulating the PI3K-AKT signaling pathway through the mitochondrial pathway. The findings of this study shed light on SB's anticancer properties and probable biological pathways in HCC and lay the groundwork for future research into SB or its active constituents as a viable adjunctive therapy to SB treatment.


Nonalcoholic Fatty Liver Disease (NAFLD) is a chronic Liver Disease prevalent all over the world. It has become more and more common in Japan, China and most western developed countries. The global prevalence rate is 25.24%, and the trend is increasing year by year. Related studies have shown that Cynarine has certain liver protection, lipid lowering and immune intervention effects. So, this study to systematically predict and analyze the mechanism of Cynarine in the treatment of non-alcoholic fatty liver disease (NAFLD) based on the integration of network pharmacology, molecular docking, and cell experiment.We performed Heatmap and Venn diagram analyses to identify genes and targets in Cynarine treat NAFLD. The network of Cynarine-therapeutic targets and the protein-protein interaction network (PPI) was constructed. We used gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to visualize associated functional pathways. The Sybyl tool was used to dock the Cynarine with key therapeutic targets molecularly. Finally, cell experiments were applied to validate the role of Cynarine in the treatment of NAFLD.The Cynarine could act on 48 targets of NAFLD, and the role of CASP3, TP53, MMP9, ELANE, NOTCH1 were more important. The PPI network showed that immune and inflammation-related targets played a pivotal role. The KEGG analysis found that the PI3K-Akt signaling pathway, cell cycle and MAPK signaling pathway may be the main pathways for Cynarine to prevent and treat NAFLD. Molecular docking studies confirmed that Cynarine has good binding activity with therapeutic targets. Cynarine reduced the fat deposition ability of NAFLD model cells, and effectively reduced the levels of ALT and AST released by liver cells due to excessive lipid accumulation. We also found that Cynarine inhibited the expression of AKT1 and MAPK1.This study revealed that Cynarine could significantly reduce the fat deposition ability of NAFLD model cells, which may be closely related to the effective regulation of AKT1 and MAPK1 expression by Cynarine.The online version contains supplementary material available at 10.1186/s41065-022-00256-7. Non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases, which affects approximately 25% of adults worldwide. It is characterized by fatty degeneration of liver parenchymal cells with the absence of heavy drinking and no other clear liver damage factors . In receCynara scolymus L.), which has anti-obesity and anti-oxidant liver effects in high fat diet-induced obese rats [Cynarine is one of the major caffeoylquinic acid compounds extracted from the TCM artichoke  [http://www.lilab-ecust.cn/pharmmapper/) [http://sea.bkslab.org/) [Homo sapiens”. What’s more, we searched potential targets of Cynarine in SwissTargetPrediction (http://www.swisstargetprediction.ch/) [We searched the 3D structure of Cynarine CAS: 30964-13-7) in the PubChem database (64-13-7 imapper/)  to prediab.org/)  was usedion.ch/)  and targhttps://www.ncbi.nlm.nih.gov/geo/), and DEGs  were selected for subsequent analysis. Then, we searched the “nonalcoholic fatty liver disease” keyword in three databases to get a comprehensive collection of NAFLD-related genes: targets with Relevance score > 10 in Genecards (https://www.genecards.org/), all targets in OMIM (https://omim.org/), and targets with Direct Evidence as marker/mechanism or therapeutic in CTD (http://ctdbase.org/) [First, we analyzed differentially expressed genes (DEGs) for GSE89632 in the GEO database (se.org/) .http://bioinformatics.psb.ugent.be/webtools/Venn/) was used to intersect the NAFLD-related targets and Cynarine target genes to obtain the common targets which were considered potential targets for Cynarine against NAFLD. Then we submitted potential targets to the String website (https://string-db.org/) [The Draw Venny Diagram online program (db.org/)  to generhttp://bio.cigb.edu.cu/bisogenet-cytoscape/) [Also, we constructed PPI networks of Cynarine targets and NAFLD-related genes by BisoGenet (oscape/) . Then weoscape/)  to calcuWe input Cynarine, its candidate targets, and NAFLD to Cytoscape 3.7.1 software to construct the Cynarine - candidate target - NAFLD network by which we could predict the main targets of Cynarine in treating NAFLD.org.Hs.eg.db package to convert the potential gene name to gene ID. Then we installed colourspace, string, DOSE, cluster Profiler, path view and other program packages to conduct GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways enrichment analysis. Both GO and KEGG enrichment analysis was statistically significant, with P < 0.05.Firstly, we used R3.6.0 software and installed the RSQLite package and We used Cytoscape 3.7.1 software to construct the gene-pathway network based on the top 30 enriched KEGG pathways and genes associated with these pathways.We used Sybyl × 2.1.1 for surfex-dock (semi-flexible docking mode). The top ten proteins were identified as receptors and Cynarine molecules as ligands. And we obtained the points of the ten proteins based on total score Function. The GSK3B protein (PDB ID: 4J1R) with the highest score was selected to map Cynarine using pymol.− 1 sodium oleate was used to culture hepatocytes to establish a non-alcoholic fatty liver cell model. HepG2 cells in the logarithmic growth phase were inoculated and cultured in a 12-well plate at 104/well. The normal medium was set as the control group; the medium containing 200 μmol·L − 1 sodium oleate was used as the model group; 5 mg·L − 1 Cynarine pre-treated the cells for 1 h [− 1 sodium oleate without washing to continue culturing the cells to make a NAFLD cell model. Each group had three repetitions.In this study, a complete medium containing 200 μmol·L  for 1 h , and the4 cells in each group separately for 48 h, wash the cell surface oil red O with PBS; add 200 μL of isopropanol to extract the oil red O in the cells, centrifuge to collect the supernatant, and then The OD value was measured at a wavelength of 510 nm. The OD value of each group represents the quantitative value of fat accumulated by the cells of the group and is statistically analyzed.Take a small number of cells from each group, discard the cell culture medium, and wash with PBS; fix with 10% formaldehyde for 10 minutes, and put the oil red O staining solution in a 60-65 °C incubator to preheat; use Rinse the cells twice with PBS, add the preheated oil red O staining solution, and place in a 60-65 °C incubator for 15 min; 80% propylene glycol differentiates for 2 to 5 min, and observe under the microscope until the background is close to colorless; Rinse the cells three times with PBS, microscopically inspect, take pictures and record. Collect about 1.0 × 10After the cells were cultured for 48 h, the culture supernatants of the cells of each group were collected and tested by an automatic biochemical analyzer for alanine transaminase (ALT) and aspartate transaminase (AST).The trizol method was used to extract the total RNA of the three groups of cells from the control group, the model group and the drug treatment group and perform reverse transcription. Using the cDNA obtained by reverse transcription as a template, qRT-PCR was performed to detect the presence of AKT1 and MPK1 in each group of cells. At the same time, GAPDH was used as the internal reference gene, and the experiment was repeated three times for each sample.Wash the three groups of cells cultured in a 6-well plate twice with PBS, add pre-cooled cell lysate for 30 min, 12,000 r/min, centrifugal radius 10 cm, centrifuge at 4 °C for 15 min, sediment cell debris Wait for impurities, take the supernatant, and determine the amount of protein by BCA method. Separate the total protein with 12% SDS-polyacrylamide gel electrophoresis, then transfer to polyvinylidene fluoride (PVDF) membrane, block with 5% BSA for 90 min, add the diluted primary antibody, and incubate at 4 °C overnight. After washing the membrane, add the secondary antibody, incubate at 37 °C for 1 h, wash twice, use the gel imaging system software to analyze the grey area of the protein band group in the film, and calculate the relative expression of AKT1 and MPK1 based on the internal reference GAPDH.P < 0.05 indicated that the difference was statistically significant.Statistical analysis was performed using SPSS 19.0 statistical software. The count data were expressed as mean ± standard deviation (x ± s). The comparison between multiple groups was performed by one-way analysis of variance, and A total of 226 human proteins targeted by Cynarine were obtained from three public databases  based on the DC and BC , molecular function (MF), cell component (CC) and KEGG pathway were sorted according to P-value, and the top 20 terms were screened to draw bubble charts which were shown in Fig. To further analyze the gene function of the target proteins and the main signal pathways involved, we analyzed the GO function and KEGG pathway enrichment analysis of the 48 targets involved in the PPI network. The GO and KEGG enriched terms were measured by gene ratio, We imported the information of targets of action and their main signal pathways enriched into Cytoscape 3.7.1 software to integrate and construct a network of “Targets-Pathways”, which visually showed the relationship between 48 targets and 30 main signal pathways in Fig. To further verify the accuracy of the core targets  obtained in the network, the molecular docking analysis between them and Cynarine molecule were performed in sybyl 2.1.1. Eight of the ten core targets had docking scores of more than 5, demonstrating their good binding activity Table . Figure P<0.01); compared with the model group, Cynarine reduced the content of ALT and AST (P<0. 01). Figure P<0.01); compared with the model group, AKT1 and MAPK1 expression levels were significantly reduced (P < 0.01) in Cynarine treatment group cells. Compared with the control group, the protein levels of AKT1 and MAPK1 in the model group were significantly reduced (P < 0.01); compared with the model group, the protein levels of AKT1 and MAPK1 in the Cynarine treatment group were significantly reduced (P < 0.01).Figure The advantages of TCM in the treatment of NAFLD have gradually emerged that single medicine or a combination of various medicines can regulate liver lipid metabolism, promote absorption and excretion, and avoid adverse reactions caused by long-term use of western medicine.In this study, the targets of Cynarine were obtained by predicting and database searching. Then 48 targets for Cynarine in treatment of NAFLD were obtained by cross-mapping them with NAFLD -related targets in GeneCards, GEO, OMIM, and CTD databases. With the help of network pharmacology and network analysis, the “ Cynarine target- NAFLD target” network topology analysis was carried out. The PPI network was constructed, which showed that these target proteins did not act alone but had complex and interlocking interactions. The targets with high degree value had better connectivity which was essential for the entire regulatory network . TherefoNAFLD is a complex disease, with lipid accumulation and inflammation being two important steps in its pathogenesis and progression, as well as two important links in the “second hit” theory. Therefore, reducing liver fat accumulation and reducing inflammation is the key to treat NAFLD.The results of GO functional enrichment analysis in this study show that NAFLD’s therapeutic targets were mainly involved in the regulation of biological processes such as transcription, translation, and inflammation. These processes have the effects of improving metabolism, promoting tissue repair and reducing inflammation, and participating in the repair process of NAFLD diseased tissues from many aspects, which are consistent with the pathogenesis of NAFLD.Analysis of target-pathway integration network based on the KEGG pathway enrichment showed that PI3K-Akt signaling pathway, cell cycle and MAPK signaling pathway were enriched by more targets, indicating that these three pathways played an important role in the treatment of NAFLD by Cynarine. The three pathways are closely related to the onset and progression of NAFLD. PI3K-Akt signaling pathway, which is a significant pathway for the metabolism of glucose and lipids, and understanding this pathway’s role in NAFLD is crucial. It has associated targets with MAPK pathway, including the predicted key targets such as GSK3, MYC and IKBKG, which can be abnormally activated by various toxic insults or cellular stimuli related to NAFLD –28. CellCell experiment results showed that Cynarine could reduce the fat deposition ability of NAFLD model cells, and effectively reduce the levels of ALT and AST released by liver cells due to excessive lipid accumulation. Additionally, we discovered that Cynarine can also inhibit the expression of AKT1 and MAPK1 , 33, 34.In summary, this study used network pharmacology to construct a network of Cynarine-targets of action, which included 48 potential targets. Analysis showed that CASP3, TP53, MMP9, ELANE, NOTCH1 played a prominent role in many targets. It may be the key to Cynarine’s prevention and treatment of NAFLD that PI3K-Akt signaling pathway, cell cycle and MAPK signaling pathway and other potential signal pathways were regulated to interfere with inflammation and immunity, metabolic homeostasis, lipotoxicity, liver cell death and other mechanisms. Also, this study revealed that Cynarine could significantly reduce the fat deposition ability of NAFLD model cells, which may be closely related to the effective regulation of AKT1 and MAPK1 expression by Cynarine. In the later stage, we will start from animal level and clinical trials to further verify the effectiveness of Cynarine in the treatment of NAFLD and other potential mechanisms, and provide more reliable ideas for the treatment of NAFLD.Additional file 1.Additional file 2.Additional file 3.


The abstract metabolic network of a given organism results in a small network that requires low computational power to be analysed and makes it a suitable model to perform a large-scale comparison of organisms’ metabolism. To explore the potentials and limits of such a basic representation, we considered a comprehensive set of KEGG organisms, represented through their abstract metabolic network. We performed pairwise comparisons using graph kernel methods and analyse the results through exploratory data analysis and machine learning techniques. The results show that abstract metabolic networks discriminate macro evolutionary events, indicating that they are expressive enough to capture key steps in metabolism evolution.Metabolism is characterised by chemical reactions linked to each other, creating a complex network structure. The whole metabolic network is divided into pathways of chemical reactions, such that every pathway is a metabolic function. A simplified representation of metabolism, which we call an  Metabolism is the organism’s machinery that breaks down complex organic molecules to produce the energy and building blocks needed for the organism’s normal functioning. It comprises all chemical and physical processes that occur within the cells of living organisms and that allow for maintaining life. These processes interact with one another, creating a complex network structure. Each process, in turn, is made up of a pathway of chemical or physical reactions, and all the pathways together form the whole metabolic network. Even though all free-living organisms perform essential metabolic functions , the underlying pathways and chemical reactions that carry them out may differ. The variety of habitats led organisms to diversity and adaptation and, in the process, to evolve their metabolic pathways to carry out similar metabolic functions under different ecological niches . MetabolLiterature on metabolomics in the last two decades ranges from the analysis of single pathways –9, to thMetabolism reconstruction and comparison can be automated thanks to the knowledge stored in metabolic databases such as BioCyc , BioModeabstract metabolic network  since it does not include any information about the single biochemical reactions within each pathway. AMNs are a minimal description of the metabolism that is attractive for two main reasons. The first one is that they are small size graphs compared to other graph-based representations of the metabolism. A mean size metabolic network contains about one thousand reactions, which means having one thousand nodes in the corresponding graph, while the mean number of metabolic pathways is around a hundred, which becomes a hundred nodes in the corresponding AMN. This makes AMNs suitable for large-scale metabolism analysis, as they require a much lower computational effort. The second reason AMNs are interesting is that they allow for metabolism comparison from a novel perspective that can yield new insights in the field. Since AMNs do not take into account the biochemical reactions information, metabolism comparison through AMNs means comparing the topological organisation of the metabolism into functional units, which is rather different than performing the comparison through a detailed reaction-based representation. The effectiveness of AMNs needs to be investigated. However, while it seems reasonable to employ AMNs to verify whether the topological organisation of metabolic pathways played a role in evolution, their use for fine-grain metabolism comparison appears unsuitable.In this paper, we are interested in exploring the potential of a simplified representation of the metabolism as a graph, where nodes are metabolic pathways, and there is an edge between two nodes if their corresponding pathways share one or more compounds. We call it an Similar representations of the metabolism have already been introduced in the literature. In , a metabThe goal of this paper is to explore the expressiveness of AMNs by performing an automatic large-scale metabolism analysis to test whether AMNs are able to discern well-known taxonomic groups. A positive answer would mean that AMNs allow for a first meaningful classification that requires low computational effort. It would also emphasise that evolutionary pressures affected not only genes and protein sequences, but also the topological organisation of the functional units in the cell.To accomplish our goal, we consider a comprehensive set of organisms whose metabolic data is stored in the KEGG database, an extensive, reliable and widely used resource explicitly designed to present data in a standardised way, a key feature in our approach. We build the AMNs of the KEGG organisms and compare them in pairs by using graph kernels  whose photosynthetic capabilities derive from acquiring photosynthetic bacteria during the primary endosymbiotic event. The clade includes plants, green algae, and red algae.In addition to these results, the analysis highlights a new paraphyletic clade that we define, for the purpose of this study, as the representation, comparison and analysis. The overall view of the approach is described in This section illustrates the details of our methodology to automatically derive AMNs from KEGG metabolic data, compare the metabolisms of different organisms using graph kernels, and analyse the comparison results. Our analytical workflow is composed of three main steps: representation step, we retrieve from KEGG the metabolic pathways of the considered organisms and build their AMN. The latter is defined as a graph where each node represents a metabolic pathway, and each edge between two nodes indicates that they share one or more metabolites. Nodes are labelled with the KEGG’s pathway identifiers, and edges are not labelled. This defines a very high-level and abstract view of the metabolism that does not include any information about the biochemical reactions within each pathway.In the comparison step, the obtained graphs are pairwise compared using graph kernels. Each kernel produces a similarity matrix containing the comparison results. We explored the use of four graph kernels: Vertex Histogram (VH), Shortest Path (SP), Weisfeiler-Lehman (WL) and Pyramid Match (PM). Each of these kernels bases the comparison on specific topological characteristics, but all of them include nodes labels among the features taken into consideration. This means that all kernels evaluate, among the various aspects, the similarity of the sets of pathways present in the two graphs.In the analysis step, we employ exploratory data analysis techniques as well as machine learning methods to analyse the similarity matrices obtained by the kernels. Exploratory data analysis comprises visualisation techniques such as Multi-Dimensional Scaling (MDS) and simple heatmaps to enhance a first qualitative view of the similarity matrices, while clustering techniques, such as k-means and hierarchical clustering, are used to classify the considered organisms and compare the obtained clusters with the taxonomic groups mention. In some experiments, we also used a Support Vector Machine (SVM) as a supervised prediction method to further support the clustering results.In the We proceed now by detailing the techniques adopted for the representation, comparison and analysis steps.Our metabolism representation is based on the metabolic data stored in the KEGG database –24. DataWe used KEGG as the sole source of metabolic data. Our approach strictly depends on the data representation and the knowledge available in KEGG, including data incompleteness, inconsistency and biases that could reflect negatively on our comparison method and the subsequent analyses. It is worth highlighting that biases exist in all metabolic databases and cannot be avoided while performing large-scale automated processes. Nonetheless, we can count on the fact that KEGG is a widely known database, whose content is constantly updated based on new knowledge.The metabolic activity in KEGG is divided into various categories. Each category is then composed of various metabolic pathways. In order to consider the whole metabolic network, all categories and all the corresponding pathways are contemplated. The list of categories and corresponding pathways is available in the Supporting information reference pathway map that is not organism specific and contains integrated information from different organisms. A reference map can be expanded into organisms-specific pathways by combining the gene information in individual genomes. This representation of pathways plays an important role in avoiding incoherence in our metabolism representation and in automatising the whole process.KEGG associates to each metabolic function a unique abstract metabolic network (AMN), whose topology reflects the metabolic pathways stored in KEGG for that organism and their interconnections. Specifically, an AMN is an undirected graph in which each node identifies a metabolic pathway and an edge between two nodes indicates that the two corresponding pathways share one or more compounds among the ones defining their reactions network. Nodes are labelled with the KEGG pathway identifiers and edges are not labelled. Note that a shared compound C between two pathways may represent different situations:We represent the metabolic network of a given organism through a graph called C is produced by one pathway and consumed by the other;C is a compound used in the shared part of two overlapping pathways;C is a compound present in both pathways, even though they express unrelated functions or work in different environments or conditions.2O, phosphate, ATP and ADP are not considered, since, though they are needed and assumed to be present everywhere, they are not “representative” compounds of any pathway and their consideration would only add noise to our representation.Ubiquitous compounds such as HNote that all AMNs share the same set of node labels, namely, the set of KEGG’s pathways identifiers. Moreover, in each AMN all labels have at most one occurrence, since there is at most one node for each pathway.In this representation, all the reactions involved in the various pathway are omitted, and just the minimal information about the presence of pathways and their interconnections is taken into consideration. This clearly results in an abstract and coarse-grain view of the metabolism.pathway map), showing the pathway content, and a textual one written in an XML format, a KGML file, where KGML stands for KEGG Markup Language. Such a file contains the information represented in the corresponding map.KEGG supplies two related representations for each pathway in its repository: a graphical representation  contains the result of the pairwise comparison between graphs i and j. In our case the matrix is normalised, that is, all similarity values are real numbers in the interval , where 0 and 1 indicate the minimum and maximum similarity values, respectively. The matrix is symmetric and the main diagonal contains only 1 values, each cell being the comparison of a graph with itself.Graph kernels can be intuitively understood as functions measuring the similarity of pairs of graphs. Given two graphs in input, a graph kernel calculates a real number that measures their similarity according to some graph’s features. More in general, given a set of We used four different kernels to compare the abstract representation of metabolisms, all implemented by the GraKel Python library . Since nA formal introduction on graph kernels can be found, e.g., in , 34–38. Vertex Histogram: This is the simplest kernel. It compares node-labelled graphs and it only considers the node labels of the graphs to compare. Given a set of node labels i. In the particular case of a labelled graph being the AMN of a given organism, every node has only one label and different nodes have different labels. Therefore, this kernel only considers the presence or absence of pathways (node labels) in the two organisms under comparison and sum up the number of common pathways. Since only nodes and their labels, and not edges, are considered, the way metabolites are exchanged between pathways does not affect the comparison result.Shortest-Path: This kernel compares graphs in terms of their shortest paths. Intuitively, this means measuring how easy or difficult it is to connect, through shared compounds, couples of pathways in the two AMNs. It first computes the shortest-paths graph of every input graph, that is, a weighted graph with the same nodes as the original graph and with weighted edges corresponding to the shortest paths between each pair of nodes. The comparison is then performed by considering each pair of shortest paths in two given graphs and comparing their length and the labels of their endpoint nodes. The resulting scores are summed up to form the final similarity score.Weisfeiler-Lehman (WL) subtree kernel: Roughly speaking, this kernel compares the subtrees rooted on each node of the two graphs under consideration, taking into account also their node labels: the more the subtrees structures and labels coincide, the higher is their similarity. The fundamental idea of the Weisfeiler-Lehman algorithm is to replace the label of each node by a multiset of labels consisting of the original label of the node and the sorted set of labels of its neighbours. The resultant multiset is then compressed into a new short label, which reflects the knowledge of the node and its neighbourhood. This relabelling process is then repeated for h iterations. By performing this procedure simultaneously on all input graphs, it follows that two nodes from different graphs will get identical new labels if and only if they have identical multiset labels. The kernel function, in this case, compares the node labels of the graphs resulting after each iteration and summarises the comparison with a real number. It can be shown that this is equivalent to comparing the number of shared subtrees between the two input graphs .Pyramid Match: Intuitively, this kernel measures the similarity of the topological features (e.g. connectivity) of the nodes with the same label in the two compared graphs. Described at a high level, the Pyramid Match graph kernel first embeds the graph nodes into a vector space by considering the eigenvectors of the k largest in magnitude eigenvalues of the adjacency matrix of the graph. Thus, the nodes become points of a k dimensional hypercube. Next, to assess the similarity of two sets of nodes, the kernel maps these points into multi-resolution histograms and compares them with a weighted histogram intersection function. In the case of labelled graphs, the kernel restricts the matchings to occur only between nodes that share the same labels.To conclude, we remark that the great advantage of graph kernels is that they provide a computationally feasible way to compare graphs. We exploit this characteristic throughout the paper to perform large-scale comparison of thousands of AMNs in a few minutes. However, we also want to point out that graph kernels do not provide insights on the agreements or disagreements of the input graphs, i.e, they do not allow for recovering the nodes and edges of the input graphs that are responsible for the obtained similarity result.i, j) in the heatmap shows the similarity value between the i-th and j-th AMNs, colour-coded so that darker colours correspond to a high degree of similarity (from dark blue to yellow). In our case the heatmaps are symmetric and cells in the main diagonal always show the darkest colour, resulting from comparing an organism with itself. As it is a challenge to annotate rows and columns with the KEGG identifier of each organism, a detailed view of the data is difficult to obtain. We used coloured bars on rows and columns to indicate the various taxonomic groups of organisms to ease this difficulty.A heatmap allows for a visual rendering of the similarity matrix produced by graph kernel. Each cell , a classic multivariate data analysis technique that allows for obtaining a low-dimensional representation of the observed similarities . Given ay1, …, yk, where k < n, such that the Euclidean distances between the coordinates of the elements for these variables are close to the original distances. In this way, the graphical representation in k dimensions will faithfully reproduce the observed structure. We executed the metric MDS using the R package [The objective of the MDS is to represent the observed distances using a set of variables  package , a languR package to perform partitioning clustering methods, such as k-means, and agglomerative hierarchical clustering [We used clustering techniques on the similarity matrices produced by graph kernels to identify patterns or groups of similar organisms. In particular, we used the ustering .k subsets, where k is pre-specified. The algorithm finds the best k groups, the best being those where the within sum of square (WSS) is the smallest possible and where the sum of squares between clusters is the largest. Each cluster is represented by the mean of the data points belonging to the cluster itself. We specified the number of clusters both according to the phylogeny and using the so-called Elbow method [k. The bend (elbow) location in the plot is generally considered an indicator of the appropriate number of clusters.K-means clustering divides the dataset into w method . The Elbd = max{d|a ∈ C, b ∈ C′}.Agglomerative hierarchical clustering is an alternative approach that does not require specifying the number of clusters to be generated. We calculated the distance between hierarchical clusters with complete linkage: The result of hierarchical clustering is a tree-based representation of the objects; the visualisation is shown through a graph known as a dendrogram.Scikit learn library [Support Vector Machine (SVM) is a well-known supervised learning method. The basic configuration works with two classes and considers linearly separable problems: data points that a hyperplane can separate. Considering data as n-dimensional labelled points, SVM searches the hyperplane separating points of different classes, maximising the distance between the boundary and data points. We use SVM as implemented in the  library .training set), k-fold cross-validation divides the dataset into k partitions and, for k times, one partition at a time is used as a validation set, while the rest of the dataset remains as the training set. Prediction indexes are measured at each iteration and, in the end, the mean value of such measures will be the final prediction results. A modified version called stratifiedk-fold cross-validation allows for dealing with imbalanced datasets by preserving the imbalanced class distribution in each fold, thereby ensuring that each class is suitably represented. In this work we use stratified 5-fold cross validation.To evaluate the SVM prediction ability, we use cross-validation. Roughly speaking, given a dataset of known data  were extracted, and all the nontruncated proteins longer than 50 residues were kept for the following analysis. Orthologs present in a single copy in all the genomes were then selected using OrthoFinder (ver 2.3.3) [For the phylogenetic reconstruction of the r 2.3.3) . Three hr 2.3.3) . For eacr 2.3.3) ; all ther 2.3.3)  using a r 2.3.3) .R package ape (ver 5.5) [The distance matrices generated during the previous analyses were used to generate cladograms using the hierarchical clustering and converted to a phylogenetic object to be compared to the phylogenetic tree using the ver 5.5) , 48. To This section presents the results of comparing the metabolisms of different species, represented as AMNs, using graph kernels. The various experiments were carried out on the entire dataset as well as on different subsets of organisms. In particular, in the first experiment, we compared the metabolisms of the entire dataset, consisting of 7141 organisms, 687 Eukaryotes and 6454 Prokaryotes, stored in KEGG. In the second experiment, we considered all the Eukaryotes and later, we also focused on each kingdom separately: Animals, Plants and, by extension, Protophotophytes, Fungi and Protists. The third test focused on Prokaryotes.The larger experiment, considering the whole dataset, ran in less than 86 seconds for VH and WL kernels, less than 7 minutes for SP and about 34 minutes for PM on a standard laptop, which reinforces the argument that AMNs and graph kernels are effective, from a computational point of view, to perform comparative analysis of large metabolomic datasets. The smaller experiments, i.e. Animals, Protophotophytes, Fungi and Protists, ran in a few seconds for all kernels.This experiment considers the metabolism of 7141 organisms stored in the KEGG database. The set comprises 370 Animals, 127 Protophotophytes, 138 Fungi, 52 Protists, 6115 Bacteria and 339 Archaea. The AMNs of all organisms were pairwise compared using the VH, SP, WL and PM graph kernels.We also applied k-means clustering to the MDS data. The MDS transformation allows for a faithful representation of the original data and, at the same time, reduces the noise of the data themselves. However, at the kingdom level, only Animals are clearly separated, meaning that, within this wide dataset of organisms, their AMNs are significantly different w.r.t. to the others. It is also interesting to observe that the WL kernel is the only kernel able to cluster almost all Archaea within one group (cluster 1).The SVM prediction analysis applied to the kernel data, see In this section, we present the analyses concerning the Eukaryotes domain. We first consider all the 687 Eukaryotes together and test whether AMNs, compared through the VH, SP, WL and PM graph kernels, allow for discerning the various kingdoms: Animals, Plants (more precisely Protophotophytes), Fungi and Protists. Subsequently, we consider each kingdom separately and evaluate the extent to which they can differentiate the organisms based on their respective phylum or class. We do not perform SVM prediction for these lower taxonomy level tests since some classes may contain a few organisms (sometimes just one), rendering the cross-validation process unusable.In this experiment, we consider all the Eukaryotes, represented through their AMNs, and use the VH, SP, WL and PM graph kernels to test if the Eukaryotes kingdoms can be distinguished. We observe a good separation among Animals, Protophotophytes, and Fungi in all heatmaps. Protists are apart, but they are generally closer to Fungi than to the rest of Eukaryotes. Also, in all heatmaps, Animals are well separated from the other kingdoms and show a further internal pattern that deserves further analysis. It is also worth noticing that all kernels highlight a pronounced difference between Animals and Protophotophytes. Animals are closer to Fungi than Protophotophytes, and, in turn, Protophotophytes are closer to Fungi than to Animals. The heatmaps view of the similarity matrices is also confirmed by the MDS plots that can be found in the Supporting Information, see Looking again at the heatmaps in We applied a 4-means clustering analysis to the kernels results, four being the different kingdoms of the organisms considered in this test. Looking at Although all kernels give an interesting and meaningful classification, VH, SP and PM perform better at kingdom level: Animals, Protophotophytes and Fungi have their own cluster while Protists are split and mixed with Fungi and algae. However, WL has the advantage of keeping all Protists together in one group and offer the interesting viewpoint of evaluating more the Animals differences (Vertebrates vs. Invertebrates) than the kingdom boundaries.The SVM prediction analysis also supports these results. In k = 4 being the optimal number of clusters, as suggested by the Elbow method [This experiment focuses on the Animals kingdom and tests whether AMNs, compared through the considered kernels methods, are useful to classify organisms at a lower taxonomic level, specifically at the Phylum level. w method .Looking at the heatmap in The other kernels also confirm the results shown here for the PM kernel. We refer to the Supporting Information for the VH, SP and WL heatmaps, MDS and k-means results, see In order to investigate whether AMNs are able to classify at a lower rank of taxonomy, we performed a further test focusing on Vertebrates only. The heatmaps obtained by the four kernels can be found in the Supporting Information, see It is noteworthy that, at this level of taxonomy, the four kernels have different classification abilities. Nonetheless, there is a trend that using more features, like WL and PM, allows for a relatively better ability to differentiate between the groups. But, as evidenced by the different performance of the two kernels, the features considered by the kernels also affect their classification ability.This experiment considers all the KEGG “Plants” (more precisely Protophotophytes). Their AMNs are compared using the four kernels to see whether the internal structure of the kingdom can be recognised. This experiment considers the KEGG Fungi and examines whether their AMNs, compared through the kernel methods, can identify further differences inside the kingdom. On the right panel of In this last experiment on the Eukaryotes domain, we consider the KEGG Protists. We observe that the Protists similarity matrix does not contain a clear pattern. This is also reflected in the k-means clustering, where all Euglenozoa are grouped in a homogeneous cluster (cluster 1). This is true also for part of the Alveolates (cluster 4), but the remaining ones are divided into clusters 2 and 3. The other kernels show similar results, as illustrated in the Supporting Information, see When looking at Prokaryotes in Pseudomonas. More precisely, we compare directly the gene-based phylogeny of the 120 Pseudomonas strain available in KEGG with the dendrogram resulting from applying the complete-linkage hierarchical clustering to the kernels similarity matrices. The best result is shown in In order to analyse the usefulness of AMNs at a lower rank of taxonomy, we considered the bacteria in the genus P. aeruginosa (turquoise lines) is relatively well-preserved in the complete linkage dendrogram, and the same is for clusters comprising respectively P. stutzeri (yellow lines) and P. putida (red lines next to the yellow ones).Looking at The VH, SP and PM kernels results can be found in the Supporting Information, see In this section, we discuss the results of the various experiments reported in the previous section from a biological and evolutionary viewpoint. We are aware that our analyses could be biased due to the use of KEGG. For instance, KEGG annotation and pathways reconstruction, and also how some pathways are included may be artefactual because of how KEGG assign genes/reactions to pathways. Nonetheless, the general agreement of all graph kernels on the results of the performed experiments suggests that the signal coming out of the metabolisms’ comparison is robust. In addition, we repeated the analyses after removing the pathways harbouring only one  or at maximum two reactions , see Graph kernels take as input two graphs and provide as output a real number measuring their similarity. They do not allow recovering the nodes and edges of the input graphs that are responsible for the similarity result. As such, the nature of the analyses does not allow us to identify the specific pathways within the AMNs that drive the clustering we observe. Nonetheless, it is still possible to link the clustering to macroevolutionary events. In this respect, as discussed below, it turns out that the clustering pattern is meaningful from the biological and evolutionary viewpoints, thus suggesting that both the AMN representation and the applied computational techniques are appropriate for comparing and analyzing the organisms’ metabolism. We proceed now by discussing the results obtained in every experiment reported in the previous section.The clustering results obtained in the whole dataset experiment, see In this experiment, we first considered all the Eukaryotes together and then each kingdom separately. In the first case, we tested whether AMNs, compared through the VH, SP, WL and PM graph kernels, allow for discerning the various kingdoms: Animals, Plants (more precisely Protophotophytes), Fungi and Protists. As shown in Protophotophytes can be either multicellular  or both  . The difThe fact that some Protists are sometimes clustered with Protophotophytes can be retraced to secondary endosymbiotic events in which unicellular non-photosynthetic eukaryotes engulfed photosynthetic eukaryotes  that later evolved into plastids. These plastids conferred onto the Protists that acquired them some metabolic pathways characteristic of photosynthetic organisms. The acquisition of these pathways sets these protists closer to Protophotophytes in terms of metabolic capabilities and network structure.Animal kingdom. Focusing on Animals, we observe that the results shown in Test performed on Vertebrates only,  where the microorganisms are mesophilic and live in similar environments, it was possible to observe a clustering pattern similar, although not identical, to their evolution. . These sequences represent a subset of the entire genomes. This subset also presents certain characteristics  that can be different from the entire genomes or at least from other subsets of genes. This data allows for a fine reconstruction of the taxonomic and evolutionary relationships between the different organisms at a very detailed level . On the other hand, the AMNs use as starting data the pathways present in the organisms and their connections. Such data, as shown in the Results section, allows to observe patterns corresponding to macroevolutionary events but is not suitable to analyse finer evolutionary and taxonomic scales. Hence, as expected, we observe more differences rather than similarities. However, we consider that it is interesting to focus on the similarities observed, since it suggests that the organismal evolution that we observe through phylogenetic studies correlates with the organization and structure of the AMNs.abstract metabolic networks, a simplified representation of metabolism. Our goal was to investigate the extent to which AMNs help discern the different taxonomic groups and capture evolutionary steps. We considered the metabolism of 7141 different species stored in the KEGG database and performed a large-scale comparison of their AMNs using graph kernels. Specifically, we employed the Vertex Histogram, Shortest Path, Weisfeiler-Lehman and the Pyramid Match graph kernels. We performed various experiments, first by considering the whole set of selected species, then by considering all the Eukaryotes and, within Eukaryotes, their kingdoms separately, and finally focusing on Prokaryotes. The comparison results were investigated through exploratory data analysis as well as machine learning techniques.In this paper, we compared the metabolism of a comprehensive set of organisms to explore the potentials of the so-called Looking at the experiments, we observe that all the results turned out to be biologically and evolutionary meaningful. Moreover, although performing differently, all the considered kernels reported a similar clustering pattern at higher taxonomic level, thus suggesting that such patterns are clear and robust. This allows us to state that AMNs, although being a straightforward representation of the metabolism, are able to reflect key evolutionary processes within the metabolism. However, it is also clear that, in general, they fail to capture fine-grain differences between species due to the lack of information on the reactions within each metabolic pathway.Our abstraction of the metabolic pathway removes detailed information. This, in turn, makes it easier to cluster together organisms that, with a more detailed network, would have been segregated. Most organisms, in fact, are able to catabolise glucose and synthesise ribose and nucleotides. Nonetheless, the metabolic reactions that form these processes may differ from organism to organism. It would have been possible to differentiate between the organisms by comparing them through a detailed metabolic network representation, reporting the information on the reactions within each metabolic pathway. With less information reported on each pathway (higher level of abstraction), it is more challenging to differentiate organisms, even at higher taxonomic levels. It is surprising that with the highest level of abstraction (i.e. presence of the pathway and its link to other pathways), it is still possible to differentiate taxa up to the phylum and sometimes the class level. This suggests that evolution did not only impact the metabolism at the fine level (i.e. reaction) but also at a higher level that affected the pathways structure and their interaction with other pathways.The effect of evolution on the higher levels of metabolic network organisation is only observable when the genetic exchange and, by extension, the exchange of functions is minimal. In the case of prokaryotes, where HGT is frequent, it is impossible to observe a relation between evolution and higher levels of metabolic network organisation. This is mainly due to the contribution of the environment in shaping these networks.We conclude by drawing some considerations about the graph kernels explored in this study. Although all of them were effective in this setting, we try to highlight their similarities and differences. The VH graph kernel performed better at a high level of taxonomy, that is, with very distant evolutionary groups. Moreover, both the SP and PM graph kernels, although being conceptually different, consider the graph’s connectivity in the comparison and often obtained similar results. They performed better than VH when descending to lower levels of taxonomy, where the presence or absence of pathways is not enough to discriminate the different taxonomic groups. Finally, the WL graph kernel obtained better results at a lower taxonomy level. It captures the local similarities of the graphs under consideration by comparing their subtrees. This detailed comparison may add noise when evolutionarily distant species are compared, while it may reveal helpful information when comparing neighbouring species.core and pan metabolism, and by extension, core and pan genomes, of a set of species interesting and relevant if applied in the context of AMNs, i.e. at the level of topological organisation of metabolic networks? They correspond to finding the common subgraph (pathways and their connections) of all the considered species (core AMNs metabolism) and finding the overall graph that considers all pathways and connections of all the considered species (pan AMNs metabolism). A further question is: Is it possible to explore the pathways dependencies of two or more symbiotic species? Being able to study the interplay of their metabolisms in terms of the topological organisation of pathways would foster the comprehension of their mutual help and relationship.This exploratory investigation on the expressiveness of AMNs gives rise to new research questions that we would like to address for future work. The first question is: How to use AMNs to highlight the relevant topological similarities and differences among the metabolism of different species? Graph kernels allow for a fast and simple comparison of AMNs, but they do not provide insights into the nodes and edges that gave rise to the similarity results. A related question is: Are the concepts of To answer these questions graph kernels are not suitable, so a different comparison technique is needed. Since AMNs are graphs, any technique in the graph theory realm can be put into action. An alternative proposal that we would like to pursue is to use a graph database (see e.g. ) to storS1 FileList of the considered KEGG organisms.(PDF)Click here for additional data file.S2 FileList of the KEGG metabolic categories and corresponding pathways.(PDF)Click here for additional data file.S3 FileMathematical formalization of the graph kernels used in the paper.(PDF)Click here for additional data file.S4 FileComplete set of analyses considering the whole set of KEGG organisms (first experiment).(PDF)Click here for additional data file.S5 FileEukaryotes analyses at kingdom level (second experiment).(PDF)Click here for additional data file.S6 FileAnimals analyses at phylum level (second experiment).(PDF)Click here for additional data file.S7 FileVertebrates analyses at class level (second experiment).(PDF)Click here for additional data file.S8 FileProtophotophytes analyses at phylum level (second experiment).(PDF)Click here for additional data file.S9 FileFungi analyses at phylum level (second experiment).(PDF)Click here for additional data file.S10 FileProtists analyses at phylum level (second experiment).(PDF)Click here for additional data file.S11 FileBacteria analyses at phylum level (third experiment). Only the phyla Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria and Tenericutes present in the list (PDF)Click here for additional data file.S12 FilePseudomonas analyses at species level (third experiment). Comparison between the Pseudomonas phylogenetic tree and the dendrograms resulting by using the VH, SP, WL and PM graph kernels.(PDF)Click here for additional data file.S13 FileAnalyses of the considered experiments after removing the pathways harbouring only one reaction (threshold 1) or at most two reactions (threshold 2). The results can be directly compared with the ones presented in the Results section.(PDF)Click here for additional data file.


This study aims to find the novel lncRNAs closely related to the progression of oral squamous cell carcinoma (OSCC) by comprehensively analyzing microarray.  Chip dataset GSE84805 was downloaded from the Gene Expression Omnibus (GEO) database, lncRNA expression profiles of OSCC and paracancerous tissue were obtained, probes sequences reannotation was conducted, and differentially expressed lncRNAs (DELs) and differentially expressed genes (DEGs) were identified. Finally, these data were analyzed by constructing the lncRNA-function enrichment network.  We found that 465 lncRNAs are differentially expressed consisting of 193 upregulated lncRNAs and 272 downregulated lncRNAs. Meanwhile, 811 DEGs were identified with 498 upregulated genes and 313 downregulated genes. Analysis of the lncRNA-function enrichment network showed that these aberrant lncRNAs may be related to focal adhesion, inflammatory response pathway, cell cycle, matrix metalloproteinases, and other biological functions. Also, we found that some key lncRNAs such as LINC00152 and HOXA11-AS have been shown to play an important role in tumor proliferation and migration.  The key lncRNAs may serve as potential molecular markers or therapeutic targets in OSCC formation and development. It can also help us to understand the molecular mechanism of occurrence and development in OSCC. Oral squamous cell carcinoma (OSCC), the 11th most prevalent cancer worldwide, accounts for more than 550,000 cases annually worldwide and is currently one of the leading causes of cancer-related death . TreatmeGene expression profiling provides a tool to search for biomarkers and explore the mechanism of development of OSCC , 10. PotThough a few lncRNAs have been functionally characterized, there are still a large number of functional transcripts which may have been unintentionally overlooked. In our study, we compare the expression of lncRNAs in paired tumor with normal tissues of OSCC based on the dataset GSE84805 deposited in the GEO (Gene Ontology Omnibus). We find some differentially expressed long noncoding RNAs (DELs) probably act as candidate biomarkers in OSCC. After lncRNA-function enrichment analysis, we point out the potential regulatory functions of lncRNAs. Our study provides new insights into the molecular discovery of OSCC that helps us understand the roles of lncRNAs in OSCC and further reveals the molecular mechanism of the development and progression of OSCC.The lncRNA and mRNA expression profiles of 6 OSCCs and 6 matched adjacent noncancerous tissues, deposited by Li and colleagues (accession number: GSE84805), were downloaded from GEO. The lncRNA and mRNA expression were detected by using an Arraystar human lncRNA microarray V3 . The raw txt data are downloaded, and the Quantile method is used to normalize the data.The probes sequences provided by the GEO platform (GPL16956) are aligned to long noncoding transcript sequences from the LNCipedia 4.0 database  and to thttp://linus.nci.nih.gov/BRB-ArrayTools.html) for preprocessing. In total, 32835 lnRNAs and 26109 genes are used for further analysis. We identify DELs and DEGs using a paired T-test with a nominal significance level for the local false discovery rate [Raw signal intensity data are imported into BRB-ArrayTools version 4.5 , lnc-C7orf11-3 (pmHG=10−8.63), and lnc-MRPL39-2 (pmHG=10−8.27). There were 14 lncRNAs correlated along the inflammatory response pathway, out of which 79% (11/14) were upregulated and 21% (3/14) downregulated, and the most relevant lncRNAs were lnc-TMEM185B-2 (10−4.97), lnc-FAM8A1-2 (pmHG=10−4.73), and lnc-C18orf26-2 (pmHG=10−4.56). Here, 11 lncRNAs were related with cell cycle pathways, with 4 upregulated and 7 downregulated. Among that, lnc-CHAF1B-2 (pmHG=10−5.94), lnc-MED11-1 (pmHG=10−5.95), and lnc-TRIM29-2 (pmHG=10−4.56) were most relevant. Besides, there were upregulated lnc-C12orf39-1 (pmHG=10−4.24), downregulated lnc-KLHL31-2 (pmHG=10−4.144), and lnc-AC136428.1.1–3 (pmHG=10−4.90) correlated to the matrix metalloproteinases.To exploring the critical biological roles of the lncRNAs in OSCC tumorigenesis and development, we integrated the lncRNAs and mRNA expression profiling data by using the power of enrichment statistics and knowledge of genomic annotation databases to assign relevant function annotations to gain better understating of the relationship between DELs and DEGs levels in the same sample. The lncRNA-pathways enrichment network showed that these DELs significantly enriched in some cancer-related pathways including focal adhesion, inflammatory response pathway, cell cycle, matrix metalloproteinases, and other biological functions . InterespmHG=10−16.52), lnc-RP11-152F13.5.1–4 (pmHG=10−16.35), lnc-C18orf26-2 (pmHG=10−15.94), lnc-FCRL3-1 (pmHG=10−15.83), and lnc-SEC11A-6 (pmHG=10−15.83) were the most relative lncRNAs. Besides, molecular function analysis showed the most significant functional groups consisted of the lncRNAs involved in platelet-derived growth factor binding, metallopeptidase activity, and metalloendopeptidase activity . Biological process analysis showed that 94% (436/465) DELs were enriched in the extracellular matrix organization and the extracellular structure organization . Among tactivity .Previous studies of gene expression profiling have focused on the exploration of the functions of mRNAs, while we turn our attention to the long noncoding RNA which is no longer regarded as transcriptional noise and has recently been regarded as valuable transcripts in tumor progression and metastasis. For example, HOTAIR has been proven as a prognostic biomarker and therapeutic target in diverse human cancers, which promotes tumorigenesis and progression by the influence on reprogramming chromatin organization, histone demethylase, activating the promoter, and combination with microRNA to regulate downstream cancer-related molecules –27. WithIn our study, we identified 193 upregulated and 272 downregulated lncRNAs in patients with OSCC. Among those lncRNAs, FEZF1-AS1 , LUCAT1 In addition, we successfully constructed lncRNA-function enrichment network to analyze the functions of lncRNAs. Our analysis results show that DELs are mainly enriched in some pathways closely related to tumor progression, including focal adhesion, inflammatory response pathway, cell cycle, matrix metalloproteinases, and so on. Also, the adhesion pathway  turns ouIn summary, some of lncRNAs associated with adhesion and cell cycle by network analysis have been supported in studies which proved that the methods of network-based function analysis by using lncRNA expression data is effective for understanding biomarker prediction and molecular mechanisms of OSCC. It may provide a new vision on therapeutic targets in OSCC by exploring the underlying mechanisms. Our research also has some limitations. First, we do not generate the microarray data by ourselves but took them from the GEO database. Second, there are few research studies on lncRNAs in OSCC, therefore lacking the verifications of other data sets or samples in our studies. Besides, we need to validate our results based on the experimental functional study.In conclusion, this study demonstrated the effectiveness of network-based function analysis for understanding lncRNA biomarker prediction and molecular mechanisms of OSCC. These novel molecules for further investigation in development and progression may provide targets for future therapies in OSCC.