Osteonecrosis of the femoral head (ONFH) is still a challenge for orthopedists worldwide and can lead to disability if patients are not treated effectively. Danyu Gukang Pill (DGP), a traditional Chinese medicine (TCM) formulation, is recognized to be effective against ONFH. Nevertheless, its molecular mechanisms remain to be clarified.The active ingredients of DGP were collected from the online databases according to oral bioavailability (OB) and drug-likeness (DL). The potential targets of DGP were retrieved from the TCMSP database, while the potential targets of ONFH were obtained from the GeneCards and NCBI databases. The functions and signaling pathways of the common targets of DGP and ONFH were enriched by GO and KEGG analyses. Subsequently, molecular docking and in vitro cell experiments were performed to further validate our findings.In total, 244 active ingredients of DGP and their corresponding 317 targets were obtained, and 40 ONFH-related targets were predicted. Afterwards, 19 common targets of DGP and ONFH were obtained and used as potential targets for the treatment of ONFH. Finally, combined with network pharmacology analysis, molecular docking and in vitro cell experiments, our study first demonstrated that the treatment effect of DGP on ONFH might be closely related to the two targets, HIF1A (HIF-1α) and VEGFA, and the HIF-1 signaling pathway.This study is the first to investigate the molecular mechanisms of DGP in the treatment of ONFH based on network pharmacology. The results showed that DGP might up-regulate the expression of HIF-1α and VEGFA by participating in the HIF-1 signaling pathway, thus playing an anti-ONFH role.The online version contains supplementary material available at 10.1186/s12906-023-03843-x. Osteonecrosis of the femoral head (ONFH) is a common progressive disease characterized by reduced blood supply to the femoral head, bone metabolic disorders, necrosis of the subchondral bone, impaired microcirculation, interrupted bone remodeling processes, and eventually resulting in femoral head collapse , 2. In CPanax notoginseng (Burk.) F. H. Chen (San Qi), Spatholobus suberectus Dunn (Ji Xue Teng), Achyranthes bidentata Bl. (Niu Xi), Dipsacus asper Wall (Xu Duan), Drynaria fortunei (Kunze) J. Sm. (Gu Sui Bu), Lycium barbarum L. (Gou Qi Zi), Salvia miltiorrhiza Bge. (Dan Shen), Angelica sinensis (Oliv.) Diels (Dang Gui), Ligusticum chuanxiong Hort. (Chuan Xiong), Acanthopanax gracilistylus W. W. Smith (Wu Jia Pi), Rehmannia glutinosa Libosch. (Shu Di Huang), Codonopsis tangshen Oliv. (Dang Shen), Atractylodes macrocephala Koidz. (Bai Zhu), Boswellia carterii Birdw. (Ru Xiang), Commiphora myrrha (Nees) Engl. (Mo Yao), Corydalis yanhusuo W. T. Wang (Yan Hu Suo), Rheum officinale Baill. (Da Huang), Curcuma wenyujin Y. H. Chen (Yu Jin), and Aucklandia lappa Decne. (Mu Xiang). Previous clinical trials have shown that DGP is a good medicine for ONFH, and the X-ray imaging and efficacy, such as the improvement in analgesia and claudication, in the DGP group are superior to those in the control group [Danyu Gukang Pill (DGP) has the effects of promoting blood circulation, removing blood stasis, dredging meridians, relieving pain, tonifying kidney, and strengthening bones, and is mainly used to treat ONFH. DGP is composed of 19 traditional Chinese medicines, including ol group . In addiol group . HoweverNetwork pharmacology is a new approach that combines systems biology, pharmacokinetics and pharmacodynamics, and can systematically reveal the biological mechanism of action of drugs in complex diseases at the molecular level –14. ThisIn the current study, we used the approaches of network pharmacology and molecular docking to investigate the potential molecular mechanisms of DGP against ONFH. Additionally, human bone marrow mesenchymal stem cells (BMSCs) play a key role in maintaining the structural and functional integrity of the femoral head to prevent ONFH due to their proliferative capacity and osteogenic differentiation. Therefore, in this study, BMSCs were used for in vitro experiments to verify the potential mechanism of DGP against ONFH. The detailed technical strategy of this study is shown in Fig. https://old.tcmsp-e.com/index.php) and Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine  databases based on the screening criteria of oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18.All ingredients of 19 Chinese herbs in DGP were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform  was used to standardize the targets of each active ingredient.We input all active ingredients into the TCMSP database to predict the potential targets of DGP and set the species as “Homo sapiens”. Meanwhile, the UniProt database (https://www.ncbi.nlm.nih.gov/gene/) and GeneCards (https://www.genecards.org) databases. We searched for targets from these two databases using the keyword “osteonecrosis of the femoral head”, and then obtained the common targets in the NCBI and GeneCards databases as ONFH-related targets.The targets associated with ONFH were collected from the NCBI (https://cn.string-db.org/) for the construction of PPI network, and then this network was visualized using Cytoscape software.The online analysis tool Venny 2.1 was used to obtain potential targets of DGP for the treatment of ONFH. The DGP-ONFH common target list was entered into the STRING database (http://www.bioinformatics.com.cn/).Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis were performed using the online analysis tools of the bioinformatics platform . The crystal structure of target proteins was obtained from the RCSB Protein Data Bank . Subsequently, molecular docking of ligands with receptors was conducted by AutoDock Vina software. At last, the results of molecular docking were analyzed using Discovery Studio 2016 Client and PyMOL.The 3D molecular structure of active ingredients was downloaded from the PubChem database (DGP (0.2 g) was dissolved in 40 mL double-distilled water to prepare a 5 mg/mL stock solution, which was then diluted to the required concentration for reserve.2.BMSCs were bought from Procell Life Science&Technology Co., Ltd , and were cultured in the Minimum Essential Medium (MEM)-alpha medium with 10% Foetal Bovine Serum (FBS), 100 U/mL penicillin, and 100 mg/mL streptomycin. Afterwards, cells were kept at 37 °C with 5% CO3, and then cells were inoculated into 96-well plates. After cells were fully attached, they were then treated with different concentrations of DGP aqueous solution  for 12, 24, 36, and 48 h. Afterwards, CCK-8 (5 mg/mL) was added to each well and the cells were incubated at 37 °C for 4 h. We then discarded the supernatant and added dimethyl sulfoxide (DMSO). Optical density (OD) at 570 nm was measured using a microplate reader. The experiments were repeated three times independently.Cell viability was determined by Cell Counting Kit-8 (CCK-8) assay. The cell density in the control and DGP groups was adjusted to 5 × 10−ΔΔCt equation.Total RNA was extracted from cells using TRIzol reagent  according to the manufacturer’s instructions. The One Step PrimeScript miRNA cDNA Synthesis Kit  was used to synthesize the cDNA of genes. The qRT-PCR was performed using the ABI 7500 Sequence Detection System . The mRNA expression of HIF-1α and VEGFA was normalized using GAPDH as an internal control. The primer sequences for PCR were: HIF-1α: F—5’-GCCTCTGTGATGAGGCTTACC-3’, R—5’-CAGTGCAATACCTTCCATGTTGC-3’; VEGFA: F—5’-ACTGCCATCCAATCGAGACC-3’, R—5’-TTGATCCGCATAATCTGCATGGT-3’; and GAPDH: F—5’-GAGTCAACGGATTTGGTCGT-3’, R—5’-GACAAGCTTCCCGTTCTCAG-3’. The results were calculated using the 2p < 0.05 for all statistical tests.Data are presented as mean ± standard deviation (SD). SPSS version 20.0 was used to analyze statistical differences. The significance level was In total, 2,350 ingredients in DGP were retrieved from the TCMSP and BATMAN-TCM databases, while 371 were selected based on the screening criteria (OB ≥ 30% and DL ≥ 0.18) . The top 10 GO terms for each category are shown in the bar chart . According to relevant literature, 36 signaling pathways related to OFFH were finally obtained. The first 20 important pathways are shown in Fig. The common targets were enriched in 1,839 GO terms, including 1,701 for biological process (BP) terms, 37 for cellular component (CC) terms, and 101 for molecular function (MF) terms , and its details are shown in Fig. In order to further clarify the molecular mechanisms of DGP in the treatment of ONFH, we constructed a target-pathway network. As shown in Fig. To investigate the reliability of molecule-target interactions and accurate binding patterns, we chose quercetin as a crucial molecule and HIF-1α and VEGFA as key targets according to the above results. The lower the binding energy, the more stable the ligand-receptor binding conformation. Binding energy below or equal to -5 kcal/mol represented a good binding ability between molecules and proteins. Our results showed that quercetin had strong interactions with HIF-1α and VEGFA with binding energies of -7.2 kcal/mol and -7.4 kcal/mol, respectively Fig. . This fuThe viability of BMSCs increased significantly after 24 h of DGP treatment Fig. a. The reThe qRT-PCR assay demonstrated that treatment with 0.1 mg/mL and 0.25 mg/mL DGP could increase the mRNA expression of HIF-1α Fig. a and VEGONFH is a refractory disease characterized by damaged subchondral microcirculation, skeletal necrosis, and microfracture accumulation without continuous remodeling . The patIt is believed that ingredients lacking appropriate pharmacokinetic properties cannot arrive to the target organ and then transmit biological activity. Thus, we screened active ingredients of DGP based on OB ≥ 30% and DL ≥ 0.18 . ImportaAs predicted, DGP might exert its pharmacological effects on the treatment of ONFH by participating in the HIF-1 signaling pathway and targeting HIF-1α and VEGFA. Studies have shown that the functional HIF-1 is composed of two subunits, HIF-1α and HIF-1β, of which HIF-1α is a pivotal regulator of the adaptive response of cells to hypoxia , 29. It In conclusion, on the basis of the network pharmacological analysis, we further verified our results by molecular docking and in vitro cell experiments. Molecular docking results showed that the binding energies between the effective chemical active ingredient of DGP (quercetin) and key target proteins (HIF-1α and VEGFA) were less than -5 kcal/mol, suggesting that stable binding of quercetin to HIF-1α and VEGFA might be critical in the treatment of ONFH. In vitro cell experiments revealed that DGP could up-regulate the expression of HIF-1α and VEGFA in the HIF-1 signaling pathway, indicating that DGP might treat ONFH by regulating the expression of HIF-1α and VEGFA in the HIF-1 signaling pathway. However, the present study still has some limitations, and the in-depth study of the underlying molecular mechanisms needs to be further explored and verified by in vivo experiments.In this study, the potential targets and pathways related to DGP in the treatment of ONFH were systematically analyzed, and the results showed that DGP might regulate the expression of HIF-1α and VEGFA in the HIF-1 signaling pathway, thus playing a crucial role in the treatment of ONFH.Additional file 1.


Saprolegnia parasitica, leads to considerable economic losses. Recently, we showed that metalaxyl, bronopol and copper sulfate are good antimicrobial agents for aquaculture. In the current study, the efficacies of metalaxyl, bronopol and copper sulfate are evaluated by in vitro antimicrobial experiments, and the mechanism of action of these three antimicrobials on S. parasitica is explored using transcriptome technology. Finally, the potential target genes of antimicrobials on S. parasitica are identified by protein–protein interaction network analysis. Copper sulfate had the best inhibitory effect on S. parasitica, followed by bronopol. A total of 1771, 723 and 2118 DEGs upregulated and 1416, 319 and 2161 DEGs downregulated S. parasitica after three drug treatments , separately. Additionally, KEGG pathway analysis also determined that there were 17, 19 and 13 significantly enriched metabolic pathways. PPI network analysis screened out three important proteins, and their corresponding genes were SPRG_08456, SPRG_03679 and SPRG_10775. Our results indicate that three antimicrobials inhibit S. parasitica growth by affecting multiple biological functions, including protein synthesis, oxidative stress, lipid metabolism and energy metabolism. Additionally, the screened key genes can be used as potential target genes of chemical antimicrobial drugs for S. parasitica.Saprolegniasis, which is caused by  Saprolegnia is responsible for devastating infections in fish in aquaculture, fish farms and hobby fish tanks [Saprolegnia genus is characterized by white mycelial spots on the skin, gills and fins of the host. The mycelium produces and releases motile zoospores, which can germinate when attached to a new host after development and then form new mycelial mats [Saprolegnia parasitica is, economically, one of the most important fish pathogens [S. parasitica infections were effectively controlled using malachite green. However, malachite green was banned worldwide in 2002 due to its carcinogenic and toxic effects [sh tanks . The infial mats ,3. Saproathogens . Outbreaathogens . In the  effects . To datePhytophthora nicotianae, Phytophthora infestans and Phytophthora cinnamomi in other plants [S. parasitica.Metalaxyl is the world’s most widely used acetylalanine fungicide, which inhibits the synthesis of ribosomal RNA in mycelium . Severalr plants ,8,9. MetBronopol  is an alcohol produced by the bromination of di(hydroxymethyl)nitromethane . In the 4·7H2O and refined trichlorfon powder had a remarkable killing effect on two parasites (Dactylogyrus and Chilodonella cyprini). In addition, copper sulfate is effective in controlling the mycelium growth of largemouth bass eggs (caused by Sapproplegnia spp.) and improving fry survival in mat spawning [Copper sulfate is a salt with high solubility in water. It can be used as an antifungal fungicide, as well as for the prevention and control of animal foot rot disease and the removal of pond algae . The comspawning .S. parasitica. Metalaxyl and copper sulfate have been shown in some reports to be effective in inhibiting the growth of S. parasitica [S. parasitica growth by metalaxyl and copper sulfate is unclear and it is necessary to study it further. No reports have been discovered that highlight that bronopol can inhibit the growth of S. parasitica, and further studies are required to prove this. In the current study, the efficacies of metalaxyl, bronopol and copper sulfate are evaluated by an in vitro inhibiting test, and the mechanism of action of these three antimicrobials on S. parasitica is explored using transcriptome technology. This provides a theoretical basis for the subsequent screening of effective drugs against S. parasitica.It is evident from the information above that metalaxyl, bronopol and copper sulfate can be used as alternatives to malachite green for saprolegniasis caused by rasitica ,19. HoweS. parasitica (ATCC 200013) was obtained from American Type Culture Collection  and cultured in potato dextrose agar .The metalaxyl, bronopol and copper sulfate used in this study were purchased from Guoyao Chemical Reagent . To determine the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of the three antimicrobials, different concentrations of the concerned antimicrobials  were prepared by dissolving their requisite amount in water. MIC and MBC were also determined by using the microdilution method and the results were also expressed in mg/L.S. parasitica cultures were prepared in potato dextrose broth to an initial optical density at 600 nm (OD600) of 0.05 and then incubated in a 250 mL cell culture flask  at 20 °C for 4 days with shaking at 120 rpm. The cultures were then split into three flask containing 2 mL of each treatment at a final concentration of three antimicrobials: metalaxyl (5.0 mg/L), bronopol (4.0 mg/L) and copper sulfate (2 mg/L). A fifth flask containing 2 mL of fresh liquid potato dextrose agar remained untreated. Cultures were grown for an additional 30 min before lysis reagent . Total RNA was isolated using TRIzol reagent  according to the manufacturer’s instructions. Experiments were conducted in triplicate and samples were removed from DNA contaminants following treatment with RNasefree DNase I . RNA integrity was confirmed using an Agilent 2100 Bioanalyzer  with all samples having RNA integrity > 9.0.Poly-(A) mRNA was enriched from total RNA using Oligo-(dT) magnetic beads, then fragmented using a fragmentation buffer. Cleaved RNA fragments were copied into first-strand complementary DNA (cDNA) with reverse transcriptase and random hexamer primers, followed by second-strand cDNA synthesis using DNA polymerase I and RNase H. The cDNA fragments underwent an end-repair process and poly(A) was added and then ligated with the Illumina paired-end sequencing adaptors. The products were purified and enriched with a polymerase chain reaction (PCR) to create the final cDNA sequencing library. Following validation on an Agilent 2100 Bioanalyzer and ABI StepOnePlus RealTime PCR System , the cDNA library was sequenced on a flow cell using high-throughput 101-bp paired-end mode on an Illumina HiSeq 2500  unit.The RNA-Seq read quality was assessed using FASTQC (version 0.11.5) and trimmed using Trimmomatic (version 0.36) with default parameters and trimmed of adaptor sequences (TruSeq3 paired-ended). Subsequently, we conducted a comparative analysis with the reference genome (ASM15154v2). For each sample, sequence alignment with the reference genome sequences was performed using Tophat .https://pypi.python.org/pypi/HTSeq (accessed on 11 April 2022)) was used to count the read numbers mapped to each gene [p < 0.05.HTSeq (version 0.6.1) (ach gene . The FPKhttps://github.com/tanghaibao/Goatools (accessed on 15 April 2022)) of the differentially expressed genes was performed, and their function was described (in combination with the GO annotation results). KEGG analysis was performed using the KEGG pathway database (http://kobas.cbi.pku.edu.cn/home.do (accessed on 15 April 2022)).GO-enrichment analysis ) was used to analyze the protein–protein interaction network of the genes of interest. After the protein–protein interaction network relationship was obtained, the NetworkX library in Python was used to visualize the network of interested genes.Protein–protein interaction (PPI) was constructed based on the corresponding protein information of genes to obtain information on the known and anticipated gene interactions ,24. STRIS.parasitica Tub-b) was used as the reference gene [To validate the RNA-seq data, 10 genes , 12.5 μL SYBR Premix Ex Taq (2×) and 9.5 μL RNase-free Hiplicate .S. parasitica by minimum inhibitory and minimum bactericidal concentration  assays  assays . We obseA total of 12 cDNA libraries were constructed and sequenced. The RNA sequencing generated a total of 639,148,432 raw reads  and 278,935,201 clean reads  were obtained after removing TruSeq adaptors and low-quality reads. The Phred-like quality scores of the clean reads at the Q30 level ranged from 93.71–96.96% and the average GC content was estimated as 61.71% . Three groups of data were compared: control vs. metalaxyl, control vs. bronopol and control vs. copper sulfate groups. In total, 8508 DEGs were identified upon pairwise comparisons: 3187 DEGs (1771 up- and 1416 downregulated) in the control vs. metalaxyl group, 1042 DEGs (723 up- and 319 downregulated) in the control vs. bronopol group and 4279 DEGs (2118 up- and 2161 downregulated) in the control vs. copper sulfate group , carbohydrate metabolism (including 13 pathways) and lipid metabolism (including 13 pathways) contained more DEGs. A number of 167 DEGs in the control vs. bronopol group were assigned to 75 KEGG pathways. Lipid metabolism (including 13 pathways), amino acid metabolism (including 12 pathways) and carbohydrate metabolism (including 12 pathways) contained more DEGs. Finally, a certain number of the 673 DEGs in the control vs. copper sulfate group were assigned to 106 KEGG pathways. The DEGs were significantly enriched in amino acid metabolism (including 14 pathways), carbohydrate metabolism (including 14 pathways) and lipid metabolism (including 14 pathways). These annotations are useful to identify functional genes and specific biological processes in S. parasitica. Further information is presented in The DEGs were characterized using the GO and KEGG databases. In the control vs. metalaxyl group, 15 terms for biological processes, 10 terms for cellular components and 11 terms for molecular functions were annotated ; glutathione metabolism (map00480); valine, leucine and isoleucine biosynthesis (map00290); phenylalanine, tyrosine and tryptophan biosynthesis (map00400) and histidine metabolism (map00340) pathways presented the most significant differences ; propanoate metabolism (map00640) and fatty acid degradation (map00071) pathways presented the most significant differences . In threFor the control vs. copper sulfate group, 14 pathways were identified as significantly enriched pathways. Among these 14 pathways, 13 pathways were metabolic pathway and 1 pathway was genetic information processing. These 13 metabolic pathways were, respectively, eight amino acid metabolic pathways; three lipid metabolic pathways; one energy metabolic pathway and one cofactors and vitamins metabolic pathway, among which arginine biosynthesis (map00220) and glutathione metabolism (map00480) pathways presented the most significant differences . In fourS. parasitica mainly by disrupting the amino acid metabolism. The results provide a direction for further analysis of the mechanism of action of the three drugs.The results above show that the pathways significantly enriched in control vs. metalaxyl, control vs. bronopol, and control vs. copper sulfate groups were all metabolic pathways, among which the amino acid metabolic pathway was the most significant and contained the most pathways, suggesting that the three antimicrobial agents could inhibit p < 0.05). The main protein interaction cluster derived from the 314 DEGs contained 63 nodes, each representing 1 protein and connected by 100 edges  and four downregulated  genes . To compS. parasitica, and the results show that the three antimicrobial agents can effectively inhibit the growth of S. parasitica. Subsequently, transcriptomics were used to study the core genes and metabolic pathways of the drug-acting S. parasitica, providing important information for an in-depth study of the molecular mechanism of drug-treated S. parasitica. At the same time, three key genes were screened out, which could be used as potential target genes of chemical antimicrobial drugs against S. parasitica.Saprolegniasis causes a considerable economic loss to aquaculture. However, there are few effective drugs to control saprolegniasis. Metalaxyl, bronopol and copper sulfate are widely used in agriculture as antiparasitic, antibacterial and antifungal agents. Therefore, we studied the effect of these three drugs against S. parasitica. At the same time, in vitro antimicrobial experiments also confirmed that copper sulfate has the strongest inhibition effect. These results suggested that copper sulfate had good control of S. parasitica and could be used as a drug additive to prevent S. parasitica infections in aquaculture.Transcriptome experiments showed that, compared to the other two drugs, after copper sulfate treatment, the genes and metabolic pathways were changed most in S. parasitica.Through the GO database annotation, it was observed that the DEGs of three groups  in the biological processes were mainly distributed in the cellular and metabolic processes. Additionally, the KEGG database annotation determined that DEGs in the three groups were mainly distributed in the metabolic pathways, similar to the results of biological process enrichment in the GO database. According to the above results, we mainly discuss the effects of the three drugs on the metabolic pathway of S. parasitica with metalaxyl, cysteine and methionine metabolism (map00270); glutathione metabolism (map00480); valine, leucine and isoleucine biosynthesis (map00290); phenylalanine, tyrosine and tryptophan biosynthesis (map00400) and histidine metabolism (map00340) pathways were observed to present the most significant differences , producing methionine sulfoxide (MetO) to improve oxidative damage [S. parasitica were downregulated by metalaxyl action, which can cause oxidative damage, and thus destroy cellular molecular functions. The disturbance of these pathways is mainly caused by the significant regulation of the genes enriched in them. Pyridoxal 5′-phosphate (PLP) is a versatile catalyst, acting as a coenzyme in a multitude of reactions. Pyridoxal-dependent enzymes are involved in cysteine, homocysteine, methionine and other metabolic processes. Aminotransferase functions similar to pyridoxal-phosphate-dependent enzymes and is involved in pyridoxal-phosphate-dependent enzymes and biological binding processes. On the basis of sequence similarity, these various enzymes can be grouped into classes I and II [Following the treatment of ferences . Map0027e damage ,29,30,31e damage . Glutathe damage . Histidiism map000 pathwayI and II . It has I and II ,37. SimiI and II . The dowI and II ,40,41. LI and II . PhenylaI and II . The degI and II . This suI and II . The dowI and II . The dowI and II . The dowS. parasitica treatment with bronopol; valine, leucine and isoleucine degradation (map00280); propanoate metabolism (map00640) and fatty acid degradation (map00071), the pathways presented the most significant differences  is an adenosylcobalamin-dependent enzyme that catalyzes the interconversion of (2R)-methylmalonyl-CoA to succinyl-CoA [S. parasitica led to the reduced uptake or mobilization of amino acids from intracellular protein stores, resulting in reduced TCA cycle production and energy [S. parasitica are affected by map00640 and map00071. Fatty acid degradation is an important metabolic pathway in organisms, and the growth of microorganisms requires fatty acids, but at the same time, some bacteria are toxic to other bacteria due to their varying sensitivity to fatty acid, so it has been proposed that fatty acid sterilization is a protective mechanism [S. parasitica. Simultaneously, the enriched genes in the pathway also confirmed this view. Acyl-CoA dehydrogenase (ACAD) can catalyze the β-oxidation of fatty acids and is the main source of ROS [S. parasitica. Enoyl-CoA hydratase is not only involved in the β-oxidation of fatty acids, but is also associated with lysine degradation, isoleucine degradation, valine degradation, tryptophan metabolism and β-alanine metabolism. As an enzyme related to fatty acid synthesis and degradation, it is an ideal target for screening antimicrobial drugs [S. parasitica.Following ferences . Map0028inyl-CoA . Methylminyl-CoA . The uprinyl-CoA . Throughd energy . The fatechanism . The wide of ROS . The upre of ROS ,54. Enoyal drugs . 3-hydroal drugs . TherefoS. parasitica treatment with copper sulfate, arginine biosynthesis (map00220); glutathione metabolism (map00480); valine, leucine and isoleucine biosynthesis (map00290) and phenylalanine, tyrosine and tryptophan biosynthesis (map00400) present the most significant differences (S. parasitica. Arginine biosynthesis (map00220) can be observed in bacteria, plants and fungi [S. parasitica was downregulated by copper sulfate treatment, and regulators in the energy supply were disturbed [S. parasitica, these amino acids were downregulated, blocking the synthesis of ATP, resulting in an ATP energy supply that was not smooth, and then led to cell death. The genes that were significantly enriched in pathway disorders were further analyzed. Glutamine synthetase (GS) is ubiquitous in all organisms and catalyzes the synthesis of glutamine from ammonia and glutamate using the energy released from the hydrolysis of ATP to ADP, and is also a key enzyme involved in nitrogen metabolism in organisms [S. parasitica and the obstruction of related metabolic processes, which is consistent with the analysis conducted previously. Glucose-6-phosphate dehydrogenase (G6PD) is the main rate-limiting enzyme of the oxidative pentose phosphate pathway [S. parasitica causes its energy supply to be inhibited as well as the synthesis of related amino acids, the life activities of cells and cell information transmission.Following ferences . The found fungi . Argininnd fungi . Map0048isturbed . Valine,isturbed ,61. Phenisturbed ,63. Thesrganisms . Nitrogerganisms . Amino arganisms . The dow pathway . G6PD pl pathway . G6PD heS. parasitica. The aldehyde dehydrogenase family (ALDH) that is regulated by SPRG_08456 plays a significant role in the enzymatic detoxification of endogenous and exogenous aldehydes and in the formation of important molecules in cellular processes. The ALDH superfamily is represented in all three taxonomic domains , suggesting a crucial role throughout evolutionary history. It has been demonstrated that members of this superfamily are used to metabolize aldehydes related to physiology and pathophysiology [S. parasitica. The function of the SPRG_03679 gene was described in detail in the discussion of the control vs. bronopol group. The upregulation of ACADs regulated by SPRG_03679 may cause an oxidative stress response and serious damage to cell structures, which may lead to the gradual apoptosis of S. parasitica. This conclusion may be one of the reasons why S. parasitica stopped growing and even died under the action of chemical drugs in the antimicrobial test. Therefore, SPRG_03679 can be used as a potential target gene for further study. Coenzyme A (CoA) transferase catalyzes the reversible transfer of CoA between an existing acyl-CoA thioester and a free carboxylic acid. CoA transferases are a superfamily of proteins central to the metabolism of acetyl-CoA and other CoA thioesters. Class I CoA transferases are mainly involved in fatty acid metabolism, including those that transfer 3-keto acid, short-chain fatty acids and (E)-glutamic acid. Acetyl-CoA and succinyl-CoA are usually CoA donors. Class II CoA transferases catalyze the exchange of acetyl and citrate (or limonyl) groups on the thiols of the prosthesis groups to ensure the continuity of the catalytic cycle. Class III CoA transferases include enzymes involved in the metabolism of oxalate, carnitine, bile acids, toluene and other aromatic compounds, as well as succinyl-CoA: citrate CoA-transferase 2 fixation is involved in the 3-hydroxypropionate cycle of CO2 fixation [S. parasitica [Through the analysis of protein–protein interaction networks (PPIs), we selected five key genes  involved in cellular and metabolic processes under the action of three antimicrobial agents. We came to understand their important roles in relation to metabolic pathways and their potential relationship with the mechanism of action of antimicrobial agents, whilst simultaneously discovering more ways to understand the way in which saprolegniasis is caused by ysiology . This abysiology . The abiysiology . Therefofixation . The uprrasitica . HoweverS. parasitica were evaluated, and copper sulfate possessed the best inhibitory effect, followed by bronopol. Through transcriptome experiments, it was observed that the three drugs inhibited the growth of S. parasitica, mainly by disrupting the amino acid metabolism, glutathione metabolism, arachidonic acid metabolism and glycerolipid metabolism pathways. The disorder of these pathways can affect multiple biological functions, including protein synthesis, oxidative stress, lipid metabolism and energy metabolism. The PPI network analysis revealed five important proteins networks corresponding to the genes SPRG_08456, SPRG_03679, SPRG_04185, SPRG_10775 and SPRG_05141, of which three key genes  were screened as potential target genes of antimicrobials on S. parasitica. This will provide a scientific basis for the screening of other drugs for the prevention and control of saprolegniasis in the future and the healthy breeding of fish.In the current study, the inhibitory effects of metalaxyl, bronopol and copper sulfate on


Gastric cancer has emerged as a key challenge in oncology research as a malignant tumour with advanced stage detection. Apart from surgical management, a pharmacotherapeutic approach to stomach cancer treatment is an appealing option to consider. Andrographolide has been shown to have anticancer and chemosensitizer properties in a variety of solid tumors, including stomach cancer but the exact molecular mechanism is skeptical. In this study, we identified and validated pharmacological mechanism involved in the treatment of GC with integrated approach of network pharmacology and molecular docking. The targets of andrographolide and GC were obtained from databases. The intersected targets between andrographolide and GC-related genes were used to construct protein–protein interaction (PPI) network. Furthermore, mechanism of action of the targets was predicted by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Finally, these results were validated by molecular docking experiments, mRNA and protein expression level. A total of 197 targets were obtained for andrographolide treating GC. Functional enrichment analysis revealed that the target genes were exerted promising therapeutic effects on GC by HIF-1 and PI3K-Akt signaling pathway. The possible mechanism of action is by inactivation of HIF-1 signaling pathway which is dependent on the inhibition of upstream PI3K-AKT pathway. The PPI network identified SRC, AKT1, TP53, STAT3, PIK3CA, MAPK1, MAPK3, VEGFA, JUN and HSP90AA1 as potential hub targets. In addition, these results were further validated with molecular docking experiments. Survival analysis indicated that the expression levels of the hub genes were significantly associated with the clinical prognosis of GC. This study provided a novel approach to reveal the therapeutic mechanisms of andrographolide on GC, making future clinical application of andrographolide in the treatment of GC. In India, it is fifth most prevalent cancer in males and sixth most prevalent cancer in females with second most common cause of cancer mortality2. In spite of enormous research the development of GC is still not very clear3. Management of GC is remains challenging due to its poor prognosis and restricted treatment options4. Chemotherapy resistance and adverse side effects of drugs limit its clinical applications. The severe side effects of currently prescribed drugs include neutropenia, chemotherapy-induced peripheral neuropathy (CIPN), stomatitis, mucositis, diarrhoea, nausea, and emesis5. Therefore, the practice of complementary and alternative medicine gained economic and sociological importance as they are cost-effective and less toxic6. These alternative medicinal dietary compounds are not harmful and extracted from plants. Most of these botanical compounds exert their effect on regulation of cancer-associated pathways to prevent tumorigenesis and as well as cancer progression7. Among the alternative medicinal compounds, Andrographolide get attention for the treatment of GC because of its anti-inflammatory, antiviral, immunomodulatory9 and anti-tumorigenic role in melanoma, leukemia, glioblastoma, breast, lung, esophageal, colorectal, bladder, pancreatic, and liver cancer19. Andrographolide is a traditional Chinese herbal medicine isolated from Andrographis paniculate20 and have the ability to circulate in the bloodstream21. Furthermore, previous studies have reported potential role of andrographolide in regulation of oxidative stress, apoptosis, necrosis, autophagy, inhibition of cell adhesion, proliferation, migration, invasion, and angiogenesis23. Various cancer-related and angiogenesis signaling pathways, such as PI3K/AKT/mTOR15 SRC/MAPKs/AP-124, TLR4/NF-κB/MMP-916, and VEGF/VEGFR2/AKT19 are also controlled by andrographolide. In recent, several studies reported that in human cancer cells andrographolide treatment promotes the rate of apoptosis and inhibit cell proliferation. Similarly, it is also evident for GC cells, apoptosis guided by tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) controlled by andrographolide12, but, in GC, the mechanistic pathways of anti-tumorigenic actions mediated by andrographolide are still unknown.Gastric cancer (GC) is the fifth most common cancer worldwide, with third highest mortality rate in the worldSo, the main aim of the present study is to investigate the pharmacological mechanisms of andrographolide in treating gastric cancer using network pharmacology and molecular docking experiments.http://www.swissadme.ch), an online tool for calculating the absorption, distribution, metabolism, excretion (ADME), oral bioavailability (OB), and drug-likeness (DL)25. The overall flowchart of this study is shown in Fig. Drug-likeness was evaluated by following Lipinski’s rule of five (RO5) for screening potential oral drugs in humans. The parameters evaluated are molecular weight (MW), XLogP3 (octanol–water partition coefficient), topological polar surface area, number of rotatable bonds, hydrogen bond acceptor, and hydrogen bond donor numbers. To explore the drug-likeness properties of andrographolide, the SMILES format CC12CCC(C(C1CCC(=C)C2CC=C3C(COC3=O)O)(C)CO)O of andrographolide was uploaded into the SwissADME server (http://www.swisstargetprediction.ch/)26, the TargetNet (http://targetnet.scbdd.com/)27 and the Comparative Toxicogenomics Database 28 were used to predict the targets of andrographolide. The 3D molecular structure file and the canonical SMILES of andrographolide were imported into the SwissTargetPrediction, TargetNet and CTD databases, respectively. Further, the predicted candidate targets were normalized by UniProt database (http://www.uniprot.org/).The SwissTargetPrediction databases (http://www.omim.org), GeneCards database (https://www.genecards.org/), Therapeutic Target Database 29, and CooLGeN database (http://ci.smu.edu.cn/CooLGeN/)30. Targets with a hit score greater than 5 in the CooLGeN database were chosen as GC-related genes. Then, the predicted targets of the andrographolide were mapped with the targets of GC, and the intersection of the two was taken to obtain the target set of andrographolide for the treatment of GC.To ensure a comprehensive collection of GC-related genes four online public database sources were explored and GC-related genes were downloaded, including the Online Mendelian Inheritance in Man (OMIM) database  to construct the compound-target network31.Andrographolide and its therapeutic targets in GC were introduced into Cytoscape . KEGG is pathway database consisting of graphical diagrams of biochemical pathways32. The Gene Ontology (GO) is a comprehensive resource for functional genomics that includes gene function definitions33. Based on pathological and clinical data pathways related to GC were selected. Histograms and bubble charts are produced through the Bioinformatics cloud platform .Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed with the Database for Annotation, Visualization and Integrated Discovery  with the species limited to “Homo sapiens” and a confidence score of > 0.9 to ensure high confidence information. The PPI network was constructed by Cytoscape , a bioinformatics software used for data visualization and integration31. To find clusters (highly interconnected regions) within the PPI network, the Cytoscape plugin cytoHubba  version 0.1) was used. Based on the degree level, the top-ranked proteins were designated as hub targets. We identified the protein clusters using MCODE35, a Cytoscape plug-in. Then, proteins contained in each cluster were entered into the STRING database to reproduce the corresponding cluster map and predict functional associations between proteins.Protein–protein interaction (PPI) plays a significant role in biological processes and is essential for understanding the intricate systems that operate within a living cellhttp://cao.labshare.cn/cb-dock. It can automatically identify the active sites of a given protein, calculate the center and size, customize the grid box size according to the query ligands, and then perform molecular docking with a popular docking program, AutoDock Vina36. The crystal structures of the hub target proteins were downloaded from the protein data bank (http://www.rcsb.org). The 3D structure of andrographolide was downloaded from the PubChem compound database . Next, the crystal structures of proteins and the ligand file of andrographolide were inputted to CB-Dock, and docking analysis was conducted to elucidate the binding activities. The docked results were then visualized and analyzed using Discovery Studio Visualizer software (Accelrys Software Inc.)37.To gain an in-depth insight into the relationship and action mechanisms between candidate proteins and andrographolide, molecular docking was conducted to assess the strength and mode of interactions between andrographolide and the hub targets. The molecular docking simulation was conducted by CB-Dock (http://gepia2.cancer-pku.cn/) is an online server providing interactive and customizable functions based on the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) database. GEPIA was used to validate the differential expression of the hub genes between GC and normal gastric tissues, and it can also analyze them according to pathological stages38.Gene Expression Profiling Interactive Analysis (https://www.proteinatlas.org/) database is an extensive proteome database based on immunohistochemical analysis. According to the intensity of staining and percentage of stained cells in the tissues, the protein expression levels of hub genes in GC tissues and normal gastric tissues were compared, and representative immunohistochemical staining pictures were obtained from the database HPA39.The Human Protein Atlas (Version 20.1) 40, which is capable of assessing the effect of genes on survival, was used to estimate the prognostic significance of each hub gene. The patients with GC were separated into two groups: those with high expression and those with low expression, and a Kaplan–Meier survival plot was used to compare the two groups. The hazard ratio (HR) with 95% confidence intervals and the log rank P value were calculated.To explore the hub targets’ influence on the overall survival (OS) of GC, a cancer genomics dataset named Kaplan–Meier Plotter  < 5, these 3 are the most important factors for drug ligand interactions. On the other hand, the rotatable bond less than 10, the hydrogen bond acceptors no more than 10, and the hydrogen bond donors no more than 5. Molecular properties of andrographolide were shown in Table Predicting for potential therapeutic ligands is the major outcome of Drug-likeness assessment. Our results showed that the property of andrographolide is according to Lipinski’s RO5. The polar surface area (PSA) less than or equal to 140 AOMIM, GeneCards, TTD, and CooLGeN databases were screened with the search term gastric cancer and stomach cancer, to determine GC-related targets. A total of 1586 targets related to GC were identified. Furthermore, targets for andrographolide were searched in SwissTargetPrediction, CTD and TargetNet databases and 640 targets were identified. A comparison between andrographolide and GC related targets identified 197 intersection genes. Figure GO enrichment analysis of the 197 targets, identified a total of 809 GO terms. Figure A total of 105 pathways were identified by KEGG pathway analysis, and the targets were closely related to pathways in cancer, PI3K-Akt signaling pathway, Proteoglycans in cancer, Focal adhesion, microRNAs in cancer, Rap1 signaling pathway, Ras signaling pathway, MAPK signaling pathway, FoxO signaling pathway and HIF-1 signaling pathway. This shows that andrographolide may play a role in the treatment of GC through the above-mentioned pathways, among which the PI3K signaling pathway is the key pathway involving 55 potential targets, including most of the hub genes. According to the number of enriched genes, the top 20 remarkably enriched pathways were shown as likely to be the major pathways in the treatment of GC  and 124 edges. Cluster 3 contained 5 nodes 10 and cluster 4 contained 11 nodes and 21 edges, respectively and were listed in Table The common targets of andrographolide and GC were put into STRING database for PPI network construction   is mutated and is most commonly mutated gene in human cancers. This gene plays significant roles in cell cycle arrest, cellular senescence, apoptosis, differentiation and metabolism47. STAT3 acts as a carcinogen in GC, which can enhance the metastatic potential of tumor cells and promote the development and progression of tumors48. Andrographolide induces apoptosis and enhances anticancer activity through inactivating STAT3 and Akt49. Andrographolide can be combined with conventional chemotherapeutic agents to be used as a potential therapeutic strategy. It enhanced chemosensitivity of tumor cells to doxorubicin through inhibition of STAT3 activity50. Andrographolide inhibited the PI3K expression and phospho-Akt coexisting with cell invasion and migration mechanism51. MAPK is a crucial signal transmitter in cells that is involved in a variety of biological processes such as cell proliferation, differentiation, and immune defense by phosphorylating nuclear transcription factors and related enzymes. The abnormal expression of MAPK3 was related to the invasion, metastasis, and drug resistance of a variety of tumor cells. Kim et al. found that MAPK3 expression is an independent prognostic index for patients after gastrectomy52. It has been reported that andrographolide has anti-angiogenic activity which is mediated through preventing VEGFA-induced phosphorylation and activation of VEGFR2 and MAPKs53. Transcription factor JUN is a risk gene for GC, and it can promote the occurrence and development of GC by participating in the regulation of the MAPK signaling pathway54. Andrographolide that involved inhibiting Hsp90 function and downregulating Hsp90 client proteins, broadening the molecular basis of andrographolide-mediated anticancer effects55.The mechanisms behind effects of andrographolide was broadly through inhibition of v-Src, NF-κB, STAT3 and PI3K/AKT activity and downregulation of mediators of cell cycle progression, inflammation, metastasis and angiogenesis6. Taken together, SRC, AKT1. TP53, STAT3, PIK3CA, MAPK1, MAPK3, VEGFA, JUN and HSP90AA1 are crucial in the pathogenesis of GC. Hence, it is promising to discover more novel anticancer compounds with low toxicity based on network pharmacology analysis. This study provides a rationale for using andrographolide for the treatment of GC.All these results suggest that andrographolide can be considered as prospective anticancer and chemosensitizer with the ability to overcome resistance to chemotherapy drugs in gastric cancer cellsIn our study we found that andrographolide is a multitarget anticancer drug. We predicted that the primary mechanism of action of andrographolide in the treatment of GC is mediated by HIF-1, PI3K-AKT and other signaling pathways to regulate the proliferation, apoptosis, migration, and angiogenesis of tumour cells, thus playing a role in the treatment of GC.In this study, based on network pharmacology analysis, we predicted 10 potential center targets for andrographolide in the treatment of GC, suggesting that andrographolide can be an herbal medicine with multicomponent, multiple targets, and multiple pathways. The network analysis revealed that andrographolide may exert its therapeutic effects against GC by modulating certain distinct targets, such as SRC, AKT1. TP53, STAT3, PIK3CA, MAPK1, MAPK3, VEGFA, JUN and HSP90AA1. The GO analysis of these targets results showed that andrographolide likely produced pharmacological effects against GC mainly by influencing different biological processes, like negative regulation of apoptotic process, response to drug, positive regulation of cell proliferation, migration, and angiogenesis. Meanwhile, the KEGG pathway analysis in the present study disclosed that andrographolide probably exerted its pharmacological action via simultaneously regulating different signaling pathways related to GC, such as pathways in cancer, HIF-1 signaling pathway and PI3K-AKT signaling pathway. These findings supported the possibility that andrographolide mediates HIF-1 inhibition which may be dependent on the inhibition of upstream PI3K-AKT pathway.To summarize, andrographolide is a promising compound, which is expected to be developed as a safe and effective multitarget drug against GC. Our network pharmacological analysis predicted that andrographolide may exert an anti-GC effect through multiple targets, pathways, and biological processes, thereby regulating the cell metabolism and cell apoptosis. Further verification studies are required to confirm the clinical efficacy of andrographolide and its mechanisms against GC.


Biological data are commonly revealed in a nominal grid data structured pattern. ATT-CNN cannot be applied directly. In order to figure out these issues, a novel method which is called the Path-ATT-CNN is proposed by us, making an explicable ATT-CNN model based on united omics data by making use of a recently characterized pathway image. Path-ATT-CNN shows brilliant predictive demonstration difference in primary lung tumor symptom (PLTS) and non-primary lung tumor symptom (non-PLTS) when applied to lung adenocarcinomas (LADCs). The imaginational tool adoption which is linked with statistical analysis enables the status of essential pathways which finally exist in LADCs. In conclusion, Path-ATT-CNN shows that it can be effectively put into use elucidating omics data in an interpretable mode. When people start to figure out key biological correlates of disease, this mode makes promising power in predicting illness.Attention convolutional neural networks (ATT-CNNs) have got a huge gain in picture operating and nature language processing. Shortage of interpretability cannot remain the adoption of deep neural networks. It is very  Processing complicated structures and dependencies in data is a difficult job . Deep nen×r originate in the Gene Expression Omnibus. Here, n and r refer to the samples and genes numbers, respectively. Primary lung tumor symptom (PLTS) is defined as presence of lung adenocarcinomas in patients, and non-primary lung tumor symptom (non-PLTS) is defined as absence of lung adenocarcinomas in patients  are identified. A total of 2,200 pathways are shown. Pathway pn matrix . The patB∈Rn×ri , where ts (PCs) . The procs types . FinallyX-Grad-CAM is used to sequence the pathways. Its goal was to figure out the key PLTO pathways in LADCs. It can find target regions. So, when reciprocal pathways are linked with pathway images, the possibilities of X-Grad-CAM identifying key pathways are wide. It is also in line with the nature of ATT-CNNs. ATT-CNN is able to localize primary pathways in input position. It is originally rational to make connected pathways. Pathways are in nearness on the pathway images. In this way, people can better find important regions. Therefore, 2,200 pathways are ranked in the conduct: person connection between pathways which is figured out on a matrix of 2,200q is generated. Combining all resultant pathway images makes up the matrix. This process occurs in 2,200 pathways. Pathways lie in among unselected situation. It was the most similar to the pathway in the row. Finally, all pathway images have the identical sequence of pathways.c∈RC×1×1. Multi-layer perceptron (MLP) has one hidden layer which is made up of a shared network. Second, the hidden layer size is set to RC/r×1×1. Here, r means the speed of reducing, which can reduce parameter overhead. Finally, we apply element-wise summary. In this way, we can combine the output. Pathway images consists of the input layer. They also have a 2 × 2 size which is followed by average-pooling and max-pooling separately, and then it is combined by the sigmoid function. The dropout rate is 0.5, and activation function is RELU. Also, it is combined when parameters are put into dilapidation convolution, 32 filters, and size of 2 × 2. The output comes from the dropout layer. The output is finally deformed to a vector. It is linked with a fully connected layer which has 64 nodes, which are ensued by a dropout layer and a Softmax layer. SGD is used as an optimizer with a learning rate of 0.001. In this way, we get the final output which is mentioned in this article. We collect spatial content of a feature map. We make it by applying average pooling and max pooling. This process generates two totally different spatial context terms: The model which uses a five-fold cross validation will be trained under 40 repeats. Data which are randomly assigned as follows undergo five-fold cross validation: 20% of data are designed as a test set. Also, the unexhausted data are allotted as a training set, and the validation set is applied to adjust hyper-parameters. The model performs by using the training collection and optimizing hyper-parameters which are in the ATT-CNN model. It is performed in each cross validation which is assessed on the test collection. Also, the area under the curve is used to test the performance.l is a score set. Also, it shows the manner of the target layer. c). Each feature diagram consists class c. Neurons which are computed weights Thinking of an input image, I, l means the scale which is in the target visual layer. Fc means a sum of weighted feature diagram. This motivation is strength pixels. Its intensities are good for increasing the score. A localization diagram for every class is regulated to exist in between 0 and 1. The class activation diagrams have the identical size, which is on the feature diagram. It is put into the figure of the input:In Because of biological interpretation which applies X-Grad-CAM, the ATT-CNN model, which is unsimilar with the model that has cross validation, is made by applying all samples. Every sample, which is input into the model, produces two activation diagrams (PLTO and non-PLTO). So, the difference map is produced. A statistical analysis, which duplicates this process for all samples, is applied. Assessing the statistical difference between the PLTO and non-PLTO groups is not an easy job. We use the Wilcoxon rank-sum test to figure out this problem for a given pixel.Several machine learning methods are compared with the predictive performance of Path-ATT-CNN. The same settings, which are used as described, are shown for all the experiments. Also, the setting is also in the aforementioned cross validation operation. There is a neural network architecture which is empirically adjusted. It composes an import layer. It comprises five hidden layers. It makes up an output layer. There are nodes in each hidden layer whose numbers are 10 k, 7.5 k, 5 k, 2.5 k, and 500. ReLU which acts as an activation function is located in the hidden layers. The number of dropout rate is calculated using the hidden layers and is set at 80%. The manner of MiNet which initially plans to figure out the identical index that is altered is identified by this method. These several famous methods which are trained by the Adam optimizer act as comparison with Path-ATT-CNN.p-value < 0.001. This method is conducted by applying a two-sample t-test. Smoking age plays a key role in the survival. The variable is in the ATT-CNN model. Also, it will be put into the fully connected layer.Our goal was to explain molecular characteristics of lung adenocarcinomas (LADCs). A total of 226 primary lung LADCs of medical stages I–II are evaluated. They undergo genome-wide expression identification. In total, 174 genes are chosen as upregulated specifically in 79 lung LADCs. A total of 79 cases are split into: 11 ALK-positive patients, 36 A triple-negative LADC patients, and 32 patients of B triple-negative LADC patients. They are unattended clustering according to the demonstration of 174 genes. Group A triple-negative LADC patients show remarkably worse prognosis for relapse than LADC patients and group B triple-negative LADC cases. A total of 9 genes are authenticated to remarkably put in group A triple-negative LADC cases which are good for finishing their prognoses. Genes, which distinguish this group of LADCs, are suggested to be helpful for patients chosen for additive chemotherapy after operation resection of stages I–II triple-negative LADCs and provide useful insights into the process of molecular target therapies for these patients. The data of LACDs are composed of RNA expression which is assessed at the gene level: RNA expression for 843 genes in 578 cases. There are patients who survived in the last follow-up ≤ 10 months. Also, those patients are removed for finishing the task and receiving better outcomes. Also, PLTO and non-PLTO groups have 155 and 432 cases, respectively. Class weights which are set in the model are important. This method can figure out imbalance data in the light of the ratio of the quantity of samples in the two groups. In total, out of 489 unusual genes we selected 346 TP53. PCA tests have shown individual pathways, respectively, on data type which transfers gene-level information to pathway-level information. The average smoking age in PLTO and non-PLTO groups is 20 and 30 years, respectively. The difference in smoking age in the two groups is remarkable with a The ATT-CNN model which is used to identify PLTO and non-PLTO groups in GSE, which uses pathway images is evaluated in a five-fold CV scheme. In total, 2,200 rows and 3 × q columns are generated by a pathway image representation. Here, q refers to the number of PCs. Also, each row represents the same pathway. In the experiment, the size of the experiment is valued with q = 1. In every experiment, which has an average AUC over 30 iterations of the five-fold CV, is reported by us. The performance of PC (q = 1) is shown in There are four different machine learning methods mentioned by us in this article. Also, four methods are compared with the performance of Path-ATT-CNN. The ATT-CNN model is used to identify PLTO and non-PLTO groups in GSE. Class weights are put into the model because the ratio of the quantity of samples is in the two groups. With regard to most cancers, Path-ATT-CNN performed better than any other methods . The detp-values. After statistical tests for all pixels, p-values are corrected using the Bonferroni correction. The fields of four methods which make use of adjusted p-values < 0.001 are observed which comprise 20 pixels. The process is repeated for all 15 pathways, and the details are shown in The ATT-CNN model, which applies samples lying in pathway images, is produced. To finish this, we should use the first two PCs for patient omics types, which is able to make helpful insights into biological interpretations. Mechanisms are linked with survival in LADC patients and self-reliant from smoking age. This is used to classify the biological mechanisms . IndividAll 20 pixels in the four hot spots are observed in PCs of RNA demonstration. Several pathways which are loaded in both PC 1 and PC 2 are found. Kaplan–Meier analysis is used to analyze the existing time of two groups which are classified by a median separation of PC values which are in the key pixels.Lung cancer has become a primary cause of cancer deaths, which annually leads to over one million deaths worldwide. There are over 1.2 million new cases which are diagnosed every year. Lung adenocarcinoma reaches an average five-year survival rate of 15%. Lung adenocarcinoma is the most common type of lung cancer. The primary reason is late examination and the scarcity of late treatments. In this situation, smoking undoubtedly becomes the primary reason why many people have been suffering from lung cancer. About 10% of cases occur in patients who have never smoked. LADCs are becoming a major public health concern. The identification of biomarkers can understand the implicit aggressiveness. It plays a key role in the biology of LADCs. Many articles have found assumed survival-associated biomarkers in LADCs. They make it by using the omics platform. The analysis of data which come from individual articles nowadays lacks the capability to find strong biomarkers that are able to make medical decisions in LADCs. United analysis which are about omics data is able to offer insightful information. This information is about the complicated biology and molecular nonuniformity of cancer. Lately, ATT-CNNs have got a huge achievement. Second, applying ATT-CNNs in bioinformatics problems and biological data shown in non-grid structures is a limitation. The application of deep learning techniques of biological data is another limitation. It is put into the interpretability which is in the trained model. Also, it is seen as a “black box.” We recently have generated a conception of “pathway image.” Our goal was to show omics data which made us to adapt ATT-CNNs in biological pathway analysis. Pathways, which are in a connected activity, are put more strongly in the pixel. It can fully leverage ATT-CNN capabilities skillfully which is connected to pixel activity. Also, the ATT-CNN model which is on the united omics data is at the pixel level that remarkably improves predictive performance. Also, it adds co-complemented message to the model. Possibility of the pathway-level analysis that removes noisy information is high. It may lie in individual omics type. This represents every pathway which is seen as a linear collection of related genes. The adoption of X-Grad-CAM and the ATT-CNN model skillfully make biological interpretations, which identify pathways linked with survival in LADC patients. The simplicity and efficiency of the ATT-CNN modeling and X-Grad-CAM lie in biological explanation . In thisIn this article, Path-ATT-CNN is designed. The method starts from the concept of “pathway image,” that generates application of omics data. Through this model, we can remarkably learn about metabolic pathways. The good pathway is helpful to patients who can learn about their body mechanism. It is able to predict patient’s lung cancer situation better than any other methods. Then, the use of X-Grad-CAM which is directly applied on the pathway image makes the identification of a plausible pathway. In conclusion, this article shows the future potential to use ATT-CNNs on omics data and X-Grad-CAM. In this way, we can detect more complicated biological correlates of disease disadvantages. We also found a key pathway. There is a limitation to the model, that is, the identification of identical pathways that need closed alignment of the pathways. This can be improved in the next 10 years.


To analyze the changes of gene expression at different timepoints after spinal cord injury (SCI) with tenth segment thoracic injury.Two SCI models, the complete paraplegia (H) and Allen’s strike (D) methods were applied to induce SCI in rats, and transcriptome sequencing was performed 1, 3, 7, 14, 56, and 70 days after SCI, respectively. Principal component analysis, differentially expressed gene analysis, and hierarchical clustering analysis were applied to analyze the differentially expressed genes (DEGs). Gene Ontology GO enrichment analysis, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and Gene Set Enrichment Analysis revealed the pathway of gene enrichment.There were 1,907, 3,120, 3,728, 978, 2,319, and 3,798 DEGs in the complete paraplegia group and 2,380, 878, 1,543, 6,040, 1,945, and 3,850 DEGs in the Allen’s strike method group and after SCI at 1, 3, 7, 14, 56, and 70 days, respectively. The transcriptome contours of D1, H1, D3, and H14 were clustered with C; the H56, D56, H70, and D70 transcriptome contours were similar and clustered together. H3, D7, and H7 were clustered together, and D14 was clustered separately. The transcriptome differences of the two SCI models were mainly concentrated during the first 2 weeks after SCI. The DEGs after SCI in the complete paraplegia group were more concentrated. Most of the early transcriptional regulation stabilized within 2 weeks after injury.There were DEGs between the two SCI models. Through the gene changes and pathway enrichment of the entire time period after SCI, the molecular mechanism of SCI repair was revealed in depth, which provided a reference for SCI treatment in the future. Spinal cord injury (SCI) is a destructive neurological and pathological state that causes major motor, sensory, and autonomic dysfunctions, often with lifelong consequences –3. AccorCurrently, many studies are also committed to developing neuroprotective and nerve regeneration therapies to promote neuronal recovery. Although this treatment has achieved varying degrees of success, the outcome remains uncertain due to the complex healing mechanisms involved. Therefore, a more intensive investigation of the mechanism and molecular events of SCI and its secondary damage is a significant approach to developing SCI treatment plans , 12. NorThe ultimate goal of SCI research is to develop and provide effective repair and treatment strategies for clinical development . PreviouTherefore, the use of RNA-seq (RNA-sequencing) technology to efficiently analyze the sequence of events during and after injury in different injury models enables a comprehensive understanding of the mechanism of SCI and its secondary injury, thereby providing data for the further study of the molecular mechanism of SCI, and providing strategies for the recovery and treatment of neurological function after severe central nervous system injury in the future. The results of the present study provided a novel insight into the mechanisms underlying SCI.A total of 39 specific-pathogen-free 8-week-old adult female Sprague–Dawley rats weighing 200–220 g were purchased from the Shanghai SLAC Laboratory Animal Co. Ltd. . Animal care in the surgical procedures and post-operation were in accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals and Guidelines and Policies for Rodent Survival Surgery provided by the Animal Care and Use Committees of Ningxia Medical University General Hospital.The 39 adult female rats were randomly assigned to a control group  and one of two SCI groups: complete paraplegia  and Allen's strike  groups. The SCI models were established after 3 days of adaptive feeding. The mice were anesthetized by intraperitoneal injection of 4% chloral hydrate. The surgical area was shaved and disinfected with 70% ethanol and betadine. A 5 cm long incision was made centered on T10, the spinous processes and lamina of the thoracic area (T9–T11) were separated and exposed, the spinous processes and lamina were removed, the spinal cord was fully exposed, and an incomplete cross-cut was made with ophthalmic scissors or scalpel for the group H. In the group D, a 5 g hammer was fixed at a height of 16 cm in a smashing device and was allowed to fall freely to impact the T9–T11 spinal cord tissue . All ratRats were euthanized 1, 3, 7, 14, 56, and 70 days after injury, and a 2 cm wound tissue sample was taken from the injury site for RNA analysis. Total RNA was extracted with TRIzol reagent  . RNA intP < 0.05 was defined as differentially expressed [FPKM (Fragments Per Kilobase Million) were considered to estimate the gene expression level . DESeq2 xpressed . In addihttp://www.genome.jp/kegg/) of DEGs were implemented using the clusterProfiler R package, in which gene length bias was corrected. P < 0.05 were considered significantly enriched by DEGs.Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG)  is a pre-defined gene set that can show a significant consistent difference between two biological states. The genes were ranked according to the degree of differential expression in the two samples, and then the predefined gene set was tested to determine whether they were enriched at the top or bottom of the list. GSEA can distinguish between subtle expression changes. We used the local version of the GSEA analysis tool For the high quality assessment of sequencing data, 39 cDNA libraries were established, including the group C, complete paraplegia group , and Allen's strike method group . RNA-Seq produced 39,435,984–52,386,642 raw reads for each sample. After filtering out the low-quality reads, there were 38,663,500–50,797,750 clean reads, with the Q20 97.46–98.58% and the Q30 92.76–95.66%. To identify the source of variation within the original data and evaluate the difference between groups and the duplication of samples within groups, PCA  was conducted. As shown in Fig. RPKM and DESeq were used to analyze the gene expression level and differential expression profiles, respectively. The results are shown in Fig. Heat map and hierarchical clustering were used to determine the gene expression patterns in the rat transcriptome at different times after SCI in different SCI models. Genes with similar expression patterns may have similar functions or may participate in the same metabolic process or cellular pathway. In this study, FPKM was applied for hierarchical cluster analysis of transcriptome abundance, with the results shown in Fig. Compared with the control group, the number of GO terms significantly enriched by upregulated and downregulated DEGs is shown in Fig. Compared with the control group, the number of KEGG terms significantly enriched by upregulated and downregulated DEGs is shown in Fig. To further investigate the changes of the transcriptome level after SCI, we analyzed the gene set enrichment of the complete paraplegia and Allen’s strike groups across all timepoints after SCI to investigate the transcriptome changes caused by SCI. Figure In recent years, research has focused on analyzing the complex pathological process and molecular mechanism of SCI to determine the impact of microenvironmental changes on SCI repair . CurrentThe results of the present study showed that the two SCI methods were quite different within 1–14 days, whereas they were relatively similar at 56 and 70 days. The difference between samples in the complete paraplegia group gradually increased with time, reached a peak at 7 days after SCI, while there was little difference between samples in this group at 14, 56, and 70 days after SCI. The Allen’s strike method group displayed considerable changes immediately on the first day after SCI, while D3 was closer to group C, but D7 and D14 were far away from group C, and was thus quite different from group C. Consistent with the complete paraplegia group, there was little difference between 56 and 70 days after SCI, and H56, H70, D56, and D70 were also gathered together. Consistent results were found in the heat map and hierarchical clustering, with D1, H1, D3, and H14 clustered with C; H56, D56, H70, and D70 transcriptome contours were similar and clustered together. H3, D7, and H7 were clustered together, whereas D14 was clustered separately. The transcriptome difference between the two SCI models was the largest on days and 14 after SCI.Siebert et al.  demonstrAt 56 and 70 day post-injury, the transcriptome characteristics of the two SCI models were relatively similar, while the relevant differential genes that were significantly enriched were in “glial cell development”, “epithelial cell proliferation”, “urogenital system development”, “gliogenesis”, “glial cell differentiation”, “regulation of angiogenesis”, “renal system development”, “glomerulus development”, “nephron development”, and other signal pathways related to glial production and renal development.Through KEGG enrichment analysis of differential genes, we found that the Allen’s strike group was the most enriched in KEGG pathway terms on day 14 after SCI, with upregulated differential genes enriched in 95 KEGG pathway terms and downregulated differential genes enriched in 70 KEGG pathway terms. Upregulated KEGG pathways included “ECM-receptor interaction”, “NOD-like receptor signaling pathway”, “cytokine-cytokine receptor interaction”, “NF-kappa B signaling pathway”, “lysosome”, “osteoclast differentiation”, “protein digestion and absorption”, “Th17 cell differentiation”, “adipocytokine signaling pathway”, and “PI3K-Akt signaling pathway”. Downregulated KEGG pathways included “dopaminergic synapse”, “neuroactive ligand-receptor interaction”, “cAMP signaling pathway”, “insulin secretion”, “aldosterone synthesis and secretion”, “biosynthesis of amino acids”, “GABAergic synapse”, “GnRH secretion”, “inflammatory mediator regulation of TRP channels”, and “oocyte meiosis”. In contrast, in the complete paraplegia group, the upregulated differential genes were not enriched in any KEGG terms at 14 days after SCI, while there were three downregulated KEGG terms, which were “Cell cycle”, “Oocyte meiosis”, and “Progesterone-mediated oocyte maturation”. “Oocyte meiosis” was downregulated in both groups at 14 days after SCI. Previous studies have shown that the key activities that drive the process of meiosis are maturation-promoting factor, cyclin B, and a heterodimer of cell division cycle 2 kinase , 34.Through the overlapping gene sets of the two SCI models, we can understand the common molecular characteristics of SCI. “L17 Signaling Pathway (RNO04657)”, “selenocompound metabolism (RNO00450)”, “non-small cell lung cancer (RNO05223)”, “melanoma (RNO05218)”, “bladder cancer (RNO05219)”, “EGFR tyrosine kinase inhibitor resistance (RNO01521)”,”adipocytokine signaling pathway (RNO04920)”, “sphingolipid signaling pathway (RNO04071)”, “MAPK signaling pathway (RNO04010)”, “estrogen signaling pathway (RNO04915)”, and “RAS signaling pathway (RNO04014)” were upregulated while “steroid hormone biosynthesis (RNO00140)”, and “oocyte meiosis (RNO04114)” were downregulated at 1 day post-injury. “lysine degradation (RNO00310)”, “renin angiotensin system (RNO04614)”, and “AMPK signaling pathway (RNO04152)” were upregulated and there was no overlapping downregulated gene set between the two SCI models at 3 day post-injury. “basal transcription factors (RNO03022)”, “ribosome biogenesis in eukaryotes (RNO03008)”, “RNA degradation (RNO03018)”, and “mRNA surveillance pathway (RNO03015)” were upregulated, whereas “steroid biosynthesis (RNO00100)” and “serotonergic synapse (RNO04726)” were downregulated at 7 day post-injury. “primary bile acid biosynthesis (RNO00120)”, “drug metabolism cytochrome P450 (RNO00982)”, “insulin resistance (RNO04931)”, “lysine degradation (RNO00310)”, and “chemical carcinogenesis (RNO05204)” were upregulated, whereas there was no overlapping downregulated gene set between the two SCI models at 14 day post-injury. “Starch and sucrose metabolism (RNO00500)” was upregulated and “steroid biosynthesis (RNO00100)” and “snare interactions in vesicular transport (RNO04130)” were downregulated at 56 day post-injury. “Lysine degradation (RNO00310)”, “circadian rhythm (RNO04710)”, “Phenylalanine metabolism (RNO00360)”, “longevity regulating pathway multiple species (RNO04213)”, “ABC transporters (RNO02010)”, “tyrosine metabolism (RNO00350)”, “AMPK signaling pathway (RNO04152)”, “renin secretion (RNO04924)”, and “purine metabolism (RNO00230)” were upregulated, whereas “fat digestion and absorption (RNO04975)” was downregulated at 70 day post-injury.In summary, through RNA-seq technology and bioinformatics analysis, it was found that the transcriptomes of the two SCI models were different, and the differences were concentrated in the first 2 weeks after the SCI occurred. The main molecular events after SCI in the complete paraplegia group were more concentrated in the week after injury, reaching a peak on day 7. The major molecular events after SCI in the Allen’s strike group occurred immediately, but they were adjusted on the third day, and the duration was relatively long of up to 14 days. This study analyzed the changes in gene expression levels of different SCI models at different timepoints, investigated signal pathways enriched by different genes, elucidated molecular changes after SCI and SCI repair pathways, and further clarified the mechanism of SCI at the level of gene expression changes. Most early transcription events appear to be stabilized within 2 weeks after the injury, such that the overall change in the average gene expression was no longer obvious, and the expression level remained relatively stable. The downregulation of “Fat digestion and absorption” at 70 days after injury may be closely associated with the formation of glial scars after SCI. On the other hand, comparing and analyzing the difference in gene expression between complete paraplegia group and Allen’s group provides a reference for explaining the entire molecular process of SCI as well as a novel direction for spinal cord injury treatment.The transcriptomes of the two SCI models were different, and these differences were mainly concentrated in the first 2 weeks after the SCI. In the complete paraplegia group, the main molecular events after SCI were more concentrated during the first week after the injury, reaching a peak on day 7. Major molecular events after SCI in the Allen’s strike group lasted for a relatively long time, up 14 days. This study found that most of the early transcription events appeared to be stabilized within 2 weeks after injury, and the down-regulation of “fat digestion and absorption” at 70 days after injury may be closely associated with the formation of glial scars after SCI. These studies may provide some meaningful insights into the differences of targeted treatment strategies by explaining the entire molecular process after SCI.


This study aimed to investigate the mechanism of action of Buxue Liqi Huatan decoction against lung cancer through network pharmacology.  The chemical composition and targets of all the drugs in the Buxue Liqi Huatan decoction were obtained through the Database and Systematic Analysis Platform of Traditional Chinese Medicine Pharmacology, the Integrated Database of Traditional Chinese Medicine, and by screening lung cancer targets with the gene map and OMIM database. The targets were then imported into Cytoscape 3.7.2 to build a target network of active ingredients and imported into the STRING database to build a protein-protein interaction network. The BisoGenet plug-in in Cytoscape 3.7.2 was used for network topology analysis. Genetic ontology (GO) enrichment analysis and Kyoto Encyclopedia of Gene and Genomes (KEGG) enrichment analysis were performed on potential targets of the Buxue Liqi Huatan decoction for lung cancer using the R-language Bioconductor platform, and results were imported from Cytoscape 3.7.2 to obtain the KEGG network connection diagram via the Autodock molecular docking software.  A total of 238 chemical components and 694 disease targets were obtained, including 133 intersecting targets. The key targets included TP53, AKT1, and MYC, and the GO functional analysis was mainly related to oxidative and cellular oxidative stress, apoptotic signaling, and antibiotic response. The results showed that the key target with the best binding performance was TP53.  The treatment of lung cancer with blood-supplementing, qi-transforming, and phlegm-transforming soups works through multiple components and targets. The active ingredients include quercetin, luteolin, naringenin, and baicalein. Among them, the core proteins of PPI protein interaction mainly include TP53, AKT1, MYC, EGRF, CCNB1, and ESR1. The enrichment analysis results show that the TNF signal pathway, PI3K-Akt signal pathway, AGE-RAGE, IL-17, etc., are the main signal pathways of Buxue Liqi Huatan decoction in treating lung cancer. This lays the foundation for further study of its mechanism. Lung cancer is one of the most common and fatal malignant tumors in China , and theNetwork pharmacology explains the occurrence and development of diseases from the perspectives of system biology and biological network balance, recognizes the interactions between drugs, and guides the discovery of new drugs to improve or restore balance to the biological network of the body. In recent years, an increasing number of researchers have explored the relationship between composite multielement targets and diseases from the perspective of system biology, thereby providing a new direction for modern TCM research . TherefoRhizoma Pinelliae, Rhizoma Arisaematis, Tianlong, Scorpio, Pericarpium Citri Reticulate, Endothelium Corneum Gigeriae Galli, and Radix Glycyrrhizae Preparata) in the Buxue Liqi Huatan decoction were searched through TCM system pharmacology platforms: Traditional Chinese Medicine Pharmacology (TCMSP) and the Integrated Database of Traditional Chinese Medicine (TCMID). TCMSP was selected based on these settings: oral biologics (OB) ≥30% and drug dependence (DL) ≥0.18 [The active chemical components of Chinese medicines (L) ≥0.18 . TCMID wLung cancer-related target genes were obtained using “gastric cancer” as the search term in the OMIM and GeneCards databases. There were too many targets retrieved from the GeneCards database, and the number of targets was filtered based on score values. A larger score value indicated that the target had a stronger correlation with the disease. Based on the assumption that a target with a score value greater than the median was the potential target for the disease, genes with scores greater than 5 that were left behind in the GeneCards database were merged and deduplicated in the OMIM database, that is, the target genes that were related to lung cancer disease.A Venn diagram was used to determine the intersection of the target of the Buxue Liqi Huatan decoction and the target of lung cancer to obtain the intersection target of the two, which was the effective target of Buxue Liqi Huatan decoction in the treatment of lung cancer.Drug active components and effective target genes were imported into Cytoscape 3.7.2 software for networking and visual analysis , and a dThe effective targets of Buxue Liqi Huatan decoction and lung cancer were imported into the STRING database to create a protein interaction network, and data with a reliability ≥0.900 were selected. Results were imported into Cytoscape 3.7.2 software for visual analysis in the tsv format. Protein-protein interaction (PPI) data were imported using R software to learn the connection points of core genes , and a hhttps://www.r-project.org/) and its base database were used to obtain the genetic ID (entrezid) of the potential action point. Other programs such as DOS, Cluster Profiler, and others were used as well. The Pathview (bioconductor) software package was used to analyze the GO function of these potential targets, including biological process (BP), cell component (CC), and molecular function (MF) analyses. The p value cutoff was 0, and the q-value cutoff was 0.05. GO concentration analysis was divided into three categories: BP, MF, and CC. Each category was classified according to the degree of importance, and the first 10 rich items are presented in a histogram and bubble chart.The software R (The targets of Buxue Liqi Huatan decoction on lung cancer were searched in the protein data bank (PDB) database and saved in PDB format. The ligands were stored in the mol2 format as the top 2 compounds by degree after topological analysis. The potential targets of Buxue Liqi Huatan decoction on lung cancer were molecularly docked with the main compounds in Buxue Liqi Huatan decoction using AutoDockTools 1.5.6.In TCMSP and TCMID, 238 active components in Buxue Liqi Huatan decoction were obtained using OB ≥30% and DL ≥0.18, a high GI absorption (pharmacokinetics), and more than two “yes” in drug-likeness as the screening conditions. The active components are shown in the supporting file  . TNF canor cells , 26. Trenous TNF , 28. Macnous TNF –32.In summary, based on network pharmacology, we preliminarily explored the possible mechanisms underlying Buxue Liqi Huatan decoction in the treatment of lung cancer. The predicted action targets in this study are consistent with those of existing studies on Buxue Liqi Huatan decoction in the treatment of lung cancer, therefore reflecting the accuracy of target prediction. In addition, this study revealed that Buxue Liqi Huatan decoction treated lung cancer based on its multicomponent, multitarget, and multipathway activities and further provided the basis for experimental verification and clinical research. This study had some limitations. The targets obtained from different databases were not identical, so there might be discrepancies. Different screening criteria were adopted for data processing. Therefore, further experimental studies are required to verify and supplement the results of this study.