Parkinson's disease (PD) is a common neurodegenerative disease in middle-aged and elderly people. Liuwei Dihuang (LWDH) pills have a good effect on PD, but its mechanism remains unclear. Network pharmacology is the result of integrating basic theories and research methods of medicine, biology, computer science, bioinformatics, and other disciplines, which can systematically and comprehensively reflect the mechanism of drug intervention in disease networks.  The main components and targets of herbs in LWDH pills were obtained through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Its active components were screened based on absorption, distribution, metabolism, and excretion (ADME); the PD-related targets were obtained from the Genecards, OMIM, TTD, and DRUGBANK databases. We used R to take the intersection of LWDH- and PD-related targets and Cytoscape software to construct the drug-component-target network. Moreover, STRING and Cytoscape software was used to analyze protein–protein interactions (PPI), construct a PPI network, and explore potential protein functional modules in the network. The Metascape platform was used to perform KEGG pathway and GO function enrichment analyses. Finally, molecular docking was performed to verify whether the compound and target have good binding activity.  After screening and deduplication, 210 effective active ingredients, 204 drug targets, 4333 disease targets, and 162 drug-disease targets were obtained. We consequently constructed a drug-component-targets network and a PPI-drug-disease-targets network. The results showed that the hub components of LWDH pills were quercetin, stigmasterol, kaempferol, and beta-sitosterol; the hub targets were AKT1, VEGFA, and IL6. GO and KEGG enrichment analyses showed that these targets are involved in neuronal death, G protein-coupled amine receptor activity, reactive oxygen species metabolic processes, membrane rafts, MAPK signaling pathways, cellular senescence, and other biological processes. Molecular docking showed that the hub components were in good agreement with the hub targets.  LWDH pills have implications for the treatment of PD since they contain several active components, target multiple ligands, and activate various pathways. The hub components possibly include quercetin, stigmasterol, kaempferol, and beta-sitosterol and act through pairing with hub targets, such as AKT1, VEGFA, and IL6, to regulate neuronal death, G protein-coupled amine receptor activity, reactive oxygen species metabolic process, membrane raft, MAPK signaling pathway, and cellular senescence for the treatment of PD. Parkinson's disease (PD) is a common neurological disorder in middle- and old-aged people. It is characterized by the progressive degeneration of dopaminergic neurons in the substantia nigra as well as pathological changes mediated by the formation of Lewy bodies, biochemical alterations by the decrease of dopamine (DA) transmitters in the striatum, and imbalance of DA and acetylcholine transmitters. PD is manifested as a multisymptomatic disorder, including symptoms such as tremor, myotonia, motor retardation, postural balance and sleep alterations, olfactory changes, autonomic nervous dysfunction, and cognitive and mental degeneration . EpidemiP < 0.05). Fang et al. [Traditional Chinese medicine (TCM) has been used for over 2,000 years for treating PD. TCM believes that kidney yin deficiency is a major key factor. Liuwei Dihuang (LWDH) pills are a classic kidney-tonifying prescription created by Qian Yi, a famous doctor during the Northern Song Dynasty. LWDH pills contain six herbal formulations: Rehmanniae radix Praeparata , Rhizoma dioscoreae , Cornus officinalis Sieb , Poria cocos , Cortex moutan , and Alisma orientale . Clinical practice has proven that LWDH pills are effective in treating PD and can alleviate its autonomic symptoms. Zhong et al.  managed g et al.  randomlyNetwork pharmacology (NP) was first proposed by the British scholar, Andrew L. Hopkins, in 2007. NP is based on the rapid development of systems biology and multidirectional pharmacology. This was a new idea of drug design, which aims to expand available drugs . It is bhttps://tcmspw.com/tcmsp.php) is an open-source database that specializes in analyzing TCM, integrating pharmacodynamics, pharmacokinetics, target prediction, and genomics [Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform  [http://db.idrblab.net/ttd/, updated June 1, 2020) [https://www.genecards.org/, version 5.0) [https://omim.org/, updated January 19, 2021) [https://www.uniprot.org/) was used to standardize the obtained targets.We obtained PD-related targets from four databases: (1) Drug Bank (n 5.1.8) , a compr1, 2020) , which pion 5.0) , which p9, 2021) , a comprhttps://cytoscape.org/, Version 3.8.0) to construct a drug-component-target network. Cytoscape is an open-source software platform for visualizing molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles, and other state data [In order to clarify the interaction between the targets of LWDH pills and the PD-related targets, R was used to select their intersection and draw the Venn diagram to obtain drug-disease targets, which were the targets of LWDH pills in the treatment of PD (disease targets). We screened out the active components that can target the drug targets and describe the relationship between LWDH pills, active components, and drug-disease targets and then imported into Cytoscape  is a database of known and predicted PPIs [k − core = 2, and node score = 0.2, using the Molecular Complex Detection (MCODE) plug-in Cytoscape. Differences were considered statistically significant at P ≤ 0.05.STRING (ted PPIs . The inthttps://metascape.org/) [P ≤ 0.01 was considered statistically significant.We applied Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses to systematically analyze the biological functions of drug-disease targets , 22. Metpe.org/) , which chttps://zinc.docking.org) and Protein Data Bank [http://www.rcsb.org), respectively. ZINC is a free database of commercially available compounds for virtual screening whereas PDB is a leading global resource for experimental data central to scientific discovery, which provides access to 3D structure data for large biological molecules. Molecular docking was performed using iGEMDOCK  [k-means and hierarchical clustering methods based on docking sites and compound properties.In order to verify whether the compound and the target have good binding activity, we selected the hub components of the drug-component-target network and the hub targets of the drug-disease target PPI network to perform molecular docking. We obtained the 3D structure of components and targets from ZINC   , a tool A total of 475 compounds from six herbs in LWDH pills were obtained from the TCMSP database. After screening and removing duplicate values using the two key ADME parameters of OB ≥ 30% and DL ≥ 0.18, we obtained 210 active compounds, including 19 compounds of FL, 32 compounds of MDP, 43 compounds of SY, 132 compounds of SZY, two compounds of SDH, and seven compounds of ZX. Finally, we obtained 204 targets of LWDH pills by compiling the corresponding targets of the active compounds of the six herbs using the TCMSP database.The number of PD-related targets obtained from the four databases Genecards, OMIM, TTD, and DrugBank was 3827, 526, 89, and 202, respectively. We summarized the obtained targets and removed duplicate values and finally obtained 4333 PD-related targets.On analyzing the identified targets of LWDH pills and PD-related targets using R , GO cellular component, GO molecular function, and KEGG pathways. Then, we saved the top 20 results for each item and created bubble charts for further analysis . It can We screened four hub components as ligands and three hub targets as binding sites for molecular docking. The energy levels of the molecular docking results were all less than -70 . Among tPD is a common neurodegenerative disease in middle-aged and elderly individuals. Because of its high prevalence, high disability rate, and chronic disease course, it has gradually become an important science and social issue in the field of population and health. DA receptor dysregulation is the pathophysiological basis of PD. However, a clear consensus on its pathogenesis does not exist. Presently, it is believed that factors such as mitochondrial dysfunction caused by oxidative stress, abnormal protein folding caused by endoplasmic reticulum stress, neuroinflammation, the microbiota-gut-brain axis, and the changes of expression of related gene expression are closely related to the occurrence and development of PD .In this study, we retrieved targets of LWDH pills and that of PD from multiple databases and combined them to obtain 162 targets of LWDH pills for the treatment of PD. Then, we constructed a PPI network and screening module and could determine that the genes encoding AKT1, VEGFA, and IL6 are important in this regard. Basic research has shown that AKT1 regulates pathological angiogenesis, vascular maturation, and permeability in vivo . Quesadaq value, the most important biological processes were neuron death, G protein-coupled amine receptor activity, reactive oxygen species metabolic process, membrane raft, MAPK signaling pathway, and cellular senescence. Some of them were the same as the biological functions of the modules. Neuronal death is the pathological basis of PD. The metabolic process of reactive oxygen species and membrane rafts may be related to mitochondrial dysfunction and thus participate in the occurrence and development of PD. Astrocytes are the bridge connecting neurons and blood vessels and are involved in activities such as underdevelopment, neurotransmitter transmission, brain metabolism, and blood flow regulation [Neuroinflammation is an important cause of DA neuron degeneration. Basic research has shown that p38 mitogen-activated protein kinase (p38MAPK) signaling pathway is also involved in the immune inflammatory response in PD. Additionally, the apoptosis signaling pathway is presently considered one of the pathways of PD pathogenesis. Subsequently, we performed KEGG pathway and GO function enrichment analyses of these targets. According to the gulation , 41. Thegulation  found thgulation .κB that causes SOD in mice with acute liver injury. Additionally, GSH level increased, MDA content decreased, and the expression of NRF-2 and HO-1 was upregulated to inhibit oxidative stress. Liao et al. [α and IL-1 beta, and activation of the MAPK signaling pathway in cells. The production of IL6 and IL8 was significantly reduced, thus, exerting an anti-inflammatory effect. Furthermore, beta-sitosterol in the diet can smoothly pass through the blood-brain barrier and can be deposited on the cell membrane. Mahmoudi et al. [Moreover, we constructed and analyzed a drug-component-target network to predict the hub components of LWDH pills in the treatment of PD and their mechanisms. Based on the degree, BC, and CC of the drug-component-target network, we predicted that quercetin, stigmasterol, kaempferol, and beta-sitosterol are the hub components of LWDH pills. Quercetin has antioxidant effects and can prevent oxidative stress-induced cell damage. It can effectively inhibit the transcription activity of the gene promoter encoding cyclooxygenase 2, which catalyzes the conversion of arachidonic acid to prostaglandins (PGs) and other inflammatory molecules and stimulates cell proliferation. It also exerts anti-inflammatory activity effects that are related to its strong antioxidative effect . Querceto et al.  found thi et al.  found thFinally, we performed molecular docking of the hub components and hub targets using iGEMDOCK to verify whether the compound and target have good binding activity. iGEMDOCK can determine the degree of binding between compounds based on the level of energy. The lower the binding energy, the stronger the binding ability of the action. The energy levels of the molecular docking results were all less than -70. This indicates that the hub active ingredients of LWDH pills have good binding activity with the hub targets of PD.In summary, based on NP, this study explained the effective active ingredients of LWDH pills and their related targets and pathways for the treatment of PD. We explained its multicomponent, multitarget, and multibiological pathway treatment for PD. The hub components may be quercetin, stigmasterol, kaempferol, and beta-sitosterol, and probably through pairing core targets such as AKT1, VEGFA, and IL6, to regulate neuronal death, G protein-coupled amine receptor activity, reactive oxygen species metabolic processes, membrane rafts, MAPK signaling pathways, and cellular senescence that can play a role in the treatment of PD. Moreover, we proved that the hub component has a good combination with the hub target through molecular docking. This provides a reference for exploring the pharmacological effects of LWDH pills. Because data on bioinformatics inventories are limited, all the active ingredients and targets for LWDH pills are not available, accounting for the present study's limitations. Our next step is to use clinical samples or animals for validating our findings and understanding further in this regard.


Chikungunya virus (CHIKV) is a mosquito-borne pathogen, within the Alphavirus genus of the Togaviridae family, that causes ~1.1 million human infections annually. CHIKV uses Aedes albopictus and Aedes aegypti mosquitoes as insect vectors. Human infections can develop arthralgia and myalgia, which results in debilitating pain for weeks, months, and even years after acute infection. No therapeutic treatments or vaccines currently exist for many alphaviruses, including CHIKV. Targeting the phagocytosis of CHIKV by macrophages after mosquito transmission plays an important role in early productive viral infection in humans, and could reduce viral replication and/or symptoms.To better characterize the transcriptional response of macrophages during early infection, we generated RNA-sequencing data from a CHIKV-infected human macrophage cell line at eight or 24 hours post-infection (hpi), together with mock-infected controls. We then calculated differential gene expression, enriched functional annotations, modulated intracellular signaling pathways, and predicted therapeutic drugs from these sequencing data.We observed 234 pathways were significantly affected 24 hpi, resulting in six potential pharmaceutical treatments to modulate the affected pathways. A subset of significant pathways at 24 hpi includes AGE-RAGE, Fc epsilon RI, Chronic myeloid leukemia, Fc gamma R-mediated phagocytosis, and Ras signaling. We found that the MAPK1 and MAPK3 proteins are shared among this subset of pathways and that Telmisartan and Dasatinib are strong candidates for repurposed small molecule therapeutics that target human processes. The results of our analysis can be further characterized in the wet lab to contribute to the development of host-based prophylactics and therapeutics. Aedes albopictus mosquitoes as active transmission vectors in addition to Aedes aegypti, which had been considered the primary vector (Chikungunya virus (CHIKV) infects approximately 1.1 million people per year in over 100 countries worldwide , with ovy vector . CHIKV iy vector . The namvia a mosquito vector  to gain a better understanding of the intracellular transcriptional response to CHIKV infection in human macrophages at 8 and 24 hpi, and (2) to apply that mechanistic knowledge to predict existing drugs that could be repurposed as either prophylactic or therapeutic treatments to minimize viral replication and spread from macrophages. Specifically, we calculated statistically significant differentially expressed genes (DEGs), functional gene annotations, and intracellular signaling pathways to improve our understanding of how macrophages respond to CHIKV infection. We then mined this information to predict existing therapeutic treatments that could be repurposed to reverse key host-pathogen interactions to reduce virus replication, infection, and pathogenesis. To our knowledge, this is the first RNA-seq experiment involving CHIKV infection of cultured human macrophages. The results obtained from this study contribute to ongoing efforts to develop effective prophylactics and/or therapeutic treatments.2 for 2 h. The virus-containing media was then removed prior to washing the cells three times with PBS and adding fresh media prior to incubation. Mock-infected cells were incubated using the same protocol and reagents, and only lacked the presence of virus.Macrophages were differentiated from the U937* human monocyte cell line and infected with CHIKV as described previously . BrieflyBiological replicates, in duplicate, of CHIKV-infected and mock-infected macrophages were harvested at eight and 24 h post-infection (hpi) by removing the supernatant and washing with PBS. RNA was extracted from all duplicate samples using Trizol reagent according to the manufacturer’s protocol as performed previously . A NanoDThe Scotty tool was used to confirm the sequencing depth and number of samples were above what was necessary to achieve sufficient statistical power .The fastq files containing the RNA-sequencing data were subjected to analysis by the Snakemake-based Automated Reproducible MOdular Workflow for Preprocessing and Differential Analysis of RNA-seq Data (ARMOR) software. Specifically, the fastq files, associated metadata, and a configuration file for each dataset were used as input to the ARMOR workflow . This woopentargets.org) database to identify known drug targets that were present in each of the significant pathways, and which could be repurposed as host-based therapeutics for CHIKV.The DEGs from ARMOR were then used as input to the signaling pathway impact analysis (SPIA) algorithm to identify intracellular signaling pathways that were significantly enriched with DEGs . SPIA im50 values and/or recommended diet restrictions, manufacturer safety statements, requirements of administration, and long-term impacts or risks that are included in drug consumption), prior publications or studies discussing the drug’s effectiveness against CHIKV, and whether any complications were indicated during trials—either during or after FDA approval.To increase the number of high-confidence potential therapeutics, a ranking strategy was implemented. For this study, a focus on small molecules was justified to facilitate ease of therapeutic distribution in geographical regions lacking adequate cold-storage resources. To simplify the multiple characteristics that were considered in the ranking of the results, a numerical score that represents a variety of metrics was assigned to each result. Contributing factors to this score include but are not exclusive to: whether the drug has been through clinical trials or has obtained US Food and Drug Administration (FDA) approval, if the drug had been approved and tested in multiple studies, the toxicity of the drug  adjusted t 24 hpi , with a t 24 hpi . A subseWe performed a Gene Ontology (GO) enrichment analysis on the DEG list to identify the cellular component, molecular mechanism, and/or biological process annotations that are assigned to each DEG result from the CHIKV-infected macrophages. The 24 hpi GO analysis identified eight statistically significant GO terms . Signifip-values also included Olfactory Transduction  signaling.We next applied a robust signaling pathway enrichment algorithm to identify a total of 234 signaling pathways that were significantly affected at 24 hpi . Seven ssduction , AGE-RAGsduction , and Stisduction . Our obssduction . The OlfIn an effort to identify DEGs that were shared across multiple pathways, we compared the members of multiple significant signaling pathways. We found that a subset of the modulated pathways shared DEGs including MAPK3 and MAPK1.opentargets.org database to identify known human drug targets within each significant signaling pathway and the associated existing small molecules that bind to each drug target in each pathway. We anticipated that this approach would identify drug targets that play a key role in fundamental viral processes, which would reduce virus replication and/or disease when targeted by a drug. As expected from the copious number of significant pathways, we predicted 136 pharmaceutical drugs from the 24 hpi pathways  a potential lag in the intracellular macrophage response to CHIKV infection, or (2) the selected MOI produced an infected macrophage population that was too small to confidently detect differential expression. Since CHIKV has a positive-sense genome, we do not expect this lagging response to be caused by a delay in viral genome replication. Rather, we believe that this observation is more likely due to the macrophages not initiating a response until after a sufficient number of CHIKV proteins have been produced in the cell. Prior studies have observed approximately 10ell line . Future While this study focuses on the response of human macrophages infected with Chikungunya virus, prior CHIKV studies have independently validated a subset of our results. A mechanistic analysis of mouse samples identified various differentially expressed genes that are also present in our results, namely MFSD1, and FAM49B . Given tAlthough minimal prior work has directly measured gene expression in CHIKV-infected macrophages, our observation of a larger number of DEGs being downregulated is somewhat expected given the primary immune functions performed by these host cells. Studies in other cell types have reported multiple cellular functions being inhibited by CHIKV infection including immune response .Prior studies examined the effect that CHIKV infection had on human skin fibroblast (HSF) cells, which showed several significant modulated signaling pathways at 24 hpi, particularly the “Wnt signaling”, “Stimuli-sensing channels”, and “ECM-receptor interaction” pathways . These cOur subsequent analysis of DEGs and the associated signaling pathways identified potential therapeutic drugs that could be repurposed for CHIKV. Prior studies have reported progress on developing therapeutics that directly target viral proteins . In contOur approach to prioritizing drugs incorporated a novel toxicity metric to facilitate ranking of existing therapeutic drugs. Small molecules have been an area of active research for their anti-viral properties, and were purposefully targeted in our analysis due to their general chemical stability and minimal need for refrigeration during transport and/or storage. This is particularly important given that many CHIKV infections occur in geographical regions that may not have consistent access to cold-storage resources. Six of our drug predictions have already been found to reduce the effects of Chikungunya virus in cells including telmisartan, sunitinib, etanercept, vorinostat, and dasatinib  Zaid et.Vorinostat, trade name Zolina, is used to treat Cutaneous T-cell Lymphoma (CTCL) as a histone deacetylase inhibitor. Recently Vorinostat has performed well as an antiviral therapy for HIV and West Nile Virus . Vorinosin silico drug repurposing study showed that Telistartan was predicted to have a docking affinity of −9.3 ± 0.1 kcal/mol to the CHIKV nsP2 protein , is used for the treatment of colorectal cancer and advanced gastrointestinal complications. It has a high level of toxicity and is only used after other treatments such as chemotherapy have been exhausted. This drug has anti-angiogenic properties and was shown to inhibit virus replication when administered prophylactically . SorafenA variety of systemic clinical signs and symptoms are associated with CHIKV which are not limited to headache, myalgia, arthralgia, retro-orbital pain . Rash anin vivo and expect that subsequent validation experiments will be needed to show whether this is the case for the therapeutics we identified in this study.Past work has documented that patients infected with CHIKV can develop side effects or adverse reactions while undergoing long-term pharmaceutical treatments . We acknAlthough this study primarily focused on predicting small molecule therapeutic treatments, an exception was made for Etanercept, a biotech fusion protein, because of its anti-inflammatory properties and acceptable safety profile (“Etanercept”; Our analysis found zero significant DEGs at eight hpi in human monocyte-derived macrophages, but 9,676 genes, and eight GO terms in the macrophages at 24 hpi. We identified 234 biological pathways that were significantly altered in human monocyte-derived macrophages during CHIKV infection that represented AGE-RAGE, innate immunity, cell cycle-related pathways, and Calcium signaling. Our drug repurposing analysis predicted that a combination of Telmisartan, Sunitinib, and/or Dasatinib could be used as a potential therapeutic for CHIKV infection since together they modulate multiple intracellular host and viral processes in macrophages at 24 hpi. We expect that this study will contribute to ongoing efforts to develop effective prophylactic and/or therapeutic treatments for CHIKV.10.7717/peerj.13090/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.13090/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.13090/supp-3Supplemental Information 3Click here for additional data file.


Code-based models overestimate the cracking load for BFRP-reinforced beams, but they underestimate the ultimate load. Exposure to an alkaline environment at temperatures as high as 60 °C caused a limited reduction in bond strength of BFRP. The durability of BFRP bars is influenced by the type of resin and sizing used to produce the bars.Reducing the fingerprint of infrastructure has become and is likely to continue to be at the forefront of stakeholders’ interests, including engineers and researchers. It necessary that future buildings produce minimal environmental impact during construction and remain durable for as long as practicably possible. The use of basalt fiber-reinforced polymer (BFRP) bars as a replacement for carbon steel is reviewed in this article by examining the literature from the past two decades with an emphasis on flexural strength, serviceability, and durability. The provisions of selected design and construction guides for flexural members are presented, compared, and discussed. The bond of BFRP bars to the surrounding concrete was reportedly superior to carbon steel when BFRP was helically wrapped and sand coated. Experimental studies confirmed that a bond coefficient  Partial replacement of cement with fly ash, slag, and/or silica fume has become a common practice and the mechanical properties of such concrete types have been studied extensively. On the other hand, exclusive use of materials such as fly ash and slag as sole cementitious materials activated using carefully selected alkalis has attracted the attention of researchers. Similarly, traditional reinforcing steel bars, despite their favorable mechanical properties, are associated with significant emission of CO2 during the manufacturing process, not to mention corrosion that may lead to a loss of cross-section. In the past two decades, interest has been renewed in reinforcing bars made of fiber-reinforced polymers (FRPs). The unidirectional fibers that constitute typically more than 75% by volume are made of glass, carbon, aramid, and, more recently, basalt. Basalt FRP (BFRP) reinforcing steel bars offer numerous favorable properties over traditional reinforcing steel bars, including, but not limited to, high tensile strength, corrosion resistance, and nonmagnetic nature. Studies by [Sustainability and durability of building structures are amongst the leading design criteria for new infrastructure. The contribution of the production of cement to the emission of COudies by  concludeIn aggressive environments, traditional reinforcing steel bars are susceptible to corrosion that could influence the structure′s life span. Marine environments and parts of the world where deicing salts must come in contact with concrete are examples of situations where traditional reinforcing steel bars may be subjected to corrosion. It is often less expensive and environmentally friendly to use sea sand for concrete structures that will be in contact with seawater, in which case the noncorroding FRP reinforcing bars would be a durable alternative to traditional steel. The magnetic properties of FRP bars make them suitable for consideration in structural elements surrounding magnetic resonance imaging (MRI) units and any other equipment sensitive to magnetic fields. Sea sand typically contains chloride irons that may cause corrosion of reinforcing steel bars. Design guides for concrete reinforced with FRP bars, such as ACI440.1R-15 , were deA basalt FRP bar is a composite material consisting of rigid polymer resin bounding unidirectional basalt fibers. Basalt fibers are produced by melting queried and crushed natural volcanic basalt rocks at a temperature of nearly 1400 °C . The molThe resulting composite material, consisting of polymeric resin and fibers, offers numerous favorable properties, including, but not limited to, high tensile strength, with applications in building new structures, such as FRP reinforcing bars, or retrofitting/strengthening deficient existing structures using FRP sheets and/or strips .BFRP bars are commonly manufactured through the pultrusion process, which involves pulling the continuous fibers through a die that is circular in cross-section and contains resin. The FRP bars are formed once the resin cures (thermosets) in the die. The amount of basalt fiber in BFRP bars is not standardized, but the fiber content most frequently reported in the literature falls in the range 75% to 90% ,11. Autog, mechanical properties of the polymer deteriorate, and its ability to transfer stresses between the fiber and the surrounding concrete decreases [FRP bars are more sensitive to fire than steel bars. However, because the FRP bars are embedded in concrete, they do not contribute to fire severity nor toxicity. Nonetheless, FRP-reinforced concrete elements have lower resistance to fire compared to steel-reinforced concrete elements . More imecreases . The stru), than traditional steel reinforcing. However, the modulus of elasticity of traditional steel may be 3.5 times or greater than BFRP. BFRP elastic moduli varying from 44.5 to 71 MPa were reported in the literature [BFRP bars may be 2.3 times stronger or more , in terms of ultimate strength  and the surrounding concrete. Specifications for BFRP bars are not explicitly mentioned in ACI440.1R-15 . HoweverHenin et al.  noted thSeveral guides, standards, and codes developed test procedures to determine bond strength that was used to develop bond–slip relationships ,25,26. Tτ , is related to the tension force, F , through Equation (1).τ = average bond stress, MPa (psi); F = tensile force, N (lbf);The bond strength, bC = effective circumference of FRP bar, calculated as 3.1416 bd where bd is the effective bar diameter of the bar, calculated according to Test Method D7250/D7250M, mm (in);l = bonded length, mm (in).l, which is defined as the length in contact with concrete.Typically, the failure load is influenced by the bond length, Studies by  concludel = 10D. Comparing l = 5D, which is less than the strength of the same bar (33 MPa) when l = 10D.The maximum bond strength increases with an increase in bond length for bars of the same material, diameter, rib spacing, and rib depth. The relationship between bond strength and slippage between BFRP and the surrounding concrete is essential to the design of flexural members. As indicated earlier, the effectiveness of the bond strength depends on several factors, including, but not limited to, surface configuration/preparation of the BFRP bars. Several studies have shown that ribbed/indented, helically wrapped, and sand-coated BFRP provides the highest bond strength compared to BFRP without wrapping or without sand coating . One stuWhen wrapped BFRP reinforcing bars are not coated with sand, the bond strength is affected by rib depth , rib spacing, and rib configuration . Therefore, large rib spacing (or lack thereof) leads to a much weaker bond strength of BFRP bars compared to unribbed bars, regardless of sand coating . The sanw, in ACI318 was also adopted in ACI440.1R [Similar to traditional carbon steel, the characteristics of the bond between FRP reinforcing bars and the surrounding concrete affects crack width under service load. The current philosophy for controlling service load crack width, CI440.1R , which rfE = design or guaranteed modulus of elasticity of FRP defined as mean modulus of sample of test specimen (fE = averageE), MPa (psi);fsf = stress level induced in FRP at service load, MPa (psi);cC = clear cover, mm (in);bk = bond-dependent coefficient.bk = 1.0 when FRP bars have the same bond characteristics as carbon steel, while a bond coefficient less than 1.0 indicates a superior bond between FRP bars and concrete in comparison to steel. On the other hand, a bond coefficient greater than 1.0 indicates that the FRP bond performance is inferior to traditional steel. Studies on various fiber manufacturers, cross-sections, and resin types indicate that the bond coefficient could vary from 0.6 to 1.72 [bk = 1.4 to be used in the absence of test data. The variation in the type of fiber  contributes to the reported wide range of bk values.The experimental bond coefficient is bk = 0.8, indicating the superior performance of BFRP bars compared to steel.The recommended bond coefficient for sand-coated, fiber-wrapped BFRP bars in CSA  is kb = Calculation of the bond coefficient is commonly obtained from the crack width model in CSA and is represented by Equation (3).fE = modulus of elasticity of FRP;fF = flexural stress in FRP bars;β = ratio of the distance from the neutral axis to extreme tension fiber to distance from neutral axis to center of tensile reinforcement;cd = thickness of concrete cover measured from extreme tension fiber to center of tension barss = spacing of longitudinal bars.bk = 0.76 [Four-point loading tests on concrete beams reinforced with deformed, sand-coated, and wrapped BFRP bars indicate superior bond performance, with b = 0.76 . This stbk = 0.77 for double sand coating of wrapped BFRP bars and bk = 0.92 for single sand coating. Clearly the bond coefficient of double sand coating is close to the recommended CSA [Experimental studies by  deduced nded CSA  value bubk value, is due to the reduced area in contact with concrete. This is consistent with the requirement for longer development length when traditional carbon reinforcing steel is bundled. bk = 1.25 was recommended for wrapped BFRP bars that are sand coated [The bond coefficient for any type of bundled BFRP bars is higher than individual bars. The reduced bond effectiveness in bundled BFRP bars, as indicated by the higher d coated .bk calculated based on service load is lower than the values calculated based on controlled crack width [The bond coefficient is often calculated based on controlled crack width, as indicated earlier. It may also be determined based on estimated service load which is sometimes taken as the load causing a stress level of 0.25ck width .−4 s−1 (simulating static loading) to 3.68 × 10−1 s−1 [−5 s−1 (simulating static loading) to 51.3 s−1 (simulating impact). Sea sand is often used to diversify the use of natural sand resources, and it is often the least expensive alternative structure in contact with seawater.Structures may be subjected to dynamic loads, such as those caused by earthquakes or blasts, which are often simulated experimentally by applying strains at a faster rate. Studies on 10 mm diameter BFRP bars showed that bond strength increases while slip decreases with increasing strain rate. The strain rate included in the study ranged from 3.68 × 1010−1 s−1 . However10−1 s−1  show thaA legitimate concern on the use of FRP reinforcing bars is their performance under elevated temperatures. While basalt fibers are naturally fire resistant, the resin that binds the fibers together cannot withstand elevated temperatures. In general, the bond strength between FRP bars and concrete decreases with an increase in temperature. However, BFRP bars maintained better bond strength compared to GFRP bars at elevated temperatures ranging from 70 °C to 220 °C . The losLongitudinal GFRP and CFRP reinforcing bars used in columns contributed as little as 5% to 12% of the ultimate axial compression capacity, therefore, ACI440.1R recommends neglecting all contributions to compression in columns as well as beams , which iIn the sequent subsections of this article, the discussion is limited to the response of flexural members reinforced with BFRP bars placed on the tension side as is typically the case in beams. The discussion in this article generally applies to the flexural response of two-way floor slabs reinforced with FRP bars. However, some experimental studies and reviews also showed FRP bars with a promising ability to resist two-way shear (punching shear) in concrete flat slab/plate, although further research is needed to understand failure modes and quantify limitations .Flexural members reinforced with BFRP bars exhibit many similarities to the response of beams reinforced with carbon steel. As indicated earlier in this article, the modulus of elasticity of BFRP bars (44 to 72 MPa) is much lower than the typical modulus of carbon steel (200 MPa), which influences flexural response. Load–deflection tests of BFRP-reinforced beams showed that stiffness remains essentially the same until cracking begins, regardless of reinforcement ratio, and whether or not the beams have shear reinforcement . As showFor beams loaded to failure in flexure, the crack width and distribution were enhanced by increasing the reinforcement ratio. This enhanced response was attributed to increased stiffness with increased BFRP reinforcement. Crack width, however, is not a durability concern in BFRP-reinforced beams as the bars do not corrode, unlike steel-reinforced beams. Crack width is only an aesthetic concern rather than a safety problem. Therefore, for aesthetic considerations, a larger crack width of 0.5 mm is permitted for exterior exposure while 0.7 mm is permitted for interior exposure .Experimental studies by  show thaThe much higher stiffness of steel reinforcing bars compared to BFRP bars increases the overall beam stiffness. The experimental study by Shamass and Cashell on the beam shown in In general, BFRP-reinforced flexural members exhibit significant cracking and large deflections, but they develop fast with a limited warning before failure . The larThe cracking moment is a property of the concrete section, which depends on the modulus of rupture and cross-sectional dimensions. It represents the moment corresponding to the initiation of flexural rupture and inception of the first crack. A fundamental assumption in its calculation is that it does not depend on the type of reinforcement, as shown in Equations (4)–(7) for the American Concrete Institute (ACI) code, Canadian code, Russian Code, and European code (EC), as well as the mechanics expression for cracking moment in Equation (8).The cracking moment may also be estimated experimentally by noting the cracking load, which corresponds to the change of stiffness of the experimentally developed load-deflection curve.cr for BFRP-reinforced beams was higher than that predicted values using ACI440.1R-15 or CSA, while the measured cracking load was very close to code-based predicted values for beams reinforced with traditional carbon steel.An experimental study by  indicateAs indicated in the previous section, studies consistently showed that cracking moments exhibited by BFRP-reinforced flexural members are higher than those predicted by various codes, although by different amounts. Similarly, the modulus of rupture increases by adding an adequate amount of basalt fibers, such as basalt macrofibers (BMFs), to the concrete mix . More thu. ACI440.1R-15 [u for structural elements reinforced with GFRP bars, and 0.3fu for AFRP bars. However, a limit of 0.55fu is set by ACI441.1R-15 for CFRP bars, which is similar to the recommendation of given by [FRP bars in general, including BFRP, have low compression capacity, therefore, their use is mostly investigated to reinforce flexural members including beams and slabs. Applications in flexural members of bridge structures are likely to require a proper understanding of fatigue response. A study by  on BFRP-40.1R-15  sets a mgiven by  for BFRPAdditionally, the reinforcement ratio of FRP bars is defined asSustained loads over an extended period of time cause progressive deformation in FRP bars, known as creep. Flexural members subjected to bending due to various external loads, including sustained loads, will induce tensile stresses in reinforcing FRP bars. After a period of time, known as endurance time, sustained tensile stresses and resulting creep can cause a failure in FRP bars known as creep rupture. When stresses are sustained, a reinforcing BFRP bar will fail at values much lower their ultimate strength of BFRP bars. It is therefore important to control the tensile stress level in FRP bars. Creep of BFRP bars is complex and affected by the properties of fibers, resin, and the interface between fiber and resin, and it is often assumed with reasonable accuracy that the creep of FRP bars is dominated by the resin properties . It is fThe most suitable mathematical model relating the ratio of the creep stress at failure/the initial ultimate strength to the sustained load duration (time) is found to be a linear relationship with logarithmic time ,41,42.When FRP reinforcing bars rupture, failure of the reinforced flexural member is catastrophic, regardless of the fiber used . Compared to steel-reinforced flexural members, FRP-reinforced beams offer a limited warning of impending failure in the form of extensive cracking and large deflections. These large deflections of the concrete membrane at failure are caused by the large elastic elongation of FRP bars before their rupture. This is especially true when the section is designed as tension controlled where failure is controlled rupture of FRP bars. In addition, the much smaller modulus of elasticity of the FRP reinforcing bars also contributes to deflections that are much larger than an equivalent steel-reinforced flexural member.The addition of 43 mm long basalt microfibers (BMFs) having 1000 MPa tensile strength tends to increase the stiffness of concrete slabs reinforced with BFRP bars when the volume fraction of BMFs exceeds 0.5%. This is demonstrated by a decrease in deflection of over-reinforced  [uf, the axial stiffness of FRP bars is lower than steel bars of the same area, due to the lower modulus of elasticity of FRP bars in general. As a result of the relatively lower axial stiffness, the neutral axis depth of the cracked FRP-reinforced concrete section is shallower than an equivalent concrete section with an equal area of reinforcing steel. As a result, ACI440.1R-15 recommends the concrete contribution to shear resistance given by Equation (15), which depends on the neutral axis depth kd. The neutral axis depth kd is dependent on the ratio of FRP reinforcement ratio, Most design codes and standards consider the shear capacity of a concrete cross-section, nt area) . Other snt area) . DespiteNoting the effect of the relatively low axial stiffness of FRP bars in general compared to steel reinforced concrete members on shear strength, it is therefore beneficial to consider increasing the reinforcement ratio and design concrete flexural members as over-reinforced. Increasing the reinforced ratio and/or modulus of elasticity of BFRP was shown to reduce shear crack width and increase the contribution of uncracked concrete to shear resistance by increasing the depth of the compression block and aggregate interlock .BFRP shear reinforcement placed perpendicular to the axis of the member is effective in resisting shear failure and increasing the load-carrying capacity, especially when the beam is tension controlled . BFRP shfbf = strength of the bent portion of FRP, MPa (psi).The sheer force carried by the stirrups is proportional to the stress, In seismic areas, where the use of lightweight concrete is of interest, studies have shown that BFRP-reinforced beams exhibited the same concrete contribution to shear resistance as that of normal-weight concrete . That coIt is important to develop a reasonable understanding of the long-term structural response of concrete members reinforced with BFRP bars in an unfavorable environment. The unique structure of FRP bars, consisting of fibers and resin, makes it important to evaluate their durability in aggressive environments such as prolonged exposure to elevated temperature, highly alkaline environments, and freezing/thawing and low temperature. In addition, moisture ingress into the resin, before placement in concrete, could lead to degradation of mechanical properties of FRP reinforcing bars. Some studies indicate that vinyl ester resin offers better resistance to moisture ingress compared to other types of resins used in making FRP bars. Studies have shown that up to 40% of the tensile strength of GFRP bars can be lost after exposure to a combination of ultraviolet rays and moisture tests with and without loading . TensileThe bond strength between ribbed BFRP bars (without wrapping or sand coating) and concrete appears to improve in samples subjected to higher temperatures of 50 °C and 60 °C for 1.5 months, compared to samples tested after 1.5 months of exposure to 40 °C [−1) [Alkalinity is defined as the condition of having or containing hydroxyl ions (OH−1) . In expe−1) . Aqueous−1) . It is nEngineering properties of BFRP bars themselves are adversely impacted by the alkaline environment, especially at temperatures higher than 40 °C. Alkaline environments with pH values between 8 and 10 are likely for mortar and concrete during service life . A pH ofConditioning in alkaline solution (pH = 12.8) at a relatively high temperature of 60 °C for 5000 h (7 months) caused a loss of 8.3% in the modulus of elasticity of BFRP bars . The fibStudies by  showed tPrediction models for ultimate tensile strength and moduli of BFRP bars in an alkaline concrete/mortar environment estimate that after 100 years of exposure at temperatures up to 60 °C, 72% of the ultimate tensile strength and 80% of the modulus of elasticity are retained . The BFRRoom temperature (20 °C ± 2 °C) studies on 8 mm diameter bars made of vinyl ester matrix that were immersed in alkaline solution (pH = 12.9) for 180 days show the relative superiority of BFRP bars compared to GFRP in terms of retention of tensile strength. BFRP bars retained 77.6% of the unconditioned tensile strength while GFRP bars retained 69.2%. CFRP experienced the least deterioration in the same alkaline environment by retaining 82.8% of the unconditioned tensile strength , the samExperimental studies conducted to date have produced data on bond durability with significant scatter in terms of the loss of bond strength due to conditioning. Such scatter is caused by research campaigns being designed with different goals set by researchers. As a result, bond durability models that take advantage of the abundant test data have proved to be challenging .The performance of BFRP-reinforced concrete structures at low temperatures and subjected to freezing and thawing cycles is also of interest to researchers and designers. Exposure to low temperature and freezing/thawing (FT) cycles did not change the bond failure mode in experimental studies . Up to a2 into the atmosphere. The major findings of this review include: Similar to all FRP reinforcing bars, the BFRP stress–strain relationship is linear until failure by tensile rupture, unlike traditional reinforcing steel which reaches yield stress, becomes inelastic, strain hardens to ultimate strength, and then softens to rupture.Characteristics of the bond between BFRP bars and the surrounding concrete are influenced by factors including rib height and rib spacing. Many studies confirm that the most effective bond strength is achieved when BFRP reinforcing bars are ribbed, helically wrapped, and sand coated. One study reported that sand-coated ribbed bars achieved better bond strength than helically wrapped and sand-coated bars. The bond coefficient of helically wrapped and sand-coated BFRP bars was found to be comparable, and sometimes superior, to the bond coefficient of traditional carbon reinforcing steel. The bond coefficient is used in design codes to control crack width, an essential serviceability consideration of FRP-reinforced flexural concrete members. Loss of bond strength of BFRP bars at elevated temperatures is equivalent to that of GFRP bars. However, at an elevated temperature of 350 °C, a significant loss of bonding occurs in BFRP bars.The modulus of elasticity of FRP bars is much lower than traditional steel bars, leading to higher deflections in equivalent FRP-reinforced flexural members. As a result, the over-reinforced design offers the relative advantage of reduced deflections compared to under-reinforced FRP flexural members. Nonetheless, most standards, including ACI4401.R-15, provide guidance on designing FRP-reinforced beams as under-reinforced or over-reinforced.Experimental studies confirmed that tensile strength, shear strength, and interlaminar shear strength deteriorate over time when BFRP bars are conditioned in alkaline solution, especially at high temperatures. At a relatively high temperature of 60 °C, BFRP bars were found to lose nearly 29% of the tensile strength after 9 months of conditioning in alkaline solution. Due to the larger exposure area, larger bar diameters were more impacted by the alkaline environment than smaller diameters. Deterioration in the modulus of elasticity was much smaller than in tensile strength.There is a need for standardization of the manufacturing of BFRP bars with the goal of providing reliable guaranteed mechanical properties, such as tensile strength and shear strength. Significant variability is reported in mechanical properties, often including bars produced by the same manufacturer. One reason for the variability in properties is the inconsistency in the application method and composition of fiber size, a thin layer of treatment applied to the fibers during manufacturing. Currently, pultrusion is the most common method for manufacturing BFRP bars.Future research needs: Limited research is available on the structural response of cast-in-place (CIP) BFRP-reinforced concrete members under compression and/or combined compression and flexure, which currently restricts the application of BFRP reinforcement to flexural members. Further research is needed to quantify the effect of rib depth and rib spacing of deformed BFRP bars on the structural response of BFRP-reinforced flexural members. Sand coating to enhance bonding of BFRP to the surrounding concrete is still an open research area in terms of effectiveness and clarity of specifications of the materials and methods of application.This article reviewed the current state-of-knowledge on research related to the application of basalt fiber-reinforced polymer (BFRP) as reinforcing bars for concrete. The review emphasized mechanical properties and durability as being the most critical for engineers in professional practice as well as researchers. The most commonly researched and used fibers for FRP bars are glass, carbon, and aramid, therefore, FRP bars with these materials have been incorporated in international design guides and standards on concrete reinforced with FRP bars, such as ACI440.R-15. The use of basalt fibers to produce concrete reinforcing FRP bars is gaining popularity due to their competitive durability, natural corrosion resistance, nonmagnetic properties, and sufficiently high tensile strength. Although carbon steel retains its environmental advantage of being 100% recyclable, its manufacturing process continues to emit significant CO


Chuankezhi injection (CKZI) was an effective traditional Chinese medicine (TCM) injection in adjuvant bronchial asthma therapy. In this report, we used a network pharmacology method to reveal the mechanisms of CKZI for the treatment of asthma.  The candidate compounds in CKZI were determined by searching the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and China National Knowledge Infrastructure website (CNKI). The targets of candidate compounds were searched in the TCMSP, DrugBank 5.0, and SwissTargetPrediction. The disease targets were screened from the Online Mendelian Inheritance in Man (OMIM) and GeneCards. The overlapping gene symbols between candidate compounds and disease were filtered via a Venn diagram and were considered as potential targets. A protein-protein interaction (PPI) network and disease-related candidate compound-target-pathway (DC-T-P) network were visualized by Cytoscape 3.6.1. Gene Ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed by metascape to determine the pathways related to asthma.  A total of 70 overlapping gene symbols were recognized as potential targets. Cytokines  and chemokines (CXCL8 and CCL2) could be recognized as hub genes. Asthma-related candidate compounds were mainly flavonoids, such as quercetin, luteolin, and kaempferol. The cytokine-mediated signaling pathway, cytokine receptor binding, and membrane craft were the most significant biological process (BP), molecular function (MF), and cellular component (CC) of GO function results, respectively. The relevant pathways of CKZI against asthma mainly include IL-17, NF-kappa B, HIF-1, calcium, and PI3K-Akt signaling pathways.  Our research provided a theoretical basis for further investigating the mechanisms of CKZI in the treatment of asthma. Bronchial asthma was reported as the most prevalent chronic inflammatory airway disease, and it affected 1–18% of the global population , 3. Asthβ-receptor agonist (LABA)  remained the first-line therapy in the treatment of chronic asthma. Combination therapy could prevent and reduce the rate of exacerbations compared with ICS treatment alone [β2-agonist (SABA), ipratropium bromide, environmental control, and targeted biologic therapy, such as anti-IgE  and anti-IL-5 [Up to now, the combination of inhaled corticosteroid (ICS) and long-acting nt alone . It coulnti-IL-5 . NeverthEpimedium brevicornu Maxim., Epimedium sagittatum (Siebold and Zucc.) Maxim., Epimedium pubescens Maxim., and Epimedium koreanum Nakai) and Bajitian . Several researches have shown that CKZI could relieve cough and asthma, exerting antiallergy, anti-inflammatory, stress response, and immunoregulatory functions. CKZI might adjust the imbalance of Th1/Th2 and reduce the expression level of IL-17 of asthma patients [Traditional Chinese Medicine (TCM), featured as having “multiple ingredients, multiple targets, and multiple pathways,” has been used for treating complex systemic diseases for thousands of years in China. TCM attracted scientists' attention as it had unique advantages, such as fewer side effects, attenuated symptoms, and life quality improvement , 9. Chuapatients –14. HoweNetwork pharmacology has emerged as an effective approach to explore the internal mechanisms of multicomponent drugs. Based on the database information of genomics, proteomics, diseases, and drugs, we could construct the visualized interaction network of “drug-component-target-pathway-disease” (D-C-T-P-D) to investigate the therapeutic effects of active ingredients and targets on the network. Network pharmacology created the “multicomponent” and multitarget” mode and has been used to reveal the potential mechanisms of compound Chinese herbal extracts or preparations –17.In this report, we used a network pharmacology method to investigate the therapeutic effects of CKZI in the treatment of asthma. We screened the candidate compounds in CKZI and their corresponding targets. The overlapping targets of asthma and candidate compounds were analyzed, and a disease-related candidate compound-target-pathway (DC-T-P) network was constructed.http://tcmspw.com/tcmsp.php/) [https://www.cnki.net/). Secondly, drug-likeness (DL)≥0.18 and oral bioavailability (OB)≥30% were set as the thresholds to select candidate compounds via ADME  analysis. Thirdly, we used TCMSP, DrugBank 5.0 (https://www.drugbank.ca/) [http://www.swisstargetprediction.ch/) [http://www.UniProt.org/). The species was selected as “Homo sapiens (Human),” and nonhuman gene symbols were eliminated.Firstly, candidate compounds in YYH and BJT were screened in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform   and Chinank.ca/)  and Swision.ch/)  to screeCandidate compounds in YYH and BJT and the screened targets (gene symbols) were input into the Cytoscape 3.6.1 software to construct a Herb-Candidate Compound-Target network. A graphical network represents the connection and interaction between target genes and compounds, and the topological properties of the network were analyzed through Network Analyzer (default setting). The degree value of a node (candidate compound or target) indicates the number of connections between the node and other nodes. Specifically, the greater the degree value is, the more critical the node is.https://omim.org/) and GeneCards (https://www.genecards.org/) [Homo sapiens (Human).” All the targets were normalized to standard gene symbols by UniProt (http://www.UniProt.org/), and duplications were removed. The overlapping gene symbols between candidate compounds and disease were filtered via a Venn diagram and were considered as potential targets of CKZI in the treatment of asthma.Bronchial-asthma-related targets were searched from the Online Mendelian Inheritance in Man   databasehttps://string-db.org/) [Homo sapiens (Human)” was chosen, and a scoring value > 0.7 was selected as a high confidence basis for protein interactions. PPI network files were imported into Cytoscape 3.6.1 software to construct the PPI network, and the topological properties of the PPI network were analyzed through Network Analyzer (default settings).PPI is the process by which two or more proteins form a protein complex through noncovalent bonds. The PPI network was constructed by importing potential targets into the STRING 11.0 database (db.org/) , 23 and https://metascape.org/) [P value < 0.01, minimum overlap≥3, and minimum enrichment≥1.5 were considered as the cutoff criteria.To reveal the potential biological function of CKZI in the treatment of asthma, Gene Ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed by metascape (pe.org/) . GO termDisease-related targets were selected by analyzing KEGG pathway enrichment, and disease-related candidate compounds in two herbs were screened by comparing with disease-related targets. Candidate compounds that did not contain disease-related targets were eliminated. To analyze the association among the disease-related potential targets, candidate compounds, and pathways, a DC-T-P network was constructed using Cytoscape 3.6.1 software, and the topological properties of the network were analyzed through Network Analyzer (default settings).The candidate compounds selected from YYH were mainly flavonoids, sterols, alkaloids, esters, and phenolic acids, and compounds selected from BJT were mainly anthraquinones, sterols, esters, and flavonoids. To analyze the association among herbs, candidate compounds, and related targets, we then constructed the herb-candidate compound-target (H–C-T) network by using Cytoscape 3.6.1 software. A graphical network is shown in Bronchial-asthma-related targets were searched from OMIM and GeneCards databases. The relevance score in GeneCards was set as 6.125. After normalized to standard gene symbols by UniProt and removing duplicates, 543 gene symbols were selected as disease-related targets. The potential targets of CKZI in treating asthma were considered as the overlapping gene symbols between candidate compounds and disease. The result is shown in Based on 70 potential targets, a PPI network was established by importing the standardized gene symbols of the potential targets into the STRING 11.0 database. Cytoscape 3.6.1 software was used to visualize the PPI network. As illustrated in P value < 0.01 was considered as the cutoff criterion. GO (Gene Ontology) enrichment analysis was annotated from three aspects: the biological process (BP), molecular function (MF), and cellular component (CC). To reveal the pharmacological mechanisms of CKZI in asthma, 70 potential targets were put into the metascape database for annotation GO enrichment analysis and a P value < 0.01, a total of 22 pathways were obtained and the top 20 pathways were shown as the core pathways, as shown in KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis of the 70 potential targets was conducted using the Metascape database. Based on the criterion of a Asthma-related candidate compounds, targets, and pathways were screened by the results of KEGG pathway enrichment analysis. A graphical network was constructed by Cytoscape 3.6.1 and is shown in Epimedium brevicornu Maxim., Epimedium sagittatum (Siebold and Zucc.) Maxim., Epimedium pubescens Maxim., and Epimedium koreanum Nakai) and Bajitian . CKZI was widely utilized in adjuvant therapy of asthma, but its mechanisms have not been systematically and comprehensively elucidated. Herein, we used a network pharmacology method to construct a disease-related candidate compound-target-pathway (DC-T-P) network to visualize the connection between CKZI and asthma.Bronchial asthma was the most prevalent chronic inflammatory airway disease, and the first-line treatment was combination treatment of ICS and LABA. However, severe side effects of ICS and LABA and poor compliance of severe asthma patients limited their applications in clinic. CKZI was a compound TCM injection that mainly included two Chinese herbal extracts, Yingyanghuo , an important cytokine in the innate immune response, can incite inflammatory response and promote airway epithelium injury and airway hyperresponsiveness. It could also stimulate the induction of inflammatory cytokines, including IL1B, IL6, and IL8 [Among the abovementioned physiological processes, cytokines and chemokines were widely investigated in the pathogenesis of asthma. Study has found that asthma was more severe in patients with excessive cytokine expression, such as IL6, IL1B, and TNF-e asthma . Tumor n and IL8 , 32. Che and IL8 .We performed GO function and KEGG pathway enrichment to reveal the related signaling pathway as cluster analysis could exhibit a refined detail of CKZI for treating asthma compared to common compound-target analysis. The cytokine-mediated signaling pathway, cytokine receptor binding, and membrane craft were the most significant biological process, molecular function, and cellular component of GO function results, respectively. The result was in accordance with hub genes selected by PPI analysis, indicating cytokines and chemokines were important biomarkers in the progression of asthma.α, reflected by an enhanced production of inflammatory mediators such as CXCL-8, and corticosteroid insensitivity [κB, PI3K-Akt, and HIF-1 were also relatively important pathways.The IL-17 signaling pathway was the most important pathway in the treatment of asthma by CKZI. IL-17 A was associated with neutrophilic inflammation and corticosteroid insensitivity in different asthma conditions, including allergic asthma, severe asthma, and asthma exacerbations. In addition, IL-17A has been shown to decrease the responsiveness of CXCL-8 production by the bronchial epithelium to corticosteroids. In the majority of asthma patients, primary bronchial epithelial cells were hyperresponsive to IL-17A and proinflammatory stimuli such as TNF-sitivity –36. Othein vitro and in vivo experiments needed to be established to verify the results analyzed by the network pharmacology method.Network pharmacology was an effective approach to explore the internal mechanisms of multicomponent drugs, particularly TCMs. After analysis, we found that flavonoids extracted from YYH and BJT played a vital role in the treatment of asthma. Cytokines  and chemokines (CXCL8 and CCL2) could be recognized as hub genes. The cytokine-mediated signaling pathway  was the main mechanism for CKZI in the treatment of asthma. However, further


The metabolic reprogramming of cancer cells creates metabolic vulnerabilities that can be therapeutically targeted. However, our understanding of metabolic dependencies and the pathway crosstalk that creates these vulnerabilities in cancer cells remains incomplete. Here, by integrating gene expression data with genetic loss-of-function and pharmacological screening data from hundreds of cancer cell lines, we identified metabolic vulnerabilities at the level of pathways rather than individual genes. This approach revealed that metabolic pathway dependencies are highly context-specific such that cancer cells are vulnerable to inhibition of one metabolic pathway only when activity of another metabolic pathway is altered. Notably, we also found that the no single metabolic pathway was universally essential, suggesting that cancer cells are not invariably dependent on any metabolic pathway. In addition, we confirmed that cell culture medium is a major confounding factor for the analysis of metabolic pathway vulnerabilities. Nevertheless, we found robust associations between metabolic pathway activity and sensitivity to clinically approved drugs that were independent of cell culture medium. Lastly, we used parallel integration of pharmacological and genetic dependency data to confidently identify metabolic pathway vulnerabilities. Taken together, this study serves as a comprehensive characterization of the landscape of metabolic pathway vulnerabilities in cancer cell lines. individual genes or metabolites. However, metabolic pathways exhibit significant cross talk and compensation for one another. We developed a computational method to answer the question: when a metabolic pathway’s activity is high, which other metabolic pathways become more essential or less essential? By integrating genetic screen data with drug response data from FDA approved drugs, we identified cancer cell line dependence on metabolic pathways as opposed to individual genes. For example, we found that identifying key regulators of metabolic pathways, such as the Pentose Phosphate Pathway, may serve as a biomarker to identify which patients may benefit from antifolate chemotherapies . The efforts outlined here serve as the first characterization of the landscape of metabolic pathway vulnerabilities in cancer cell lines. Our results demonstrate the benefit of analyzing dependencies on metabolic pathways as opposed to metabolic genes.Cancer cells rewire their metabolism, which creates targetable metabolic vulnerabilities. Previous analyses of metabolic vulnerabilities in cancer cells have been limited to the analysis of  MTAP)  A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Jason W. Locasale, Ph.D.Guest EditorPLOS Computational BiologyDouglas LauffenburgerDeputy EditorPLOS Computational Biology***********************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Manuscript title: The landscape of metabolic pathway dependencies in cancer cell linesSummary:The manuscript by Joly et al., describes an integration of genetic dependency data (DepMap CRISPR/CAS screens), gene expression data (RNAseq), and pharmacological drug sensitivity information (DepMap PRISM) on CCLE cell lines and provides a means for metabolic pathway-level interpretation. Their approach identifies novel and previously known links between metabolic pathway activity (expression levels) in a given cell line, and essentiality of genes in various metabolic pathways as well as sensitivity of cell lines to drugs targeting various metabolic pathways. Overall, they developed a useful analysis pipeline for interpretation of multi-level treatment data on cancer cell lines and provide valuable insights into future attempts for drug discovery, personalized medicine, and combination therapies. Overall, I think it is a solid work that fits well the scope and standards of the journal PLOS Computational Biology. I believe this manuscript is suitable for publication subject to the following revisions.Major points:1- Figure 3 legend is inconsistent with the text. Line 215 mistakenly states that folate concentration is 4 times higher in RPMI than DMEM, while the reverse is true and consistent with the main text (line 226). Also, line 216 in the legend mistakenly states dependency on Folate is higher in DMEM, while the reverse is true and consistent with the figure and main text.2- Figure 3, right color bar, shows Folate twice, with different corresponding dependency values. Please correct.3- Line 232: The authors attribute the difference seen between RPMI and DMEM in dependency to Oxidative phosphorylation to the fact that RPMI and DMEM contain 150 μM and 0 μM aspartate, respectively. They should add a plausible explanation as to why this difference is not also reflected in the dependency to Aspartate, Alanine, Glutamate metabolic pathway which also involves aspartate.4- The authors need to add some discussion/clarification about the effects of media composition on dependencies. Many instances shown in Figure 3 are not consistent with the overall conclusions of this section (lines 234-236) claiming media composition affects pathway dependency. For instance, Glutathione, Histidine, and Phenylalanine are example of reagents with considerable differences in fold abundance between the two media conditions, but no apparent effect on relative dependencies. Overall, I think in order to be able to claim a general effect from media composition (or culture mode) on dependencies, a robust statistical method such as ANOVA should be used and appropriate p-values should be reported.Minor points:1- The authors used ssGSEA, which is a rank-based enrichment method, for inferring metabolic pathway activities based on expression levels of individual genes. The choice of this approach over alternative methods such as normalized weighted average expression should be justified either by direct comparison or at least by a logical explanation.2- Throughout the manuscript, the authors only consider 69 of a total of 91 metabolic pathways defined by KEGG. The rationale behind this choice is not clear.3- Line 153-154 is vague. Instead of using the word “association”, the authors should use “enrichment” (GSEA FDR<0.05).4- Line 194: The authors raise an interesting question about whether there might be a link between a metabolic pathway’s essentiality and its own activity level. They report that of the 69 metabolic pathways queried, 36 had a negative NES and 33 had a positive NES. From this, they conclude that there is no general rule regarding pathway expression and essentiality. A question that bears asking is whether any meaningful differences could be detected between the pathways that fall into the above two group . The authors should at least include the names of the pathways in each of the two groups in a supplementary table rather than only reporting the numbers 33 and 36 to facilitate further investigation beyond what is currently stated in the manuscript.5- Line 244: Please describe how this AUC is measured and why IC-50 is not used instead.6- Line 276: Typo: “activity can be associated with anti-cancer drug sensitivity”7- I am curious to see if the method introduced in the present manuscript could be used to test the experimental findings by Gao et al. ) showing that methionine deprivation has a synergistic anti-cancer effect with 5-Fluorouracil therapy? I think this could potentially add an interesting validation to the present manuscript.Reviewer #2: OVERVIEW:In the manuscript, "The landscape of metabolic pathway dependencies in cancer cell lines", the authors describe a novel pipeline which attempts to draw a correlation between CRISPR-Cas9 loss-of-function essentiality screens, pharmacological, and gene expression data. Indeed, the authors were able to find correlations. The pipeline is poorly described and it is very difficult for the reader to find crucial details about the analysis. Also the need for the pipeline is not clearly delineated by the authors in the Introduction, discussion, methods or results. The authors have written a lengthy and redundant manuscript for example methods section of simulation studies is basically same as what they wrote in Results section. The authors apply their pipeline to sample data to show its sensitivity. However, no comparison between the sample data and real data is available. The pipeline described by the author is unclear to me; however, I feel like the analysis presented in the paper still needs more clarity and fleshing out. Further, the pipeline makes use of a lot of data only to recapitulate existing knowledge and very little new findings. The state of the manuscript still feels preliminary to me, in terms of both analysis and presentation, and leaves a lot to be desired. Keeping all this in mind, I recommend a major revision.MAJOR CONCERNS:1. The authors literally gave two paragraphs of background in the introduction before jumping to what they did in the paper. We often write Introductions in the paper to establish the relevance of the work and give the readers some context on where authors are coming from. Metabolic pathway dependencies in cancer are studied often and should have plenty of information for the authors to discuss. Please rewrite the entire section with some relevant examples as discussed in the results section. I would make some suggestions but I would review rather than write the paper for them.2. The shape of the gene expression distributions of different cell lines may be significantly different. How similar are the gene expression distributions of simulated data from the real data (of the 16643 genes) for each of the 300 cell lines? Further, some of the cell lines are similar which the real data (both gene dependency and expression) will reflect. Do the authors introduce some "meaning" in their simulated data that their pipeline is supposed to catch? How do the authors ensure the pipeline will catch meaningful signals? Also, Could the authors provide a better way to understand 1B and 1C? What is the sensitivity of Genetic PDEA conveying? What does it mean? In line 118, what is a significant results here? The method can be sensitve, but can it capture accurate results? How do the authors know this? Perhaps some benchmarking and comparison of this simulated data would be useful for the reader to understand. The authors may have this in the supplementary information.3. What does the metabolic pathway activity mean here? In Figure 1, what are the dimensions of sub-figure A1. The pipeline description is not clear.4. If the authors are using DepMap dependency data which tells you the essentiality of the gene, why is there a need summarize the data at the level of pathway so early in the analysis. The authors could be loosing information by doing this. Can't we correlate the genes themselves and then finding the pathways with high positive or negative correlation coefficients.5. Analysis in Fig 2, did that involve extraction before or after Genetic PDEA? Also how were genes belonging to multiple pathway treated?6. At line 201, by context, do the authors mean environment (media)? Also, it should be obvious, no? that the pathway dependencies were influenced by media? A metabolic gene that is essential in one media doesn't need to be essential in another. However, it would be more curious if this were true for significant number of non-metabolic genes and pathways. Also, Could the authors show how much influence media has over the NES and correlation coefficients?7. The way the whole analysis is done, the authors continue to integrate any new data they find. However, it would be nice if authors could validate some of their analysis. Alternatively, present new targets or novel unexplored pathway vulnerabilities, for e.g. using analysis in Fig 6. As of now, it seems like a method that is very expensive that needed transcriptomic data, gene essentiality data, and then pharmacological data, to produce known findings.MINOR CONCERNS:1. Line 101: Should that ssNES?2. Line 133: "Then, a synthetic gene set of 25 genes was perturbed..." For better visualization of the perturbation, could authors also say how much does the value of each gene possibly change. Also what is X and its value?3. Something doesn't see right with the sentence in line 159. Are authors trying to say "...dependecies are clustered together based on pathway activities more than activities are clustered based on dependencies..."? How? It is not clear from just looking at the figure 2A or Supp Fig 1?Reviewer #3: The present study is intended to provide a comprehensive characterization of metabolic pathway vulnerabilities in cancer cell lines. The authors describe computational value scores of metabolic pathway activity and calculate associations between such scores and sensitivity to clinically approved drugs. They report that metabolic pathway dependencies are highly context-specific and that cancer cells are vulnerable to inhibition of one metabolic pathway only in conjunction with another specific metabolic pathway. As an example, they argue that their approach implemented for Pentose Phosphate Pathway may serve to identify which patients that respond to antifolate chemotherapies. Overall, while the study has potential value for future applications, the authors should provide at least some experimental evidence, for example test the effects of combinations predicted by the computational model. Such experiments should be done in various media and O2 concentrations to recapitulate tumor microenvironment heterogeneity. In absence of any experiment, it is difficult to judge the value of such computational predictions.More specific concerns highlighted below:1. Starting at line 264, the authors state that they “found a strong association between decreased Core Glycolysis (hsa_M00001) pathway activity and increased sensitivity to AZD8931, an inhibitor of EGFR and ERBB2. This should be a good test case in principle, but still unclear to me how it would be done exactly. For example treat a collection of cells with a combination of AZD8931 and inhibitors of various glycolytic enzymes? (how many and on which exact enzymes?). I presume more than one, otherwise it would undermine the foundation of the paper .2. This reviewer remains unsure about the meaning (and usefulness) of metabolic pathway score/value as described. For example, in the case of cholesterol biosynthesis, I understand the essentiality score for HMGCR, or sensitivity scores for statins. When it comes to the entire pathway, my understanding is that the calculation is based on every component of the pathway. However, within any given pathway only a few steps are druggable and/or are rate limiting for the respective metabolic flux. To shut down an entire pathway one need only block a few select steps.3. The reported correlations are based on datasets generated using CRISPR screens. The authors should consider recapitulating the analysis using the RNAi screen data . There are many cases of discrepancies between shRNA and CRISPR based screens . In absence of any experimental approach, it would only be prudent to generate computational models using both types of data.The figures depict CCLE collection as a “nondescript cloud” without further analyses based on lineages, key mutations, etc. Any correlations between the pathway score and individual scores for rate limiting enzymes?**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, Data Requirements:http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: Reproducibility:http://journals.plos.org/compbiol/s/submission-guidelines#loc-materials-and-methodsTo enhance the reproducibility of your results, PLOS recommends that you deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions, please see  25 Mar 2021AttachmentResponse to Reviewers FINAL.pdfSubmitted filename: Click here for additional data file. 6 Apr 2021Dear Dr. Graham,We are pleased to inform you that your manuscript 'The landscape of metabolic pathway dependencies in cancer cell lines' has been provisionally accepted for publication in PLOS Computational Biology.Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. Best regards,Jason W. Locasale, Ph.D.Guest EditorPLOS Computational BiologyDouglas LauffenburgerDeputy EditorPLOS Computational Biology***********************************************************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: I think the authors have adequately addressed my concerns and I have no further comments.**********Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  1223-442824 | ploscompbiol.org | @PLOSCompBiolPLOS Computational Biology | Carlyle House, Carlyle Road, Cambridge CB4 3DN | United Kingdom


Recently, many studies have investigated the role of gene-signature on the prognostic assessment of breast cancer (BC), however, the tumor heterogeneity and sequencing noise have limited the clinical usage of these models. Pathway-based approaches are more stable to the perturbation of certain gene expression. In this study, we constructed a prognostic classifier based on survival-related pathway crosstalk analysis. We estimated pathway’s deregulation scores (PDSs) for samples collected from public databases to select survival-related pathways. After pathway crosstalk analysis, we conducted K-means clustering analysis to cluster the patients into G1 and G2 subgroups. The survival outcome of the G2 subgroup was significantly worse than the G1 subgroup. Internal and external dataset exhibits high consistency with the training dataset. Significant differences were found between G2 and G1 subgroups on pathway activity, gene mutation, immune cell infiltration levels, and in particular immune cells/pathway’s activities were significantly negatively associated with BC patient’s outcomes. In conclusion, we established a novel classifier reflecting the overall survival risk of BC and successfully validated its clinical usage on multiple BC datasets, which could offer clinicians inspiration in formulating the clinical treatment plan. As a highly metastatic and invasive malignant tumor with high incidence, breast cancer (BC) seriously threatens women’s health and quality of life . BC occuWith the development of RNA-Seq high-throughput sequencing technology, various gene expression profiles of BC have been accumulated. Plenty of excellent models have been constructed to decode BC, the majority of them were built based on a single gene list. For example, In this study, we constructed a novel classifier reflecting the overall survival risk of BC based on survival-related pathway crosstalk analysis. We calculated the PAS for each pathway obtained from KEGG and GO resources based on the expression matrix. And then investigated the influence of crosstalk between these selected pathways on different cohorts to select the most critical 100 sub-pathways among all cohorts. we further conducted a K-means clustering analysis to cluster the patients into G1 (moderate) and G2 (aggressive) subgroups. Internal and external dataset exhibits high consistency with the training dataset.https://xenabrowser.net/datapages), while GSE16446, GSE42568, GSE7390, GSE20711, GSE1456A, GSE1456B and GSE20685 microarray data and relevant clinical information were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/gds/). After removing normal and non-survival information samples, we finally obtained 1,090 (TCGA), 107 (GSE16446), 104 (GSE42568), 198 (GSE7390), 88 (GSE20711), 159 (GSE1456A), 159 (GSE1456B) and 327 (GSE20685) BC samples for each dataset.We collected BC gene expression profiles from TCGA and GEO datasets, and the dataset with less than 20 samples or without overall survival information was excluded from our selection. TCGA mRNA expression data (level 3) and clinical features were downloaded from the UCSC Xena webserver (http://geneontology.org/) and generated a new mRNA expression matrix that contains only genes that exist in it for each pathway. After that, for each gene, based on its expression level, we classified the tumors into two subgroups, the samples in the higher group were scored +1, while the others were scored −1. Finally, we averaged all gene scores in this pathway as the pathway activity score for each tumor sample. A higher PAS indicates a higher pathway activity in the sample, and otherwise, a lower score means lower activity in the sample.The pathway activity score (PAS) for each dataset was calculated based on the method proposed by Bhandari et al. . We downp-value for each pathway by regression analysis. The pathways were then ranked based on the log-rank p-value. To minimize the false positive rate, we used the common significant pathways of these three large breast cancer cohorts, instead of using any single data set. The combined rank of each pathway was determined by the sure independence screening (SIS) method. We further selected the top n pathways for pathway crosstalk analysis. The threshold n was set to 100, which is much bigger than N/log(N), where N is the sample size of each cohort.Based on PASs and survival information, we calculated the log-rank A∩PB, while the unique genes that specifically exist in pathway A or pathway B were defined as PA–(PA∩PB) and PB–(PA∩PB). Based on this classifier, each pathway pair could generate three sub-pathways.Different pathways often share some of the same genes, which can lead to crosstalk in the prognostic associations of different pathways. Considering the influence of overlapping genes on the PAS quantification of the two pathways can help identify cancer-related features. We further identified the crosstalk among the 100 selected survival-related pathways to define sub-pathways related to survival. The crosstalk between two pathways with at least three genes in common could be classified into three types. The overlapped genes between pathway A and pathway B could be defined as Pp-value based on the recalculated PAS for each sub-pathway. After Bonferroni correction, we identified critical survival-related pathways and sub-pathways  for each dataset, and the overlapped pathways were finally adopted for further modeling.To make sure each sub-pathway contains enough genes for further analysis, we obtained sub-pathways that consist of at least three genes. The Cox-PH model was used to calculate the survival risk p-value to redefine the samples as G1 and G2 subgroups. To predict these two subgroups for other datasets, we used several machine learning methods, including SVM, Adaboost, and Gaussian, to build a prediction model and obtained the best performance based on the pathway activity matrix. The robustness of the model was evaluated in the TCGA testing dataset and several external individual GEO datasets. We further built a classification model using several machine learning methods, including SVM, Adaboost, and Gaussian, based on these labels. We used the grid search to slightly turn the hyperparameters of the classifier. In the cross-validation procedure, TCGA samples were divided into training and testing datasets in a 4:1 ratio, and the training dataset was used to perform 10-fold cross-validation. To predict the GEO dataset, we used all TCGA samples to build the classification model.With the pathways generated above, we constructed a pathway activity matrix with the row names are sub-pathways and the column names are sample IDs for each dataset. We performed consensus clustering with the pathway features acquired above to classify the TCGA samples and obtained the best cluster number as 2 based on three metrics, including C-index, Brier score, and log-rank p-value, to evaluate the model’s performance. These metrics can quantify the proportion of patient pairs in a cohort whose risk prediction is highly consistent with survival outcomes. Usually, a higher C-index indicates more precise prediction performance, and 1 means perfect prediction, while 0.5 means the prediction performance is similar to random prediction. To calculate the C-index, we built a Cox-PH model based on the clustering labels and the patient’s survival information in the TCGA training dataset and predicted the survival rate in the testing dataset, which was calculated by the R “survcomp” package. Brier score reflects the mean difference between observed and predicted survival after a certain period in survival analysis, and a lower score means good performance. Log-rank p-value was calculated by the R “survival” package to show the survival difference between the two groups, and a lower score means a more significant survival difference.We then compared three metrics, including C-index, Brier score, and log-rank The log-rank test compares the survival difference of two groups at each observed event time was performed by R “survival” package. Kaplan-Meier analysis was applied to obtain a survival-curve plot of BRCA subtypes. Multivariate Cox regression analysis determined the independent role of this newly established predictor. Besides, we adopted Fisher’s exact test to compare the census gene mutation differences between G2 and G1 subgroups in the TCGA cohort. We also compared the distributions of G2 and G1 in different clinicopathological features, such as tumor stage, new tumor event, and sex, by using Fisher exact tests.We used the “DESeq2” R package  to real GSEA analysis was used to compare the pathway activity difference between G2 and G1, in which the R “ClusterProfier” package was performed. We adopted Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways to perform GSEA analysis and the top 20 significant pathways were displayed.p-value of less than 0.05 was considered significant.The R “maftools” package was utilized to analyze and visualize the mutation data. The mutation data were compared between one group and the other groups using the chi-square test. A p = 0.0053 and p = 0.0031, respectively; log-rank test). We further built a classifier based on the TCGA training dataset with the label defined by k-means clustering analysis .We obtained seven BC datasets  gene expression profiles and available clinical survival information from the TCGA and GEO databases. After calculating the PAS for each pathway obtained from KEGG and GO resources and selecting the survival-related pathways, we investigated the influence of crosstalk between these selected pathways on different cohorts, and then the most critical 100 sub-pathways among all cohorts were identified . K-meansp-values between G2 and G1. The TCGA test cohort generated a high C-index (0.661), low Brier score (0.179), and significant average log-rank p-value (p = 0.00123) on survival difference. In different datasets, our classifier can significantly divide the samples into a good prognostic group and a poor prognostic group, which suggested that the newly developed classifier is able to universally predict the overall survival outcomes for patients with BC.To evaluate the robustness of OS risk prediction of the model, we tested the model performance on the TCGA testing dataset and several external GEO datasets, including GSE1456A, GSE7390, GSE16446, GSE20685, GSE20711, and GSE42568. As shown in p < 0.05 in all datasets). We further introduced several published transcriptomic-based predictors as previous study  in the TCGA dataset as well as the external validation datasets. As shown in us study , includius study , interfeus study  as well us study , and perus study . In thisus study  of each and PRF1 . As showor stage . This rePI3KCA and CDH1 were significantly higher in the G1 group than G2 group . No significant tumor mutation burden (TMB) level difference was found between G1 and G2 subgroups    based on > 0.05) . The dow > 0.05) . These r > 0.05) .via NFκB were enriched in the G1 subgroup, while metabolic-related pathways such as oxidative phosphorylation signaling were activated in the G2 subgroup . The result revealed that significant differences were obtained between two groups in CD8+T cells, CD4+T memory cells (resting), CD4+T memory (activated), γδ T cells, Macrophages M0, Macrophages M1, Macrophages M2, Dendritic cells (resting), and Mast cells (resting)  . Among mresting) , which cConsidering that the tumor tissue has tumor cells, stromal cells and immune cells, we measured stromal score and immune score based on specific gene expression signature to represent the level of immune infiltration and stroma infiltration of each tumor following the previous reported method-ESTIMATE . Also, aIn the era of personalized medicine, there is an urgent need for a molecular marker-based approach to predict the prognostic outcomes of cancer patients accurately. Previous studies have reported many gene-based signatures to subtype BC  Tekpli  Parker . Here, wp-value of the high-age group of TCGA does not reach statistical significance (0.076), a clear trend can still be seen. However, it is challenging to explain why our classifier is unable to distinguish OS in the low-stage samples of TCGA with prognostic significance, though it performed well in the other two verification sets , our classifier can basically distinguish patients with different overall survival periods, although the high-age group and the low-stage group of TCGA have not reached statistical significance. Although the ion sets .We found a significant mutation rate difference of PI3KCA and CDH1 gene between the G1 and G2 subgroups. It is not yet clear whether PIK3CA mutation is associated with clinical outcome, PI3KCA mutation can be associated with a better prognosis  or a worWe also found that the G1 subgroup displayed significant higher level of immune infiltration, stromal infiltration level than the G2 subgroup. As reported, Th1 and cytotoxic types of memory T cells and CD8+ T cells can predict better prognosis in diverse cancers . Several