Integrated network pharmacology analysis and in vitro validation revealed the underlying mechanism of Xiyanping injection in treating coronavirus disease 2019.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide, leading to a pandemic. In China, Xiyanping injection (XYP) has been recommended as a drug for COVID-19 treatment in the Guideline on Diagnosis and Treatment of COVID-19 by the National Health Commission of the People Republic of China and National Administration of Traditional Chinese Medicine (Trial eighth Edition). However, the relevant mechanisms at the molecular-level need to be further elucidated. METHODS: In this study, XYP related active ingredients, potential targets and COVID-19 related genes were searched in public databases. Protein-protein interaction network and module analyzes were used to screen for key targets. gene ontology and Kyoto encyclopedia of genes and genomes were performed to investigate the potentially relevant signaling pathways. Molecular docking was performed using Autodock Tools and Vina. For the validation of potential mechanism, PolyI:C was used to induce human lung epithelial cells for an inflammation model. Subsequently, CCK-8 assays, enzyme-linked immunosorbent assay, reverse transcription quantitative polymerase chain reaction and western blot were employed to determine the effect of XYP on the expression of key genes. RESULTS: Seven effective active ingredients in XYP were searched for 123 targets in the relevant databases. Furthermore, 6446 COVID-19 disease targets were identified. Sodium 9-dehydro-17-hydro-andrographolide-19-yl sulfate was identified as the vital active compounds, and IL-6, TNF, IL-1ß, CXCL8, STAT3, MAPK1, MAPK14, and MAPK8 were considered as the key targets. In addition, molecular docking revealed that the active compound and the targets showed good binding affinities. The enrichment analysis predicted that the XYP could regulate the IL-17, Toll-like receptor, PI3K-Akt and JAK-STAT signaling pathways. Consistently, further in vitro experiments demonstrated that XYP could slow down the cytokine storm in the lung tissue of COVID-19 patients by down-regulating IL-6, TNF-α, IL-1ß, CXCL8, and p-STAT3. CONCLUSION: Through effective network pharmacology analysis and molecular docking, this study suggests that XYP contains many effective compounds that may target COVID-19 related signaling pathways. Moreover, the in vitro experiment confirmed that XYP could inhibit the cytokine storm by regulating genes or proteins related to immune and inflammatory responses.

Identification of an immune-related 6-lncRNA panel with a good performance for prognostic prediction in hepatocellular carcinoma by integrated bioinformatics analysis.

Hepatocellular carcinoma (HCC) is one of the most malignant tumors with a poor prognosis. The long non-coding RNA (lncRNA) has been found to have great potential as a prognostic biomarker or therapeutic target for cancer patients. However, the prognostic value and tumor immune infiltration of lncRNAs in HCC has yet to be fully elucidated. To identify prognostic biomarkers of lncRNA in HCC by integrated bioinformatics analysis and explore their functions and relationship with tumor immune infiltration. The prognostic risk assessment model for HCC was constructed by comprehensively using univariate/multivariate Cox regression analysis, Kaplan-Meier survival analysis, and the least absolute shrinkage and selection operator regression analysis. Subsequently, the accuracy, independence, and sensitivity of our model were evaluated, and a nomogram for individual prediction in the clinic was constructed. Tumor immune microenvironment (TIME), immune checkpoints, and human leukocyte antigen alleles were compared in high- and low-risk patients. Finally, the functions of our lncRNA signature were examined using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and gene set enrichment analysis. A 6-lncRNA panel of HCC consisting of RHPN1-AS1, LINC01224, CTD-2510F5.4, RP1-228H13.5, LINC01011, and RP11-324I22.4 was eventually identified, and show good performance in predicting the survivals of patients with HCC and distinguishing the immunomodulation of TIME of high- and low-risk patients. Functional analysis also suggested that this 6-lncRNA panel may play an essential role in promoting tumor progression and immune regulation of TIME. In this study, 6 potential lncRNAs were identified as the prognostic biomarkers in HCC, and the regulatory mechanisms involved in HCC were initially explored.

Real-world 10-year retrospective study of the guidelines for diagnosis and treatment of primary liver cancer in China.

BACKGROUND: Hepatocellular carcinoma (HCC) is a common malignant tumor worldwide. Many regions across the world have issued various HCC diagnosis and treatment protocols to improve the diagnosis and targeted treatment of patients with HCC. However, real-world studies analysing the practice, application value, and existing problems of the China Liver Cancer (CNLC) staging system are scarce. AIM: To analyze the current situation and problems associated with the Guidelines for Diagnosis and Treatment of Primary Liver Cancer in China. METHODS: We collected the medical records of all patients with HCC admitted to the First Affiliated Hospital of Zhengzhou University from January 1, 2011 to December 31, 2019, and recorded the hospitalization information of those patients until December 31, 2020. All information on the diagnosis and treatment of the target patients was recorded, and their demographic and sociological characteristics, CNLC stages, screening situations, and treatment methods and effects were analyzed. The survival status of the patients was obtained from follow-up data. RESULTS: This study included the medical records of 3022 patients with HCC. Among these cases, 304 patients were screened before HCC diagnosis; their early-stage diagnosis rate was 69.08%, which was significantly higher than that of patients with HCC who were diagnosed without screening and early detection (33.74%). Herein, patients with no clinical outcome at discharge were followed up, and the survival information of 1128 patients was obtained. A Cox model was used to analyse independent risk factors affecting overall survival, which were revealed as age > 50 years, no screening, alpha-fetoprotein > 400 ng/mL, Child-Pugh grade B, and middle and late CNLC stages. Based on the Cox model survival analysis, in our study, patients with HCC identified via screening had significant advantages in overall and tumor-free survival after hepatectomy. CONCLUSION: Early diagnosis and treatment can be achieved by screening groups at high risk for HCC based on the guidelines; however, real-world compliance is poor.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels T cell-related prognostic risk model and tumor immune microenvironment modulation in triple-negative breast cancer.

BACKGROUND: Triple negative breast cancer (TNBC) is an aggressive and fatal malignancy. The current success of tumor immunotherapy has focused attention on intermediate T-cell subsets and the tumor microenvironment, which are essential for activation of the anti-tumor response. Therefore, both areas require further research to accelerate progress in developing tailored immunotherapeutic approaches for patients with TNBC. METHODS: We obtained scRNA-seq data of TNBC from the GEO database. A multiplex strategy was used to analyze and identify the T-cell heterogeneity of TNBC. By combining the METABRIC and GEO databases, a prognostic risk model for T-cell marker genes was constructed and validated. In addition, the immune-infiltrating cells of TNBC was analyzed using CIBERSORT, and the association between the risk model and response to immunotherapy was investigated. RESULTS: Based on scRNA-seq data, 25,932 cells were identified for multiple analyzes. T cells were studied with a focus on 2 subtypes, including CD8+ and CD4+. There were also communication relationships between T cells and multiple cell types. The results of the enrichment analysis showed that the T-cell marker genes were focused in pathways related to the immune system. In addition, OPTN, TMEM176A, PKM and HES1 deserve attention as prognostic markers in TNBC. The immune infiltration results showed that the high-risk group had significant immune cell infiltration and immunosuppression status. CONCLUSION: This study provides a resource for understanding T-cell heterogeneity and the associated prognostic risk model for TNBC. The results show that the model helps predict prognosis and response to treatment in breast cancer.

Data mining combines bioinformatics discover immunoinfiltration-related gene SERPINE1 as a biomarker for diagnosis and prognosis of stomach adenocarcinoma.

Stomach adenocarcinoma (STAD) is a type of cancer which often at itsadvanced stage apon diagnosis and mortality in clinical practice. Several factors influencethe prognosis of STAD, including the expression and regulation of immune cells in the tumor microenvironment. We here investigated the biomarkers related to the diagnosis and prognosis of gastric cancer, hoping to provide insights for the diagnosis and treatment of gastric cancer in the future. STAD and normal patient RNA sequencing data sets were accessed from the cancer genome atlas (TCGA database). Differential genes were determined and obtained by using the R package DESeq2. The stromal, immune, and ESTIMATE scores are calculated by the ESTIMATE algorithm, followed by the modular genes screening using the R package WGCNA. Subsequently, the intersection between the modular gene and the differential gene was taken and the STRING database was used for PPI network module analysis. The R packages clusterProfiler, enrichplot, and ggplot2 were used for GO and KEGG enrichment analysis. Cox regression analysis was used to screen survival-related genes, and finally, the R package Venn Diagram was used to take the intersection and obtain 7 hub genes. The time-dependent ROC curve and Kaplan-Meier survival curve were used to find the SERPINE1 gene, which plays a critical role in prognosis. Finally, the expression pattern, clinical characteristics, and regulatory mechanism of SERPINE1 were analyzed in STAD. We revealed that the expression of SERPINE1 was significantly increased in the samples from STAD compared with normal samples. Cox regression, time-dependent ROC, and Kaplan-Meier survival analyses demonstrated that SERPINE1 was significantly related to the adverse prognosis of STAD patients. The expression of SERPINE1 increased with the progression of T, N, and M classification of the tumor. In addition, the results of immune infiltration analysis indicated that the immune cells' expression were higher in high SERPINE1 expression group than that in low SERPINE1 expression group, including CD4+ T cells, B cells, CD8+ T cells, macrophages, neutrophils and other immune cells. SERPINE1 was closely related to immune cells in the STAD immune microenvironment and had a synergistic effect with the immune checkpoints PD1 and PD-L1. In conclusion, we proved that SERPINE1 is a promising prognostic and diagnostic biomarker for STAD and a potential target for immunotherapy.

T cell-related prognostic risk model and tumor immune environment modulation in lung adenocarcinoma based on single-cell and bulk RNA sequencing.

BACKGROUND: T cells are present in all stages of tumor formation and play an important role in the tumor microenvironment. We aimed to explore the expression profile of T cell marker genes, constructed a prognostic risk model based on these genes in Lung adenocarcinoma (LUAD), and investigated the link between this risk model and the immunotherapy response. METHODS: We obtained the single-cell sequencing data of LUAD from the literature, and screened out 6 tissue biopsy samples, including 32,108 cells from patients with non-small cell lung cancer, to identify T cell marker genes in LUAD. Combined with TCGA database, a prognostic risk model based on T-cell marker gene was constructed, and the data from GEO database was used for verification. We also investigated the association between this risk model and immunotherapy response. RESULTS: Based on scRNA-seq data 1839 T-cell marker genes were identified, after which a risk model consisting of 9 gene signatures for prognosis was constructed in combination with the TCGA dataset. This risk model divided patients into high-risk and low-risk groups based on overall survival. The multivariate analysis demonstrated that the risk model was an independent prognostic factor. Analysis of immune profiles showed that high-risk groups presented discriminative immune-cell infiltrations and immune-suppressive states. Risk scores of the model were closely correlated with Linoleic acid metabolism, intestinal immune network for IgA production and drug metabolism cytochrome P450. CONCLUSION: Our study proposed a novel prognostic risk model based on T cell marker genes for LUAD patients. The survival of LUAD patients as well as treatment outcomes may be accurately predicted by the prognostic risk model, and make the high-risk population present different immune cell infiltration and immunosuppression state.

Lactoferrin Alleviates Lipopolysaccharide-Induced Infantile Intestinal Immune Barrier Damage by Regulating an ELAVL1-Related Signaling Pathway.

As the most important intestinal mucosal barrier of the main body, the innate immune barrier in intestinal tract plays especially pivotal roles in the overall health conditions of infants and young children; therefore, how to strengthen the innate immune barrier is pivotal. A variety of bioactivities of lactoferrin (LF) has been widely proved, including alleviating enteritis and inhibiting colon cancer; however, the effects of LF on intestinal immune barrier in infants and young children are still unclear, and the specific mechanism on how LF inhibits infantile enteritis by regulating immune signaling pathways is unrevealed. In the present study, we firstly performed pharmacokinetic analyses of LF in mice intestinal tissues, stomach tissues and blood, through different administration methods, to confirm the metabolic method of LF in mammals. Then we constructed in Vitro and in Vivo infantile intestinal immune barrier damage models utilizing lipopolysaccharide (LPS), and evaluated the effects of LF in alleviating LPS-induced intestinal immune barrier damage. Next, the related immune molecular mechanism on how LF exerted protective effects was investigated, through RNA-seq analyses of the mouse primary intestinal epithelial cells, and the specific genes were analyzed and screened out. Finally, the genes and their related immune pathway were validated in mRNA and protein levels; the portions of special immune cells (CD4+ T cells and CD8+ T cells) were also detected to further support our experimental results. Pharmacokinetic analyses demonstrated that the integrity of LF could reach mice stomach and intestine after oral gavage within 12 h, and the proper administration of LF should be the oral route. LF was proven to down-regulate the expression levels of inflammatory cytokines in both the primary intestinal epithelial cells and mice blood, especially LF without iron (Apo-LF), indicating LF alleviated infantile intestinal immune barrier damage induced by LPS. And through RNA-seq analyses of the mouse primary intestinal epithelial cells treated with LPS and LF, embryonic lethal abnormal vision Drosophila 1 (ELAVL1) was selected as one of the key genes, then the ELAVL1/PI3K/NF-κB pathway regulated by LF was verified to participate in the protection of infantile intestinal immune barrier damage in our study. Additionally, the ratio of blood CD4+/CD8+ T cells was significantly higher in the LF-treated mice than in the control mice, indicating that LF distinctly reinforced the overall immunity of infantile mice, further validating the strengthening bioactivity of LF on infantile intestinal immune barrier. In summary, LF was proven to alleviate LPS-induced intestinal immune barrier damage in young mice through regulating ELAVL1-related immune signaling pathways, which would expand current knowledge of the functions of bioactive proteins in foods within different research layers, as well as benefit preclinical and clinical researches in a long run.

Single-cell RNA-sequencing uncovers compound kushen injection synergistically improves the efficacy of chemotherapy by modulating the tumor environment of breast cancer.

Background: Due to lack of enough specific targets and the immunosuppressive tumor microenvironment (TME) of triple-negative breast cancer (TNBC), TNBC patients often cannot benefit from a single treatment option. This study aims to explore the regulatory effects of Compound kushen injection (CKI) plus chemotherapy on the TME of TNBC from a single cell level. Methods: A mouse TNBC model in BALB/c mice was established to evaluate the antitumor efficacy and toxicity of CKI combined with chemotherapy. Flow cytometry was used to observe the influence of CKI on the lymphocyte populations in the tumor bearing mice. Both bulk RNA sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) were applied to portray the modulation of CKI combined with chemotherapy on the TME of TNBC mice. Results: CKI significantly enhanced the anticancer activity of chemotherapy in vivo with no obvious side effects. Flow cytometry results revealed a significantly higher activation of CD8+ T lymphocytes in the spleens and tumors of the mice with combination therapy. Bulk RNA-seq indicated that CKI could promote the cytotoxic immune cell infiltrating into tumor tissues. Meanwhile, scRNA-seq further revealed that CKI combined with chemotherapy could enhance the percentage of tumor-infiltrating CD8+ T cells, inhibit tumor-promoting signaling pathways, and promote T cell activation and positive regulation of immune response. In addition, CKI showed obvious anticancer activity against MDA-MB-231 breast tumor cells in vitro. Conclusions: The combination of CKI and chemotherapy might provide a higher efficiency and lower toxicity strategy than a single chemotherapy drug for TNBC. CKI potentiates the anti-TNBC effects of chemotherapy by activating anti-tumor immune response in mice.

Integrated bioinformatics analysis reveals potential mechanisms associated with intestinal flora intervention in nonalcoholic fatty liver disease.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is a common chronic liver disease that imposes a huge economic burden on global public health. And the gut-liver axis theory supports the therapeutic role of intestinal flora in the development and progression of NAFLD. To this end, we designed bioinformatics study on the relationship between intestinal flora disorder and NAFLD, to explore the possible molecular mechanism of intestinal flora interfering with NAFLD. METHODS: Differentially expressed genes for NAFLD were obtained from the GEO database. And the disease genes for NAFLD and intestinal flora disorder were obtained from the disease databases. The protein-protein interaction network was established by string 11.0 database and visualized by Cytoscape 3.7.2 software. Cytoscape plug-in MCODE and cytoHubba were used to screen the potential genes of intestinal flora disorder and NAFLD, to obtain potential targets for intestinal flora to interfere in the occurrence and process of NAFLD. Enrichment analysis of potential targets was carried out using R 4.0.2 software. RESULTS: The results showed that 7 targets might be the key genes for intestinal flora to interfere with NAFLD. CCL2, IL6, IL1B, and FOS are mainly related to the occurrence and development mechanism of NAFLD, while PTGS2, SPINK1, and C5AR1 are mainly related to the intervention of intestinal flora in the occurrence and development of NAFLD. The gene function is mainly reflected in basic biological processes, including the regulation of metabolic process, epithelial development, and immune influence. The pathway is mainly related to signal transduction, immune regulation, and physiological metabolism. The TNF signaling pathway, AGE-RAGE signaling pathway in diabetic activity, and NF-Kappa B signaling pathways are important pathways for intestinal flora to interfere with NAFLD. According to the analysis results, there is a certain correlation between intestinal flora disorder and NAFLD. CONCLUSION: It is speculated that the mechanism by which intestinal flora may interfere with the occurrence and development of NAFLD is mainly related to inflammatory response and insulin resistance. Nevertheless, further research is needed to explore the specific molecular mechanisms.

Single-Cell and Bulk RNA Sequencing Reveal Malignant Epithelial Cell Heterogeneity and Prognosis Signatures in Gastric Carcinoma.

Gastric carcinoma (GC) heterogeneity represents a major barrier to accurate diagnosis and treatment. Here, we established a comprehensive single-cell transcriptional atlas to identify the cellular heterogeneity in malignant epithelial cells of GC using single-cell RNA sequencing (scRNA-seq). A total of 49,994 cells from nine patients with paired primary tumor and normal tissues were analyzed by multiple strategies. This study focused on the malignant epithelial cells, which were divided into three subtypes, including pit mucous cells, chief cells, and gastric and intestinal cells. The trajectory analysis results suggest that the differentiation of the three subtypes could be from the pit mucous cells to the chief cells and then to the gastric and intestinal cells. Lauren's histopathology of GC might originate from various subtypes of malignant epithelial cells. The functional enrichment analysis results show that the three subtypes focused on different biological processes (BP) and pathways related to tumor development. In addition, we generated and validated the prognostic signatures for predicting the OS in GC patients by combining the scRNA-seq and bulk RNA sequencing (bulk RNA-seq) datasets. Overall, our study provides a resource for understanding the heterogeneity of GC that will contribute to accurate diagnosis and prognosis.

An advanced network pharmacology study to explore the novel molecular mechanism of Compound Kushen Injection for treating hepatocellular carcinoma by bioinformatics and experimental verification.

BACKGROUND: Compound Kushen Injection (CKI) is a Chinese patent drug that exerts curative effects in the clinical treatment of hepatocellular carcinoma (HCC). This study aimed to explore the targets and potential pharmacological mechanisms of CKI in the treatment of HCC. METHODS: In this study, network pharmacology was used in combination with molecular biology experiments to predict and verify the molecular mechanism of CKI in the treatment of HCC. The constituents of CKI were identified by UHPLC-MS/MS and literature search. The targets corresponding to these compounds and the targets related to HCC were collected based on public databases. To screen out the potential hub targets of CKI in the treatment of HCC, a compound-HCC target network was constructed. The underlying pharmacological mechanism was explored through the subsequent enrichment analysis. Interactive Gene Expression Profiling Analysis and Kaplan-Meier plotter were used to examine the expression and prognostic value of hub genes. Furthermore, the effects of CKI on HCC were verified through molecular docking simulations and cell experiments in vitro. RESULTS: Network analysis revealed that BCHE, SRD5A2, EPHX2, ADH1C, ADH1A and CDK1 were the key targets of CKI in the treatment of HCC. Among them, only CDK1 was highly expressed in HCC tissues, while the other 5 targets were lowly expressed. Furthermore, the six hub genes were all closely related to the prognosis of HCC patients in survival analysis. Molecular docking revealed that there was an efficient binding potential between the constituents of CKI and BCHE. Experiments in vitro proved that CKI inhibited the proliferation of HepG2 cells and up-regulated SRD5A2 and ADH1A, while down-regulated CDK1 and EPHX2. CONCLUSIONS: This study revealed and verified the targets of CKI on HCC based on network pharmacology and experiments and provided a scientific reference for further mechanism research.

Exploring the Multicomponent Synergy Mechanism of Yinzhihuang Granule in Inhibiting Inflammation-Cancer Transformation of Hepar Based on Integrated Bioinformatics and Network Pharmacology.

Background: The Chinese patent drug Yinzhihuang granule (YZHG) is used to treat hepatitis B. This research is aimed at exploring the multicomponent synergistic mechanism of YZHG in the treatment of inflammation-cancer transformation of hepar and at providing new evidence and insights for its clinical application. Methods: To retrieve the components and targets of Yinzhihuang granules. The differentially expressed genes (DEGs) of hepar inflammation-cancer transformation were obtained from TTD, PharmGKB, and GEO databases. Construct the compound-prediction target network and the key module network using Cytoscape 3.7.1. Results: The results show that hepatitis B and hepatitis C shared a common target, MMP2. CDK1 and TOP2A may play an important role in the treatment with YZHG in hepatitis B inflammatory cancer transformation. KEGG pathway enrichment showed that key genes of modules 1, 2, and 4 were mainly enriched in the progesterone-mediated oocyte maturation signaling pathway and oocyte meiosis signaling pathway. Conclusion: The multicomponent, multitarget, and multichannel pharmacological benefits of YZHG in the therapy of inflammation-cancer transition of hepar are directly demonstrated by network pharmacology, providing a scientific basis for its mechanism.

Exploring potential mechanisms of Suhexiang Pill against COVID-19 based on network pharmacology and molecular docking.

BACKGROUND: The traditional Chinese medicine prescription Suhexiang Pill (SHXP), a classic prescription for the treatment of plague, has been recommended in the 2019 Guideline for coronavirus disease 2019 (COVID-19) diagnosis and treatment of a severe type of COVID-19. However, the bioactive compounds and underlying mechanisms of SHXP for COVID-19 prevention and treatment have not yet been elucidated. This study investigates the mechanisms of SHXP in the treatment of COVID-19 based on network pharmacology and molecular docking. METHODS: First, the bioactive ingredients and corresponding target genes of the SHXP were screened from the traditional Chinese medicine systems pharmacology database and analysis platform database. Then, we compiled COVID-19 disease targets from the GeneCards gene database and literature search. Subsequently, we constructed the core compound-target network, the protein-protein interaction network of the intersection of compound targets and disease targets, the drug-core compound-hub gene-pathway network, module analysis, and hub gene search by the Cytoscape software. The Metascape database and R language software were applied to analyze gene ontology biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Finally, AutoDock software was used for molecular docking of hub genes and core compounds. RESULTS: A total of 326 compounds, 2450 target genes of SHXP, and 251 genes related to COVID-19 were collected, among which there were 6 hub genes of SHXP associated with the treatment of COVID-19, namely interleukin 6, interleukin 10, vascular endothelial growth factor A, signal transducer and activator of transcription 3 (STAT3), tumor necrosis factor (TNF), and epidermal growth factor. Functional enrichment analysis suggested that the effect of SHXP against COVID-19 is mediated by synergistic regulation of several biological signaling pathways, including Janus kinase/ STAT3, phosphatidylinositol 3-kinase (PI3K)-protein kinase B (Akt), T cell receptor, TNF, Nuclear factor kappa-B, Toll-like receptor, interleukin 17, Chemokine, and hypoxia-inducible factor 1 signaling pathways. SHXP may play a vital role in the treatment of COVID-19 by suppressing the inflammatory storm, regulating immune function, and resisting viral invasion. Furthermore, the molecular docking results showed an excellent binding affinity between the core compounds and the hub genes. CONCLUSION: This study preliminarily predicted the potential therapeutic targets, signaling pathways, and molecular mechanisms of SHXP in the treatment of severe COVID-19, which include the moderate immune system, relieves the "cytokine storm," and anti-viral entry into cells.

A network pharmacology approach to reveal the pharmacological targets and biological mechanism of compound kushen injection for treating pancreatic cancer based on WGCNA and in vitro experiment validation.

BACKGROUND: Compound kushen injection (CKI), a Chinese patent drug, is widely used in the treatment of various cancers, especially neoplasms of the digestive system. However, the underlying mechanism of CKI in pancreatic cancer (PC) treatment has not been totally elucidated. METHODS: Here, to overcome the limitation of conventional network pharmacology methods with a weak combination with clinical information, this study proposes a network pharmacology approach of integrated bioinformatics that applies a weighted gene co-expression network analysis (WGCNA) to conventional network pharmacology, and then integrates molecular docking technology and biological experiments to verify the results of this network pharmacology analysis. RESULTS: The WGCNA analysis revealed 2 gene modules closely associated with classification, staging and survival status of PC. Further CytoHubba analysis revealed 10 hub genes (NCAPG, BUB1, CDK1, TPX2, DLGAP5, INAVA, MST1R, TMPRSS4, TMEM92 and SFN) associated with the development of PC, and survival analysis found 5 genes (TSPOAP1, ADGRG6, GPR87, FAM111B and MMP28) associated with the prognosis and survival of PC. By integrating these results into the conventional network pharmacology study of CKI treating PC, we found that the mechanism of CKI for PC treatment was related to cell cycle, JAK-STAT, ErbB, PI3K-Akt and mTOR signalling pathways. Finally, we found that CDK1, JAK1, EGFR, MAPK1 and MAPK3 served as core genes regulated by CKI in PC treatment, and were further verified by molecular docking, cell proliferation assay, RT-qPCR and western blot analysis. CONCLUSIONS: Overall, this study suggests that the optimized network pharmacology approach is suitable to explore the molecular mechanism of CKI in the treatment of PC, which provides a reference for further investigating biomarkers for diagnosis and prognosis of PC and even the clinical rational application of CKI.

An Advanced Systems Pharmacology Strategy Reveals AKR1B1, MMP2, PTGER3 as Key Genes in the Competing Endogenous RNA Network of Compound Kushen Injection Treating Gastric Carcinoma by Integrated Bioinformatics and Experimental Verification.

Gastric carcinoma (GC) is a severe tumor of the digestive tract with high morbidity and mortality and poor prognosis, for which novel treatment options are urgently needed. Compound Kushen injection (CKI), a classical injection of Chinese medicine, has been widely used to treat various tumors in clinical practice for decades. In recent years, a growing number of studies have confirmed that CKI has a beneficial therapeutic effect on GC, However, there are few reports on the potential molecular mechanism of action. Here, using systems pharmacology combined with proteomics analysis as a core concept, we identified the ceRNA network, key targets and signaling pathways regulated by CKI in the treatment of GC. To further explore the role of these key targets in the development of GC, we performed a meta-analysis to compare the expression differences between GC and normal gastric mucosa tissues. Functional enrichment analysis was further used to understand the biological pathways significantly regulated by the key genes. In addition, we determined the significance of the key genes in the prognosis of GC by survival analysis and immune infiltration analysis. Finally, molecular docking simulation was performed to verify the combination of CKI components and key targets. The anti-gastric cancer effect of CKI and its key targets was verified by in vivo and in vitro experiments. The analysis of ceRNA network of CKI on GC revealed that the potential molecular mechanism of CKI can regulate PI3K/AKT and Toll-like receptor signaling pathways by interfering with hub genes such as AKR1B1, MMP2 and PTGERR3. In conclusion, this study not only partially highlighted the molecular mechanism of CKI in GC therapy but also provided a novel and advanced systems pharmacology strategy to explore the mechanisms of traditional Chinese medicine formulations.

Investigation on the mechanisms of guiqi huoxue capsule for treating cervical spondylosis based on network pharmacology and molecular docking.

BACKGROUND: Guiqi huoxue capsule (GQHXC) is a patented Chinese medicine used for treating a liver and kidney deficiency and blood stasis syndrome due to qi deficiency. It is caused by cervical spondylosis (cervical spondylotic radiculopathy (CSR), mixed cervical spondylosis mainly composed of nerve root type). Its underlying mechanisms need, however, to be further clarified. METHODS: In this study, collecting compounds, predicting therapeutic targets, constructing networks, and analyzing biological functions and pathways were based on network pharmacology analysis. In addition, molecular docking verification was engaged to assess the binding potential of selected target-compound pairs. RESULTS: We established 5 networks: compound-putative target network of GQHXC, protein-protein interaction (PPI) network related to CSR, compound-CSR target network, potential therapeutic targets PPI network, and herb-compound-target-pathway network. Network analysis indicated that 7 targets (tumor necrosis factor [TNF], interleukin 6 [IL6], nitric oxide synthase 3 [NOS3], Interleukin-8 [CXCL8], prostaglandin-endoperoxide synthase 2 [PTGS2], vascular endothelial growth factor A [VEGFA], and AP-1 transcription factor subunit [JUN]) might be the therapeutic targets of GQHXC in CSR. Moreover, molecular docking verification showed that TNF, IL6, NOS3, CXCL8, PTGS2, VEGFA, and JUN had a good is interaction with the corresponding compounds. Furthermore, enrichment analysis indicated that GQHXC might exert a curative role in CSR by regulating some important pathways, such as TNF signaling pathway, NF-kappa B signaling pathway, AGE-RAGE signaling pathway in diabetic complications, and so on. CONCLUSION: Our study preliminarily explained the underlying mechanisms of GQHXC for treating CSR, and molecular docking verification was adopted as an additional verification. These findings laid a valuable foundation for experimental research and further application of GQHXC in the clinical treatment of CSR.

High Throughput Transcriptome Data Analysis and Computational Verification Reveal Immunotherapy Biomarkers of Compound Kushen Injection for Treating Triple-Negative Breast Cancer.

BACKGROUND: Although notable therapeutic and prognostic benefits of compound kushen injection (CKI) have been found when it was used alone or in combination with chemotherapy or radiotherapy for triple-negative breast cancer (TNBC) treatment, the effects of CKI on TNBC microenvironment remain largely unclear. This study aims to construct and validate a predictive immunotherapy signature of CKI on TNBC. METHODS: The UPLC-Q-TOF-MS technology was firstly used to investigate major constituents of CKI. RNA sequencing data of CKI-perturbed TNBC cells were analyzed to detect differential expression genes (DEGs), and the GSVA algorithm was applied to explore significantly changed pathways regulated by CKI. Additionally, the ssGSEA algorithm was used to quantify immune cell abundance in TNBC patients, and these patients were classified into distinct immune infiltration subgroups by unsupervised clustering. Then, prognosis-related genes were screened from DEGs among these subgroups and were further overlapped with the DEGs regulated by CKI. Finally, a predictive immunotherapy signature of CKI on TNBC was constructed based on the LASSO regression algorithm to predict mortality risks of TNBC patients, and the signature was also validated in another TNBC cohort. RESULTS: Twenty-three chemical components in CKI were identified by UPLC-Q-TOF-MS analysis. A total of 3692 DEGs were detected in CKI-treated versus control groups, and CKI significantly activated biological processes associated with activation of T, natural killer and natural killer T cells. Three immune cell infiltration subgroups with 1593 DEGs were identified in TNBC patients. Then, two genes that can be down-regulated by CKI with hazard ratio (HR) > 1 and 26 genes that can be up-regulated by CKI with HR < 1 were selected as key immune- and prognosis-related genes regulated by CKI. Lastly, a five-gene prognostic signature comprising two risky genes (MARVELD2 and DYNC2I2) that can be down-regulated by CKI and three protective genes (RASSF2, FERMT3 and RASSF5) that can be up-regulated by CKI was developed, and it showed a good performance in both training and test sets. CONCLUSIONS: This study proposes a predictive immunotherapy signature of CKI on TNBC, which would provide more evidence for survival prediction and treatment guidance in TNBC as well as a paradigm for exploring immunotherapy biomarkers in compound medicines.

Identification of Key Genes Associated With the Process of Hepatitis B Inflammation and Cancer Transformation by Integrated Bioinformatics Analysis.

BACKGROUND: Hepatocellular carcinoma (HCC) has become the main cause of cancer death worldwide. More than half of hepatocellular carcinoma developed from hepatitis B virus infection (HBV). The purpose of this study is to find the key genes in the transformation process of liver inflammation and cancer and to inhibit the development of chronic inflammation and the transformation from disease to cancer. METHODS: Two groups of GEO data (including normal/HBV and HBV/HBV-HCC) were selected for differential expression analysis. The differential expression genes of HBV-HCC in TCGA were verified to coincide with the above genes to obtain overlapping genes. Then, functional enrichment analysis, modular analysis, and survival analysis were carried out on the key genes. RESULTS: We identified nine central genes (CDK1, MAD2L1, CCNA2, PTTG1, NEK2) that may be closely related to the transformation of hepatitis B. The survival and prognosis gene markers composed of PTTG1, MAD2L1, RRM2, TPX2, CDK1, NEK2, DEPDC1, and ZWINT were constructed, which performed well in predicting the overall survival rate. CONCLUSION: The findings of this study have certain guiding significance for further research on the transformation of hepatitis B inflammatory cancer, inhibition of chronic inflammation, and molecular targeted therapy of cancer.

Performance evaluation of medical service for breast cancer patients based on diagnosis related groups.

BACKGROUND: To evaluate the performance of medical service for patients with breast cancer in Henan Province, China, using diagnosis related groups (DRGs) indicators and to provide data to inform practices and policies for the prevention and control of breast cancer. METHODS: The data were collected from the front pages of medical records (FPMR) of all hospitals above class II that admitted breast cancer patients in Henan Province between 2016 and 2019. Breast cancer patients were the subjects in our study. China DRGs (CN-DRGs) was used as a risk adjustment tool. Three indicators, including the case mix index (CMI), number of DRGs, and total weight, were used to evaluate the range of available services for patients with breast cancer, while indicators including the charge efficiency index (CEI), time efficiency index (TEI) and inpatient mortality of low-risk group cases (IMLRG) were used to evaluate medical service efficiency and medical safety. RESULTS: Between 2016 and 2019, there were 103,760 patients with breast cancer. The total weight increased over the study period at an average annual rate of 21.71%. The TEI decreased over the study period by 15.60%. The CEI exhibited an increasing trend, but the average annual rate of increase was small (2.94%). The IMLRP was 0.02, 0, 0 and 0.01% in 2016, 2017, 2018 and 2019, respectively. CONCLUSION: The performance of medical service improved between 2016 and 2019 for breast cancer patients discharged from study hospitals in Henan Province. The main area of improvement was in the range of available services, but medical institutions must still make efforts to improve the efficiency of medical services and ensure medical safety. DRGs is an effective evaluation tool.

Identification and analysis of key genes associated with acute myocardial infarction by integrated bioinformatics methods.

BACKGROUND: Acute myocardial infarction (AMI) is a common disease leading threat to human health around the world. Here we aimed to explore new biomarkers and potential therapeutic targets in AMI through adopting integrated bioinformatics tools. METHODS: The gene expression Omnibus (GEO) database was used to obtain genes data of AMI and no-AMI whole blood. Furthermore, differentially expressed genes (DEGs) were screened using the "Limma" package in R 3.6.1 software. Functional and pathway enrichment analyses of DEGs were performed via "Bioconductor" and "GOplot" package in R 3.6.1 software. In order to screen hub DEGs, the STRING version 11.0 database, Cytoscape and molecular complex detection (MCODE) were applied. Correlation among the hub DEGs was evaluated using Pearson's correlation analysis. RESULTS: By performing DEGs analysis, 289 upregulated and 62 downregulated DEGs were successfully identified from GSE66360, respectively. And they were mainly enriched in the terms of neutrophil activation, immune response, cytokine, nuclear factor kappa-B (NF-κB) signaling pathway, IL-17 signaling pathway, and tumor necrosis factor (TNF) signaling pathway. Based on the data of protein-protein interaction (PPI), the top 10 hub genes were ranked, including interleukin-8 (CXCL8), TNF, N-formyl peptide receptor 2 (FPR2), growth-regulated alpha protein (CXCL1), transcription factor AP-1 (JUN), interleukin-1 beta (IL1B), platelet basic protein (PPBP), matrix metalloproteinase-9 (MMP9), toll-like receptor 2 (TLR2), and high affinity immunoglobulin epsilon receptor subunit gamma (FCER1G). What's more, the results of correlation analysis demonstrated that there was positive correlation between the 10 hub DEGs. CONCLUSION: Ten DEGs were identified as potential candidate diagnostic biomarkers for patients with AMI in present study. However, further experiments are needed to confirm the functional pathways and hub genes associated with AMI.