Molecular insights into gastric cancer: The impact of TGFBR2 and hsa-mir-107 revealed by microarray sequencing and bioinformatics.

BACKGROUND: Gastric carcinoma (GC) remains a significant therapeutic challenge, garnering widespread attention. Oxymatrine (OMT), an active component of the traditional Chinese medicine compound Kushen injection (CKI), has shown promising results in combination with chemotherapy for the treatment of GC. However, the molecular mechanisms underlying OMT's therapeutic effects in GC have yet to be elucidated. METHODS: The transcriptomic expression data of HGC-27 post-OMT intervention were obtained through microarray sequencing, while the miRNA and mRNA sequencing data for GC patients were sourced from the TCGA database. The mechanism of OMT intervention in GC is analyzed in multiple aspects, including Protein-Protein Interactions (PPI), Competitive Endogenous RNA (ceRNA) networks, correlation and co-expression analyses, immune infiltration, and clinical implications. RESULTS: By analyzing key modules, five critical mRNAs were identified, and their interacting miRNAs were predicted to construct a ceRNA network. Among these, TGFBR2 and hsa-miR-107 have correlations or co-expression relationships with other genes in the network. They are differentially expressed in most other cancers, associated with prognosis, and have diagnostic value. TGFBR2 also exhibits immune infiltration phenomena, and its high expression is linked to poor patient prognosis. Low expression of hsa-miR-107 is associated with poor patient prognosis. OMT may act on the TGFß/Smad signaling pathway or negatively regulate the WNT signaling pathway through the hsa-miR-107/BTRC axis, thereby inhibiting the onset and progression of GC. CONCLUSION: The mechanisms of OMT intervention in GC are diverse, TGFBR2 and hsa-miR-107 may serve as prognostic molecular biomarkers or potential therapeutic targets.

A new strategy to identify ADAM12 and PDGFRB as a novel prognostic biomarker for matrine regulates gastric cancer via high throughput chip mining and computational verification.

BACKGROUND: Gastric cancer is a life-threatening disease that poses a serious risk to human health. Although there are numerous molecular targets for gastric cancer in clinical practice, they often exhibit low specificity and sensitivity. Consequently, this can result in a low early diagnosis rate, delayed treatment, and poor prognosis for patients with gastric cancer. Hence, it remains crucial to identify more precise diagnostic markers for this disease. METHODS: This study utilized ceRNA chips and bioinformatics methods to investigate the key genes and mechanisms involved in matrine intervention in gastric cancer cells. RESULTS: ADAM12 and PDGFRB are the key genes that are down-regulated after matrine intervention in gastric cancer cells. By conducting bioinformatics analysis, two ceRNA regulatory axes were identified, which are associated with the prognosis of gastric cancer. These axes are lncRNA DGCR5/hsa-miR-206/ADAM12 and circRNA ITGA3/hsa-miR-24-3p/PDGFRB. CONCLUSION: The low expression of ADAM12 may weaken the digestion of extracellular matrix (ECM) molecules, which can result in the invasion and metastasis of tumor cells. This occurs without the catalysis of ECM proteases, thereby impacting the invasion and metastasis of gastric cancer cells. Additionally, the analysis of immune infiltration suggests that ADAM12 and PDGFRB may influence changes in the tumor immune microenvironment, thereby affecting the occurrence and development of gastric cancer. This study contributes to a deeper understanding of the role of the matrine-related ceRNA network in gastric cancer, providing a reference for clinical diagnosis and treatment. It holds significant importance in discovering new drug treatment targets.

Single-cell RNA sequencing integrated with bulk RNA sequencing analysis reveals diagnostic and prognostic signatures and immunoinfiltration in gastric cancer.

BACKGROUND: Early diagnosis and prognostic predication of gastric cancer (GC) pose significant challenges in current clinical practice of GC treatments. Therefore, our aim was to explore relevant gene signatures that can predict the prognosis of GC patients. METHODS: Here, we established a single-cell transcriptional atlas of GC, focusing on the expression of T-cell-related genes for cell-cell communication analysis, trajectory analysis, and transcription factor regulatory network analysis. Additionally, we conducted validation and prediction of immune-related prognostic gene signatures in GC patients using TCGA and GEO data. Based on these prognostic gene signatures, we predicted the immune infiltration status of GC patients by grouping the patient samples into high or low-risk groups. RESULTS: Based on 10 tumor samples and corresponding normal samples from GC patients, we selected 18,416 cells for subsequent analysis using single-cell sequencing. From these, we identified 3,284 T-cells and obtained 641 differentially expressed genes related to T-cells from 5 different T-cell subtypes. By integrating bulk RNA sequencing data, we identified prognostic signatures associated with T-cells. Stratifying patients based on these prognostic signatures into high-risk or low-risk groups allowed us to effectively predict their survival rates and the immunoinfiltration status of the tumor microenvironment. CONCLUSION: This study explored prognostic gene signatures associated with T-cells in GC patients, providing insights into predicting patients' survival rates and immunoinfiltration levels.

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.

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.

A Prospective, Randomized, Placebo-Controlled Study Assessing the Efficacy of Chinese Herbal Medicine (Huangqi Guizhi Wuwu Decoction) in the Treatment of Albumin-Bound Paclitaxel-Induced Peripheral Neuropathy.

Objective: This study aimed to evaluate the efficacy and safety of Huangqi Guizhi Wuwu decoction (HGWD), which is composed of five crude drugs (Astragali Radix, Cinnamomi Ramulus, Paeoniae Radix Alba, Zingiberis Rhizoma Recens, and Jujubae Fructus), in the treatment of albumin-bound paclitaxel (nab-PTX)-induced peripheral neuropathy (PN) in Chinese patients with breast cancer (BC). Methods: This trial was conducted at the National Cancer Center in China from January 2020 to June 2022. The eligible participants were assigned randomly in a 1:1 ratio to an HGWD group or a control group. The outcome measure was EORTC QLQ-CIPN20 questionnaire. Results: 92 patients diagnosed with BC were enrolled and randomized to either HGWD group (n = 46) or control group (n = 46). There were no significant differences in baseline characteristics between the two groups (p > 0.05). A statistical analysis of the sensory and motor functions of the EORTC QLQ-CIPN20 scores showed that patients in the HGWD group reported a larger decrease in CIPN sensory scores than those in the control group (p < 0.001). The EORTC QLQ-CIPN20 autonomic scores showed no statistical significance between the two groups (p > 0.05). Conclusions: HGWD packs could significantly improve patients' nab-PTX-induced PN, increase the tolerance for nab-PTX-containing chemotherapy, and further improve the quality of life of patients with BC.

Integrating transcriptomics and network analysis-based multiplexed drug repurposing to screen drug candidates for M2 macrophage-associated castration-resistant prostate cancer bone metastases.

Metastatic castration-resistant prostate cancer (CRPC) has long been considered to be associated with patient mortality. Among metastatic organs, bone is the most common metastatic site, with more than 90% of advanced patients developing bone metastases (BMs) before 24 months of death. Although patients were recommended to use bone-targeted drugs represented by bisphosphonates to treat BMs of CRPC, there was no significant improvement in patient survival. In addition, the use of immunotherapy and androgen deprivation therapy is limited due to the immunosuppressed state and resistance to antiandrogen agents in patients with bone metastases. Therefore, it is still essential to develop a safe and effective therapeutic schedule for CRPC patients with BMs. To this end, we propose a multiplex drug repurposing scheme targeting differences in patient immune cell composition. The identified drug candidates were ranked from the perspective of M2 macrophages by integrating transcriptome and network-based analysis. Meanwhile, computational chemistry and clinical trials were used to generate a comprehensive drug candidate list for the BMs of CRPC by drug redundancy structure filtering. In addition to docetaxel, which has been approved for clinical trials, the list includes norethindrone, testosterone, menthol and foretinib. This study provides a new scheme for BMs of CRPC from the perspective of M2 macrophages. It is undeniable that this multiplex drug repurposing scheme specifically for immune cell-related bone metastases can be used for drug screening of any immune-related disease, helping clinicians find promising therapeutic schedules more quickly, and providing reference information for drug R&D and clinical trials.

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.

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.

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.

Biodata Mining of Differentially Expressed Genes between Acute Myocardial Infarction and Unstable Angina Based on Integrated Bioinformatics.

Acute coronary syndrome (ACS) is a complex syndrome of clinical symptoms. In order to accurately diagnose the type of disease in ACS patients, this study is aimed at exploring the differentially expressed genes (DEGs) and biological pathways between acute myocardial infarction (AMI) and unstable angina (UA). The GSE29111 and GSE60993 datasets containing microarray data from AMI and UA patients were downloaded from the Gene Expression Omnibus (GEO) database. DEG analysis of these 2 datasets is performed using the "limma" package in R software. DEGs were also analyzed using protein-protein interaction (PPI), Molecular Complex Detection (MCODE) algorithm, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Correlation analysis and "cytoHubba" were used to analyze the hub genes. A total of 286 DEGs were obtained from GSE29111 and GSE60993, including 132 upregulated genes and 154 downregulated genes. Subsequent comprehensive analysis identified 20 key genes that may be related to the occurrence and development of AMI and UA and were involved in the inflammatory response, interaction of neuroactive ligand-receptor, calcium signaling pathway, inflammatory mediator regulation of TRP channels, viral protein interaction with cytokine and cytokine receptor, human cytomegalovirus infection, and cytokine-cytokine receptor interaction pathway. The integrated bioinformatical analysis could improve our understanding of DEGs between AMI and UA. The results of this study might provide a new perspective and reference for the early diagnosis and treatment of ACS.

A Multidimensional Bayesian Network Meta-Analysis of Chinese Herbal Injections for Treating Non-small Cell Lung Cancer With Gemcitabine and Cisplatin.

Introduction: As non-small cell lung cancer (NSCLC) seriously threatens human health, several clinical studies have reported that Chinese herbal injections (CHIs) in combination with and gemcitabine plus cisplatin (GP) are beneficial. This multidimensional network meta-analysis aimed to compare the clinical efficacy and safety of different CHIs in combination with GP against NSCLC. Methods: Randomized controlled trials (RCTs) for the treatment of NSCLC were retrieved from seven electronic databases from inception to April 30, 2020. Study selection and data extraction were based on a priori criteria. Data analysis was performed using Stata 13.0, WinBUGS 14.0 software. Multidimensional cluster analysis was performed using the "scatterplot3d" package in R 3.6.1 software. Results: This network meta-analysis included 71 eligible RCTs and 10 Chinese herbal injections. Delisheng injection and Kangai injection had the highest probability in terms of clinical effectiveness rate (94.60%) and gastrointestinal reactions (82.62%) when combined with GP compared with the other interventions. Compound Kushen injection combined with GP ranked ahead of the other interventions in terms of performance status (73.36%) and abnormal liver function (87.17%). Shenmai injection combined with GP had the highest probability in terms of leukopenia (94.59%) and thrombocytopenia (99.18%). Conclusion: The current evidence revealed that CHIs combined with GP have a better impact on patients with NSCLC than GP alone. Aidi injection, Compound kushen injection, and Kanglaite injection deserve more attention of clinicians when combined with GP in patients with NSCLC. Additionally, due to the limitations of this network meta-analysis, further well-designed, large-sample, multicenter RCTs are required to support our findings adequately.

Investigation on the clinical efficacy and mechanism of compound kushen injection in treating esophageal cancer based on multi-dimensional network meta-analysis and in vitro experiment.

ETHNOPHARMACOLOGICAL RELEVANCE: Esophageal cancer, as a high incidence of gastrointestinal cancer, has an indelible impact on human life and health. The combination of Chinese herbal injections and chemotherapy is commonly applied in the treatment of Esophageal cancer. AIM OF THE STUDY: This study aimed to confirm the clinical advantage of Compound Kushen Injection to treat esophageal cancer and explore its molecular mechanism. METHODS: The network meta-analysis method was used for the clinical evaluation of anti-tumor Chinese herbal injections. Initially, several electronic databases were searched to identify randomized controlled trials regarding Chinese herbal injections to treat esophageal cancer from their inception to September 5, 2020. Then, WinBugs and Stata software was used to calculate and analyze the outcome indicators, including total clinical efficiency, improvement of quality of life and adverse reactions. Furthermore, the surface under the cumulative ranking curve and three-dimensional cluster analysis were used to rank the efficacy and safety of Chinese herbal injections about each outcome. Cell Counting Kit-8 assay was used to observe the effect of Compound Kushen Injection on the proliferation of esophageal cancer cells. Real-Time Quantitative PCR and Western Blot analysis were used to detect the mRNA and protein expression of EGFR and AURKA in ESCA cells. RESULTS: The surface under the cumulative ranking curve of Compound Kushen Injection combined with chemotherapy in total clinical efficiency, quality of life, reduction of nausea and vomiting were ranking at 89.1%, 81.8% and 92.4%, respectively. Compound Kushen Injection was determined as the dominant variety in the treatment of esophageal cancer which can inhibit the proliferation of esophageal cancer cells and downregulate the overexpression of EGFR and AURKA mRNA and protein. CONCLUSION: In this study, network meta-analysis was applied to confirm that Compound Kushen Injection has a curative effect on esophageal cancer and is superior to other anti-tumor Chinese herbal injections. Combined with the network pharmacology and in vitro experiment, the mechanism of Compound Kushen Injection inhibiting the proliferation of esophageal cancer cells by regulating the abnormal expression of EGFR and AURKA was revealed.

Using single-patient (n-of-1) trials to determine effectiveness of traditional Chinese medicine on chemotherapy-induced leukopenia in gastric cancer: a feasibility study.

BACKGROUND: Gastric cancer has been the second cause of cancer death worldwide. Chemical comprehensive treatment programs primarily were the main therapy method with modest efficacy to gastric cancer. Traditional Chinese medicine (TCM) has been reported to alleviate adverse events induced by chemotherapy, but has not yet developed clinical trials to test and needs scientific evidence for making policy. Single-patient (N-of-1) trials might be an eligible study design for TCM since it well represented the individualized treatment philosophy of TCM. The aim of this study is to obtain information necessary to design a more series trial. METHODS: Individuals who underwent gastrectomy were included. Each patient suffered 3-week standard chemotherapy and 3-day treatment periods (decoction with Astragalus mongholicus and Semen Cuscutae or placebo: decoction without Astragalus mongholicus and Semen Cuscutae). Each trial lasted up to a maximum of 30 weeks or a minimum of 20 weeks. Staffs and participants were blinded to the randomization. This study was approved by Ethics Committee of First Hospital, Lanzhou University in November, 2014. RESULTS: From August, 2014 to March, 2015, 6 participants were included. There were 16 cycles compared between intervention and control decoction (2.28, 95% CI: 1.24-5.47), P<0.0001. The quality of life (QoL) score after the trial was reported is a little higher than before, t=3.87, P=0.01. Two participants reported symptoms had improved after taken trial decoction. CONCLUSIONS: This is the first N-of-1 trials of testing the effectiveness of TCM decoction on alleviative treatment to gastric cancer. The feasibility study will help to develop a practical design for the more series trial.

Research Progress in Reversal of Tumor Multi-drug Resistance via Natural Products.

Multidrug resistance occurs when a tumor develops resistance to multiple chemotherapeutic drugs, which may include antitumor drugs with different chemical structures and mechanisms. Multidrug resistance limits the treatment effects of antitumor drugs, and is the main cause of chemotherapy failure. Multidrug resistance is caused by numerous factors including changes in ATP-binding cassette transporters, target proteins, detoxification, deoxyribonucleic acid repair, drug metabolic enzymes, and signal pathways of apoptosis. Clinical research indicates that natural products have great potential to treat tumors and reverse multidrug resistance. Natural products, which often have multiple targets, could play an important role in tumor treatment, have beneficial effects on tumor inhibition, improve symptoms, reduce radiotherapy and chemotherapy side effects, enhance immunity, and prolong survival. Because natural products often have few adverse reactions and less drug resistance, the antitumor activities of natural products have attracted extensive research. We aimed to review the basic research and clinical application of natural products in the reversal of multidrug resistance.

Clinical research of compound zhebei granules for increasing the therapeutic effect of chemotherapy in refractory acute leukemia patients.

OBJECTIVE: To observe the effects of Compound Zhebei Granules (CZG) in chemotherapy for refractory acute leukemia. METHOD: Using a randomized, double-blind and multi-central concurrent control clinical research project, the patients conformed with the diagnostic criteria, according to the drug randomized method, were divided into a CZG group and a control group. The patients of the two groups respectively took the observation drug or a placebo 3 days before chemotherapy, and the therapeutic effects were evaluated after one course of chemotherapy. According to the clinical research project, 137 patients were enrolled, including 71 cases in the CZG group and 66 cases in the control group. RESULTS: The clinical complete remission (CR) rate was 42.3% in the CZG group with a total effective rate of 73.2%, and it was 25.8% in the control group with a total effective rate of 53.0%, showing a statistically significant difference between the two groups (P < 0.05). CONCLUSION: CZG can increase the clinical remission rate for refractory acute leukemia during chemotherapy.