Identification of the Key Active Pharmaceutical Ingredients of Yishen Qutong Granule, A Chinese Medicine Formula, In The Treatment of Primary Lung Cancer.

BACKGROUND: Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Traditional Chinese medicine (TCM) reportedly has potential therapeutic effects against LC. OBJECTIVE: This study aimed to investigate the antitumor efficacy of Yishen Qutong granule (YSQTG) in primary LC treatment, to identify its key active pharmaceutical ingredients (APIs), and to explore its possible mechanism of action. METHODS: The antitumor role of YSQTG was validated via cell function assays and a xenograft tumor model. Then, high-performance liquid chromatography-mass spectrometry (HPLC-MS) was performed to determine the objective precipitation components of YSQTG, followed by target prediction through reference to databases. Subsequently, the proportion of the predicted targets that underwent actual changes was identified via RNA-sequencing. Enrichment analysis was performed to explore the possible mechanisms of action. Hub genes were screened, and western blotting was used to verify their protein expression levels to identify the core target. Molecular docking between the active compounds and the verified core target was performed, combined with an evaluation of the potential efficacy of candidate compounds using meta-analysis to screen the candidate key APIs. RESULTS: Experiments confirmed that YSQTG could inhibit LC cell proliferation, induce apoptosis in vitro, and inhibit lung tumor growth in vivo. HPLC-MS, RNA-seq, and enrichment analysis showed that oxidative stress-related pathways were the possible mechanism of YSQTG in primary LC treatment. Western blot verification indicated that heme oxygenase 1 (HMOX1, HO-1) could be the core target. Molecular docking and meta-analysis suggested that genistein and quercetin were the candidate key APIs. CONCLUSION: YSQTG and its active ingredients, genistein and quercetin, may have therapeutic effects against LC through their action on the downregulation of oxidative stress-related HMOX1 protein expression.

EGFL7 affects the migration of epidermal stem cells in refractory diabetic wounds by regulating Notch signaling pathway.

Aim: This study aimed to explore the role of EGFL7 in the healing process of refractory diabetic wounds. Methods: Epidermal stem cells (ESCs) were isolated from healthy mice and diabetic mice, identified by immunofluorescence, transfected with EGFL7 overexpression and silencing lentiviral vectors, and treated with Notch pathway inhibitor (DAPT). Results: SiEGFL7 significantly inhibited the proliferation, invasion and migration of ESCs of healthy mice. DAPT prominently inhibited the expressions of Notch1, Notch2, Hes1 and Jag1 in ESCs of healthy mice induced by overexpressed EGFL7. Overexpressed EGFL7 promoted wound healing in diabetic mice with refractory wounds. Conclusion: EGFL7 affects the proliferation and migration of ESCs in refractory diabetic wounds by regulating the Notch signaling pathway.

EGFL7 silencing inhibited the proliferation, invasion and migration of ESCs of healthy mice, which was reversed by Notch signaling inhibition. Overexpressed EGFL7 promoted wound healing in diabetic mice with refractory wounds, providing a promising potential for the treatment of diabetic wound.

Network Pharmacology and in vitro Experimental Verification on Intervention of Quercetin, Present in Chinese Medicine Yishen Qutong Granules, on Esophageal Cancer.

OBJECTIVE: To explore the potential mechanism of Yishen Qutong Granules (YSQTG) for the treatment of esophageal cancer using network pharmacology and experimental research. METHODS: The effective components and molecular mechanism of YSQTG in treating esophageal cancer were expounded based on network pharmacology and molecular docking. The key compound was identified by high-performance liquid chromatography and mass spectrometry (HPLC-MS) to verify the malignant phenotype of the key compounds in the treatment of esophageal cancer. Then, the interaction proteins of key compounds were screened by pull-down assay combined with mass spectrometry. RNA-seq was used to screen the differential genes in the treatment of esophageal cancer by key compounds, and the potential mechanism of key compounds on the main therapeutic targets was verified. RESULTS: Totally 76 effective compounds of YSQTG were found, as well as 309 related targets, and 102 drug and disease interaction targets. The drug-compound-target network of YSQTG was constructed, suggesting that quercetin, luteolin, wogonin, kaempferol and baicalein may be the most important compounds, while quercetin had higher degree value and degree centrality, which might be the key compound in YSQTG. The HPLC-MS results also showed the stable presence of quercetin in YSQTG. By establishing a protein interaction network, the main therapeutic targets of YSQTG in treating esophageal cancer were Jun proto-oncogene, interleukin-6, tumor necrosis factor, and RELA proto-oncogene. The results of cell function experiments in vitro showed that quercetin could inhibit proliferation, invasion, and clonal formation of esophageal carcinoma cells. Quercetin mainly affected the biological processes of esophageal cancer cells, such as proliferation, cell cycle, and cell metastasis. A total of 357 quercetin interacting proteins were screened, and 531 genes were significantly changed. Further pathway enrichment analysis showed that quercetin mainly affects the metabolic pathway, MAPK signaling pathway, and nuclear factor kappa B (NF- κ B) signaling pathway, etc. Quercetin, the key compound of YSQTG, had stronger binding activity by molecular docking. Pull-down assay confirmed that NF- κ B was a quercetin-specific interaction protein, and quercetin could significantly reduce the protein level of NF- κ B, the main therapeutic target. CONCLUSION: YSQTG can be multi-component, multi-target, multi-channel treatment of esophageal cancer, it is a potential drug for the treatment of esophageal cancer.

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.

The Molecular Mechanism of Traditional Chinese Medicine Prescription: Gu-tong Formula in Relieving Osteolytic Bone Destruction.

Bone metastasis is a common complication in patients with advanced tumors, causing pain and bone destruction and affecting their quality of life. Typically, complementary and alternative medicine (CAM), with unique theoretical guidance, has played key roles in the treatment of tumor-related diseases. Gu-tong formula (GTF), as a representative prescription of traditional Chinese medicine, has been demonstrated to be an effective clinical medication for the relief of cancer pain. However, the molecular mechanism of GTF in the treatment of osteolytic metastasis is still unclear. Herein, we employ network pharmacology and molecular dynamics methods to uncover the potential treatment mechanism, indicating that GTF can reduce the levels of serum IL6 and TGFB1 and thus limit the scope of bone cortical damage. Among the active compounds, sesamin and deltoin can bind stably with IL6 and TGFB1, respectively, and have the potential to become anti-inflammatory and anticancer drugs. Although the reasons for the therapeutic effect of GTF are complex and comprehensive, this work provides biological plausibility in the treatment of osteolytic metastases, which has a guiding significance for the treatment of cancer pain with CAM.

Effect of Oral Chinese Medicine Combined with Western Medicine on Cancer Pain: A Meta-Analysis.

OBJECTIVE: To assess the effect of oral Chinese medicine (OCM) combined with Western medicine (WM) on cancer pain. METHODS: PubMed, Embase, Cochrane Library, Clinical Trials Registry Platform, Chinese National Knowledge Infrastructure (CNKI), Wanfang and VIP databases were searched from their inception to September {dy2019}. Randomized controlled trials (RCTs) treating cancer pain by Chinese medicine (CM) combined with WM were included. The primary outcome were total pain relief rate and the quality of life (QOL), and the other outcomes were the average daily dosage of analgesics, the primary time of pain, the analgesic duration time, and adverse events. The methodological quality of RCTs was assessed in accordance with Cochrane 5.1.0 handbook of systematic reviews of interventions. Evidence level was assessed by the Grades of Recommendation, Assessment, Development and Evaluation (GRADE) approach. RESULTS: There were 1,087 patients in the 14 studies, with 544 in the experiment group and 543 in the control group. These studies were all conducted in China, and published between 2006 and {dy2019}. Compared with the WM, OCM combined with WM could significantly relieve the cancer pain [risk ratio (RR)=1.43, 95% confidence interval (CI): 1.32, 1.56), improve QOL (RR=8.57, 95% CI: 4.25, 12.89), decrease the primary time of pain (RR=-0.20, 95% CI: -0.24, -0.16], prolong the analgesic duration time (RR=3.47, 95% CI: 2.09, 4.85), reduce the dosage of analgesics (RR=-19.52, 95% CI: -36.32, -2.72), and reduce side events (RR=0.49, 95% CI: 0.37, 0.65). Evidence levels for total pain relief rate, primary time of pain and side events were low, evidence level for QOL, analgesic duration time and average daily dosage of analgesics were very low. CONCLUSIONS: Compared with the WM, OCM combined with WM could significantly relieve the cancer pain, improve the QOL, decrease the primary time of pain, prolong the analgesic duration time, reduce the dosage of analgesics and side events. The evidence levels were low or very low.

Investigating the Multitarget Mechanism of Traditional Chinese Medicine Prescription for Cancer-Related Pain by Using Network Pharmacology and Molecular Docking Approach.

Gu-tong formula (GTF) has achieved good curative effects in the treatment of cancer-related pain. However, its potential mechanisms have not been explored. We used network pharmacology and molecular docking to investigate the molecular mechanism and the effective compounds of the prescription. Through the analysis and research in this paper, we obtained 74 effective compounds and 125 drug-disease intersection targets to construct a network, indicating that quercetin, kaempferol, and ß-sitosterol were possibly the most important compounds in GTF. The key targets of GTF for cancer-related pain were Jun proto-oncogene (JUN), mitogen-activated protein kinase 1 (MAPK1), and RELA proto-oncogene (RELA). 2204 GO entries and 148 pathways were obtained by GO and KEGG enrichment, respectively, which proved that chemokine, MAPK, and transient receptor potential (TRP) channels can be regulated by GTF. The results of molecular docking showed that stigmasterol had strong binding activity with arginine vasopressin receptor 2 (AVPR2) and C-X3-C motif chemokine ligand 1 (CX3CL1) and cholesterol was more stable with p38 MAPK, prostaglandin-endoperoxide synthase 2 (PTGS2), and transient receptor potential vanilloid-1 (TRPV1). In conclusion, the therapeutic effect of GTF on cancer-related pain is based on the comprehensive pharmacological effect of multicomponent, multitarget, and multichannel pathways. This study provides a theoretical basis for further experimental research in the future.

Closed Rhinoplasty with a Mushroom-Shaped Costal Cartilage Graft in East Asian Patients.

BACKGROUND: The most challenging part of rhinoplasty is nasal tip management. For East Asian patients with a bulbous under projected nasal tip with thick skin, autologous cartilage is considered the gold-standard graft material to provide strong support to the nasal tip and effectively increase tip projection. The present study aimed to evaluate the outcomes of closed rhinoplasty with a mushroom-shaped costal cartilage graft in East Asian patients. METHODS: From February 2018 to May 2019, 52 patients underwent rhinoplasty with a mushroom-shaped costal cartilage graft in our institution. Rhinoplasty was performed through a bilateral endonasal incision. Postoperatively, all patients were photographed and asked to complete a satisfaction survey online or by telephone. RESULTS: The mean follow-up period was 15.8 months (range 12-21 months). Twenty-four of 52 patients agreed to participate in this study. Postoperatively, the mean nasofrontal angle was 137.7 ± 3.7°, mean nasolabial angle was 94.1 ± 6.2°, mean nasal tip angle was 79.4 ± 5.2°, and mean columellar/lobular angle was 44.7 ± 2.4°. The mean tip projection/nasal length index was 0.53 ± 0.07, and the mean columellar/lobular length index was 1.21 ± 0.22. There were no prolonged functional complications. Most patients (23/24; 95.8%) were satisfied with the aesthetic results. CONCLUSION: The present results suggest that the mushroom-shaped costal cartilage graft in closed rhinoplasty is a good choice for the correction of a bulbous under projected nasal tip. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Simulation-based hypothesis testing of high dimensional means under covariance heterogeneity.

In this article, we study the problem of testing the mean vectors of high dimensional data in both one-sample and two-sample cases. The proposed testing procedures employ maximum-type statistics and the parametric bootstrap techniques to compute the critical values. Different from the existing tests that heavily rely on the structural conditions on the unknown covariance matrices, the proposed tests allow general covariance structures of the data and therefore enjoy wide scope of applicability in practice. To enhance powers of the tests against sparse alternatives, we further propose two-step procedures with a preliminary feature screening step. Theoretical properties of the proposed tests are investigated. Through extensive numerical experiments on synthetic data sets and an human acute lymphoblastic leukemia gene expression data set, we illustrate the performance of the new tests and how they may provide assistance on detecting disease-associated gene-sets. The proposed methods have been implemented in an R-package HDtest and are available on CRAN.

Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering.

Comparing large covariance matrices has important applications in modern genomics, where scientists are often interested in understanding whether relationships (e.g., dependencies or co-regulations) among a large number of genes vary between different biological states. We propose a computationally fast procedure for testing the equality of two large covariance matrices when the dimensions of the covariance matrices are much larger than the sample sizes. A distinguishing feature of the new procedure is that it imposes no structural assumptions on the unknown covariance matrices. Hence, the test is robust with respect to various complex dependence structures that frequently arise in genomics. We prove that the proposed procedure is asymptotically valid under weak moment conditions. As an interesting application, we derive a new gene clustering algorithm which shares the same nice property of avoiding restrictive structural assumptions for high-dimensional genomics data. Using an asthma gene expression dataset, we illustrate how the new test helps compare the covariance matrices of the genes across different gene sets/pathways between the disease group and the control group, and how the gene clustering algorithm provides new insights on the way gene clustering patterns differ between the two groups. The proposed methods have been implemented in an R-package HDtest and are available on CRAN.

LOCAL INDEPENDENCE FEATURE SCREENING FOR NONPARAMETRIC AND SEMIPARAMETRIC MODELS BY MARGINAL EMPIRICAL LIKELIHOOD.

We consider an independence feature screening technique for identifying explanatory variables that locally contribute to the response variable in high-dimensional regression analysis. Without requiring a specific parametric form of the underlying data model, our approach accommodates a wide spectrum of nonparametric and semiparametric model families. To detect the local contributions of explanatory variables, our approach constructs empirical likelihood locally in conjunction with marginal nonparametric regressions. Since our approach actually requires no estimation, it is advantageous in scenarios such as the single-index models where even specification and identification of a marginal model is an issue. By automatically incorporating the level of variation of the nonparametric regression and directly assessing the strength of data evidence supporting local contribution from each explanatory variable, our approach provides a unique perspective for solving feature screening problems. Theoretical analysis shows that our approach can handle data dimensionality growing exponentially with the sample size. With extensive theoretical illustrations and numerical examples, we show that the local independence screening approach performs promisingly.

MARGINAL EMPIRICAL LIKELIHOOD AND SURE INDEPENDENCE FEATURE SCREENING.

We study a marginal empirical likelihood approach in scenarios when the number of variables grows exponentially with the sample size. The marginal empirical likelihood ratios as functions of the parameters of interest are systematically examined, and we find that the marginal empirical likelihood ratio evaluated at zero can be used to differentiate whether an explanatory variable is contributing to a response variable or not. Based on this finding, we propose a unified feature screening procedure for linear models and the generalized linear models. Different from most existing feature screening approaches that rely on the magnitudes of some marginal estimators to identify true signals, the proposed screening approach is capable of further incorporating the level of uncertainties of such estimators. Such a merit inherits the self-studentization property of the empirical likelihood approach, and extends the insights of existing feature screening methods. Moreover, we show that our screening approach is less restrictive to distributional assumptions, and can be conveniently adapted to be applied in a broad range of scenarios such as models specified using general moment conditions. Our theoretical results and extensive numerical examples by simulations and data analysis demonstrate the merits of the marginal empirical likelihood approach.