Background Head and neck cancer (HNC) is one of the more common malignant tumors that threaten human health worldwide. Multidisciplinary team management (MDTM) in HNC treatment has been introduced in the past several decades to improve patient survival rates. This study reviewed the impact of MDTM on survival rates in patients with HNC compared to conventional treatment methods. Methods Only cohort studies were identified for this meta-analysis that included an exposure group that utilized MDTM and a control group. Heterogeneity and sensitivity also were assessed. Survival rate data for HNC patients were analyzed using RevMan 5.2 software. Results Five cohort studies (n = 39,070) that examined survival rates among HNC patients were included. Hazard ratios (HR) were calculated using the random effect model. The results revealed that exposure groups treated using MDTM exhibited a higher survival rate [HR = 0.84, 95% CI (0.76-0.92), P = 0.0004] with moderate heterogeneity (I 2 = 68%, p = 0.01). For two studies that examined the effect of MDTM on the survival rate for patients specifically with stage IV HNC, MDTM did not produce any statistically significant improvement in survival rates [HR = 0.81, 95% CI (0.59-1.10), p = 0.18]. Conclusions The application of MDTM based on conventional surgery, radiotherapy, and chemotherapy improved the overall survival rate of patients with HNC. Future research should examine the efficacy of MDTM in patients with cancer at different stages. Endometrial cancer (EC) is one of the most common gynecologic malignancies. The present study aims to identify a metabolism-related biosignature for EC and explore the molecular immune-related mechanisms underlying the tumorigenesis of EC. Transcriptomics and clinical data of EC were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Common differentially expressed metabolism-related genes were extracted and a risk signature was identified by using the least absolute shrinkage and selection operator (LASSO) regression analysis method. A nomogram integrating the prognostic model and the clinicopathological characteristics was established and validated by a cohort of clinical EC patients. Furthermore, the immune and stromal scores were observed and the infiltration of immune cells in EC cells was analyzed. Six genes, including CA3, HNMT, PHGDH, CD38, PSAT1, and GPI, were selected for the development of the risk prediction model. The Kaplan-Meier curve indicated that patients in the low-risk group had considerably better overall survival (OS) (P = 7.874e-05). Then a nomogram was constructed and could accurately predict the OS (AUC = 0.827, 0.821, 0.845 at 3-, 5-, and 7-year of OS). External validation with clinical patients showed that patients with low risk scores had a longer OS (p = 0.04). Immune/stromal scores and infiltrating density of six types of immune cells were lower in high-risk group. In summary, our work provided six potential metabolism-related biomarkers as well as a nomogram for the prognosis of EC patients, and explored the underlying mechanism involved in the progression of EC. In summary, our work provided six potential metabolism-related biomarkers as well as a nomogram for the prognosis of EC patients, and explored the underlying mechanism involved in the progression of EC.Background The prevalence of Helicobacter pylori infection (HPI) is still high around the world, which induces gastric diseases, such as gastric cancer (GC). The epidemiological investigation showed that there was an association between HPI and asthma (AST). Coptidis rhizoma (CR) has been reported as an herbal medicine with anti-inflammatory and anti-bacterial effects. Purpose The present study was aimed to investigate the protective mechanism of HPI on AST and its adverse effects on the development of GC. Coptis chinensis was used to neutralize the damage of HPI in GC and to hopefully intensify certain protective pathways for AST. Method The information about HPI was obtained from the public database Comparative Toxicogenomics Database (CTD). https://www.selleckchem.com/products/baf312-siponimod.html The related targets in AST and GC were obtained from the public database GeneCards. The ingredients of CR were obtained from the public database Traditional Chinese Medicine Systems Pharmacology (TCMSP). The network pharmacology including gene ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and molecular docking were utilized. Protein-protein interaction was constructed to analyze the functional link of target genes. The molecular docking was employed to study the potential effects of active ingredients from CR on key target genes. Result The top 10 key targets of HPI for AST were CXCL9, CX3CL1, CCL20, CCL4, PF4, CCL27, C5AR1, PPBP, KNG1, and ADORA1. The GO biological process involved mainly leukocyte migration, which responded to bacterium. The (R)-canadine and quercetin were selected from C. chinensis, which were employed to explore if they inhibited the HPI synchronously and protect against AST. The targets of (R)-canadine were SLC6A4 and OPRM1. For ingredient quercetin, the targets were AKR1B1 and VCAM1. Conclusion CXCL9 and VCAM1 were the common targets of AST and HPI, which might be one of the imported targets of HPI for AST. Quercetin could be an effective ingredient to suppress HPI and help prevent AST. Previous studies have indicated that the changes in body composition during treatment are prognostic in lung cancer. The question which follows is it may be too late to identify vulnerable patients after treatment and to improve outcomes for these patients. In our study, we sought to explore the alterations of body composition and weight before the outset of the antiangiogenic treatment and its role in predicting clinical response and outcomes. In this retrospective study, 122 patients with advanced lung cancer treated with anlotinib or apatinib were analyzed. The changes in weight and body composition including skeletal muscle index (SMI), subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT) for 3 months before the outset of antiangiogenic treatment and other clinical characteristics were evaluated with LASSO Cox regression and multivariate Cox regression analysis, which were applied to construct nomograms. The performance of the nomograms was validated internally by using bootstrap method with 1,000 resamples models and was assessed by the concordance index (C-index), calibration plots, decision curve analysis (DCA).