No abstract

BackgroundThere is a crosstalk between gut microbiota (GM) and cancer immunotherapy (CI). The purpose of this study is to use bibliometric analysis to identify the highly cited papers relating to GM/CI and explore the research status and development trends of the GM/CI research.MethodsA literature search regarding GM/CI publications from 2012 to 2021 was undertaken on July 4, 2022. The article titles, journals, authors, institutions, countries, total citations, keywords, and other information were extracted from the Science Citation Index Expanded (SCIE) of Web of Science Core Collection (WoSCC). The Bibliometrix of R package and VOSviewer were used for bibliometric analysis.ResultsA total of 665 papers were extracted. The number of papers has increased rapidly over the past decade, especially after 2018. The United States and China had the most publications and made great contributions to this field. Th5e Univ Texas MD Anderson Canc Ctr and Univ Paris Saclay were absolutely in the leading position in GM/CI. The most influential authors were Zitvogel L and Routy B. Frontiers in Immunology had the most publications and Science had the most total citations. Historical direct citation analysis explained the historical evolution in GM/CI. Highly cited papers and high-frequency keywords illustrated the current status and trends of GM/CI. Four clusters were identified and the important topics included the role of GM and antibiotics in CI, the methods of targeting GM to improve CI outcomes, the mechanism by which GM affects CI and the application of ICIs in melanoma. “Tumor microbiome”, “proton pump inhibitors” and “prognosis” may be the new focus of attention in the next few years.ConclusionThis study filtered global publications on GM/CI correlation and analyzed their bibliometric characteristics, identified the most cited papers in GM/CI, and gained insight into the status, hotspots and trends of global GM/CI research, which may inform researchers and practitioners of future directions.

No abstract

BackgroundWe aimed to assess the differences in the gut microbiome among participants with different uric acid levels (hyperuricemia [HUA] patients, low serum uric acid [LSU] patients, and controls with normal levels) and to develop a model to predict HUA based on microbial biomarkers.MethodsWe sequenced the V3-V4 variable region of the 16S rDNA gene in 168 fecal samples from HUA patients (n=50), LSU patients (n=61), and controls (n=57). We then analyzed the differences in the gut microbiome between these groups. To identify gut microbial biomarkers, the 107 HUA patients and controls were randomly divided (2:1) into development and validation groups and 10-fold cross-validation of a random forest model was performed. We then established three diagnostic models: a clinical model, microbial biomarker model, and combined model.ResultsThe gut microbial α diversity, in terms of the Shannon and Simpson indices, was decreased in LSU and HUA patients compared to controls, but only the decreases in the HUA group were significant (P=0.0029 and P=0.013, respectively). The phylum Proteobacteria (P<0.001) and genus Bacteroides (P=0.02) were significantly increased in HUA patients compared to controls, while the genus Ruminococcaceae_Ruminococcus was decreased (P=0.02). Twelve microbial biomarkers were identified. The area under the curve (AUC) for these biomarkers in the development group was 84.9% (P<0.001). Notably, an AUC of 89.1% (P<0.001) was achieved by combining the microbial biomarkers and clinical factors.ConclusionsThe combined model is a reliable tool for predicting HUA and could be used to assist in the clinical evaluation of patients and prevention of HUA.