Visual analysis of global research on immunotherapy for gastric cancer: A literature mining from 2012 to 2022

Relationship between the microenvironment and survival in kidney transplantation: a bibliometric analysis from 2013 to 2023

Background

Kidney transplantation is considered the most effective treatment for end-stage renal failure. Recent studies have shown that the significance of the immune microenvironment after kidney transplantation in determining prognosis of patients. Therefore, this study aimed to conduct a bibliometric analysis to provide an overview of the knowledge structure and research trends regarding the immune microenvironment and survival in kidney transplantation.

Methods

Our search included relevant publications from 2013 to 2023 retrieved from the Web of Science core repository and finally included 865 articles. To perform the bibliometric analysis, we utilized tools such as VOSviewer, CiteSpace, and the R package "bibliometrix". The analysis focused on various aspects, including country, author, year, topic, reference, and keyword clustering.

Results

Based on the inclusion criteria, a total of 865 articles were found, with a trend of steady increase. China and the United States were the countries with the most publications. Nanjing Medical University was the most productive institution. High-frequency keywords were clustered into 6 areas, including kidney transplantation, transforming growth factor β, macrophage, antibody-mediated rejection, necrosis factor alpha, and dysfunction. Antibody mediated rejection (2019-2023) was the main area of research in recent years.

Conclusion

This groundbreaking bibliometric study comprehensively summarizes the research trends and advances related to the immune microenvironment and survival after kidney transplantation. It identifies recent frontiers of research and highlights promising directions for future studies, potentially offering fresh perspectives to scholars in the field.

Knock down of APE1 suppressed gastric cancer metastasis via improving immune disorders caused by myeloid-derived suppressor cells

Gastric cancer is a highly immunogenic malignancy. Immune tolerance facilitated by myeloid-derived suppressor cells (MDSCs) has been implicated in gastric cancer resistance mechanisms. The potential role of APE1 in regulating gastric cancer metastasis by targeting MDSCs remains uncertain. In this study, the plasmid Plxpsp-mGM-CSF was used to induce high expression of granulocyte-macrophage colony-stimulating factor (GM-CSF) in GES-1 cells. For tumor transplantation experiments, AGS, AGS+GM-CSF and AGS+GM-CSF-siAPE1 cell lines were established by transfection, followed by subcutaneous implantation of tumor cells. MDSCs, Treg cells, IgG, CD3 and CD8 levels were assessed. Transfection with siAPE1 significantly inhibited tumor growth compared to the AGS+GM-CSF group. APE1 gene knockdown modulated the immune system in gastric cancer mice, characterized by a decrease in MDSCs and an increase in Treg cells, IgG, CD3 and CD8. In addition, APE1 gene knockdown resulted in decreased levels of pro-MDSC cytokines (HGF, CCL5, IL-6, CCL12). Furthermore, APE1 gene knockdown inhibited proliferation, migration and invasion of AGS and MKN45 cells. AGS-GM-CSF cell transplantation increased MDSC levels and accelerated tumor growth, whereas APE1 knockdown reduced MDSC levels, inhibited tumor growth and attenuated inflammatory infiltration in gastric cancer tissues. Strategies targeting the APE1/MDSC axis offer a promising approach to the prevention and treatment of gastric cancer, providing new insights into its management.

Unveiling the landscape of cytokine research in glioma immunotherapy: a scientometrics analysis

Background: Cytokines modulate the glioma tumor microenvironment, influencing occurrence, progression, and treatment response. Strategic cytokine application may improve glioma immunotherapy outcomes. Gliomas remain refractory to standard therapeutic modalities, but immunotherapy shows promise given the integral immunomodulatory roles of cytokines. However, systematic evaluation of cytokine glioma immunotherapy research is absent. Bibliometric mapping of the research landscape, recognition of impactful contributions, and elucidation of evolutive trajectories and hot topics has yet to occur, potentially guiding future efforts. Here, we analyzed the structure, evolution, trends, and hotspots of the cytokine glioma immunotherapy research field, subsequently focusing on avenues for future investigation. Methods: This investigation conducted comprehensive bibliometric analyses on a corpus of 1529 English-language publications, from 1 January 2000, to 4 October 2023, extracted from the Web of Science database. The study employed tools including Microsoft Excel, Origin, VOSviewer, CiteSpace, and the Bibliometrix R package, to systematically assess trends in publication, contributions from various countries, institutions, authors, and journals, as well as to examine literature co-citation and keyword distributions within the domain of cytokines for glioma immunotherapy. The application of these methodologies facilitated a detailed exploration of the hotspots, the underlying knowledge structure, and the developments in the field of cytokines for glioma immunotherapy. Results: This bibliometric analysis revealed an exponential growth in annual publications, with the United States, China, and Germany as top contributors. Reviews constituted 17% and research articles 83% of total publications. Analysis of keywords like "interleukin-13," "TGF-beta," and "dendritic cells" indicated progression from foundational cytokine therapies to sophisticated understanding of the tumor microenvironment and immune dynamics. Key research avenues encompassed the tumor microenvironment, epidermal growth factor receptor, clinical trials, and interleukin pathways. This comprehensive quantitative mapping of the glioma immunotherapy cytokine literature provides valuable insights to advance future research and therapeutic development. Conclusion: This study has identified remaining knowledge gaps regarding the role of cytokines in glioma immunotherapy. Future research will likely focus on the tumor microenvironment, cancer vaccines, epidermal growth factor receptor, and interleukin-13 receptor alpha 2. Glioma immunotherapy development will continue through investigations into resistance mechanisms, microglia and macrophage biology, and interactions within the complex tumor microenvironment.

A tumor-associated endothelial signature score model in immunotherapy and prognosis across pan-cancers

Background: The tumor-associated endothelial cell (TAE) component plays a vital role in tumor immunity. However, systematic tumor-associated endothelial-related gene assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers have not been explored. Herein, we investigated a TAE gene risk model to predict CIT responses and patient survival in a pan-cancer analysis. Methods: We analyzed publicly available datasets of tumor samples with gene expression and clinical information, including gastric cancer, metastatic urothelial cancer, metastatic melanoma, non-small cell lung cancer, primary bladder cancer, and renal cell carcinoma. We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm. Results: The model demonstrated a high predictive accuracy in both training and validation cohorts. The response rate of the high score group to immunotherapy in the training cohort was significantly higher than that of the low score group, with CIT response rates of 51% and 27%, respectively. The survival analysis showed that the prognosis of the high score group was significantly better than that of the low score group (all p < 0·001). Tumor-associated endothelial gene signature scores positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may benefit patients in the high score group. The analysis of TAE scores across 33 human cancers revealed that the TAE model could reflect immune cell infiltration and predict the survival of cancer patients. Conclusion: The TAE signature model could represent a CIT response prediction model with a prognostic value in multiple cancer types.

Prognostic Implication of Plasma Metabolites in Gastric Cancer

Gastric cancer (GC) typically carries a poor prognosis as it is often diagnosed at a late stage. Altered metabolism has been found to impact cancer outcomes and affect patients' quality of life, and the role of metabolites in gastric cancer prognosis has not been sufficiently understood. We aimed to establish a prognostic prediction model for GC patients based on a metabolism-associated signature and identify the unique role of metabolites in the prognosis of GC. Thus, we conducted untargeted metabolomics to detect the plasma metabolites of 218 patients with gastric adenocarcinoma and explored the metabolites related to the survival of patients with gastric cancer. Firstly, we divided patients into two groups based on the cutoff value of the abundance of each of the 60 metabolites and compared the differences using Kaplan-Meier (K-M) survival analysis. As a result, 23 metabolites associated with gastric cancer survival were identified. To establish a risk score model, we performed LASSO regression and Cox regression analysis on the 60 metabolites and identified 8 metabolites as an independent prognostic factor. Furthermore, a nomogram incorporating clinical parameters and the metabolic signature was constructed to help individualize outcome predictions. The results of the ROC curve and nomogram plot showed good predictive performance of metabolic risk features. Finally, we performed pathway analysis on the 24 metabolites identified in the two parts, and the results indicated that purine metabolism and arachidonic acid metabolism play important roles in gastric cancer prognosis. Our study highlights the important role of metabolites in the progression of gastric cancer and newly identified metabolites could be potential biomarkers or therapeutic targets for gastric cancer patients.

Analyzing fulminant myocarditis research trends and characteristics using the follower-leading clustering algorithm (FLCA): A bibliometric study

BACKGROUND

Myocarditis can be classified into 2 categories: fulminant myocarditis (FM) and nonfulminant myocarditis. FM is the most severe type, characterized by its acute and explosive nature, posing a sudden and life-threatening risk with a high fatality rate. Limited research has been conducted on FM characteristics using cluster analysis. This study introduces the following-leading clustering algorithm (`) as a unique method and utilizes it to generate a dual map and timeline view of FM themes, aiming to gain a better understanding of FM.

METHODS

The metadata were obtained from the Web of Science (WoS) database using an advanced search strategy based on the topic (TS= (("Fulminant") AND ("Myocarditis"))). The analysis comprised 3 main components: descriptive analytics, which involved identifying the most influential entities using CJAL scores and analyzing publication trends, author collaborations using the FLCA algorithm, and generating a dual map and timeline view of FM themes using the FLCA algorithm. The visualizations included radar plots divided into 4 quadrants, stacked bar and line charts, network charts, chord diagrams, a dual map overlay, and a timeline view.

RESULTS

The findings reveal that the prominent entities in terms of countries, institutes, departments, and authors were the United States, Huazhong University of Science and Technology (China), Cardiology, and Enrico Ammirati from Italy. A dual map, based on the research category, was created to analyze the relationship between citing and cited articles. It showed that articles related to cells and clinical medicine/surgery were frequently cited by articles in the fields of general health/public/nursing and clinical medicine/surgery. Additionally, a visual timeline view was presented on Google Maps, showcasing the themes extracted from the top 100 cited articles. These visualizations were successfully and reliably generated using the FLCA algorithm, offering insights from various perspectives.

CONCLUSION

A new FLCA algorithm was utilized to examine bibliometric data from 1989 to 2022, specifically focusing on FM. The results of this analysis can serve as a valuable guide for researchers, offering insights into the thematic trends and characteristics of FM research development. This, in turn, can facilitate and promote future research endeavors in this field.