Causal Associations Between Gut Microbiota and Cerebrovascular Diseases.

BACKGROUND: Numerous studies suggest that the gut microbiota closely linked to cerebrovascular diseases, such as Intracranial aneurysm (IA) and aneurysmal subarachnoid hemorrhage (aSAH). Nevertheless, the confirmation of a definitive causal connection between gut microbiota, IA, and aSAH is still pending. The aim of our research is to explore the potential bidirectional causality among them. METHODS: This bidirectional Mendelian Randomization (MR) study used single nucleotide polymorphisms linked to gut microbiota, IA, and aSAH from Genome-Wide Association Studies. The Inverse Variance Weighted (IVW) method was used to explore causality. To assess the robustness of the result, sensitivity analyses were further performed, including weighted-median method, MR-Egger regression, Maximum-likelihood method, MR pleiotropy residual sum and outlier test and leave-one-out analysis. RESULTS: In the IVW method, the family Porphyromonadaceae (odds ratio [OR] 0.63; 95% CI 0.47-0.85; P: 0.002) and genus Bilophila (OR 0.66; 95% CI 0.50-0.86; P: 0.002) showed a significant negative association with the risk of IA. Similarly, the genus Bilophila (OR: 0.68; 95% CI: 0.50-0.93; P: 0.017) and genus Ruminococcus1 (OR: 0.48; 95% CI: 0.30-0.78; P: 0.003) were linked to reduced risk of aSAH. The sensitivity analysis yielded similar outcomes in the IVW approach. Through the adoption of reverse MR analysis, a potential correlation between IA and decreased abundance of genus Ruminococcus1 was identified (OR 0.94; 95% CI 0.90-0.99; P 0.024). CONCLUSIONS: This MR analysis investigated the causal associations between gut microbiota, IA, and aSAH risks. The findings expanded current knowledge of the microbiota-gut-brain axis and offered novel perspectives on preventing and managing these conditions.

Development and external validation of dual online tools for prognostic assessment in elderly patients with high-grade glioma: a comprehensive study using SEER and Chinese cohorts.

Background: Elderly individuals diagnosed with high-grade gliomas frequently experience unfavorable outcomes. We aimed to design two web-based instruments for prognosis to predict overall survival (OS) and cancer-specific survival (CSS), assisting clinical decision-making. Methods: We scrutinized data from the SEER database on 5,245 elderly patients diagnosed with high-grade glioma between 2000-2020, segmenting them into training (3,672) and validation (1,573) subsets. An additional external validation cohort was obtained from our institution. Prognostic determinants were pinpointed using Cox regression analyses, which facilitated the construction of the nomogram. The nomogram's predictive precision for OS and CSS was gauged using calibration and ROC curves, the C-index, and decision curve analysis (DCA). Based on risk scores, patients were stratified into high or low-risk categories, and survival disparities were explored. Results: Using multivariate Cox regression, we identified several prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in elderly patients with high-grade gliomas, including age, tumor location, size, surgical technique, and therapies. Two digital nomograms were formulated anchored on these determinants. For OS, the C-index values in the training, internal, and external validation cohorts were 0.734, 0.729, and 0.701, respectively. We also derived AUC values for 3-, 6-, and 12-month periods. For CSS, the C-index values for the training and validation groups were 0.733 and 0.727, with analogous AUC metrics. The efficacy and clinical relevance of the nomograms were corroborated via ROC curves, calibration plots, and DCA for both cohorts. Conclusion: Our investigation pinpointed pivotal risk factors in elderly glioma patients, leading to the development of an instrumental prognostic nomogram for OS and CSS. This instrument offers invaluable insights to optimize treatment strategies.

Global research trends in immunotherapy for glioma: a comprehensive visualization and bibliometric analysis.

Background: Glioma is a prevalent and lethal brain malignancy; despite current treatment options, the prognosis remains poor. Therefore, immunotherapy has emerged as a promising therapeutic strategy. However, research trends and hotspots in glioma immunotherapy have not been systematically analyzed. This study aimed to elucidate global research trends and knowledge structures regarding immunotherapy for glioma using bibliometric analysis. Methods: Publications related to immunotherapy for glioma from 2000-2023 were retrieved from Web of Science Core Collection database (WoSCC). We conducted quantitative analysis and visualization of research trends using various tools, including VOSviewer (1.6.18), CiteSpace (5.7 R3), Microsoft Charticulator, and the Bibliometrix package in R. Results: A total of 4910 publications were included. The number of annual publications exhibited an obvious upward trend since 2019. The USA was the dominant country in terms of publication output and centrality. Frontiers in Immunology published the most articles. Harvard Medical School ranked first in productivity among institutions. Sampson, John H. Ph.D. is the most prolific author in the field with 88 articles and a total of 7055 citations. Clinical Cancer Research has the largest total number and impact factor. Analysis of keywords showed immunotherapy, glioblastoma, immunotherapy, and clinical trials as hot topics. The tumor microenvironment, cell death pathways, chimeric antigen receptor engineering, tumor-associated macrophages, and nivolumab treatment represent indicating shifts in the direction of future glioma immunotherapy development. Conclusion: This bibliometric analysis systematically delineated global landscapes and emerging trends in glioma immunotherapy research. This study highlighted the prominence of Chimeric Antigen Receptor T-cell (CAR-T), Programmed Death-1 (PD-1), and nivolumab in current glioma immunotherapy research. The growing emphasis on specific neoantigens and prognostic tumor markers suggests potential avenues for future exploration. Furthermore, the data underscores the importance of strengthened international collaboration in advancing the field.

Development and external validation of a novel prognostic nomogram for overall survival in prostate cancer patients with bone metastatic: a retrospective study of the SEER-based and a single Chinese center.

BACKGROUND: Prostate cancer (PCa) patients with bone metastases (BM) often face a poor prognosis, a leading contributor to mortality within this group. This study aims to develop a novel prognostic nomogram to predict overall survival for them. METHODS: We retrospectively analyzed PCa patients with BM from Surveillance, Epidemiology, and End Results (SEER) database and our hospital. Independent prognostic factors were identified using univariate and multivariate Cox regression analyses for the creation of a nomogram. Calibration curves and receiver operating characteristic (ROC) curves, along with the concordance index (C-index) and decision curve analysis (DCA), were employed to evaluate the performance of the constructed nomogram. RESULTS: A total of 12,344 PCa patients with BM, derived from 2010 to 2019 SEER database, were randomly allocated into a training cohort (n = 8640) and an internal validation cohort (n = 3704). Additionally, an external validation cohort (n = 126) from our hospital. The novel nomogram integrates multiple factors: age, race, histopathology, organ metastasis, chemotherapy, Gleason score, and prostate-specific antigen (PSA). C-index for the training, internal validation, and external validation cohorts were 0.770 (0.766-0.774), 0.756 (0.749-0.763), and 0.751 (0.745-0.757) respectively. Similarly, the area under the curve (AUC) for each cohort exhibited comparable results (training cohort-3-year: 0.682, 6-year: 0.775, 9-year: 0.824; internal validation cohort-3-year: 0.681, 6-year: 0.750, 9-year: 0.806; external validation cohort-2-year: 0.667, 3-year: 0.744, 4-year: 0.800), indicating that the nomogram possesses robust discriminative ability. Calibration curve and DCA curve further proved the reliability and accuracy of the prognostic nomogram. CONCLUSION: This study determined the independent risk factors for prostate cancer (PCa) patients with bone metastasis (BM) and subsequently developed a robust prognostic nomogram to predict overall survival (OS). This tool can serve to guide precise clinical treatment strategies for these patients.

A novel staging system based on deep learning for overall survival in patients with esophageal squamous cell carcinoma.

PURPOSE: We developed DeepSurv, a deep learning approach for predicting overall survival (OS) in patients with esophageal squamous cell carcinoma (ESCC). We validated and visualized the novel staging system based on DeepSurv using data from multiple cohorts. METHODS: Totally 6020 ESCC patients diagnosed from January 2010 to December 2018 were included in the present study from the Surveillance, Epidemiology, and End Results database (SEER), randomly assigned to the training and test cohorts. We developed, validated and visualized a deep learning model that included 16 prognostic factors; then a novel staging system was further constructed based on the total risk score derived from the deep learning model. The classification performance at 3-year and 5-year OS was assessed by the receiver-operating characteristic (ROC) curve. Calibration curve and the Harrell's concordance index (C-index) were also used to comprehensively assess the predictive performance of the deep learning model. Decision curve analysis (DCA) was utilized to assess the clinical utility of the novel staging system. RESULTS: A more applicable and accurate deep learning model was established, which outperformed the traditional nomogram in predicting OS in the test cohort (C-index: 0.732 [95% CI 0.714-0.750] versus 0.671 [95% CI 0.647-0.695]). The ROC curves at 3-year and 5-year OS for the model also showed good discrimination ability in the test cohort (Area Under the Curve [AUC] at 3-/5-year OS = 0.805/0.825). Moreover, using our novel staging system, we observed a clear survival difference among different risk groups (P < 0.001) and a significant positive net benefit in the DCA. CONCLUSIONS: A novel deep learning-based staging system was constructed for patients with ESCC, which performed a significant discriminability for survival probability. Moreover, an easy-to-use web-based tool based on the deep learning model was also implemented, offering convenience for personalized survival prediction. We developed a deep learning-based system that stages patients with ESCC according to their survival probability. We also created a web-based tool that uses this system to predict individual survival outcomes.

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.