Clinical and Experimental Medicine最新文献

As a novel dietary assessment tool, the dietary index for gut microbiota (DI-GM) measured food intake patterns that influenced GM balance. The association of DI-GM with advanced cardiovascular-kidney-metabolic syndrome (CKM) remained unclear. We aimed to explore the relationship between DI-GM and the advanced CKM risk. Participants from the National Health and Nutrition Examination Survey (NHANES) between 2007 and 2018 were included. The DI-GM was defined based on intake levels of 10 beneficial and 4 unfavorable foods. To assess the association of DI-GM with advanced CKM risk, we employed multivariate logistic regression, receiver operating characteristic (ROC) analysis, and weighted quantile sum (WQS) regression. About 13,226 eligible participants were enrolled. Multivariable logistic regression showed that each 1-point increment in the DI-GM and beneficial food scores were associated with a 6% (95% CI: 0.92-0.97) and 7% (95% CI: 0.90-0.97) reduction in advanced CKM risk, respectively. Unfavorable food scores showed no significant association with advanced CKM risk. ROC analysis indicated that compared with the baseline model, the addition of the DI-GM (AUC: 0.743 vs. 0.741; P = 0.013) and the beneficial food scores (AUC: 0.743 vs. 0.741; P = 0.005) significantly improved the discriminatory ability of the baseline model for advanced CKM. WQS regression further identified broccoli, soybeans, fermented dairy products, and dietary fiber as the key dietary components strongly associated with advanced CKM. The DI-GM can function as a promising tool for assessing advanced CKM risk. For the general population, especially individuals with metabolic risk factors, increasing the intake of broccoli, soybeans, fermented dairy products, and dietary fiber may help reduce advanced CKM risk.

Renal cell carcinoma (RCC) presents treatment challenges in advanced stages owing to its metastatic potential. This study explored the role of NME8, a nucleotide metabolism-related protein, in the metastasis and prognosis of RCC. Analysis of the GSE66272 dataset and of The Cancer Genome Atlas (TCGA) specimens revealed that NME8 is overexpressed in renal cancer and is correlated with poor patient outcomes, as determined by Cox regression. Functional assessments, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses and immune infiltration studies, were conducted. In vitro experiments involving the creation of stable NME8-knockdown cell lines revealed that NME8 knockdown significantly reduced renal cancer cell proliferation, migration, and invasion while increasing apoptosis. Immunoblotting indicated that NME8 was associated with epithelial-mesenchymal transition (EMT) proteins such as E-cadherin, vimentin, and Twist. Furthermore, exposure to the Janus kinase (JAK) inhibitor ruxolitinib and the signal transducer and activator of transcription (STAT)3 inhibitor stattic demonstrated that NME8 promoted RCC metastasis by activating the JAK/STAT signaling pathway. Elevated NME8 levels were linked to immune cell infiltration. In conclusion, our findings suggest that NME8 contributes to RCC metastasis by promoting JAK/STAT-mediated EMT and modulating the tumor immune microenvironment. While elevated NME8 expression correlates with poor prognosis in public cohorts, its utility as a clinical prognostic biomarker requires further validation in independent patient populations.

Colon cancer (CC) stands as one of the most prevalent malignant neoplasms worldwide. Despite extensive investigations on the function of T cells in antitumor immunity and dynamics of tumor microenvironment (TME), their precise molecular contributions to the CC progression remain incompletely characterized. Differential gene expression analysis was implemented leveraging TCGA-COAD transcriptomic data, followed by the identification of T cell signature genes using single-cell RNA sequencing (scRNA-seq) dataset. Through intersectional analysis and subsequent prognosis-related gene screening using least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox proportional hazards models, a prognostic model was established. Moreover, its performance was evaluated via receiver operating characteristic (ROC) curves. Participants were split into high-risk and low-risk cohorts based on risk scores, to explore potential immunological differences between groups. A prognostic model was developed based on seven genes, encompassing UBE2N, TUBA1C, FXR1, CBLB, YTHDC1, GPRIN3, and AGPAT2. The area under the ROC curve (AUC) for the training cohort at 3, 5, and 7 years reached 0.676, 0.715, and 0.721, respectively. External validation using three GEO datasets demonstrated consistent predictive performance of the model. The AUC values at 3, 5, and 7 years were 0.632, 0.617, and 0.582 in GSE39582, 0.689, 0.755, and 0.951 in GSE17537, and 0.667, 0.653, and 0.649 in GSE161158. The identified T cell signature genes may function as potential therapeutic targets, while the developed prognostic model and nomogram may facilitate clinical decision-making for CC management.

Rheumatic diseases(RD), a collection of autoimmune disorders affecting connective tissues, have a global prevalence of approximately 3%-5%. Characterized by immune-mediated attacks on the body's own tissues, these conditions lead to inflammation and tissue damage. Rheumatic disease patients frequently receive glucocorticoids, immunosuppressants, and biological agents, potentially elevating the risk of latent tuberculosis infection (LTBI) reactivation and Mycobacterium Tuberculosis (MTB) dissemination. Tumor necrosis factor-α(TNF-α)is crucial in immune responses, and TNF-targeted therapies could raise the risk of advancing from LTBI to active tuberculosis(TB). Compared to TNF inhibitors, non-TNF-targeted drugs-including IL-6, JAK, and B-cell inhibitors-pose a lower TB reactivation risk but may still modulate immune responses and defense against MTB. Our review synthesizes clinical research advancements in non-TNF-targeted therapies for rheumatic diseases, focusing on their association with TB reactivation risk. Non-TNF-targeted therapies demonstrate a reduced TB reactivation risk in rheumatic disease treatment, yet they necessitate rigorous TB screening and surveillance. Comprehensive infection status evaluation and prophylactic anti-tuberculosis treatment are essential for LTBI patients prior to therapy. The risk of TB infection is heightened in rheumatic disease patients undergoing biological therapy, particularly with TNF inhibitors. Despite the lower risk with non-TNF-targeted therapies, stringent screening and ongoing monitoring are imperative. Clinical practice should involve thorough patient assessment, personalized treatment planning, and preventive anti-tuberculosis strategies to balance therapeutic efficacy and infection risk. Further studies are warranted to elucidate the link between non-TNF-targeted therapies and TB risk and to refine treatment strategies for rheumatic diseases.This review seeks to investigate the potential TB reactivation risk associated with non-TNF-targeted therapies in rheumatic diseases and to offer theoretical foundations and novel perspectives for their clinical use.