DYNLT1 gene expression is downregulated in whole blood of patients at different Huntington's disease stages

Advances in Huntington's Disease Biomarkers: A 10-Year Bibliometric Analysis and a Comprehensive Review

Neurodegenerative disorders (NDs) cause progressive neuronal loss and are a significant public health concern, with NDs projected to become the second leading global cause of death within two decades. Huntington's disease (HD) is a rare, progressive ND caused by an autosomal-dominant mutation in the huntingtin (HTT) gene, leading to severe neuronal loss in the brain and resulting in debilitating motor, cognitive, and psychiatric symptoms. Given the complex pathology of HD, biomarkers are essential for performing early diagnosis, monitoring disease progression, and evaluating treatment efficacy. However, the identification of consistent HD biomarkers is challenging due to the prolonged premanifest HD stage, HD's heterogeneous presentation, and its multiple underlying biological pathways. This study involves a 10-year bibliometric analysis of HD biomarker research, revealing key research trends and gaps. The study also features a comprehensive literature review of emerging HD biomarkers, concluding the need for better stratification of HD patients and well-designed longitudinal studies to validate HD biomarkers. Promising candidate wet HD biomarkers- including neurofilament light chain protein (NfL), microRNAs, the mutant HTT protein, and specific metabolic and inflammatory markers- are discussed, with emphasis on their potential utility in the premanifest HD stage. Additionally, biomarkers reflecting brain structural deficits and motor or behavioral impairments, such as neurophysiological (e.g., motor tapping, speech, EEG, and event-related potentials) and imaging (e.g., MRI, PET, and diffusion tensor imaging) biomarkers, are evaluated. The findings underscore that the discovery and validation of reliable HD biomarkers urgently require improved patient stratification and well-designed longitudinal studies. Reliable biomarkers, particularly in the premanifest HD stage, are crucial for optimizing HD clinical management strategies, enabling personalized treatment approaches, and advancing clinical trials of HD-modifying therapies.

Identification of DYNLT1 associated with proliferation, relapse, and metastasis in breast cancer

Background

Breast cancer (BC) is the most common malignant disease worldwide. Although the survival rate is improved in recent years, the prognosis is still bleak once recurrence and metastasis occur. It is vital to investigate more efficient biomarkers for predicting the metastasis and relapse of BC. DYNLT1 has been reported that participating in the progression of multiple cancers. However, there is still a lack of study about the correlation between DYNLT1 and BC.

Methods

In this study, we evaluated and validated the expression pattern and prognostic implication of DYNLT1 in BC with multiple public cohorts and BC tumor microarrays (TMAs) of paraffin-embedded tissues collected from the Affiliated Hospital of Jining Medical University. The response biomarkers for immune therapy, such as tumor mutational burden (TMB), between different DYNLT1 expression level BC samples were investigated using data from the TCGA-BRCA cohort utilizing public online tools. In addition, colony formation and transwell assay were conducted to verify the effects of DYNLT1 in BC cell line proliferation and invasion.

Results

The results demonstrated that DYNLT1 overexpressed in BC and predicted poor relapse-free survival in our own BC TMA cohort. In addition, DYNLT1 induced BC development by promoting MDA-MB-231 cell proliferation migration, and metastasis.

Conclusion

Altogether, our findings proposed that DYNLT1 could be a diagnostic and prognostic indicator in BC.

Novel Immune-Related Genetic Expression for Primary Sjögren's Syndrome

Objective: To identify novel immune-related genes expressed in primary Sjögren's syndrome (pSS).

Methods: Gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were screened. The differences in immune cell proportion between normal and diseased tissues were compared, weighted gene co-expression network analysis was conducted to identify key modules, followed by a protein–protein interaction (PPI) network generation and enrichment analysis. The feature genes were screened and verified using the GEO datasets and quantitative real-time PCR (RT-qPCR).

Results: A total of 345 DEGs were identified, and the proportions of gamma delta T cells, memory B cells, regulatory T cells (Tregs), and activated dendritic cells differed significantly between the control and pSS groups. The turquoise module indicated the highest correlation with pSS, and 252 key genes were identified. The PPI network of key genes showed that RPL9, RBX1, and RPL31 had a relatively higher degree. In addition, the key genes were mainly enriched in coronavirus disease-COVID-2019, hepatitis C, and influenza A. Fourteen feature genes were obtained using the support vector machine model, and two subtypes were identified. The genes in the two subtypes were mainly enriched in the JAK-STAT, p53, and toll-like receptor signaling pathways. The majority of the feature genes were upregulated in the pSS group, verified using the GEO datasets and RT-qPCR analysis.

Conclusions: Memory B cells, gamma delta T cells, Tregs, activated dendritic cells, RPL9, RBX1, RPL31, and the feature genes possible play vital roles in the development of pSS.