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

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.