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Recently, the role of long non-coding RNA (lncRNA) in rheumatic immune diseases has attracted widespread attention. However, knowledge of lncRNA in connective tissue disease-associated interstitial lung disease (CTD-ILD) is limited. This study explored the expression profile and possible mechanisms of lncRNA and mRNA in peripheral blood mononuclear cells (PBMCs) of CTD-ILD patients, especially systemic sclerosis (SSc)-ILD and rheumatoid arthritis (RA)-ILD. LncRNA microarray analysis identified 240 diferentially expressed lncRNAs and 218 diferentially expressed mRNA in the CTD-ILD group and the connective tissue disease without associated interstitial lung disease (CTD-NILD) group. The bioinformatics analysis of diferential genes has identified several important biological processes and signal pathways, including nuclear factor kappa B (NF-kappa B) signaling pathway, interleukin 17 (IL-17) signaling pathway, B cell receptor signaling pathway. Relative expression levels of five diferentially expressed lncRNAs and one mRNA in 120 SSc and RA patients with or without ILD were detected by quantitative reverse-transcription (PCR). The ENST00000604692 expression level was significantly higher in the ILD than the without interstitial lung disease (NILD) group; T311354 and arginase-1 were significantly higher in SSc than RA group. These data suggest that the specific profile of lncRNA in PBMCs of CTD-ILD patients and the potential signal pathways related to the pathogenesis of CTD-ILD, which may provide newfound insights for the diagnosis and treatment of CTD-ILD patients. [ABSTRACT FROM AUTHOR]

Background: Dermatomyositis (DM) is an inflammatory muscle disease that increases the risk of cancer, although the precise connection is not fully understood. The aim of this study was to investigate the mechanisms linking DM to cancer and identify potential therapeutic targets., Methods: We conducted differential gene expression analysis on the GSE128470 dataset and employed WGCNA to pinpoint key genes related to DM. Central genes were identified with the LASSO and SVM-RFE methods. The expression levels and diagnostic relevance of these genes were confirmed via the GSE1551 dataset. Immune cell infiltration was analyzed in relation to central genes, and RT‒qPCR was utilized to evaluate the expression of key genes across various cancers., Results: In total, differentially expressed genes (DEGs), involved mainly in innate immunity, cytokine responses, and autoimmune diseases, were identified. In the WGCNA, 399 significant genes related to DM were identified, with central genes including MIF, C1QA, and CDKN1A. Immune infiltration analysis revealed diverse immune cell populations in DM patients, with significant correlations between central genes and these immune cells. MIF levels were notably elevated in various tumors and correlated with the prognosis of specific cancers. Furthermore, MIF was negatively associated with most immune cells but positively correlated with CD4+ Th1 cells, NKT cells, and MDSCs. Factors such as immune regulatory elements, TMB, and MSI indicated that MIF may affect immunotherapy outcomes. The increased expression of MIF mRNA was confirmed via RT‒qPCR., Conclusion: The findings demonstrate that MIF, C1QA, and CDKN1A are differentially expressed in DM patients, with MIF showing significant alterations in DM patients with cancer. MIF may serve as a crucial prognostic biomarker and therapeutic target for various cancers, playing a pivotal role in linking DM to cancer through the modulation of CD4+ Th1 cells, NKT cells, and MDSCs., Competing Interests: Jianwei Guo and Tianyi Lei contributed equally to this work and share first authorship. The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results., (© 2024 Guo et al.)

Background: Sentinel lymph node metastasis (SLNM) is a critical factor in the prognosis and treatment planning for breast cancer (BC), as it indicates the potential spread of cancer to other parts of the body. The accurate prediction and diagnosis of SLNM are essential for improving clinical outcomes and guiding treatment decisions., Objective: This study aimed to construct a Lasso regression model by integrating multimodal ultrasound (US) techniques, including US, shear wave elastography (SWE), and contrast-enhanced ultrasound (CEUS), to improve the predictive accuracy of sentinel lymph node metastasis in breast cancer and provide more precise guidance for clinical treatment., Results: A total of 253 eligible samples were screened, of which 148 were group benign and 105 were group malignant. There were statistically significant differences ( p < 0.05) between group malignant patients in terms of age, palpable mass, body mass index, distance to nipple, maximum diameter, blood flow, microcalcification, 2D border, 2D morphology, and 2D uniformity and group benign. The Lasso regression model was useful in the diagnosis of benign and malignant nodules with an AUC of 0.966 and in diagnosing SLNM with an AUC of 0.832., Conclusion: In this study, we successfully constructed and validated a Lasso regression model based on the multimodal ultrasound technique for predicting whether SLNM occurs in BCs, showing high diagnostic accuracy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Li, Chen, Liu, Bao, Zhang and Xu.)