Jiaxi Chen

Objectives: This study aimed to investigate plasma metabolomic and lipidomic profiles in rheumatoid arthritis (RA) to identify potential biomarkers for distinguishing treatment responses. Methods: Plasma samples were collected from 106 RA patients and 10 healthy controls, with 30 RA samples selected based on predefined inclusion criteria. Using liquid chromatography–mass spectrometry (LC-MS)-based untargeted metabolomics and lipidomics analysis, a total of 2,279 metabolites and 2,987 lipids were tentatively annotated. Results: Through rigorous statistical evaluation (variable importance in the projection (VIP) > 1 and false discovery rate (FDR) < 0.05), 22 metabolites and lipids were found to be positively associated with RA risk based on logistic regression analysis. These were further refined into 12 core features using least absolute shrinkage and selection operator (LASSO) regression. The 12 core features identified in the treatment non-response group included lipids Cer(d18:1/16:0), PS(16:0/20:0), and Palmitic acid; metabolites Mimosine and D-Xylose; drug-related metabolites Dihydralazine, Tridihexethyl, Acyclovir monophosphate, Indoline, and Melleolide; and pollution-related metabolites Norcotinine and 2-Chloro-1-(chloromethyl)ethyl carbamate. Machine learning models utilizing these features showed promising preliminary discriminatory performance in internal cross-validation, achieving area under the curve (AUC) values exceeding 0.90 in this cohort, suggesting the potential utility of these candidate biomarkers in differentiating treatment-responsive from non-responsive RA patients. Additionally, the identified features showed significant correlations with high disease activity, with preliminary evidence of potential associations across different stages of disease progression. Conclusion: The findings suggest significant metabolic and lipidomic alterations in RA, and indicate that these candidate biomarkers may serve as preliminary indicators of treatment response.

This study investigates the causal relationship between rheumatoid arthritis (RA) and idiopathic pulmonary fibrosis (IPF), aiming to identify shared biomarkers between the two conditions. Utilizing bidirectional Mendelian randomization (MR), we assessed causality by analyzing genome‐wide association study (GWAS) data from European cohorts, which included 14,361 RA cases and 42,923 controls, as well as 1028 IPF cases and 196,986 controls. In addition, we examined transcriptomic and single‐cell RNA sequencing (scRNA‐seq) data derived from RA patients to pinpoint shared genetic features. The MR analysis confirmed that RA is a causal factor for IPF, yielding an odds ratio of 1.156 (95% CI: 1.054‐1.267, p = 0.002), whereas no evidence supported the reverse causation. Notably, CCL2 and CXCL2 were identified as key biomarkers, displaying elevated expression levels in RA plasma ( p < 0.05) and specific immune cell types, particularly macrophages. These findings indicate that RA significantly contributes to the development of IPF and highlight CCL2 and CXCL2 as potential diagnostic markers. This research provides valuable insights that may facilitate early detection and intervention strategies for patients at risk of developing IPF in the context of RA.

Sepsis remains a significant global health burden and contributor to mortality, yet the precise molecular mechanisms underlying the immune response are not fully elucidated. To gain insight into this issue, we performed a comprehensive analysis using a variety of techniques including bulk RNA sequencing, single-cell RNA sequencing, and enzyme-linked immunosorbent assay (ELISA). We performed enrichment analysis of differentially expressed genes in sepsis and healthy individuals by utilizing Gene Ontology (GO) analysis and indicated significant enrichment of immune-related response. Following Weighted Gene Co-Expression Network Analysis (WGCNA) and protein-protein interaction analysis (PPI) were used to identify key immune-related hub genes and validated by ELISA to show that NLRC4 is highly expressed in sepsis. Additionally, an analysis of scRNA-seq data from newly diagnosed sepsis, sepsis diagnosis at 6 hours, and healthy samples demonstrates a significant increase in both the expression levels and proportions of NLRC4 in sepsis monocytes and neutrophils. In addition, using pySCENIC we identified upstream transcription factors that regulate NLRC4. Our study provides valuable insights into the identification of NLRC4 in peripheral blood as a potential candidate gene for the diagnosis and treatment of sepsis.

Purpose: The pathogenesis of T cell subsets in sepsis during the body’s resistance to infection is currently unknown. We aimed to investigate the dynamics and molecular mechanisms of T cells during the development of sepsis. Patients and Methods: Perform single-cell data analysis on peripheral blood mononuclear cells (PBMCs) specimen samples from seven healthy controls, five early-stage sepsis patients, and four late sepsis patients, and the atlas were mapped and analyzed using reference mapping to identify the T cell subpopulations specific to early sepsis. Expression network upstream to investigate the changes of regulatory transcription factors and pathways by pySCENIC. Results: Twenty-two CD4⁺ T-cell subpopulations and 10 CD8⁺ T-cell subpopulations were identified by mapping analysis. At the early stage of sepsis, we observed altered ratios of multiple immune cells in PBMCs. Three cell types CD4 Tn cells, CD8 (GZMK⁺ early Tem), and CD8 (ZNF683⁺CXCR6⁻ Tm) showed an upward trend (p < 0.05) in the early stages of sepsis compared to normal and returned to normal levels after two weeks. In addition, we found the presence of four sepsis-specific transcription factors (MXI1, CHD1, ARID5A, KLF9) in these three types of cells, which were validated in two external datasets. The differentially expressed genes in CD4 Tn cells, CD8 (GZMK⁺ early Tem), and CD8 (ZNF683⁺CXCR6⁻ Tm) cells between the healthy group and the early-stage sepsis group are commonly enriched in the allograft rejection pathway. In addition, it was found that CD8 cells exhibit a trend towards differentiation into CD8 Temra cells in sepsis. Conclusion: We successfully depicted the dynamic changes of T cell subsets during sepsis onset and progression, which provides important clues for an in-depth understanding of T cells’ function and regulatory mechanisms during sepsis pathogenesis.

The long non-coding RNA, Negative Regulator of Antiviral Response (NRAV) has been identified as a participant in both respiratory virus replication and immune checkpoints, however, its involvement in pan-cancer immune regulation and prognosis, particularly those of hepatocellular carcinoma (HCC), remains unclear. To address this knowledge gap, we analyzed expression profiles obtained from The Cancer Genome Atlas (TCGA) database, comparing normal and malignant tumor tissues. We found that NRAV expression is significantly upregulated in tumor tissues compared to adjacent nontumor tissues. Kaplan-Meier (K-M) analysis revealed the prognostic power of NRAV, wherein overexpression was significantly linked to reduced overall survival in a diverse range of tumor patients. Furthermore, noteworthy associations were observed between NRAV, immune checkpoints, immune cell infiltration, genes related to autophagy, epithelial-mesenchymal transition (EMT), pyroptosis, tumor mutational burden (TMB), and microsatellite instability (MSI) across different cancer types, including HCC. Moreover, NRAV upregulation expression was associated with multiple pathological stages by clinical observations. Furthermore, our investigation revealed a substantial elevation in the expression of NRAV in both HCC tumor tissues and cells compared to normal tissues and cells. The inhibition of NRAV resulted in the inhibition of cell proliferation, migration, and invasion in HCC cells, while also influencing the expression of CD274 (PD-L1) and CD44, along with various biomarkers associated with EMT, autophagy, and pyroptosis. The aforementioned results propose NRAV as a promising prognostic biomarker for HCC.