Chronic inflammation response as a key factor in polycystic ovary syndrome among patients with bipolar disorderLiu, Chen, Teng
et alJ Affect Disord (2025) 377, 264-274
Abstract: This study aimed to investigate serum inflammatory factor levels of polycystic ovary syndrome (PCOS) in female patients with bipolar disorder (BD) to explore the related inflammatory molecular mechanisms preliminarily.The study recruited 72 female drug-naïve patients with BD and 98 female healthy controls (HCs). Demographic information, menstrual cycles, sex hormone levels, and ovarian ultrasound data were collected from them. Additionally, their serum inflammatory factor levels and the proteomics of peripheral blood mononuclear cells were analyzed.The levels of interleukin (IL)-8 and IL-13 were significantly higher in patients with BD than in HCs (p < 0.05), and the IL-8 level was higher in BD patients with PCOS than in those without (adjusted p = 0.07). Bioinformatics analysis revealed that downregulated genes with significant differences between the two groups were all involved in immune-inflammatory-related pathways, and the expression of downregulated genes BTN3A2, MAP2K5, JCHAIN-B, and DMAP1 showed substantial differences and consistent trends between the two groups.IL-8-related chronic inflammatory response is closely associated with PCOS in BD patients, and genes such as BTN3A2 may mediate this chronic inflammatory response by negatively regulating the abnormal differentiation of T helper 17 cells, serving as one of the mechanisms underlying its pathogenesis.Copyright © 2025 Elsevier B.V. All rights reserved.
Integrative Multi-omics Analysis and Mendelian Randomization Reveal Potential Therapeutic Targets and their Stratification in Lung Squamous Cell CarcinomaChen, Li, Chang
et alCurr Med Chem (2025)
Abstract: Lung Squamous Cell Carcinoma (LUSC), a major subtype of non-small cell lung cancer, presents significant treatment challenges due to limited targeted therapy options. This study aims to identify novel therapeutic targets to improve therapeutic strategies for LUSC.By employing bulk RNA sequencing, Weighted Gene Co-expression Network Analysis (WGCNA), survival analysis, and Mendelian Randomization (MR), we pinpointed genes with prognostic relevance to LUSC. These genes were further scrutinized for their therapeutic potential through LASSO regression, Protein-Protein Interaction (PPI) network analysis, and immune infiltration assessments. To delve into the roles and cell-specific expressions of these genes within the LUSC microenvironment, pathway enrichment analysis, single-cell RNA sequencing (scRNA-seq), and pseudotime analysis were conducted.Our integrative approach identified 23 prognostically significant therapeutic targets, categorized into tier-one, tier-two, and tier-three genes based on their potential therapeutic relevance. Functional enrichment analyses highlighted the significant role of these genes in immune response regulation, particularly in T-cell receptor signaling and the complement system. scRNA-seq analysis revealed cell-type-specific expression patterns and pseudotime analysis provided insights into cellular heterogeneity and developmental trajectories in LUSC.In this study, we identified 3 tier-one genes (MCM6, C4B, CTC-463A16.1), 7 tier-two genes (C4A, HLA-DRB9, LIMS2, LINC00654, MYO7B, SIGLEC5, TIE1), and 13 tier-three genes (AC007743.1, AC147651.4, ALDH2, BTN3A2, BTNL9, CCR1, GIPC3, HLA-DQB1, ICAM5, LIMD1, PM20D1, RP11-302L19.3, RP11-768F21.1).Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.
Identification and validation of depression-associated genetic variants in the UK Biobank cohort with transcriptome and DNA methylation analyses in independent cohortsLafta, Sokolov, Rukh
et alHeliyon (2025) 11 (2), e41865
Abstract: Depression is one of the most common psychiatric conditions resulting from a complex interaction of genetic, epigenetic and environmental factors. The present study aimed to identify independent genetic variants in the protein-coding genes that associate with depression and to analyze their transcriptomic and methylation profile. Data from the GWAS Catalogue was used to identify independent genetic variants for depression. The identified genetic variants were validated in the UK Biobank cohort and used to calculate a genetic risk score for depression. Data was also used from publicly available cohorts to conduct transcriptome and methylation analyses. Eight SNPs corresponding to six protein-coding genes (TNXB, NCAM1, LTBP3, BTN3A2, DAG1, FHIT) were identified that were highly associated with depression. These validated genetic variants for depression were used to calculate a genetic risk score that showed a significant association with depression (p < 0.05) but not with co-morbid traits. The transcriptome and methylation analyses suggested nominal significance for some gene probes (TNXB- and NCAM1) with depressed phenotype. The present study identified six protein-coding genes associated with depression and primarily involved in inflammation (TNXB), neuroplasticity (NCAM1 and LTBP3), immune response (BTN3A2), cell survival (DAG1) and circadian clock modification (FHIT). Our findings confirmed previous evidence for TNXB- and NCAM1 in the pathophysiology of depression and suggested new potential candidate genes (LTBP3, BTN3A2, DAG1 and FHIT) that warrant further investigation.© 2025 The Authors.
Genome-wide Mendelian randomization mapping the influence of plasma proteome on major depressive disorderLi, Zhang, Zhao
J Affect Disord (2025) 376, 1-9
Abstract: Plasma proteins play critical roles in a series of biological processes and represent a major source of translational biomarkers and drug targets. In this study, we performed Mendelian randomization (MR) to explore potential causal associations of protein quantitative trait loci (pQTL, n = 54,219) with major depressive disorder (MDD) using summary statistics from the PGC (n = 143,265) and further replicated in FinnGen cohort (n = 406,986). Subsequently, gene expression quantitative trait loci (eQTL) of identified proteins were leveraged to validate the primary findings in both PGC and FinnGen cohorts. We implemented reverse causality detection using bidirectional MR analysis, Steiger test, Bayesian co-localization and phenotype scanning to further strengthen the MR findings. In primary analyses, MR analysis revealed 2 plasma protein significantly associated with MDD risk at Bonferroni correction (P < 3.720 × 10-5), including butyrophilin subfamily 2 member A1 (BTN2A1, OR = 0.860; 95 % CI, 0.825-0.895; P = 1.79 × 10-5) and butyrophilin subfamily 3 member A2 (BTN3A2, OR = 1.071; 95 % CI, 1.056-1.086; P = 3.89 × 10-6). Both the identified proteins had no reverse causality. Bayesian co-localization indicated that BTN2A1 (coloc.abf-PPH4 = 0.620) and BTN3A2 (coloc.abf-PPH4 = 0.872) exhibited a shared variant with MDD, a finding that was subsequently validated by HEIDI test. In the replication stage, BTN2A1 and BTN3A2 were successfully validated in the FinnGen cohort. This study genetically determined BTN2A1 and BTN3A2 were associated with MDD and these findings may have clinical implications for MDD prevention.Copyright © 2025 Elsevier B.V. All rights reserved.
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