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The pathogenesis of major depressive disorder (MDD) is currently unclear and lacks objective diagnostic criteria. The complexity and heterogeneity of MDD also limit precise treatment. Using bioinformatics methods, we identified 18 gene signatures of MDD from the GSE98793 dataset, and validated them in an independent dataset GSE44593 (Area under the curve values were 0.92, 0.72, and 0.70 for the training, validation, and test sets, respectively). Among the gene signatures, TLR4 had the largest absolute coefficient value (coefficient = -6.13). We further identified three CD 14 + monocyte-associated gene signatures and two immune-related subtypes. The expression of TLR4 is significantly increased in subtype A of MDD (lower predicted probability), and is significantly correlated with the composition of multiple immune cells (P < 0.05). We validated that TLR4 acts as a protective factor in MDD (OR = 0.91, 95% CI = 0.85 to 0.98, P = 0.012), and its expression is driven by the same causal variants as MDD (H4/(H3 + H4) = 98.62%). Further analysis showed that the relationship between TLR4 and MDD is influenced by eight immune cell signatures. Our research provided genetic support that the immune factors may play an important role in MDD, and proposed a possible strategy for the diagnosis and treatment of MDD.
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http://dx.doi.org/10.1038/s41598-025-95663-x | DOI Listing |
Eur J Clin Microbiol Infect Dis
September 2025
School of Bioengineering and Biosciences, Department of Biochemistry, Lovely Professional University, Punjab, 144411, India.
Purpose: This study investigates codon usage and amino acid usage bias in the genus Acinetobacter to uncover the evolutionary forces shaping these patterns and their implications for pathogenicity and biotechnology.
Methods: Codon usage patterns were examined in representative genomes of the genus Acinetobacter using standard codon bias indices, including GC content, relative synonymous codon usage (RSCU), effective number of codons (ENC), and codon adaptation index (CAI). Neutrality and parity plots were employed to evaluate the relative influence of mutational pressure and natural selection on codon preferences.
Funct Integr Genomics
September 2025
Department of Plastic Surgery, the First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, China.
Keloid scarring and Metabolic Syndrome (MS) are distinct conditions marked by chronic inflammation and tissue dysregulation, suggesting shared pathogenic mechanisms. Identifying common regulatory genes could unveil novel therapeutic targets. Methods.
View Article and Find Full Text PDFBiomed Environ Sci
August 2025
Gastrointestinal Disease Centre, Hebei Key Laboratory of Colorectal Cancer Precision Diagnosis and Treatment, The First Hospital of Hebei Medical University, Shijiazhuang 050031, Hebei, China.
Objective: To explore the correlation between chromosome 8 open reading frame 76 (C8orf76) and cyclin-dependent kinase 4 (CDK4) and the potential predictive effect of C8orf76 and CDK4 on the prognosis of colorectal cancer (CRC).
Methods: We constructed a protein-protein interaction network of C8orf76-related genes and analyzed the prognostic signatures of C8orf76 and CDK4. Clinicopathological features of C8orf76 and CDK4 were visualized using a nomogram.
Int J Gen Med
September 2025
Department of Geriatrics, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, People's Republic of China.
Background: Sepsis is characterized by profound immune and metabolic perturbations, with glycolysis serving as a pivotal modulator of immune responses. However, the molecular mechanisms linking glycolytic reprogramming to immune dysfunction remain poorly defined.
Methods: Transcriptomic profiles of sepsis were obtained from the Gene Expression Omnibus.
Biochem Biophys Rep
December 2025
Division of Breast Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, 112, Taiwan.
Purpose: This study aimed to conduct functional proteomics across breast cancer subtypes with bioinformatics analyses.
Methods: Candidate proteins were identified using nanoscale liquid chromatography with tandem mass spectrometry (NanoLC-MS/MS) from core needle biopsy samples of early stage (0-III) breast cancers, followed by external validation with public domain gene-expression datasets (TCGA TARGET GTEx and TCGA BRCA).
Results: Seventeen proteins demonstrated significantly differential expression and protein-protein interaction (PPI) found the strong networks including COL2A1, COL11A1, COL6A1, COL6A2, THBS1 and LUM.