Biomarkers prediction and immune landscape in ulcerative colitis: Findings based on bioinformatics and machine learning.

BACKGROUND: Ulcerative colitis (UC) presents diagnostic and therapeutic difficulties. The primary objective of this study is to identify efficacious biomarkers for diagnosis and treatment, as well as acquire a deeper understanding of the immuneological characteristics associated with the disease. METHODS: Datasets relating to UC were obtained from GEO database. Among these, three datasets were merged to create a metadata for bioinformatics analysis and machine learning. Additionally, one dataset specifically utilized for external validation. Least absolute shrinkage and selection operator (LASSO) and random forest (RF) were employed to screen signature genes. The artificial neural network (ANN) model and receiver operating characteristic (ROC) curve were used to assess the diagnostic performance of signature genes. The single sample gene set enrichment analysis (ssGSEA) was applied to reveal the immune landscape. Finally, the relationship between the signature genes, immune infiltration, and clinical characteristics was investigated through correlation analysis. RESULT: By intersecting the result of LASSO, RF and WGCNA, 8 signature genes were identified, including S100A8, IL-1B, CXCL1, TCN1, MMP10, GREM1, DUOX2 and SLC6A14. The biological progress of this gene mostly encompasses acute inflammatory response, aggregation and chemotaxis of leukocyte, and response to lipopolysaccharide by mediating IL-17 signaling pathway, NF-kappa B signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway. Immune infiltration analysis shows 25 immune cells are significantly elevated in UC samples. Moreover, these signature genes exhibit a strong correlation with various immune cells and a mild to moderate correlation with the Mayo score. CONCLUSION: S100A8, IL-1B, CXCL1, TCN1, MMP10, GREM1, DUOX2 and SLC6A14 have been identified as credible potential biomarkers for the diagnosis and therapy of UC. The immune response mediated by these signature biomarkers plays a crucial role in the occurrence and advancement of UC by means of the reciprocal interaction between the signature biomarkers and immune-infiltrated cells.

Hec1 inhibition alters spindle morphology and chromosome alignment in porcine oocytes.

Aneuploidy is caused by incorrect chromosome segregation and can result in cancer or birth defects. The spindle assembly checkpoint (SAC) guarantees proper cell cycle progression. Highly Expressed in Cancer protein 1 (Hec1, also called Ndc80) is the core component of the Ndc80 complex and is involved in regulating both kinetochore-microtubule interactions and the SAC during mitosis in multiple cell types. However, its involvement in pig oocyte meiotic maturation remains uncertain. Thus, we investigated Hec1 expression, localization, and possible functions during porcine oocyte meiosis. Immunofluorescent staining showed that Hec1 was expressed in porcine oocytes and was associated with centromeres at both the metaphase I and metaphase II stages. Disrupting Hec1 function with its inhibitor INH1 resulted in polar body extrusion defects in porcine oocytes. Moreover, inhibiting Hec1 activity also resulted in severe chromosome misalignments and aberrant spindle morphology. Our results showed a unique localization pattern for Hec1 in porcine oocytes and suggested that Hec1 was required for chromosome alignment and spindle organization. Thus, Hec1 might regulate spindle checkpoint activity during mammalian oocyte meiosis.