Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with poor prognosis and high mortality. Although a large number of studies have explored its potential prognostic markers using traditional RNA sequencing (RNA-Seq) data, they have not achieved good prediction effect. In order to explore the possible prognostic signaling pathways leading to the difference in prognosis, we identified differentially expressed genes from one scRNA-seq cohort and four GEO cohorts, respectively. Then Cox and Lasso regression analysis showed that 12 genes were independent prognostic factors for PDAC. AUC and calibration curve analysis showed that the prognostic model had good discrimination and calibration. Compared with the low-risk group, the high-risk group had a higher proportion of gene mutations than the low-risk group. Immune infiltration analysis revealed differences in macrophages and monocytes between the two groups. Prognosis related genes were mainly distributed in fibroblasts, macrophages and type 2 ducts. The results of cell communication analysis showed that there was a strong communication between cancer-associated fibroblasts (CAF) and type 2 ductal cells, and collagen formation was the main interaction pathway.

Background: Myasthenia gravis (MG) is an autoimmune disease that affects neuromuscular junction. The literature suggests the involvement of circulating cytokines (CK), gut microbiota (GM), and serum metabolites (SM) with MG. However, this research is limited to observational trials, and comprehensive causal relationship studies have not been conducted. Based on published datasets, this investigation employed Mendelian Randomization (MR) to analyze the known and suspected risk factors and biomarkers causal association of MG and its subtypes. Methods: This research used two-sample MR and linkage disequilibrium score (LDSC) regression of multiple datasets to aggregate datasets acquired from the genome-wide association studies (GWAS) to assess the association of MG with 41-CK, 221-GM, and 486-SM. For sensitivity analysis and to validate the robustness of the acquired data, six methods were utilized, including MR-Egger regression, inverse variance weighting (IVW), weighted median, and MR-PRESSO. Results: The MR method identified 20 factors significantly associated with MG, including 2 CKs, 6 GMs, and 9 SMs. Further analysis of the factors related to the two MG subtypes, early-onset MG (EOMG) and late-onset MG (LOMG), showed that EOMG had a high overlap with MG in the intestinal flora, while LOMG had a greater similarity in CKs and SMs. Furthermore, LDSC regression analysis indicated that Peptococcaceae, oxidized biliverdin, and Kynurenine had significant genetic correlations with general MG, whereas EOMG was highly correlated with Intestinibacter, while LOMG had significant genetic associations with Kynurenine and Glucose. Conclusion: This research furnishes evidence for the potential causal associations of various risk factors with MG and indicates a heterogeneous relationship between CKs, GMs, and SMs with MG subtypes.

Hepatocellular carcinoma (HCC) represents one of the most lethal malignancies worldwide and is characterized by profound metabolic reprogramming during its development. Paraoxonase 1 (PON1), a liver-synthesized secretory protein involved in lipid metabolism, has an incompletely defined role in cancer biology. This study aimed to systematically investigate the expression pattern, clinical features, and biological function of PON1 in HCC through an integrated approach combining data mining, RNA-seq and experimental verification. Our results demonstrated that PON1 expression is significantly downregulated in HCC tissues compared with adjacent tissues. Clinically, significant disparities were observed in gender (χ² = 19.305, p < 0.0001), tumor stage (χ² = 18.030, p = 0.0004), and tumor grade (χ² = 13.391, p = 0.0039) between patients with high and low PON1 expression in HCC. Low PON1 expression was associated with poor prognosis (TCGA_LIHC, log-rank: χ² = 9.290, p = 0.0023; ICGC_LIRI, log-rank: χ² = 8.469, p = 0.0036; GSE14520, log-rank: χ² = 9.746, p = 0.0018). Univariate and multivariate Cox regression analyses revealed PON1 as an independent prognostic biomarker. Pathway analysis showed that PON1-positively correlated genes enriched in pathways such as peroxisome and fatty acid degradation, whereas PON1-negatively correlated genes mainly in the cell cycle pathway. Functional experiments confirmed that knockdown of PON1 promoted HCC cell proliferation, migration, invasion and inhibited apoptosis, whereas overexpression of PON1 reversed these malignant phenotypes. Mechanistically, we uncovered that PON1 exerts its tumor-suppressive effects by negatively regulating TANK and CXCL3, key molecules of the NOD-like receptor signaling pathway. In summary, our findings identify PON1 as an independent prognostic biomarker in HCC and demonstrate the tumor-suppressive role of PON1, indicating its potential as a therapeutic target for HCC.