Oxeiptosis is a recently discovered caspase-independent, non-inflammatory programmed cell death modality. Current studies suggest that oxeiptosis has crucial effects on biological processes in a variety of diseases. However, the mechanism of oxeiptosis in hepatocellular carcinoma (HCC) remains unclear and no relevant studies have been published. Therefore, this study is intended to investigate the mechanism and prognostic role of oxeiptosis-related genes in HCC. We explored the mechanisms and molecular phenotypes underlying the role of oxeiptosis in HCC through multi-omics analysis. Firstly, we obtained RNA-sequencing and clinical data from public database and divided the samples into trial and validation cohorts in subsequent analyses. We then screened oxeiptosis core genes (OCGs) and screened prognosis-related genes. Based on different molecular markers, we identified the molecular phenotypes of HCC, and the potential OCGs molecular mechanisms were explored. Subsequently, we construct a prognostic prediction system for HCC. Finally, we analyzed the tumor microenvironment and the immune escape phenomenon. We screened a total of 69 OCGs, most of which were prognostic risk factors for HCC. A majority of OCGs were enriched in cell cycle regulation and mitotic processes, which were related to both tumor cell proliferation and death. We identified 2 different molecular typing options with significant differences in prognosis, function, and signaling pathway enrichment between different molecular subtypes. The prognostic prediction model combined with molecular phenotypes and had a good predictive effect. Finally, we found CD4 + T-cell exhaustion in samples with specific molecular phenotypes. Through multi-omics analysis of OCGs, we not only revealed the possible molecular mechanisms of OCGs in HCC but also provided a prognostic prediction system for clinical application through molecular typing and risk scoring model. Meanwhile, we found immune escape mechanisms in HCC.

Background: The challenge of systemic treatment for hepatocellular carcinoma (HCC) stems from the development of drug resistance, primarily driven by the interplay between cancer stem cells (CSCs) and the tumor microenvironment (TME). However, there is a notable dearth of comprehensive research investigating the crosstalk between CSCs and stromal cells or immune cells within the TME of HCC.

Background and objective: Heavy metals, ubiquitous in the environment, pose a global public health concern. The correlation between these and diabetic kidney disease (DKD) remains unclear. Our objective was to explore the correlation between heavy metal exposures and the incidence of DKD.Methods: We analyzed data from the NHANES (2005–2020), using machine learning, and cross-sectional survey. Our study also involved a bidirectional two-sample Mendelian randomization (MR) analysis.Results: Machine learning reveals correlation coefficients of −0.5059 and − 0.6510 for urinary Ba and urinary Tl with DKD, respectively. Multifactorial logistic regression implicates urinary Ba, urinary Pb, blood Cd, and blood Pb as potential associates of DKD. When adjusted for all covariates, the odds ratios and 95% confidence intervals are 0.87 (0.78, 0.98) (p = 0.023), 0.70 (0.53, 0.92) (p = 0.012), 0.53 (0.34, 0.82) (p = 0.005), and 0.76 (0.64, 0.90) (p = 0.002) in order. Furthermore, multiplicative interactions between urinary Ba and urinary Sb, urinary Cd and urinary Co, urinary Cd and urinary Pb, and blood Cd and blood Hg might be present. Among the diabetic population, the OR of urinary Tl with DKD is a mere 0.10, with a 95%CI of (0.01, 0.74), urinary Co 0.73 (0.54, 0.98) in Model 3, and urinary Pb 0.72 (0.55, 0.95) in Model 2. Restricted Cubic Splines (RCS) indicate a linear linkage between blood Cd in the general population and urinary Co, urinary Pb, and urinary Tl with DKD among diabetics. An observable trend effect is present between urinary Pb and urinary Tl with DKD. MR analysis reveals odds ratios and 95% confidence intervals of 1.16 (1.03, 1.32) (p = 0.018) and 1.17 (1.00, 1.36) (p = 0.044) for blood Cd and blood Mn, respectively.Conclusion: In the general population, urinary Ba demonstrates a nonlinear inverse association with DKD, whereas in the diabetic population, urinary Tl displays a linear inverse relationship with DKD.

Background: Liver metastasis (LM) stands as a primary cause of mortality in metastatic colorectal cancer (mCRC), posing a significant impediment to long-term survival benefits from targeted therapy and immunotherapy. However, there is currently a lack of comprehensive investigation into how senescent and exhausted immune cells contribute to LM. Methods: We gathered single-cell sequencing data from primary colorectal cancer (pCRC) and their corresponding matched LM tissues from 16 mCRC patients. In this study, we identified senescent and exhausted immune cells, performed enrichment analysis, cell communication, cell trajectory, and cell-based in vitro experiments to validate the results of single-cell multi-omics. This process allowed us to construct a regulatory network explaining the occurrence of LM. Finally, we utilized weighted gene co-expression network analysis (WGCNA) and 12 machine learning algorithms to create prognostic risk model. Results: We identified senescent-like myeloid cells (SMCs) and exhausted T cells (TEXs) as the primary senescent and exhausted immune cells. Our findings indicate that SMCs and TEXs can potentially activate transcription factors downstream via ANGPTL4-SDC1/SDC4, this activation plays a role in regulating the epithelial-mesenchymal transition (EMT) program and facilitates the development of LM, the results of cell-based in vitro experiments have provided confirmation of this conclusion. We also developed and validated a prognostic risk model composed of 12 machine learning algorithms. Conclusion: This study elucidates the potential molecular mechanisms underlying the occurrence of LM from various angles through single-cell multi-omics analysis in CRC. It also constructs a network illustrating the role of senescent or exhausted immune cells in regulating EMT.

Fluorouracil (5-FU) is widely used to treat metastatic colorectal cancer (mCRC), but real-world safety data is limited. Our study aimed to evaluate 5-FU's safety profile in a large mCRC population using the FAERS database. We conducted disproportionality analyses to identify adverse drug events associated with 5-FU use in mCRC patients from 2004 to 2023. Subgroup analyses, gender difference analyses, and logistic regression were also performed. We identified 1,458 reports with 5-FU as the primary suspected drug, with males accounting for 48.8% of reports. Gastrointestinal disorders were the most common adverse event (864 cases), while pregnancy-related conditions showed the strongest signal intensity (ROR = 2.97). We found 19 preferred terms with positive signals, including ischemic hepatitis (ROR = 59.32), blood iron increased (ROR = 59.32), and stress cardiomyopathy (ROR = 51.94). Males were more susceptible to weight loss and skin toxicity. Most adverse events occurred within the first month of 5-FU administration. Our study provides a comprehensive analysis of 5-FU's safety profile in mCRC patients, helping healthcare professionals mitigate risks in clinical practice.

The four-drug regimen for heart failure with reduced ejection fraction (HFrEF) significantly reduces the risks of hospitalization and mortality. To identify key adverse drug events (ADEs) warranting attention with this regimen, we conducted a real-world pharmacovigilance analysis based on the FDA Adverse Event Reporting System (FAERS) events. We collected ADE reports of the four-drug regimen from FAERS that matched this regimen over a 10-year period. Disproportionality analysis and subgroup analysis were performed using four algorithms. Time-to-onset (TTO) analysis was used to assess the temporal risk patterns of ADE occurrence. Lastly, logistic regression was applied to investigate the relationship-value between patient characteristics and ADEs. A total of 1,237 cases with 6,580 ADE reports were collected. Disproportionality analysis identified the most frequent ADEs as hypotension, acute kidney injury (AKI), and hyperkalemia. TTO analysis revealed a median TTO of 39 days for all important medical events, and the median TTO for AKI was 28 days, both fitting an early failure curve. In the comprehensive management of HFrEF with the four-drug regimen, in addition to routine monitoring of ADEs such as hypotension and hyperalemia, early-onset AKI should be a particular focus.