Several studies have demonstrated that chronic hepatitis delta virus (HDV) infection is associated with a worsening of hepatitis B virus (HBV) infection and increased risk of hepatocellular carcinoma (HCC). However, there is limited data on the role of HDV in the oncogenesis of HCC. This study is aimed at assessing the potential mechanisms of HDV‐associated hepatocarcinogenesis, especially to screen and identify key genes and pathways possibly involved in the pathogenesis of HCC. We selected three microarray datasets: GSE55092 contains 39 cancer specimens and 81 paracancer specimens from 11 HBV‐associated HCC patients, GSE98383 contains 11 cancer specimens and 24 paracancer specimens from 5 HDV‐associated HCC patients, and 371 HCC patients with the RNA‐sequencing data combined with their clinical data from the Cancer Genome Atlas (TCGA). Afterwards, 948 differentially expressed genes (DEGs) closely related to HDV‐associated HCC were obtained using the R package and filtering with a Venn diagram. We then performed gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to determine the biological processes (BP), cellular component (CC), molecular function (MF), and KEGG signaling pathways most enriched for DEGs. Additionally, we performed Weighted Gene Coexpression Network Analysis (WGCNA) and protein‐to‐protein interaction (PPI) network construction with 948 DEGs, from which one module was identified by WGCNA and three modules were identified by the PPI network. Subsequently, we validated the expression of 52 hub genes from the PPI network with an independent set of HCC dataset stored in the Gene Expression Profiling Interactive Analysis (GEPIA) database. Finally, seven potential key genes were identified by intersecting with key modules from WGCNA, including 3 reported genes, namely, CDCA5, CENPH, and MCM7, and 4 novel genes, namely, CDC6, CDC45, CDCA8, and MCM4, which are associated with nucleoplasm, cell cycle, DNA replication, and mitotic cell cycle. The CDCA8 and stage of HCC were the independent factors associated with overall survival of HDV‐associated HCC. All the related findings of these genes can help gain a better understanding of the role of HDV in the underlying mechanism of HCC carcinogenesis.

The primary aim of this study was to closely monitor and identify adverse events (AEs) linked to lenvatinib, a pharmacotherapeutic agent employed for the management of renal cell carcinoma, thyroid cancer, and hepatocellular carcinoma. The ultimate goal was to optimize patient safety and provide evidence-based guidance for the appropriate utilization of this medication. A comprehensive collection and analysis of reports from the FDA Adverse Event Reporting System (FAERS) database was conducted, encompassing the period from the first quarter of 2015 to the first quarter of 2023. Disproportionality analysis, employing robust algorithms including ROR, PRR, BCPNN, and EBGM was employed for effective data mining to quantify signals associated with lenvatinib-related AEs. Among the collected reports, a total of 15,193 cases were identified where lenvatinib was the "primary suspected (PS)" drug, resulting in 50,508 lenvatinib-induced AEs. An analysis was conducted to examine the occurrence of lenvatinib-induced adverse drug reactions (ADRs) across 26 organ systems. The findings revealed the presence of expected ADRs, including diarrhea, vomiting, stomatitis, hepatic encephalopathy, decreased appetite, dehydration, decreased weight, and electrolyte imbalances, which were consistent with the information provided in the drug labels. Furthermore, unexpected significant ADRs were observed at the preferred terms (PT) level, such as interstitial lung disease, pneumothorax, hypophysitis, failure to thrive, polycythemia, hypopituitarism, spontaneous pneumothorax, pulmonary cavitation, and limbic encephalitis. These findings indicated the potential occurrence of adverse effects that are currently not documented in the drug instructions. This study has successfully detected novel and unforeseen signals pertaining to ADRs associated with the administration of lenvatinib, thereby contributing significant insights into the intricate correlation between ADRs and the utilization of lenvatinib. The outcomes of this investigation underscore the utmost significance of continuous monitoring and vigilant surveillance in order to promptly identify and effectively manage AEs, consequently enhancing overall patient safety and well-being in the context of lenvatinib therapy.

Background: The EGF domain-specific O-GlcNAc transferase (EOGT), a migrasome marker, plays emerging roles in cancer biology through O-GlcNAcylation modifications, yet its pan-cancer functions and therapeutic implications remain underexplored. This study aimed to systematically characterize EOGT’s oncogenic mechanisms across malignancies, with particular focus on hepatocellular carcinoma (HCC) progression and sorafenib resistance. Methods: Multi-omics analysis integrated TCGA/GTEx data from 33 cancer types with spatial/single-cell transcriptomics and 10 HCC cohorts. Functional validation employed Huh7 cell models with EOGT modulation, RNA sequencing, and ceRNA network construction. Drug sensitivity analysis leveraged GDSC/CTRP/PRISM databases, while immune microenvironment assessment utilized ESTIMATE/TIMER algorithms. Results: EOGT showed cancer-specific dysregulation, marked by significant upregulation in HCC correlating with advanced stages and poor survival. Pan-cancer analysis revealed EOGT’s association with genomic instability, tumor stemness, and angiogenesis. Experimental validation demonstrated EOGT’s promotion of HCC proliferation and migration. A novel exosomal circ_0058189/miR-130a-3p/EOGT axis was identified, showing that circ_0058189 was upregulated in HCC tissues, plasma samples and exosomes of sorafenib-resistant cells. Conclusion: This study establishes EOGT as a pan-cancer angiogenesis biomarker, while elucidating its role in therapeutic resistance via exosomal circRNA-mediated regulation, providing mechanistic insights for targeted intervention strategies.

To develop a signature based on anoikis-related genes (ARGs) for predicting the prognosis of patients with hepatocellular carcinoma (HCC), and to elucidate the molecular mechanisms involved. In this study, bioinformatic algorithms were applied to integrate and analyze 777 HCC RNA-seq samples from the cancer genome atlas and international cancer genome consortium repositories. A prognostic signature was developed via the least absolute shrinkage and selection operator-cox regression method. To evaluate the accuracy of the signature in predicting events, multi-type technical means, such as Kaplan–Meier plots, receiver operating characteristic curve analysis, nomogram construction, and univariate and multivariate Cox regression studies were performed. We investigated the underlying molecular biological mechanisms and immune mechanisms of the signature using gene set enrichment analysis and the CIBERSORT R package, respectively. Meanwhile, immunohistochemical staining acquired from the human protein atlas was used to confirm the differential expression levels of hub genes involved in the prognostic signature. We developed an HCC prognostic signature with a collection of 5 ARGs, and the prognostic value was successfully assessed and verified in both the test and validation cohorts. The risk scores calculated by the prognostic signature were proved to be an independent negative prognostic factor for overall survival. A set of nomograms based on risk scores was established and found to be effective in predicting OS. Further investigation of the underlying molecular biological mechanisms and immune mechanisms indicated that the signature may be relevant to metabolic dysregulation and infiltration of gamma delta T cells in the tumor. The survival prognosis of HCC patients can be predicted by the anoikis-related prognostic signature, and it serves as a valuable reference for individualized HCC therapy.

The relationship between total caffeine intake (TCI) and aging remains understudied. α-Klotho (KL), a key aging-related circulating biomarker, is clinically valuable. Unlike prior studies focusing on coffee intake, we analyzed TCI (from diet and supplements) to isolate its independent association with KL and explore mechanisms via molecular docking. We thus conducted a cross-sectional analysis with NHANES 2007 to 2016 data to investigate TCI and serum KL levels in adults aged 40 to 79. Serum KL levels were determined using ELISA kits, while trained interviewers assessed TCI through 24-hour dietary recalls. Generalized linear regression models were employed to evaluate the correlation between TCI and serum KL levels. We utilized restricted cubic spline analysis to explore the dose–response relationship between the 2 factors. Subgroup analyses and molecular docking studies were also conducted to further understand the association. After adjusting for potential confounders, higher TCI was significantly associated with lower serum KL levels. In the fully adjusted model, compared to the lowest TCI group, KL levels decreased by −30.21 pg/mL (95% CI = −50.49 to −9.93 pg/mL) and −27.31 pg/mL (95% CI = −51.94 to −2.67 pg/mL) in the third and fourth groups, respectively, indicating a significant trend ( P for trend = .005). Restricted cubic spline analysis revealed a linear dose–response relationship ( P for nonlinearity = .362). Subgroup analyses showed that the negative correlation was more pronounced in participants aged < 60 years, those who were overweight/obese, and females. Molecular docking analysis suggested a direct interaction between caffeine and KL, with a binding energy of −5.299 kcal/mol, implying a stable interaction. These results suggest a potential pro-aging effect of caffeine. Further prospective studies and experimental validation are needed to clarify caffeine’s effects on KL. Notably, molecular docking indicates caffeine may interfere with α-Klotho immunoassay detection, emphasizing the need for caffeine abstinence before testing to enhance biomarker accuracy.