Objective: To investigate the effect of COVID-19 infection on pancreatic cancer. Methods: Based on the mRNA-Seq data of COVID-19 patients and pancreatic cancer (PC) patients in the GEO database, we used a support vector machine (SVM), LASSO-Cox regression analysis and random forest tree (RF) to screen the common signature genes of the two diseases and further investigate their effects and functional characteristics on PC, respectively. The above procedures were performed in R software. Results: The proteins COL10A1/FAP/FN1 were found to be common signature genes for COVID-19 and PC, were significantly up-regulated in both diseases and showed good diagnostic efficacy for PC. The risk model based on COL10A1/FAP/FN1 showed good PC risk prediction ability and clinical application potential. Tumor typing based on COL10A1/FAP/FN1 expression levels effectively classified PC into different subtypes and showed significant differences between the two subtypes in terms of survival prognosis, immune levels, immune checkpoint expression levels, mutation status of common tumor mutation sites, and drug sensitivity analysis. While pathway analysis also revealed that FN1 as an extracellular matrix component may be involved in the biological process of PC by regulating the PI3K-AKT signaling axis. Conclusion: The upregulated expression of COL10A1/FAP/FN1, the characteristic genes of COVID-19, are potential diagnostic targets for PC, and the upregulated expression of FN1 may promote the progression of PC by activating the PI3K-AKT signaling pathway. The COL10A1/FAP/FN1-based typing provides a new typing approach for PC, and also provides a good reference and idea for the refinement of PC treatment and subsequent clinical research.

Objective: To explore dermatomyositis signature genes as potential biomarkers of hepatocellular carcinoma and their associated molecular regulatory mechanisms. Methods: Based on the mRNA-Seq data of dermatomyositis and hepatocellular carcinoma in public databases, five dermatomyositis signature genes were screened by LASSO regression analysis and support vector machine (SVM) algorithm, and their biological functions in dermatomyositis with hepatocellular carcinoma were investigated, and a nomogram risk prediction model for hepatocellular carcinoma was constructed and its predictive efficiency was initially evaluated. The immune profile in hepatocellular carcinoma was examined based on the CIBERSORT and ssGSEA algorithms, and the correlation between five dermatomyositis signature genes and tumor immune cell infiltration and immune checkpoints in hepatocellular carcinoma was investigated. Results: The expression levels of five dermatomyositis signature genes were significantly altered in hepatocellular carcinoma and showed good diagnostic efficacy for hepatocellular carcinoma, suggesting that they may be potential predictive targets for hepatocellular carcinoma, and the risk prediction model based on five dermatomyositis signature genes showed good risk prediction efficacy for hepatocellular carcinoma and has good potential for clinical application. In addition, we also found that the upregulation of SPP1 expression may activate the PI3K/ART signaling pathway through integrin-mediated activation, which in turn regulates the development and progression of hepatocellular carcinoma. Conclusion: LY6E, IFITM1, GADD45A, MT1M, and SPP1 are potential predictive targets for new-onset hepatocellular carcinoma in patients with dermatomyositis, and the upregulation of SPP1 expression may activate the PI3K/ART signaling pathway through the mediation of integrins to promote the development and progression of hepatocellular carcinoma.

Patients with malignant biliary obstruction (MBO) are often treated with endoscopic retrograde cholangiopancreatography (ERCP) combined with biliary stent placement for tumor progression. However, certain patients die within 30 days after the procedure, increasing healthcare resource consumption and patient burden. Therefore, the development of early mortality prediction models is important for optimizing treatment decisions. The present study retrospectively analyzed the clinical data of 285 patients with MBO, including demographic information, laboratory indicators and tumor‑related factors. Logistic regression and artificial neural network (ANN) models were used to construct a prediction tool, and the model performance was evaluated using area under the curve (AUC), accuracy, sensitivity and specificity. The logistic regression model, which identified the cancer antigen 19‑9 (CA19‑9) level and a history of previous ERCP surgery as independent risk factors, had an AUC of 0.727 and an accuracy of 65.0%. The ANN model, which combined five variables, namely CA19‑9, history of previous ERCP surgery, neutrophil‑lymphocyte ratio (NLR), liver metastasis and carcinoembryonic antigen, demonstrated that NLR was the most weighted predictor. Furthermore, the ANN model had an AUC of 0.813, an accuracy of 88.2% and a specificity that was markedly higher than that of the logistic regression model (95.5 vs. 83.3%). However, the ANN model was revealed to be slightly less sensitive compared with the logistic regression model (61.1 vs. 61.2%). In conclusion, compared with logistic regression, the ANN model had a greater performance level in terms of predictive power and specificity, and is suitable for capturing complex non‑linear relationships. However, its complexity and risk of overfitting need to be further optimized. The present study provides a new tool for the accurate prediction of the risk of early death after ERCP in patients with MBO, which could help improve individualized treatment strategies.

Primary liver cancer (PLC) and metformin are not well understood to be associated. We conducted a Mendelian randomization (MR) analysis using genetic data from IEU OpenGWAS and FinnGen R10, with metformin as the exposure and PLC as the outcome. The inverse variance weighting (IVW) method was the primary analytical approach, with heterogeneity assessed by Cochran's Q test, pleiotropy by MR‐Egger intercept, and outliers by MR‐PRESSO. Bioinformatics analyses further explored potential mechanisms, including differential gene expression, protein–protein interactions (PPI), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, immune cell infiltration analysis, and drug sensitivity analysis. MR results demonstrated a significant association between metformin use and reduced risk of PLC ( β = −5.6046, OR = 0.0037, p = 0.026), with a Benjamini‐Hochberg false discovery rate (FDR) adjusted p value of 0.13. However, no causal effect was observed for hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICC). By cross‐referencing transcriptome data from the GEO database GSE241466 with metformin‐related gene loci, 34 overlapping genes were identified. Differentially expressed genes (DEGs) were filtered using |log2FC| > 0 and p < 0.05, with five hub genes (DDX52, KIF11, GCDH, MRPL45, and TICRR) being particularly prominent. Functional enrichment analysis revealed involvement in cGMP‐PKG signaling and fatty acid metabolism pathways. Further validation with GEPIA2, TIMER, and TISCH showed correlations between these genes and immune infiltration, while GSCA‐based drug sensitivity analysis suggested therapeutic relevance. In summary, these findings indicate that metformin may reduce PLC risk by modulating metabolic and immune‐related pathways, supporting its potential value as an adjunct therapeutic agent. However, further validation through large‐scale clinical and basic research is warranted.

Cancer secondary to dermatomyositis (DM) is defined as paraneoplastic dermatomyositis, which is one of the major subtypes of DM. However, cases of DM with intrahepatic cholangiocarcinoma (ICC) are rarely reported. In the course of our clinical work, we encountered a case of a middle-aged female patient who was diagnosed with DM for 7 years and then diagnosed with ICC, and we would like to share this case. In addition, in order to further investigate the deeper mechanism of ICC associated with DM, we also analyzed the dataset related to DM and ICC in the Gene Expression Omnibus (GEO) database based on the machine learning methods and found that poly(ADP-ribose) polymerase family member 12 (PARP12) and metallothionein 1M (MT1M) were closely associated with ICC secondary to DM. They are potentially important biomarkers for predicting the occurrence of ICC in patients with DM.

Serum uric acid (SUA) is the end product of purine metabolism, and its elevated levels are closely associated with a variety of metabolic disorders. Although some studies have mentioned the possible link between SUA and metabolic dysfunction-associated steatotic liver disease (MASLD), no study has yet clearly defined the inevitable association between the 2. This study aims to provide evidence of the association between the 2 and explore their targets of action based on network toxicology. Weighted multivariate-adjusted logistic regression was employed to assess the relationship between SUA and the risk of MASLD. A two-sample Mendelian randomization (MR) study was conducted to further corroborate the causal relationship between SUA and MASLD. Inverse variance weighting (IVW) was used as the primary MR analysis method, with other supplementary MR methods aiding in validating the result. Further explored the toxic action targets of SUA on MASLD based on network toxicology analysis. The weighted multivariate-adjusted logistic regression analysis revealed a positive correlation between SUA and the risk of MASLD [odds ratio (OR) = 1.300; 95% confidence intervals (CI) = 1.175–1.438]. Additionally, SUA was independently associated with the median control attenuation parameter (CAP) (OR = 17,032.865, 95% CI = 1722.045–168,473.206). The results of the MR analysis also supported a causal relationship between SUA and MASLD (OR = 1.242; 95% CI = 1.060–1.454). Furthermore, molecular blind docking revealed the specific binding interactions and sites between SUA and target proteins. SUA is associated with an increased risk of MASLD incidence. Toxicological molecular docking simulation results show significant binding specificity between SUA components and target genes. The next step could be to develop preventive and screening programs for MASLD targeting individuals with high levels of SUA.