RESUMO
Epidermal growth factor receptor inhibitors (EGFRIs) induced cutaneous toxicity is a common adverse event (AE), although it is not as severe as major cancers, we still need to pay enough attention to them. Therefore, it is necessary to evaluate the diversity of EGFRI class drugs. The objective of this study was to conduct a scientific and systematic investigation into the correlation between EGFRI and cutaneous toxicities. The data accessed from the FDA adverse event reporting system database (FAERS) encompass a time frame spanning from January 2013 to March 2023. By utilizing reporting odds ratios (RORs), information components (ICs), proportional reporting ratios (PRRs), and chi-squared (χ2), the relationship between drugs and adverse reactions was evaluated through disproportionality analysis. Within the FAERS database, a total of 29,559 skin adverse events were recorded. A robust indication of the correlation between EGFRI and elderly patients (≥65 years) was identified. Among EGFRIs, erlotinib accounted for the largest proportion of skin adverse events (39.72%). Rash, dry skin, and pruritus ranked top among all preferred terms, and signals such as rash, skin lesions, and acneiform dermatitis were detected in every single drug. Clinicians should guide patients customize the treatment plan for each patient.
RESUMO
MicroRNAs (miRNAs) are important types of noncoding RNAs, and there is a lack of holistic and systematic understanding of the functions they play in disease. We proposed a research strategy, including two parts network analysis and network modelling, to analyze, model, and predict the regulatory network of miRNAs from a network perspective, using unstable angina pectoris as an example. In the network analysis section, we proposed the WGCNA & SimCluster method using both correlation and similarity to find hub miRNAs, and validation on two datasets showed better results than the methods using correlation or similarity alone. In the network modelling section, we used six knowledge graph or graph neural network models for link prediction of three types of edges and multilabel classification of two types of nodes. Comparative experiments showed that the RotatE model was a good model for link prediction, while the RGCN model was the best model for multilabel classification. Potential target genes were predicted for hub miRNAs and validation of hub miRNA-target gene interactions, target genes as biomarkers and target gene functions were performed using a three-step validation approach. In conclusion, our study provides a new strategy to analyze and model miRNA regulatory networks.