New perspectives in prognostication of hepatocellular carcinoma: The role and clinical implications of transient receptor potential family genes.

The study titled "Transient receptor potential-related risk model predicts prognosis of hepatocellular carcinoma patients" is a significant contribution to hepatocellular carcinoma (HCC) research, highlighting the role of transient receptor potential (TRP) family genes in the disease's progression and prognosis. Utilizing data from The Cancer Genome Atlas database, it establishes a new risk assessment model, emphasizing the interaction of TRP genes with tumor proliferation pathways, key metabolic reactions like retinol metabolism, and the tumor immune microenvironment. Notably, the overexpression of the TRPC1 gene in HCC correlates with poorer patient survival outcomes, suggesting its potential as a prognostic biomarker and a target for personalized therapy, particularly in strategies combining immunotherapy and anti-TRP agents.

Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique.

BACKGROUND: Postoperative delirium, particularly prevalent in elderly patients after abdominal cancer surgery, presents significant challenges in clinical management. AIM: To develop a synthetic minority oversampling technique (SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients. METHODS: In this retrospective cohort study, we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022. The incidence of postoperative delirium was recorded for 7 d post-surgery. Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not. A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium. The SMOTE technique was applied to enhance the model by oversampling the delirium cases. The model's predictive accuracy was then validated. RESULTS: In our study involving 611 elderly patients with abdominal malignant tumors, multivariate logistic regression analysis identified significant risk factors for postoperative delirium. These included the Charlson comorbidity index, American Society of Anesthesiologists classification, history of cerebrovascular disease, surgical duration, perioperative blood transfusion, and postoperative pain score. The incidence rate of postoperative delirium in our study was 22.91%. The original predictive model (P1) exhibited an area under the receiver operating characteristic curve of 0.862. In comparison, the SMOTE-based logistic early warning model (P2), which utilized the SMOTE oversampling algorithm, showed a slightly lower but comparable area under the curve of 0.856, suggesting no significant difference in performance between the two predictive approaches. CONCLUSION: This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods, effectively addressing data imbalance.