ObjectiveBy combining biological detection and imaging evaluation, a clinical prediction model is constructed based on a large cohort to improve the accuracy of distinguishing between benign and malignant pulmonary nodules. MethodsA retrospective analysis was conducted on the clinical data of the 32 627 patients with pulmonary nodules who underwent chest CT and testing for 7 types of lung cancer-related serum autoantibodies (7-AABs) at our hospital from January 2020 to April 2024. The univariate and multivariate logistic regression models were performed to screen independent risk factors for benign and malignant pulmonary nodules, based on which a nomogram model was established. The performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). ResultsA total of 1 017 patients with pulmonary nodules were included in the study. The training set consisted of 712 patients, including 291 males and 421 females, with a mean age of (58±12) years. The validation set included 305 patients, comprising 129 males and 176 females, with a mean age of (58±13) years. Univariate ROC curve analysis indicated that the combination of CT and 7-AABs testing achieved the highest area under the curve (AUC) value (0.794), surpassing the diagnostic efficacy of CT alone (AUC=0.667) or 7-AABs alone (AUC=0.514). Multivariate logistic regression analysis showed that radiological nodule diameter, nodule nature, and CT combined with 7-AABs detection were independent predictors, which were used to construct a nomogram prediction model. The AUC values for this model were 0.826 and 0.862 in the training and validation sets, respectively, demonstrating excellent performance in DCA. ConclusionThe combination of 7-AABs with CT significantly enhances the accuracy of distinguishing between benign and malignant pulmonary nodules. The developed predictive model provides strong support for clinical decision-making and contributes to achieving precise diagnosis and treatment of pulmonary nodules.
ObjectiveTo investigate the pan-cancer expression profile, prognostic value, co-expression networks, immune regulatory roles of BRF1, and its biological functions and molecular mechanisms in esophageal squamous cell carcinoma (ESCC). MethodsIntegrated analysis of TCGA pan-cancer datasets was performed to evaluate BRF1 expression differences between tumor/normal tissues, survival correlations, co-expressed gene-enriched pathways, and immune features (immune checkpoints, cytokines, immune cell infiltration). GEO datasets were used to validate BRF1 expression in ESCC. BRF1 was knocked down using siRNA in ESCC cells, with MTT and Transwell assays assessing proliferation/migration, and Western blot analyzing proliferation- (PCNA) and migration-related proteins (Vimentin, MMP, E-Cadherin). TCGA data were analyzed to explore BRF1-ferroptosis correlations. ResultsBRF1 was significantly upregulated in over 20 cancer types. High BRF1 expression predicted poor prognosis in adrenocortical carcinoma (ACC) and prostate adenocarcinoma (PRAD). BRF1 positively regulated T cell-mediated cell death pathways in ESCA and circadian rhythm pathways in PAAD. BRF1 exhibited cancer-type-specific correlations with immune checkpoints, cytokine networks, and immune cell infiltration. In vitro, BRF1 knockdown suppressed ESCC proliferation (PCNA downregulation) and migration (Vimentin/MMP downregulation, E-Cadherin upregulation). BRF1 expression positively correlated with ferroptosis antagonists (GPX4, HSPA5, SLC7A11). ConclusionBRF1 demonstrates complex pan-cancer expression and functional heterogeneity, modulating tumor progression and immune infiltration. BRF1 promotes ESCC proliferation and migration, potentially via ferroptosis resistance regulation, highlighting its potential as a therapeutic target in ESCC.