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Background: Adrenocortical carcinoma (ACC) is an extremely rare malignant tumor with poor prognosis.Existing treatment options have limited effects, and new therapeutic targets urgently need to be discovered. TNFSF13B has been reported to be associated with the prognosis of clear cell renal cell carcinoma, but it has not been studied in ACC.Methods: TNFSF13B expression was analyzed and compared between ACC tumors and normal tissues by using public datasets from TCGA and GTEx. Kaplan-Meier analysis was employed to evaluate survival, and Cox regression was employed to evaluate clinicopathologic features. The upstream and downstream regulatory mechanisms of TNFSF13B were also analyzed. GSEA was performed to explore the mechanisms of TNFSF13B in ACC. Finally, 14 ACC clinical samples were used to verify the relationships between TNFSF13B expression and disease-free survival (DFS) and overall survival (OS).Results: TNFSF13B expression was significantly higher in ACC tissues than in normal tissues. The prognosis of ACC patients with high TNFSF13B expression was worse than that of patients with low TNFSF13B expression. High TNFSF13B expression was strongly correlated with poor prognosis, and TNFSF13B was a prognostic factor. TNFSF13B expression is modified by upstream miRNAs, methylation and ubiquitination, and downstream, it interacts with other proteins. GSEA showed that regulation of cholesterol biosynthesis by SREBP and SREBF, downstream signaling events of the B cell receptor (BCR) and activation of gene expression by SREBF and SREBP were significantly enriched in the TNFSF13B highexpression phenotype. Clinical samples confirmed that TNFSF13B expression was significantly associated with DFS but not with OS.Conclusions: TNFSF13B may be a potential prognostic molecular marker of poor survival in ACC patients, offering a new therapeutic target.

Background: Functional adrenal tumors (FATs) are mainly diagnosed by biochemical analysis. Traditional imaging tests have limitations and cannot be used alone to diagnose FATs. In this study, we aimed to establish an artificially intelligent diagnostic model based on computed tomography (CT) images to distinguish different types of FATs. Methods: A cohort study of 375 patients diagnosed with hyperaldosteronism (HA), Cushing's syndrome (CS), and pheochromocytoma in our center between March 2015 and June 2020 was conducted. Retrospectively, patients were randomly divided into three data sets: the training set (270 cases), the testing set (60 cases), and the retrospective trial set (45 cases). An artificially intelligent diagnostic model based on CT images was established by transferring data from the training set into the deep learning network. The testing set was then used to evaluate the accuracy of the model compared to that of physicians' judgments. The retrospective trial set was used to evaluate the quantification and distinction performance. Results: The deep learning model achieved an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.915, and the AUCs in all three FAT types were greater than 0.882. The AUC of the model tested on the retrospective dataset reached above 0.849. In the quantitative evaluation of tumor lesion area recognition, the diagnostic model also obtained a segmentation Dice coefficient of 0.69. With the help of the proposed model, clinicians reached 92.5% accuracy in distinguishing FATs, compared to 80.6% accuracy when using only their judgment (P<0.05). Conclusions: The result of our study shows that the diagnostic model based on a deep learning network can distinguish and quantify three common FAT types based on texture features of contrast-enhanced CT images. The model can quantify and distinguish functional tumors without any endocrine tests and can assist clinicians in the diagnostic procedure.