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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.

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Background: Recent studies have suggested that macrophages are significantly involved in different renal diseases. However, the role of these renal infiltrating macrophages has not been entirely uncovered. To further clarify the underlying mechanism and identify therapeutic targets, a bioinformatic analysis based on transcriptome profiles was performed.Methods: Three transcription profiling datasets, GSE27045, GSE51466 and GSE75808, were obtained from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were assessed by Gene Ontology (GO) functional annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and gene set enrichment analysis (GSEA). Results:The classic signaling pathways and metabolic pathways of macrophages infiltrating the kidney in different pathophysiological processes, including lupus nephritis (LN), renal crystal formation and renal ischemia-reperfusion injury (IRI), were analysed. Furthermore, the common classical pathways significantly altered in the three renal disorders were the oxidative phosphorylation, VEGF signaling and JAK/STAT signaling pathways, while the renin-angiotensin system was uniquely altered in LN, the glycolysis and gluconeogenesis pathways were uniquely altered in models of renal crystal formation, and the calcium signaling pathway was specific to renal IRI.Conclusions: Via bioinformatics analysis, this study revealed the transcriptional features of macrophages in murine LN, renal crystal formation and IRI models, which may serve as promising targets for mechanistic research and the clinical treatment of multiple renal diseases.

ObjectiveOur previous work found COX4I2 was associated with angiogenesis in pheochromocytoma. The purpose of this study was to explore the role of COX4I2 in regulating angiogenesis in pheochromocytoma.MethodsDistribution of COX4I2 was evaluated by scRNA-seq in one case of pheochromocytoma and the findings were verified by immunostaining. COX4I2 was further knocked down in target cells. Changes of angiogenesis-related genes were evaluated by qPCR in target cells.ResultsThe scRNA-seq revealed high mRNA expression of COX4I2 in fibroblasts rather than tumor cells. Immunostaining of COX4I2 confirmed its distribution in fibroblasts. Knocking down COX4I2 in NIH3T3 cell line led to significant reduction of angiogenesis-related genes, especially ANG1 and HGF.ConclusionsFibroblasts mediate the angiogenesis of pheochromocytoma by increasing COX4I2 expression, possibly by affecting ANG1 and HGF.

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There are series of Cu-Ni sulfide-bearing mafic-ultramafic intrusions widespread in north JilinProvince,Northeastern China. The intrusions formed in Xing’an-Mongolian Orogenic Belt near to the northeastern margin of North China Craton. The complexes were formed in almost same period according to the zircon U-Pb dating reported recently, which means that the complexes were formed in same tectonic period and belong to one tectonic magmatic event. The rock assemblages are different from the ophiolite type and Yidun type in orogenic belt. The mafic-ultramafic complexes formed in the range from 217 Ma to 232 Ma coeval with A-type granites in the area, which formed bimodal igneous rock assemblage. According to the regional angular unconformities, there were existed the orogenies of Caledonian, Hercynian, Early Indosinian, Late Indosinian and Yanshanian. The Early Indosinian coeval with orogenic I-type granites and sanukitie that suggesting the lithosphere thickening in the extrusion tectonic setting of orogenic processes, however the Late Indosinian coeval with bimodal igneous rock assemblage that suggesting the lithosphere thinning in the extension tectonic setting of post-orogenic processes in the Xing’an-Mongolian Orogenic Belt. Chemical composition of the mafic-ultramafic rocks has the characteristics of high-Mg and low-K tholeiites related with inter-continental post-orogenic tectonic setting. The trace elements indicate their formed in conditions of continental extension belt or initial rift and has the characteristics of revolution from oceanic island arc, volcanic arc of continental margin to continental extended belt. The low initial Sr isotopic ratios and positive ε_Nd(t) values suggest that the initial m...

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.