PDCD10 promotes proliferation, migration, and invasion of osteosarcoma by inhibiting apoptosis and activating EMT pathway.

BACKGROUND: Osteosarcoma, a common primary malignant tumor, occurs in children and adolescents with a poor prognosis. The current treatment methods are various, while the five-year survival rate of patients has not been significantly improved. As a member of the programmed death factor (PDCD) family, programmed death factor 10 (PDCD10) plays a role in regulating cell apoptosis. Several studies of PDCD10 in CCM and cancers have been reported before. However, there are no relevant research reports on the effects of PDCD10 on osteosarcoma. METHODS: We used bioinformatics analysis, IHC, and clinical data to confirm the expression of PDCD10 and its correlation with prognosis in osteosarcoma. Then, we used shRNAs and cDNA to knock down or overexpress PDCD10 in U2OS and MG63 cell lines. A series of function assays such as CCK8, Wound healing test, Plate cloning formation assay, and Transwell were done to confirm how PDCD10 affects osteosarcoma. Animal assays were done to confirm the conclusions in cell lines. At last, WB was used to measure the protein expression levels of apoptosis and the EMT pathway. RESULTS: PDCD10 was highly expressed in patients with osteosarcoma and correlated with prognosis; PDCD10 knockdown inhibited osteosarcoma growth, proliferation, migration, and invasion; PDCD10 overexpression promoted osteosarcoma growth, proliferation, migration, and invasion. In vivo experiments confirmed the conclusions in cell lines; PDCD10 inhibited apoptosis and activated the EMT pathway. CONCLUSIONS: In this study, it was found that PDCD10 was highly expressed in patients with osteosarcoma, and it was closely related to patient prognosis. PDCD10 inhibited tumor cell apoptosis and promoted tumor progression by activating the EMT pathway. These findings may provide a potential target for gene therapy of osteosarcoma.

Prediction of ISUP grading of clear cell renal cell carcinoma using support vector machine model based on CT images.

BACKGROUND: To explore whether radiomics combined with computed tomography (CT) images can be used to establish a model for differentiating high grade (International Society of Urological Pathology [ISUP] grade III-IV) from low-grade (ISUP I-II) clear cell renal cell carcinoma (ccRCC). METHODS: For this retrospective study, 3-phase contrast-enhanced CT images were collected from 227 patients with pathologically confirmed ISUP-grade ccRCC (155 cases in the low-grade group and 72 cases in the high-grade group). First, we delineated the largest dimension of the tumor in the corticomedullary and nephrographic CT images to obtain the region of interest. Second, variance selection, single variable selection, and the least absolute shrinkage and selection operator were used to select features in the corticomedullary phase, nephrographic phase, and 2-phase union samples, respectively. Finally, a model was constructed using the optimal features, and the receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the predictive performance of the features in the training and validation queues. A Z test was employed to compare the differences in AUC values. RESULTS: The support vector machine (SVM) model constructed using the screening features for the 2-stage joint samples can effectively distinguish between high- and low-grade ccRCC, and obtained the highest prediction accuracy. Its AUC values in the training queue and the validation queue were 0.88 and 0.91, respectively. The results of the Z test showed that the differences between the 3 groups were not statistically significant. CONCLUSION: The SVM model constructed by CT-based radiomic features can effectively identify the ISUP grades of ccRCC.