Background Exosomes derived from mesenchymal stem cells (MSCs) have shown to have effective application prospects in the medical field, but exosome yield is very low. The production of exosome mimetic vesicles (EMVs) by continuous cell extrusion leads to more EMVs than exosomes, but whether the protein compositions of MSC-derived EMVs (MSC-EMVs) and exosomes (MSC-exosomes) are substantially different remains unknown. The purpose of this study was to conduct a comprehensive proteomic analysis of MSC-EMVs and MSC-exosomes and to simply explore the effects of exosomes and EMVs on wound healing ability. This study provides a theoretical basis for the application of EMVs and exosomes. Methods In this study, EMVs from human umbilical cord MSCs (hUC MSCs) were isolated by continuous extrusion, and exosomes were identified after hUC MSC ultracentrifugation. A proteomic analysis was performed, and 2315 proteins were identified. The effects of EMVs and exosomes on the proliferation, migration and angiogenesis of human umbilical vein endothelial cells (HUVECs) were evaluated by cell counting kit-8, scratch wound, transwell and tubule formation assays. A mouse mode was used to evaluate the effects of EMVs and exosomes on wound healing. Results Bioinformatics analyses revealed that 1669 proteins in both hUC MSC-EMVs and hUC MSC-exosomes play roles in retrograde vesicle-mediated transport and vesicle budding from the membrane. The 382 proteins unique to exosomes participate in extracellular matrix organization and extracellular structural organization, and the 264 proteins unique to EMVs target the cell membrane. EMVs and exosomes can promote wound healing and angiogenesis in mice and promote the proliferation, migration and angiogenesis of HUVECs. Conclusions This study presents a comprehensive proteomic analysis of hUC MSC-derived exosomes and EMVs generated by different methods. The tissue repair function of EMVs and exosomes was herein verified by wound healing experiments, and these results reveal their potential applications in different fields based on analyses of their shared and unique proteins.

Osteosarcoma is a bone tumor with a high incidence in children and adolescents. Chemotherapy for osteosarcoma is limited, and effective targeted drugs are urgently needed to treat osteosarcoma. Exosomes as a natural nano drug delivery platform have been widely studied and proven to have good drug delivery performance. However, the low production of exosomes hinders its development as a carrier. Exosome mimetics (EMs) as an alternative product of exosomes solve the problem of low production of exosomes and maintain the good performance of exosomes as carriers. In this study, bone marrow mesenchymal stem cells (BMSCs) were sequentially extruded to generate EMs to encapsulate doxorubicin (EM-Dox) to treat osteosarcoma. The results showed that we successfully prepared EMs of BMSC, and EM-Dox was prepared using an active-loading approach. Our engineered EM-Dox demonstrated significantly more potent tumor inhibition activity and fewer side effects than free doxorubicin. This novel biological nanomedicine system provides a promising opportunity to develop novel precision medicine for osteosarcoma.

BackgroundOsteosarcoma (OSC) and Ewing's sarcoma (EWS) are children's most common primary bone tumors. The purpose of the study is to develop and validate a new nomogram to predict the cancer-specific survival (CSS) of childhood OSC and EWS.MethodsThe clinicopathological information of all children with OSC and EWS from 2004 to 2018 was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analyses were used to screen children's independent risk factors for CSS. These risk factors were used to construct a nomogram to predict the CSS of children with OSC and EWS. A series of validation methods, including calibration plots, consistency index (C-index), and area under the receiver operating characteristic curve (AUC), were used to validate the accuracy and reliability of the prediction model. Decision curve analysis (DCA) was used to validate the clinical application efficacy of predictive models. All patients were divided into low- and high-risk groups based on the nomogram score. Kaplan-Meier curve and log-rank test were used to compare survival differences between the two groups.ResultsA total of 2059 children with OSC and EWS were included. All patients were randomly divided into training cohort 60% (N = 1215) and validation cohort 40% (N = 844). Univariate and multivariate analysis suggested that age, surgery, stage, primary site, tumor size, and histological type were independent risk factors. Nomograms were established based on these factors to predict 3-, 5-, and 8-years CSS of children with OSC and EWS. The calibration plots showed that the predicted value was highly consistent with the actual value. In the training cohort and validation cohort, the C-index was 0.729 (0.702–0.756) and 0.735 (0.702–0.768), respectively. The AUC of the training cohort and the validation cohort also showed similar results. The DCA showed that the nomogram had good clinical value.ConclusionWe constructed a new nomogram to predict the CSS of OSC and EWS in children. This predictive model has good accuracy and reliability and can help doctors and patients develop clinical strategies.

BackgroundRenal cell carcinoma (RCC) is the most common renal malignant tumor in elderly patients. The prognosis of renal cell carcinoma with distant metastasis is poor. We aim to construct a nomogram to predict the risk of distant metastasis in elderly patients with RCC to help doctors and patients with early intervention and improve the survival rate.MethodsThe clinicopathological information of patients was downloaded from SEER to identify all elderly patients with RCC over 65 years old from 2010 to 2018. Univariate and multivariate logistic regression analyzed the training cohort's independent risk factors for distant metastasis. A nomogram was established to predict the distant metastasis of elderly patients with RCC based on these risk factors. We used the consistency index (C-index), calibration curve, and area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model. Decision curve analysis (DCA) was used to assess the clinical application value of the model.ResultsA total of 36,365 elderly patients with RCC were included in the study. They were randomly divided into the training cohort (N = 25,321) and the validation cohort (N = 11,044). In the training cohort, univariate and multivariate logistic regression analysis suggested that race, tumor histological type, histological grade, T stage, N stage, tumor size, surgery, radiotherapy, and chemotherapy were independent risk factors for distant metastasis elderly patients with RCC. A nomogram was constructed to predict the risk of distant metastasis in elderly patients with RCC. The training and validation cohort's C-indexes are 0.949 and 0.954, respectively, indicating that the nomogram has excellent accuracy. AUC of the training and validation cohorts indicated excellent predictive ability. DCA suggested that the nomogram had a better clinical application value than the traditional TN staging.ConclusionThis study constructed a new nomogram to predict the risk of distant metastasis in elderly patients with RCC. The nomogram has excellent accuracy and reliability, which can help doctors and patients actively monitor and follow up patients to prevent distant metastasis of tumors.

BackgroundMalignant rhabdoid tumor of the kidney (MRTK) is a rare type of tumor that lacks typical clinical manifestations. Herein, we presented clinical data of 2 children with MRTK. In addition, we used a high-throughput RNA-sequencing (RNA-seq), GO analysis, and KEGG signaling pathway analysis to examine gene expression differences at the transcripts level between 2 patients with MRTK and 3 patients with non-tumor diseases without other symptoms.Case reportPreoperative B-scan ultrasonography and computed tomography (CT) examination in 2 cases suggested nephroblastoma. Both patients were treated with radical nephrectomy. After the operation, MRTK was confirmed by pathological examination. Child 1 and Child 2 then received 7 courses and 12 courses of regular chemotherapy, respectively. Child 1 was followed up for 2 years, and Child 2 for 3.1 years without showing symptoms. RNA-seq results showed 2203 differential genes (DEGs) in the kidney tissue of children with MRTK compared to normal tissue (p <0.01). GO analysis suggested that most DEGs participate in protein binding. KEGG results showed that the DEGs were mainly involved in the PI3K-Akt signaling pathway and microRNA-related proteins.ConclusionThe PI3K-Akt signaling pathway and microRNA-related proteins as targets have extremely high potential value for the diagnosis and treatment of MRTK.

ObjectiveWith the development of osteosarcoma treatment, limb salvage surgery is gradually replacing amputation as the primary surgical option. Most pediatric osteosarcomas of the limbs undergo limb-salvage surgery. We aimed to use propensity score matching (PSM) analysis test the difference in cancer-specific mortality (CSM) between amputation and limb-salvage surgery in pediatric patients with Osteosarcoma of the limbs. PSM is a statistical method used to deal with data from an Observational Study. The PSM method is designed to reduce the influence of biases and confounding variables to make a more reasonable comparison between experimental and control groups.MethodsPatient information was downloaded from the SEER (surveillance, epidemiology, and End Results) database from 2004 to 2018. We included all primary pediatric osteosarcoma patients who underwent limb salvage or amputation. Multivariate logistic regression models were used to explore the factors influencing patient choice of amputation. Differences in CSM and other causes of mortality (OSM) between limb salvage and amputation were analyzed using cumulative incidence plots and competitive risk regression tests after 1:1 proportional propensity score matching.ResultsA total of 1,058 pediatric patients with limbs Osteosarcoma were included. Patients who underwent amputations were more likely to be male (OR 1.4, P = 0.024) and more likely to have distant metastasis (OR 2.1, P < 0.001). Before propensity matching, CSM was 1.4 times higher in patients undergoing amputation than in patients undergoing limb salvage (P = 0.017) and 3.4 times higher in OSM (P = 0.007). After adjustment for propensity matching, CSM was 1.5 times higher in patients undergoing amputation than in patients undergoing limb salvage (P = 0.028), but there was no significant difference in OSM (HR 3.2, P = 0.078).ConclusionsOur results suggested that amputation is associated with a 1.5-fold increase in CSM in pediatric patients with limbs Osteosarcoma. Therefore, in the surgical selection of pediatric patients with Osteosarcoma, limb salvage surgery should be the first choice in the absence of other contraindications.

ObjectiveProstate cancer (PC) is the most common non-cutaneous malignancy in men worldwide. Accurate predicting the survival of elderly PC patients can help reduce mortality in patients. We aimed to construct nomograms to predict cancer-specific survival (CSS) and overall survival (OS) in elderly PC patients.MethodsInformation on PC patients aged 65 years and older was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to determine independent risk factors for PC patients. Nomograms were developed to predict the CSS and OS of elderly PC patients based on a multivariate Cox regression model. The accuracy and discrimination of the prediction model were tested by the consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve. Decision curve analysis (DCA) was used to test the clinical value of the nomograms compared with the TNM staging system and D’Amico risk stratification system.Results135183 elderly PC patients in 2010-2018 were included. All patients were randomly assigned to the training set (N=94764) and the validation set (N=40419). Univariate and multivariate Cox regression model analysis revealed that age, race, marriage, histological grade, TNM stage, surgery, chemotherapy, radiotherapy, biopsy Gleason score (GS), and prostate-specific antigen (PSA) were independent risk factors for predicting CSS and OS in elderly patients with PC. The C-index of the training set and the validation set for predicting CSS was 0.883(95%CI:0.877-0.889) and 0.887(95%CI:0.877-0.897), respectively. The C-index of the training set and the validation set for predicting OS was 0.77(95%CI:0.766-0.774)and 0.767(95%CI:0.759-0.775), respectively. It showed that the proposed model has excellent discriminative ability. The AUC and the calibration curves also showed good accuracy and discriminability. The DCA showed that the nomograms for CSS and OS have good clinical potential value.ConclusionsWe developed new nomograms to predict CSS and OS in elderly PC patients. The models have been internally validated with good accuracy and reliability and can help doctors and patients to make better clinical decisions.

BackgroundClear cell renal cell carcinoma (ccRCC) is expected in the elderly and poor prognosis. We aim to explore prognostic factors of ccRCC in the elderly and construct a nomogram to predict cancer-specific survival (CSS) in elderly patients with ccRCC.MethodsClinicopathological information for all elderly patients with ccRCC from 2004 to 2018 was downloaded from the Surveillance, Epidemiology, and End Results (SEER) program. All patients were randomly assigned to a training cohort (70%) or a validation cohort (30%). Univariate and multivariate Cox regression models were used to identify the independent risk factors for CSS. A new nomogram was constructed to predict CSS at 1-, 3-, and 5 years in elderly patients with ccRCC based on independent risk factors. Subsequently, we used the consistency index (C-index), calibration curves, and the area under the receiver operating curve (AUC) and decision curve analysis (DCA) to test the prediction accuracy of the model.ResultsA total of 33,509 elderly patients with ccRCC were enrolled. Univariate and multivariate Cox regression analyses results showed that age, sex, race, marriage, tumor size, histological grade, tumor, nodes, and metastases (TNM) stage, and surgery were independent risk factors for CSS in elderly patients with ccRCC. We constructed a nomogram to predict CSS in elderly patients with ccRCC. The C-index of the training cohort and validation cohort was 0.81 (95% CI: 0.802–0.818) and 0.818 (95% CI: 0.806–0.830), respectively. The AUC of the training cohort and validation cohort also suggested that the prediction model had good accuracy. The calibration curve showed that the observed value of the prediction model was highly consistent with the predicted value. DCA showed good clinical application value of the nomogram.ConclusionIn this study, we explored prognostic factors in elderly patients with ccRCC. We found that age, sex, marriage, TNM stage, surgery, and tumor size were independent risk factors for CSS. We constructed a new nomogram to predict CSS in elderly patients with ccRCC with good accuracy and reliability, providing clinical guidance for patients and physicians.