Background: Recent studies have shown the great value of cell therapy over the past few decades. Mesenchymal stem cells (MSCs) have been reported to treat various degenerative diseases not through their differentiation potential but through their paracrine factors of the extracellular vesicle (EV) including exosomes. Exosomes are nanosized (70~150 nm) membrane-bound extracellular vesicles, not only involved in cell-to-cell communication but also in the development of tissue injury repair. Objective: As more researchers proved the enormous potential of exosomes in the field of repairing damaged tissue currently, it is urgent to explore the concrete mechanism and make exosomes to be a practical treatment tool in clinical medicine. In our study, we analyzed and summarized the work on tissue repair via exosomes in order to give some suggestions about the application of exosomes in clinical reality in the future. Results: MSC-derived exosomes (MSC-Ex) contain a wide variety of functional proteins, mRNAs, miRNAs and signaling lipids. Compared with their parent cells, MSC-Ex are more stable and can reduce the inherent safety risks in administering viable cells such as the risk of occlusion in microvasculature. MSC-Ex can be used to develop a cell-free exosome-based therapy for regenerative medicine, and may provide an alternative to MSC-based therapy. Conclusion: This review summarizes the most recent knowledge of therapeutic potential of MSC-Ex in the liver, heart, kidney, bone, brain diseases and cancer, as well as their associated challenges and opportunities.

Objectives: It is reported that inflammation- and nutrition-related indicators have a prognostic impact on multiple cancers. Here we aimed to identify a prognostic nomogram model for prediction of overall survival (OS) in surgical patients with tongue squamous cell carcinoma (TSCC). Methods: The retrospective data of 172 TSCC patients were charted from the Cancer Hospital of Shantou University Medical College between 2008 and 2019. A Cox regression analysis was performed to determine prognostic factors to establish a nomogram and predict OS. The predictive accuracy of the model was analyzed by the calibration curves and the concordance index (C-index). The difference of OS was analyzed by Kaplan–Meier survival analysis. Results: Multivariate analysis showed age, tumor node metastasis (TNM) stage, red blood cell, platelets, and platelet-to-lymphocyte ratio were independent prognostic factors for OS, which were used to build the prognostic nomogram model. The C-index of the model for OS was 0.794 (95% CI = 0.729-0.860), which was higher than that of TNM stage 0.685 (95% CI = 0.605-0.765). In addition, decision curve analysis also showed the nomogram model had improved predictive accuracy and discriminatory performance for OS, compared to the TNM stage. According to the prognostic model risk score, patients in the high-risk subgroup had a lower 5-year OS rate than that in a low-risk subgroup (23% vs 49%, P < .0001). Conclusions: The nomogram model based on clinicopathological features inflammation- and nutrition-related indicators represents a promising tool that might complement the TNM stage in the prognosis of TSCC.

Background Oral tongue squamous cell carcinoma (OTSCC) is a prevalent malignant disease that is characterized by high rates of metastasis and postoperative recurrence. The aim of this study was to establish a nomogram to predict the outcome of OTSCC patients after surgery. Methods We retrospectively analyzed 169 OTSCC patients who underwent treatments in the Cancer Hospital of Shantou University Medical College from 2008 to 2019. The Cox regression analysis was performed to determine the independent prognostic factors associated with patient’s overall survival (OS). A nomogram based on these prognostic factors was established and internally validated using a bootstrap resampling method. Results Multivariate Cox regression analysis revealed the independent prognostic factors for OS were TNM stage, age, lymphocyte-to-monocyte ratio and immunoglobulin G, all of which were identified to create the nomogram. The Akaike Information Criterion and Bayesian Information Criterion of the nomogram were lower than those of TNM stage (292.222 vs. 305.480; 298.444 vs. 307.036, respectively), indicating a better goodness-of-fit of the nomogram for predicting OS. The bootstrap-corrected of concordance index (C-index) of nomogram was 0.784 (95% CI 0.708–0.860), which was higher than that of TNM stage (0.685, 95% CI 0.603–0.767, P = 0.017). The results of time-dependent C-index for OS also showed that the nomogram had a better discriminative ability than that of TNM stage. The calibration curves of the nomogram showed good consistency between the probabilities and observed values. The decision curve analysis also revealed the potential clinical usefulness of the nomogram. Based on the cutoff value obtained from the nomogram, the proposed high-risk group had poorer OS than low-risk group (P < 0.0001). Conclusions The nomogram based on clinical characteristics and serological inflammation markers might be useful for outcome prediction of OTSCC patient.