BackgroundFibronectin (FN) is a high-molecular-weight glycoprotein component of the extracellular matrix involved in cell adhesion, migration, metastasis, proliferation and differentiation, as well as embryogenesis, wound healing, and blood coagulation. Considerable recent research has established that tumor expression of FN is closely associated with tumor formation and development as well as disease prognosis. However, the mechanisms underlying this relationship have remained unclear. The aim of this study was to investigate FN protein expression in esophageal squamous cell carcinoma (ESCC) and determine its potential prognostic relevance, while also elucidating the source and function of FN.MethodsWe conducted immunohistochemical analyses of protein expression in primary tumors of ESCC patients and analyzed their association with standard prognostic parameters and clinical outcomes. Expression of FN in two ESCC cell lines (Eca-109 and TE-1) was also examined by RT-PCR, immunofluorescence, and ELISA. ESCC cells were cultured in a microenvironment containing a high FN content, and changes in their morphology and migration ability were assessed by microscopy, wound-healing assays, and Transwell assays.ResultsFN expression in ESCC specimens was mainly detected in the tumor stroma, with very little FN detected in tumor cells. Stromal FN content in ESCC specimens was associated with lymphatic metastasis (P = 0.032) and prognosis. In this latter context, patients with high tumor stromal expression of FN showed worse overall survival (P = 0.002) and progression-free survival (P < 0.001) than those with low expression of FN. Interestingly, FN expression and secretion in ESCC cell lines (Eca-109 and TE-1) was found to be low, but these cells adopted a more migratory phenotype when cultured in vitro in a microenvironment containing high levels of FN.ConclusionsHigh FN expression in the stroma of ESCC tumors is closely associated with poor prognosis of patients. High stromal FN content facilitates tumor cell metastasis by promoting morphological changes and improving the motility and migratory ability of ESCC cells.

Immune checkpoint inhibitor (ICI) treatment has been used to treat advanced urothelial cancer. Molecular markers might improve risk stratification and prediction of ICI benefit for urothelial cancer patients. We analyzed 406 cases of bladder urothelial cancer from The Cancer Genome Atlas (TCGA) data set and identified 161 messenger RNAs (mRNAs) as differentially expressed immunity genes (DEIGs). Using the LASSO Cox regression model, an eight-mRNA-based risk signature was built. We validated the prognostic and predictive accuracy of this immune-related risk signature in 348 metastatic urothelial cancer (mUC) samples treated with anti-PD-L1 (atezolizumab) from IMvigor210. We built an immune-related risk signature based on the eight mRNAs: ANXA1, IL22, IL9R, KLRK1, LRP1, NRG3, SEMA6D, and STAP2. The eight-mRNA-based risk signature successfully categorizes patients into high-risk and low-risk groups. Overall survival was significantly different between these groups, regardless if the initial TCGA training set, the internal TCGA testing set, all TCGA set, or the ICI treatment set. The hazard ratio (HR) of the high-risk group to the low-risk group was 3.65 (p &lt; 0.0001), 2.56 (p &lt; 0.0001), 3.36 (p &lt; 0.0001), and 2.42 (p = 0.0009). The risk signature was an independent prognostic factor for prediction survival. Moreover, the risk signature was related to immunity characteristics. In different tumor mutational burden (TMB) subgroups, it successfully categorizes patients into high-risk and low-risk groups, with significant differences of clinical outcome. Our eight-mRNA-based risk signature is a stable biomarker for urothelial cancer and might be able to predict which patients benefit from ICI treatment. It might play a role in precision individualized immunotherapy.

Background The length of time for clinical improvement of patients with thymomatous myasthenia gravis (MG) after extended thymectomy is not clear. The purpose of this study was to determine the length of time after thymectomy in patients with thymomatous MG to achieve a 3-point reduction of Quantitative Myasthenia Gravis Score (QMGS), and identify variables associated with a failure to achieve the reduction. Methods The records of patients with thymomatous MG who underwent extended thymectomy from January 2005 to December 2018 were retrospectively reviewed. The primary end point was a reduction of 3 points of QMGs and the secondary end point was another reduction of 3 points of QMGs. Results A total of 481 patients were included in the analysis, the mean age of the patients was 41.63 ± 8.55 years, and approximately 60% were male. The median time to achieve a 3 point decrease in QMGS was 6 months, and the median time to achieve another 3 point decrease was 30 months. Multivariable analysis indicated that age ≥ 42 years and Masaoka-Koga stage > I were associated with a lower probability of achieving a 3 point decrease in QMGS (HR = 0.55 and 0.65, respectively). Likewise, multivariable analysis indicated that age ≥ 42 years and Masaoka-Koga stage > I were associated with a lower probability of achieving a second 3 point decrease in QMGS (HR = 0.53 and 0.53, respectively). Conclusions In patients with thymomatous MG who receive thymectomy, age ≥ 42 years and Masaoka-Koga stage > I are associated with a worse prognosis and failure to achieve a 3 point decrease in QMGS.

Background To provide multivariable prognostic models for severe complications prediction after heart valve surgery, including low cardiac output syndrome (LCOS), acute kidney injury requiring hemodialysis (AKI-rH) and multiple organ dysfunction syndrome (MODS). Methods We developed multivariate logistic regression models to predict severe complications after heart valve surgery using 930 patients collected retrospectively from the first affiliated hospital of Sun Yat-Sen University from January 2014 to December 2015. The validation was conducted using a retrospective dataset of 713 patients from the same hospital from January 2016 to March 2017. We considered two kinds of prognostic models: the PRF models which were built by using the preoperative risk factors only, and the PIRF models which were built by using both of the preoperative and intraoperative risk factors. The least absolute shrinkage selector operator was used for developing the models. We assessed and compared the discriminative abilities for both of the PRF and PIRF models via the receiver operating characteristic (ROC) curve. Results Compared with the PRF models, the PIRF modes selected additional intraoperative factors, such as auxiliary cardiopulmonary bypass time and combined tricuspid valve replacement. Area under the ROC curves (AUCs) of PRF models for predicting LCOS, AKI-rH and MODS are 0.565 (0.466, 0.664), 0.688 (0.62, 0.757) and 0.657 (0.563, 0.751), respectively. As a comparison, the AUCs of the PIRF models for predicting LOCS, AKI-rH and MODS are 0.821 (0.747, 0.896), 0.78 (0.717, 0.843) and 0.774 (0.7, 0.847), respectively. Conclusions Adding the intraoperative factors can increase the predictive power of the prognostic models for severe complications prediction after heart valve surgery.

Objectives This study aimed to examine the expression of programmed cell death 1 ligand 2 (PD-L2) in thymoma and thymomatous myasthenia gravis (MG). Methods The records of 70 patients with thymoma receiving surgical resection between January 2017 and December 2018 were retrospectively reviewed. Thymoma PD-L2 expression was evaluated by immunohistochemistry staining. Associations between PD-L2 expression and clinicopathologic features were examined. Results PD-L2 expression was positive in 41 patients (58.6%) and negative in 29 patients (41.4%). Of them, 33 had thymomatous MG. Patients with MG were more likely to be 50 years of age or younger (69.70% vs 35.14%); have more World Health Organization (WHO) type B thymomas (84.85% vs 64.86%); have tumors of smaller size (4.09 ± 2.33 cm vs 6.47 ± 2.42 cm); have positive PD-L2 expression (78.79% vs 40.54%); and have a higher percentage of PD-L2–positive cells, higher PD-L2 expression intensity, and score (all P &lt; .05). Positive PD-L2 expression was associated with more type B thymomas, higher Masaoka-Koga stage, smaller tumor size, ectopic thymus, and MG (all P &lt; .05). Factors significantly associated with MG were age under 50 years, tumor size less than 5 cm, and positive PD-L2 expression (all P &lt; .05). Conclusions Thymoma PD-L2 expression is significantly associated with thymomatous MG and WHO histologic types B2 and B3.

Purpose: The purpose of this study was to examine the expression of PD-L2 in thymoma and thymomatous myasthenia gravis (MG). Methods: The records of 70 patients with thymoma who underwent surgical resection between January 2017 and December 2018 were retrospectively reviewed. Thymoma PD-L2 expression was evaluated by immunohistochemistry (IHC) staining. Associations between PD-L2 expression and clinicopathological features were examined. Results: PD-L2 expression was positive in 41 patients (58.6%) and negative in 29 patients (41.4%). Of the patients, 33 had thymomatous MG. Patients with MG were more likely to be ≤ 50 years of age (69.70% vs. 35.14%), have higher WHO type (84.85% vs. 64.86%), smaller tumor size (4.09 ± 2.33 vs. 6.47 ± 2.42 cm), have positive PD-L2 expression (78.79% vs. 40.54%), and have higher PD-L2 expression intensity, ratio, and score (all, P < 0.05). Positive PD-L2 expression was associated with higher WHO type, higher Masaoka-Koga stage, smaller tumor size, ectopic thymus, and MG (all, P < 0.05). Factors significantly associated with MG were age > 50 years (OR = 0.09, 95% CI: 0.02 to 0.39), tumor size > 5 cm (OR = 0.22, 95% CI: 0.06 to 0.82), and positive PD-L2 expression (OR = 9.25, 95% CI: 1.93 to 44.30) (all, P < 0.05; Table 3).Conclusion: Thymoma PD-L2 expression is significantly associated with thymomatous MG and WHO histological type B2 and B3.

No abstract

Background: To provide prognostic multivariate models for severe complications prediction after heart valvular surgery, such as low cardiac output syndrome (LOCS), acute kidney injury requiring hemodialysis (AKI-rH) and multiple organ dysfunction syndrome (MODS).Methods: We developed multivariate logistic regression models to predict the severe complications after heart valvular surgery using 930 patients collected retrospectively from the first affiliated hospital of Sun Yat-Sen University from January 2014 to December 2015. The validation was conducted using a retrospective dataset of 713 patients from the same hospital from January 2016 to March 2017. We considered two kinds of prognostic models: the PRF models which were built by using the preoperative risk factors only, and the PIRF models which were built by using both of the preoperative and intraoperative risk factors. The Least absolute shrinkage selector operator was used for developing the models. We assessed and compared the discriminative abilities for both of the PRF and PIRF models via the receiver operating characteristic curve. Results: Compared with the PRF models, the PIRF modes selected additional intraoperative factors, such as auxiliary cardiopulmonary bypass time and combined tricuspid valve replacement. The area under the curves (AUCs) of the PRF models for predicting LOCS, AKI-rH and MODS are 0.565 (0.466, 0.664), 0.688 (0.62, 0.757) and 0.657 (0.563, 0.751), respectively. As a comparison, the AUCs of the PIRF models for predicting LOCS, AKI-rH and MODS are 0.821 (0.747, 0.896), 0.78 (0.717, 0.843) and 0.774 (0.7, 0.847), respectively. Conclusions: Adding the intraoperative factors can increase the predictive power of the prognostic models for severe complications prediction after heart valvular surgery.