Integrated bioinformatics and machine-learning screening for immune-related genes in diagnosing non-alcoholic fatty liver disease with ischemic stroke and RRS1 pan-cancer analysis.

Background: The occurrence of ischemic stroke (IS) is associated with nonalcoholic fatty liver disease (NAFLD). The cancer burden of NAFLD complicated by IS also warrants attention. This study aimed to identify candidate immune biomarkers linked to NAFLD and IS and analyze their association with cancer. Methods: Two of each of the NAFLD and IS datasets were downloaded, differentially expressed genes (DEGs) were identified, and module genes were screened via weighted gene coexpression network analysis (WGCNA). Subsequently, utilizing machine learning (least absolute shrinkage and selection operator regression, random forest and support vector machine-recursive feature elimination) and immune cell infiltration analysis, immune-related candidate biomarkers for NAFLD with IS were determined. Simultaneously, a nomogram was established, the diagnostic efficacy was assessed, and the role of candidate biomarkers in cancer was ascertained through pan-cancer analyses. Results: In this study, 117 and 98 DEGs were identified from the combined NAFLD and IS datasets, respectively, and 279 genes were obtained from the most significant modules of NAFLD. NAFLD module genes and IS DEGs were intersected to obtain nine genes, which were enriched in the inflammatory response and immune regulation. After overlapping the results of the three machine learning algorithms, six candidate genes were obtained, based on which a nomogram was constructed. The calibration curve demonstrated good accuracy, and the candidate genes had high diagnostic values. The genes were found to be related to the immune dysregulation of stroke, and RRS1 was strongly associated with the prognosis, immune cell infiltration, microsatellite instability (MSI), and tumor mutation burden (TMB). Conclusion: Six common candidate immune-related genes (PTGS2, FCGR1A, MMP9, VNN3, S100A12, and RRS1) of NAFLD and IS were identified, and a nomogram for diagnosing NAFLD with IS was established. RRS1 may serve as a candidate gene for predicting the prognosis of patients with cancer who have NAFLD complicated by IS, which could aid in their diagnosis and treatment.

MRI-based random survival Forest model improves prediction of progression-free survival to induction chemotherapy plus concurrent Chemoradiotherapy in Locoregionally Advanced nasopharyngeal carcinoma.

BACKGROUND: The present study aimed to explore the application value of random survival forest (RSF) model and Cox model in predicting the progression-free survival (PFS) among patients with locoregionally advanced nasopharyngeal carcinoma (LANPC) after induction chemotherapy plus concurrent chemoradiotherapy (IC + CCRT). METHODS: Eligible LANPC patients underwent magnetic resonance imaging (MRI) scan before treatment were subjected to radiomics feature extraction. Radiomics and clinical features of patients in the training cohort were subjected to RSF analysis to predict PFS and were tested in the testing cohort. The performance of an RSF model with clinical and radiologic predictors was assessed with the area under the receiver operating characteristic (ROC) curve (AUC) and Delong test and compared with Cox models based on clinical and radiologic parameters. Further, the Kaplan-Meier method was used for risk stratification of patients. RESULTS: A total of 294 LANPC patients (206 in the training cohort; 88 in the testing cohort) were enrolled and underwent magnetic resonance imaging (MRI) scans before treatment. The AUC value of the clinical Cox model, radiomics Cox model, clinical + radiomics Cox model, and clinical + radiomics RSF model in predicting 3- and 5-year PFS for LANPC patients was [0.545 vs 0.648 vs 0.648 vs 0.899 (training cohort), and 0.566 vs 0.736 vs 0.730 vs 0.861 (testing cohort); 0.556 vs 0.604 vs 0.611 vs 0.897 (training cohort), and 0.591 vs 0.661 vs 0.676 vs 0.847 (testing cohort), respectively]. Delong test showed that the RSF model and the other three Cox models were statistically significant, and the RSF model markedly improved prediction performance (P < 0.001). Additionally, the PFS of the high-risk group was lower than that of the low-risk group in the RSF model (P < 0.001), while comparable in the Cox model (P > 0.05). CONCLUSION: The RSF model may be a potential tool for prognostic prediction and risk stratification of LANPC patients.

MRI-Based Back Propagation Neural Network Model as a Powerful Tool for Predicting the Response to Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma.

BACKGROUND: Pretreatment individualized assessment of tumor response to induction chemotherapy (ICT) is a need in locoregionally advanced nasopharyngeal carcinoma (LANPC). Imaging method plays vital role in tumor response assessment. However, powerful imaging method for ICT response prediction in LANPC is insufficient. PURPOSE: To establish a robust model for predicting response to ICT in LANPC by comparing the performance of back propagation neural network (BPNN) model with logistic regression model. STUDY TYPE: Retrospective. POPULATION: A total of 286 LANPC patients were assigned to training (N = 200, 43.8 ± 10.9 years, 152 male) and testing (N = 86, 43.5 ± 11.3 years, 57 male) cohorts. FIELD STRENGTH/SEQUENCE: T2 -weighted imaging, contrast enhanced-T1 -weighted imaging using fast spin echo sequences at 1.5 T scanner. ASSESSMENT: Predictive clinical factors were selected by univariate and multivariate logistic models. Radiomic features were screened by interclass correlation coefficient, single-factor analysis, and the least absolute shrinkage selection operator (LASSO). Four models based on clinical factors (Modelclinic ), radiomics features (Modelradiomics ), and clinical factors + radiomics signatures using logistic (Modelcombined ), and BPNN (ModelBPNN ) methods were established, and model performances were compared. STATISTICAL TESTS: Student's t-test, Mann-Whitney U-test, and Chi-square test or Fisher's exact test were used for comparison analysis. The performance of models was assessed by area under the receiver operating characteristic (ROC) curve (AUC) and Delong test. P < 0.05 was considered statistical significance. RESULTS: Three significant clinical factors: Epstein-Barr virus-DNA (odds ratio [OR] = 1.748; 95% confidence interval [CI], 0.969-3.171), sex (OR = 2.883; 95% CI, 1.364-6.745), and T stage (OR = 1.853; 95% CI, 1.201-3.052) were identified via univariate and multivariate logistic models. Twenty-four radiomics features were associated with treatment response. ModelBPNN demonstrated the highest performance among Modelcombined , Modelradiomics , and Modelclinic (AUC of training cohort: 0.917 vs. 0.808 vs. 0.795 vs. 0.707; testing cohort: 0.897 vs. 0.755 vs. 0.698 vs. 0.695). CONCLUSION: A machine-learning approach using BPNN showed better ability than logistic regression model to predict tumor response to ICT in LANPC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Targeting Extracellular Heat Shock Protein 70 Ameliorates Doxorubicin-Induced Heart Failure Through Resolution of Toll-Like Receptor 2-Mediated Myocardial Inflammation.

Background Heart failure (HF) is one of the most significant causes of morbidity and mortality for the cardiovascular risk population. We found previously that extracellular HSP70 (heat shock protein) is an important trigger in cardiac hypertrophy and fibrosis, which are associated with the development of heart dysfunction. However, the potential role of HSP70 in response to HF and whether it could be a target for the therapy of HF remain unknown. Methods and Results An HF mouse model was generated by a single IP injection of doxorubicin at a dose of 15 mg/kg. Ten days later, these mice were treated with an HSP70 neutralizing antibody for 5 times. We observed that doxorubicin treatment increased circulating HSP70 and expression of HSP70 in myocardium and promoted its extracellular release in the heart. Blocking extracellular HSP70 activity by its antibody significantly ameliorated doxorubicin-induced left ventricular dilation and dysfunction, which was accompanied by a significant inhibition of cardiac fibrosis. The cardioprotective effect of the anti-HSP70 antibody was largely attributed to its ability to promote the resolution of myocardial inflammation, as evidenced by its suppression of the toll-like receptor 2-associated signaling cascade and modulation of the intracellular distribution of the p50 and p65 subunits of nuclear factor-κB. Conclusions Extracellular HSP70 serves as a noninfectious inflammatory factor in the development of HF, and blocking extracellular HSP70 activity may provide potential therapeutic benefits for the treatment of HF.

Toll-like receptor (TLR) 2 and TLR4 differentially regulate doxorubicin induced cardiomyopathy in mice.

Recent evidence indicates that toll-like receptor (TLR) 2 and 4 are involved in the pathogenesis of dilated cardiomyopathy (DCM), but the exact mechanisms of their actions have not been elucidated. We explored the therapeutic potential of blocking TLRs in mice with established cardiomyopathy. Cardiomyopathy was generated by a single intraperitoneal injection of doxorubicin (10 mg/kg). Two weeks later, the mice were treated with TLR2 or TLR4 neutralizing antibody. Blocking TLR2, but not TLR4, activity not only reduced mortality, but also attenuated doxorubicin-induced cardiac dysfunction by 20% and inhibited myocardial fibrosis. To determine the differential effects of blocking TLR2 and TLR4 in chronic cardiomyopathy, mice were injected with doxorubicin (3.5 mg/kg) once a week for 8 weeks, followed by treatment with TLR2 or TLR4 neutralizing antibody for 40 days. Blocking TLR2 activity blunted cardiac dysfunction by 13% and inhibited cardiac fibrosis, which was associated with a significant suppression of myocardial inflammation. The underlying mechanism involved interrupting the interaction of TLR2 with its endogenous ligands, resulting in attenuation of inflammation and fibrosis. In contrast, blocking TLR4 exacerbated cardiac dysfunction and fibrosis by amplifying inflammation and suppressing autophagy. Our studies demonstrate that TLR2 and TLR4 play distinct roles in the progression of doxorubicin-induced DCM. TLR4 activity is crucial for the resolution of inflammation and cardiac fibrosis, while blocking TLR2 activity has therapeutic potential for the treatment of DCM.

[Blocking extracellular HMGB1 activity protects against doxorubicin induced cardiac injury in mice].

This study aims to investigate the preventive role and potential mechanisms of blocking extracellular HMGB1 function on doxorubicin induced cardiac injury. Mice were treated with HMGB1 blocker glycyrrhizin 1 h before and one time every day (intraperitoneal, 10 mg per mouse) after doxorubicin injection, and sacrificed on the day 14 after doxorubicin challenge. Cardiac function was evaluated by echocardiography and hemodynamic measurement. Myocardial inflammation and collagen deposition were analyzed by immunohistochemistry and picrosirius red staining. The interaction of HMGB1 and TLR2 was assessed by co-immunoprecipitation and confocal microscopy. The protein contents of HMGB1, MyD88, p65NF-kappaB and phospho-p65NF-kappaB were measured by Immunoblot. Compared with mice treated with saline, doxorubicin treatment led to an upregulation in HMGB1 expression. Blocking HMGB1 activity with glycyrrhizin protected mice against cardiac dysfunction, inflammatory response, and cardiac fibrosis induced by doxorubicin challenge. Glycyrrhizin inhibited the interaction of HMGB1 and TLR2, and blocked the downstream signaling of TLR2. In conclusion, blocking HMGB1 protected against doxorubicin induced cardiac injury by inhibiting TLR2 signaling pathway.